@InProceedings{	  aarts93a,
  author	= {E. H. L. Aarts and H. P. Stehouwer},
  title		= {Neural Networks and the Travelling Salesman Problem},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {950--955},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  abdallah95a,
  author	= {M. A. Abdallah and T. I. Samu and W. A. Grissom},
  title		= {Automatic target identification using neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2588},
  pages		= {556--65},
  note		= {(Intelligent Robots and Computer Vision XIV: Algorithms,
		  Techniques, Active Vision, and Materials Handling Conf.
		  Date: 23--26 Oct. 1995 Conf. Loc: Philadelphia, PA, USA
		  Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  abdel-aty-zohdy95a,
  author	= {Abdel-Aty-Zohdy, H. S. and Zondy, M. A. },
  title		= {Neural networks for pattern discovery and optimization in
		  signal processing and applications},
  booktitle	= {1995 Canadian Conference on Electrical and Computer
		  Engineering},
  year		= {1995},
  editor	= {Gagnon, F. },
  volume	= {1},
  pages		= {202--6},
  organization	= {Dept. of Electr. \& Syst. Eng. , Oakland Univ. ,
		  Rochester, MI, USA},
  publisher	= {IEE},
  address	= {New York, NY, USA},
  abstract	= {This work is intended to advance both conceptual and
		  implementation techniques of supervised and
		  discovery-driven neural networks in noisy time varying
		  signal processing applications. Successful neural networks
		  and their significance in applications are based on;
		  selection of proper theoretical algorithms for learning,
		  appropriate selection of the sequencing of signal
		  processing tasks, and efficient VLSI system implementation.
		  In this paper, we present pattern discovery Self Organizing
		  Feature Map (SOFM), followed by a Recurrent Dynamic Neural
		  Network (RDNN) algorithm for signal representation and
		  processing. This approach combines the benefits of RDNNs
		  with SOFM counter part. Preliminary designs,
		  implementations, test results and validation of
		  silicon-chips for each of the above neural network
		  approaches are also presented.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  abdel-aty-zohdy96a,
  author	= {H. S. Abdel-Aty-Zohdy and M. A. Zohdy},
  title		= {Analog/digital implementation of neural networks for
		  pattern discovery and optimization in signal processing
		  applications},
  booktitle	= {38th Midwest Symposium on Circuits and Systems.
		  Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  editor	= {L. P. Caloba and P. S. R. Diniz and A. C. M. {de Querioz}
		  and E. H. Watanabe},
  address	= {New York, NY, USA},
  pages		= {277},
  dbinsdate	= {oldtimer}
}

@InCollection{	  abdelazim96a,
  author	= {H. Y. Abdelazim},
  title		= {A hybrid fuzzy-neural approach to the recognition of
		  Arabic script},
  booktitle	= {Proceedings of the 5th International Conference and
		  Exhibition on Multi-Lingual Computing},
  publisher	= {Univ. Cambridge},
  year		= {1996},
  address	= {Cambridge, UK},
  pages		= {2/3/1--14},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  abdulkader00a,
  author	= {Abdulkader, Hasan and Ibnkahla, Mohamed},
  title		= {Convergence properties of self-organizing maps applied for
		  communication channel equalization},
  booktitle	= {ICASSP, IEEE International Conference on Acoustics, Speech
		  and Signal Processing---Proceedings},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {3502--3505},
  organization	= {Natl Polytechnics Inst of Toulouse},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper studies the convergence properties of a Self
		  Organizing Map (SOM) equalizer. The transmitted signal is
		  assumed to be a 4-QAM signal corrupted by an additive white
		  gaussian noise (AWGN). Several theoretical results such as
		  map ordering and stability conditions are given in the
		  paper. These results show good fit with computer
		  simulations.},
  dbinsdate	= {2002/1}
}

@Article{	  abel96a,
  author	= {Abel, E. W. and Zacharia, P. C. and Forster, A. and
		  Farrow, T. L. },
  title		= {Neural network analysis of the {EMG} interference
		  pattern},
  journal	= {Medical Engineering \& Physics},
  year		= {1996},
  volume	= {18},
  number	= {1},
  pages		= {12--17},
  dbinsdate	= {oldtimer}
}

@Article{	  abidi94a,
  author	= {Abidi, M. A. and Yasuki, S. and Crilly, P. B. },
  title		= {Image compression using hybrid neural networks combining
		  the auto-associative multi-layer perceptron and the
		  \mbox{self-organizing} feature map},
  journal	= {IEEE Transactions on Consumer Electronics},
  year		= {1994},
  volume	= {40},
  number	= {4},
  pages		= {796--811},
  month		= {Nov},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  abidi94b,
  author	= {Abidi, M. A. and Yasuki, S. and Crilly, P. B.},
  title		= {Image compression using hybrid neural networks},
  journal	= {Digest of Technical Papers---IEEE International Conference
		  on Consumer Electronics},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1994},
  number	= {},
  volume	= {},
  pages		= {70--71},
  abstract	= {A new image compression technique using hybrid neural
		  networks combining two different learning network-s (1)
		  AMLP (Auto-associative multi-layer perceptron) and (2) SOFM
		  (self-organizing feature map) is presented. The neural
		  networks simultaneously perform dimensionality reduction
		  with AMLP and categorization with SOFM to compress the
		  image. The two hybrid neural networks which form a parallel
		  and a serial architecture are examined using theoretical
		  analysis and computer simulation. These hybrid neural
		  networks achieve clear performance improvement with respect
		  to decoded picture quality and compression ratios, compared
		  to existing image compression techniques.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  abidi96a,
  author	= {S. S. R. Abidi},
  title		= {Neural networks and child language development: a
		  simulation using a modular neural network architecture},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {840--5},
  dbinsdate	= {oldtimer}
}

@Article{	  abidi97a,
  author	= {S. S. R. Abidi},
  title		= {Using neural networks to explicate human category
		  learning: a simulation of concept learning and
		  lexicalisation},
  journal	= {Malaysian Journal of Computer Science},
  year		= {1997},
  volume	= {10},
  number	= {2},
  pages		= {60--71},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  aboelela94a,
  author	= {Aboelela, M. A. S. },
  title		= {Short term forecasting of electric daily loads},
  booktitle	= {MEPCON 94. Middle East Power System Conference.
		  Proceedings},
  year		= {1994},
  pages		= {13--17},
  organization	= {Fac. of Eng. , Cairo Univ. , Giza, Egypt},
  publisher	= {Cairo Univ},
  address	= {Giza, Egypt},
  dbinsdate	= {oldtimer}
}

@Article{	  abou01a,
  author	= {Abou Chadi, F. E. Z. and Nashar, A. and Saad, M.},
  title		= {Automatic analysis and classification of surface
		  electromyography},
  journal	= {Frontiers-of-Medical-and-Biological-Engineering},
  year		= {2001},
  volume	= {11},
  pages		= {13--29},
  abstract	= {Parametric modeling of surface electromyography (EMG)
		  algorithms that facilitates automatic SEMG feature
		  extraction and artificial neural networks (ANN) are
		  combined for providing an integrated system for the
		  automatic analysis and diagnosis of myopathic disorders.
		  Three paradigms of ANN were investigated: the multilayer
		  backpropagation algorithm, the self-organizing feature map
		  algorithm and a probabilistic neural network model. The
		  performance of the three classifiers was compared with that
		  of the old Fisher linear discriminant (FLD) classifiers.
		  The results have shown that the three ANN models give
		  higher performance. The percentage of correct
		  classification reaches 90%. Poorer diagnostic performance
		  was obtained from the FLD classifier. The system presented
		  here indicates that surface EMG, when properly processed,
		  can be used to provide the physician with a diagnostic
		  assist device.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  abramson01a,
  author	= {Abramson, M. and Wechsler, H.},
  title		= {Competitive reinforcement learning for combinatorial
		  problems},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2333--2338},
  organization	= {George Mason University},
  publisher	= {},
  address	= {},
  abstract	= {This paper shows that the competitive learning rule found
		  in Learning Vector Quantization (LVQ) serves as a promising
		  function approximator to enable reinforcement learning
		  methods to cope with a large decision search space, defined
		  in terms of different classes of input patterns, like those
		  found in the game of Go. In particular, this paper
		  describes S[arsa]LVQ, a novel reinforcement learning
		  algorithm and shows its feasibility for Go. As the
		  distributed LVQ representation corresponds to a (quantized)
		  codebook of compressed and generalized pattern templates,
		  the state space requirements for online reinforcement
		  methods are significantly reduced, thus decreasing the
		  complexity of the decision space and consequently improving
		  the play performance. As a result of competitive learning,
		  SLVQ can win against heuristic players and starts to level
		  off against stronger opponents such as Wally. SLVQ
		  outperforms S[arsa]Linear when playing against both a
		  heuristic player and Wally. Furthermore, while playing Go,
		  SLVQ learns to stay alive while SLinear fails to do so.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  abrantes93a,
  author	= {Abrantes, A. J. and Marques, J. S. },
  title		= {A common framework for snakes and {K}ohonen networks},
  booktitle	= {Neural Networks for Signal Processing III, Proceedings of
		  the 1993 IEEE-SP Workshop},
  year		= {1993},
  editor	= {Kamm, C. A. and Kuhn, G. M. and Yoon, B. and Chellappa, R.
		  and Kung, S. Y. },
  pages		= {251--60},
  organization	= {INESC/ISEL, Lisbon, Portugal},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  abrantes95a,
  author	= {Abrantes, A. J. and Marques, J. S. },
  title		= {Unified approach to snakes, elastic nets and {K}ohonen
		  maps},
  booktitle	= {Proceedings of the 1995 International Conference on
		  Acoustics, Speech, and Signal Processing},
  year		= {1995},
  volume	= {5},
  pages		= {3427--30},
  organization	= {INESC, Polytech. Inst. of Lisbon, Portugal},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  abrantes95b,
  author	= {A. J. Abrantes and J. S. Marques},
  title		= {Exploiting the common structure of {SOM} edge linking
		  algorithms: an experimental study},
  booktitle	= {Proceedings of the International Conference on Image
		  Processing},
  publisher	= {IEEE Computer Society Press},
  year		= {1995},
  volume	= {3},
  address	= {Los Alamitos, CA, USA},
  pages		= {624--7},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  abuelgasim94a,
  author	= {Abuelgasim, A. and Gopal, S. },
  title		= {Classification of multiangle and multispectral {ASAS} data
		  using a hybrid neural network model},
  booktitle	= {IGARSS '94. International Geoscience and Remote Sensing
		  Symposium. Surface and Atmospheric Remote Sensing:
		  Technologies, Data Analysis and Interpretation},
  year		= {1994},
  volume	= {3},
  pages		= {1670--2},
  organization	= {Dept. of Geogr. , Boston Univ. , MA, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  acciani93a,
  author	= {Acciani, G. and Bellomo, A. and Chiarantoni, E. and
		  Paradiso, A. },
  title		= {Validation of neural network analysis to predict prognosis
		  in breast cancer patients},
  booktitle	= {Proceedings of the 36th Midwest Symposium on Circuits and
		  Systems},
  year		= {1993},
  volume	= {1},
  pages		= {453--6},
  organization	= {Dipartimento di Elettrotecnica ed Elettronica, Politecnico
		  di Bari, Italy},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  acciani96a,
  author	= {G. Acciani and E. Chiarantoni and M. Minenna and F.
		  Vacca},
  title		= {Multivariate Data Projection Techniques based on a Network
		  of Enhanced Neural Elements},
  booktitle	= {ICNN'96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {211--216},
  dbinsdate	= {oldtimer}
}

@Article{	  acharya97a,
  author	= {S. Acharya and R. Sadananda},
  title		= {Promoting software reuse using \mbox{self-organizing}
		  maps},
  journal	= {Neural Processing Letters},
  year		= {1997},
  volume	= {5},
  number	= {3},
  pages		= {219--26},
  dbinsdate	= {oldtimer}
}

@Article{	  addison88a,
  author	= {Edwin R. Addison and William Dedmond},
  title		= {Criteria for choosing connectionist paradigms for
		  real-time data fusion and adaptive discrimination},
  journal	= {Neural Networks},
  year		= {1988},
  volume	= {1},
  number	= {1 SUPPL},
  pages		= {419},
  dbinsdate	= {oldtimer}
}

@Article{	  ae91a,
  author	= {T. Ae},
  title		= {Neural networks and functional memories},
  journal	= {Joho Shori},
  year		= {1991},
  volume	= {32},
  number	= {12},
  pages		= {1301--1309},
  note		= {(in Japanese)},
  x		= {Discusses artificial neural networks; . . . Kohonen
		  networks; learning vector quantization; topology-preserving
		  maps;},
  dbinsdate	= {oldtimer}
}

@Article{	  ae93a,
  author	= {Tadashi Ae and Reiji Aibara},
  title		= {Non von {N}eumann Chip Architecture---Present and Future},
  journal	= {IEICE Trans. Elecronics},
  year		= {1993},
  volume	= {E76-C},
  number	= {7},
  pages		= {1034--1044},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ae96a,
  author	= {T. Ae and K. Sakai and T. Toyosaki},
  title		= {Neural artificial intelligence system},
  booktitle	= {14th International Congress on Cybernetics. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {J. Parisi and S. C. Muller and W. Zimmermann},
  address	= {Berlin, Germany},
  pages		= {471--6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  aggarwal96a,
  author	= {R. K. Aggarwal and Q. Y. Xuan and A. T. Johns},
  title		= {Fault classification for double-circuits using
		  self-organization mapping},
  booktitle	= {32nd Universities Power Engineering Conference. UPEC '97},
  publisher	= {ACM},
  year		= {1996},
  volume	= {1},
  editor	= {E. A. Fox and G. Marchionini},
  address	= {New York, NY, USA},
  pages		= {440--3},
  dbinsdate	= {oldtimer}
}

@Article{	  aghajan93a,
  author	= {H. K. Aghajan and C. D. Schaper and T. Kailath},
  title		= {Machine vision techniques for subpixel estimation of
		  critical dimensions},
  journal	= {Opt. Eng. },
  year		= {1993},
  volume	= {32},
  number	= {4},
  pages		= {828--839},
  month		= {April},
  annote	= {{SOFM} is investigated for edge detection purposes. },
  dbinsdate	= {oldtimer}
}

@Article{	  aguilera01a,
  author	= {Aguilera, P. A. and Frenich, A. G. and Torres, J. A. and
		  Castro, H. and Vidal, J. L. M. and Canton, M},
  title		= {Application of the Kohonen neural network in coastal water
		  management: Methodological development for the assessment
		  and prediction of water quality},
  journal	= {WATER RESEARCH},
  year		= {2001},
  volume	= {35},
  number	= {17},
  month		= {DEC},
  pages		= {4053--4062},
  abstract	= {Kohonen neural network (KNN) was applied to nutrient data
		  (ammonia, nitrite, nitrate and phosphate) taken from
		  coastal waters in a Spanish tourist area. The activation
		  maps obtained were not sufficient to evaluate and predict
		  the trophic status of coastal waters. To achieve this aim,
		  a new methodology is proposed which uses as its starting
		  point the activation maps obtained from KNN. Firstly, to
		  evaluate the trophic status of the coastal waters. it
		  consists of the development of a quadrat system which
		  enables a better classification than the traditional
		  classification based simply on standardized data. The new
		  classification allows clear differentiation of water
		  quality within the mesotrophic band. Secondly, and in order
		  to use the activation maps as predictive tools, the trophic
		  classification. obtained from activation maps, was
		  transposed onto new activation maps. To do this, the
		  activation maps of the sampling points which defined each
		  trophic group were superimposed. To avoid unnecessary
		  complexity and to facilitate the process, this
		  superimposition was undertaken only where the frequency
		  exceeded 0.05. In this way, four frequency maps related to
		  the trophic status of coastal waters (potentially
		  eutrophic, high mesotrophic, low mesotrophic and
		  oligotrophic) were obtained. There was no loss of relevant
		  information in the new maps thus obtained. These frequency
		  maps served as the basis for the successful prediction of
		  the trophic status of random samples of coastal waters.
		  This methodology, based on KNN, is proposed as a tool to
		  aid the decision-making in coastal water quality
		  management. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  ahalt89a,
  author	= {S. C. Ahalt and T. P. Jung and A. K. Krishnamurthy},
  title		= {Radar target identification using the learning vector
		  quantization neural network},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {2},
  pages		= {605},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ahalt90a,
  author	= {S. C. Ahalt and T. Jung and A. K. Krishnamurthy},
  title		= {A comparison of radar signal classifiers},
  booktitle	= {Proc. IEEE International Conference on Systems
		  Engineering},
  year		= {1990},
  pages		= {609--612},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  ahalt90b,
  author	= {Stanley C. Ahalt and Ashok K. Krishnamurty and Prakoon
		  Chen and Douglas E. Melton},
  title		= {Competitive Learning Algorithms for Vector Quantization},
  journal	= {Neural Networks},
  year		= {1990},
  volume	= {3},
  number	= {3},
  pages		= {277--290},
  dbinsdate	= {oldtimer}
}

@Article{	  ahmad01a,
  author	= {Ahmad, K. and Vrusias, B. L. and Ledford, A.},
  title		= {Choosing feature sets for training and testing
		  self-organising maps: A case study},
  journal	= {NEURAL COMPUTING \& APPLICATIONS},
  year		= {2001},
  volume	= {10},
  number	= {1},
  pages		= {56--66},
  abstract	= {Statistical pattern recognition techniques, supervised and
		  unsupervised classification techniques being two good
		  examples here, rely on the computations of similarity and
		  distance metrics. The distances are computed in a
		  multi-dimensional space. The axes of this space in
		  principle relate to the features inherent in the input
		  data. Usually, such features are chosen by neural network
		  developers, thereby introducing a possible bias. A method
		  of automatically generating feature sets is discussed, with
		  specific reference to the categorisation of streams of
		  free-text news items. The feature sets were generated by a
		  procedure that automatically selects a group of keywords
		  based on a lexico-semantic analysis. Three different types
		  of text streams---headlines only, news summaries and full
		  news items including the body of the text---have been
		  categorised using Self-Organising Feature Maps (SOFM). A
		  method for assessing the discrimination ability of a SOFM,
		  based on Fisher's Linear Discriminant Rule suggests that
		  the maps trained on vectors related to summaries only
		  provides a fairly accurate cluster when compared with
		  vectors related to full text. The use of summaries as
		  document surrogates for document categorisation is
		  suggested.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ahmad99a,
  author	= {Ahmad, K. and Bale, T. A. and Burford, D.},
  title		= {Text classification and minimal-bias training vectors},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {2816--19},
  abstract	= {The categorisation of text using neural networks has been
		  pursued in the context of emerging digital libraries.
		  Frequency of key terms in a document set are used to create
		  training vectors and a feature map can be trained such that
		  texts are topologically ordered. The choice of training
		  vectors and training regimes remains an open question in
		  neural network research. In order to minimise training bias
		  we have developed a method which uses the linguistic
		  characteristics of domain-specific texts for the creation
		  of training vectors. This method has been evaluated by
		  classifying a standard free-text document set (TIPSTER's
		  SUMMAC AP news wire collection) using a Kohonen feature
		  map. This method is particularly relevant to the domain of
		  financial prediction, considering the large volume of news
		  reports available to financial analysts.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ahmed00a,
  author	= {Ahmed, Mohamed and Cooper, Brian and Love, Shaun},
  title		= {Document image segmentation using a two-stage neural
		  network},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {3962},
  pages		= {25--33},
  organization	= {Lexmark Int Inc},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  abstract	= {In this paper, we present a new system to segment and
		  label document images into text, halftone images, and
		  background using feature extraction and unsupervised
		  clustering. Each pixel is assigned a feature pattern
		  consisting of a scaled family of differential geometrical
		  invariant features and texture features extracted from the
		  cooccurence matrix. The invariant feature pattern is then
		  assigned to a specific region using a two-stage neural
		  network system. The first stage is a self-organizing
		  principal components analysis (SOPCA) network that is used
		  to project the feature vector onto its leading principal
		  axes found by using principal components analysis (PCA).
		  Using the SOPCA algorithm, we can train the SOPCA network
		  to project our feature vector orthogonally onto the
		  subspace spanned by the eigenvectors belonging to the
		  largest eigenvalues. By doing that we ensure that the
		  vector is represented by a reduced number of effective
		  features. The next step is to cluster the output of the
		  SOPCA network into different regions. This is accomplished
		  using a self-organizing feature-map (SOFM) network. In this
		  paper, we demonstrate the power of the SOPCA-SOFM approach
		  to segment document images into text, halftone, and
		  background.},
  dbinsdate	= {2002/1}
}

@InCollection{	  ahmed97a,
  author	= {Mohamed N. Ahmed and Aly A. Farag},
  title		= {Two-Stage Neural Network for Volume Segmentation of
		  Medical Images},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {III},
  pages		= {1373--1378},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ahmed97b,
  author	= {M. N. {A}hmed and A. A. Farag},
  title		= {{3D} segmentation and labeling using
		  \mbox{self-organizing} {K}ohonen network for volumetric
		  measurements on brain {CT} imaging to quantify {TBI}
		  recovery},
  booktitle	= {Proceedings of the 18th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  `Bridging Disciplines for Biomedicine'},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {2},
  editor	= {H. Boom and C. Robinson and W. Rutten and M. Neuman and H.
		  Wijkstra},
  address	= {New York, NY, USA},
  pages		= {738--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ahola99a,
  author	= {Ahola, J. and Alhoniemi, E. and Simula, O.},
  title		= {Monitoring industrial processes using the
		  \mbox{self-organizing} map},
  booktitle	= {SMCia/99 Proceedings of the 1999 IEEE Midnight---Sun
		  Workshop on Soft Computing Methods in Industrial
		  Applications},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {},
  pages		= {22--7},
  abstract	= {In this paper, three process monitoring methods based on
		  the self-organizing map (SOM) are presented: trajectory
		  display, fuzzy response and probabilistic response. These
		  approaches are compared with each other and also
		  demonstrated in two case studies: continuous pulping and
		  hot rolling of steel strips.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ahrns95a,
  author	= {Ingo Ahrns and J{\"{o}}rg Bruske and Gerald Sommer},
  title		= {On-Line Learning with Dynamic Cell Structure},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {141--146},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  aiello00a,
  author	= {Aiello, A. and Grimaldi, D. and Daponte, P.},
  title		= {Non-intrusive estimation of the carrier frequency in
		  {GMSK} signals},
  booktitle	= {AUTOTESTCON (Proceedings)},
  year		= {2000},
  editor	= {},
  volume	= {},
  pages		= {621--625},
  organization	= {Universita della Calabria},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The paper presents an efficient technique for evaluating
		  the carrier frequency in GMSK communication systems. This
		  technique operates in a non-intrusive way and is based on
		  measurement of the frequency shift from the expected value.
		  It utilizes the Learning Vector Quantisation neural network
		  based demodulator for reconstructing the transmitted phase.
		  From this and the received phase the mean value of the
		  frequency shift is estimated. The technique does not
		  require a high frequency sampling rate because the
		  base-band signal is processed. Tests performed on
		  experimental GSMK signals show that the technique is quite
		  attractive and is also fast and more accurate compared to
		  others.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  aiello01a,
  author	= {Aiello, A. and Grimaldi, D. and Veltri, S.},
  title		= {Frequency error measurement in {GMSK} signals in a
		  multipath propagation environment},
  booktitle	= {Conference Record---IEEE Instrumentation and Measurement
		  Technology Conference},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {507--512},
  organization	= {Dip. di Elettronica, Informatica e Sistemistica,
		  Universita della Calabria},
  publisher	= {},
  address	= {},
  abstract	= {The paper presents an efficient method for evaluating the
		  carrier frequency in GMSK communication systems. This
		  method operates in a non-intrusive way. It utilizes the
		  Learning Vector Quantisation neural network based
		  demodulator for reconstructing the transmitted phases. From
		  these and the expected phases is estimated the carrier
		  frequency error. The method is able to operate both in
		  static and multi-path propagation cases and it does not
		  require a high frequency sampling rate because the
		  base-band signal is processed. In order to apply the method
		  two procedures, PSP (Procedure for Static Propagation) and
		  PMP (Procedure for Multi-path Propagation), are set-up.
		  Tests performed on GMSK signals show that the method is
		  quite attractive, fast and more accurate if compared with
		  other approaches.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  aiello01c,
  author	= {Aiello, A. and Grimaldi, D. and Rapuano, S.},
  title		= {{GMSK} neural network based demodulator},
  booktitle	= {Proceedings of the International Workshop on Intelligent
		  Data Acquisition and Advanced Computing Systems: Technology
		  and Applications. IDAACS'2001. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {},
  pages		= {2--6},
  abstract	= {The pattern recognition characteristics of the Artificial
		  Neural Networks are used to realise a real demodulator for
		  Gaussian Minimum Shift-Keying signals, used in the GSM
		  telecommunications. The demodulator utilizes the learning
		  vector quantization (LVQ) neural network. It offers both
		  greater efficiency in demodulating and less sensitivity to
		  noise. In order to solve the problem regarding input signal
		  synchronization, a pre-processing phase is organised. The
		  demodulator prototype has been realised by implementing the
		  pre-processing phase and the LVQ neural network on
		  TMS320C30 digital signal processor. The demodulator has
		  been tested according to the European Telecommunication
		  Standard Institute recommendations.},
  dbinsdate	= {2002/1}
}

@Article{	  aigno98a,
  author	= {Song Aigno and Lu Jiren},
  title		= {Evolutionary programming based {K}ohonen network for
		  passive sonar targets clustering analysis},
  journal	= {Acta Electronica Sinica},
  year		= {1998},
  volume	= {26},
  number	= {7},
  pages		= {128--32},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  aike95a,
  author	= {Guo Aike and Sun Haijian and Yang Xian Yi},
  title		= {A multilayer neural network model for the perception of
		  rotational motion},
  booktitle	= {Proceedings of International Conference on Neural
		  Information Processing (ICONIP `95)},
  year		= {1995},
  editor	= {Zhong, Y. and Yang, Y. and Wang, M. },
  volume	= {1},
  pages		= {121--4},
  organization	= {Inst. of Biophys. , Acad. Sinica, Beijing, China},
  publisher	= {Publishing House of Electron. Ind},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@Article{	  aires-de-sousa02a,
  author	= {Aires-de-Sousa, Joao and Gasteiger, Johann},
  title		= {Prediction of enantiomeric selectivity in chromatography:
		  Application of conformation-dependent and
		  conformation-independent descriptors of molecular
		  chirality},
  journal	= {Journal of Molecular Graphics and Modelling},
  year		= {2002},
  volume	= {20},
  number	= {5},
  month		= {},
  pages		= {373--388},
  organization	= {Departamento de Quimica, Campus Fac. de Ciencias e
		  Tecnologia, Universidade Nova de Lisboa},
  publisher	= {},
  address	= {},
  abstract	= {In order to process molecular chirality by computational
		  methods and to obtain predictions for properties that are
		  influenced by chirality, a fixed-length
		  conformation-dependent chirality code is introduced. The
		  code consists of a set of molecular descriptors
		  representing the chirality of a 3D molecular structure. It
		  includes information about molecular geometry and atomic
		  properties, and can distinguish between enantiomers, even
		  if chirality does not result from chiral centers. The new
		  molecular transform was applied to two datasets of chiral
		  compounds, each of them containing pairs of enantiomers
		  that had been separated by chiral chromatography. The
		  elution order within each pair of isomers was predicted by
		  means of Kohonen neural networks (NN) using the chirality
		  codes as input. A previously described
		  conformation-independent chirality code was also applied
		  and the results were compared. In both applications
		  clustering of the two classes of enantiomers (first eluted
		  and last eluted enantiomers) could be successfully achieved
		  by NN and accurate predictions could be obtained for
		  independent test sets. The chirality code described here
		  has a potential for a broad range of applications from
		  stereoselective reactions to analytical chemistry and to
		  the study of biological activity of chiral compounds. },
  dbinsdate	= {2002/1}
}

@InCollection{	  aitsab96a,
  author	= {O. Aitsab and R. Pyndiah and B. Solaiman},
  title		= {Joint optimization of multi-dimensional {SOFM} codebooks
		  with QAM modulations for vector quantized image
		  transmission},
  booktitle	= {Proceedings IWISPO '96. Third International Workshop on
		  Image and Signal Processing on the Theme of Advances in
		  Computational Intelligence},
  publisher	= {Elsevier},
  year		= {1996},
  editor	= {B. G. Mertzios and P. Liatsis},
  address	= {Amsterdam, Netherlands},
  pages		= {3--6},
  dbinsdate	= {oldtimer}
}

@Article{	  ajjimarangsee89a,
  author	= {P. Ajjimarangsee and T. L. Huntsberger},
  title		= {Neural network model for fusion of visible and infrared
		  sensor outputs},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1989},
  volume	= {1003},
  pages		= {153--160},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  note		= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ajjimarangsee89b,
  author	= {P. Ajjimarangsee and T. L. Huntsberger},
  title		= {Unsupervised pattern recognition using parallel
		  \mbox{self-organizing} feature maps},
  booktitle	= {Proc. 4th Conference on Hypercubes, Concurrent Computers
		  and Applications},
  year		= {1989},
  volume	= {II},
  pages		= {1093--1096},
  organization	= {D. O. E. ; US Air Force; NASA},
  publisher	= {Golden Gate Enterprises},
  address	= {Los Altos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  akingbehin90a,
  author	= {K. Akingbehin and K. Khorasani and A. Shaout},
  title		= {Alternative models for neural computing},
  booktitle	= {Proc. 2nd IASTED Int. Symp. Expert Systems and Neural
		  Networks},
  year		= {1990},
  editor	= {M. H. Hamza},
  pages		= {66--69},
  organization	= {IASTED},
  publisher	= {Acta Press},
  address	= {Anaheim, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  al-sulaiman93a,
  author	= {M. A. Al-Sulaiman and S. I. Ahson and M. I. Al-Kanhal},
  title		= {Construction of {A}rabic Phoneme Maps using {L}earning
		  {V}ector {Q}uantization},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {IV},
  pages		= {84--90},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  alahakoon00a,
  author	= {Alahakoon, Damminda and Halgamuge, Saman K. and
		  Srinivasan, Bala},
  title		= {Dynamic self-organizing maps with controlled growth for
		  knowledge discovery},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  volume	= {11},
  number	= {3},
  month		= {May},
  pages		= {601--614},
  organization	= {Monash Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The growing self-organizing map (GSOM) has been presented
		  as an extended version of the self-organizing map (SOM),
		  which has significant advantages for knowledge discovery
		  applications. In this paper, the GSOM algorithm is
		  presented in detail and the effect of a spread factor,
		  which can be used to measure and control the spread of the
		  GSOM, is investigated. The spread factor is independent of
		  the dimensionality of the data and as such can be used as a
		  controlling measure for generating maps with different
		  dimensionality, which can then be compared and analyzed
		  with better accuracy. The spread factor is also presented
		  as a method of achieving hierarchical clustering of a data
		  set with the GSOM. Such hierarchical clustering allows the
		  data analyst to identify significant and interesting
		  clusters at a higher level of the hierarchy, and as such
		  continue with finer clustering of only the interesting
		  clusters. Therefore, only a small map is created in the
		  beginning with a low spread factor, which can be generated
		  for even a very large data set. Further analysis is
		  conducted on selected sections of the data and as such of
		  smaller volume. Therefore, this method facilitates the
		  analysis of even very large data sets.},
  dbinsdate	= {2002/1}
}

@Article{	  alahakoon98a,
  author	= {Alahakoon, D. and Halgamuge, S. K. and Srinivasan, B.},
  title		= {Self growing cluster development approach to data mining},
  journal	= {IEEE International Conference on Systems, Man, and
		  Cybernetics},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  number	= {},
  volume	= {3},
  pages		= {2901--2906},
  abstract	= {We describe a data analysis method using a structure
		  adapting neural network with two additional layers. The
		  neural network used is an extended version of a self
		  organizing feature map which can adapt it's structure to
		  better represent the clusters in data. Once the clusters
		  are identified, we use two additional layers on the feature
		  map to analyze the clusters and the representation of
		  attributes in the clusters. Simulations and initial results
		  with two simple benchmark data sets are also described.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  alahakoon98b,
  author	= {Alahakoon, D. and Halgamuge, S. K.},
  title		= {Knowledge discovery with supervised and unsupervised self
		  evolving neural networks},
  booktitle	= {Proceedings of the 5th International Conference on Soft
		  Computing and Information/Intelligent Systems.
		  Methodologies for the Conception, Design and Application of
		  Soft Computing},
  publisher	= {World Scientific},
  address	= {Singapore},
  year		= {1998},
  volume	= {2},
  pages		= {907--10},
  abstract	= {We describe two extended algorithms useful in knowledge
		  discovery and data mining. Firstly the self organising
		  feature map (SOFM) extended by acquiring the ability of
		  self growing nodes is described. The second method is a
		  generalised form of radial basis function (RBF) networks
		  with the capability of expanding the hidden layer
		  automatically. The main contribution of this paper is to
		  highlight the fact that the extension of the existing fixed
		  structure neural networks into self evolving neural
		  networks, convert them into useful tools for data mining
		  and knowledge discovery.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  alahakoon99a,
  author	= {Alahakoon, L. D. and Halgamuge, S. K. and Srinivasan, B.},
  title		= {A self generating neural architecture for data analysis},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3548--51},
  abstract	= {Supervised and unsupervised self generating neural network
		  architectures have been used in the recent past. Our
		  previous work (1998) has described an unsupervised self
		  generating feature map, called the growing self organising
		  map (GSOM). In this paper we describe some extensions to
		  the GSOM such that it could be used to map and analyse more
		  realistic data sets.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  alahakoon99b,
  author	= {Alahakoon, D. and Halgamuge, S. K. and Srinivasan, B.},
  title		= {Data mining with self generating neuro-fuzzy classifiers},
  booktitle	= {FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems.
		  Conference Proceedings},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1096--101},
  abstract	= {Self generating neural networks have been presented as a
		  better alternative to fixed structure networks in data
		  mining applications. It has also been shown that the
		  nearest prototype classifier is functionally equivalent to
		  an alternative fuzzy classifier model. Several supervised
		  neural networks have been developed to generate nearest
		  prototypes which can be converted to fuzzy rules. We
		  present an extended version of our growing self-organising
		  map (GSOM) model which can also be used to identify nearest
		  prototypes for generating fuzzy rules.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  alander90a,
  author	= {Jarmo T. Alander and Antti Autere and Lasse
		  Holmstr{\"{o}}m and Peter Holmstr{\"{o}}m and Ari
		  H{\"{a}}m{\"{a}}l{\"{a}}inen and Juha Tuominen},
  title		= {Surface Type Recognition by a Hair Sensor},
  booktitle	= {Communication Control and Signal Processing},
  editor	= {E. Arikan},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  year		= {1990},
  pages		= {1757--1764},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  alander91a,
  author	= {Jarmo T. Alander and Matti Frisk and Lasse Holmstr{\"{o}}m
		  and Ari H{\"{a}}m{\"{a}}l{\"{a}}inen and Juha Tuominen},
  title		= {Process Error Detection using Self-Organizing Feature
		  Maps},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1229--1232},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  alander91b,
  author	= {Jarmo T. Alander and Matti Frisk and Lasse Holmstr{\"{o}}m
		  and Ari H{\"{a}}m{\"{a}}l{\"{a}}inen and Juha Tuominen},
  title		= {Process Error Detection Using \mbox{Self-organizing}
		  Feature Maps},
  institution	= {Rolf Nevanlinna Institute},
  address	= {Helsinki, Finland},
  type		= {Res. Reports},
  number	= {A5},
  year		= {1991},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  alba01a,
  author	= {Alba, J. L. and Pujol, A. and Villanueva, J. J.},
  title		= {Separating geometry from texture to improve face
		  analysis},
  booktitle	= {IEEE International Conference on Image Processing},
  year		= {2001},
  editor	= {},
  volume	= {2},
  pages		= {673--676},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {This article studies the effect of preprocessing a
		  classical PCA decomposition using a modified Self
		  Organizing Map (SOM) in order to find shape clusters to
		  improve the texture analysis by means of a pool of PCAs. In
		  most successfully view-based recognition systems, shape and
		  texture are jointly used to statistically model a linear or
		  piece-wise linear subspace that optimally explains the face
		  space for a specific database. Our work is aimed to
		  separate the influence that variance in face shape stamps
		  on the set of eigenfaces in the classical PCA
		  decomposition. A set of experiments show the reliability of
		  this new system.},
  dbinsdate	= {2002/1}
}

@Article{	  albeverio97a,
  author	= {S. Albeverio and N. Kruger and B. Tirozzi},
  title		= {An extended {K}ohonen phonetic map},
  journal	= {Mathematical and Computer Modelling},
  year		= {1997},
  volume	= {25},
  number	= {2},
  pages		= {69--73},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  albrecht94a,
  author	= {Albrecht, T. and Matz, G. and Hunte, T. and Hildemann, J.
		  },
  title		= {An intelligent gas sensor system for the identification of
		  hazardous airborne compounds using an array of
		  semiconductor gas sensors and {K}ohonen feature map neural
		  networks},
  booktitle	= {Second Internatinal Conference on 'Intelligent Systems
		  Engineering'},
  year		= {1994},
  pages		= {130--7},
  organization	= {Tech. Univ. Hamburg-Harburg, Germany},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  alder90a,
  author	= {Michael Alder and Roberto Togneri and Edmund Lai and
		  Yianni Attikiouzel},
  title		= {{K}ohonen's algorithm for the numerical parametrisation of
		  manifolds},
  journal	= {Pattern Recognition Letters},
  year		= {1990},
  volume	= {11},
  pages		= {313--319},
  dbinsdate	= {oldtimer}
}

@Article{	  alder91a,
  author	= {M. D. Alder and R. Togneri and Y. Attikiouzel},
  title		= {Dimension of the speech space},
  journal	= {IEE Proc. I [Communications, Speech and Vision]},
  year		= {1991},
  volume	= {138},
  number	= {3},
  pages		= {207--214},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@Article{	  alhoniemi00a,
  author	= {Alhoniemi, Esa},
  title		= {Analysis of pulping data using the Self-Organizing Map},
  journal	= {TAPPI Journal},
  year		= {2000},
  volume	= {83},
  number	= {7},
  month		= {Jul},
  pages		= {66},
  organization	= {Helsinki Univ of Technology},
  publisher	= {TAPPI Press},
  address	= {Norcross, GA},
  abstract	= {Analysis of process data makes it possible to obtain
		  useful information from processes or phenomena that are
		  analytically difficult to deal with. To demonstrate the
		  possibilities of this kind of approach, an analysis of the
		  faults that occurred in a continuous digester is presented.
		  The process data are fed to an artificial neural network,
		  the self organizing map (SOM), which is used to form visual
		  presentations of the data. By interpreting these
		  visualizations, the reason for the faults can be
		  determined.},
  dbinsdate	= {2002/1}
}

@InCollection{	  alhoniemi96a,
  author	= {E. Alhoniemi and O. Simula and J. Vesanto},
  title		= {Monitoring and modeling of complex processes using the
		  \mbox{self-organizing} map},
  booktitle	= {Progress in Neural Information Processing. Proceedings of
		  the International Conference on Neural Information
		  Processing},
  publisher	= {Springer-Verlag},
  year		= {1996},
  volume	= {2},
  editor	= {S. -I. Amari and L. Xu and L. -W. Chan and I. King and K.
		  -S. Leung},
  address	= {Singapore},
  pages		= {1169--74},
  dbinsdate	= {oldtimer}
}

@Article{	  alhoniemi99a,
  author	= {Alhoniemi, Esa and Hollm\'en, Jaakko and Simula, Olli and
		  Vesanto, Juha},
  title		= {Process monitoring and modeling using the
		  \mbox{self-organizing} map},
  journal	= {Integrated Computer-Aided Engineering},
  year		= {1999},
  number	= {1},
  volume	= {6},
  pages		= {3--14},
  abstract	= {The Self-Organizing Map (SOM) is a powerful neural network
		  method for analysis and visualization of high-dimensional
		  data. It maps nonlinear statistical dependencies between
		  high-dimensional measurement data into simple geometric
		  relationships on a usually two-dimensional grid. The
		  mapping roughly preserves the most important topological
		  and metric relationships of the original data elements and,
		  thus, inherently clusters the data. The need for
		  visualization and clustering occurs, for instance, in the
		  analysis of various engineering problems. In this paper,
		  the {SOM} has been applied in monitoring and modeling of
		  complex industrial processes. Case studies, including pulp
		  process, steel production, and paper industry axe
		  described.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  alhoniemi99b,
  author	= {Alhoniemi, E. and Himberg, J. and Vesanto, J.},
  title		= {Probabilistic measures for responses of self-organizing
		  map units},
  booktitle	= {Proceedings of the International ICSC Congress on
		  Computational Intelligence Methods and Applications. ICSC
		  Academic Press, Zurich, Switzerland},
  year		= {1999},
  volume	= {},
  pages		= {},
  abstract	= {The self-organizing map (SOM) is a widely used data
		  visualization tool in engineering applications. The
		  algorithm performs a nonlinear mapping from a
		  high-dimensional data space to a low-dimensional space,
		  which is typically a two-dimensional, rectangular grid.
		  This makes it possible to present multidimensional data in
		  two dimensions. Often the model vectors of the SOM and a
		  new data sample need to be compared. The SOM, however,
		  gives no probability measures to determine if the sample
		  belongs to data sets determined by map units. For this
		  purpose a modified batch version of reduced kernel density
		  estimator (RXDE) was tested. The results were compared with
		  Gaussian mixture model (GMM) and S-Map.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  alhumaidi96a,
  author	= {S. M. Alhumaidi and W. L. Jones and Jun-Dong Park and S.
		  Ferguson and M. H. Thursby and S. H. Yueh},
  title		= {A neural network sea ice edge classifier for the {NASA}
		  Scatterometer},
  booktitle	= {IGARSS '96. 1996 International Geoscience and Remote
		  Sensing Symposium. Remote Sensing for a Sustainable
		  Future},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  editor	= {T. I. Stein},
  address	= {New York, NY, USA},
  pages		= {1526--8},
  dbinsdate	= {oldtimer}
}

@Article{	  ali93a,
  author	= {Ali, K. S. },
  title		= {Self learning for autonomous systems},
  journal	= {Computers \& Industrial Engineering},
  year		= {1993},
  volume	= {25},
  pages		= {401--4},
  month		= {Sept},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  alici96a,
  author	= {Y. Alici},
  title		= {Neural networks in corporate failure prediction: the UK
		  experience},
  booktitle	= {Neural Networks in Financial Engineering. Proceedings of
		  the Third International Conference on Neural Networks in
		  the Capital Markets},
  publisher	= {World Scientific},
  year		= {1996},
  editor	= {A. -P. N. Refenes and Y. Abu-Mostafa and J. Moody and A.
		  Weigend},
  address	= {Singapore},
  pages		= {393--406},
  dbinsdate	= {oldtimer}
}

@Article{	  alici96b,
  author	= {Alici, Yurt},
  title		= {Corporate solvency map through \mbox{self-organising}
		  neural networks},
  journal	= {IEEE IAFE Conference on Computational Intelligence for
		  Financial Engineering (CIFEr)},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1996},
  number	= {},
  volume	= {},
  pages		= {286--292},
  abstract	= {Corporate failure prediction has been used in the
		  application of both parametric classical classification and
		  non-parametric artificial neural network techniques.
		  Although discriminant and logistic regression analysis have
		  been accepted as standard pattern recognition devices,
		  different kinds of neural network technology have recently
		  demonstrated promising outcomes, in terms of accuracy, when
		  compared with results from classical pattern recognition
		  techniques. Most of the neural net studies in corporate
		  failure prediction have centred on implementing a large
		  variety of supervised learning algorithms. Considering
		  stochastic properties of financial ratios due to creative
		  accounting practices, different accounting policies and
		  deviant patterns of so-called healthy companies, little
		  work has been conducted in identifying different patterns
		  of both failed and solvent firms. Therefore, the purpose of
		  this study is to extract solvency maps of UK listed
		  manufacturing firms, by employing self-organising maps. The
		  results obtained from this research indicate that there is
		  marked difference between failed and non-failed firms in
		  terms of financial characteristics although different
		  financial structures exist amongst both bankrupt and
		  solvent companies.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  alirezaie95a,
  author	= {J. Alirezaie and M. E. Jernigan and C. Nahmias},
  title		= {Neural network based segmentation of magnetic resonance
		  images of the brain},
  booktitle	= {1995 IEEE Nuclear Science Symposium and Medical Imaging
		  Conference Record},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {3},
  editor	= {P. A. Moonier},
  address	= {New York, NY, USA},
  pages		= {1397--401},
  dbinsdate	= {oldtimer}
}

@Article{	  alirezaie97a,
  author	= {J. Alirezaie and M. E. Jernigan and C. Nahmias},
  title		= {Neural network-based segmentation of magnetic resonance
		  images of the brain},
  journal	= {IEEE Transactions on Nuclear Science},
  year		= {1997},
  volume	= {44},
  number	= {2},
  pages		= {194--8},
  note		= {(1995 Nuclear Science Symposium and Medical Imaging
		  (NSS/MIC) Conf. Date: 21--28 Oct. 1995 Conf. Loc: San
		  Francisco, CA, USA)},
  dbinsdate	= {oldtimer}
}

@Article{	  alirezaie97b,
  author	= {J. Alirezaie and M. E. Jernigan and C. Nahmias},
  title		= {Automatic segmentation of {MR} images using
		  \mbox{self-organizing} feature mapping and neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3034},
  number	= {pt. 1--2},
  pages		= {138--49},
  note		= {(Medical Imaging 1997: Image Processing Conf. Date: 25--28
		  Feb. 1997 Conf. Loc: Newport Beach, CA, USA Conf. Sponsor:
		  SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  allinson89a,
  author	= {Nigel M. Allinson and Martin J. Johnson and Kevin J.
		  Moon},
  title		= {Digital realisation of {S}elf-{O}rganising {M}aps},
  booktitle	= {Advances in Neural Information Processing Systems I},
  year		= {1989},
  editor	= {David S. Touretzky},
  pages		= {728--738},
  publisher	= {Morgan Kaufmann},
  address	= {San Mateo, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  allinson89b,
  author	= {N. M. Allinson and M. T. Brown and M. J. Johnson},
  title		= {{{0,1}N} space \mbox{self-organising} feature
		  maps---extensions and hardware},
  booktitle	= {IEE International Conference on Artificial Neural
		  Networks, Publication 313},
  year		= {1989},
  pages		= {261--264},
  publisher	= {IEE},
  address	= {London, UK},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  allinson89c,
  author	= {N. M. Allinson and M. J. Johnson},
  title		= {Realisation of self organising neural maps in {{0,1}N}
		  space},
  booktitle	= {{New Developments in Neural Computing}},
  publisher	= {Adam-Hilger},
  year		= {1989},
  editor	= {J. G. Taylor and C. L T. Mannion},
  chapter	= {},
  pages		= {79--86},
  address	= {Bristol, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  allinson92a,
  author	= {N. M. Allinson and A. W. Ellis},
  title		= {Face recognition: combining cognitive psychology and image
		  engineering},
  journal	= {IEE Electronics and Communication J. },
  year		= {1992},
  volume	= {4},
  number	= {},
  pages		= {291--300},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  allinson92b,
  author	= {N. M. Allinson and M. J. Johnson},
  title		= {Application of \mbox{self-organising} digital neural
		  networks in attentive vision systems},
  booktitle	= {Proc. Fourth Int. IEE Conference on Image Processing and
		  its Applications, Maastricht, Netherlands},
  year		= {1992},
  pages		= {193--196},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  allinson92c,
  author	= {N. M. Allinson},
  title		= {Self-organising neural maps and their applications},
  booktitle	= {Theory and Applications of Neural Networks},
  publisher	= {Springer},
  year		= {1992},
  editor	= {J. G. Taylor and C. L. T. Mannion},
  chapter	= {},
  pages		= {101--120},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  allinson93a,
  author	= {N. M. Allinson and H. Yin},
  title		= {Comparison of {K}ohonen \mbox{self-organising} maps and
		  {K}alman filtering},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  allinson98a,
  author	= {N. M. Allinson and Hujun Yin},
  title		= {Interactive and semantic data visualisation using
		  \mbox{self-organising} maps},
  booktitle	= {IEE Colloquium Neural Networks in Interactive Multimedia
		  Systems},
  publisher	= {IEE},
  year		= {1998},
  address	= {London, UK},
  pages		= {1/1--10},
  dbinsdate	= {oldtimer}
}

@InCollection{	  allinson99a,
  author	= {N. M. Allinson and H. Yin},
  title		= {Self-Organising Maps for pattern recognition},
  booktitle	= {Kohonen Maps},
  pages		= {111--20},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Proceedings{	  allison01a,
  title		= {Advances in Self-Organising Maps},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  almajali01a,
  author	= {Almajali, S. and Sharieh, A. and Qutiashat, M.},
  title		= {Arabic speech recognition using {SOM}-{LVQ} neural
		  networks},
  journal	= {Advances-in-Modelling-\&-Analysis-B:-Signals,-Information,-Patterns,-Data-Acquisition,-Transmission,-Processing,-Classification}
		  ,
  year		= {2001},
  volume	= {44},
  pages		= {1--15},
  abstract	= {In this research, an Arabic speech recognition system
		  (ASRS) has been developed using self-organized map (SOM)
		  neural networks, which adopts learning vector quantization
		  (LVQ). In this system, Arabic speech recognition was
		  successfully developed and tested for both phonemes and a
		  set of words. To increase recognition accuracy, the 38
		  Arabic phonemes were arranged into a set of classes based
		  on linguistics and acoustics principles. For each class of
		  phonemes, a neural network that performs the recognition
		  task for that class was built. For vowel-semivowel,
		  nasal-glide, and subset of unvoiced phoneme classes, the
		  recognition generalization accuracy (GA) of tests reached
		  around 92%, 96%, 98%, respectively. The ASRS produced GA
		  equals to 80.49% for all Arabic phonemes, 86.9% for 33
		  phonemes. 89.5% for selected 25 phonemes, and 96% for
		  selected 20 phonemes. A set of words that found in the
		  Arabic menus of Microsoft Word package was selected to be
		  tested by the ASRS. For each menu, a separate neural
		  network/map has been created. The GA using SOM for the nine
		  menus was around 88%. The GA after 1500 LVQ steps was
		  around 91% and after 3000 LVQ steps was around 92%.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  alpaydin93a,
  author	= {R. Alpaydin and U. {\"{U}}nl{\"{u}}akin and F.
		  G{\"{u}}rgen and E. Alpaydin},
  title		= {Comparing Distributed and Local Neural Classifiers for the
		  Recognition of Japanese Phonemes},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {239--242},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  alrashdan00a,
  author	= {Alrashdan, A. and Motavalli, S. and Fallahi, B.},
  title		= {Automatic segmentation of digitized data for reverse
		  engineering applications},
  journal	= {IIE Transactions},
  year		= {2000},
  volume	= {32},
  pages		= {59--69},
  abstract	= {Reverse engineering is the process of developing a CAD
		  model and a manufacturing database for an existing part.
		  The paper is about the automatic segmentation of 3D
		  digitized data for such applications. This is achieved
		  using a combination of region and edge based approaches. It
		  is assumed that the part surface contains planar as well as
		  curved surfaces that are embedded in a base surface. The
		  part surface should be visible to a single scanning probe
		  (2 1/2 D object). Neural net algorithms are developed. A
		  backpropagation net is used to segment part surfaces into
		  surface primitives homogenous in their intrinsic
		  differential geometric properties. The method is based on
		  the computation of Gaussian and mean curvatures, obtained
		  by locally approximating the object surface using quadratic
		  polynomials. They are used as input to the neural net which
		  outputs an initial region-based segmentation in the form of
		  a curvature sign map. An edge based segmentation is also
		  performed using the partial derivatives of depth values.
		  Here, the output of the Laplacian operator and the unit
		  surface normal are computed and used as input to a
		  self-organized mapping (SOM) net which is used to find the
		  edge points. The combination of approaches segments the
		  data into primitive surface regions. The uniqueness of our
		  approach is in automatic calculation of the threshold level
		  for segmentation, and on the adaptability of the method to
		  various noise levels in the digitized data. The developed
		  algorithms and sample results are described in the paper.},
  dbinsdate	= {oldtimer}
}

@Article{	  altinel00a,
  author	= {Altinel, I. K. and Aras, N. and Oommen, B. J.},
  title		= {Fast, efficient and accurate solutions to the Hamiltonian
		  path problem using neural approaches},
  journal	= {COMPUTERS \& OPERATIONS RESEARCH},
  year		= {2000},
  volume	= {27},
  number	= {5},
  month		= {APR},
  pages		= {461--494},
  abstract	= {Unlike its cousin, the Euclidean Traveling Salesman
		  Problem (TSP), to the best of our knowledge, there has been
		  no documented all-neural solution to the Euclidean
		  Hamiltonian Path Problem (HPP). The reason for this is the
		  fact that the heuristics which map the cities onto the
		  neurons "lose their credibility" because the underlying
		  cyclic property of the order of the neurons used in the TSP
		  is lost in the HPP. In this paper we present three neural
		  solutions to the HPP. The first of these, GSOM_HPP, is a
		  generalization of Kohonen's self-organizing map (SOM) as
		  modified by Angeniol et al. (Neural Networks
		  1988;1:289--93). The second and third methods use the
		  recently-introduced self-organizing neural network, the
		  Kohonen Network Incorporating Explicit Statistics (KNIES)
		  (Oommen et al., Proceedings of WIRN/VIETRI-98, the Tenth
		  Italian Workshop on Neural Nets, Vietri Sul Mare, Italy,
		  May 1998. p. 273--282). The primary difference between
		  KNIES and Kohonen's SOM is the fact that unlike SOM, every
		  iteration in the training phase includes two distinct
		  modules---the attracting module and the dispersing module.
		  As a result of SOM and the dispersing module introduced in
		  KNIES the neurons individually find their places both
		  statistically and topologically, and also collectively
		  maintain their mean to be the mean of the data points which
		  they represent. The new philosophy, which has previously
		  (Oommen et al. Proceedings of WIRN/VIETRI-98, the Tenth
		  Italian Workshop on Neural Nets, Vietri Sul Mare, Italy,
		  May 1998. p. 273--282) been used to effectively solve the
		  Euclidean Traveling Salesman Problem (TSP), is now extended
		  to solve the Euclidean Hamiltonian Path (HPP). These
		  algorithms which are the first all-neural solutions to the
		  HPP, have also been rigorously tested. Experimental results
		  for problems obtained by modifying selected instances from
		  the traveling salesman problem library (TSPLIB) (Reinett.
		  ORSA Journal on Computing 1991;3:376--84) for the HPP
		  indicate that they are both accurate and efficient. Apart
		  from the computational results presented, the paper also
		  contains a systematic strategy by which the quality of any
		  HPP algorithm can be quantified. Scope and purpose Over the
		  past two decades an enormous amount of work has been done
		  in designing neural networks (NNs) which utilize a variety
		  of learning principles. There are many works in the
		  literature that are noteworthy in the context, we list only
		  a few like CS,621, which describe the various families of
		  NNs, and how their learning compares to underlying
		  biological learning models. In this paper we concentrate
		  our attention on Kohonen's Self-Organizing Map (SOM) [21].
		  The SOM has een used in solving certain optimization
		  problems such as the Euclidean Traveling Salesman Problem
		  [3,4] which has been one of the oldest "hard nuts" of
		  Operations Research and Mathematical Programming. Any
		  algorithm devised to solve the TSP tries to answer the
		  following question: Given a set of N cities and distances
		  for each pair of cities what is the shortest tour that
		  visits each city exactly once? The beauty of the SOM is the
		  fact that the individual neurons adaptively tend to learn
		  the properties of the underlying distribution of the space
		  in which they operate. Additionally, they also tend to
		  learn their places topologically. This feature is
		  particularly important for problems which involve two and
		  three-dimensional physical spaces, and is indeed, the
		  principal motivation for the SOM being used in solving the
		  TSP [7,8]. More recently, we have added to this collection
		  of methods a scheme which uses the recently introduced
		  self-organizing neural network, the Kohonen Network
		  Incorporating Explicit Statistics (KNIES) [9]. The primary
		  difference between KNIES and the SOM is that, in KNIES, the
		  neurons not only individually find their places
		  statistically and topologically, but also collectively
		  maintain their mean to be the mean of the data points which
		  they represent. The Euclidean Hamiltonian Path Problem
		  (HPP) is closely related to the TSP: Given a set {X-i:1
		  less than or equal to i less than or equal to N} of cities
		  with starting and terminal cities X-s and X-t,
		  respectively, and distances for each pair of cities, what
		  is the shortest path that starts at X-s, terminates at X-t,
		  respectively, and visits each city exactly once? There are
		  only a few independent solutions to the HPP; indeed most
		  solutions utilize the underlying solution to the TSP. In
		  this paper we try to solve the HPP without resorting to an
		  underlying TSP solution method, and adapt our new NN
		  methods, KNIES. Our new neural algorithms are the first
		  all-neural solutions to the HPP to our knowledge, and
		  experimental results indicate that they are accurate and
		  efficient. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  alvarez94a,
  author	= {M. Alvarez and A. Varfis},
  title		= {Decoding Functions for {K}ohonen Maps},
  booktitle	= {Proc. ESANN'94, European Symp. on Artificial Neural
		  Networks},
  year		= {1994},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {245--250},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  alvarez95a,
  author	= {M. Alvarez and J. -M. Auger and A. Varfis},
  title		= {On Self-Organised Regression Curves},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {21--26},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InCollection{	  alvarez96a,
  author	= {G. Alvarez and S. Guevara and C. Piedrahita and O.
		  Tapias},
  title		= {The application of fuzzy {K}ohonen neural networks to
		  trace editing in seismic data processing},
  booktitle	= {Fourth European Congress on Intelligent Techniques and
		  Soft Computing Proceedings, EUFIT '96},
  publisher	= {Verlag Mainz},
  year		= {1996},
  volume	= {2},
  address	= {Aachen, Germany},
  pages		= {1146--50},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  alves_de_barros93a,
  author	= {Marcelo {Alves de Barros} and Mohamed Akil and Ren{\'{e}}
		  Natowicz},
  title		= {A Reconfigurable Architecture for Real Time Segmentation
		  of Image Sequences using Self-Organizing Feature Maps},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {197--202},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  amarasingham98a,
  author	= {A. Amarasingham and W. B. Levy},
  title		= {Predicting the Distribution of Synaptic Strengths and Cell
		  Firing Correlations in a Self Organizing, Sequence
		  Prediction Model},
  journal	= {Neural Computation},
  volume	= {10},
  pages		= {25--57},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@InCollection{	  amari89a,
  author	= {{S. -i. } Amari},
  title		= {Dynamical Study of the Formation of Cortical Maps},
  booktitle	= {Dynamic Interactions in Neural Networks: Models and Data},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  year		= 1989,
  editor	= {M. A. Arbib and S. -i. Amari},
  pages		= {15--34},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ambroise95a,
  author	= {Cristophe Ambroise and G{\'{e}}rard Govaert},
  title		= {Self-Organization for {G}aussian Parsimonious Clustering},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {I},
  pages		= {425--430},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ambuhl97a,
  author	= {J. Amb{\"u}hl and D. Cattani and P. Eckert},
  title		= {Classification of Meteorological Patterns},
  booktitle	= {Proc. ICANN'97, 7th International Conference on Artificial
		  Neural Networks},
  publisher	= {Springer},
  year		= 1997,
  volume	= 1327,
  series	= {Lecture Notes in Computer Science},
  address	= {Berlin},
  pages		= {1119--1124},
  dbinsdate	= {oldtimer}
}

@Article{	  amerijckx98a,
  author	= {Amerijckx, Christophe and Verleysen, Michel and Thissen,
		  Philippe and Legat, Jean Didier},
  title		= {Image compression by self-organized {K}ohonen map},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1998},
  number	= {3},
  volume	= {9},
  pages		= {503--507},
  abstract	= {This paper presents a compression scheme for digital still
		  images, by using the Kohonen's neural network algorithm,
		  not only for its vector quantization feature, but also for
		  its topological property. This property allows an increase
		  of about 80% for the compression rate. Compared to the JPEG
		  standard, this compression scheme shows better performances
		  (in terms of PSNR) for compression rates higher than 30.},
  dbinsdate	= {oldtimer}
}

@Article{	  amin94a,
  author	= {Shara Amin},
  title		= {A Self-Organized Travelling Salesman},
  journal	= {Neural Computing {\&} Applications},
  year		= {1994},
  volume	= {2},
  number	= {3},
  pages		= {129--133},
  dbinsdate	= {oldtimer}
}

@Article{	  aminian93a,
  author	= {Aminian, K. and Robert, P. and Jequier, E. and Schutz, Y.
		  },
  title		= {Level, downhill and uphill walking identification using
		  neural networks},
  journal	= {Electronics Letters},
  year		= {1993},
  volume	= {29},
  number	= {17},
  pages		= {1563--5},
  month		= {Aug},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  anagnostopoulos01a,
  author	= {Anagnostopoulos, C. and Anagnostopoulos, J. and Vergados,
		  D.D. and Kayafas, E. and Loumos, V. and Theodoropoulos,
		  G.},
  title		= {Training a learning vector quantization network for
		  biomedical classification},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2506--2511},
  organization	= {Natl. Tech. Univ. of Athens (NTUA), Electrical and
		  Computer Eng. Dept.},
  publisher	= {},
  address	= {},
  abstract	= {A competitive Learning Vector Quantization (LVQ)
		  Artificial Neural Network (ANN) was trained to identify
		  third stage parasitic strongyle larvae from domestic
		  animals on the basis of quantitative data obtained from
		  processed digital images of larvae. For this reason various
		  novel quantitative features obtained from processed digital
		  images of larvae were tested whether they are variant or
		  invariant to the shape taken by the motile larvae during
		  image recording. A total of 255 images of 57 individual
		  larvae in various shapes belonging to 5 genera were
		  recorded. Following image processing 16 novel features were
		  measured of which 7 were selected as invariant to larva
		  shape. By trial and error two of those novel features
		  "area" and "perimeter" along with the quantitative features
		  used in conventional identification, "overall body length",
		  "width" and "tail of sheath" were used as an effective
		  training data set for the ANN. This ANN coupled with an
		  image analysis facility and a knowledge relational database
		  became the basis for developing a computer-based larva
		  identification system whose overall identification
		  performance was 91.9%. The advantages of this system are
		  its speed and objectivity. The objectivity of the system is
		  based on the fact that it is not subject to inter- and
		  intra-observer variability arising from the user's profile
		  of competency in interpreting subjective and
		  non-quantifiable descriptions. The limitations of the
		  system are that it can not handle raw images but only data
		  extracted from images, its performance depends on the
		  reliability of the input vectors used as training data for
		  the ANN and its use is restricted only in well equipped
		  laboratories due to its requirement for expensive
		  instrumentation.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  anand91a,
  author	= {R. Anand and K. Mehrotra and C. K. Mohan and S. Ranka},
  title		= {Analyzing Images Containing Multiple Sparse Patterns with
		  Neural Networks},
  booktitle	= {Proc. International Joint Conference on Artificial
		  Intelligence (IJCAI)},
  publisher	= {University of Sydney},
  year		= {1991},
  address	= {Sydney, Australia},
  dbinsdate	= {oldtimer}
}

@Article{	  anand93a,
  author	= {R. Anand and K. Mehrotra and C. K. Mohan and S. Ranka},
  title		= {Analyzing Images Containing Multiple Sparse Patterns with
		  Neural Networks},
  journal	= {Pattern Recognition},
  year		= 1993,
  volume	= 26,
  pages		= {1717--1724},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  anderson90a,
  author	= {Ove Anderson and Piero Cosi and Paul Dalsgaard},
  title		= {A {SONN}-based Architecture for Automatic Speech
		  Segmentation and Alignment},
  editor	= {Andrea Paoloni},
  pages		= {18--29},
  booktitle	= {Proc. 1st Workshop on Neural Networks and Speech
		  Processing, November 89, Roma},
  address	= {Roma, Italy},
  year		= {1990},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  anderson91a,
  author	= {T. R. Anderson. },
  title		= {Speaker independent phoneme recognition with an auditory
		  model and a neural network: a comparison with traditional
		  techniques},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {I},
  pages		= {149--152},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  anderson92a,
  author	= {Anderson, T. R. },
  title		= {Phoneme recognition using an auditory model and a
		  recurrent \mbox{self-organizing} neural network},
  booktitle	= {ICASSP-92: 1992 IEEE International Conference on
		  Acoustics, Speech and Signal Processing},
  year		= {1992},
  volume	= {2},
  pages		= {337--40},
  organization	= {Armstrong Lab. , Wright-Patterson AFB, OH, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  anderson94a,
  author	= {Timothy Anderson},
  title		= {Auditory Models with {K}ohonen {SOFM} and {LVQ} for
		  Speaker Independent Phoneme Recognition},
  pages		= {4466--4469},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, speech recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  anderson94b,
  author	= {Anderson, T. R. and Patterson, R. D. },
  title		= {Speaker recognition with the auditory image model and
		  \mbox{self-organizing} feature maps: A comparison with
		  traditional techniques},
  booktitle	= {ESCA Workshop on Automatic Speaker Recognition
		  Identification and Verification},
  year		= {1994},
  pages		= {153--6},
  organization	= {Armstrong Lab. , Wright Res. \& Dev. Center,
		  Wright-Patterson AFB, OH, USA},
  publisher	= {IDIAP},
  address	= {Martingny, Switzerland},
  dbinsdate	= {oldtimer}
}

@Article{	  anderson99a,
  author	= {Anderson, B.},
  title		= {{K}ohonen Neural Networks and Language},
  journal	= {Brain and Language},
  year		= {1999},
  volume	= {70},
  number	= {1},
  pages		= {86--94},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ando91a,
  author	= {Akio Ando and Kazuhiko Ozeki},
  title		= {A Multi-Template Learning Algorithm Based on Minimization
		  of Recognition Error Function},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {Kohonen, Teuvo and M{\"{a}}kisara, Kai and Simula, Olli
		  and Kangas, Jari},
  pages		= {421--426},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  andrade97a,
  author	= {Andrade,M. A. and Casari,G. and Sander,C. and Valencia,A.
		  },
  title		= {Classification of Protein Families and Detection of the
		  Determinant Residues with an Improved Self Organizing Map},
  journal	= {Biol. Cyb. },
  year		= {1997},
  pages		= {441--50},
  volume	= {76},
  dbinsdate	= {oldtimer}
}

@Article{	  andrare93a,
  author	= {M. A. Andrare and P. Chac{\'{o}}n and J. J. Merelo and F.
		  Mor{\'{a}}n},
  title		= {Evaluation of Secondary Structure of Proteins from {UV}
		  Circular Dichroism Spectra using an Unsupervised Learning
		  Neural Network},
  journal	= {Protein Engineering},
  year		= {1993},
  volume	= {6},
  number	= {4},
  pages		= {383--390},
  dbinsdate	= {oldtimer}
}

@Article{	  andreou89a,
  author	= {A. G. Andreou and K. A. Boahen},
  title		= {Synthetic neural circuits using current-domain signal
		  representations},
  journal	= {Neural Computation},
  year		= {1989},
  volume	= {1},
  number	= {4},
  pages		= {489--501},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  andres92a,
  author	= {Andres, M. and Mallot, H. and Giefing, G. -J. },
  title		= {Selforganization of binocular receptive fields},
  booktitle	= {Artificial Neural Networks, 2. Proceedings of the 1992
		  International Conference (ICANN-92)},
  year		= {1992},
  editor	= {Aleksander, I. },
  volume	= {1},
  pages		= {553--6},
  organization	= {Inst. fur Neuroinf. , Ruhr-Univ. Bochum, Germany},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  andres94a,
  author	= {Marianne Andres and Oliver Schl{\"{u}}ter and Friederike
		  Spengler and Hubert R. Dinse},
  title		= {A Model of Fast and Reversible Representation Plasticity
		  using {K}ohonen Mapping},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  pages		= {306--309},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, physiological modeling},
  dbinsdate	= {oldtimer}
}

@InCollection{	  andres96a,
  author	= {M. Andres and O. Schluter and F. Spengler and H. R.
		  Dinse},
  title		= {Modification of {K}ohonen's {SOFM} to simulate cortical
		  plasticity induced by coactivation input patterns},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {421--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  andreu96a,
  author	= {Andreu, G. and Crespo, A.},
  title		= {Estimating the organization degree for a toroidal
		  \mbox{self-organizing} feature map ({TSOFM})},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96)},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1996},
  volume	= {2},
  pages		= {649--52},
  abstract	= {Feature maps (SOFM) are an important tool to visualize
		  high-dimensional data as a two-dimensional image. One of
		  the possible application of this network is to image
		  recognition. However, this architecture presents some
		  problems mainly due to the border effects. In this paper a
		  new organization of the feature maps named toroidal
		  self-organizing feature maps ({TSOFM}) is presented. Its
		  main advantage consist on the elimination of the the border
		  effects and, consequently, the increasing of the
		  recognition rate. Another important aspect presented in
		  this paper is the measurement of how well organized is the
		  networks during the training phase. This proposal have been
		  experimented in an industrial application demonstrating the
		  better quality results.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  andreu97a,
  author	= {G. Andreu and A. Crespo and J. M. Valiente},
  title		= {Selecting the Toroidal Self-Organizing Feature Maps
		  ({TSOFM}) Best Organized to Object Recognition},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {II},
  pages		= {1341--1346},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  andrew94a,
  author	= {Lachlan L. H. Andrew and M. Palaniswami},
  title		= {A Study on the Effect of Neighbourhood Functions for Noise
		  Robust Vector Quantisers},
  pages		= {4159--4162},
  booktitle	= {Proc. ICNN'94 IEEE International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, vector quantization, noise tolerance},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  andrew94b,
  author	= {Lachlan L. H. Andrew},
  title		= {Neuron Splitting for Efficient Feature Map Formation},
  booktitle	= {Proc. ANZIIS'94, Aust. New Zealand Intell. Info. Systems
		  Conference },
  pages		= {10--13},
  year		= 1994,
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  andrew95a,
  author	= {Lachlan L. H. Andrew and M. Palaniswami},
  title		= {A New Adaptive Image Sequence Coding Scheme Using
		  {K}ohonen's {SOFM}},
  volume	= {IV},
  pages		= {2071--2076},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, vector quantization, image sequence coding},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  andrew95b,
  author	= {Colin Andrew and Miroslaw Kubat and Gert Pfurtscheller},
  title		= {Trimming the Inputs of {RBF} Networks},
  booktitle	= {Proc. ESANN'95, European Symp. on Artificial Neural
		  Networks},
  year		= {1995},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {291--296},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  andrew96a,
  author	= {Lachlan L. H. Andrew},
  title		= {Neural Networks for Adaptive Image Sequence Vector
		  Quantization},
  pages		= {569--573},
  booktitle	= {Proc. IPCS'6 Int. Picture Coding Symposium},
  year		= {1996},
  annote	= {application, vector quantization, image sequence coding},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  andrianasy95a,
  author	= {Fidimahery Andianasy and Maurice Milgram},
  title		= {A Learning Scheme for On-Line Handwritten Recognition
		  Using Elastic Matching},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {61--65},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@Article{	  angelakis01a,
  author	= {Angelakis, C. and Loukis, E. N. and Pouliezos, A. D. and
		  Stavrakakis, G.S.},
  title		= {A neural network-based method for gas turbine blading
		  fault diagnosis},
  journal	= {International Journal of Modelling and Simulation},
  year		= {2001},
  volume	= {21},
  number	= {1},
  month		= {},
  pages		= {51--60},
  organization	= {Technical University of Crete, Department of Electronic
		  Engineering},
  publisher	= {},
  address	= {},
  abstract	= {In this paper artificial neural networks are used with
		  promising results in a critical, and at the same time, very
		  difficult problem concerning the diagnosis of gas turbine
		  blading faults. Neural network-based fault diagnosis is
		  treated as a pattern recognition problem, based on
		  measurements and feature selection. Emphasis is given to
		  the design of the appropriate neural network architecture
		  and the selection of the appropriate measuring instruments,
		  which are of critical importance for achieving good
		  performance (high success rates and generalization
		  capabilities). Initially the performance of the classical
		  neural network architectures, namely MultiLayer Perceptron
		  (MLP), Learning Vector Quantization (LVQ), Modular
		  MultiLayer Perceptron and Radial Basis Function (RBF), are
		  investigated for this problem. The implemented neural
		  network structures are trained to classify faulty and
		  healthy patterns coming from twelve different measuring
		  instruments. The performance of the above neural network
		  structures is investigated, and the diagnostic capabilities
		  of the measuring instruments are examined. Next, in order
		  to improve the generalization capabilities, which are
		  critical for the specific diagnostic problem, a new
		  multinet architecture is developed, based on the idea of
		  'majority rule' decision. Compared with the classical
		  architectures, this new multinet architecture is
		  characterized by higher generalization capabilities and
		  robustness. A first approach to the design of the
		  appropriate multinet architecture and the selection of the
		  appropriate measuring instruments, in order to provide the
		  basis of a high-performance automated diagnostic system, is
		  proposed. The conclusions derived are of general interest
		  and applicability.},
  dbinsdate	= {2002/1}
}

@Article{	  angeniol88a,
  author	= {B. Ang{\`{e}}niol and G. D. L. C. Vaubois and J. Y. L.
		  Texier},
  title		= {Self-organizing feature maps and the {T}ravelling
		  {S}alesman {P}roblem},
  journal	= {Neural Networks},
  year		= {1988},
  volume	= {1},
  number	= {4},
  pages		= {289--293},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  anguita95a,
  author	= {Davide Anguita and Filippo Passaggio and Rodolfo Zunino},
  title		= {{SOM}-based Interpolation for Image Compression},
  volume	= {I},
  pages		= {739--742},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InCollection{	  anouar96a,
  author	= {F. Anouar and F. Badran and S. Thiria},
  title		= {Topological maps for mixture densities},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {833--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  anouar97a,
  author	= {F. Anouar and F. Badran and S. Thiria},
  title		= {Probabilistic self organized map. {A}pplication to
		  classification},
  booktitle	= {5th European Symposium on Artificial Neural Networks ESANN
		  '97. Proceedings},
  publisher	= {D facto},
  year		= {1997},
  editor	= {M. Verleysen},
  address	= {Brussels, Belgium},
  pages		= {13--18},
  dbinsdate	= {oldtimer}
}

@InCollection{	  anouar97b,
  author	= {F. Anouar and F. Badran and S. Thiria},
  title		= {Self organizing map, a probabilistic approach},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {339--344},
  dbinsdate	= {oldtimer}
}

@Article{	  anouar98a,
  author	= {Anouar, F. and Badran, F. and Thiria, S.},
  title		= {Probabilistic \mbox{self-organizing} map and radial basis
		  function networks},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {20},
  pages		= {83--96},
  abstract	= {We propose in this paper a new learning algorithm
		  probabilistic self-organizing map (PRSOM) using a
		  probabilistic formalism for topological maps. This
		  algorithm approximates the density distribution of the
		  input set with a mixture of normal distributions. The
		  unsupervised learning is based on the dynamic clusters
		  principle and optimizes the likelihood function. A
		  supervised version of this algorithm based on radial basis
		  functions (RBF) is proposed. In order to validate the
		  theoretical approach, we achieve regression tasks on
		  simulated and real data using the PRSOM algorithm.
		  Moreover, our results are compared with normalized Gaussian
		  basis functions (NGBF) algorithm.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ansamaki00a,
  author	= {Ansamaki, J. and Toivanen, P. J. and Parkinnen, J.},
  title		= {New multispectral edge detection methods based on
		  roughness and differential Prewitt},
  booktitle	= {Signal Processing X Theories and Applications. Proceedings
		  of EUSIPCO 2000. Tenth European Signal Processing
		  Conference. Tampere Univ. Technology, Tampere, Finland},
  year		= {2000},
  volume	= {3},
  pages		= {1349--52},
  abstract	= {Two new edge detection methods for multispectral images
		  are presented. The first method is based on calculating
		  Euclidean distances between pixel vectors inside a suitable
		  mask. This method approximates the roughness of the pixel
		  neighborhood. The result is a scalar edge image. This
		  method is tested using an airborne remote sensing image and
		  two artificial images. One artificial image contains
		  metameric colors and the other has a group of colors
		  arranged using the self organizing map. In all the cases,
		  the edges are clearly visible without further filtering.
		  The second method is a differential Prewitt method using
		  vectors for calculating the gradient value. In this case
		  the edge image is a vector image. This method is tested
		  using the same test images as in the first case.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ansari90a,
  author	= {N. Ansari and Y. Chen},
  title		= {A neural network model to configure maps for a satellite
		  communication network},
  booktitle	= {Proc. GLOBECOM'90, IEEE Global Telecommunications
		  Conference and Exhibition. 'Communications: Connecting the
		  Future'},
  year		= {1990},
  volume	= {II},
  pages		= {1042--1046},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ansari91a,
  author	= {N. Ansari and D. Liu},
  title		= {The performance evaluation of a new neural network based
		  traffic management scheme for a satellite communication
		  network},
  booktitle	= {Proc. GLOBECOM'91, IEEE Global Telecommunications
		  Conference Countdown to the New Millennium. Featuring a
		  Mini-Theme on: 'Personal Communications Services (PCS). '},
  year		= {1991},
  volume	= {I},
  pages		= {110--114},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  ansari95a,
  author	= {Ansari, N. and Dequan Liu},
  title		= {The performance evaluation of a new neural network-based
		  traffic management scheme for a satellite communication
		  network},
  journal	= {Neurocomputing},
  year		= {1995},
  volume	= {8},
  number	= {3},
  pages		= {263--82},
  month		= {Aug},
  dbinsdate	= {oldtimer}
}

@Article{	  antikainen00a,
  author	= {Antikainen, O. K. and Rantanen, J. T. and Yliruusi, J.
		  K.},
  title		= {Use of the Kohonen self-organising map to predict the
		  flowability of powders},
  journal	= {STP PHARMA SCIENCES},
  year		= {2000},
  volume	= {10},
  number	= {5},
  month		= {SEP-OCT},
  pages		= {349--354},
  abstract	= {A true-dimensional Kohonen self-organising map was used to
		  predict weight variation in tablets on the basis of flow
		  properties of powders. Flow test data of the powders
		  consisted of four different angles of repose, flowing time
		  and the non- uniformity of flow determined front thirty-six
		  powder batches. The network was trained with three
		  microcrystalline cellulose grades and their mixture.
		  Microcrystalline cellulose fillers contained various
		  amounts of a poor flowing drug substance, paracetamol. The
		  final network was tested using a few silicified
		  microcrystalline cellulose powders which also contained
		  paracetamol. The Kohonen self-organising map is able to
		  combine numerous parameters that describe powder
		  flowability, and to present the data in an
		  easily-understood, visual form. It is practically
		  impossible for any other technique to achieve this. The
		  self-organising map appeared to he able to cluster
		  flowability data so that the best flowing powders were
		  localised in the same part on the map. The predictive
		  ability of the map was satisfactory.},
  dbinsdate	= {2002/1}
}

@Article{	  antonini90a,
  author	= {M. Antonini and M. Barlaud and P. Mathieu and J. C.
		  Feauveau},
  title		= {Multiscale image coding using the {K}ohonen Neural
		  Network},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1990},
  volume	= {1360},
  number	= {1},
  pages		= {14--26},
  note		= {Conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  antonini90b,
  author	= {M. Antonini and M. Barlaud and P. Mathieu},
  title		= {Predictive interscale image coding using vector
		  quantization},
  booktitle	= {Signal Processing V. Theories and Applications. Proc.
		  EUSIPCO-90, Fifth European Signal Processing Conference},
  year		= {1990},
  editor	= {L. Torres and E. Masgrau and M. A. Lagunas},
  volume	= {II},
  pages		= {1091--1094},
  organization	= {CIDEM; CIRIT; IBM; et al},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  anzali98a,
  author	= {S. Anzali and W. W. K. R. Mederski and M. Osswald and D.
		  Dorsch},
  title		= {1 Endothelin Antagonists Search for Surrogates of
		  Methylendioxyphenyl by Means of a {K}ohonen Neural
		  Network},
  journal	= {Bioorganic \& Medicinal Chemistry Letters},
  volume	= {8},
  pages		= {11--16},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  anzali98b,
  author	= {S. Anzali and J. Gasteiger and U. Holzgrabe and J.
		  Polanski and J. Sadowski and A. Teckentrup and M. Wagener},
  title		= {The Use of Self Organizing Neural Networks in Drug
		  Design},
  journal	= {Perspectives in Drug Discovery and Design},
  volume	= {9--11},
  pages		= {273--299},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  aoki00a,
  author	= {Aoki, H. and Saito, T.},
  title		= {On classification function of self-organizing map with
		  threshold operation},
  journal	= {Transactions-of-the-Institute-of-Electronics,-Information-and-Communication-Engineers-A}
		  ,
  year		= {2000},
  volume	= {},
  pages		= {1122--4},
  abstract	= {For a basic self-organizing map, we propose a simple
		  learning algorithm with threshold operation. Preliminary
		  numerical experimental results suggest that, adjusting the
		  threshold appropriately, undesired interventions among
		  input classes can be suppressed and the classification
		  function can be reinforced.},
  dbinsdate	= {2002/1}
}

@Article{	  ara97a,
  author	= {M. Ara and N. Suzuki and E. Suzuki and H. Mukae},
  title		= {Application of \mbox{self-organizing} feature map to
		  failure diagnosis through sound data},
  journal	= {Research Reports of Kogakuin University},
  year		= {1997},
  publisher	= {Kogakuin Univ},
  volume	= {4},
  number	= {82},
  pages		= {129--33},
  dbinsdate	= {oldtimer}
}

@Article{	  arabshahi96a,
  author	= {Payman Arabshahi and Jai J. Choi and Robert J. {Marks II}
		  and Thomas P. Caudell},
  title		= {Fuzzy Parameter Adaptation in Optimization: {SOM} Neural
		  Net Training Examples},
  journal	= {IEEE Computational Science \& Engineering},
  year		= 1996,
  volume	= 3,
  pages		= {57--65},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  arai00a,
  author	= {Arai, Y. and Hakura, J.},
  title		= {Teleoperation system for real world robots-adaptive robot
		  navigation based on sensor fusion},
  booktitle	= {Proceedings Seventh International Conference on Parallel
		  and Distributed Systems: Workshops. IEEE Comput. Soc, Los
		  Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {487--92},
  abstract	= {The authors propose a teleoperation system with an
		  autonomous robot which is able to solve tasks even without
		  a large load for the operator and the system. Most
		  teleoperation systems require skilled operators and
		  expensive interfaces to solve tasks because they assume
		  that the operator controls a robot completely. For these
		  problems, we propose a teleoperation system which consists
		  of an operation system and an autonomous robot. The
		  operation system has a man-machine interface and allows a
		  user to specify the working space and the tasks to be done.
		  The autonomous robot follows the instruction from the
		  operation system to solve the specific tasks. The paper
		  focuses on navigation problems of the autonomous robot as
		  an essential part of the proposed system. Namely, the
		  autonomous robot should keep on the instructed paths in the
		  real world to achieve a goal of the tasks. Our approach is
		  based on a sensor fusion method based on two learning
		  schemes: self-organizing map (SOM) and reinforcement
		  learning. These learning schemes allow the system to be
		  able to solve the tasks in an unreliable environment such
		  as outdoors. Computational simulations reveal the
		  effectiveness and robustness of the proposed method in the
		  navigation problem.},
  dbinsdate	= {2002/1}
}

@Article{	  arakawa01a,
  author	= {Arakawa, Y. and Yoshioka, M. and Omatu, S.},
  title		= {Land cover mapping of synthetic aperture radar data by
		  using texture analysis and self-organizing map},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-C}
		  ,
  year		= {2001},
  volume	= {121},
  pages		= {234--9},
  abstract	= {We propose a method to apply a texture analysis to land
		  cover mapping of synthetic aperture radar (SAR) data.
		  Texture is a fundamental characteristic in image analysis.
		  By using the texture analysis it is possible to find
		  patterns in an aerial image. For classification of
		  categories we adopt the Kohonen's self-organizing map and
		  compare the results with those of the maximum likelihood
		  classifier.},
  dbinsdate	= {2002/1}
}

@Article{	  araki96a,
  author	= {H. Araki and H. Fukumoto and T. Ae},
  title		= {Image processing using simplified {K}ohonen network},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2661},
  pages		= {24--33},
  note		= {(Real-Time Imaging Conf. Date: 29--30 Jan. 1996 Conf. Loc:
		  San Jose, CA, USA Conf. Sponsor: SPIE; Soc. Imaging Sci. \&
		  Technol)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  aras00a,
  author	= {Aras, N. and Altmel, I. K. and Oommen, J.},
  title		= {A Kohonen-like decomposition method for the traveling
		  salesman problem {KNIES}-{DECOMPOSE}},
  booktitle	= {ECAI 2000. 14th European Conference on Artificial
		  Intelligence. including Prestigious Applications of
		  Intelligent Systems (PAIS-2000). Proceedings (Frontiers in
		  Artificial Intelligence and Applications Vol.54). IOS
		  Press, Amsterdam, Netherlands},
  year		= {2000},
  volume	= {},
  pages		= {261--5},
  abstract	= {In addition to the classical heuristic algorithms of
		  operations research, there have also been several
		  approaches based on artificial neural networks which solve
		  the traveling salesman problem (TSP). Their efficiency,
		  however, decreases as the problem size (number of cities)
		  increases. An idea to reduce the complexity of a
		  large-scale TSP instance is to decompose or partition it
		  into smaller subproblems, which are easier to solve. We
		  introduce an all-neural decomposition heuristic that is
		  based on a recent self-organizing map called KNIES (Kohonen
		  Network Incorporating Explicit Statistics) (N. Aras, 1999),
		  which has been successfully implemented in solving both the
		  Euclidean TSP and the Euclidean Hamiltonian path problem.},
  dbinsdate	= {2002/1}
}

@Article{	  aras99a,
  author	= {Aras, N. and Oommen, B. J. and Altinel, I. K.},
  title		= {{K}ohonen network incorporating explicit statistics and
		  its application to the travelling salesman problem},
  journal	= {Neural Networks},
  year		= {1999},
  number	= {9},
  volume	= {12},
  pages		= {1273--1284},
  abstract	= {In this paper we introduce a new self-organizing neural
		  network, the Kohonen Network Incorporating Explicit
		  Statistics (KNIES) that is based on Kohonen's
		  Self-Organizing Map (SOM). The primary difference between
		  the {SOM} and the KNIES is the fact that every iteration in
		  the training phase includes two distinct modules---the
		  attracting module and the dispersing module. As a result of
		  the newly introduced dispersing module the neurons maintain
		  the overall statistical properties of the data points.
		  Thus, although in {SOM} the neurons individually find their
		  places both statistically and topologically, in KNIES they
		  collectively maintain their mean to be the mean of the data
		  points, which they represent. Although the scheme as it is
		  currently implemented maintains the mean as its invariant,
		  the scheme can easily be generalized to maintain higher
		  order central moments as invariants. The new scheme has
		  been used to solve the Euclidean Travelling Salesman
		  Problem (TSP). Experimental results for problems taken from
		  TSPLIB indicate that it is a very accurate NN strategy for
		  the TSP---probably the most accurate neural solutions
		  available in the literature.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  arciniegas01a,
  author	= {I. Arciniegas and B. Daniel and M. J. Embrechts},
  title		= {Exploring financial crises data with self-organisinf maps
		  ({SOM})},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {39--46},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  ardizzone00a,
  author	= {Ardizzone, E. and Capra, A. and La Cascia, M.},
  title		= {Using temporal texture for content-based video retrieval},
  journal	= {Journal-of-Visual-Languages-and-Computing},
  year		= {2000},
  volume	= {11},
  pages		= {241--52},
  abstract	= {Textures evolving over time are called temporal textures
		  and are very common in everyday life. Examples are the
		  smoke flowing or the wavy water of a river. The idea
		  explored in the paper is that image features based on
		  temporal texture could allow a better performance of
		  current content based video retrieval systems that are
		  mainly based on static characteristics of representative
		  frames, like color and texture. To this aim we analyze the
		  spatio-temporal nature of texture and its application in
		  content-based access to video databases. In particular, we
		  represent temporal texture using the spatiotemporal
		  autoregressive (STAR) model and a variation of
		  self-organizing maps (SOM) where each node is an
		  autoregressive model. These representation schemes have
		  been implemented in a query by example framework to analyze
		  the weaknesses and the strengths of the different
		  approaches. Preliminary experimental results are
		  reported.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ardizzone91a,
  author	= {E. Ardizzone and A. Chella and F. Sorbello},
  title		= {A Digital Architecture Implementing the Self-Organizing
		  Feature Maps},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {721--727},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ardizzone94a,
  author	= {E. Ardizzone and A. Chella and R. Rizzo},
  title		= {Color Image Segmentation Based on a Neural Gas Network},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1161--1164},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, image segmentation, neural gas algorithm},
  dbinsdate	= {oldtimer}
}

@Article{	  arrigo91a,
  author	= {Arrigo, P. and Giuliano, F. and Scalia, F. and Rapallo, A.
		  and Damiani, G},
  title		= {Identification of a new motif on nucleic acid sequence
		  data using {K}ohonen's \mbox{self-organizing} map.},
  journal	= {COMP. APPLIC. BIOSCI.},
  year		= {1991},
  number	= {3},
  volume	= {7},
  pages		= {353--357},
  abstract	= {The authors present a performance test of a Kohonen
		  features map applied to the fast extraction of uncommon
		  sequences from the coding region of the human insulin
		  receptor gene. They used a network with 30 neurons and with
		  a variable input window. The program was aimed at detecting
		  unique or uncommon DNA regions present in crude sequence
		  data and was able to automatically detect the signal
		  peptide coding regions of a set of human insulin receptor
		  gene data. The testing of this program with HSIRPR cDNA
		  release (EMBL data bank) indicated the presence of unique
		  features in the signal peptide coding region. This program
		  can automatically detect "singularity" from crude
		  sequencing data and it does not require knowledge of the
		  features to be found.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  arrigo94a,
  author	= {Arrigo, P. and Giuliano, F. and Damiani, G. },
  title		= {Identification of singular domains on nucleotidic
		  sequences by {SOFM}},
  booktitle	= {IEE Colloquium on 'Molecular Bioinformatics' (Digest No.
		  1994/029)},
  year		= {1994},
  pages		= {4/1},
  organization	= {Istituto per i Circuiti Elettronici, Consiglio Nazinale
		  delle Ricerche, Genova, Italy},
  publisher	= {IEE},
  address	= {London, UK},
  abstract	= {The availability of new powerful hardware architectures
		  allows to implement efficient computational models. The
		  Artificial Neural Classifiers are one of the more
		  successful class. The peculiar characteristic of Neural
		  Classifiers induced many Computer Scientist and Molecular
		  Biologist to apply they to the analysis of many kind of
		  experimental data or to the development of integrated
		  systems of information retrieval. The Molecular Biology
		  offers an very important application field in order to test
		  the performances of different neural architectures. The
		  analysis of biosequences represents one of the more
		  important tasks. The major part of the literature on this
		  applicative field is based on the 'supervised' neural
		  models. The genomic sequences could be considered like
		  noisy signals and the knowledge about the syntactical rules
		  that regulate the gene expression are not well known. This
		  reason induced us to refuse all the knowledge about the
		  genomic features an to make a new 'blind' search on
		  complete sequence, in order to take in account the
		  contextual effect on this kind of pattern recognition. In
		  this paper we present an application of Kohonen's
		  Self-Organizing Maps (KFM) to the recognition of uncommon
		  domains on cDNA sequences.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  arrowsmith99a,
  author	= {Arrowsmith, M. J. and Varley, M. R. and Picton, P. D. and
		  Heys, J. D.},
  title		= {Hybrid neural network system for texture analysis},
  booktitle	= {Seventh International Conference on Image Processing and
		  Its Applications (Conf. Publ. No.465)},
  year		= {1999},
  publisher	= {IEE},
  address	= {London, UK},
  volume	= {1},
  pages		= {339--43},
  abstract	= {Texture classification and segmentation in digital images
		  is commonly achieved using spatial grey level dependence
		  matrices (SGLDMs), often referred to as co-occurrence
		  matrices. This involves the computation of many matrices
		  over a range of different spatial separations and
		  orientations. The approach proposed in this paper uses a
		  hybrid neural network system, consisting of a
		  self-organising map followed by a backpropagation network,
		  to restrict the number of SGLDMs that need to be computed.
		  The system is trained in two phases on images with known
		  texture content. The trained system is able to provide
		  information, in the form of pixel spacing and orientation,
		  on the texture content of unseen images. This information
		  may be used to select appropriate SGLDMs for further
		  texture classification. Experimental results are presented
		  which demonstrate the effective performance of the
		  system.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  arroyo01a,
  author	= {Arroyo, J. M. R. and Beddoes, A. J. and Allinson, N. M.},
  title		= {Condition monitoring of 11 {KV} paper insulated cables
		  using self-organising maps},
  booktitle	= {Canadian Conference on Electrical and Computer
		  Engineering},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {259--264},
  organization	= {EA Technology Ltd},
  publisher	= {},
  address	= {},
  abstract	= {This paper concerns the feasibility of using
		  Self-Organising Feature Maps for the insulation assessment
		  of paper insulated cables. This class of neural networks is
		  able to isolate different clusters within the discharge
		  activity obtained throughout a degradation process.
		  However, once trained, they are incapable of identifying
		  novel states in the insulation of the sample. As a possible
		  solution of this problem, the authors present a variation
		  of the SOM based on the expansion of the trained map. With
		  this modification, SOM can be used for the condition
		  monitoring of the cables and the prediction of incipient
		  faults.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  arroyo01b,
  author	= {Arroyo, J. M. R. and Beddoes, A. J. and Alinson, N. M.},
  title		= {Insulation condition assessment of 11 kV paper cables
		  using neural networks},
  booktitle	= {IEE Symposium Pulsed Power 2001 (Digest No.01/156). IEE,
		  London, UK},
  year		= {2001},
  volume	= {1},
  pages		= {},
  abstract	= {The increasing demand of energy and the cost associated
		  with the installation of new power systems are intensifying
		  the design and implementation of condition monitoring
		  techniques, which are able to estimate the remaining active
		  life of existing systems and, therefore, provide decision
		  support for their maintenance or replacement. For paper
		  insulated power cables, a common method for their condition
		  assessment is the detection and analysis of partial
		  discharges (PD). These discharges in the cavities of the
		  paper insulation increase the size of the voids and cause
		  the development of carbonaceous paths between the fibres of
		  the paper layers. These defects may lead to fatal breakdown
		  of the cable. Although this physical deterioration process
		  is well known, the essential parameters required for the
		  control of the different stages of the degradation remain
		  uncertain. Therefore, a large number of variables have to
		  be monitored to develop an efficient and reliable condition
		  monitoring system for paper insulated cables.
		  Self-organising maps (SOM), a type of artificial neural
		  network, are a suitable technique for the processing and
		  analysis of high dimensional problems involving large
		  amounts of data, being particularly appropriate for the
		  recognition of trends and feature clustering. They form the
		  basis of the condition monitoring technique presented in
		  this paper.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  arseneau94a,
  author	= {J. Brant Arseneau and Tim Spracklen},
  title		= {Reengineering Software Modularity using Artificicial
		  Neural Networks},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1384--1387},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, visualization, clustering},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  arseneau94b,
  author	= {J. Brant Arseneau and Tim Spracklen},
  title		= {Reengineering Software Modularity using Artificial Neural
		  Networks},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {467--470},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, symbolic data, clustering},
  dbinsdate	= {oldtimer}
}

@InCollection{	  asanovic97a,
  author	= {K. Asanovic},
  title		= {A fast {K}ohonen net implementation for Spert-II},
  booktitle	= {Biological and Artificial Computation: From Neuroscience
		  to Technology. International Work Conference on Artificial
		  and Natural Neural Networks, IWANN'97. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {J. Mira and R. Moreno-Diaz and J. Cabestany},
  address	= {Berlin, Germany},
  pages		= {792--800},
  dbinsdate	= {oldtimer}
}

@Article{	  assadollahi01a,
  author	= {Assadollahi, R. and Pulvermuller, F.},
  title		= {Neural network classification of word evoked neuromagnetic
		  brain activity},
  journal	= {Emergent neural computational architectures based on
		  neuroscience. Towards neuroscience-inspired computing.
		  Springer-Verlag, Berlin, Germany; 2001; x+576
		  pp.p.311--19},
  year		= {2001},
  volume	= {},
  pages		= {311--19},
  abstract	= {The brain-physiological signatures of words are modulated
		  by their psycholinguistic and physical properties. The
		  fine-grained differences in complex spatio-temporal
		  patterns of a single word induced brain response may be
		  detected using unsupervised neuronal networks. Objective of
		  this study was to motivate and explore an architecture of a
		  Kohonen net and its performance, even when physical
		  stimulus properties are kept constant over the classes. We
		  investigated 16 words from four lexico-semantic classes.
		  The items from the four classes were matched for word
		  length and frequency. A Kohonen net was trained on the data
		  recorded from a single subject. After learning, the network
		  performed above chance on new testing data. The results
		  obtained suggest that the research on single trial
		  recognition of brain responses is feasible and a rich field
		  to explore.},
  dbinsdate	= {2002/1}
}

@InCollection{	  atlas95a,
  author	= {L. Atlas and L. Owsley and J. McLaughlin and G. Bernard},
  title		= {Automatic feature-finding for time-frequency
		  distributions},
  booktitle	= {Proceedings of the IEEE-SP International Symposium on
		  Time-Frequency and Time-Scale Analysis},
  publisher	= {Gordon \& Breach},
  year		= {1995},
  editor	= {G. F. Forsyth and M. Ali},
  address	= {Newark, NJ, USA},
  pages		= {333--6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  atmaca96a,
  author	= {H. Atmaca and M. Bulut and D. Demir and S. Pazar},
  title		= {A new fuzzy {K}ohonen clustering network based on
		  histogram for image segmentation},
  booktitle	= {Proceedings of the Eleventh International Symposium on
		  Computer and Information Sciences. ISCIS},
  publisher	= {Middle East Tech. Univ},
  year		= {1996},
  volume	= {2},
  editor	= {V. Atalay and U. Halici and K. Inan and N. Yalabik and A.
		  Yazici},
  address	= {Ankara, Turkey},
  pages		= {845--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  atsalakis01a,
  author	= {Atsalakis, A. and Andreadis, I. and Papamarkos, N.},
  title		= {Histogram based color reduction through Self-Organized
		  neural networks},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {470--476},
  abstract	= {A now technique suitable for reduction of the number of
		  colors in an image is presented in this paper. It is based
		  on histogram processing and the use of Kohonen Self
		  Organizing Feature Map (SOFM) neural networks. Initially,
		  the dominant colors of each primary image are extracted
		  through a simple linear piece-wise histogram approximation
		  process. Then, using a SOFM the dominant color components
		  of each primary color band are obtained and a look up table
		  is constructed containing all possible color triplets. The
		  final dominant colors are extracted from the look-up table
		  entries using a SOFM by specifying the number of output
		  neurons equal to the number of the dominant colors. Thus,
		  the final image has all the dominant color classes,
		  Experimental and comparative results demonstrate the
		  applicability of the proposed technique.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  atsalakis01b,
  author	= {Atsalakis, A. and Papamarkos, N. and Strouthopoulos, C.},
  title		= {A new adaptive color quantization technique},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {1006--1012},
  abstract	= {This paper proposes a new algorithm for color
		  quantization. The proposed approach achieves color
		  quantization using an adaptive tree clustering procedure.
		  In each node of the tree a self- organized Neural Network
		  Classifier (NNC) is used which is fed by image color values
		  and additional local spatial features. The NNC consists of
		  a Principal Component Analyzer (PCA) and a Kohonen
		  self-organized feature map (SOFM) neural network. The
		  output neurons of the NNTC define the color classes for
		  each node. The final image not only has the dominant image
		  colors, but also its texture approaches the image local
		  characteristics used. For better classification, split and
		  merging conditions are used in order to define if color
		  classes must be split or merged. To speed-up the entire
		  algorithm and reduce memory requirements, a fractal
		  scanning sub-sampling technique is used.},
  dbinsdate	= {2002/1}
}

@Article{	  audouze00a,
  author	= {Audouze, K. and Ros, F. and Pintore, M. and Chretien,
		  J.R.},
  title		= {Prediction of odours of aliphatic alcohols and
		  carbonylated compounds using fuzzy partition and self
		  organising maps ({SOM})},
  journal	= {Analusis},
  year		= {2000},
  volume	= {28},
  number	= {7},
  month		= {Sep},
  pages		= {625--632},
  organization	= {Univ of Orleans},
  publisher	= {EDP Sci},
  address	= {New York, NY},
  abstract	= {A set of 114 olfactory molecules divided into fruity,
		  ethereal and camphoraceous compounds, was submitted to an
		  analysis by Kohonen Neural Networks, also known as Self
		  Organising Map (SOM). The compounds are represented in a
		  hyperspace derived from their molecular descriptors and SOM
		  gives a useful projection of this hyperspace onto a 2D map.
		  Owing to the complexity of the olfaction mechanism,
		  evidenced by the fact that one compound can exhibit
		  simultaneously different properties, SOM alone is unable to
		  take into account the olfaction diversity of the original
		  114 compounds. Then, a Fuzzy Partition method was applied
		  on the Kohonen map previously developed. The obtained
		  results allowed delineating different representative zones
		  for the three odours, expressing more closely the olfactory
		  richness. The ability of the Hybrid System combining SOM
		  and Fuzzy Partition to model the three odours was validated
		  by dividing the 114 compounds into a training set and a
		  test set, including 86 and 28 molecules, respectively. The
		  most important olfactory characteristics were reproduced
		  satisfactorily for the entire test set compounds.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  auger91a,
  author	= {J. M. Auger},
  title		= {Parallel implementation on transputer of {K}ohonen's
		  algorithm},
  booktitle	= {Computing with Parallel Architectures: T. Node},
  year		= {1991},
  editor	= {D. Gassilloud and J. C. Grossetie},
  pages		= {215--226},
  organization	= {Inst. Syst. Eng. Inf},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  auger92a,
  author	= {Jean-Marie Auger and Yizhak Idan and Raymond Chevallier
		  and Bernadette Dorizzi},
  title		= {Complementary Aspects of Topological Maps and Time Delay
		  Neural Networks for Character Recognition},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {444--449},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  augusteijn93a,
  author	= {Marijke F. Augusteijn and Tammy L. Skufca},
  title		= {Identification of Human Faces through Texture-Based
		  Feature Recognition and Neural Network Technology},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {I},
  pages		= {392--398},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  augusteijn95a,
  author	= {M. F. Augusteijn and K. K. McCarthy},
  title		= {Image indexing applied to character font recognition by
		  means of a {K}ohonen neural network hierarchy},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks. Vol. 5. Fuzzy Logic and Evolutionary Programming.
		  Proceedings of the Artificial Neural Networks in
		  Engineering (ANNIE'95)},
  publisher	= {ASME Press},
  year		= {1995},
  editor	= {C. H. Dagli and M. Akay and C. L. P. Chen and B. R.
		  Fernandez and J. Ghosh},
  address	= {New York, NY, USA},
  pages		= {431--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  aupetit00a,
  author	= {Michael Aupetit and Pierre Couturier and Pierre Massotte},
  title		= {Function Approximation with Continuous Self-Organizing
		  Maps using Neighboring Influence Interpolation},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {LGI2P - Site EERIE - EMA},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  aupetit01a,
  author	= {M. Aupetit and P. Couturier and P. Massotte},
  title		= {Vector quantisation with $\gamma$-observable neighbors},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {230--7},
  year		= {2001},
  editor	= {Nigel Allison and Hujun Yin and Lesley Allison and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InCollection{	  austermeier96a,
  author	= {H. Austermeier and G. Hartmann and R. Hilker},
  title		= {Color-calibration of a robot vision system using
		  \mbox{self-organizing} feature maps},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {257--62},
  dbinsdate	= {oldtimer}
}

@TechReport{	  autere90a,
  author	= {Antti Autere and Jarmo T. Alander and Lasse
		  Holmstr{\"{o}}m and Peter Holmstr{\"{o}}m and Ari
		  H{\"{a}}m{\"{a}}l{\"{a}}inen and Juha Tuominen},
  title		= {Surface Type Recognition by a Hair Sensor},
  type		= {Res. Reports},
  number	= {A2},
  institution	= {Rolf Nevanlinna Institute},
  address	= {Helsinki, Finland},
  year		= {1990},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  avcibas00a,
  author	= {Avcibas, I. and Sankur, B.},
  title		= {Statistical analysis of image quality measures},
  booktitle	= {Signal Processing X Theories and Applications. Proceedings
		  of EUSIPCO 2000. Tenth European Signal Processing
		  Conference. Tampere Univ. Technology, Tampere, Finland},
  year		= {2000},
  volume	= {4},
  pages		= {2181--4},
  abstract	= {In this paper, we conduct a statistical analysis of the
		  sensitivity and consistency behavior of objective image
		  quality measures. We categorize the quality measures and
		  compare them for still image compression applications. The
		  measures have been categorized into pixel difference-based,
		  correlation-based, edge-based, spectral-based,
		  context-based and HVS-based (human visual system-based)
		  measures. The mutual relationships between the measures are
		  visualized by plotting their Kohonen maps. Their
		  consistency and sensitivity to coding as well as additive
		  noise and blur are investigated via ANOVA analysis of their
		  scores. It has been found that measures based on HVS, on
		  phase spectrum and on multiresolution mean square error are
		  most discriminative to coding artifacts.},
  dbinsdate	= {2002/1}
}

@Article{	  avcibas02a,
  author	= {Avcibas, I. and Sankur, B. and Sayood, K.},
  title		= {Statistical evaluation of image quality measures},
  journal	= {Journal-of-Electronic-Imaging},
  year		= {2002},
  volume	= {11},
  pages		= {206--23},
  abstract	= {In this work we comprehensively categorize image quality
		  measures, extend measures defined for gray scale images to
		  their multispectral case, and propose novel image quality
		  measures. They are categorized into pixel difference-based,
		  correlation-based, edge-based, spectral-based,
		  context-based and human visual system (HVS)-based measures.
		  Furthermore we compare these measures statistically for
		  still image compression applications. The statistical
		  behavior of the measures and their sensitivity to coding
		  artifacts are investigated via analysis of variance
		  techniques. Their similarities or differences are
		  illustrated by plotting their Kohonen maps. Measures that
		  give consistent scores across an image class and that are
		  sensitive to coding artifacts are pointed out. It was found
		  that measures based on the phase spectrum, the
		  multiresolution distance or the HVS filtered mean square
		  error are computationally simple and are more responsive to
		  coding artifacts. We also demonstrate the utility of
		  combining selected quality metrics in building a
		  steganalysis tool.},
  dbinsdate	= {2002/1}
}

@InCollection{	  azam98a,
  author	= {F. Azam and H. F. Van Landingham},
  title		= {Adaptive self organizing feature map neuro-fuzzy technique
		  for dynamic system identification},
  booktitle	= {Proceedings of the 1998 IEEE International Symposium on
		  Intelligent Control (ISIC) held jointly with IEEE
		  International Symposium on Computational Intelligence in
		  Robotics and Automation (CIRA) Intelligent Systems and
		  Semiotics (ISAS)},
  publisher	= {IEEE},
  year		= {1998},
  address	= {New York, NY, USA},
  pages		= {337--41},
  dbinsdate	= {oldtimer}
}

@InCollection{	  azam98b,
  author	= {Farooq Azam and H. F. VanLandingham},
  title		= {A New Approach for Modular Neural Network Design and
		  Learning},
  booktitle	= {Proc. JCIS'98},
  publisher	= {Association for Intelligent Machinery, Inc},
  year		= 1998,
  editor	= {Paul P. Wang},
  volume	= {II},
  pages		= {147--189},
  abstract	= {This paper presents a new efficient design and learning
		  methodology for a modularly structured neural network. This
		  design scheme is based on the input space partitioning
		  using an adaptive fuzzy partitioning module implementing an
		  adaptive self organizing feature map paradigm. The input
		  space partitioning serves the dual purpose of the
		  determination of optimal number of expert neural networks
		  in the modular structure along with facilitation of the
		  neural architecture training. This approach leads to a
		  simple and optimal modular neural architecture along with a
		  simplified training algorithm as compared to generally used
		  expectation maximization (EM) algorithm. The experimental
		  results indicate that this approach has good capabilities
		  for nonlinear system identification.},
  dbinsdate	= {oldtimer}
}

@Article{	  azami00a,
  author	= {Azami, Seyed Bahram Zahir and Feng, Gang},
  title		= {Robust vector quantizer design using self-organizing
		  neural networks},
  journal	= {Signal Processing},
  year		= {2000},
  volume	= {80},
  number	= {7},
  month		= {Jul},
  pages		= {1289--1298},
  organization	= {Univ of Ottawa},
  publisher	= {Elsevier Science Publishers B.V.},
  address	= {Amsterdam},
  abstract	= {In this paper we propose a new method to design vector
		  quantizers for noisy channels. Self-organizing neural
		  networks are known for their efficiency in voice and image
		  data compression; we use self-organizing algorithm to
		  create a topological similarity between the input space and
		  the index space. This similarity reduces the effect of
		  channel noise because any single bit error in a transmitted
		  index will be translated to a close codevector in the input
		  space which yields relatively small distortion. For an
		  8-bit vector quantizer, the proposed system resulted in
		  4.59 dB spectral distortion in a highly noisy channel while
		  a simple LBG, LBG with splitting and 2-D self-organizing
		  map (SOM) resulted in 5.96, 5.46 and 5.02 dB of distortion,
		  respectively.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  azcarraga00a,
  author	= {Azcarraga, Arnulfo P.},
  title		= {Assessing self-organization using order metrics},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {159--164},
  organization	= {Natl Univ of Singapore},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Self-Organizing Maps (SOM) are proving to be useful as
		  data analysis and visualization tools because they can
		  visually render the data analysis results in 2D or 3D, and
		  do not need category information for each input pattern.
		  But this unsupervised nature of the training process makes
		  it difficult to have separate training and test sets to
		  determine the quality of the training process, which is
		  done quite naturally for supervised Neural Network learning
		  algorithms. In applications like data analysis, where there
		  is little clue as to the way the SOM is supposed to look
		  like after training, it is important to be able to assess
		  the quality of the self-organization process independent of
		  the application, and without need for category information.
		  The Average Unit Disorder has been used to assess the
		  quality of the ordering of a self-organized map. It is
		  shown here that this same order metric can be used to
		  assess the quality of the self-organization process itself.
		  Based on this order metric, it can be determined whether
		  the SOM has adequately learned, whether the parameters used
		  to train the SOM have been correctly specified, and whether
		  the SOM variant used is well-suited to the specific problem
		  at hand.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  azcarraga00b,
  author	= {Azcarraga, A. P.},
  title		= {Evaluating {SOM}s using order metrics},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {255--60},
  abstract	= {It has been shown that self-organized maps, when
		  adequately trained with the set of integers 1 to 32, lay
		  out real numbers in a 2D map in an ordering that is
		  superior to any of the known 2D orderings, such as the
		  Cantor-diagonal, Morton, Peano-Hilbert, raster-scan,
		  row-prime, spiral, and random orderings. Two 2D order
		  metrics (average direct neighbor distance and average unit
		  disorder) have been used to assess the quality of a map's
		  2D ordering. It is shown that these same order metrics are
		  useful in assessing the quality of the self-organization
		  process itself. Based on these metrics, it can be
		  determined whether the SOM has already adequately learned
		  and whether the parameters used to train the SOM have been
		  correctly specified. In applications like data analysis,
		  where there is little clue as to the way the SOM is
		  supposed to look like after training, it is important to be
		  able to assess the quality of the self-organization process
		  independent of the application.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  azcarraga01a,
  author	= {Azcarraga, A. P. and Yap, T. N. Jr},
  title		= {Comparing keyword extraction techniques for {WEBSOM} text
		  archives},
  booktitle	= {Proceedings 13th IEEE International Conference on Tools
		  with Artificial Intelligence. ICTAI 2001. IEEE Comput. Soc,
		  Los Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {187--94},
  abstract	= {The WEBSOM methodology for building very large text
		  archives has a very slow method for extracting meaningful
		  unit labels. This is because the method computes for the
		  relative frequencies of all the words of all the documents
		  associated to each unit and then compares these to the
		  relative frequencies of all the words of all the other
		  units of the map. Since maps may have more than 100,000
		  units and the archive may contain up to 7 million
		  documents, the existing WEBSOM method is not practical. A
		  fast alternative method is based on the distribution of
		  weights in the weight vectors of the trained map, plus a
		  simple manipulation of the random projection matrix used
		  for input data compression. Comparisons made using a WEBSOM
		  archive of the Reuters text collection reveal that a high
		  percentage of keywords extracted using this method match
		  the keywords extracted for the same map units using the
		  original WEBSOM method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  azcarraga01b,
  author	= {Azcarraga, A. P. and Yap, T. N. Jr},
  title		= {{SOM}-based methodology for building large text archives},
  booktitle	= {Proceedings Seventh International Conference on Database
		  Systems for Advanced Applications. DASFAA 2001. IEEE
		  Comput. Soc, Los Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {66--73},
  abstract	= {Not only have self-organizing maps (SOMs), such as the
		  WEBSOM, been shown to scale up to very large datasets,
		  these maps also allow for a novel mode of navigating
		  through a large collection of text documents. The entire
		  text collection is presented to a user as a regular map,
		  where each point in the map is associated to a group of
		  documents that are likely to be composed of similar terms
		  and phrases. In addition, the closer two points are in the
		  map, the more similar are their respective associated
		  documents. Thus, once an interesting document is found in
		  the map, the user just has to click around the vicinity of
		  that document to retrieve other similar documents. A major
		  drawback of SOMs, however, is the long training time
		  required, especially for document collections where both
		  the volume and the dimensionality are huge. We demonstrate
		  how the size of the initial text collection is
		  progressively and drastically reduced from the raw document
		  collection to the final SOM-based text archive. We
		  demonstrate this using a widely studied Reuters
		  collection.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  azcarraga90a,
  author	= {A. P. Azcarraga and B. Amy},
  title		= {{K}ohonen Features Maps: toward invariant character
		  recognition},
  booktitle	= {Artificial Intelligence IV. Methodology, Systems,
		  Applications. Proc. of the Fourth International Conference
		  (AIMSA '90)},
  year		= {1990},
  editor	= {P. Jorrand and V. Sgurev},
  pages		= {209--217},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  azema-barac92a,
  author	= {M. E. Azema-Barac},
  title		= {A conceptual framework for implementing neural networks on
		  massively parallel machines},
  booktitle	= {Proc. Sixth Int. Parallel Processing Symp. },
  year		= {1992},
  editor	= {V. K. Prasanna and L. H. Canter},
  pages		= {527--530},
  publisher	= {IEEE Computer Soc. Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  azema-barac92b,
  author	= {M. E. Azema-Barac},
  title		= {A generic strategy for mapping neural network models on
		  transputer-based machines},
  booktitle	= {Transputing in numerical and neural network applications},
  year		= {1992},
  pages		= {244--9},
  editor	= {Reijns, G. L. and Jian Luo},
  publisher	= {IOS Press},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InCollection{	  azimi-sadjadi96a,
  author	= {M. R. Azimi-Sadjadi and M. A. Shaikh and Bin Tian and K.
		  E. Eis and D. Reinke},
  title		= {Neural network-based cloud detection/classification using
		  textural and spectral features},
  booktitle	= {IGARSS '96. 1996 International Geoscience and Remote
		  Sensing Symposium. Remote Sensing for a Sustainable
		  Future},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  editor	= {T. I. Stein},
  address	= {New York, NY, USA},
  pages		= {1105--7},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baader94a,
  author	= {Baader, A. and Hirzinger, G. },
  title		= {A \mbox{self-organizing} algorithm for multisensory
		  surface reconstruction},
  booktitle	= {IROS '94. Proceedings of the IEEE/RSJ/GI International
		  Conference on Intelligent Robots and Systems. Advanced
		  Robotic Systems and the Real World},
  year		= {1994},
  volume	= {1},
  pages		= {81--8},
  organization	= {Inst. for Roboptics \& Syst. Dynamics, German Aerosp. Res.
		  Establ. , Wessling, Germany},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baader95a,
  author	= {Baader, A. and Hirzinger, G. },
  title		= {World modeling for a sensor-in-hand robot arm},
  booktitle	= {Proceedings of the 1995 IEEE/RSJ International Conference
		  on Intelligent Robots and Systems. Human Robot Interaction
		  and Cooperative Robots},
  year		= {1995},
  volume	= {2},
  pages		= {110--15},
  organization	= {Inst. of Robotics \& Syst. Dynamics, German Aerosp. Res.
		  Establ. , Wessling, Germany},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  babri96a,
  author	= {H. A. Babri and A. A. Osman-Gani},
  title		= {Decision making using neural networks: an application to
		  cross-cultural management},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {4},
  address	= {New York, NY, USA},
  pages		= {2060--5},
  abstract	= {Clustering various countries according to their relative
		  similarity in terms of relevant organizational variables is
		  a very useful management tool for multinational
		  enterprises(MNs). The effects of the nature of population
		  and type of 'similarity' variables on the cluster
		  compositions are generally well understood. However, the
		  differences on cluster compositions arising from the
		  underlying differences of various techniques have not been
		  well investigated. This paper is the first empirical study
		  using neural networks (specifically Kohonen's SOFM) as a
		  tool to identify country clusters based on managers'
		  perceptions of various management and human resource
		  development(HRD) practices in a large MNE. A method of
		  obtaining near-optimum number of country clusters is
		  described. The clusters developed by the SOFM network are
		  also compared with those obtained using a popular
		  clustering technique such as Q-factor analysis.},
  dbinsdate	= {oldtimer}
}

@Article{	  babu97a,
  author	= {G. P. Babu},
  title		= {Self-organizing neural networks for spatial data},
  journal	= {Pattern Recognition Letters},
  year		= {1997},
  volume	= {18},
  number	= {2},
  pages		= {133--42},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bachmann01a,
  author	= {Bachmann, Charles M. and Fusina, Robert A. and Donato,
		  Timothy F.},
  title		= {Effects of time series imagery on automated classification
		  of coastal wetland environments using projection pursuit
		  methods},
  booktitle	= {International Geoscience and Remote Sensing Symposium
		  (IGARSS)},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {1868--1870},
  organization	= {Naval Research Laboratory, Remote Sensing Division, Code
		  7212},
  publisher	= {Institute of Electrical and Electronics Engineers Inc.},
  address	= {},
  abstract	= {Using a time series of Radarsat SAR and Land-sat TM
		  imagery, we developed automated classification models for
		  coastal land-cover in Northampton County, VA. The database
		  consisted of three pairs of images from each sensor taken
		  within one week of each other. We compared the results of
		  models based on data from one set with the results based on
		  data from all three sets for Radarsat only, Landsat only
		  and both sensors combined. For Radarsat only, there is an
		  increase on the order of 23--30% in the overall
		  classification score for the three-date set compared to the
		  one-date set. This is consistent with previous results
		  based on different classification methods. We found a
		  smaller increase, on the order of 1--5%, for the Landsat
		  only. For combined sensor cases, three-date Inputs did not
		  improve results when compared with single-date inputs. For
		  the feature extraction stage, we compared a Projection
		  Pursuit method with Principal Component Analysis. In the
		  final classifier stage, we compared a vector quantization
		  algorithm, LVQ, and the backward propagation of error model
		  with a cross-entropy cost function. The differences given
		  above for one-date versus three-date results hold for all
		  the methods studied. Best model agreement with a National
		  Wetlands Inventory (NWI) map was found for three-date
		  atmospherically corrected Landsat inputs, for which
		  generalization performance was 80.5% of pixels correct,
		  with 86.4% obtained on the cross-validation set used to
		  find a stopping point for model optimization, and 87.9% for
		  the training set.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  back94a,
  author	= {B. Back and G. Oosterom and K. Sere and M. {van Wezel}},
  title		= {A Comparative Study of Neural Networks in Bankrupty
		  Prediction},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conference Proc. of Finnish Artificial Intelligence
		  Society},
  pages		= {140--148},
  booktitle	= {Proc. Conference on Artificial Intelligence Res. in
		  Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {application, prediction, comparison},
  dbinsdate	= {oldtimer}
}

@InCollection{	  back96a,
  author	= {B. Back and K. Sere and H. Vanharanta},
  title		= {Data mining accounting numbers using
		  \mbox{self-organizing} maps},
  booktitle	= {STeP '96---Genes, Nets and Symbols. Finnish Artificial
		  Intelligence Conference},
  publisher	= {Univ. Vaasa},
  year		= {1996},
  editor	= {J. Alander and T. Honkela and M. Jakobsson},
  address	= {Vaasa, Finland},
  pages		= {35--47},
  dbinsdate	= {oldtimer}
}

@InCollection{	  back97a,
  author	= {Barbro Back and Kaisa Sere and Hannu Vanharanta},
  title		= {Analyzing financial performance with
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {356--361},
  dbinsdate	= {oldtimer}
}

@Article{	  back98,
  author	= {Back, B.~ and Sere, K.~ and Vanharanta, H.~},
  title		= {Managing Complexity in Large Databases Using
		  Self-Organized Maps},
  journal	= {Accounting, Management and Information Technologies},
  year		= {1998},
  volume	= {8},
  number	= {4},
  pages		= {191--210},
  dbinsdate	= {oldtimer}
}

@InCollection{	  back98a,
  author	= {B. Back and K. Sere and H. Vanharanta},
  title		= {Analyzing financial performance with
		  \mbox{self-organizing} maps},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {266--70},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  badran92a,
  author	= {Badran, F. and Thiria, S. and Main, B. },
  title		= {Smoothing by use of \mbox{self-organizing} maps},
  booktitle	= {Fifth International Conference. Neural Networks and their
		  Applications. NEURO NIMES 92},
  year		= {1992},
  pages		= {107--15},
  organization	= {CEDRIC/CNAM, Paris, France},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Article{	  bahlmann99a,
  author	= {Bahlmann, Claus and Heidemann, Gunther and Ritter, Helge},
  title		= {Artificial neural networks for automated quality control
		  of textile seams},
  journal	= {Pattern Recognition},
  year		= {1999},
  number	= {6},
  volume	= {32},
  pages		= {1049--1060},
  abstract	= {We present a method for an automated quality control of
		  textile seams, which is aimed to establish a standardized
		  quality measure and to lower costs in manufacturing. The
		  system consists of a suitable image acquisition setup, an
		  algorithm for locating the seam, a feature extraction stage
		  and a neural network of the self-organizing map type for
		  feature classification. A procedure to select an optimized
		  feature set carrying the information relevant for
		  classification is described.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bai01a,
  author	= {Bai Bo Feng and Guo Lie Jin and Chen Xue Jun},
  title		= {The technology and theory of online recognition of
		  gas-liquid two-phase flow regime},
  booktitle	= {Proceedings-of-the-CSEE. vol.21, no.7},
  year		= {2001},
  volume	= {21},
  pages		= {46--50},
  abstract	= {It is of important industrial background and scientific
		  significance to study the online recognition of a
		  gas-liquid two-phase flow regime. The key problem of the
		  online recognition is how to map to the space of flow
		  regimes from the space of character of the parameter
		  fluctuations with the shortest time. A counter propagation
		  network (CPN) can train the results of self-organized
		  mapping with supervision. At the same time, it takes
		  advantage of a simple algorithm and doesn't require lots of
		  training samples. Therefore, it meets the online and
		  automatic recognition of gas-liquid two-phase flow regimes.
		  In the present test case, the online criterion was 8.2 s
		  with the FFT of pressure fluctuations from the vertical
		  upward section of the U-type tube. The presented technology
		  and theory has some obvious advantages, which provides a
		  feasible solution of online recognition without a databank
		  of flow regime and can partially overcome the problems
		  induced by not enough experiments in advance. In addition,
		  the problems to shorten the identification time and to
		  increase the identification possibility are discussed in
		  the paper.},
  dbinsdate	= {2002/1}
}

@InCollection{	  bailey95a,
  author	= {M. Bailey and C. Solomon and N. Kasabov and S. Greig},
  title		= {Hybrid systems for medical data analysis and decision
		  making-a case study on varicose vein disorders},
  booktitle	= {Proceedings of the Second New Zealand International
		  Two-Stream Conference on Artificial Neural Networks and
		  Expert Systems},
  publisher	= {IEEE Computer Society Press},
  year		= {1995},
  editor	= {N. K. Kasabov and G. Coghill},
  address	= {Los Alamitos, CA, USA},
  pages		= {265--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  balachander97a,
  author	= {T. Balachander and R. Kothar and H. Cualing},
  title		= {An empirical comparison of dimensionality reduction
		  techniques for pattern classification},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {589--94},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ball90a,
  author	= {N. R. Ball and K. Warwick},
  title		= {Application of augmented-output self organizing feature
		  maps to the adaptive control problem},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conference},
  year		= {1990},
  volume	= {I},
  pages		= {242},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ball92a,
  author	= {Nigel Ball and Kevin Warwick},
  title		= {Applying \mbox{self-organizing} feature maps to the
		  control of artificial organisms in maze running tasks},
  booktitle	= {Proc. American Control Conference },
  year		= {1992},
  pages		= {3062--3063},
  organization	= {American Automatic Control Council},
  publisher	= {American Automatic Control Council},
  address	= {Green Valley, AZ},
  x		= {cp93---DIALOG No: 03560102 Variations on the now standard
		  Kobonen feature map enable an ordering of the map state
		  space by using only a limited subset of the complete input
		  vector. Also it is possible to employ merely a local
		  adaptation procedure to order the map, rather than having
		  to rely on global variables and objectives. Such variations
		  have been included as part of a Hybrid Learning System
		  (HLS) which has arisen out of a genetic-based classifier
		  system. Inthis paper a description of the modified feature
		  map is given, thisconstituting the HLS's long term memory,
		  and results on the control of simplemaze running task are
		  presented, thereby demonstrating the value of goalrelated
		  feedback within the overall network. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ball92b,
  author	= {N. R. Ball},
  title		= {Competitive Learning in Classifier Feature Maps},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {703--706},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  ball92c,
  author	= {N. Ball and L. Kierman and K. Warwick and E. Cahill and D.
		  Esp and J. Macqueen},
  title		= {Neural networks for power systems alarm handling},
  journal	= {Neurocomputing},
  year		= {1992},
  volume	= {4},
  number	= {1--2},
  pages		= {5--8},
  dbinsdate	= {oldtimer}
}

@Article{	  ball93a,
  author	= {N. R. Ball and K. Warwick},
  title		= {Using \mbox{self-organizing} feature maps for the control
		  of artificial organisms},
  journal	= {IEE Proc. D (Control Theory and Applications)},
  year		= {1993},
  volume	= {140},
  number	= {3},
  pages		= {176--180},
  month		= {May},
  abstract	= {Variations on the standard Kohonen feature map can enable
		  an ordering of the map state space by using only a limited
		  subset of the complete input vector. Also it is possible to
		  employ merely a local adaptation procedure to order the
		  map, rather than having to rely on global variables and
		  objectives. Such variations have been included as part of a
		  hybrid learning system (HLS) which has arisen out of a
		  genetic-based classifier system. In this paper a
		  description of the modified feature map is given, which
		  constitutes the HLSs long term memory, and results on the
		  control of a simple maze running task are presented,
		  thereby demonstrating the value of goal related feedback
		  within the overall network.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ball93b,
  author	= {Ball, N. R. },
  title		= {Towards the development of cognitive maps in classifier
		  systems},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  year		= {1993},
  editor	= {Albrecht, R. F. and Reeves, C. R. and Steele, N. C. },
  pages		= {712--18},
  organization	= {Dept. of Eng. , Cambridge Univ. , UK},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ball94a,
  author	= {N. R. Ball},
  title		= {Reinforcement Learning in {K}ohonen Feature Maps},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {663--666},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, control, modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ball94b,
  author	= {N. R. Ball},
  title		= {Application of a Neural Network based classifier system to
		  {ABV} obstacle avoidance},
  booktitle	= {Proc. IMACS Int. Symp. on Signal Processing, Robotics and
		  Neural Networks},
  year		= {1994},
  pages		= {294--297},
  publisher	= {IMACS},
  address	= {Lille, France},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ball94c,
  author	= {N. Ball},
  title		= {Organizing an animat`s behavioural repertoires using
		  {K}ohonen feature maps},
  booktitle	= {From Animals to Animats 3. Proceedings of the Third
		  International Conference on Simulation of Adaptive
		  Behavior},
  publisher	= {MIT Press},
  year		= {1994},
  editor	= {D. Cliff and P. Husbands and J. -A. Meyer and S. W.
		  Wilson},
  address	= {Cambridge, MA, USA},
  pages		= {128--37},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ball96a,
  author	= {N. R. Ball},
  title		= {Representation of obstacles in a Neural Network based
		  Classifier System},
  booktitle	= {Proc. ESANN'96, European Symp. on Artificial Neural
		  Networks},
  year		= {1996},
  publisher	= {D facto conference services},
  address	= {Bruges, Belgium},
  editor	= {Michel Verleysen},
  pages		= {155--160},
  dbinsdate	= {oldtimer}
}

@Article{	  ball96b,
  author	= {N. R. Ball},
  title		= {Application of a neural network based classifier system to
		  {AGV} obstacle avoidance},
  journal	= {Mathematics and Computers in Simulation},
  year		= {1996},
  volume	= {41},
  number	= {3--4},
  pages		= {285--96},
  note		= {(IMACS Symposium on Signal Processing Robotics and Neural
		  Networks Conference Date: April 1994 Conf. Loc: Lille,
		  France)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  balmat91a,
  author	= {Balmat, J. and Abellard, P. and Maifret, R. },
  title		= {Modeling {K}ohonen type neural networks using a data flow
		  Petri net},
  booktitle	= {Proceedings of the Fourth ISMM/IASTED International
		  Conference Parallel and Distributed Computing and
		  Systems---II},
  year		= {1991},
  editor	= {Ammar, R. A. },
  pages		= {32--4},
  organization	= {DCN Toulon, France},
  publisher	= {Acta Press},
  address	= {Anaheim, CA, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  balmelli96a,
  author	= {L. Balmelli},
  title		= {Adaptive sampling for very large particle systems using an
		  incremental \mbox{self-organizing} feature map: an
		  application in molecular dynamic},
  booktitle	= {Computer Animation and Simulation '96. Proceedings of the
		  Eurographics Workshop},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {R. Boulic and G. Hegron},
  address	= {Wien, Austria},
  pages		= {15--29},
  dbinsdate	= {oldtimer}
}

@Article{	  ban00a,
  author	= {Ban, Sang-Woo and Cho, Jun-ki and Jung, Soon-ki and Lee,
		  Minho},
  title		= {Active vision system based on human eye saccadic
		  movement},
  journal	= {IEICE Transactions on Fundamentals of Electronics,
		  Communications and Computer Sciences},
  year		= {2000},
  volume	= {E83-A},
  number	= {6},
  month		= {Jun},
  pages		= {1066--1073},
  organization	= {Kyungpook Natl Univ},
  publisher	= {IEICE of Japan},
  address	= {Tokyo},
  abstract	= {We propose a new active vision system that mimics a
		  saccadic movement of human eye. It is implemented based on
		  a new computational model using neural networks. In this
		  model, the visual pathway was divided in order to
		  categorize a saccadic eye movement into three parts, each
		  of which was then individually modeled using different
		  neural networks to reflect a principal functionality of
		  brain structures related with the saccadic eye movement in
		  our brain. Initially, the visual cortex for saccadic eye
		  movements was modeled using a self-organizing feature map,
		  then a modified learning vector quantization network was
		  applied to imitate the activity of the superior colliculus
		  relative to a visual stimulus. In addition, a multilayer
		  recurrent neural network, which is learned by an
		  evolutionary computation algorithm, was used to model the
		  visual pathway from the superior colliculus to the
		  oculomotor neurons. Results from a computer simulation show
		  that the proposed computational model is effective in
		  mimicking the human eye movements during a saccade. Based
		  on the proposed model, an active vision system using a CCD
		  type camera and motor system was developed and demonstrated
		  with experimental results.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  bandeira00a,
  author	= {Bandeira, N. and Lobo, V. and Moura Pires, F.},
  title		= {Analysis of {EEG} of shooters},
  booktitle	= {Proceedings of the International Conference on Mathematics
		  and Engineering Techniques in Medicine and Biological
		  Sciences. METMBS'00. CSREA Press---Univ. Georgia, Athens,
		  GA, USA},
  year		= {2000},
  volume	= {1},
  pages		= {35--9},
  abstract	= {Electroencephalogram (EEG) data was collected from
		  competition-level shooters during practice sessions and
		  searched for relations between this EEG data and the
		  shooter's score on target. For this purpose self-organizing
		  maps (SOM), cross-correlation analysis and scatter matrix
		  based techniques were applied to identify which variables
		  were relevant for the correct classification of EEG data
		  vectors into the corresponding target score classes.},
  dbinsdate	= {2002/1}
}

@InCollection{	  bandeira98a,
  author	= {N. Bandeira and V. J. Lobo and F. Moura-Pires},
  title		= {Training a \mbox{self-organizing} map distributed on a
		  {PVM} network},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {457--61},
  dbinsdate	= {oldtimer}
}

@Article{	  banzhaf90a,
  author	= {W. Banzhaf and H. Haken},
  title		= {Learning in a competitive network},
  journal	= {Neural Networks},
  year		= {1990},
  volume	= {3},
  number	= {4},
  pages		= {423--435},
  x		= {. . . The rule which was originally introduced by Kohonen
		  is appropriately modified and applied to the competitive
		  network under study. . . . },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baraghimian89a,
  author	= {K. A. Baraghimian},
  title		= {Connected component labeling using \mbox{self-organizing}
		  feature maps},
  booktitle	= {Proc. 13th Annual Int. Computer Software and Applications
		  Conference },
  year		= {1989},
  pages		= {680--684},
  publisher	= {IEEE Computer Soc. Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  baraldi02a,
  author	= {Baraldi, A. and Alpaydin, E.},
  title		= {Constructive feedforward {ART} clustering networks---Part
		  {II}},
  journal	= {IEEE TRANSACTIONS ON NEURAL NETWORKS},
  year		= {2002},
  volume	= {13},
  number	= {3},
  month		= {MAY},
  pages		= {662--677},
  abstract	= {Part I of this paper defines the class of constructive
		  unsupervised on-line learning simplified adaptive resonance
		  theory (SART) clustering networks. Proposed instances of
		  class SART are the symmetric Fuzzy ART (S-Fuzzy ART) and
		  the Gaussian ART (GART) network. In Part 11 of our work, a
		  third network belonging to class SART, termed fully
		  self-organizing SART (FOSART), is presented and discussed.
		  FOSART is a constructive, soft-to-hard competitive,
		  topology-preserving, minimum- distance-to-means clustering
		  algorithm capable of: 1) generating processing units and
		  lateral connections on an example-driven basis and 2)
		  removing processing units and lateral connections on a
		  minibatch basis. FOSART is compared with Fuzzy ART, S-Fuzzy
		  ART, GART and other well-known clustering techniques (e.g.,
		  neural gas and self-organizing map) in several unsupervised
		  learning tasks, such as vector quantization, perceptual
		  grouping and 3-D surface reconstruction. These experiments
		  prove that when compared with other unsupervised learning
		  networks, FOSART provides an interesting balance between
		  easy user interaction, performance accuracy, efficiency,
		  robustness, and flexibility.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  baraldi95a,
  author	= {A. Baraldi and F. Parmiggiani},
  title		= {A Self-Organizing Neural Network Merging {K}ohonen's and
		  {ART} Models},
  volume	= {V},
  pages		= {2444--2449},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  baraldi95b,
  author	= {Baraldi, A. and Parmiggiani, F. },
  title		= {A neural network for unsupervised categorization of
		  multivalued input patterns: an application to satellite
		  image clustering},
  journal	= {IEEE Transactions on Geoscience and Remote Sensing},
  year		= {1995},
  volume	= {33},
  number	= {2},
  pages		= {305--16},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  baraldi96a,
  author	= {A. Baraldi and F. Parmiggiani},
  title		= {Fuzzy clustering: critical analysis of the contextual
		  mechanisms employed by three neural network models},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2761},
  pages		= {261--70},
  note		= {(Applications of Fuzzy Logic Technology III Conf. Date:
		  10--12 April 1996 Conf. Loc: Orlando, FL, USA Conf.
		  Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  baraldi97a,
  author	= {A. Baraldi and F. Parmiggiani},
  title		= {Neural network fuzzification: a critical review of the
		  fuzzy learning vector quantization model},
  booktitle	= {Neural Nets WIRN VIETRI-96. Proceedings of the 8th Italian
		  Workshop on Neural Nets},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {M. Marinaro and R. Tagliaferri},
  address	= {London, UK},
  pages		= {93--9},
  dbinsdate	= {oldtimer}
}

@Article{	  baraldi97b,
  author	= {A. Baraldi and F. Parmiggiani},
  title		= {Novel neural network model combining radial basis
		  function, competitive Hebbian learning rule, and fuzzy
		  simplified adaptive resonance theory},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3165},
  pages		= {98--112},
  note		= {(Applications of Soft Computing Conf. Date: 28--29 July
		  1997 Conf. Loc: San Diego, CA, USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  baraldi97c,
  author	= {A. Baraldi and F. Parmiggiani},
  title		= {Fuzzy Combination of {K}ohonen's and {ART} Neural Network
		  Models to Detect Statistical Regularities in a Random
		  Sequence of Multi-Valued Input Patterns},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {I},
  pages		= {281--286},
  dbinsdate	= {oldtimer}
}

@InCollection{	  baraldi98a,
  author	= {A. Baraldi and P. Blonda and A. Petrosino},
  title		= {Fuzzy neural networks for pattern recognition},
  booktitle	= {Neural Nets WIRN-VIETRI-97. Proceedings of the 9th Italian
		  Workshop on Neural Nets},
  publisher	= {Springer-Verlag London},
  year		= {1998},
  editor	= {M. Marinaro and R. Tagliaferri},
  address	= {London, UK},
  pages		= {35--83},
  dbinsdate	= {oldtimer}
}

@Article{	  baraldi98b,
  author	= {Baraldi, A. and Blonda, P. and Parmiggiani, F. and
		  Pasquariello, G. and Satalino, G.},
  title		= {Model transitions in descending {F LVQ} },
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1998},
  number	= {5},
  volume	= {9},
  pages		= {724--738},
  abstract	= {Fuzzy learning vector quantization (FLVQ), also known as
		  the fuzzy Kohonen clustering network, was developed to
		  improve performance and usability of on-line
		  hard-competitive Kohonen's vector quantization and
		  soft-competitive self organizing map (SOM) algorithms. The
		  FLVQ effectiveness seems to depend on the range of change
		  of the weighting exponent m(t). In the first part of this
		  work, extreme m(t) values (1 and infinity , respectively)
		  are employed to investigate FLVQ asymptotic behaviors. This
		  analysis shows that when m(t) tends to either one of its
		  extremes, FLVQ is affected by trivial vector quantization,
		  which causes centroids collapse in the grand mean of the
		  input data set. No analytical criterion has been found to
		  improve the heuristic choice of the range of m(t) change.
		  In the second part of this paper, two FLVQ and {SOM}
		  classification experiments of remote sensed data are
		  presented. In these experiments the two nets are connected
		  in cascade to a supervised second stage, based on the delta
		  rule. Experimental results confirm that FLVQ performance
		  can be greatly affected by the user's definition of the
		  range of change of the weighting exponent. Moreover, FLVQ
		  shows instability when its traditional termination
		  criterion is applied. Empirical recommendations are
		  proposed for the enhancement of FLVQ robustness. Both the
		  analytical and the experimental data reported seem to
		  indicate that the choice of the range of m(t) change is
		  still open to discussion and that alternative clustering
		  neural-network approaches should be developed to pursue
		  during training: 1) monotone reduction of the neurons'
		  learning rate and 2) monotone reduction of the overlap
		  among neuron receptive fields.},
  dbinsdate	= {oldtimer}
}

@Article{	  baraldi99a,
  author	= {Baraldi, A. and Blonda, P.},
  title		= {A survey of fuzzy clustering algorithms for pattern
		  recognition. {II}},
  journal	= {IEEE Transactions on Systems, Man and Cybernetics, Part B
		  (Cybernetics)},
  year		= {1999},
  volume	= {29},
  pages		= {786--801},
  abstract	= {For pt.I see ibid., p.775--85. In part I an equivalence
		  between the concepts of fuzzy clustering and soft
		  competitive learning in clustering algorithms is proposed
		  on the basis of the existing literature. Moreover, a set of
		  functional attributes is selected for use as dictionary
		  entries in the comparison of clustering algorithms. In this
		  paper, five clustering algorithms taken from the literature
		  are reviewed, assessed and compared on the basis of the
		  selected properties of interest. These clustering models
		  are (1) self-organizing map (SOM); (2) fuzzy learning
		  vector quantization (FLVQ); (3) fuzzy adaptive resonance
		  theory (fuzzy ART); (4) growing neural gas (GNG); (5) fully
		  self-organizing simplified adaptive resonance theory
		  (FOSART). Although our theoretical comparison is fairly
		  simple, it yields observations that may appear parodoxical.
		  First, only FLVQ, fuzzy ART, and FOSART exploit concepts
		  derived from fuzzy set theory (e.g., relative and/or
		  absolute fuzzy membership functions). Secondly, only {SOM},
		  FLVQ, GNG, and FOSART employ soft competitive learning
		  mechanisms, which are affected by asymptotic misbehaviors
		  in the case of FLVQ, i.e., only {SOM}, GNG, and FOSART are
		  considered effective fuzzy clustering algorithms.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baras90a,
  author	= {J. S. Baras and A. LaVigna},
  title		= {Convergence of {K}ohonen's learning vector quantization},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, San Diego},
  year		= {1990},
  volume	= {III},
  pages		= {17--20},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baras90b,
  author	= {J. S. Baras and A. LaVigna},
  title		= {Convergence of the vectors in {K}ohonen's learning vector
		  quantization},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conference },
  year		= {1990},
  volume	= {II},
  pages		= {1028--1031},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baras90c,
  author	= {J. S. Baras and A. La Vigna},
  title		= {Convergence of a neural network classifier},
  booktitle	= {Proc. 29th IEEE Conference on Decision and Control},
  year		= {1990},
  volume	= {III},
  pages		= {1735--1740},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baras99a,
  author	= {Baras, J. S. and Dey, S.},
  title		= {Adaptive classification based on compressed data using
		  learning vector quantization},
  booktitle	= {Proceedings of the 38th IEEE Conference on Decision and
		  Control},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {3677--83},
  abstract	= {Classification problems using compressed data are becoming
		  increasingly important in many applications with large
		  amounts of sensory data and large sets of classes. These
		  applications range from aided target recognition (ATR), to
		  medical diagnosis, to speech recognition, to fault
		  detection and identification in manufacturing systems. In
		  this paper, we develop and analyze a learning vector
		  quantization (LVQ) based algorithm for the combined
		  compression and classification problem. We show convergence
		  of the algorithm using techniques from stochastic
		  approximation, namely, the ODE method.},
  dbinsdate	= {oldtimer}
}

@Article{	  baras99b,
  author	= {Baras, J. S. and Dey, S.},
  title		= {Combined compression and classification with learning
		  vector quantization},
  journal	= {IEEE Transactions on Information Theory},
  year		= {1999},
  volume	= {45},
  pages		= {1911--20},
  abstract	= {Combined compression and classification problems are
		  becoming increasingly important in many applications with
		  large amounts of sensory data and large sets of classes.
		  These applications range from automatic target recognition
		  (ATR) to medical diagnosis, speech recognition, and fault
		  detection and identification in manufacturing systems. In
		  this paper, we develop and analyze a learning vector
		  quantization (LVQ) based algorithm for combined compression
		  and classification. We show convergence of the algorithm
		  using the ODE method from stochastic approximation. We
		  illustrate the performance of the algorithm with some
		  examples.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  barbalho01a,
  author	= {Barbalho, J. M. and Neto, A. D. D. and Costa, J. A. E. and
		  Netto, M. L. A.},
  title		= {Hierarchical {SOM} applied to image compression},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {442--447},
  organization	= {Univ. Federal do Rio Grande do Norte, Dept. of Electrical
		  Engineering, Lab. of Computer Eng. and Automation},
  publisher	= {},
  address	= {},
  abstract	= {The increase of the need for image storage and
		  transmission in computer systems has increased the
		  importance of signal and image compression algorithms. The
		  approach involving vector quantization (VQ) relies on
		  designing of a finite set of codes which will substitute
		  the original signal during transmission with a minimal of
		  distortion, taking advantage of the spatial redundancy of
		  image to compress them. Algorithms such as LGB and SOM work
		  in an unsupervised way toward finding a good codebook for a
		  given training data. However, the number of code vectors
		  (N) needed for VQ increases with the vector dimension, and
		  full-search algorithms such as LGB and SOM can lead to
		  large training and coding times. An alternative for
		  reducing the computational complexity is the use of a
		  tree-structured vector quantization algorithm. This paper
		  presents an application of a hierarchical SOM for image
		  compression in which reduces the search complexity from
		  O(N) to O(log N), enabling a faster training and image
		  coding. Results are given for conventional SOM, LBG and
		  HSOM showing the advantage of the proposed method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  barber93a,
  author	= {Steven M. Barber and Jose G. Delgado-Frias and Stamatis
		  Vassiliadis and Gerald G. Pechanek},
  title		= {{SPIN-L}: Sequential Pipelined Neuroemulator with Learning
		  Capabilities},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1927--1930},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  barea98a,
  author	= {Barea, R. and Boquete, L. and Garcia, R. and Mazo, M. and
		  Lopez, E. and Escudero M. and Rodriguez F. J.},
  title		= {{ECG} identification using self-organising maps},
  booktitle	= {Proceedings of NC 1998. International ICSC/IFAC Symposium
		  on Neural Computation. ICSC Academic Press, Zurich,
		  Switzerland},
  year		= {1998},
  volume	= {},
  pages		= {378--83},
  abstract	= {The use of automatic systems for the analysis of ECG
		  signals has been growing since the early sixties due to the
		  possibilities opened up by the new technologies. This paper
		  presents the results of the use of self-organising Kohonen
		  maps for the automatic identification of the various
		  cardiac arrhythmias. The algorithm proposed has two
		  options: automatic identification of the R wave in the QRS
		  complex and classification the static projection of the
		  cardiac cycle. Tests carried out with synthetic,
		  noise-contaminated signals suggest that the system could be
		  useful, though further corroborative tests need to be
		  carried out with real signals.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  barge95a,
  author	= {Barge, M. and Chevallier, R. and Curatu, E. and Maruani,
		  A. },
  title		= {Optical digit recognition based on {K}ohonen maps},
  booktitle	= {Optical Computing. Proceedings of the International
		  Conference},
  year		= {1995},
  editor	= {Wherrett, B. S. and Chavel, P. },
  pages		= {451--4},
  organization	= {Dept. Images, ENST, Paris, France},
  publisher	= {IOP Publishing},
  address	= {Bristol, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  barge96a,
  author	= {M. Barge and K. Heggarty and Y. Idan and R. Chevallier},
  title		= {64-channel correlator implementing a {K}ohonen-like neural
		  network for handwritten-digit recognition},
  journal	= {Applied Optics},
  year		= {1996},
  volume	= {35},
  number	= {23},
  pages		= {4655--65},
  dbinsdate	= {oldtimer}
}

@Article{	  barhak01a,
  author	= {Barhak, J. and Fischer, A.},
  title		= {Parameterization and reconstruction from 3D scattered
		  points based on neural network and {PDE} techniques},
  journal	= {IEEE Transactions on Visualization and Computer Graphics},
  year		= {2001},
  volume	= {7},
  number	= {1},
  month		= {January/March 2001},
  pages		= {1--16},
  organization	= {CMSR Lab. for Comp. Graphics and CAD, Department of
		  Mechanical Engineering, Technion},
  publisher	= {},
  address	= {},
  abstract	= {Reverse engineering ordinarily uses laser scanners since
		  they can sample 3D data quickly and accurately relative to
		  other systems. These laser scanner systems, however, yield
		  an enormous amount of irregular and scattered digitized
		  point data that requires intensive reconstruction
		  processing. Reconstruction of freeform objects consists of
		  two main stages: 1) parameterization and 2) surface
		  fitting. Selection of an appropriate parameterization is
		  essential for topology reconstruction as well as surface
		  fitness. Current parameterization methods have topological
		  problems that lead to undesired surface fitting results,
		  such as noisy self-intersecting surfaces. Such problems are
		  particularly common with concave shapes whose parametric
		  grid is self-intersecting, resulting in a fitted surface
		  that considerably twists and changes its original shape. In
		  such cases, other parameterization approaches should be
		  used in order to guarantee non-self-intersecting behavior.
		  The parameterization method described in this paper is
		  based on two stages: 1) 2D initial parameterization and 2)
		  3D adaptive parameterization. Two methods were developed
		  for the first stage: Partial Differential Equation (PDE)
		  parameterization and neural network Self Organizing Maps
		  (SOM) parameterization. PDE parameterization yields a
		  parametric grid without self-intersections. Neural network
		  SOM parameterization creates a grid where all the sampled
		  points, not only the boundary points, affect the grid,
		  leading to a uniform and smooth surface. In the second
		  stage, a 3D base surface was created and then adaptively
		  modified. To this end, the Gradient Descent Algorithm (GDA)
		  and Random Surface Error Correction (RSEC), both of which
		  are iterative surface fitting methods, were developed and
		  implemented. The feasibility of the developed
		  parameterization methods and fitting algorithms is
		  demonstrated on several examples using sculptured free
		  objects.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  barhak01b,
  author	= {Barhak, J. and Fischer, A.},
  title		= {Adaptive reconstruction of freeform objects with 3D {SOM}
		  neural network grids},
  booktitle	= {Proceedings Ninth Pacific Conference on Computer Graphics
		  and Applications. Pacific Graphics 2001. IEEE Comput. Soc,
		  Los Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {97--105},
  abstract	= {Reverse engineering is an important process in CAD systems
		  today. Yet several open problems lead to a bottleneck in
		  the reverse engineering process. First, because the
		  topology of the object to be reconstructed is unknown,
		  point connectivity relations are undefined. Second, the
		  fitted surface must satisfy global and local shape
		  preservation criteria that are undefined explicitly. In
		  reverse engineering, object reconstruction is based both on
		  parameterization and on fitting. Nevertheless, the above
		  problems are influenced mainly by parameterization. In
		  order to overcome the above problems, the paper proposes a
		  neural network, Self Organizing Map (SOM) method, for
		  creating a 3D parametric grid. The main advantage of the
		  proposed SOM method is that it detects both the orientation
		  of the grid and the position of the sub-boundaries. The
		  neural network grid converges to the sampled shape through
		  an adaptive learning process. The SOM method is applied
		  directly on 3D sampled data and avoids the projection
		  anomalies common to other methods. The paper also presents
		  boundary correction and growing grid extensions to the SOM
		  method. In the surface fitting stage, an RSEC (Random
		  Surface Error Correction) fitting method based on the SOM
		  method was developed and implemented.},
  dbinsdate	= {2002/1}
}

@MastersThesis{	  barmore88a,
  author	= {G. D. Barmore},
  title		= {Speech Recognition Using Neural Nets and Dynamic Time
		  Warping},
  school	= {Air Force Inst. of Tech. },
  year		= {1988},
  address	= {Wright-Patterson AFB, OH},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@TechReport{	  barna87a,
  author	= {G. Barna},
  title		= {Modification of {K}ohonen's Self-Organizing Algorithm:
		  Numerical Studies},
  institution	= {Helsinki Univ. of Technology, Lab. of Computer and
		  Information Science},
  year		= {1987},
  type		= {Report {A4}},
  address	= {Espoo, Finland},
  month		= {October},
  dbinsdate	= {oldtimer}
}

@Article{	  barna88a,
  author	= {Gy{\"{o}}rgy Barna and Ronald Chrisley and Teuvo Kohonen},
  title		= {Statistical Practical Pattern Recognition with Neural
		  Networks},
  journal	= {Neural Networks},
  year		= {1988},
  volume	= {1},
  number	= {Supplement 1},
  pages		= {7},
  annotate	= {},
  abstract	= {Successful recognition of natural signals, e.g., speech,
		  requires substantial statistical pattern recognition
		  capabilities. It is thus desirable to compare the
		  performance of the neural approaches with each other and to
		  the performance of more conventional approaches on tasks
		  that are explicitly statistical. Although (Huang and
		  Lippmann, l987) compares a Backpropagation network (BP) to
		  conventional Bayesian classifiers on some tasks that have
		  some statistical aspects, this work is novel in several
		  respect: 1) tasks of varying dimensionality are considered;
		  2) none of the tasks include a uniform distribution
		  component that, although popular in non-statistical pattern
		  recognition tasks, is rarely encountered in natural
		  applications; and 3) several networks are compared: a BP,
		  two Boltzmann machines (BM1 and BM2), and the Learning
		  Vector Quantization method (LVQ), developed previously by
		  one of the authors (Kohonen, l988).},
  dbinsdate	= {oldtimer}
}

@Article{	  barna89a,
  author	= {G. Barna and K. Kaski},
  title		= {Variations on the {B}oltzmann machine},
  journal	= {J. Physics A [Mathematical and General]},
  year		= {1989},
  volume	= {22},
  number	= {23},
  pages		= {5174--5151},
  dbinsdate	= {oldtimer}
}

@Article{	  barna90a,
  author	= {G. Barna and K. Kaski},
  title		= {Stochastic vs. deterministic neural networks for pattern
		  recognition},
  journal	= {Physica Scripta},
  year		= {1990},
  volume	= {T33},
  pages		= {110--115},
  note		= {Conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  barnickel97a,
  author	= {Barnickel,G. and Anzali,S},
  title		= {Evaluation of High Throughput Screening Hits by Means of
		  {K}ohonen Neural Networks},
  journal	= {Abstr. Pap. Amer. Chem. Soc. },
  year		= {1997},
  pages		= {29-?},
  volume	= {214},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  barone95a,
  author	= {Dante Augusto Couto Barone and Ant{\^{o}}nio Rog{\`{e}}rio
		  Machado Ramos},
  title		= {Application of a Hybrid System in Engineering Pattern
		  Recognition Problems},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {95--98},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@Article{	  barragan00a,
  author	= {Barragan, M. J. R. and Bote, V. P. G. and Guerrero, A. P.
		  and Alonso, F. Z.},
  title		= {Scientific journals: determination of the requirements of
		  use},
  journal	= {Revista-Espanola-de-Documentacion-Cientifica},
  year		= {2000},
  volume	= {23},
  pages		= {417--36},
  abstract	= {The information requirements of the scientific community
		  of the areas of science and technology in the University of
		  Extremadura were analysed quantitatively and qualitatively
		  through journal citations. For this purpose, a
		  retrospective search was made in the Science Citation Index
		  database, considering multiple variables such as use by
		  department, impact factor, journal citations, availability,
		  localization, accessibility, and coverage. A set of (simple
		  and complex) indicators was applied to these variables,
		  using multiple association models (in particular, Kohonen's
		  algorithm) to determine the degree of interdepartmental
		  overlap in the real use of information.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  barreto01a,
  author	= {Barreto, G. A. and Araujo, A. F. R.},
  title		= {A self-organizing {NARX} network and its application to
		  prediction of chaotic time series},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {2144--2149},
  organization	= {Depto. de Engenharia Electrica USP},
  publisher	= {},
  address	= {},
  abstract	= {This paper introduces the concept of dynamic embedding
		  manifold (DEM), which allows the Kohonen self-organizing
		  map (SOM) to learn dynamic, nonlinear input-output
		  mappings. The combination of the DEM concept with the SOM
		  results in a new modelling technique that we called
		  Vector-Quantized Temporal Association Memory (VQTAM). We
		  use VQTAM to propose an unsupervised neural algorithm
		  called Self-Organizing NARX (SONARX) network. The SONARX
		  network is evaluated on the problem of modeling and
		  prediction of three chaotic time series and compared with
		  MLP, RBF and autoregressive (AR) models. Its is shown that
		  SONARX exhibits similar performance when compared to MLP
		  and RBF, while producing much better results than the AR
		  model. The influence of the number of neurons, the memory
		  order, the number of training epochs and the size of the
		  training set in the final prediction error is also
		  evaluated.},
  dbinsdate	= {2002/1}
}

@Article{	  barreto01b,
  author	= {G. de A. Barreto and Aluizio F. R. Ara{\'u}jo},
  title		= {Time in Self-Organizing Maps: An Overview of Models},
  journal	= {International Journal of Computer Research, Special Issue:
		  Past, Present and Future of Neural Networks},
  year		= {2001},
  key		= {},
  volume	= {10},
  number	= {2},
  pages		= {139--79},
  month		= {},
  note		= {Guest Editors: P.G. Anderson and G. Antoniou and V.
		  Mladenov and E. Oja and M. Paprzycki and N. C. Steele},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  barritt92a,
  author	= {Barritt, B. A. and Rau, N. J. },
  title		= {Enhancing electronic combat system digital signal
		  processing using neural networks},
  booktitle	= {Proceedings of the IEEE 1992 National Aerospace and
		  Electronics Conference, NAECON 1992},
  year		= {1992},
  volume	= {3},
  pages		= {887--93},
  organization	= {Logicon Inc. , Dayton, OH, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  barro93a,
  author	= {S. Barro and M. G. Penedo and D. Cabello and J. M. Pardo},
  title		= {Artificial neural network based processing in a system for
		  lung nodule detection},
  booktitle	= {Conference Proceedings DICTA-93 Digital Image Computing:
		  Techniques and Applications},
  publisher	= {Australian Pattern Recognition Soc},
  year		= {1993},
  volume	= {1},
  editor	= {K. K. Fung and A. Ginige},
  address	= {Broadway, NSW, Australia},
  pages		= {79--86},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  barry93a,
  author	= {William Barry and Paul Dalsgaard},
  title		= {Speech Database Annotation. The Importance of a
		  Multi-Lingual Approach},
  booktitle	= {Proc. EUROSPEECH'93, 3rd European Conference on Speech,
		  Communication and Technology},
  year		= {1993},
  volume	= {I},
  pages		= {13--20},
  address	= {Berlin},
  dbinsdate	= {oldtimer}
}

@InCollection{	  barschdorff94a,
  author	= {D. Barschdorff and U. Femmer},
  title		= {Artificial neural networks for wear estimation},
  booktitle	= {Intelligent Manufacturing Systems 1994 (IMS`94). A
		  Postprint Volume from the IFAC Workshop},
  publisher	= {Pergamon},
  year		= {1994},
  editor	= {P. Kopacek},
  address	= {Oxford, UK},
  pages		= {151--5},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  barson95a,
  author	= {P. Barson and N. Davey and S. Field and R. Frank and D. S.
		  W. Tansley},
  title		= {Dynamic Competitive Learning Applied to the Clone
		  Detection Problem},
  booktitle	= {Proc. Int. Workshop on Applications of Neural Networks to
		  Telecommunications 2},
  year		= {1995},
  editor	= {Joshua Alspector and Rodney Goodman and Timothy X. Brown},
  pages		= {234--241},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bartal94a,
  author	= {Yair Bartal and Jie Lin and Robert E. Uhrig},
  title		= {Nuclear Power Plant Transient Diagnostics Using {LVQ} or
		  {SOM} networks don't know that they don't know},
  pages		= {3744--3749},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, diagnostics, monitoring},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baruah91a,
  author	= {Arati B. Baruah and Les E. Atlas and Alistair D. C.
		  Holden},
  title		= {{K}ohonen's feature maps applied to ordered clustering
		  applications},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {596--601},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bastos99a,
  author	= {Bastos, R. C. and Bastos, L. C.},
  title		= {Success factors determination for entrepreneurs in Santa
		  Catarina by using neural networks},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3556--9},
  abstract	= {The entrepreneurs has a fundamental role in the success of
		  an undertaking. Besides the features and attributes of
		  their personality, the attitude they take has an important
		  influence in the undertaking success. This paper presents
		  the neural networks utilization as a tool directed to
		  investigate the existence of patterns for successful
		  entrepreneurs defined according to a set of features. The
		  use of neural networks makes possible the classification
		  and recognition of patterns associated to the entrepreneurs
		  success or failure. From real data about entrepreneurs in
		  Santa Catarina State, a set of LVQ neural network
		  architectures is tested in order to establish a
		  classification model of successful entrepreneurs. The
		  creation of profiles which allow relevant feature
		  evaluation in the identification of a successful
		  entrepreneur is also discussed.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bastos99b,
  author	= {Bastos, L. C. and Bastos, R. C. and Nishida, W.},
  title		= {Radial basis function for classification of remote sensing
		  images},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1959--62},
  abstract	= {This work presents a hybrid classifier for multispectral
		  images using radial basis function networks (RBF). A
		  Kohonen self-organization-map is used in substitution of
		  the k-means algorithm in unsupervised stage of training.
		  The algorithm of the pseudo-inverse is used for the
		  determination of the weights of the supervised stage. The
		  architecture proposed reduces the time required for
		  processing. Also, it presents satisfactory results with
		  small training samples. A practical application is
		  accomplished and the results obtained between the
		  classifier of maximum likelihood and the proposed hybrid
		  classifier are compared.},
  dbinsdate	= {oldtimer}
}

@Article{	  bathen00a,
  author	= {Bathen, T. F. and Engan, T. and Krane, J. and Axelson,
		  D.},
  title		= {Analysis and classification of proton {NMR} spectra of
		  lipoprotein fractions from healthy volunteers and patients
		  with cancer or {CHD}},
  journal	= {ANTICANCER RESEARCH},
  year		= {2000},
  volume	= {20},
  number	= {4},
  month		= {JUL-AUG},
  pages		= {2393--2408},
  abstract	= {Human blood plasma samples from 52 subjects were collected
		  and the very low density lipoprotein (VLDL) intermediate
		  density lipoprotein (IDL) low density lipoprotein (LDL) and
		  high density lipoprotein were isolated by serial ultra
		  centrifugation. 600 MHz H-1 NMR spectra of the lipoprotein
		  fractions were acquired. The methyl and methylene regions
		  in the spectra of VLDL, LDL and HDL were utilised in
		  further analyses via Kohonen neural networks (KNN) and
		  generative topographic mapping (GTM) two related examples
		  of (unsupervised learning) self-organising feature mapping
		  techniques. Systematic variations in lipoprotein profiles
		  can be substantially visualised through the use of KNN and
		  GTM. The relationship between the sample positions in the
		  Kohonen plot was visualised by surface plots of the
		  corresponding VLDL and HDL cholesterol and VLDL
		  triglyceride contents. The GTM maps of the VLDL and HDL
		  fractions were used to investigate the individual
		  properties of selected samples. A large number of the
		  cancer patients were found clustered in the VLDL GTM map,
		  and GTM map positions of samples in relation to CHD,
		  diabetes and renal failure could be found. Although the
		  study group here considered is heterogeneous in respect to
		  age, sex, type of disease and medications within each
		  defined class, classification of VLDL and HDL data with
		  probabilistic neural network (PNN) was quite successful
		  with respect to the groupings: cancer, CHD, volunteers and
		  other (comprising patients with other diseases). Statistics
		  based on 15 independent sets of PNN calculations gave true
		  positive fractions usually higher than 0.83 and false
		  positive fractions lower than 0.088. Attempts to use the
		  corresponding LDL data and four classes were uniformly poor
		  although some classifications (e.g, volunteer versus CHD)
		  were easily performed.},
  dbinsdate	= {2002/1}
}

@Article{	  bauer92a,
  author	= {Hans-Ulrich Bauer and Klaus R. Pawelzik},
  title		= {Quantifying the neighborhood preservation of
		  {S}elf-{O}rganizing {F}eature {M}aps},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1992},
  volume	= {3},
  number	= {4},
  pages		= {570--579},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bauer92b,
  author	= {Hans-Ulrich Bauer and Klaus Pawelzik and Theo Geisel},
  title		= {A Topographic Product for the Optimization of
		  Self-Organizing Feature Maps},
  booktitle	= {Advances in Neural Information Processing Systems 4},
  editor	= {John E. Moody and Stephen J. Hanson and Richard P.
		  Lippmann},
  publisher	= {Morgan Kaufmann},
  year		= {1992},
  pages		= {1141--1147},
  address	= {San Mateo, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bauer94a,
  author	= {H. -U. Bauer},
  title		= {Oriented Ocular Dominance Bands in the {S}elf-{O}rganizing
		  {F}eature {M}ap},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {42--45},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {analysis, visual system},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bauer95a,
  author	= {H. -U. Bauer and Th. Villmann},
  title		= {A Growth Algorithm for Hypercubical Output Spaces in
		  Self-Organizing Feature Maps},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {I},
  pages		= {69--74},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Article{	  bauer95b,
  author	= {Bauer, H. -U. },
  title		= {Development of oriented ocular dominance bands as a
		  consequence of areal geometry},
  journal	= {Neural Computation},
  year		= {1995},
  volume	= {7},
  number	= {1},
  pages		= {36--50},
  month		= {Jan},
  dbinsdate	= {oldtimer}
}

@Article{	  bauer96a,
  author	= {H. -U. Bauer and R. Der and M. Herrmann},
  title		= {Controlling the magnification factor of
		  \mbox{self-organizing} feature maps},
  journal	= {Neural Computation},
  year		= {1996},
  volume	= {8},
  number	= {4},
  pages		= {757--71},
  dbinsdate	= {oldtimer}
}

@Article{	  bauer96b,
  author	= {H. -U. Bauer and M. Riesenhuber and T. Geisel},
  title		= {Phase diagrams of \mbox{self-organizing} maps},
  journal	= {Physical Review E [Statistical Physics, Plasmas, Fluids,
		  and Related Interdisciplinary Topics]},
  year		= {1996},
  volume	= {54},
  number	= {3},
  pages		= {2807--10},
  dbinsdate	= {oldtimer}
}

@Article{	  bauer97a,
  author	= {H. -U. Bauer and T. Villmann},
  title		= {Growing a hypercubical output space in a
		  \mbox{self-organizing} feature map},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  volume	= {8},
  number	= {2},
  pages		= {218--26},
  abstract	= {Neural maps project data from an input space onto a neuron
		  position in a (often lower dimensional) output space grid
		  in a neighborhood preserving way, with neighboring neurons
		  in the output space responding to neighboring data points
		  in the input space. A map-learning algorithm can achieve an
		  optimal neighborhood preservation only, if the output space
		  topology roughly matches the effective structure of the
		  data in the input space. We here present a growth
		  algorithm, called the GSOM or growing self-organizing map,
		  which enhances a widespread map self-organization process,
		  Kohonen's self-organizing feature map (SOFM), by an
		  adaptation of the output space grid during learning. The
		  GSOM restricts the output space structure to the shape of a
		  general hypercubical shape, with the overall dimensionality
		  of the grid and its extensions along the different
		  directions being subject of the adaptation. This constraint
		  meets the demands of many larger information processing
		  systems, of which the neural map can be a part. We apply
		  our GSOM-algorithm to three examples, two of which involve
		  real world data. Using recently developed methods for
		  measuring the degree of neighborhood preservation in neural
		  maps, we find the GSOM-algorithm to produce maps which
		  preserve neighborhoods in a nearly optimal fashion.},
  dbinsdate	= {oldtimer}
}

@Article{	  bauer97b,
  author	= {H. -U. Bauer and D. Brockmann and T. Geisel},
  title		= {Analysis of ocular dominance pattern formation in a
		  high-dimensional \mbox{self-organizing}-map model},
  journal	= {Network: Computation in Neural Systems},
  year		= {1997},
  volume	= {8},
  number	= {1},
  pages		= {17--33},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bauer97c,
  author	= {H. -U. Bauer and M. Riesenhuber and D. Brockmann and T.
		  Geisel},
  title		= {Analysis of {SOM}-based models for the development of
		  visual maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {233--238},
  dbinsdate	= {oldtimer}
}

@Article{	  bauer97d,
  author	= {H. U. Bauer and W. Sch{\"o}llhorn},
  title		= {Self-Organizing Maps for the Analysis of Complex Movement
		  Patterns},
  journal	= {Neural Processing Letters},
  year		= 1997,
  volume	= 5,
  pages		= {193--199},
  dbinsdate	= {oldtimer}
}

@Article{	  bauer98a,
  author	= {Bauer, H. U.},
  title		= {Exploiting topography of neural maps: A case study on
		  investment strategies for emerging markets},
  journal	= {IEEE IAFE Conference on Computational Intelligence for
		  Financial Engineering (CIFEr)},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  number	= {},
  volume	= {},
  pages		= {216--219},
  abstract	= {Investments can be spread over many possible assets to
		  avoid risk (at the cost of obtaining only an average
		  performance) or it can be focused on clusters of only a few
		  promising assets (at the cost of increased risk). A
		  trade-off between these two objectives can be reached by
		  using the Self-Organizing Map (SOM), a neural network
		  paradigm which achieves a clustering of data points while
		  simultaneously preserving their inherent neighborhood
		  relations (topography). This amounts to a combination of
		  clustering with local smoothing. In a case study involving
		  investments in emerging stock markets I illustrate the
		  application of {SOM}s in investment decisions, with an
		  improvement of about 30% in returns over other, more simple
		  investment strategies.},
  dbinsdate	= {oldtimer}
}

@Article{	  bauer99a,
  author	= {Bauer, H. U and Herrmann, M. and Villmann, T.},
  title		= {Neural maps and topographic vector quantization},
  journal	= {Neural Networks},
  year		= {1999},
  number	= {4},
  volume	= {12},
  pages		= {659--676},
  abstract	= {Neural maps combine the representation of data by codebook
		  vectors, like a vector quantizer, with the property of
		  topography, like a continuous function. While the
		  quantization error is simple to compute and to compare
		  between different maps, topography of a map is difficult to
		  define and to quantify. Yet, topography of a neural map is
		  an advantageous property, e.g. in the presence of noise in
		  a transmission channel, in data visualization, and in
		  numerous other applications. In this article we review some
		  conceptual aspects of definitions of topography, and some
		  recently proposed measures to quantify topography. We apply
		  the measures first to neural maps trained on synthetic data
		  sets, and check the measures for properties like
		  reproducibility, scalability, systematic dependence of the
		  value of the measure on the topology of the map, etc. We
		  then test the measures on maps generated for four
		  real-world data sets, a chaotic time series, speech data,
		  and two sets of image data. The measures are found to do an
		  imperfect, but an adequate job in selecting a
		  topographically optimal output space dimension, while they
		  consistently single out particular maps as
		  non-topographic.},
  dbinsdate	= {oldtimer}
}

@Article{	  bauknecht96a,
  author	= {H. Bauknecht and A. Zell and H. Bayer and P. Levi and M.
		  Wagner and J. Sadowski and J. Gasteiger},
  title		= {Locating biologically active compounds in medium-sized
		  heterogeneous datasets by topical autocorrelation vectors:
		  Dopamine and benzodiazepine agonists},
  journal	= {Journal of Chemical Information and Computer Sciences},
  year		= 1996,
  volume	= 36,
  pages		= {1205--1213},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baumann91a,
  author	= {Thomas Baumann and Alain Germond and Daniel Tschudi},
  title		= {Impulse Test Fault Diagnosis on Power Transformers Using
		  {K}ohonen's Self-Organizing Neural Network},
  booktitle	= {Proc. Third Symp. on Expert Systems Application to Power
		  Systems},
  year		= {1991},
  address	= {Tokyo \& Kobe},
  dbinsdate	= {oldtimer}
}

@Article{	  baumann93a,
  author	= {Baumann, E. W. and Williams, D. L. },
  title		= {Stochastic associative memory},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1993},
  volume	= {1966},
  pages		= {132--9},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baumann93b,
  author	= {Baumann, T. and Germond, A. J. },
  title		= {Application of the {K}ohonen network to short-term load
		  forecasting},
  booktitle	= {ANNPS '93. Proceedings of the Second International Forum
		  on Applications of Neural Networks to Power Systems},
  year		= {1993},
  editor	= {Tamura, Y. and Suzuki, H. and Mori, H. },
  pages		= {407--12},
  organization	= {Siemens AG, Vienna, Austria},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baumann93c,
  author	= {Baumann, T. and Strasser, H. and Landrichter, H. },
  title		= {Short-term load forecasting methods in comparison:
		  {K}ohonen learning, backpropagation learning, multiple
		  regression analysis and {K}alman filters},
  booktitle	= {PSCC. Proceedings of the Eleventh Power Systems
		  Computation Conference},
  year		= {1993},
  volume	= {1},
  pages		= {445--51},
  organization	= {Siemens AG, Wien, Austria},
  publisher	= {Power Syst. Comput. Conference},
  address	= {Zurich, Switzerland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bayer93a,
  author	= {Harald Bayer},
  title		= {{SUSOM} 'SUpervised' Self-Organizing Maps},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {620},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baykal91a,
  author	= {N. Baykal and N. Yalabik and A. H. Goktogan},
  title		= {Character recognition using {K}ohonen's feature map},
  booktitle	= {Computer and Information Sciences VI. Proc. 1991 Int.
		  Symposium},
  year		= {1991},
  editor	= {M. Baray and B. Ozguc},
  volume	= {II},
  pages		= {923--932},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  baykal92a,
  author	= {Baykal, N. and Yalabik, N. },
  title		= {Object orientation detection and character recognition
		  using optimal feedforward network and {K}ohonen's feature
		  map},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 1},
  pages		= {292--303},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  baykal98a,
  author	= {Baykal, N. and Erkmen, A. M.},
  title		= {An elastic potential field approach to self-organizing
		  map},
  booktitle	= {IEEE ICIPS'98. Proceedings of the Second IEEE
		  International Conference on Intelligent Processing Systems.
		  IEEE, Piscataway, NJ, USA},
  year		= {1998},
  volume	= {},
  pages		= {555--9},
  abstract	= {This paper proposes an escape methodology to the local
		  minima problem of self-organizing feature maps generated in
		  the overlapping regions which are equidistant to the
		  corresponding winners. The method is based on the
		  artificial potential field concept. Our approach introduces
		  an excitation term, which increases the convergence speed
		  and efficiency of the algorithm while increasing the
		  probability of escaping from local minima. We also
		  associate a learning set which specifies variable
		  attractive and repulsive field of output neurons. Results
		  indicate that accuracy percentile of this method are higher
		  than the original algorithm while it has the ability to
		  escape from local minima and distribute learning in
		  decoupled regions of the winning frequency map.},
  dbinsdate	= {2002/1}
}

@Article{	  baykal99a,
  author	= {Baykal, Nazife and Erkmen, Aydan M.},
  title		= {Extended Self Organizing Feature Map: A tagged potential
		  field approach},
  journal	= {Neural Processing Letters},
  year		= {1999},
  number	= {1},
  volume	= {10},
  pages		= {57--72},
  abstract	= {This paper proposes an escape methodology to the local
		  minima problem of self organizing feature maps generated in
		  the overlapping regions which are equidistant to the
		  corresponding winners. Two new versions of the Self
		  Organizing Feature Map are derived equipped with such a
		  methodology. The first approach introduces an excitation
		  term, which increases the convergence speed and efficiency
		  of the algorithm, while increasing the probability of
		  escaping from local minima. In the second approach, we
		  associate a learning set which specifies the attractive and
		  repulsive field of output neurons. Results indicate that
		  accuracy percentile of the new methods are higher than the
		  original algorithm while they have the ability to escape
		  from local minima.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  beard89a,
  author	= {R. A. Beard and K. S. Rattan},
  title		= {A neural network system for robot vision},
  booktitle	= {Proc. NAECON 1989, IEEE 1989 National Aerospace and
		  Electronics Conference },
  year		= {1989},
  volume	= {IV},
  pages		= {1920--1921},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  beauge93a,
  author	= {Beauge, L. and Durand, S. and Alexandre, F. },
  title		= {Plausible \mbox{self-organizing} maps for speech
		  recognition},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  year		= {1993},
  editor	= {Albrecht, R. F. and Reeves, C. R. and Steele, N. C. },
  pages		= {221--6},
  organization	= {CRIN-CNRS/INRIA Lorraine, Vandoeuvre-les-Nancy, France},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bebis91a,
  author	= {G. N. Bebis and G. M. Papadourakis},
  title		= {Model-based object recognition using artificial neural
		  networks},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1111--1115},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  bebis92a,
  author	= {G. N. Bebis and G. M. Papadourakis},
  title		= {Object recognition using invariant object boundary
		  representations and neural network models},
  journal	= {Pattern Recognition},
  year		= {1992},
  volume	= {25},
  number	= {1},
  pages		= {25--44},
  month		= {January},
  x		= {Several approaches for classifying two-dimensional objects
		  which are based on the use of both invariant boundary
		  transformations and artificial neural networks (ANNs) were
		  implemented and compared. . . . In particular, the
		  multilayer ANN trained with the prediction backpropagation
		  rule and the Kohonen ANN were utilized. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bebis95a,
  author	= {Bebis, G. and Georgiopoulos, M. and {da Vitoria Lobo}, N.
		  },
  title		= {Learning geometric hashing functions for model-based
		  object recognition},
  booktitle	= {Proceedings of the Fifth International Conference on
		  Computer Vision},
  year		= {1995},
  pages		= {543--8},
  organization	= {Dept. of Electr. \& Comput. Eng. , Univ. of Central
		  Florida, Orlando, FL, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  bebis98a,
  author	= {George Bebis and Michael Georgiopoulos and Niels {da
		  Vitoria Lobo}},
  title		= {Using Self-Organizing Maps to Learn Geometric Hash
		  Functions for Model-Based Object Recognition},
  journal	= {IEEE Transactions on Neural Networks},
  year		= 1998,
  volume	= 9,
  pages		= {560--570},
  abstract	= {A new approach to address the nonuniform distribution of
		  invariants over the hash space during geometric hashing and
		  methods which have emerged from it is proposed. The
		  approach, which is based on an elastic hash table, involves
		  the distribution of the hash bins over the invariants. The
		  key idea is to associate the hash bins with the output
		  nodes of a self-organizing feature map (SOFM) neural
		  network which is trained using the invariants as training
		  examples. In this way, the location of a hash bin in the
		  space of invariants is determined by the weight vector of
		  the node associated with the hash bin. The advantage of
		  this approach is that it is a process that adapts to the
		  invariants through learning.},
  dbinsdate	= {oldtimer}
}

@Article{	  becanovic00a,
  author	= {Becanovic, Vlatko},
  title		= {Image object classification using saccadic search,
		  spatio-temporal pattern encoding and self-organization},
  journal	= {Pattern Recognition Letters},
  year		= {2000},
  number	= {3},
  volume	= {21},
  pages		= {253--263},
  abstract	= {A method for extracting features from photographic images
		  is investigated. The input image is through a saccadic
		  search algorithm divided into a set of sub-images,
		  segmented and coded by a spatio-temporal encoding engine.
		  The input image is thus represented by a set of
		  characteristic pattern signatures, well suited for
		  classification by an unsupervised neural network. A
		  strategy using multiple self-organizing feature maps (SOM)
		  in a hierarchical manner is used. With this approach, using
		  a certain degree of user selection, a database of
		  sub-images is grouped according to similarities in
		  signature space. In English 22 Refs.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  beckenkamp00a,
  author	= {Fábio Ghignatti Beckenkamp and Wolfgang Pree},
  title		= {Building Neural Network components},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {C. Doppler Lab for Software Research, University of
		  Constance},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  beckers97a,
  author	= {M. L. M. Beckers and W. J. Melssen and L. M. C. Buydens},
  title		= {A \mbox{self-organizing} feature map for clustering
		  nucleic acids. Application to a data matrix containing
		  A-DNA and B-DNA dinucleotides},
  journal	= {Computers \& Chemistry},
  year		= {1997},
  volume	= {21},
  number	= {6},
  pages		= {377--90},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  becks92a,
  author	= {K. -H. Becks and J. Dahm and F. Seidel},
  title		= {Analysing particle jets with artificial neural networks},
  booktitle	= {Industrial and Engineering Applications of Artificial
		  Intelligence and Expert Systems. 5th International
		  Conference, IEA/AIE-92},
  year		= {1992},
  editor	= {F. Belli and F. J. Radermacher},
  pages		= {109--112},
  organization	= {Univ. Paderborn; Southwest Texas State Univ; et al},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@Article{	  bednar00a,
  author	= {Bednar, J. A. and Miikkulainen, R.},
  title		= {Tilt aftereffects in a self-organizing model of the
		  primary visual cortex},
  journal	= {NEURAL COMPUTATION},
  year		= {2000},
  volume	= {12},
  number	= {7},
  month		= {JUL},
  pages		= {1721--1740},
  abstract	= {RF-LISSOM, a self-organizing model of laterally connected
		  orientation maps in the primary visual cortex, was used to
		  study the psychological phenomenon known as the tilt
		  aftereffect. The same self-organizing processes that are
		  responsible for the long-term development of the map are
		  shown to result in tilt aftereffects over short timescales
		  in the adult. The model permits simultaneous observation of
		  large numbers of neurons and connections, making it
		  possible to relate high-level phenomena to low-level
		  events, which is difficult to do experimentally. The
		  results give detailed computational support for the
		  long-standing conjecture that the direct tilt aftereffect
		  arises from adaptive lateral interactions between feature
		  detectors. They also make a new prediction that the
		  indirect effect results from the normalization of synaptic
		  efficacies during this process. The model thus provides a
		  unified computational explanation of self-organization and
		  both the direct and indirect tilt aftereffect in the
		  primary visual cortex.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  bednar00b,
  author	= {Bednar, J. A. and Miikkulainen, R.},
  title		= {Self-organization of innate face preferences: could
		  genetics be expressed through learning?},
  booktitle	= {Proceedings Seventeenth National Conference on Artificial
		  Intelligence (AAAI-2000). Twelfth Innovative Applications
		  of Artificial Intelligence Conference (IAAI-2000). AAAI
		  Press, Menlo Park, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {117--22},
  abstract	= {Self-organizing models develop realistic cortical
		  structures when given approximations of the visual
		  environment as input, and are an effective way to model the
		  development of face recognition abilities. However,
		  environment-driven self-organization alone cannot account
		  for the fact that newborn human infants will preferentially
		  attend to face-like stimuli even immediately after birth.
		  Previously it has been proposed that internally generated
		  input patterns, such as those found in the developing
		  retina and in PGO waves during REM sleep, may have the same
		  effect on self-organization as does the external
		  environment. Internal pattern generators constitute an
		  efficient way to specify, develop, and maintain
		  functionally appropriate perceptual organization. They may
		  help express complex structures from minimal genetic
		  information, and retain this genetic structure within a
		  highly plastic system. Simulations with the CRF-LISSOM
		  model show that such preorganization can account for
		  newborn face preferences, providing a computational
		  framework for examining how genetic influences interact
		  with experience to construct a complex system.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  bednar97a,
  author	= {J. A. Bednar and R. Miikkulainen},
  title		= {A Neural Network Model of Visual Tilt Aftereffects},
  booktitle	= {Proceedings of the 19th Annual Meeting of the Cognitive
		  Science Society (COGSCI-97)},
  year		= {1997},
  publisher	= {Erlbaum},
  address	= {Hillsdale, NJ},
  pages		= {37--42},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bednar98a,
  author	= {J. Bednar and R. Miikkulainen},
  title		= {Pattern-Generator-Driven Development in Self-Organizing
		  Models},
  booktitle	= {Computational Neuroscience: Trends in Research},
  publisher	= {Plenum},
  year		= {1998},
  editor	= {J. M. Bower},
  address	= {New York},
  pages		= {317--323},
  dbinsdate	= {oldtimer}
}

@Article{	  beger01a,
  author	= {Beger, R. D. and Wilkes, J. G.},
  title		= {Developing C-13 {NMR} quantitative spectrometric
		  data-activity relationship ({QSDAR}) models of steroid
		  binding to the corticosteroid binding globulin},
  journal	= {JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN},
  year		= {2001},
  volume	= {15},
  number	= {7},
  month		= {JUL},
  pages		= {659--669},
  abstract	= {We have developed four quantitative spectrometric
		  data-activity relationship (QSDAR) models for 30 steroids
		  binding to corticosteroid binding globulin, based on
		  comparative spectral analysis (CoSA) of simulated C-13
		  nuclear magnetic resonance (NMR) data. A QSDAR model based
		  on 3 spectral bins had an explained variance (r(2)) of 0.80
		  and a cross-validated variance (q(2)) of 0.78. Another
		  QSDAR model using the 3 atoms from the comparative
		  structurally assigned spectral analysis (CoSASA) of
		  simulated C-13 NMR on a steroid backbone template gave an
		  explained variance (r(2)) of 0.80 and a cross-validated
		  variance (q(2)) of 0.73. Positions 3 and 14 from the
		  steroid backbone template have correlations with the
		  relative binding activity to corticosteroid binding
		  globulin that are greater than 0.52. The explained
		  correlation and cross-validated correlation of these QSDAR
		  models are as good as previously published quantitative
		  structure-activity relationship (QSAR), self-organizing map
		  (SOM) and electrotopological state (E- state) models. One
		  reason that the cross-validated variance of QSDAR models
		  were as good as the other models is that simulated C-13 NMR
		  spectral data are more accurate than the errors introduced
		  by the assumptions and approximations used in calculated
		  electrostatic potentials, E-states, HE-states, and the
		  molecular alignment process of QSAR modeling. The QSDAR
		  models developed provide a rapid, simple way to predict the
		  binding activity of a steroid to corticosteroid binding
		  globulin.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  behera95a,
  author	= {Behera, L. and Gopal, M. and Chaudhury, S. },
  title		= {Self-organizing neural networks for learning inverse
		  dynamics of robot manipulator},
  booktitle	= {1995 IEEE/IAS International Conference on Industrial
		  Automation and Control (I A \& C'95)},
  year		= {1995},
  pages		= {457--60},
  organization	= {Dept. of Electr. Eng. , Indian Inst. of Technol. , New
		  Delhi, India},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  behera98a,
  author	= {L. Behera and S. Chaudhury and M. Gopal},
  title		= {Application of Self Organizing Neural Networks in Robot
		  Tracking Control},
  journal	= {IEE Proceedings---Control Theory and Applications},
  volume	= {145},
  pages		= {135--140},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  behera99a,
  author	= {Behera, Laxmidhar and Krishna, GPrem},
  title		= {Neural controller based binocular vision system for object
		  tracking},
  journal	= {IETE Journal of Research},
  year		= {1999},
  number	= {1},
  volume	= {45},
  pages		= {63--71},
  abstract	= {ln this paper, we present a learning control approach to
		  the problem of visual tracking using an active binocular
		  vision system. The simulated vision system used here is
		  composed of two cameras which have freedom of pan and are
		  mounted on a plate that can tilt. A self organizing map is
		  trained to learn the mapping between visual feedback as
		  received from the simulated CCD cameras and the incremental
		  values in the orientation angles of the vision system to
		  fixate continuously on a mobile target. A direct
		  implication of this is that the object should be centered
		  at the vergence point of the cameras. This is accomplished
		  by ensuring minimum disparity of the target vector from the
		  center of the images of both the cameras. The learning
		  algorithm is presented and the simulation study shows an
		  average focussing error of 0.15 pixels after 1200
		  iterations for a small, defined joint angle space of the
		  vision system. Thus, a significant contribution of this
		  work is the application of a self organizing map in dynamic
		  object tracking.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  behme93a,
  author	= {Holger Behme and Wolf Dieter Brandt and Hans Werner
		  Strube},
  title		= {Speech Recognition by Hierarchical Segment
		  Classification},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {416--419},
  publisher	= {Springer},
  address	= {London, UK},
  abstract	= {A neural network for speech processing is presented. Its
		  complex architecture, incorporating self-organizing feature
		  maps [1], allows the construction of a hierarchy of layers,
		  where each layer operates on a larger time scale and deals
		  with higher units of speech, like phonemes, syllables, word
		  parts and so on. Tasks the network has to deal with include
		  representation of speech, segmentation of the speech signal
		  and classifying segments.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  behme93b,
  author	= {Holger Behme and Wolf Dieter Brandt and Hans Werner
		  Strube},
  title		= {Speech Processing by Hierarchical Segment Classification},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {279--282},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  beilharz95a,
  author	= {Beilharz, J. and Ropke, K. and Filbert, D. },
  title		= {Statistical and neural concepts of unsupervised classifier
		  design for motor diagnosis},
  journal	= {Automatisierungstechnik},
  year		= {1995},
  volume	= {43},
  number	= {1},
  pages		= {46--53},
  month		= {Jan},
  dbinsdate	= {oldtimer}
}

@Article{	  belic97a,
  author	= {I. Belic and L. Gyergyek},
  title		= {Neural network methodologies for mass spectra
		  recognition},
  journal	= {Vacuum},
  year		= {1997},
  volume	= {48},
  number	= {7--9},
  pages		= {633--7},
  note		= {(5th European Vacuum Conference, EVC-5 Conf. Date: 23--27
		  Sept. 1996 Conf. Loc: Salamanca, Spain)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bellando96a,
  author	= {J. Bellando and R. Kothari},
  title		= {On image correspondence using topology preserving
		  mappings},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1784--9},
  abstract	= {A computational approach for establishing correspondence
		  between two image views is presented. We show that a
		  self-organizing feature map trained with tokens (features)
		  from the first frame and subsequently with tokens from the
		  second frame (without re-initialization) is capable of
		  indicating the underlying transformation which results in
		  the second frame. The reliance of the self-organizing
		  feature map on the underlying probability density of the
		  features makes the proposed approach insensitive to missing
		  tokens. Simulation results under three different observer
		  movements (panning, zooming, and rotation) are presented to
		  illustrate the proposed method.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bellido93a,
  author	= {I. Bellido and E. Fiesler},
  title		= {Do Backpropagation Trained Neural Networks have Normal
		  Weight Distributions},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {772--775},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  bellotti95a,
  author	= {Bellotti, R. and Castellano, M. and {De Marzo}, C. and
		  Satalino, G. },
  title		= {Signal/background classification in a cosmic ray space
		  experiment by a modular neural system},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2492},
  number	= {pt. 2},
  pages		= {1153--61},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  benaim93a,
  author	= {Michel Benaim},
  title		= {The 'Off Line Learning Approximation' in Continuous Time
		  Neural Networks: An Adiabatic Theorem},
  journal	= {Neural Networks},
  year		= {1993},
  volume	= {6},
  number	= {5},
  pages		= {655--665},
  dbinsdate	= {oldtimer}
}

@InCollection{	  benaim97a,
  author	= {M. Benaim and J. -C. Fort and G. Pages},
  title		= {Almost sure convergence of the
		  \mbox{\mbox{one-dimensional}} {K}ohonen algorithm},
  booktitle	= {5th European Symposium on Artificial Neural Networks ESANN
		  '97. Proceedings},
  publisher	= {D facto},
  year		= {1997},
  editor	= {M. Verleysen},
  address	= {Brussels, Belgium},
  pages		= {193--8},
  dbinsdate	= {oldtimer}
}

@Article{	  benaim98a,
  author	= {M. Benaim and J. -C. Fort and G. Pages},
  title		= {Convergence of the \mbox{\mbox{one-dimensional}} {K}ohonen
		  algorithm},
  journal	= {Advances in Applied Probability},
  year		= {1998},
  volume	= {30},
  number	= {3},
  pages		= {850--69},
  dbinsdate	= {oldtimer}
}

@InCollection{	  benaki94a,
  author	= {A. Benaki and B. Gatos and I. Karamani and D. Karras and
		  S. Perantonis and N. Vassilas and N. Gaitanis},
  title		= {A robot hand-eye coordination system for {3-D} object
		  recognition using novel neural networks trained with
		  multiview moments},
  booktitle	= {Proceedings EURISCON `94. European Robotics and
		  Intelligent Systems Conference},
  publisher	= {Univ. Bristol},
  year		= {1994},
  volume	= {3},
  address	= {Bristol, UK},
  pages		= {1692--701},
  dbinsdate	= {oldtimer}
}

@Book{		  bengtsson96a,
  author	= {Bengtsson, M.},
  title		= {Hierarchical Clustering in a Mean Field Theory. Scientific
		  rept.},
  year		= {1996},
  abstract	= {A hierarchical clustering model, which is built on a Potts
		  glass in a mean field approximation, is developed. It
		  gives, we believe, the optimal clustering solution to
		  randomly positioned Gaussian clusters of identical size and
		  standard deviation. The phase transition temperatures are
		  estimated in advance, facilitating a completely parameter
		  free model, and thereby avoiding parameter tuning that
		  plagues other self-organizing models. Explored numerically,
		  the model performs well when clustering problems are scaled
		  up. Furthermore, it is demonstrated that standard vector
		  quantization and the Kohonen self-organizing map are unable
		  to solve these clustering tasks in general.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  benitez-diaz94a,
  author	= {Benitez-Diaz, D. and Carrabina, J. and Gonzalez-Rodriguez,
		  M. },
  title		= {Neural-like network model for color images analysis
		  systems},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {3},
  pages		= {1415--20},
  organization	= {Departamento de Inf. y Sistemas, Campus Univ. de Tafira,
		  Las Palmas, Spain},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  benitez-diaz95a,
  author	= {D. Benitez-Diaz and J. Garcia-Quesada},
  title		= {Learning algorithm with {G}aussian membership function for
		  fuzzy {RBF} neural networks},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {527--34},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  benitez00a,
  author	= {Benitez, Cesar and Lander, Daniel Kvedaras and Ramirez,
		  Jose},
  title		= {Reduction of dimensionality for perceptual clustering},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {148--151},
  organization	= {Universidad Simon Bolivar},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Multidimensionality is one of the problems to be solved
		  for a robust methodology in order to be capable of
		  resolving simple and realistic problems. This work
		  establishes a complete methodology based on self-organized
		  maps (SOM) and the expectation-maximization (EM) algorithm
		  that finds an abstract probability function, which is a mix
		  of local experts. An application of this methodology is
		  presented as a case study, where the problem is robot
		  navigation in noisy environments. Readings from seven robot
		  sonars were taken as input for the system, mapped into a
		  two dimension space and grouped into abstract observations,
		  in order to make recognition of navigation space
		  environment dependant and accurate. The goal is to build
		  the capability of predicting observations and of
		  recognizing abstractions that were defined over the data
		  itself.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  benitez00b,
  author	= {Benitez, L. M. and Ferreira, A. and Iparraguirre, D. I.
		  P.},
  title		= {Chaotic time series prediction using the Kohonen
		  algorithm},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {347--52},
  abstract	= {Deterministic nonlinear prediction is a powerful technique
		  for the analysis and prediction of time series generated by
		  nonlinear dynamical systems. In this paper, the use of a
		  Kohonen network as a component of one deterministic
		  nonlinear prediction algorithm is suggested. In order to
		  evaluate the performance of the proposed algorithm, it was
		  applied to the prediction of time series generated by two
		  well-known chaotic dynamical systems, and the results were
		  compared with those obtained using the modified method of
		  analogues with the same time series. The generated time
		  series were corrupted by superimposed observational noise.
		  The experimental results have shown that the Kohonen
		  network can learn the neighborhood relations present in the
		  reconstructed attractor of the time series and that good
		  predictions can also be obtained with the proposed
		  algorithm.},
  dbinsdate	= {2002/1}
}

@Article{	  benitez00c,
  author	= {Benitez, M. C. and Rubio, A. and Garcia, P. and {de la
		  Torre}, A.},
  title		= {Different confidence measures for word verification in
		  speech recognition},
  journal	= {Speech Communication},
  year		= {2000},
  volume	= {32},
  number	= {1},
  month		= {Sep},
  pages		= {79--94},
  organization	= {Universidad de Granada},
  publisher	= {Elsevier Science Publishers B.V.},
  address	= {Amsterdam},
  abstract	= {Recent research in Automatic Speech Recognition (ASR)
		  technologies has shown the key-word spotting (KWS) systems
		  as one of the most interesting options for accessing
		  information using speech. KWS systems can accept
		  spontaneous speech, which allows potential users to ask for
		  information without learning complex protocols for the
		  human-machine communication. One of the most relevant
		  aspects in KWS systems is the verification of key-word
		  candidates. Utterances detected as key-words could be
		  either `false alarms' (non-key-words or incorrectly
		  recognized key-words) or `correct key-words'. The use of
		  confidence measurements allows (by additional processing of
		  the spoken sentence) the verification of the candidates and
		  the decision as to whether each utterance must be accepted
		  as a correctly recognized key-word or rejected as a false
		  alarm. In this work we propose a novel method for
		  verification in those KWS systems based on phone models.
		  Under our new approach, a phonematic speech recognizer
		  decodes the spoken sentence in parallel with the KWS
		  recognizer. The first one produces a phone string as output
		  while the second one generates a key-word/filler-model
		  string. By aligning both strings, a set of characteristics
		  is extracted which are used to verify the putatives
		  key-word. For that we have built two classifiers; in the
		  first one the euclidean metric is modified and adapted in a
		  local and iterative way in order to give greater importance
		  to the most discriminate directions between the classes.
		  The second is a vector quantizer which was trained using
		  adaptative technique learning. We have applied the proposed
		  method to several KWS tasks. Experimental results presented
		  in this paper show that the proposed verification method
		  improves the performance of the KWS systems by reducing the
		  false alarm rate without a significant increase in the
		  rejection of correctly detected keywords.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  benitez_rochel00a,
  author	= {{Benitez Rochel}, R. and {Trella Lopez}, M. and {Conejo
		  Mu\~{n}oz}, R.},
  title		= {Neural networks applied to Item Response Theory},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Departamento de Lenguajes y Ciencias de la Computación
		  Universidad de Málaga},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bennani90a,
  author	= {Y. Bennani and F. Fogelman-Souli{\'{e}} and P. Gallinari},
  title		= {A connectionist approach for automatic speaker
		  identification},
  booktitle	= {Proc. ICASSP-90, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1990},
  volume	= {I},
  pages		= {265--268},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bennani90b,
  author	= {Y. Bennani and N. Chaourar and P. Gallinari and A.
		  Mellouk},
  title		= {Comparing neural net models on speech recognition tasks},
  booktitle	= {Proc. Neuro-N\^{i}mes '90, Third Int. Workshop. Neural
		  Networks and Their Applications},
  year		= {1990},
  pages		= {455--467},
  organization	= {ARC; JSAI; SEE},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bennani90c,
  author	= {Y. Bennani and F. Fogelman-Souli{\'{e}} and P. Gallinari},
  title		= {Text-dependent speaker identification using learning
		  vector quantization},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conference },
  year		= {1990},
  volume	= {II},
  pages		= {1087--1090},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  annote	= {page numbers may be incorrect},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bennani91a,
  author	= {Y. Bennani and N. Chaourar and P. Gallinari and A.
		  Mellouk},
  title		= {Validation of neural net architectures on speech
		  recognition tasks},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing },
  year		= {1991},
  volume	= {I},
  pages		= {97--100},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bennani99a,
  author	= {Bennani, Y.},
  title		= {Adaptive weighting of pattern features during learning},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3008--13},
  abstract	= {Irrelevant or redundant features may have negative effects
		  on classification algorithms. The designer of a
		  classification algorithm typically require a few very
		  significant features characterising the class membership of
		  the patterns. The discriminatory information is encoded in
		  a very complex manner and features which are the most
		  important for pattern classification may not be apparent.
		  One way to address this problem is the use of feature
		  weighting procedure as a data preprocessing step. In this
		  paper we propose a two-step algorithm as an extension of
		  the learning vector quantization algorithm (LVQ). This
		  approach is based on weighting features depending on their
		  contribution to discrimination. Adapting weighting
		  coefficients and codewords is done simultaneously by using
		  a new global learning algorithm named omega LVQ2.
		  Experiments are undertaken on a synthetic problem and on
		  real problems in speech and speaker recognition domain, to
		  show significant improvement over the standard learning
		  algorithm.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  benson00a,
  author	= {Benson, Karl},
  title		= {Evolving Finite State Machines with embedded Genetic
		  Programming for Automatic Target Detection},
  booktitle	= {Proceedings of the IEEE Conference on Evolutionary
		  Computation, ICEC},
  year		= {2000},
  editor	= {},
  volume	= {2},
  pages		= {1543--1549},
  organization	= {Defence Evaluation and Research Agency Malvern},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper presents a model comprising Finite State
		  Machines (FSMs) with embedded Genetic Programs (GPs) which
		  co-evolve to perform the task of Automatic Target Detection
		  (ATD). The fusion of a FSM and GPs allows for a control
		  structure (main program), the FSM, and sub-programs, the
		  GPs, to co-evolve in a symbiotic relationship. The GP
		  outputs along with the FSM state transition levels are used
		  to construct confidence intervals that enable each pixel
		  within the image to be classified as either target or
		  non-target, or to cause a state transition to take place
		  and further analysis of the pixel to be performed. The
		  algorithms produced using this method consist of nominally
		  four GPs, with a typical node cardinality of less than ten,
		  that are executed in an order dictated by the FSM. The
		  results of the experimentation performed are compared to
		  those obtained in two independent studies of the same
		  problem using Kohonen Neural Networks and a two stage
		  Genetic Programming strategy.},
  dbinsdate	= {2002/1}
}

@Article{	  benson00b,
  author	= {Benson, M. W. and Hu, J.},
  title		= {Asynchronous self-organizing maps},
  journal	= {IEEE TRANSACTIONS ON NEURAL NETWORKS},
  year		= {2000},
  volume	= {11},
  number	= {6},
  month		= {NOV},
  pages		= {1315--1322},
  abstract	= {A recently defined energy function which leads to a self-
		  organizing map is used as a foundation for an asynchronous
		  neural-network algorithm, We generalize the existing
		  stochastic gradient approach to an asynchronous parallel
		  stochastic gradient method for generating a topological map
		  on a distributed computer system (MIMD). A convergence
		  proof is presented and simulation results on a set of
		  problems are included. A practical problem using the energy
		  function approach is that a summation over the entire
		  network is required during the computation of updates.
		  Using simulations we demonstrate effective algorithms that
		  use efficient sampling for the approximation of these
		  sums.},
  dbinsdate	= {2002/1}
}

@TechReport{	  beppu90a,
  author	= {T. Beppu and M. Sase and Y. Kosugi},
  title		= {Self-Organizing Feature Map using Classified Neural
		  Units},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1990},
  number	= {PRU90--96},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  berenji93a,
  author	= {Hamid R. Berenji},
  title		= {Neural Networks for Fuzzy Logic Inference},
  booktitle	= {Proc. International Conference on Fuzzy Systems},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1993},
  pages		= {1395},
  abstract	= {The non-adaptive behavior of current fuzzy inference
		  systems can significantly be improved by using artificial
		  neural networks. Recent interests in combining fuzzy logic
		  and neural networks have led to the development of new
		  algorithms that provide adaptive behavior while maintaining
		  the strong knowledge representation characteristic of fuzzy
		  systems. Many perspectives on the impact of neural networks
		  on fuzzy logic inference exist, however, in this plenary
		  talk, we focus on four important categories: (a)- pattern
		  recognition, (b)- formation of fuzzy rules from data, (c)-
		  refinement of fuzzy membership functions, and (d)-
		  implementation of fuzzy inference systems using neural net
		  works. In pattern recognition area, we review the hybrid
		  neural networks and fuzzy logic methods for cluster
		  analysis in fuzzy sets. The Kohonen's Feature Maps, Fuzzy
		  ART, and Bezdek's Fuzzy-LVQ are considered. The temporal
		  back-propagation algorithm, and radial basis functions can
		  be used to extract rules from data. On the other hand,
		  reinforcement learning, and supervised learning techniques
		  such as backpropagation can be used in fine-tuning the
		  membership functions. Finally, the advantages of using
		  neural networks for implementing fuzzy rules will be
		  discussed and examples of their applications will be
		  demonstrated.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  berger97a,
  author	= {A. Berger and D. P. F. Moller and M. Renter},
  title		= {Detection of sleep with new preprocessing methods for EEG
		  analysing},
  booktitle	= {Computational Intelligence Theory and Applications.
		  International Conference, 5th Fuzzy Days. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {B. Reusch},
  address	= {Berlin, Germany},
  pages		= {304--10},
  dbinsdate	= {oldtimer}
}

@Article{	  bermejo00a,
  author	= {Bermejo, Sergio and Cabestany, Joan},
  title		= {Batch learning vector quantization algorithm for nearest
		  neighbour classification},
  journal	= {Neural Processing Letters},
  year		= {2000},
  volume	= {11},
  number	= {3},
  month		= {Jun},
  pages		= {173--184},
  organization	= {Universitat Politecnica de Catalunya (UPC)},
  publisher	= {Kluwer Academic Publishers},
  address	= {Dordrecht},
  abstract	= {We introduce a batch learning algorithm to design the set
		  of prototypes of 1 nearest-neighbour classifiers. Like
		  Kohonen's LVQ algorithms, this procedure tends to perform
		  vector quantization over a probability density function
		  that has zero points at Bayes borders. Although it differs
		  significantly from their online counterparts since: (1) its
		  statistical goal is clearer and better defined; and (2) it
		  converges superlinearly due to its use of the very fast
		  Newton's optimization method. Experiments results using
		  artificial data confirm faster training time and better
		  classification performance than Kohonen's LVQ algorithms.},
  dbinsdate	= {2002/1}
}

@Article{	  bermejo01a,
  author	= {Bermejo, S. and Cabestany, J.},
  title		= {Learning with nearest neighbour classifiers},
  journal	= {Neural Processing Letters},
  year		= {2001},
  volume	= {13},
  number	= {2},
  month		= {April 2001},
  pages		= {159--181},
  organization	= {Department of Electronic Engineering, Univ. Politecnica de
		  Catalunya, C4 building},
  publisher	= {},
  address	= {},
  abstract	= {This paper introduces a learning strategy for designing a
		  set of prototypes for a 1-nearest-neighbour (NN)
		  classifier. In learning phase, we transform the 1-NN
		  classifier into a maximum classifier whose discriminant
		  functions use the nearest models of a mixture. Then the
		  computation of the set of prototypes is viewed as a problem
		  of estimating the centres of a mixture model. However,
		  instead of computing these centres using standard
		  procedures like the EM algorithm, we derive to compute a
		  learning algorithm based on minimising the
		  misclassification accuracy of the 1-NN classifier on the
		  training set. One possible implementation of the learning
		  algorithm is presented. It is based on the online gradient
		  descent method and the use of radial gaussian kernels for
		  the models of the mixture. Experimental results using
		  hand-written NIST databases show the superiority of the
		  proposed method over Kohonen's LVQ algorithms.},
  dbinsdate	= {2002/1}
}

@Article{	  bermejo01b,
  author	= {Bermejo, S. and Cabestany, J.},
  title		= {Finite-sample convergence properties of the {LVQ}1
		  algorithm and the batch {LVQ}1 algorithm},
  journal	= {Neural Processing Letters},
  year		= {2001},
  volume	= {13},
  number	= {2},
  month		= {April 2001},
  pages		= {135--157},
  organization	= {Department of Electronic Engineering, Univ. Politecnica de
		  Catalunya, C4 building},
  publisher	= {},
  address	= {},
  abstract	= {This letter addresses the asymptotic convergence of
		  Kohonen's LVQ1 algorithm when the number of training
		  samples are finite with an analysis that uses the dynamical
		  systems and optimisation theories. It establishes the
		  sufficient conditions to ensure the convergence of LVQ1
		  near a minimum of its cost function for constant step sizes
		  and cyclic sampling. It also proposes a batch version of
		  LVQ1 based on the very fast Newton optimisation method that
		  cancels the dependence of the on-line version on the order
		  of supplied training samples.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  bermejo98a,
  author	= {Bermejo, S. and Cabestany, J. and Payeras, M.},
  title		= {A new dynamic {LVQ} -based classifier and its application
		  to handwritten character recognition},
  booktitle	= {6th European Symposium on Artificial Neural Networks.
		  ESANN'98. Proceedings},
  publisher	= {D-Facto},
  address	= {Brussels, Belgium},
  year		= {1998},
  volume	= {},
  pages		= {203--8},
  abstract	= {In this paper we present a new dynamic learning strategy
		  in LVQ's framework that consists of a growing process and a
		  posteriori pruning process. This schema allows building a
		  family of dynamic LVQ learning systems. To evaluate this
		  proposal, we have employed LVQ3 as a core of the learning
		  system. We have done an empirical analysis of this system
		  (DLVQ3) in order to characterize its parameters and to
		  compare it with nondynamical LVQ algorithms. Finally we
		  present results on applying PCA/sub 64/+DLVQ3 to upper and
		  lower handwritten character recognition, obtaining on
		  average test classification error (with no rejection) 7.22%
		  and 15.275% respectively in front of 9.16% and 17.01%
		  achieved with PCA/sub 64/+nondynamic LVQx's of similar
		  size, 14.7% and 23.1% with PCA/sub 64/+PNN and 10.1% and
		  20.3% with PCA/sub 128/+MLP.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bermejo99a,
  author	= {Bermejo, S. and Cabestany, J.},
  title		= {Online gradient learning algorithms for k-nearest neighbor
		  classifiers},
  booktitle	= {Foundations and Tools for Neural Modeling. International
		  Work-Conference on Artificial and Natural Neural Networks,
		  IWANN'99. Proceedings, (Lecture Notes in Computer Science
		  Vol.1606)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {1},
  pages		= {546--55},
  abstract	= {We present two online gradient learning algorithms to
		  design condensed k-nearest neighbor (NN) classifiers. The
		  goal of these learning procedures is to minimize a measure
		  of performance closely related to the expected
		  misclassification rate of the k-NN classifier. One possible
		  implementation of the algorithm is given. Convergence
		  properties are analyzed and connections with other works
		  are established. We compare these learning procedures with
		  Kohonen's LVQ algorithms (1996) and k-NN classification
		  using the handwritten NIST databases. Experimental results
		  demonstrate the potential of the proposed learning
		  algorithms.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bermudez96a,
  author	= {J. L. Bermudez and A. Piras and M. Rubinstein},
  title		= {Classification of lightning electromagnetic waveforms with
		  a \mbox{self-organizing} {K}ohonen map},
  booktitle	= {Thirteenth International Wroclaw Symposium Electromagnetic
		  Compatibility 1996},
  publisher	= {Inst. Telecommun},
  year		= {1996},
  editor	= {J. M. Janiszewski and W. Moron and W. Sega},
  address	= {Warsaw, Poland},
  pages		= {517--21},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bernard01a,
  author	= {Bernard, S. and Boujemaa, N. and Vitale, D. and Bricot,
		  C.},
  title		= {Fingerprint classification using Kohonen topological map},
  booktitle	= {IEEE International Conference on Image Processing},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {230--233},
  organization	= {INRIA Rocquencourt},
  publisher	= {},
  address	= {},
  abstract	= {Self Organizing Maps are efficient and usual for dimension
		  reduction and data clustering. In our present work, we
		  propose the use of Kohonen Topologic Map for fingerprint
		  pattern classification. The learning process takes into
		  account the large intra-class diversity and the continuum
		  of fingerprint pattern types. After a brief introduction to
		  fingerprint domain-specific knowledge and the expert
		  approach, we present an original and intuitive description
		  of the algorithm. For a classification based on the global
		  shape of the fingerprint, we adopted a suitable feature
		  space. Indeed we obtained 88% of correct classification on
		  a database composed of 1600 NIST fingerprints.},
  dbinsdate	= {2002/1}
}

@InCollection{	  bernard97a,
  author	= {Gilles Bernard},
  title		= {Experiments on distributional categorization of lexical
		  items with self organizing maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {304--309},
  dbinsdate	= {oldtimer}
}

@Article{	  bernard98a,
  author	= {P. Bernard and A. Golbraikh and D. Kireev and J. R.
		  Chretien and N. Rozhkova},
  title		= {Comparison of Chemical Databases Analysis of Molecular
		  Diversity with Self Organizing Maps {SOM} },
  journal	= {Analusis, An International Journal on Analytical
		  Chemistry},
  volume	= {26},
  pages		= {333--341},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  berns93a,
  author	= {Berns, K. and Muller, B. and Dillmann, R. },
  title		= {Dynamic control of a robot leg with \mbox{self-organizing}
		  feature maps},
  booktitle	= {IROS '93. Proceedings of the 1993 IEEE/RSJ International
		  Conference on Intelligent Robots and Systems. Intelligent
		  Robots for Flexibility},
  year		= {1993},
  volume	= {1},
  pages		= {553--60},
  organization	= {Gruppe Interaktive Planuungstechnik, Forschungszentrum
		  Inf. , Karlsruhe, Germany},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  bertin97a,
  author	= {E. Bertin},
  title		= {Self organizing maps and imaging surveys},
  journal	= {Astrophysics and Space Science Library},
  year		= {1997},
  volume	= {210},
  pages		= {221--2},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bertsch87a,
  author	= {H. Bertsch and J. Dengler},
  title		= {Klassifizierung und {S}egmentierung medizinischer {B}ilder
		  mit {H}ilfe der selbstlernenden topologischen {K}arte},
  booktitle	= {9. DAGM-Symp. Mustererkennung},
  year		= {1987},
  editor	= {E. Paulus},
  pages		= {166--170},
  organization	= {Deutche Arbeitsgruppe f{\"{u}}r Mustererkennung},
  publisher	= {Springer},
  address	= {Berlin},
  x		= {tiedostosta leila. bib, viite aika sekava: {Bertsch H,
		  Dengler J (1987) Klassifizierung und Segmentierung
		  medizinischerBilder mit Hilfe der selbstlernenden
		  topologischen Karte,E Paulus (ed. ), 9. DAGM-Symposium
		  Mustererkennung, 166--170,Springer Informatik Fachberichte
		  149, Berlin, Heidelberg. }},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  betke99a,
  author	= {Betke, M. and Kawai, J.},
  title		= {Gaze detection via \mbox{self-organizing} gray-scale
		  units},
  booktitle	= {Proceedings International Workshop on Recognition,
		  Analysis, and Tracking of Faces and Gestures in Real-Time
		  Systems. In Conjunction with ICCV'99},
  year		= {1999},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  volume	= {},
  pages		= {70--6},
  abstract	= {We present a gaze estimation algorithm that detects an eye
		  in a face image and estimates the gaze direction by
		  computing the position of the pupil with respect to the
		  center of the eye. The algorithm is information conserving
		  and based on unsupervised learning. It creates a map of
		  self-organized gray-scale image units that collectively
		  learn to describe the eye outline.},
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  beveridge93a,
  author	= {Martin Beveridge},
  title		= {Using Self Organizing Maps for the Objective Assesment of
		  /s/ Misarticualtions by Patients with Intra-Oral Cancers},
  school	= {University of Edinburgh, Department of Linguistics},
  year		= {1993},
  address	= {Edinburgh, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  bezdek01a,
  author	= {Bezdek, J. C. and Kuncheva, L. I.},
  title		= {Nearest prototype classifier designs: An experimental
		  study},
  journal	= {International Journal of Intelligent Systems},
  year		= {2001},
  volume	= {16},
  number	= {12},
  month		= {December },
  pages		= {1445--1473},
  organization	= {Computer Science Department, University of West Florida},
  publisher	= {},
  address	= {},
  abstract	= {We compare eleven methods for finding prototypes upon
		  which to base the nearest prototype classifier. Four
		  methods for prototype selection are discussed: Wilson +
		  Hart (a condensation + error-editing method), and three
		  types of combinatorial search-random search, genetic
		  algorithm, and tabu search. Seven methods for prototype
		  extraction are discussed: unsupervised vector quantization,
		  supervised learning vector quantization (with and without
		  training counters), decision surface mapping, a fuzzy
		  version of vector quantization, c-means clustering, and
		  bootstrap editing. These eleven methods can be usefully
		  divided two other ways: by whether they employ pre- or
		  postsupervision; and by whether the number of prototypes
		  found is user-defined or "automatic." Generalization error
		  rates of the 11 methods are estimated on two synthetic and
		  two real data sets. Offering the usual disclaimer that
		  these are just a limited set of experiments, we feel
		  confident in asserting that presupervised, extraction
		  methods offer a better chance for success to the casual
		  user than postsupervised, selection schemes. Finally, our
		  calculations do not suggest that methods which find the
		  "best" number of prototypes "automatically" are superior to
		  methods for which the user simply specifies the number of
		  prototypes. },
  dbinsdate	= {2002/1}
}

@Article{	  bezdek90a,
  author	= {J. C. Bezdek},
  title		= {A note on generalized \mbox{self-organizing} network
		  algorithms},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1990},
  volume	= {1293},
  number	= {pt. 1},
  pages		= {260--267},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  note		= {Conference paper in journal},
  abstract	= {We identify some similarities and differences between two
		  clustering models, viz., the fuzzy c-means (FCM) and
		  Kohonen self-organizing (KSO) feature map approaches. This
		  leads us to suggest that there is an important unknown
		  relationship between the two methodologies. Consequently,
		  we propose several avenues of research which, if
		  successfully resolved, will strengthen both the FCM and
		  Kohonen models and their utility for applications in
		  clustering and classifier design.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bezdek91a,
  author	= {J. C. Bezdek},
  title		= {Self-Organization and Clustering Algorithms},
  booktitle	= {Proc. 2nd Joint Technology Workshop on Neural Networks and
		  Fuzzy Logic},
  year		= {1991},
  volume	= {I},
  pages		= {143--158},
  x		= {nt9093. bib---AN ACCESSION NUMBER: N91217836XSP},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bezdek92a,
  author	= {J. C. Bezdek and E. C. -K. Tsao and N. R. Pal},
  title		= {Fuzzy {K}ohonen clustering networks},
  booktitle	= {Proc. IEEE International Conference on Fuzzy Systems},
  year		= {1992},
  pages		= {1035--1043},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bezdek92b,
  author	= {James C. Bezdek},
  title		= {Integration and generalization of {LVQ} and c-means
		  clustering},
  booktitle	= {SPIE Vol. 1826, Intelligent Robots and Computer Vision XI:
		  Biological, Neural Net, and 3-D Methods},
  year		= {1992},
  pages		= {280--299},
  volume	= {1826},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  abstract	= {This paper discusses the relationship between the
		  sequential hard c-means (SHCM), learning vector
		  quantization (LVQ), and fuzzy c-means (FCM) clustering
		  algorithms. LVQ and SHCM suffer from several major
		  problems. For example, they depend heavily on
		  initialization. If the initial values of the cluster
		  centers are outside the convex hull of the input data, such
		  algorithms, even if they terminate, may not produce
		  meaningful results in terms of prototypes for cluster
		  representation. This is due in part to the fact that they
		  update only the winning prototype for every input vector.
		  We also discuss the impact and interaction of these two
		  families with Kohonen's self-organizing feature mapping
		  (SOFM), which is not a clustering method, but which often
		  lends itself to clustering algorithms. Then we present two
		  generalizations of LVQ that are explicitly designed as
		  clustering algorithms: we refer to these algorithms as
		  generalized LVQ = GLVQ; and fuzzy LVQ = FLVQ. Learning
		  rules are derived to optimize an objective function whose
		  goal is to produce 'good clusters'. GLVQ/FLVQ (may) update
		  every node in the clustering net for each input vector. We
		  use Anderson's IRIS data to compare the performance of
		  GLVQ/FLVQ with a standard version of LVQ. Experiments show
		  that the final centroids produced by GLVQ are independent
		  of node initialization and learning coefficients. Neither
		  GLVQ nor FLVQ depends upon a choice for the update
		  neighborhood or learning rate distribution---these are
		  taken care of automatically.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bezdek93a,
  author	= {James C. Bezdek and Nikhil R. Pal and Eric C. K. Tsao},
  title		= {Two Generalizations of {K}ohonen Clustering},
  booktitle	= {Proc. of the Third Int. Workshop on Neural Networks and
		  Fuzzy Logic, Houston, Texas, NASA Conference Publication
		  10111},
  year		= {1993},
  editor	= {Christopher J. Culbert},
  volume	= {II},
  pages		= {199--226},
  publisher	= {NASA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bezdek93b,
  author	= {James C. Bezdek and Nikhil R. Pal},
  title		= {Prototype Generating Clustering Algorithms},
  booktitle	= {Proc. 5th IFSA World Congress '93---Seoul, Fifth Int.
		  Fuzzy Systems Association World Congress},
  year		= {1993},
  volume	= {I},
  pages		= {36--43},
  publisher	= {Korea Fuzzy Mathematics and Systems Society},
  address	= {Seoul, Korea},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bezdek93c,
  author	= {James C. Bezdek and Nikhil R. Pal},
  title		= {An Index of Topological Preservation and its Application
		  to Self-Organizing Feature Maps},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2435--2440},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {We discuss topological preservation under feature
		  extraction transformations. Transformations that preserve
		  the order of all distances in any neighborhood of vectors
		  in p-space are defined as metric topology preserving (MTP)
		  transformations. We give a necessary and sufficient
		  condition for this property in terms of Spearman's rank
		  correlation coefficient. A modification of Kohonen's
		  self-organizing feature map algorithm that extracts vectors
		  in q-space from data in p-space is given. Three methods are
		  empirically compared: principal components analysis:
		  Sammon's algorithm; and our extension of the
		  self-organizing feature map algorithm. Our MTP index shows
		  that the first two methods preserve distance ranks on six
		  data sets much more effectively than extended SOFM.},
  dbinsdate	= {oldtimer}
}

@Article{	  bezdek93d,
  author	= {Bezdek, J. and Pal, N. R. },
  title		= {Fuzzification of the \mbox{self-organizing} feature map:
		  will it work?},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1993},
  volume	= {2061},
  pages		= {142--62},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  bezdek95a,
  author	= {James C. Bezdek and Nikhil R. Pal},
  title		= {A Note on Self-Organizing Semantic Maps},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1995},
  volume	= {6},
  number	= {5},
  pages		= {1029--1036},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {This paper discusses Kohonen's self-organizing semantic
		  map (SOSM). We show that augmentation and normalization of
		  numerical feature data as recommended for the SOSM is
		  entirely unnecessary to obtain semantic maps that exhibit
		  semantic similarities between objects represented by the
		  data. Visual displays of a small data set of 13 animals
		  based on principal components, Sammon's algorithm, and
		  Kohonen's (unsupervised) self-organizing feature map (SOFM)
		  possess exactly the same qualitative information as the
		  much more complicated SOSM display does.},
  dbinsdate	= {oldtimer}
}

@Article{	  bezdek95b,
  author	= {James C. Bezdek and Nikhil R. Pal},
  title		= {Two Soft Relatives of Learning Vector Quantization},
  journal	= {Neural Networks},
  year		= {1995},
  volume	= {8},
  number	= {5},
  pages		= {729--743},
  publisher	= {Elsevier Science Ltd},
  dbinsdate	= {oldtimer}
}

@Article{	  bezdek95c,
  author	= {Bezdek, J. C. and Pal, N. R. },
  title		= {Index of topological preservation for feature extraction},
  journal	= {Pattern Recognition},
  year		= {1995},
  volume	= {28},
  number	= {3},
  pages		= {381--91},
  month		= {March},
  abstract	= {This paper is about the ability of principal components
		  analysis, the Sammon algorithm, and an extension of the
		  Kohonen self-organizing feature map to preserve spatial
		  order during feature extraction on unlabeled data.
		  Transformations to q-space that preserve the order of all
		  pairwise distances in any set of vectors in p-space are
		  defined as metric topology preserving (MTP)
		  transformations. We give a necessary and sufficient
		  condition for this new property in terms of the Spearman
		  rank correlation coefficient. Unlike many other measures of
		  extracted feature quality, the MTP index is independent of
		  the extraction method. A modification of the Kohonen
		  self-organizing feature map algorithm that extracts vectors
		  in q-space from data in p-space is developed. The extent to
		  which principal components, Sammon's algorithm and our
		  extension of the self-organizing feature map (SOFM)
		  preserve the MTP property is discussed. Our MTP index shows
		  that the first two methods preserve distance ranks on seven
		  data sets much more effectively than extended SOFM.},
  dbinsdate	= {oldtimer}
}

@Article{	  bezdek95d,
  author	= {J. C. Bezdek and N. R. Pal and R. J. Hathaway and N. B.
		  Karayiannis},
  title		= {Some new competitive learning schemes},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2492},
  number	= {pt. 1},
  pages		= {538--49},
  note		= {(Applications and Science of Artificial Neural Networks
		  Conference Date: 17--21 April 1995 Conference Loc: Orlando,
		  FL, USA Conference Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bezdek96a,
  author	= {J. C. Bezdek and T. R. Reichherzer and G. Lim and Y.
		  Attikiouzel},
  title		= {Classification with multiple prototypes},
  booktitle	= {Proceedings of the Fifth IEEE International Conference on
		  Fuzzy Systems. FUZZ-IEEE '96},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {626--32},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bezdek99a,
  author	= {J. C. Bezdek and L. I. Kuncheva},
  title		= {Point prototype generation and classifier design},
  booktitle	= {Kohonen Maps},
  pages		= {71--96},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Nearest prototypes, self-organising feature maps,
		  classifier design,, pre-supervision, post-supervision,},
  dbinsdate	= {oldtimer}
}

@Article{	  bezerianos00a,
  author	= {Bezerianos, A. and Vladutu, L. and Papadimitriou, S.},
  title		= {Hierarchical state space partitioning with a network
		  self-organizing map for the recognition of {ST}-T segment
		  changes},
  journal	= {Medical and Biological Engineering and Computing},
  year		= {2000},
  volume	= {38},
  number	= {4},
  month		= {Jul},
  pages		= {406--415},
  organization	= {Univ of Patras},
  publisher	= {Peter Peregrinus Ltd},
  address	= {Stevenage},
  abstract	= {The problem of maximizing the performance of ST-T segment
		  automatic recognition for ischaemia detection is a
		  difficult pattern classification problem. The paper
		  proposes the network self-organizing map (NetSOM) model as
		  an enhancement to the Kohonen self-organized map (SOM)
		  model This model is capable of effectively decomposing
		  complex large-scale pattern classification problems into a
		  number of partitions, each of which is more manageable with
		  a local classification device. The NetSOM attempts to
		  generalize the regularization and ordering potential of the
		  basic SOM from the space of vectors to the space of
		  approximating functions. It becomes a device for the
		  ordering of local experts (i.e. independent neural
		  networks) over its lattice of neurons and for their
		  selection and co-ordination. Each local expert is an
		  independent neural network that is trained and activated
		  under the control of the NetSOM. This method is evaluated
		  with examples from the European ST-T database. The first
		  results obtained after the application of NetSOM to ST-T
		  segment change recognition show a significant improvement
		  in the performance compared with that obtained with
		  monolithic approaches, i.e. with single network types. The
		  basic SOM model has attained an average ischaemic beat
		  sensitivity of 73.6% and an average ischaemic beat
		  predictivity of 68.3%. The work reports and discusses the
		  improvements that have been obtained from the
		  implementation of a NetSOM classification system with both
		  multilayer perceptrons and radial basis function (RBF)
		  networks as local experts for the ST-T segment change
		  problem. Specifically, the NetSOM with multilayer
		  perceptrons (radial basis functions) as local experts has
		  improved the results over the basic SOM to an average
		  ischaemic beat sensitivity of 75.9% (77.7%) and an average
		  ischaemic beat predictivity of 72.5% (74.1%).},
  dbinsdate	= {2002/1}
}

@InProceedings{	  bhandarkar01a,
  author	= {Bhandarkar, S. M. and Nammalwar, P.},
  title		= {Segmentation of multispectral {MR} images using a
		  hierarchical self-organizing map},
  booktitle	= {Proceedings of the IEEE Symposium on Computer-Based
		  Medical Systems},
  year		= {2001},
  editor	= {},
  volume	= {},
  pages		= {294--299},
  organization	= {Department of Computer Science, University of Georgia},
  publisher	= {},
  address	= {},
  abstract	= {The application of a hierarchical self-organizing map
		  (HSOM) to the problem of segmentation of multispectral
		  magneti resonance (MR) images is investigated. The HSOM is
		  composed of several layers of the self-organizing map (SOM)
		  organized in a pyramidal fashion. The SOM has been used for
		  the segmentation of multispectral MR images but the results
		  often suffer from undersegmentation and oversegmentation.
		  By combining the concepts of self-organization and
		  topographic mapping with multiscale image segmentation, the
		  HSOM is seen to overcome the major drawbacks of the SOM.
		  The segmentation results of the HSOM are compared with
		  those of the SOM and the k-means clustering algorithm on
		  multispectral MR images of the human brain representing
		  both, normal conditions and pathological conditions such as
		  multiple sclerosis. The multiscale segmentation results of
		  the HSOM are shown to have interesting consequences from
		  the viewpoint of clinical diagnosis of pathological
		  conditions.},
  dbinsdate	= {2002/1}
}

@Article{	  bhandarkar96a,
  author	= {S. M. Bhandarkar and J. Koh and Minsoo Suk},
  title		= {A hierarchical neural network and its application to image
		  segmentation},
  journal	= {Mathematics and Computers in Simulation},
  year		= {1996},
  volume	= {41},
  number	= {3--4},
  pages		= {337--55},
  note		= {(IMACS Symposium on Signal Processing Robotics and Neural
		  Networks Conference Date: April 1994 Conference Loc: Lille,
		  France)},
  abstract	= {The problem of image segmentation can be formulated as one
		  of vector quantization. Although self-organizing networks
		  with competitive learning are useful for vector
		  quantization, they, in their original single-layer
		  structure, are inadequate for image segmentation. This
		  paper proposes and describes a hierarchical self-organizing
		  neural network for image segmentation. The hierarchical
		  self-organizing feature map (HSOFM) which is an extension
		  of the traditional (single-layer) self-organizing feature
		  map (SOFM) is seen to alleviate the shortcomings of the
		  latter in the context of image segmentation. The problem of
		  image segmentation is formulated as one of vector
		  quantization and mapped onto the HSOFM. The HSOFM combines
		  the ideas of self-organization and topographic mapping with
		  those of multi-scale image segmentation. Experimental
		  results using intensity and range images bring out the
		  advantages of the HSOFM over the conventional SOFM.},
  dbinsdate	= {oldtimer}
}

@Article{	  bhandarkar97a,
  author	= {Bhandarkar, Suchendra M and Koh, Jean and Suk, Minsoo},
  title		= {Multiscale image segmentation using a hierarchical
		  \mbox{self-organizing} map},
  journal	= {Neurocomputing},
  year		= {1997},
  number	= {3},
  volume	= {14},
  pages		= {241--272},
  abstract	= {Multiscale structures and algorithms that unify the
		  treatment of local and global scene information are of
		  particular importance in image segmentation. Vector
		  quantization, owing to its versatility, has proved to be an
		  effective means of image segmentation. Although vector
		  quantization can be achieved using self-organizing maps
		  with competitive learning, self-organizing maps in their
		  original single-layer structure, are inadequate for image
		  segmentation. A hierarchical self-organizing neural network
		  for image segmentation is presented. The Hierarchical
		  Self-Organizing Map (HSOM) is an extension of the
		  conventional (single-layer) Self-Organizing Map (SOM). The
		  problem of image segmentation is formulated as one of
		  vector quantization and mapped onto the HSOM. By combining
		  the concepts of self-organization and topographic mapping
		  with those of multiscale image segmentation the HSOM
		  alleviates the shortcomings of the conventional {SOM} in
		  the context of image segmentation.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bharitkar01a,
  author	= {Bharitkar, S. and Filev, D.},
  title		= {An online learning vector quantization algorithm},
  booktitle	= {Proceedings of the Sixth International Symposium on Signal
		  Processing and its Applications. IEEE, Piscataway, NJ,
		  USA},
  year		= {2001},
  volume	= {2},
  pages		= {394--7},
  abstract	= {We propose an online learning algorithm for the learning
		  vector quantization (LVQ) approach in nonlinear supervised
		  classification. The advantage of this approach is the
		  ability of the LVQ to adjust its codebook vectors as new
		  patterns become available, so as to accurately model the
		  class representation of the patterns. Moreover this
		  algorithm does not significantly increase the computational
		  complexity over the original LVQ algorithm.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  bi94a,
  author	= {Hao Bi and Guangguo Bi and Yimin Mao},
  title		= {Stochastically Competitive Learning Algorithm for Vector
		  Quantizer Design},
  pages		= {622--626},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {vector quantization, modification, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bi94b,
  author	= {Hao Bi and Guangguo Bi and Yimin Mao},
  title		= {Globally Optimal Vector Quantizer Design Using
		  Stochastically Competitive Learning Algorithm},
  booktitle	= {Proc. Int. Symp. on Speech, Image Processing and Neural
		  Networks},
  year		= {1994},
  volume	= {II},
  pages		= {650--653},
  organization	= {{IEEE} Hong Kong Chapter of Signal Processing},
  address	= {Hong Kong},
  annote	= {application, vector quantization, optimization,
		  modification},
  dbinsdate	= {oldtimer}
}

@Article{	  bian99a,
  author	= {Bian, J.~C. and Chen, H.~B. and Yang, P.~C. and Lu,
		  D.~R.},
  title		= {Retrievals of over-Ocean Precipitable Water from {S}sm/{I}
		  by {SOM} Network Model},
  journal	= {Chinese Science Bulletin},
  year		= {1999},
  volume	= {44},
  number	= {11},
  pages		= {1038--1041},
  dbinsdate	= {oldtimer}
}

@Article{	  biebelmann96a,
  author	= {E. Biebelmann and M. Koppen and B. Nickolay},
  title		= {Practical applications of neural networks in texture
		  analysis},
  journal	= {Neurocomputing},
  year		= {1996},
  volume	= {13},
  number	= {2--4},
  pages		= {261--79},
  note		= {(3rd International Conference on Fuzzy Logic, Neural Nets
		  and Soft Computing (IIZUKA'94) Conf. Date: 1--7 Aug. 1994
		  Conf. Loc: Iizuka, Japan Conf. Sponsor: Int. Fuzzy Syst.
		  Assoc. ; Int. Neural Network Soc. ; Japan Soc. Fuzzy Theory
		  \& Syst. ; et al)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bieler94a,
  author	= {Bieler, K. and Glavitsch, H. },
  title		= {Evaluation of different AI-methods for fault diagnosis in
		  power systems},
  booktitle	= {ISAP '94. International Conference on Intelligent System
		  Application to Power Systems},
  year		= {1994},
  editor	= {Hertz, A. and Holen, A. T. and Rault, J. -C. },
  volume	= {1},
  pages		= {209--16},
  organization	= {Eidgenossische Tech. Hochschule, Zurich, Switzerland},
  publisher	= {EC2},
  address	= {Nanterre Cedex, France},
  dbinsdate	= {oldtimer}
}

@Article{	  bienfait94a,
  author	= {Bienfait, B. },
  title		= {Applications of high-resolution \mbox{self-organizing}
		  maps to retrosynthetic and {QSAR} analysis},
  journal	= {Journal of Chemical Information and Computer Sciences},
  year		= {1994},
  volume	= {34},
  number	= {4},
  pages		= {890--8},
  month		= {July-Aug},
  dbinsdate	= {oldtimer}
}

@Article{	  bienfait97a,
  author	= {B. Bienfait and J. Gasteiger},
  title		= {Checking the projection display of multivariate data with
		  colored graphs},
  journal	= {Journal of Molecular Graphics \& Modelling},
  year		= 1997,
  volume	= 15,
  pages		= {203--215,254--258},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bigus94a,
  author	= {Joseph P. Bigus},
  title		= {Applying Neural Networks to Computer System Performance
		  Tuning},
  pages		= {2442--2447},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, initialization, hybrid},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bing91a,
  author	= {Z. Bing and E. Grant},
  title		= {A neural network approach to adaptive state-space
		  partitioning},
  booktitle	= {Proc. IEEE Int. Symp. on Intelligent Control},
  year		= {1991},
  pages		= {180--183},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  binks91a,
  author	= {David L. Binks and Nigel M. Allinson},
  title		= {Financial Data Recognition and Prediction using Neural
		  Networks},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1709--1712},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bishop91a,
  author	= {J. M. Bishop and R. J. Mitchell},
  title		= {Neural networks---an introduction},
  booktitle	= {Proc. IEE Colloquium on 'Neural Networks for Systems:
		  Principles and Applications' (Digest No. 019)},
  year		= {1991},
  pages		= {1--3},
  organization	= {IEE},
  publisher	= {IEE},
  address	= {London, UK},
  x		= {In this paper, neural networks are introduced, . . .
		  Kohonen networks; . . . },
  dbinsdate	= {oldtimer}
}

@TechReport{	  bishop96a,
  author	= {Christopher M. Bishop and Markus Svens{\'e}n and
		  Christopher K. I. Williams},
  title		= {{GTM}: A Principled Alternative to the Self-Organizing
		  Map},
  institution	= {Neural Computing Research Group, Aston University},
  year		= 1996,
  number	= {NCRG/96/015},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bishop96b,
  author	= {C. M. Bishop and M. Svensen and C. K. I. Williams},
  title		= {{GTM}: a principled alternative to the
		  \mbox{self-organizing} map},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {165--70},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bishop97a,
  author	= {Christopher M. Bishop and Markus Svensen and Christopher
		  K. I. Williams},
  title		= {Magnification factors for the {SOM} and {GTM} algorithms},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {333--338},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bishop97b,
  author	= {Christopher M. Bishop and Markus Svens{\'e}n and
		  Christopher K. I. Williams},
  title		= {{GTM}: A Principled Alternative to the Self-Organizing
		  Map},
  booktitle	= {Advances in Neural Information Processing Systems 9},
  publisher	= {The MIT Press},
  year		= 1997,
  editor	= {Michael C. Mozer and Michael I. Jordan and Thomas
		  Petsche},
  pages		= {354--360},
  address	= {Cambridge, MA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bishop98a,
  author	= {C. M. Bishop},
  title		= {Latent variables, topographic mappings and data
		  visualization},
  booktitle	= {Neural Nets WIRN-VIETRI-97. Proceedings of the 9th Italian
		  Workshop on Neural Nets},
  publisher	= {Springer-Verlag London},
  year		= {1998},
  editor	= {M. Marinaro and R. Tagliaferri},
  address	= {London, UK},
  pages		= {3--32},
  dbinsdate	= {oldtimer}
}

@Article{	  bishop98b,
  author	= {Bishop, Christopher M and Svensen, Markus and Williams,
		  Christopher KI},
  title		= {Developments of the generative topographic mapping},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {203--224},
  abstract	= {The generative topographic mapping (GTM) model was
		  introduced by Bishop et al. as a probabilistic
		  re-formulation of the self-organizing map (SOM). It offers
		  a number of advantages compared with the standard {SOM},
		  and has already been used in a variety of applications. In
		  this paper we report on several extensions of the GTM,
		  including an incremental version of the EM algorithm for
		  estimating the model parameters, the use of local subspace
		  models, extensions to mixed discrete and continuous data,
		  semi-linear models which permit the use of high-dimensional
		  manifolds whilst avoiding computational intractability,
		  Bayesian inference applied to hyper-parameters, and an
		  alternative framework for the GTM based on Gaussian
		  processes. All of these developments directly exploit the
		  probabilistic structure of the GTM, thereby allowing the
		  underlying modelling assumptions to be made explicit. They
		  also highlight the advantages of adopting a consistent
		  probabilistic framework for the formulation of pattern
		  recognition algorithms.},
  dbinsdate	= {oldtimer}
}

@Article{	  bishop98c,
  author	= {Christopher M. Bishop and Markus Svens{\'e}n and
		  Christopher K. I. Williams},
  title		= {{GTM}: The Generative Topographic Mapping},
  journal	= {Neural Computation},
  year		= 1998,
  volume	= 10,
  pages		= {215--234},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  black93a,
  author	= {Black, J. V. },
  title		= {Comparison of the performance of vector quantiser training
		  algorithms},
  booktitle	= {Third International Conference on Artificial Neural
		  Networks},
  year		= {1993},
  pages		= {71--5},
  organization	= {Defence Res. Agency, Malvern, UK},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@TechReport{	  blackmore92a,
  author	= {Justine Blackmore and Risto Miikkulainen},
  title		= {Incremental grid growing: encoding high-dimensional
		  structure into a two-dimensional feature map},
  institution	= {University of Texas at Austin},
  number	= {TR AI92--192},
  address	= {Austin, TX},
  year		= {1992 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  blackmore93a,
  author	= {Justine Blackmore and Risto Miikkulainen},
  title		= {Incremental Grid Growing: Encoding High-Dimensional
		  Structure into a Two-Dimensional Feature Map},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {I},
  pages		= {450--455},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  blackmore95a,
  author	= {J. Blackmore and R. Miikkulainen},
  title		= {Visualizing high-dimensional structure with the
		  incremental grid growing neural network},
  booktitle	= {Machine Learning. Proceedings of the Twelfth International
		  Conference on Machine Learning},
  publisher	= {Morgan Kaufmann Publishers},
  year		= {1995},
  editor	= {A. Prieditis and S. Russell},
  address	= {San Francisco, CA, USA},
  pages		= {55--63},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  blanch00a,
  author	= {Blanch Perez del Notario C. and Malanda Trigueros, A.},
  title		= {Kohonen-{GLA} algorithms for efficient vector
		  quantization},
  booktitle	= {ICECS 2000. 7th IEEE International Conference on
		  Electronics, Circuits and Systems. IEEE, Piscataway, NJ,
		  USA},
  year		= {2000},
  volume	= {2},
  pages		= {671--4},
  abstract	= {Two new methods for vector quantization (VQ) codebook
		  design are presented, consisting of a sequential
		  combination of two well-known VQ design techniques: the
		  Kohonen self organizing feature maps and the generalized
		  Lloyd algorithm. The combined methods profit from the best
		  features of their constituents achieving a superior
		  performance to either of them.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  blanchet92a,
  author	= {Max Blanchet and Shuji Yoshizawa and Nobuyuki Okudaira and
		  {Shun-ichi} Amari},
  title		= {Self-Adaptive System for Automatic Sleep Cycle Recognition
		  Using Heart Rate. Application for a Biological Rythme
		  Dependent Alarm Clock},
  booktitle	= {Proc. 7'th Symp. on Biological and Physiological
		  Engineering},
  year		= {1992},
  pages		= {171--174},
  publisher	= {Toyohashi University of Technology},
  address	= {Toyohashi, Japan},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  blanchet93a,
  author	= {Max Blanchet and Shuji Yoshizawa and {Shun-ichi} Amari},
  title		= {Modified {K}ohonen's Self-Organizing Feature Map and Its
		  Application to Automatic Sleep Cycle Recognition},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2476--2479},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {In order to increase the adaptive ability of the Kohonen's
		  self-organizing feature map algorithm, we modify the
		  updating rule of its partition boundary. The modified
		  algorithm, together with the Genetic Algorithm, is applied
		  to the detection of the REM (Rapid Eye Movement) cycle of
		  the sleep, which is said to be related to comfortableness
		  of the sleep. We used the heart-rate alone (not relying on
		  complicated EEG analysis) and succeeded to achieve a high
		  detection accuracy.},
  dbinsdate	= {oldtimer}
}

@Article{	  blanco00a,
  author	= {Blanco, A. and Delgado, M. and Pegalajar, M. C.},
  title		= {Extracting rules from a (fuzzy/crisp) recurrent neural
		  network using a self-organizing map},
  journal	= {International Journal of Intelligent Systems},
  year		= {2000},
  volume	= {15},
  number	= {7},
  month		= {},
  pages		= {595--621},
  organization	= {Universidad de Granada},
  publisher	= {John Wiley \& Sons Inc},
  address	= {New York, NY},
  abstract	= {Although the extraction of symbolic knowledge from trained
		  feedforward neural networks has been widely studied,
		  research in recurrent neural networks (RNN) has been more
		  neglected, even though it performs better in areas such as
		  control, speech recognition, time series prediction, etc.
		  Nowadays, a subject of particular interest is (crisp/fuzzy)
		  grammatical inference, in which the application of these
		  neural networks has proven to be suitable. In this paper,
		  we present a method using a self-organizing map (SOM) for
		  extracting knowledge from a recurrent neural network able
		  to infer a (crisp/fuzzy) regular language. Identification
		  of this language is done only from a (crisp/fuzzy) example
		  set of the language.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  blanco99a,
  author	= {Blanco, I. D. and {Diez Gonzalez}, A. B. and {Cuadrado
		  Vega}, A. A. and {Enguita Gonzalez}, J.},
  title		= {{RBF} approach for trajectory interpolation in
		  \mbox{self-organizing} map based condition monitoring},
  booktitle	= {1999 7th IEEE International Conference on Emerging
		  Technologies and Factory Automation. Proceedings ETFA '99.
		  },
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1003--10},
  abstract	= {Self-organizing map trajectory interpolation using radial
		  basis functions is proposed for process condition
		  monitoring. Self-organizing maps are a powerful means to
		  visualize the state of a process. However, a quantization
		  process takes place when input vectors are mapped onto the
		  grid (output) space. The discrete nature of the output
		  space gives rise to a considerable loss of information
		  which is particularly harmful to detect incipient faults
		  which are often revealed by drifts or slight changes in the
		  state trajectory. A continuous projection from the input
		  space onto the {SOM} grid space using RBF-based
		  interpolation is proposed and tested with experimental
		  data.},
  dbinsdate	= {oldtimer}
}

@Article{	  blasco00a,
  author	= {Blasco, J. A. and Fueyo, N. and Dopazo, C. and Chen, J.
		  Y.},
  title		= {A self-organizing-map approach to chemistry representation
		  in combustion applications},
  journal	= {COMBUSTION THEORY AND MODELLING},
  year		= {2000},
  volume	= {4},
  number	= {1},
  month		= {MAR},
  pages		= {61--76},
  abstract	= {Several alternative techniques have been proposed in the
		  literature in order to avoid the CPU-intensive numerical
		  integration of the thermochemical equations in the
		  simulation of combustion processes. The present paper
		  introduces a new approach, which is based on two artificial
		  neural-network (ANN) paradigms, namely the self-organizing
		  map (SOM) and the multilayer perceptron (MLP). The SOM is
		  first employed for the automatic partitioning of the
		  thermochemical space into subdomains. Then, a specialized
		  MLP is trained in order to fit the thermochemical points
		  belonging to a given subdomain. The presented strategy is
		  tested on a partially stirred reactor (PaSR) with a reduced
		  methane-air mechanism, and encouraging results are
		  reported. The relatively modest CPU-time and memory
		  requirements of the method make the SOM-MLP approach a
		  promising technique for the inclusion of large chemical
		  mechanisms in the context of complex applications, such as
		  the multidimensional simulation of combustion.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  blayo90a,
  author	= {F. Blayo and C. Lehmann},
  title		= {A systolic implementation of the self organization
		  algorithm},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  blayo91a,
  author	= {F. Blayo and P. Demartines},
  title		= {Data analysis: how to compare {K}ohonen neural networks to
		  other techniques?},
  booktitle	= {Proc. IWANN'91, Int. Workshop on Artificial Neural
		  Networks},
  year		= {1991},
  editor	= {A. Prieto},
  pages		= {469--476},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@Article{	  blayo92a,
  author	= {F. Blayo and P. Demartines},
  title		= {{K}ohonen algorithms. {A}pplication to the analysis of
		  economic data},
  journal	= {Bull. des Schweizerischen Elektrotechnischen Vereins {\&}
		  des Verbandes Schweizerischer Elektrizit{\"{a}}tswerke},
  year		= {1992},
  volume	= {83},
  number	= {5},
  pages		= {23--26},
  note		= {(in French)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  blight93a,
  author	= {Blight, D. C. and McLeod, R. D. },
  title		= {Self-organizing {K}ohonen maps for {FPGA} placement},
  booktitle	= {Field-Programmable Gate Arrays: Architectures and Tools
		  for Rapid Prototyping. Second International Workshop on
		  Field Programmable Logic and Applications},
  year		= {1993},
  editor	= {Grunbacher, H. and Hartenstein, R. W. },
  pages		= {88--95},
  organization	= {Dept. of Electr. \& Comput. Eng. , Manitoba Univ. ,
		  Winnipeg, Man. , Canada},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  blonda93a,
  author	= {P. Blonda and G. Pasquariello and J. Smith},
  title		= {Comparison of Backpropagation, Cascade-Correlation and
		  {K}ohonen Algorithms for Cloud Retrieval},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1231--1234},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {The expected high volume imagery data from NASA Mission to
		  the Planet Earth (e.g. one terabyte for EOS AM) is one of
		  the target application areas for automated cloud retrieval,
		  and more generally for automated image classification. We
		  used the backpropagation (BP), the Cascade-Correlation (CC)
		  and Kohonen self-organizing map (SOM) neural network
		  architectures for cloud retrieval from satellite imagery.
		  We have used a simple scene (a mixed scene containing only
		  cloud and ocean). This simple scene allows us to evaluate
		  the accuracy of the classification (and the trend in
		  misclassifications). better than a complicated scene. BP
		  and CC performed at the same accuracy level. CC was
		  slightly more efficient than BP in terms of the number
		  epochs. BP requires the user to set the number of hidden
		  units. CC demonstrated a built -in flexibility in terms of
		  the (variable) number of hidden units necessary to
		  accomplish the learning phase of the algorithm. The {SOM}
		  algorithm, was slightly less accurate, do to its
		  unsupervised nature, than CC and BP for our test data ( 97%
		  accuracy versus 99% accuracy level for BP and CC). This
		  study shows that for simple scenes, which are abundant in
		  global monitoring satellite imagery, a simple
		  pixel-by-pixel or 3-by-3 window approaches provide high
		  accuracy classification without using complicated
		  contextual information.},
  dbinsdate	= {oldtimer}
}

@Article{	  blonda93b,
  author	= {Blonda, P. and Carella, A. and DeBlasi, R. and Dicuonzo,
		  F. and LaForgia, V. and Milella, D. Pasquariello, G. and
		  Satalino, G.},
  title		= {Neural network modular system for object classification in
		  brain {MR} images},
  journal	= {Artificial Intelligence in Medicine},
  year		= {1993},
  number	= {},
  volume	= {10},
  pages		= {477--480},
  abstract	= {In this paper we present a modular system for segmentation
		  and classification of brain Magnetic Resonance (MR) images.
		  It consists of two Neural Network architectures, a Self
		  Organizing Map and a Multilayer Perceptron. The objective
		  of the work is to evaluate the effectiveness of a Neural
		  Network approach for automatic recognition of anatomic
		  structures in {MR} images. The system has been useful to
		  discriminate 20 different anatomic classes.},
  dbinsdate	= {oldtimer}
}

@Article{	  blonda94a,
  author	= {Blonda, P. and La Forgia, V. and Pasquariello, G. and
		  Satalino, G. },
  title		= {Feature extraction and pattern classification for remotely
		  sensed data analysis by a modular neural system},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1994},
  volume	= {2315},
  pages		= {48--55},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  blonda95a,
  author	= {Blonda, P. and Bennardo, A. and la Forgia, V. and
		  Satalino, G. },
  title		= {Modular neural system, based on a fuzzy clustering
		  network, for classification},
  booktitle	= {1995 International Geoscience and Remote Sensing
		  Symposium, IGARSS '95. Quantitative Remote Sensing for
		  Science and Applications},
  year		= {1995},
  editor	= {Stein, T. I. },
  volume	= {1},
  pages		= {449--51},
  organization	= {IESI-CNR, Bari, Italy},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  blonda96a,
  author	= {P. Blonda and A. Bennardo and G. Satalino and V. la
		  Forgia},
  title		= {Application of the unsupervised Fuzzy {K}ohonen Clustering
		  Network for remote sensed data segmentation},
  booktitle	= {Proceedings of the WILF '95. Italian Workshop on Fuzzy
		  Logic 1995. New Trends in Fuzzy Logic},
  publisher	= {World Scientific},
  year		= {1996},
  editor	= {A. Bonarini and D. Mancini and F. Masulli and A.
		  Petrosino},
  address	= {Singapore},
  pages		= {143--50},
  dbinsdate	= {oldtimer}
}

@Article{	  blonda96b,
  author	= {P. Blonda and V. la Forgia and G. Pasquariello and G.
		  Satalino},
  title		= {Feature extraction and pattern classification of remote
		  sensing data by a modular neural system},
  journal	= {Optical Engineering},
  year		= {1996},
  volume	= {35},
  number	= {2},
  pages		= {536--42},
  dbinsdate	= {oldtimer}
}

@Article{	  blonda96c,
  author	= {P. Blonda and A. Bennardo and G. Satalino and G.
		  Pasquariello and R. {De Blasi} and D. Milella},
  title		= {Fuzzy neural network based segmentation of multispectral
		  magnetic resonance brain images},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2761},
  pages		= {146--53},
  note		= {(Applications of Fuzzy Logic Technology III Conf. Date:
		  10--12 April 1996 Conf. Loc: Orlando, FL, USA Conf.
		  Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@Article{	  blonda96d,
  author	= {P. Blonda and A. Bennardo and G. Pasquariello and G.
		  Satalino and V. la Forgia},
  title		= {Application of the fuzzy {K}ohonen clustering network to
		  remote sensed data processing},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2761},
  pages		= {119--29},
  note		= {(Applications of Fuzzy Logic Technology III Conf. Date:
		  10--12 April 1996 Conf. Loc: Orlando, FL, USA Conf.
		  Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  blonda97a,
  author	= {P. Blonda and A. Bennardo and G. Satalino},
  title		= {Neuro-fuzzy processing of remote sensed data},
  booktitle	= {Neural Nets WIRN VIETRI-96. Proceedings of the 8th Italian
		  Workshop on Neural Nets},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {M. Marinaro and R. Tagliaferri},
  address	= {London, UK},
  pages		= {153--63},
  dbinsdate	= {oldtimer}
}

@Article{	  blonda97b,
  author	= {P. Blonda and A. Baraldi and G. Bafunno and G. Satalino
		  and G. Ria},
  title		= {Experimental comparison of {FOSART} and {F {LVQ} } in a
		  remotely sensed image classification task},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3165},
  pages		= {113--22},
  note		= {(Applications of Soft Computing Conf. Date: 28--29 July
		  1997 Conf. Loc: San Diego, CA, USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  blonda99a,
  author	= {Blonda, P. and Satalino, G. and Wasowski, J. and Parise,
		  M. and Baraldi A and Refice A},
  title		= {Neural techniques for {SAR} intensity and coherence data
		  classification},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.3871},
  year		= {1999},
  volume	= {3871},
  pages		= {374--81},
  abstract	= {Previously it has been proved that combined analysis of
		  SAR intensity and interferometric correlation images is a
		  valuable tool in classification tasks where traditional
		  techniques such as crisp thresholding schemes and classical
		  maximum likelihood classifiers have been employed. In this
		  work, developed in the framework of the ESA A03--320
		  project titled Application of ERS data to landslide
		  activity monitoring in the southern Apennines, Italy, our
		  goal is to investigate: 1) usefulness of SAR
		  interferometric correlation information in mapping areas
		  with diffuse erosional activity, including landslides; and
		  2) effectiveness of soft computing techniques in the
		  combined analysis of SAR intensity and interferometric
		  correlation images. Two neural classifiers are selected
		  from the literature. The first classifier is a one-stage
		  error-driven multilayer perceptron (MLP) and the second
		  classifier is a two-stage hybrid (TSH) learning system,
		  consisting of a sequence of an unsupervised data-driven
		  first stage with a supervised error-driven second stage.
		  The TSH unsupervised first stage is implemented as either:
		  a) the on-line learning, dynamic-sizing, dynamic-linking
		  fully self organizing simplified adaptive resonance theory
		  (FOSART) clustering model; b) the batch-learning,
		  static-sizing, no-linking fuzzy learning vector
		  quantization (FLVQ) algorithm; or c) the on-line learning,
		  static-sizing, static-linking self-organizing map (SOM).
		  The input data set consists of three SAR ERS-1/ERS-2 tandem
		  pair images depicting an area featuring slope instability
		  phenomena in the Campanian Apennines of Southern Italy.
		  From each tandem pair, four pixel-based features are
		  extracted: the backscattering mean intensity, the
		  interferometric coherence, the backscattering intensity
		  texture and the backscattering intensity change. Our
		  classification task is focused on the discrimination of
		  land cover types useful for hazard evaluation, i.e.,
		  evaluation of areas affected by erosion.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  blumel94a,
  author	= {Blumel, R. },
  title		= {Application of {K}ohonen's \mbox{self-organizing}
		  artificial neural networks to {PWM} inverter drives},
  booktitle	= {IECON '94. 20th International Conference on Industrial
		  Electronics, Control and Instrumentation},
  year		= {1994},
  volume	= {2},
  pages		= {1242--6},
  organization	= {Univ. der Bundeswehr Munchen, Neubiberg, Germany},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  bobrovskii96a,
  author	= {A. L. Bobrovskii and V. V. Efirnov},
  title		= {Intelligent information systems with parallel data
		  processing},
  journal	= {Elektronnoe Modelirovanie},
  year		= {1996},
  volume	= {18},
  number	= {1},
  pages		= {24--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bock96a,
  author	= {Hans H. Bock},
  title		= {Simultaneous Visualization and Classification Methods as
		  an Alternative to {K}ohonen's Neural Networks},
  booktitle	= {Classification and Multivariate Graphics: Models, Software
		  and Applications},
  year		= 1996,
  editor	= {Hans-Joachim Mucha and Hans-Hermann Bock},
  number	= {Report No. 10},
  series	= {Weierstrass-Institut f{\"u}r Angewandte Analysis und
		  Stochastik},
  address	= {Berlin},
  pages		= {15--23},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  boda94a,
  author	= {P{\'{e}}ter Boda and Gy{\"{o}}rgy G. Vass},
  title		= {Neural Networks and Fuzzy Systems in Speech Processing:
		  Applications to Voiced/Unvoiced Decision},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conf. Proc. of Finnish Artificial Intelligence Society},
  pages		= {47--54},
  booktitle	= {Proc. Conf. on Artificial Intelligence Res. in Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {application, pattern recognition, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  boda95a,
  author	= {Boda, P. P. },
  title		= {Robust voiced/unvoiced speech classification with
		  \mbox{self-organizing} maps},
  booktitle	= {1995 IEEE Symposium on Circuits and Systems},
  year		= {1995},
  volume	= {2},
  pages		= {1516--19},
  organization	= {Acoust. Lab. , Helsinki Univ. of Technol. , Espoo,
		  Finland},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  boddy94a,
  author	= {L. Boddy and A. M. Gimblett and C. W. Morris and J. E. M.
		  Mordue},
  title		= {Neural network analysis of fungal spore morphometric data
		  for identification of species in the genus Pestalotiopsis},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks.},
  publisher	= {ASME},
  volume	= {4},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {605--12},
  dbinsdate	= {oldtimer}
}

@Article{	  bode01a,
  author	= {Bode, M. and Freyd, O. and Fischer, J. and
		  Niedernostheide, F.-J. and Schulze, H.-J.},
  title		= {Hybrid hardware for a highly parallel search in the
		  context of learning classifiers},
  journal	= {Artificial Intelligence},
  year		= {2001},
  volume	= {130},
  number	= {1},
  month		= {July 2001},
  pages		= {75--84},
  organization	= {Westfalische Wilhelms-Univ. Munster, Institut fur
		  Angewandte Physik},
  publisher	= {},
  address	= {},
  abstract	= {Based on a comparison of input data with a set of
		  prototypes, classifier systems identify the most
		  appropriate representative for a given sample pattern. One
		  remarkable classifier is Kohonen's Self-Organizing Map and
		  the related learning vector quantizer, as these algorithms
		  are highly parallel. For real-time applications the
		  classifier search may be one of the time critical
		  processes. We discuss specialized hardware being able to
		  execute such a search in a fully parallel manner. Also the
		  learning and updating of prototypes is performed in
		  parallel controlled by a propagating front. Finally, we
		  present experimental results concerning an unsupervised
		  learning vector quantizer (LVQ) and a self-organizing map
		  (SOM) obtained from our thyristor-based analog-digital
		  hybrid system. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  bodruzzaman95a,
  author	= {M. Bodruzzaman and S. Zein-Sabatto and O. Omitowoju and M.
		  Malkani},
  title		= {Electromyographic {(EMG)} Signal Decomposition using
		  {K}ohonen Neural Net and Wavelet Network},
  volume	= {II},
  pages		= {854--862},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@Article{	  boehm94a,
  author	= {Boehm, K. and Broll, W. and Sokolewicz, M. },
  title		= {Dynamic gesture recognition using neural networks; a
		  fundament for advanced interaction construction},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1994},
  volume	= {2177},
  pages		= {336--46},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  boehme94a,
  author	= {H. -J. Boehme and U. -D. Braumann and H. -M. Gross},
  title		= {A Neural Network Architecture for Sensory Controlled
		  Internal Simulation},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1189--1192},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, knowledge database, internal state},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bogdan01a,
  author	= {Bogdan, M. and Rosenstiel, W.},
  title		= {Detection of cluster in self-organizing maps for
		  controlling a prostheses using nerve signals},
  booktitle	= {9th European Symposium on Artificial Neural Networks.
		  ESANN'2001. Proceedings. D-Facto, Evere, Belgium},
  year		= {2001},
  volume	= {},
  pages		= {131--6},
  abstract	= {In order to control a prostheses by means of biological
		  nerve signals, a self-organizing map (SOM) was used to
		  classify nerve signals recorded by a regeneration type
		  neurosensor. The trained SOM contains information about the
		  relation between the recorded nerve signal and the winning
		  neuron of the SOM. Classes of nerve signals fired by
		  defined axons can be found in a cluster on the SOM. In
		  order to control a prostheses, the clusters on the SOM must
		  be assigned to an action of the prostheses. Since medical
		  staff are usually not experienced at identifying the
		  clusters within an SOM, we have developed Clusot, an
		  algorithm that automatically defines clusters within SOMs.
		  After a short introduction to the project, we present the
		  signal processing of the project. We focus on the automatic
		  detection of clusters within a trained SOM using Clusot.
		  Clusot is explained within the context of the project in
		  question.},
  dbinsdate	= {2002/1}
}

@Article{	  bogdan92a,
  author	= {Bogdan, A. and Meadows, H. E. },
  title		= {{K}ohonen neural network for image coding based on
		  iteration transformation theory},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1766},
  pages		= {425--36},
  annote	= {A conference paper in journal},
  abstract	= {Iterated transformation theory (ITT), also known as
		  fractal coding, is a relatively new block compression
		  method which removes redundancies between different scale
		  representations of the uncompressed signal. In ITT coding
		  we are looking for a piecewise continuous mapping from the
		  space of all images with the same support onto itself which
		  has a close approximation of the desired image as a unique
		  fixed point. The mapping is then the code for the image,
		  and for decoding we iterate the mapping on any initial
		  image, orders of magnitude faster than encoding. We have
		  reduced the computational load of finding the piecewise
		  continuous transformation by using a self-organizing
		  feature map (SOFM) artificial neural network which finds
		  similar features in different resolution representations of
		  the image. The patterns are mapped onto a two-dimensional
		  array of formal neurons forming a code book similar to
		  vector quantization (VQ) coding. We use the (SOFM) ordering
		  properties by searching for mapping not only to the best
		  feature match neuron but also to its neighbors in the
		  network. In this paper we describe the ITT-SOFM algorithm
		  and its software implementation with application to image
		  coding of still gray images. Computer simulations show
		  compression results comparable to or better than
		  state-of-the-art VQ coders, and computational complexity
		  better than most of the well known clustering algorithms.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  boggess94a,
  author	= {Boggess, J. E. , III and Nation, P. B. and Harmon, M. E.
		  },
  title		= {Compression of color information in digitized images using
		  an artificial neural network},
  booktitle	= {Proceedings of the IEEE 1994 National Aerospace and
		  Electronics Conference NAECON 1994},
  year		= {1994},
  volume	= {2},
  pages		= {772--8},
  organization	= {Dept. of Comput. Sci. , Mississippi State Univ. , MS,
		  USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  boggess94b,
  author	= {Boggess, J. E. , III and Nation, P. B. and Harmon, M. E.
		  },
  title		= {Using artificial neural networks for data compression of
		  color information in digitized images},
  booktitle	= {Proceedings of the 32nd Annual Southeast Conference},
  year		= {1994},
  editor	= {Cordes, D. W. and Vrbsky, V. },
  pages		= {298--304},
  organization	= {Dept. of Comput. Sci. , Mississippi State Univ. , MS,
		  USA},
  publisher	= {ACM},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bohn98a,
  author	= {ChristianA. Bohn},
  title		= {{K}ohonen Feature Mapping through Graphics Hardware},
  booktitle	= {Proc. JCIS'98},
  publisher	= {Association for Intelligent Machinery, Inc},
  year		= 1998,
  editor	= {Paul P. Wang},
  volume	= {II},
  pages		= {64--67},
  abstract	= {This work describes the utilization of the inherent
		  parallelism of commonly available hardware graphics
		  accelerators for the realization of the Kohonen feature
		  map. The result is an essential reduction of computing time
		  compared to standard software implementations.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bojer01a,
  author	= {Bojer, T. and Hammer, B. and Schunk, D. and Tluk von
		  Toschanowitz K.},
  title		= {Relevance determination in learning vector quantization},
  booktitle	= {9th European Symposium on Artificial Neural Networks.
		  ESANN'2001. Proceedings. D-Facto, Evere, Belgium},
  year		= {2001},
  volume	= {},
  pages		= {271--6},
  abstract	= {We propose a method to automatically determine the
		  relevance of the input dimensions of a learning vector
		  quantization (LVQ) architecture during training. The method
		  is based on Hebbian learning and introduces weighting
		  factors of the input dimensions which are automatically
		  adapted to the specific problem. The benefits are twofold.
		  On the one hand, the incorporation of relevance factors in
		  the LVQ architecture increases the overall performance of
		  the classification and adapts the metric to the specific
		  data used for training. On the other hand, the method
		  induces a pruning algorithm, i.e. an automatic detection of
		  the input dimensions which do not contribute to the overall
		  classifier. Hence we obtain a possibly more efficient
		  classification and we gain insight to the role of the data
		  dimensions.},
  dbinsdate	= {2002/1}
}

@InCollection{	  bologna94a,
  author	= {G. Bologna and C. Pellegrini},
  title		= {Internal knowledge analysis in a feed-forward neural
		  network},
  booktitle	= {Neural Networks in Biomedicine. Proceedings of the
		  Advanced School of the Italian Biomedical Physics
		  Association},
  publisher	= {World Scientific},
  year		= {1994},
  editor	= {F. Masulli and P. G. Morasso and A. Schenone},
  address	= {Singapore},
  pages		= {37--56},
  dbinsdate	= {oldtimer}
}

@Article{	  bonabeau02a,
  author	= {Bonabeau, Eric},
  title		= {Graph multidimensional scaling with self-organizing maps},
  journal	= {Information Sciences},
  year		= {2002},
  volume	= {143},
  number	= {1--4},
  month		= {June },
  pages		= {159--180},
  organization	= {Icosystem Corporation},
  publisher	= {Elsevier Science Inc.},
  address	= {},
  abstract	= {Self-organizing maps (SOM) are unsupervised, competitive
		  neural networks used to project high-dimensional data onto
		  a low-dimensional space. In this paper it is shown that SOM
		  can be used to perform multidimensional scaling (MDS) on
		  graphs. The SOM-based approach is applied to two families
		  of random graphs and three real-world networks. },
  dbinsdate	= {2002/1}
}

@Article{	  bonabeau98a,
  author	= {E. Bonabeau and F. Henaux},
  title		= {\mbox{Self-organizing} maps for drawing large graphs},
  journal	= {Information Processing Letters},
  year		= {1998},
  volume	= {67},
  number	= {4},
  pages		= {177--84},
  dbinsdate	= {oldtimer}
}

@Article{	  bonabeau98b,
  author	= {E. Bonabeau and F. Henaux},
  title		= {Monte {C}arlo Partitioning of Graphs with a Self
		  Organizing Map},
  journal	= {International Journal of Modern Physics C},
  volume	= {9},
  pages		= {1107--1119},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  bonnet95a,
  author	= {N. Bonnet},
  title		= {Preliminary Investication of two Methods for the Automatic
		  Handling of Multivariate Maps in Microanalysis},
  journal	= {Ultramicroscopy},
  year		= {1995},
  volume	= {57},
  number	= {1},
  pages		= {17--27},
  publisher	= {Elsevier},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bors94a,
  author	= {Adrian G. Bor{\c{s}} and I. Pitas},
  title		= {Robust Estimation for Radial Basis Functions},
  booktitle	= {Proc. NNSP'94, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1994},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {105--114},
  annote	= {application, modification},
  dbinsdate	= {oldtimer}
}

@Article{	  bors96a,
  author	= {A. G. Bors and I. Pitas},
  title		= {Median radial basis function neural network},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1996},
  volume	= {7},
  number	= {6},
  pages		= {1351--64},
  dbinsdate	= {oldtimer}
}

@Article{	  bortolan02a,
  author	= {Bortolan, G. and Pedrycz, W.},
  title		= {An interactive framework for an analysis of {ECG}
		  signals},
  journal	= {ARTIFICIAL INTELLIGENCE IN MEDICINE},
  year		= {2002},
  volume	= {24},
  number	= {2},
  month		= {FEB},
  pages		= {109--132},
  abstract	= {In this study, we introduce and discuss a development of a
		  highly interactive and user-friendly environment for an ECG
		  signal analysis. The underlying neural architecture being a
		  crux of this environment comes in the form of a
		  self-organizing map. This map helps discover a structure in
		  a set of ECG patterns and visualize a topology of the data.
		  The role of the designer is to choose from some already
		  visualized regions of the self-organizing map characterized
		  by a significant level of data homogeneity and substantial
		  difference from other regions. In the sequel, the regions
		  are described by means of information granules-fuzzy sets
		  that are essential in the characterization of the main
		  relationships existing in the ECG data. The study
		  introduces an original method of constructing membership
		  functions that incorporates class membership as an
		  important factor affecting changes in membership grades.
		  The study includes a comprehensive descriptive modeling of
		  highly dimensional ECG data.},
  dbinsdate	= {2002/1}
}

@Book{		  bose96a,
  author	= {Bose, S. and Murthy, C.A},
  title		= {New {LVQ} Model. Technical rept.},
  year		= {1996},
  abstract	= {A new LVQ model has been proposed here. An exponential
		  membership function has been considered in this regard. The
		  performance of the new model in relation to other existing
		  models has been studied experimentally with the help of an
		  artificial data set as well as IRIS data. Finally the
		  proposed algorithm is applied on a satellite image data.
		  The proposed model has been found to provide satisfactory
		  results with all these data sets.},
  dbinsdate	= {oldtimer}
}

@Article{	  bottazzi90a,
  author	= {C. Bottazzi},
  title		= {Neuro-computers},
  journal	= {Informazione Elettronica},
  year		= {1990},
  volume	= {18},
  number	= {10},
  pages		= {21--27},
  month		= {October},
  note		= {(in Italian)},
  x		= {. . . author discusses progress in the technology of the
		  application of neutral networks and . . . with special
		  reference to work being done by T. Kohonen . . . },
  dbinsdate	= {oldtimer}
}

@InCollection{	  bouchired98a,
  author	= {S. Bouchired and M. Ibnkahla and D. Roviras and F.
		  Castanie},
  title		= {Equalization of satellite mobile communication channels
		  using combined \mbox{self-organizing} maps and {RBF}
		  networks},
  booktitle	= {Proceedings of the 1998 IEEE International Conference on
		  Acoustics, Speech and Signal Processing, ICASSP '98},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {6},
  address	= {New York, NY, USA},
  pages		= {3377--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bouton91a,
  author	= {C. Bouton and M. Cottrell and J. -C. Fort and G.
		  Pag{\`{e}}s},
  title		= {Self-organization and convergence of the {K}ohonen
		  algorithm},
  booktitle	= {Probabilit{\'{e}}s Num{\'{e}}riques},
  publisher	= {INRIA},
  address	= {Paris, France},
  year		= {1991},
  editor	= {N. Bouleau and D. Talay},
  chapter	= {V. 2},
  pages		= {163--180},
  dbinsdate	= {oldtimer}
}

@TechReport{	  bouton92a,
  author	= {C. Bouton and G. Pag{\`{e}}s},
  title		= {Self-organization and convergence of the
		  \mbox{\mbox{one-dimensional}} {K}ohonen algorithm with non
		  uniformly distributed stimuli (version 2)},
  institution	= {Laboratoire de Probabilit{\'{e}}s, Universit{\'{e}} Paris
		  VI},
  year		= {1992},
  address	= {Paris, France},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@TechReport{	  bouton92b,
  author	= {C. Bouton and G. Pag{\`{e}}s},
  title		= {Convergence in distribution of the
		  \mbox{\mbox{one-dimensional}} {K}ohonen algorithms when the
		  stimuli are not uniform},
  institution	= {Laboratoire de Probabilit{\'{e}}s, Universit{\'{e}} Paris
		  VI},
  year		= {1992},
  address	= {France},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bouton92c,
  author	= {Catherine Bouton and Gilles Pag{\`e}s},
  title		= {Auto-Organisation de l'Algorithme de {K}ohonen en
		  Dimension~1},
  year		= {1992},
  booktitle	= {Proc. Workshop `Aspects Theoriques des Reseaux de
		  Neurones'},
  editor	= {M. Cottrell and M. Chaleyat-Maurel},
  publisher	= {Universit\'e Paris~{I}},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bouton92d,
  author	= {Catherine Bouton and Gilles Pag{\`e}s},
  title		= {Convergence p. s. et en Loi de l'Algorithme de {K}ohonen
		  en Dimension~1},
  year		= {1992},
  booktitle	= {Proc. of the workshop `Aspects Theoriques des Reseaux de
		  Neurones'},
  editor	= {M. Cottrell and M. Chaleyat-Maurel},
  publisher	= {Universit\'e Paris~{I}},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@Article{	  bouton93a,
  author	= {Catherine Bouton and Gilles Pag{\`{e}}s},
  title		= {Self-Organization and a. s. convergence of the
		  \mbox{\mbox{one-dimensional}} {K}ohonen algorithm with
		  non-uniformly distributed stimuli},
  journal	= {Stochastic Processes and Their Applications},
  year		= {1993},
  volume	= {47},
  pages		= {249--274},
  dbinsdate	= {oldtimer}
}

@Article{	  bouton94a,
  author	= {C. Bouton and G. Pag\`es},
  title		= {Convergence in distribution of the
		  \mbox{\mbox{one-dimensional}} {{K}ohonen} algorithms when
		  the stimuli are not uniform},
  journal	= {Advances in Applied Probability},
  volume	= {26},
  pages		= {80--103},
  year		= {1994},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bovbel00a,
  author	= {Bovbel, E. E. and Kheidorov, I. E. and Kotlyar, M. E.},
  title		= {Speaker identification using autoregressive hidden Markov
		  models and adaptive vector quantisation},
  booktitle	= {Text, Speech and Dialogue. Third International Workshop,
		  TSD 2000. Proceedings (Lecture Notes in Artificial
		  Intelligence Vol.1902). Springer-Verlag, Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {207--10},
  abstract	= {Wide-frequency spectral analysis, autoregressive hidden
		  Markov models (ARHMM) and self-organising neural networks
		  (SOM) have been used for high accuracy speaker features
		  modelling. The initial ARHMM parameters estimation based on
		  Kalman filter is proposed. The five-keyword speaker
		  identification system has been built and tested. The
		  experiments show that this approach provides high accuracy
		  of speaker identification even if the same words are
		  pronounced by different speakers.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  bowles99a,
  author	= {Bowles, Jeffrey and Clamons, Dean and Gillis, David and
		  Palmadesso, Peter and Antoniades, John and Baumback, Mark
		  and Daniel, Mark and Grossmann, John and Haas, Daniel and
		  Skibo, Jeffrey},
  title		= {New results from the ORASIS/NEMO compression algorithm},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3753},
  pages		= {226--234},
  abstract	= {We present results from an improved ORASIS (Optical
		  Real-time Adaptive Spectral Identification System)
		  hyperspectral-data compression-algorithm that is being
		  implemented on the Naval EarthMap Observer (NEMO)
		  satellite. The algorithm is shown to produce results that
		  are statistically improved from previous findings. To
		  augment the statistical testing, the re-inflated data are
		  run through analysis programs such as unsupervised
		  classification. ORASIS compression is a series of
		  algorithms. The first algorithm, the exemplar selector
		  process (ESP), is a variation of Learned Vector
		  Quantization (LVQ) that builds up a relatively small set of
		  spectra to represent the full data set. Subsequent
		  algorithms find approximate endmembers for the exemplar set
		  and project the set into the space defined by the
		  endmembers. Both the ESP and the projection process
		  contribute to the compression of the data. The obtainable
		  compression ratios vary with scene content and other
		  factors but ratios between 10:1 and 30:1 are possible. The
		  compressed data format is designed to allow direct access
		  to individual pieces of the data without reinflation of the
		  entire data set. Details of the hardware implementation of
		  the Imagery On-Board Processor (IOBP) of NEMO is discussed,
		  as well as the use of the compressed data on the ground.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  boznar97a,
  author	= {M. Boznar},
  title		= {Pattern selection strategies for a neural network-based
		  short term air pollution prediction model},
  booktitle	= {Proceedings. Intelligent Information Systems. IIS'97},
  publisher	= {IEEE Computer Society},
  year		= {1997},
  editor	= {H. Adeli},
  address	= {Los Alamitos, CA, USA},
  pages		= {340--4},
  dbinsdate	= {oldtimer}
}

@InCollection{	  braccini97a,
  author	= {G. Braccini and L. Edenbrandt and M. Lagerholm and C.
		  Peterson and O. Rauer and R. Rittner and L. Sornmo},
  title		= {\mbox{Self-organizing} maps and {H}ermite functions for
		  classification of {ECG} complexes},
  booktitle	= {Computers in Cardiology 1997},
  publisher	= {IEEE},
  year		= {1997},
  address	= {New York, NY, USA},
  pages		= {425--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bradburn89a,
  author	= {David S. Bradburn},
  title		= {Reducing Transmission Error Effects Using a
		  Self-Organizing Network},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  pages		= {531--537},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  bradburn91a,
  author	= {Bradburn, D. S.},
  title		= {Self-organization of non-numeric data sets.},
  journal	= {International Joint Conference on Neural Networks},
  year		= {1991},
  number	= {},
  volume	= {},
  pages		= {37--42},
  abstract	= {A topology-preserving map can be constructed by simulated
		  annealing. This map shares some useful properties with
		  those generated by classical self-organization, but it does
		  not require that the input data have numeric values. An
		  application to mail routing is presented, showing that
		  self-organization can be accomplished given only a local
		  adjacency list. A string-matching application shows the
		  extension of the algorithm to cases where a scalar distance
		  between input points is available. The map in the latter
		  application, like a Kohonen map, exhibits local smoothness
		  permitting shortcuts to the nearest-neighbor search.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  branston99a,
  author	= {Branston, N. M. and {El Deredy}, W.},
  title		= {Capturing movement responses of single cells in the human
		  basal ganglia using hidden {M}arkov models},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  year		= {1999},
  publisher	= {IEE},
  address	= {London, UK},
  volume	= {1},
  pages		= {395--400},
  abstract	= {We used hidden {M}arkov models (HMMs) to characterise the
		  firing activity of single neurons in the basal ganglia
		  recorded during voluntary limb movements made by patients
		  undergoing surgery for Parkinson's disease. The parameters
		  of these HMMs were then used as input patterns for training
		  the generative topographic mapping (GTM) algorithm. Data
		  were strongly clustered in the GTM latent space, and
		  separated both by brain region and by movement type, when
		  as few as two states were modelled in the HMM. Our results
		  support the hypotheses that (1) cells in these brain
		  regions are not involved in the preparation or execution of
		  a single type of movement but rather participate to some
		  extent in many different movements, and (2) neural networks
		  in these regions operate as systems with distinct and
		  relatively stable configurations of activity.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  brauer89a,
  author	= {P. Brauer and P. Knagenhjelm},
  title		= {Infrastructure in {K}ohonen maps},
  booktitle	= {Proc. ICASSP-89, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1989},
  pages		= {647--650},
  address	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  brauer91a,
  author	= {P. Brauer and P. Hedelin and D. Huber and P. Knagenhjelm},
  title		= {Probability based optimization for network classifiers},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {I},
  pages		= {133--136},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {. . . The authors propose a probability based optimization
		  (PBO) algorithm . . . The performance of this algorithm has
		  been systematically evaluated using a MAP (maximum a
		  posteriori) estimator and a Kohonen (1988) map. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  brause90a,
  author	= {R. Brause},
  title		= {Optimal performance and storage requirements of
		  neighbourhood-conserving mappings for robot control},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conference},
  year		= {1990},
  volume	= {I},
  pages		= {221--224},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  brause90b,
  author	= {R. Brause},
  title		= {Optimal information distribution and performance in
		  neighbourhood-conserving maps for robot control},
  booktitle	= {Proc. 2nd Int. IEEE Conference on Tools for Artificial
		  Intelligence},
  year		= {1990},
  pages		= {451--456},
  organization	= {IEEE},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  brause92a,
  author	= {R. Brause},
  title		= {Optimal Information Distribution and Performance in
		  Neighbourhood-Conserving Maps for Robot Control},
  journal	= {Int. J. Computers and Artificial Intelligence},
  year		= {1992},
  volume	= {11},
  number	= {2},
  pages		= {173--199},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  brause94a,
  author	= {R{\"{u}}diger W. Brause},
  title		= {An Approximation Network with Maximal Transinformation},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {701--704},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, approximation, modification},
  dbinsdate	= {oldtimer}
}

@Article{	  brause96a,
  author	= {Brause, R. W. },
  title		= {Sensor encoding using lateral inhibited self-organized
		  cellular neural networks},
  journal	= {Neural Networks},
  year		= {1996},
  volume	= {9},
  number	= {1},
  pages		= {99--120},
  abstract	= {The paper focuses on the division of the sensor field into
		  subsets of sensor events and proposes the linear
		  transformation with the smallest achievable error for
		  reproduction: the transform coding approach using the
		  principal component analysis (PCA). For the implementation
		  of the PCA, this paper introduces a new symmetrical,
		  lateral inhibited neural network model, proposes an
		  objective function for it and deduces the corresponding
		  learning rules. The necessary conditions for the learning
		  rate and the inhibition parameter for balancing the
		  crosscorrelations vs the autocorrelations are computed. The
		  simulation reveals that an increasing inhibition can speed
		  up the convergence process in the beginning slightly. In
		  the remaining part of the paper, the application of the
		  network in picture encoding is discussed. Here, the use of
		  non-completely connected networks for the self-organized
		  formation of templates in cellular neural networks is
		  shown. It turns out that the self-organizing Kohonen map is
		  just the non-linear, first order approximation of a general
		  self-organizing scheme. Hereby, the classical transform
		  picture coding is changed to a parallel, local model of
		  linear transformation by locally changing sets of
		  self-organized eigenvector projections with overlapping
		  input receptive fields. This approach favours an effective,
		  cheap implementation of sensor encoding directly on the
		  sensor chip.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  breazu99a,
  author	= {Breazu, M. and Toderean, G. and Volovici, D. and Iridon,
		  M.},
  title		= {Speeding up fractal image compression by working in
		  {K}arhunen-{L}oeve transform space},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {2694--7},
  abstract	= {The main weakness of fractal image compression is its long
		  encoding time needed to search the entire domain pool to
		  find the best domain-range mapping. To solve the problem,
		  some solutions were proposed but most of them do not employ
		  neural networks (only the use of Kohonen {SOM} for
		  clustering was reported). The paper proposes a new method
		  based on {K}arhunen-{L}oeve transform (PCA networks), which
		  attempts to use neural networks' well-known adaptability in
		  order to find a good feature vector for a block.
		  Performance regarding network generality, quantization of
		  the transform coefficients, comparison with DCT and kd-tree
		  search, was explored. Results prove that the proposed
		  method slightly outperforms state-of-the-art methods.},
  dbinsdate	= {oldtimer}
}

@Book{		  breidegrad00a,
  author	= {Björn Breidegard},
  title		= {En datorexekverbar modell för lärande},
  publisher	= {Licentiatuppsats Certec, LTH},
  year		= {2000},
  number	= {1},
  dbinsdate	= {2002/1}
}

@Article{	  breining00a,
  author	= {Breining, Christina},
  title		= {State detection for hands-free telephone sets by means of
		  fuzzy {LVQ} and {SOM}},
  journal	= {Signal Processing},
  year		= {2000},
  volume	= {80},
  number	= {7},
  month		= {Jul},
  pages		= {1361--1372},
  organization	= {Technische Universitaet Darmstadt},
  publisher	= {Elsevier Science Publishers B.V.},
  address	= {Amsterdam},
  abstract	= {The step gain of adaptive algorithms for acoustic echo
		  cancellation should vary with time in dependence of the
		  far-end signal, the local signal, and the adaptation
		  quality. It can be described as state-dependent, which is
		  also the case for the control parameters of other
		  algorithms in a hands-free telephone set, such as adaptive
		  loss control or noise reduction. Most of the known state
		  detection methods can only distinguish between subsets of
		  the states, and are unreliable for certain state
		  transitions, so that they should be combined for improved
		  reliability of the state detection. In this contribution,
		  state detection is performed by a fuzzy learning vector
		  quantization/self-organizing map approach to combine
		  several state detectors. The step gain is inferred from
		  state information using fuzzy logic concepts. Finally,
		  adaptation results are compared to the best conventional
		  step-gain control method used in the complete structure.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  breton95a,
  author	= {S. Breton and J. P. Urban and H. Kihl},
  title		= {A Recursive Sensorimotor Map-based Algorithm for the
		  Learning of Saccades},
  volume	= {II},
  pages		= {406--409},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InCollection{	  briscoe97a,
  author	= {G. Briscoe and T. Caelli},
  title		= {Learning temporal sequences in recurrent
		  \mbox{self-organising} neural nets},
  booktitle	= {Advanced Topics in Artificial Intelligence. 10th
		  Australian Joint Conference on Artificial Intelligence,
		  AI'97. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {A. Sattar},
  address	= {Berlin, Germany},
  pages		= {427--35},
  dbinsdate	= {oldtimer}
}

@InCollection{	  brockmann97a,
  author	= {D. Brockmann and H. -U. Bauer and M. Riesenhuber and T.
		  Geisel},
  title		= {{SOM}-model for the development of oriented receptive
		  fields and orientation maps from non-oriented ON-center
		  OFF-center inputs},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {207--12},
  dbinsdate	= {oldtimer}
}

@Article{	  brosse01a,
  author	= {Brosse, S. and Giraudel, J. L. and Lek, S.},
  title		= {Utilisation of non-supervised neural networks and
		  principal component analysis to study fish assemblages},
  journal	= {ECOLOGICAL MODELLING},
  year		= {2001},
  volume	= {146},
  number	= {1--3},
  month		= {DEC 1},
  pages		= {159--166},
  abstract	= {Kohonen self-organizing maps (SOM) belong to the
		  non-supervised artificial neural network modelling methods.
		  It typically displays a high dimensional data set in a
		  lower dimensional space. In this way, that method can be
		  considered as a non- linear surrogate to the principal
		  component analysis (PCA). In order to test the efficiency
		  of SOM on complex ecological data gathered in the natural
		  environment, we made a comparison between PCA and SOM
		  capabilities to analyse the spatial occupancy of several
		  European freshwater fish species in the littoral zone of a
		  large French lake. The same data matrix consisting of 710
		  samples and 15 species was analysed using PCA and SOM. Both
		  methods provided insights on the major trends in fish
		  spatial occupancy. However, a more detailed analysis showed
		  that only SOM was able to reliably visualise the entire
		  fish assemblage in a two dimensional space (i.e. both
		  dominant and scarce species). On the contrary PCA provided
		  irrelevant ecological information for some species. These
		  drawbacks were afforded to data heterogeneity, scarce
		  species being poorly represented on the PCA plane. These
		  results led us to conclude that SOM constitute a more
		  reliable data representation method than PCA when complex
		  ecological data sets are used. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  brozek98a,
  author	= {Brozek, J. and Kulczycki, J. and Szpyra, W. and Tylek,
		  W.},
  title		= {Designing the m-loops electric power distribution networks
		  using artificial neural networks},
  booktitle	= {Engineering Benefits from Neural Networks. Proceedings of
		  the International Conference EANN '98},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1998},
  volume	= {},
  pages		= {261--4},
  abstract	= {The use of artificial neural networks for designing m-loop
		  electric power distribution networks is presented. The task
		  is to connect transformer stations (with given locations
		  and loads) into m loops (m Hamilton cycles) in order to
		  minimize a given aim function, subject to technical
		  constraints. The problem of designing the described network
		  is similar to the travelling salesman problem and is a
		  NP-hard problem. The described task can be solved in three
		  stages: dividing a transformer station set into m subsets
		  using a competitive neural network with winner-takes-all
		  (WTA) learning rule; connecting each subset element using
		  the Kohonen self-organizing map (SOM), after connecting
		  every subset to the main feeding point (MFP); improving the
		  obtained result. As the optimization criterion the minimal
		  annual cost of the electric power network (i.e., fixed
		  cost+variable cost+cost of undelivered energy) was chosen.
		  The definition of the problem as well as a short algorithm
		  and some results of tests performed on m-loop electric
		  power network models are presented.},
  dbinsdate	= {oldtimer}
}

@InBook{	  bruckner02a,
  author	= {Bernd Br{\"u}ckner and Thomas Wesarg},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Modeling Speech Processing and Recognition in the Auditory
		  System Using the Multilevel Hypermap Architecture},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {145--64},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  bruckner92a,
  author	= {B. Br{\"{u}}ckner and M. Franz and A. Richter},
  title		= {A Modified Hypermap Architecture for Classification of
		  Biological Signals},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1167--1170},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bruckner93a,
  author	= {B. Br{\"{u}}ckner and W. Zander},
  title		= {Classification of Speech using a Modified {H}ypermap
		  Architecture},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {III},
  pages		= {75--78},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bruckner93b,
  author	= {B. Br{\"{u}}ckner and W. Zander},
  title		= {Neurobiological Modelling and Structured Neural Networks},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {43--46},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bruckner95a,
  author	= {B. Br{\"{u}}ckenr and T. Wesarg and C. Blumenstein},
  title		= {Improvements of the modified Hypermap Architecture for
		  Speech Recognition},
  volume	= {V},
  pages		= {2891--2895},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bruckner97a,
  author	= {B. Br{\"u}ckner},
  title		= {Improvements in the analysis of structured data with the
		  Multilevel Hypermap Architecture},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {1},
  address	= {Singapore},
  pages		= {342--345},
  dbinsdate	= {oldtimer}
}

@Article{	  bruske95a,
  author	= {Bruske, J. and Sommer, G. },
  title		= {Dynamic cell structure learns perfectly topology
		  preserving map},
  journal	= {Neural Computation},
  year		= {1995},
  volume	= {7},
  number	= {4},
  pages		= {845--65},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bruske95b,
  author	= {Bruske, J. and Sommer, G. },
  title		= {Dynamic cell structures},
  booktitle	= {Advances in Neural Information Processing Systems 7},
  year		= {1995},
  editor	= {Tesauro, G. and Touretzky, D. and Leen, T. },
  pages		= {497--504},
  organization	= {Dept. of Cognitive Syst. , Kiel Univ. , Germany},
  publisher	= {MIT Press},
  address	= {Cambridge, MA, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  bruske96a,
  author	= {J. Bruske and I. Ahrns and G. Sommer},
  title		= {Practicing Q-learning},
  booktitle	= {4th European Symposium on Artificial Neural Networks,
		  ESANN '96. Proceedings},
  publisher	= {D Facto},
  year		= {1996},
  editor	= {M. Verleysen},
  address	= {Brussels, Belgium},
  pages		= {25--30},
  dbinsdate	= {oldtimer}
}

@Article{	  bruske97a,
  author	= {J. Bruske and M. Hansen and L. Riehn and G. Sommer},
  title		= {Biologically inspired calibration-free adaptive saccade
		  control of a binocular camera-head},
  journal	= {Biological Cybernetics},
  year		= {1997},
  volume	= {77},
  number	= {6},
  pages		= {433--46},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bryant93a,
  author	= {Bryant, B. D. and Gowdy, J. N. },
  title		= {Speaker-independent voiced-stop-consonant recognition
		  using a block-windowed neural network architecture},
  booktitle	= {Proceedings SSST '93 The Twenty-Fifth Southeastern
		  Symposium on System Theory},
  year		= {1993},
  pages		= {400--4},
  organization	= {Dept. of Electr. \& Comput. Eng. , Clemson Univ. , SC,
		  USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  brzakovic92a,
  author	= {Brzakovic, D. and Wang, D. and Beck, H. },
  title		= {Modular neural network architecture for flaw
		  classification},
  booktitle	= {Southcon /92. Conference Record},
  year		= {1992},
  pages		= {315--19},
  organization	= {Dept. of Electr. \& Comput. Eng. , Tennessee Univ. ,
		  Knoxville, TN, USA},
  publisher	= {Electron. Conventions Manage},
  address	= {Los Angeles, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  buckle01a,
  author	= {Buckle, M. and Strey, A.},
  title		= {Specification and simulation of neural networks using
		  epsilo{NN}},
  journal	= {Neural-Network-World},
  year		= {2001},
  volume	= {11},
  pages		= {73--89},
  abstract	= {The language EpsiloNN allows a high-level specification of
		  arbitrary neural network structures. It is especially
		  designed for the automatic generation of simulation code,
		  which can run efficiently on different parallel computer
		  architectures. In this paper some applications of EpsiloNN
		  are presented. First, the basic syntactical and semantical
		  aspects of the language are described briefly. Then the
		  EpsiloNN specifications of a popular multilayer perceptron
		  (MLP) and of a more complex hybrid LVQ/RBF neural network
		  architecture are presented. Further features of the
		  language are explained by example.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  budinich94a,
  author	= {Marco Budinich and John G. Taylor},
  title		= {On the Ordering Conditions for {S}elf-{O}rganizing
		  {M}aps},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {347--349},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {analysis, ordering considerations, multidimensional},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  budinich94b,
  author	= {Marco Budinich},
  title		= {A {S}elf-{O}rganizing Neural Network for the Traveling
		  Salesman Problem that is Competitive with Simulated
		  Annealing},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {358--361},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, TSP, optimization},
  dbinsdate	= {oldtimer}
}

@Article{	  budinich95a,
  author	= {Marco Budinich and John G. Taylor},
  journal	= {Neural Computation},
  title		= {On the Ordering Conditions for {S}elf-{O}rganizing
		  {M}aps},
  pages		= {284--289},
  volume	= {7},
  number	= {2},
  year		= {1995},
  dbinsdate	= {oldtimer}
}

@Article{	  budinich95b,
  author	= {Marco Budinich},
  journal	= {Neural Computation},
  title		= {Sorting with {S}elf-{O}rganizing {M}aps},
  pages		= {1188--1190},
  volume	= {7},
  number	= {6},
  year		= {1995},
  dbinsdate	= {oldtimer}
}

@Article{	  budinich96a,
  author	= {Marco Budinich},
  journal	= {Neural Computation},
  title		= {A {S}elf-{O}rganizing Neural Network for the Traveling
		  Salesman Problem That is Competitive with Simulated
		  Annealing},
  pages		= {416--424},
  volume	= {8},
  number	= {2},
  year		= {1996},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  buessler00a,
  author	= {Buessler, J. L. and Urban, J. P.},
  title		= {Neurobiology suggests the design of modular architectures
		  for neural control},
  booktitle	= {IEEE International Conference on Intelligent Robots and
		  Systems},
  year		= {2000},
  editor	= {},
  volume	= {1},
  pages		= {64--69},
  organization	= {TROP Research Group, University of Mulhouse},
  publisher	= {},
  address	= {},
  abstract	= {The existence of modular structures in the biological
		  world strongly suggests that the training of this kind of
		  structures is actually feasible. It is a key indication for
		  the development of neural networks applications, especially
		  in the field of robotics. Indeed, a single network can only
		  efficiently treat problems with few independent variables;
		  the combining of several networks is necessary to address
		  more complex tasks. We investigate learning techniques and
		  show that using a particular form of architecture can ease
		  the training of a modular structure: a bi-directional
		  structure that allows combining several neural networks.
		  The approach is illustrated with Kohonen's self-organizing
		  maps for a robotic visual servoing task.},
  dbinsdate	= {2002/1}
}

@Article{	  buessler02a,
  author	= {Buessler, Jean-Luc and Urban, Jean-Philippe and Gresser,
		  Julien},
  title		= {Additive composition of supervised self-organizing maps},
  journal	= {Neural Processing Letters},
  year		= {2002},
  volume	= {15},
  number	= {1},
  month		= {February },
  pages		= {9--20},
  organization	= {TROP Research Group},
  publisher	= {},
  address	= {},
  abstract	= {The learning of complex relationships can be decomposed
		  into several neural networks. The modular organization is
		  determined by prior knowledge of the problem that permits
		  to split the processing into tasks of small dimensionality.
		  The sub-tasks can be implemented with neural networks,
		  although the learning examples cannot be used anymore to
		  supervise directly each of the networks. This article
		  addresses the problem of learning in a modular context,
		  developing in particular additive compositions. A simple
		  rule allows defining efficient training, and combining, for
		  example, several Supervised-SOM networks. This technique is
		  important because it introduces interesting generalizations
		  in many modular compositions, permitting data fusion or
		  sequential combinations of neural networks.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  buessler95a,
  author	= {J. L. Buessler and D. Kuhn and J. P. Urban},
  title		= {Learning Self-Organizing Maps Using Input-Output
		  Associations Applied to Robotics},
  volume	= {II},
  pages		= {384--388},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  buessler99a,
  author	= {Buessler, J. L. and Kara, R. and Wira, P. and Kihl, H. and
		  Urban, J. P.},
  title		= {Multiple \mbox{self-organizing} maps to facilitate the
		  learning of visuo-motor correlations},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and Cybernetics.
		  },
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {470--5},
  abstract	= {This paper presents an application of bi-directional
		  neural modularity: a chaining of several self-organizing
		  maps (SOM) is used to represent the motor and sensorial
		  position correlations of a robotic platform. Two active
		  cameras follow the movements of a robot manipulator in 3-D
		  space. The mapping of image positions and camera
		  orientations into arm angular joint positions can be
		  learned by a neural network. However, decomposing the
		  problem and using several neural networks turns out to be a
		  better way. In our approach, the neural modules do not need
		  to be adapted independently. Based on the principle of
		  bi-directionality, the modular architecture can be adapted
		  globally, using the sensor-motor data directly.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  buhmann89a,
  author	= {Joachim Buhmann and Robert Divko and Klaus Schulten},
  title		= {On Sparsely Coded Associative Memories},
  booktitle	= {Neural Networks from Models to Applications,
		  {N'EURO~'88}},
  pages		= {360--371},
  editor	= {L. Personnaz and G. Dreyfus},
  publisher	= {{EZIDET}},
  address	= {Paris},
  year		= 1989,
  dbinsdate	= {oldtimer}
}

@InProceedings{	  buhmann92a,
  author	= {J. Buhmann and H. K{\"u}hnel},
  title		= {Complexity optimized vector quantization: a neural network
		  approach},
  booktitle	= {Proc. DCC '92, Data Compression Conf. },
  year		= {1992},
  editor	= {J. A. Storer and M. Cohn},
  pages		= {12--21},
  organization	= {IEEE; NASA/CESDIS},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  x		= {The authors discuss a vector quantization strategy which
		  jointly optimizes distortion errors and complexity costs. .
		  . . Their approach establishes a unifying framework for
		  different quantization methods like . . . self-organizing
		  topological maps. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  buhmann92b,
  author	= {J. Buhmann and H. K{\"u}hnel},
  title		= {Unsupervised and Supervised Data Clustering with
		  Competitive Neural Networks},
  booktitle	= {Proc. IJCNN'92, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {796--801},
  year		= {1992},
  dbinsdate	= {oldtimer}
}

@Article{	  buhmann93a,
  author	= {J. Buhmann and H. K{\"u}hnel},
  journal	= {IEEE Trans. Information Theory},
  title		= {Vector Quantization with Complexity Costs},
  month		= {July},
  volume	= 39,
  number	= 4,
  year		= {1993},
  pages		= {1133--1145},
  dbinsdate	= {oldtimer}
}

@Article{	  buhmann93b,
  author	= {J. Buhmann and H. K{\"u}hnel},
  title		= {Complexity optimized data clustering by competitive neural
		  networks},
  journal	= {Neural Computation},
  year		= {1993},
  volume	= {5},
  number	= {1},
  pages		= {75--88},
  month		= {January},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  buhusi93a,
  author	= {Catalin V. Buhusi},
  title		= {Neural Learning in Automatic Fuzzy Systems Synthesis},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {786--789},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  buhusi93b,
  author	= {Buhusi, C. V. },
  title		= {Parallel implementation of \mbox{self-organizing} neural
		  networks},
  booktitle	= {Romanian Symposium on Computer Science. 9th Symposium,
		  ROSYCS'93. Proceedings},
  year		= {1993},
  editor	= {Felea, V. and Ciobanu, G. },
  pages		= {51--8},
  organization	= {Institute for Comput. Sci. , Acad. of Sci. , Lasi,
		  Romania},
  publisher	= {Univ. Al. I. Cuza},
  address	= {Iasi, Romania},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  bunke01b,
  author	= {Bunke, H. and Gunter, S. and Jiang, X.},
  title		= {Towards bridging the gap between statistical and
		  structural pattern recognition: two new concepts in graph
		  matching},
  booktitle	= {Advances in Pattern Recognition---ICAPR 2001. Second
		  International Conference. Proceedings (Lecture Notes in
		  Computer Science Vol.2013). Springer-Verlag, Berlin,
		  Germany},
  year		= {2001},
  volume	= {},
  pages		= {1--11},
  abstract	= {Two concepts in structural pattern recognition are
		  discussed. The first, the median of a set of graphs, can be
		  used to characterize a set of graphs by just a single
		  prototype. Such a characterization is needed in various
		  tasks, for example, in clustering. The second concept is
		  the weighted mean of a pair of graphs. It can be used to
		  synthesize a graph that has a specified degree of
		  similarity, or distance, to each of a pair of given graphs.
		  Such an operation is needed in many machine-learning tasks.
		  It is argued that with these new concepts various
		  well-established techniques from statistical pattern
		  recognition become applicable in the structural domain,
		  particularly to graph representations. Specific examples
		  include k-means clustering, vector quantization, and
		  Kohonen maps.},
  dbinsdate	= {2002/1}
}

@Article{	  burel91a,
  author	= {G. Burel and I. Pottier},
  title		= {Vector quantization of images using {K}ohonen algorithm.
		  {T}heory and implementation},
  journal	= {Revue Technique Thomson-CSF},
  year		= {1991},
  volume	= {23},
  number	= {1},
  pages		= {137--159},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  burel92a,
  author	= {Gilles Burel},
  title		= {Nouveaux r{\'{e}}sultats th{\'{e}}oriques concernant les
		  cartes topologiques},
  journal	= {Bull. d'information des Laboratoires Centraux de Thomson
		  CSF},
  year		= {1992},
  publisher	= {Thomson CSF},
  address	= {Paris, France},
  volume	= {4},
  pages		= {3--13},
  note		= {(in french)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  burel93a,
  author	= {Gilles Burel and Jean-Yves Catros},
  title		= {Image Compression using Topological Maps and {MLP}},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {II},
  pages		= {727--731},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  burel93b,
  author	= {Gilles Burel},
  title		= {Une nouvelle approche pour les r{\'{e}}seaux de neurones:
		  la repr{\'{e}}presentation scalaire Distribu{\'{e}}e},
  journal	= {Traitement du Signal},
  year		= {1993},
  volume	= {10},
  number	= {1},
  pages		= {41--51},
  note		= {(in french)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  burger97a,
  author	= {M. Burger and T. Graepel and K. Obermayer},
  title		= {Phase transitions in soft topographic vector
		  quantization},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {619--24},
  dbinsdate	= {oldtimer}
}

@InCollection{	  burger98a,
  author	= {M. Burger and T. Graepel and K. Obermayer},
  title		= {An Annealed Self-Organizing Map for Source Channel
		  Coding},
  booktitle	= {Advances in Neural Information Processing Systems},
  year		= {1998},
  editor	= {M. Jordan and M. Kearns and S. Solla},
  volume	= {10},
  pages		= {430--436},
  dbinsdate	= {oldtimer}
}

@Article{	  burke94a,
  author	= {Burke, L. I. },
  title		= {Neural methods for the traveling salesman problem:
		  insights from operations research},
  journal	= {Neural Networks},
  year		= {1994},
  volume	= {7},
  number	= {4},
  pages		= {681--90},
  abstract	= {Adaptive neural approaches to the traveling salesman
		  problem appear to hold great promise, as evidenced by the
		  work of Angeniol et al., among others. An adaptive approach
		  based on self-organizing feature maps with conscience---the
		  guilty net---has also exhibited the ability to give short
		  tour lengths for the TSP. Neural approaches rarely compare
		  in speed or efficacy, however, with good heuristics from
		  operations research. In this paper, experiments with the
		  guilty net and a new extension, the vigilant net, show that
		  adaptive approaches can benefit from established results in
		  operations research. Specifically, a simple starting
		  position strategy derives from operations research
		  literature, and proves an important force in reducing tour
		  lengths. In addition, operations research yields insights
		  into the kinds of TSP instances that should be studied.
		  Nonuniformly distributed cities pose an important problem,
		  and the guilty and vigilant nets yield tour lengths shorter
		  than even a comparable geometry preserving operations
		  research heuristic. Experimentation also shows the effect
		  of neighborhood size and conscience.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  burr88a,
  author	= {D. Burr},
  title		= {An improved elastic net method for the {T}ravelling
		  {S}alesman {P}roblem},
  booktitle	= {Proc. ICNN'88, International Conference on Neural
		  Networks},
  year		= {1988},
  volume	= {I},
  pages		= {69--76},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  burrascano90a,
  author	= {P. Burrascano and P. Lucci and G. Martinelli and R.
		  Perfetti},
  title		= {Velotopic maps in well-log inversion},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {I},
  pages		= {311--316},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  burrascano90b,
  author	= {P. Burrascano and P. Lucci and G. Martinelli and R.
		  Perfetti},
  title		= {Shear velocity estimation by the combined use of
		  supervised and unsupervised neural networks},
  booktitle	= {Proc. ICASSP-90, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1990},
  volume	= {IV},
  pages		= {1921--1924},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {A neural estimator composed of two different neural
		  networks, namely, a perceptron and a Kohonen map, is
		  proposed. . . . },
  dbinsdate	= {oldtimer}
}

@Article{	  burrascano91a,
  author	= {P. Burrascano},
  title		= {Learning vector quantization for the probabilistic neural
		  network},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1991},
  volume	= {2},
  number	= {4},
  pages		= {458--461},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@Article{	  burrascano99a,
  author	= {Burrascano, P. and Fiori, S. and Mongiardo, M.},
  title		= {A review of artificial neural networks applications in
		  microwave computer-aided design},
  journal	= {International-Journal-of-RF-and-Microwave-Computer-Aided-Engineering}
		  ,
  year		= {1999},
  volume	= {9},
  pages		= {158--74},
  abstract	= {Neural networks found significant applications in
		  microwave CAD. In this paper, after providing a brief
		  description of neural networks employed so far in this
		  context, we illustrate some of their most significant
		  applications and typical issues arising in practical
		  implementation. We also summarize current research
		  tendencies and introduce use of self-organizing maps
		  enhancing model accuracy and applicability. We conclude
		  considering some future developments and exciting
		  perspectives opened from use of neural networks in
		  microwave CAD.},
  dbinsdate	= {2002/1}
}

@Article{	  burton99a,
  author	= {Burton, R.~M. and Plaehn, D.~C.},
  title		= {One-Dimensional {K}ohonen Maps Are Super-Stable with
		  Exponential Rate},
  journal	= {Advances in Applied Probability},
  year		= {1999},
  volume	= {31},
  number	= {2},
  pages		= {367--393},
  dbinsdate	= {oldtimer}
}

@Article{	  busch94a,
  author	= {C. Busch and M. H. Gross},
  title		= {Interactive Neural Network Texture Analysis and
		  Visualization for Surface Reconstruction in Medical
		  Imaging},
  journal	= {EUROGRAPHICS'93},
  year		= {1993},
  volume	= {12},
  number	= {3},
  pages		= {C-49--60},
  dbinsdate	= {oldtimer}
}

@Article{	  busch97a,
  author	= {C. Busch},
  title		= {Wavelet based texture segmentation of multi-modal
		  tomographic images},
  journal	= {Computers \& Graphics},
  year		= {1997},
  volume	= {21},
  number	= {3},
  pages		= {347--58},
  dbinsdate	= {oldtimer}
}

@InCollection{	  butchart95a,
  author	= {K. Butchart and N. Davey and R. Adams},
  title		= {A comparative study of three neural networks that use soft
		  competition},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {308--14},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  buttress95a,
  author	= {Buttress, J. and Frith, A. M. and Gent, C. R. and
		  Beaumont, A. J. },
  title		= {Using the {K}ohonen self organising map for novel data
		  handling in adaptive learning},
  booktitle	= {Neural Networks---Producing Dependable Systems (ERA
		  95--0973)},
  year		= {1995},
  pages		= {5. 1. 1--9},
  organization	= {EDS Defence Ltd. , UK},
  publisher	= {ERA Technol},
  address	= {Leatherhead, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  byrne94a,
  author	= {Byrne, W. and Mastrogiannis, K. and Meyer, G. F. },
  title		= {Classification of multi-spectral remote sensing data with
		  neural networks: a comparative study},
  booktitle	= {IEE Colloquium on 'Applications of Neural Networks to
		  Signal Processing' (Digest No. 1994/248)},
  year		= {1994},
  pages		= {5/1--2},
  organization	= {Dept. of Comput. Sci. , Keele Univ. , UK},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cabello93a,
  author	= {Cabello, D. and Penedo, M. G. and Barro, S. and Pardo, J.
		  M. and Heras, J. },
  title		= {CT image segmentation by \mbox{self-organizing} learning},
  booktitle	= {New Trends in Neural Computation. International Workshop
		  on Artificial Neural Networks. IWANN '93 Proceedings},
  year		= {1993},
  editor	= {Mira, J. and Cabestany, J. and Prieto, A. },
  pages		= {651--6},
  organization	= {Univ. de Santiago de Compostela, Spain},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cadieux00a,
  author	= {Cadieux, S. and Michaud, F. and Lalonde, F.},
  title		= {Intelligent system for automated fish sorting and
		  counting},
  booktitle	= {IEEE International Conference on Intelligent Robots and
		  Systems},
  year		= {2000},
  editor	= {},
  volume	= {2},
  pages		= {1279--1284},
  organization	= {Syst. d'automatismes et de mesure, Hydro-Quebec's Research
		  Cent. (IREQ)},
  publisher	= {},
  address	= {},
  abstract	= {This paper presents an automated system for counting fish
		  by species. This system is to be used in fishways for
		  monitoring and surveying fish. The system requires very few
		  adjustments and no special installation. An infrared
		  silhouette sensor is used to acquire the fish silhouettes.
		  These silhouettes are then processed on a personal computer
		  for fish counting and classification by species. The system
		  allows the operator to select the species of interest
		  according to the fauna of the specified river.
		  Classification is made based on the combined results of a
		  Bayes maximum likelihood classifier, a Learning Vector
		  Quantification classifier and a One-Class-One-Network
		  (OCON) neural network classifier. Through the use of
		  specialized classifiers of different types, a robust,
		  modular and expandable recognition system is created.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  cagnoni94a,
  author	= {Stefano Cagnoni and Guido Valli},
  title		= {{OS {LVQ} }: a training strategy for optimum-size Learning
		  Vector Quantization classifiers},
  pages		= {762--765},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {modification, multi-step training},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cagnoni97a,
  author	= {Cagnoni, A. and Mauri, G. and Pensini, M. P.},
  title		= {An hybrid architecture for on-line defects classification
		  in automatic inspection systems},
  booktitle	= {Neural Networks in Engineering Systems. Proceedings of the
		  1997 International Conference on Engineering Applications
		  of Neural Networks},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1997},
  volume	= {1},
  pages		= {329--32},
  abstract	= {The paper describes a new general-purpose neural
		  architecture for an industrial quality control system,
		  BABINet. The architecture is especially tailored for defect
		  patterns whose parameters are detected by an automatic
		  inspection system, but it can be employed with success in
		  other similar classification problems. The BABINet system
		  is composed of two modules: an unsupervised module (whose
		  basic components are ART2 nets) and a supervised module
		  based on a new type of neural network model called ART2SUP.
		  Each module is composed of some networks arranged in
		  different ways: a tree structure for the unsupervised
		  module and a linear structure for the supervised one. The
		  unsupervised module creates groups of defect patterns which
		  are in detail classified by the supervised module.
		  Classification of the available defect patterns results in
		  a meaningful improvement (more than 10%) of the percentage
		  of correctness with respect to other studied neural models
		  (backpropagation, radial basis functions, learning vector
		  quantization) differently arranged in linear or complex
		  structures. At the moment the system is available on single
		  processor machines, but an implementation on parallel
		  machines is foreseen.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cai93a,
  author	= {S. Cai and H. Toral and J. Qiu},
  title		= {Flow Regime Identification by a Self-Organizing Neural
		  Network},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {868},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cai93b,
  author	= {Shiqian Cai and Haluk Toral},
  title		= {Flowrate Measurement in Air-Water Horizontal Pipeline by
		  Neural Network},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {2013--2016},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {The Kohonen self-organising feature map (KSOFM) and the
		  multi-layer back propagation network (MBPN) were applied in
		  a hybrid network model to measure the flow rate of
		  individual phases in horizontal air-water flow. Feature
		  sets derived from turbulent absolute and differential
		  pressure signals obtained from a range of flow regimes were
		  classified into clusters by the KSOFM according to flow
		  regime. Samples belonging to each cluster were trained by
		  the MBPN to measure the flow rate of individual phases. Two
		  thirds of the samples were randomly selected to train the
		  MBPN, the remainder was used for testing. Individual phase
		  flow rates were identified with 10% accuracy.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cai94a,
  author	= {Yudong Cai},
  title		= {The Application of the Artificial Neural Network in the
		  Grading of Beer Quality},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {516--520},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, pattern recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  calinescu94a,
  author	= {Calinescu, R. and Grigoras, D. },
  title		= {A neural \mbox{self-organizing} scheme for dynamic load
		  allocation},
  booktitle	= {Transputer Applications and Systems'94. Proceedings of the
		  1994 World Transputer Congress},
  year		= {1994},
  editor	= {de Gloria, A. and Jane, M. R. and Marini, D. },
  pages		= {860--8},
  organization	= {Dept. of Comput. Sci. , Tech. Univ. Gh. Asachi, Iasi,
		  Romania},
  publisher	= {IOS Press},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  callan99a,
  author	= {Callan, D. E. and Kent, R. D. and Roy, N. and Tasko, S.
		  M.},
  title		= {Self-organizing map for the classification of normal and
		  disordered female voices},
  journal	= {Journal of Speech, Language, and Hearing Research},
  year		= {1999},
  volume	= {42},
  pages		= {355--66},
  abstract	= {The goal of this research was to train a self-organizing
		  map (SOM) on various acoustic measures (amplitude
		  perturbation quotient, degree of voice breaks, rahmonic
		  amplitude, soft phonation index, standard deviation of the
		  fundamental frequency, and peak amplitude variation) of the
		  sustained vowel /a/ to enhance visualization of the
		  multidimensional nonlinear regularities inherent in the
		  input data space. The {SOM} was trained using 30 spasmodic
		  dysphonia exemplars, 30 pretreatment functional dysphonia
		  exemplars, 30 post-treatment functional dysphonia
		  exemplars, and 30 normal voice exemplars. After training,
		  the classification performance of the {SOM} was evaluated.
		  The results indicated that the {SOM} had better
		  classification performance than that of a stepwise
		  discriminant analysis over the original data. Analysis of
		  the weight values across the {SOM}, by means of stepwise
		  discriminant analysis, revealed the relative importance of
		  the acoustic measures in classification of the various
		  groups. The {SOM} provided both an easy way to visualize
		  multidimensional data, and enhanced statistical
		  predictability at distinguishing between the various groups
		  (over that conducted on the original data set). The authors
		  regard the results of this study as a promising initial
		  step into the use of {SOM}s with multiple acoustic measures
		  to assess phonatory function.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  calonge97a,
  author	= {T. Calonge and L. Alonso and R. Ralha and A. L. Sanchez},
  title		= {Parallel implementation of non-recurrent neural networks},
  booktitle	= {Vector and Parallel Processing---VECPAR '96. Second
		  International Conference on Vector and Parallel Processing
		  ---Systems and Applications. Selected Papers},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {J. M. L. M. Palma and J. Dongarra},
  address	= {Berlin, Germany},
  pages		= {314--25},
  dbinsdate	= {oldtimer}
}

@Article{	  camastra01a,
  author	= {Camastra, F. and Vinciarelli, A.},
  title		= {Cursive character recognition by learning vector
		  quantization},
  journal	= {Pattern Recognition Letters},
  year		= {2001},
  volume	= {22},
  number	= {6--7},
  month		= {May},
  pages		= {625--629},
  organization	= {Elsag spa},
  publisher	= {},
  address	= {},
  abstract	= {This paper presents a cursive character recognizer
		  embedded in an off-line cursive script recognition system.
		  The recognizer is composed of two modules: The first one is
		  a feature extractor, the second one a learning vector
		  quantizer. The selected feature set was compared to Zernike
		  polynomials using the same classifier. Experiments are
		  reported on a database of about 49,000 isolated characters.
		  },
  dbinsdate	= {2002/1}
}

@InCollection{	  cameron95a,
  author	= {B. M. Cameron and A. Manduca and R. A. Robb},
  title		= {Surface generation for virtual reality displays with a
		  limited polygonal budget},
  booktitle	= {Proceedings of the International Conference on Image
		  Processing},
  publisher	= {IEEE Computer Society Press},
  year		= {1995},
  volume	= {1},
  address	= {Los Alamitos, CA, USA},
  pages		= {438--41},
  dbinsdate	= {oldtimer}
}

@Article{	  cammarata89a,
  author	= {S. Cammarata},
  title		= {Introduction to neural computation},
  journal	= {Sistemi et Impresa},
  year		= {1989},
  volume	= {35},
  number	= {302},
  pages		= {688--697},
  month		= {April},
  note		= {(in Italian)},
  x		= { Outlines the historical development of neural network
		  approaches to computation. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cammarata93a,
  author	= {G. Cammarata and S. Cavalieri and A. Fichera and L.
		  Marletta},
  title		= {Self-Organizing Map to Filter Acoustic Mapping Survey in
		  Noise Pollutation Analysis},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {2017--2020},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {In this paper the authors propose a neural approach to
		  filter the data provided by acoustic measurements. It is
		  based on the use of a Kohonen Self-Organizing Map network
		  which, in the learning phase receives correct acoustic
		  measurements. The Kohonen neural network learning on the
		  basis of this set of measurements would allow the network
		  to be used as a filter. Having received a set of acoustic
		  measurements in input, it would be able, in the production
		  phase, to discard any acoustic measurements which were
		  insignificant or affected by errors.},
  dbinsdate	= {oldtimer}
}

@Article{	  campanario95a,
  author	= {Juan Miguel Campanario},
  title		= {Using Neural Networks to Study Networks of Scientific
		  Journals},
  journal	= {Scientometrics},
  year		= {1995},
  volume	= {33},
  number	= {1},
  pages		= {23--40},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  campbell96a,
  author	= {Campbell, N. W. and Thomas, B. T. and Troscianko, T.},
  title		= {Neural networks for the segmentation of outdoor images},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {343--6},
  abstract	= {One of the most crucial steps in many engineering
		  applications of computer vision is that of segmentation.
		  This is the process by which images are divided into
		  coherent regions based upon the colour, texture or other
		  properties of their pixels. This paper has two objectives.
		  The first of these is to demonstrate how a self-organising
		  feature map (SOM) may be used to automatically segment
		  images. The second aim is to examine the quality of these
		  segmentations when solely colour information is used, and
		  also when both colour and texture information is used. The
		  images used here are taken from the Bristol image database
		  and consist of a large number of high-quality colour
		  outdoor scenes. Every object in these scenes has been
		  assigned a label by hand. Hence a human-derived `ideal'
		  segmentation is available for each image, and hence a
		  quantitative performance measure for the segmentation
		  process is available.},
  dbinsdate	= {oldtimer}
}

@Article{	  campbell97a,
  author	= {N. W. Campbell and B. T. Thomas and T. Troscianko},
  title		= {Automatic segmentation and classification of outdoor
		  images using neural networks},
  journal	= {International Journal of Neural Systems},
  year		= {1997},
  volume	= {8},
  number	= {1},
  pages		= {137--44},
  dbinsdate	= {oldtimer}
}

@Article{	  campos00a,
  author	= {Campos, Marcos M. and Carpenter, Gail A.},
  title		= {Building adaptive basis functions with a continuous
		  \mbox{self-organizing} map},
  journal	= {Neural Processing Letters},
  year		= {2000},
  number	= {1},
  volume	= {11},
  pages		= {59--78},
  abstract	= {This paper introduces CSOM, a continuous version of the
		  Self-Organizing Map (SOM). The CSOM network generates maps
		  similar to those created with the original {SOM} algorithm
		  but, due to the continuous nature of the mapping, CSOM
		  outperforms the {SOM} on function approximation tasks. CSOM
		  integrates self-organization and smooth prediction into a
		  single process. This is a departure from previous work that
		  required two training phases, one to self-organize a map
		  using the {SOM} algorithm, and another to learn a smooth
		  approximation of a function. System performance is
		  illustrated with three examples.},
  dbinsdate	= {oldtimer}
}

@Article{	  campos01a,
  author	= {Campos, M. M. and Carpenter, G. A.},
  title		= {S-{TREE}: Self-organizing trees for data clustering and
		  online vector quantization},
  journal	= {Neural Networks},
  year		= {2001},
  volume	= {14},
  number	= {4--5},
  month		= {},
  pages		= {505--525},
  organization	= {Center for Adaptive Systems, Department Cognitive/Neural
		  Systems, Boston University},
  publisher	= {},
  address	= {},
  abstract	= {This paper introduces S-TREE (Self-Organizing Tree), a
		  family of models that use unsupervised learning to
		  construct hierarchical representations of data and online
		  tree-structured vector quantizers. The S-TREE1 model, which
		  features a new tree-building algorithm, can be implemented
		  with various cost functions. An alternative implementation,
		  S-TREE2, which uses a new double-path search procedure, is
		  also developed. The performance of the S-TREE algorithms is
		  illustrated with data clustering and vector quantization
		  examples, including a Gauss-Markov source benchmark and an
		  image compression application. S-TREE performance on these
		  tasks is compared with the standard tree-structured vector
		  quantizer (TSVQ) and the generalized Lloyd algorithm (GLA).
		  The image reconstruction quality with S-TREE2 approaches
		  that of GLA while taking less than 10% of computer time.
		  S-TREE1 and S-TREE2 also compare favorably with the
		  standard TSVQ in both the time needed to create the
		  codebook and the quality of image reconstruction. Copyright
		  },
  dbinsdate	= {2002/1}
}

@Article{	  campos96a,
  author	= {T. P. R. Campos},
  title		= {Connectionist modeling for arm kinematics using visual
		  information},
  journal	= {IEEE Transactions on Systems, Man and Cybernetics, Part B
		  [Cybernetics]},
  year		= {1996},
  volume	= {26},
  number	= {1},
  pages		= {89--99},
  dbinsdate	= {oldtimer}
}

@InCollection{	  campos98a,
  author	= {M. M. Campos and G. A. Carpenter},
  title		= {{WSOM}: building adaptive wavelets with
		  \mbox{self-organizing} maps},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {763--7},
  dbinsdate	= {oldtimer}
}

@InCollection{	  campos98b,
  author	= {Marcos M. Campos and Gail A. Carpenter},
  title		= {Building Adaptive Basis Functions with a Continuous
		  {SOM}},
  booktitle	= {Proc. JCIS'98},
  publisher	= {Association for Intelligent Machinery, Inc},
  year		= 1998,
  editor	= {Paul P. Wang},
  volume	= {II},
  pages		= {68--71},
  abstract	= {This paper introduces CSOM, a distributed version of the
		  self-organizing map network capable of generating maps
		  similar to those created with the original algorithm: due
		  to the continuous nature of the mapping, CSOM outperforms
		  the traditional {SOM} algorithm in function approximation
		  tasks. System performance is illustrated with three
		  examples.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  canas91a,
  author	= {A. Canas and J. Ortega and F. J. Fernandez and A. Prieto
		  and F. J. Pelayo},
  title		= {An approach to isolated word recognition using multilayer
		  perceptrons},
  booktitle	= {Proc. IWANN'91, Int. Workshop on Artificial Neural
		  Networks},
  year		= {1991},
  editor	= {A. Prieto},
  pages		= {340--347},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cao95a,
  author	= {Yuanda Cao and Yifeng Chen},
  title		= {A neural Spatio-Temporal Feature Detector},
  booktitle	= {Proceedings of International Conference on Neural
		  Information Processing (ICONIP `95)},
  year		= {1995},
  editor	= {Zhong, Y. and Yang, Y. and Wang, M. },
  volume	= {1},
  pages		= {201--4},
  organization	= {Inst. of Artificial Intelligence, Beijing Inst. of
		  Technol. , China},
  publisher	= {Publishing House of Electron. Ind},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@Article{	  capitan-vallvey00a,
  author	= {Capitan-Vallvey, L. F. and Navas, N. and del Olmo, M. and
		  Consonni, V. and Todeschini, R.},
  title		= {Resolution of mixtures of three nonsteroidal
		  anti-inflammatory drugs by fluorescence using partial least
		  squares multivariate calibration with previous wavelength
		  selection by Kohonen artificial neural networks},
  journal	= {Talanta},
  year		= {2000},
  volume	= {52},
  number	= {6},
  month		= {Sep},
  pages		= {1069--1079},
  organization	= {Univ of Granada},
  publisher	= {Elsevier Science Publ Co Inc},
  address	= {New York, NY},
  abstract	= {A spectrofluorometric method for the quantitative
		  determination of flufenamic, mefenamic and meclofenamic
		  acids in mixtures has been developed by recording emission
		  fluorescence spectra between 370 and 550 nm with an
		  excitation wavelength of 352 nm. The excitation-emission
		  spectra of these compounds are deeply overlapped which does
		  not allow their direct determination without previous
		  separation. The proposed method applies partial least
		  squares (PLS) multivariate calibration to the resolution of
		  this mixture using a set of wavelengths previously selected
		  by Kohonen artificial neural networks (K-ANN). The linear
		  calibration graphs used to construct the calibration matrix
		  were selected in the ranges from 0.25 to 1.00 \mu{}g
		  ml<sup>-1</sup> for flufenamic and meclofenamic acids, and
		  from 1.00 to 4.00 \mu{}g ml<sup>-1</sup> for mefenamic
		  acid. A cross-validation procedure was used to select the
		  number of factors. The selected calibration model has been
		  applied to the determination of these compounds in
		  synthetic mixtures and pharmaceutical formulations.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  cappelli95a,
  author	= {Marco Cappelli and Rodolfo Zunino},
  title		= {{D {LVQ} }: Dynamic model for {L}earning {V}ector
		  {Q}uantization},
  volume	= {I},
  pages		= {652--655},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  caravaglia01a,
  author	= {Susan Caravaglia},
  title		= {Statistical Analysis of the {T}animoto Coefficient
		  Self-Organizing Map ({TCSOM}) Applied to Health Behavioral
		  Survey Data},
  booktitle	= {International Joint Conference on Neural Networks,
		  Washington, DC, USA, July 15--19},
  pages		= {},
  year		= {2001},
  month		= {July},
  note		= {CD-ROM},
  dbinsdate	= {2002/1}
}

@InProceedings{	  card95a,
  author	= {H. C. Card and SriGouri Kamarsu},
  title		= {Limited Precision Unsupervised Learning Algorithms for
		  Speech Coding},
  volume	= {I},
  pages		= {128--131},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@Book{		  cardei97a,
  author	= {Cardei, V. C. and Hadley, R. F.},
  title		= {Predicting semantic categories using a hybrid neural
		  network. Technical report no. CSS-IS TR97--02.},
  year		= {1997},
  abstract	= {Describes a hybrid neural network that can predict the
		  semantic categories and some characteristic features of the
		  words from a given sentence. Section 1 presents the general
		  description of the neural network and the way it functions,
		  and the considerations which led to the network
		  architecture. Section 2 describes the Kohonen map and
		  output network, along with their corresponding learning
		  algorithms in the form of a pseudo-code. Section 3
		  describes the structure of the program written for
		  implementing the hybrid network and discusses a sample of a
		  test run. The final section presents some experimental data
		  and comparisons between average activations for nouns in
		  novel positions versus the nouns from the training set.
		  Other data that indicates the degree of systematicity of
		  the network are also presented.},
  dbinsdate	= {oldtimer}
}

@Article{	  cardiel01a,
  author	= {Cardiel, G. B. and {De los Angeles Fabian Alvarez}, M. and
		  Martinez, M. V. and Villasenor, L.},
  title		= {Inteligencias artificiales y ensayos ultrasonicos para la
		  deteccion de defectos},
  journal	= {Revista de Metalurgia (Madrid)},
  year		= {2001},
  volume	= {37},
  number	= {3},
  month		= {May/June },
  pages		= {403--411},
  organization	= {Instituto de Invest. Metalurgicas, Universidad
		  Michoacana},
  publisher	= {},
  address	= {},
  abstract	= {One of the most serious problems in the quality control of
		  welded unions is the location, identification and
		  classification of defects. As a solution to this problem, a
		  technique for classification, applicable to welded unions
		  done by electric arc welding as well as by friction, is
		  proposed; it is based on ultrasonic signals. The neuronal
		  networks proposed are Kohonen and Multilayer Perceptron,
		  all in a virtual instrument environment. Currently the
		  techniques most used in this field are: radiological
		  analysis (X-rays) and ultrasonic analysis (ultrasonic
		  waves). The X-ray technique in addition to being dangerous
		  requires highly specialized personnel and equipment,
		  therefore its use is restricted. The ultrasonic technique,
		  in spite of being one of the most used for detection of
		  discontinuities, requires personnel with wide experience in
		  the interpretation of ultrasonic signals; this is a
		  time-consuming process which necessarily increases its
		  operation cost. The classification techniques that we
		  propose turn out to be safe, reliable, inexpensive and easy
		  to implement for the solution of this important problem.},
  dbinsdate	= {2002/1}
}

@Article{	  carl94a,
  author	= {Carl, S. and Kraft, R. },
  title		= {Land use classification of {ERS-1} images with an
		  artificial neural network},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1994},
  volume	= {2315},
  pages		= {452--9},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  carlen93a,
  author	= {Carlen, E. T. and Abdel-Aty-Zohdy, H. S. },
  title		= {{VLSI} implementation of a feature mapping neural
		  network},
  booktitle	= {Proceedings of the 36th Midwest Symposium on Circuits and
		  Systems},
  year		= {1993},
  volume	= {2},
  pages		= {958--62},
  organization	= {Dept. of Electr. \& Syst. Eng. , Oakland Univ. ,
		  Rochester, MI, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  carlini97a,
  author	= {P. Carlini},
  title		= {Self organizing maps, vector quantization, and fractal
		  image coding},
  journal	= {Fractals},
  year		= {1997},
  volume	= {5},
  number	= {suppl. issue},
  pages		= {201--14},
  note		= {(Fractal Image Encoding and Analysis Conf. Date: 8--17
		  July 1995 Conf. Loc: Trondheim, Norway Conf. Sponsor:
		  NATO)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  carlson91a,
  author	= {Eero Carlson},
  title		= {Self-Organizing Feature Maps for Appraisal of Land Value
		  of Shore Parcels},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1309--1312},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  carlson93a,
  author	= {Eero Carlson},
  title		= {Cognitive Grammar and Map Digitization},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {1018},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  carlson97a,
  author	= {Eero Carlson},
  title		= {Scaling and sensitivity in appraisal},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {57--62},
  dbinsdate	= {oldtimer}
}

@InCollection{	  carlson98a,
  author	= {E. Carlson},
  title		= {Real Estate Investment Appraisal of Land Properties using
		  {SOM}},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {117--127},
  dbinsdate	= {oldtimer}
}

@Article{	  carosone95a,
  author	= {F. Carosone and A. Cenedese and G. Querzoli},
  title		= {Recognition of partially overlapped particle images using
		  the {K}ohonen neural network},
  journal	= {Experiments in Fluids},
  year		= {1995},
  volume	= {19},
  number	= {4},
  pages		= {225--232},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  carpinteiro00a,
  author	= {Carpinteiro, Otavio A. S. and {da Silva}, Alexandre P. A.
		  and Feichas, Carlos H. L.},
  title		= {Hierarchical neural model in short-term load forecasting},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {241--246},
  organization	= {Escola Federal de Engenharia de Itajuba},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper proposes a novel neural model to the problem of
		  short-term load forecasting. The neural model is made up of
		  two self-organizing map nets---one on top of the other. It
		  has been successfully applied to domains in which the
		  context information given by former events plays a primary
		  role. The model was trained and assessed on load data
		  extracted from a Brazilian electric utility. It was
		  required to predict once every hour the electric load
		  during the next 24 hours. The paper presents the results,
		  and evaluates them.},
  dbinsdate	= {2002/1}
}

@Article{	  carpinteiro00b,
  author	= {Carpinteiro, O. A. S.},
  title		= {A hierarchical self-organising map model for sequence
		  recognition},
  journal	= {PATTERN ANALYSIS AND APPLICATIONS},
  year		= {2000},
  volume	= {3},
  number	= {3},
  pages		= {279--287},
  abstract	= {This raper presents art analysis of an original
		  hierarchical neural model on a complex sequence---che
		  complete sixteenth four-part fugue in G minor of the
		  Well-Tempered Clavier (vol. I) of J. S. Bach. The model
		  makes an effective use of context information, through in
		  hierarchical topology and its embedded time integrators,
		  and that enables it to keep a very good account of
		  patterns. The model peri;,rms sequence classification and
		  discrimination efficiently. It has application in domains
		  which require pattern recognition, or particularly, which
		  demand recognising either a set of sequences of vectors in
		  time, or sub-sequences into a unique and large sequence of
		  vectors in time.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  carpinteiro00c,
  author	= {Carpinteiro, O. A. S. and {Alves da Silva} A. P.},
  title		= {A hierarchical neural model in short-term load
		  forecasting},
  booktitle	= {Proceedings. Vol.1. Sixth Brazilian Symposium on Neural
		  Networks. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {120--4},
  abstract	= {This paper proposes a novel neural model for the
		  short-term load forecasting problem. The neural model is
		  made up of two self-organizing map nets-one on top of the
		  other. It has been successfully applied to domains in which
		  the context information given by former events plays a
		  primary role. The model was trained and assessed on the
		  load data extracted from a Brazilian electric utility. It
		  was required to predict once every hour the electric load
		  during the next 24 hours. The paper presents the results
		  and evaluates them.},
  dbinsdate	= {2002/1}
}

@Article{	  carpinteiro01a,
  author	= {Carpinteiro, O. A. S. and {Alves Da Silva}, A. P.},
  title		= {A hierarchical self-organizing map model in short-term
		  load forecasting},
  journal	= {Journal of Intelligent and Robotic Systems: Theory and
		  Applications},
  year		= {2001},
  volume	= {31},
  number	= {1--3},
  month		= {May/July },
  pages		= {105--113},
  organization	= {Instituto de Engenharia Eletrica, Escola Fed. de
		  Engenharia de Itajuba},
  publisher	= {},
  address	= {},
  abstract	= {This paper proposes a novel neural model to the problem of
		  short-term load forecasting. The neural model is made up of
		  two self-organizing map nets---one on top of the other. It
		  has been successfully applied to domains in which the
		  context information given by former events plays a primary
		  role. The model was trained and assessed on load data
		  extracted from a Brazilian electric utility. It was
		  required to predict once every hour the electric load
		  during the next 24 hours. The paper presents the results,
		  and evaluates them.},
  dbinsdate	= {2002/1}
}

@InCollection{	  carpinteiro98a,
  author	= {O. A. S. Carpinteiro},
  title		= {A \mbox{self-organizing} map model for analysis of musical
		  time series},
  booktitle	= {Proceedings 5th Brazilian Symposium on Neural Networks},
  publisher	= {IEEE Computer Society},
  year		= {1998},
  editor	= {A. de Padua Braga and T. B. Ludermir},
  address	= {Los Alamitos, CA, USA},
  pages		= {140--5},
  dbinsdate	= {oldtimer}
}

@InCollection{	  carpinteiro98b,
  author	= {Ot{\'a}vio Augusto S. Carpinteiro},
  title		= {A Hierarchical Self-Organizing Map Model for Sequence
		  Recognition},
  booktitle	= {Proceedings of ICANN98, the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer},
  year		= 1998,
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= 2,
  address	= {London},
  pages		= {815--820},
  abstract	= {Proposes a neural model made up of two self-organizing map
		  nets-one on top of the other. The model makes an effective
		  use of context information, and that enables it to perform
		  sequence classification and discrimination efficiently. It
		  was trained and assessed on a four-part fugue of J.S. Bach.
		  The model has application in domains which require pattern
		  recognition, or in particular, which demand recognizing
		  either a set of sequences of vectors in time or
		  sub-sequences into a unique and large sequence of vectors
		  in time.},
  dbinsdate	= {oldtimer}
}

@Article{	  carpinteiro99a,
  author	= {Carpinteiro, Otavio Augusto S.},
  title		= {Hierarchical \mbox{self-organizing} map model for sequence
		  recognition},
  journal	= {Neural Processing Letters},
  year		= {1999},
  number	= {3},
  volume	= {9},
  pages		= {209--220},
  abstract	= {A novel neural model made up of two self-organizing maps
		  nets---one on top of the other---is introduced and analyzed
		  experimentally. The model makes effective use of context
		  information, and that enables it to perform sequence
		  classification and discrimination efficiently. It was
		  successfully applied to real sequences, taken from the
		  third voice of the sixteenth four-part fugue in G minor of
		  the Well-Tempered Clavier (vol. I) of J.S. Bach. The model
		  has an application in domains which require pattern
		  recognition, or more specifically, which demand the
		  recognition of either a set of sequences of vectors in time
		  or sub-sequences into a unique and large sequence of
		  vectors in time.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  carpinteiro99b,
  author	= {Carpinteiro, O. A. S. and {Alves da Silva}, A. P.},
  title		= {A hierarchical \mbox{self-organizing} map model in
		  short-term load forecasting},
  booktitle	= {Engineering Applications of Neural Networks. Proceedings
		  of the 5th International Conference on Engineering
		  Applications of Neural Networks (EANN'99)},
  publisher	= {Wydawnictwo Adam Marszalek},
  address	= {Torun, Poland},
  year		= {1999},
  volume	= {},
  pages		= {75--80},
  abstract	= {This paper proposes a novel neural model to the problem of
		  short-term load forecasting. The neural model is made up of
		  two self-organizing map nets-one on top of the other. It
		  has been successfully applied to domains in which the
		  context information given by former events plays a primary
		  role. The model was trained and assessed on load data
		  extracted from a Brazilian electric utility. It was
		  required to predict once every hour the electric load
		  during the next 24 hours. The paper presents the results,
		  and evaluates them.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  carraro89a,
  author	= {A. Carraro and E. Chilton and H. McGurk},
  title		= {A telephonic lipreading device for the hearing impaired},
  booktitle	= {IEE Colloquium on 'Biomedical Applications of Digital
		  Signal Processing' (Digest No. 144)},
  year		= {1989},
  organization	= {IEE},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  carrato93a,
  author	= {Sergio Carrato and Giovanni L. Sicuranza and Luigi Manzo},
  title		= {Application of Ordered Codebooks to Image Coding},
  booktitle	= {Neural Networks for Signal Processing 3---Proceedings of
		  the 1993 IEEE Workshop},
  year		= {1993},
  editor	= {Kamm, C. A. and Kung, S. Y. and Yoon, B. and Chellappa, R.
		  and Kung, S. Y. },
  pages		= {291--300},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, New Jersey, USA},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@Article{	  carrato94a,
  author	= {Sergio Carrato},
  title		= {Image vector quantization using ordered codebooks:
		  Properties and applications},
  journal	= {Signal Processing},
  year		= {1994},
  volume	= {40},
  number	= {1},
  pages		= {87--103},
  annote	= {vector quantization, image compression},
  abstract	= {We consider the application of self-organizing feature
		  maps (SOFMs) to the problem of image vector quantization.
		  We show that, using a SOFM-based codebook, vector
		  quantizers which are better than conventionally designed
		  ones can be obtained if their property of ordering is
		  suitably exploited. In fact, there are at least three
		  possible applications, which are described in the paper,
		  where they give better results at no extra cost. We first
		  introduce a measure of disorder, which is used for
		  evaluating the quality of the ordering of SOFM-based
		  codebooks. Then, we exploit the ordering of these codebooks
		  for two purposes: a differential coding technique which
		  relies on the correlation between successive addresses
		  related to adjacent blocks, and a fast search algorithm
		  based on a `first guess' of the winning codeword. We also
		  show that an ordered codebook is robust with respect to
		  channel errors. Simulation results are presented, which
		  confirm the useful properties of SOFM-based vector
		  quantizers.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  carreira00a,
  author	= {Carreira, M. J. and Mirmehdi, M. and Thomas, B. T. and
		  Haddon, J. F.},
  title		= {Grouping of directional features using an extended Hough
		  transform},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {3},
  pages		= {990--3},
  abstract	= {Directional features extracted from Gabor responses are
		  used as primitives for perceptual grouping. In previous
		  work, we extracted Gabor features in 8 directions and then
		  applied two self-organising maps, thus classifying each
		  pixel in the image within a neuron-map, each corner of
		  which represents one of four main directions. In this work
		  we group pixels with similar directional features to detect
		  salient structures within an image. Results obtained from
		  application to forward-looking infrared (FLIR) images are
		  very promising.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  carter93a,
  author	= {S. Carter and R. J. Frank and D. S. W. Tansley},
  title		= {Clone Detection in Telecommunications Software Systems: A
		  Neural Net Approach},
  booktitle	= {Proc. Int. Workshop on Application of Neural Networks to
		  Telecommunications},
  year		= {1993},
  editor	= {Joshua Alspector and Rodney Goodman and Timothy X. Brown},
  pages		= {273--287},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, classification, symbolic data},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  caselli94a,
  author	= {Caselli, S. and Faldella, E. and Fringuelli, B. and Rosi,
		  L. },
  title		= {A neural approach to robotic haptic recognition of {3-D}
		  objects based on a {K}ohonen \mbox{self-organizing} feature
		  map},
  booktitle	= {IECON '94. 20th International Conference on Industrial
		  Electronics, Control and Instrumentation},
  year		= {1994},
  volume	= {2},
  pages		= {835--40},
  organization	= {Dipartimento di Ingegneria dell'Inf. , Parma Univ. ,
		  Italy},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {The paper describes a versatile robotic haptic recognition
		  system of three-dimensional objects. The adopted design
		  methodology foresees a learning phase of the geometric
		  properties of the objects, followed by the operative phase
		  of actual recognition in which the robot explores the
		  objects with its end-effector, correlating the sensorial
		  data with the preceding perceptive experiences. These
		  phases are mapped on the training and classification
		  activities typical of the unsupervised Kohonen neural
		  networks. The validity of the novel approach pursued for
		  the design of the haptic recognition system has been
		  ascertained with reference to a high-dexterity 3-finger,
		  10-degree of freedom robotic hand (the University of
		  Bologna hand), but the underlying methodological issues can
		  be specialized to any dextrous end-effector. The developed
		  prototype system, even though currently referring to a
		  simulated environment and to some working assumptions, such
		  as the convexity of the objects and their immobility during
		  exploration, has already shown a satisfactory operative
		  level in recognizing objects belonging to a set of
		  significant cardinality.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  castellano00a,
  author	= {Castellano, G. and Fanelli, A. M.},
  title		= {Self-organizing neural fuzzy inference network},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {14--19},
  organization	= {Universita degli Studi di Bari},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A self-organizing neural network is proposed which is
		  inherently a fuzzy inference system with the capability of
		  learning fuzzy rules from data. The learning strategy
		  consists of two phases: a self-organizing clustering to
		  establish the structure of the network as well as the
		  initial values of its parameters and a supervised learning
		  phase for optimal adjustment of these parameters. After
		  learning, the network encodes in its structure the
		  essential design parameters of a fuzzy system. An example
		  is given to illustrate the characteristics and capabilities
		  of the proposed network.},
  dbinsdate	= {2002/1}
}

@Article{	  castillo00a,
  author	= {Castillo, P. A. and Merelo, J. J. and Prieto, A. and
		  Rivas, V. and Romero, G.},
  title		= {G-Prop: Global optimization of multilayer perceptrons
		  using {GA}s},
  journal	= {Neurocomputing},
  year		= {2000},
  volume	= {35},
  number	= {},
  month		= {Nov},
  pages		= {149--163},
  organization	= {Universidad de Granada},
  publisher	= {Elsevier Science B.V.},
  address	= {Amsterdam},
  abstract	= {A general problem in model selection is to obtain the
		  right parameters that make a model fit observed data. For a
		  multilayer perceptron (MLP) trained with back-propagation
		  (BP), this means finding appropriate layer size and initial
		  weights. This paper proposes a method (G-Prop, genetic
		  backpropagation) that attempts to solve that problem by
		  combining a genetic algorithm (GA) and BP to train MLPs
		  with a single hidden layer. The GA selects the initial
		  weights and changes the number of neurons in the hidden
		  layer through the application of specific genetic
		  operators. G-Prop combines the advantages of the global
		  search performed by the GA over the MLP parameter space and
		  the local search of the BP algorithm. The application of
		  the G-Prop algorithm to several real-world and benchmark
		  problems shows that MLPs evolved using G-Prop are smaller
		  and achieve a higher level of generalization than other
		  perceptron training algorithms, such as Quick-Propagation
		  or RPROP, and other evolutive algorithms, such as G-LVQ.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  castillo99a,
  author	= {Castillo, P. A. and Merelo, J. J. and Gonzalez, J. and
		  Rivas, V. and Romero, G.},
  title		= {{SA}-{P}rop: optimization of multilayer perceptron
		  parameters using simulated annealing},
  booktitle	= {Foundations and Tools for Neural Modeling. International
		  Work-Conference on Artificial and Natural Neural Networks,
		  IWANN'99. Proceedings, (Lecture Notes in Computer Science
		  Vol.1606)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {1},
  pages		= {661--70},
  abstract	= {A general problem in model selection is to obtain the
		  right parameters that make a model fit observed data. If
		  the model selected is a multilayer perceptron (MLP) trained
		  with backpropagation (BP), it is necessary to find
		  appropriate initial weights and learning parameters. This
		  paper proposes a method that combines simulated annealing
		  (SA) and BP to train MLPs with a single hidden layer,
		  termed SA-Prop. SA selects the initial weights and the
		  learning rate of the network. SA-Prop combines the
		  advantages of the stochastic search performed by the SA
		  over the MLP parameter space and the local search of the BP
		  algorithm. The application of the proposed method to
		  several real-world benchmark problems shows that MLPs
		  evolved using SA-Prop achieve a higher level of
		  generalization than other perceptron training algorithms,
		  such as QuickPropagation (QP) or RPROP, and other evolutive
		  algorithms, such as G-LVQ.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  castillo99b,
  author	= {Castillo, P. A. and Rivas, V. and Merelo, J. J. and
		  Gonzalez, J. and Prieto, A. and Romero, G.},
  title		= {{G}-{P}rop-{II}: global optimization of multilayer
		  perceptrons using {GA}s},
  booktitle	= {Proceedings of the 1999 Congress on Evolutionary
		  Computation---CEC99. },
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {},
  pages		= {2022--7},
  abstract	= {A general problem in model selection is to obtain the
		  right parameters that make a model fit observed data. For a
		  multilayer perceptron (MLP) trained with backpropagation
		  (BP), this means finding the right hidden layer size,
		  appropriate initial weights and learning parameters. The
		  paper proposes a method (G-Prop-II) that attempts to solve
		  that problem by combining a genetic algorithm (GA) and BP
		  to train MLPs with a single hidden layer. The GA selects
		  the initial weights and the learning rate of the network,
		  and changes the number of neurons in the hidden layer
		  through the application of specific genetic operators.
		  G-Prop-II combines the advantages of the global search
		  performed by the GA over the MLP parameter space and the
		  local search of the BP algorithm. The application of the
		  G-Prop-II algorithm to several real world and benchmark
		  problems shows that MLPs evolved using G-Prop-II are
		  smaller and achieve a higher level of generalization than
		  other perceptron training algorithms, such as
		  QuickPropagation or RPROP, and other evolutive algorithms,
		  such as G-LVQ. It also shows some improvement over previous
		  versions of the algorithm.},
  dbinsdate	= {oldtimer}
}

@Article{	  catrina93a,
  author	= {Catrina, S. },
  title		= {Nested network method for robot control},
  journal	= {Revue Roumaine des Sciences Techniques, Serie
		  Electrotechnique et Energetique},
  year		= {1993},
  volume	= {38},
  number	= {3},
  pages		= {421--8},
  month		= {July-Sept},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  caudill88a,
  author	= {M. Caudill},
  title		= {Network paradigm selection guidelines for application
		  development},
  booktitle	= {Proc. Fourth Annual Artificial Intelligence and Advanced
		  Computer Technology Conference},
  year		= {1988},
  pages		= {298--302},
  publisher	= {Tower Conf. Management},
  address	= {Glen Ellyn, IL},
  dbinsdate	= {oldtimer}
}

@Article{	  caudill93a,
  author	= {M. Caudill},
  title		= {A little knowledge is a dangerous thing (neural nets)},
  journal	= {AI Expert},
  year		= {1993},
  volume	= {8},
  number	= {6},
  pages		= {16--22},
  month		= {June},
  annote	= {A review of {SOFM}},
  dbinsdate	= {oldtimer}
}

@Article{	  cavazos00a,
  author	= {Cavazos, Tereza},
  title		= {Using self-organizing maps to investigate extreme climate
		  events: An application to wintertime precipitation in the
		  Balkans},
  journal	= {Journal of Climate},
  year		= {2000},
  volume	= {13},
  number	= {10},
  month		= {},
  pages		= {1718--1732},
  organization	= {Pennsylvania State Univ},
  publisher	= {American Meteorological Soc},
  address	= {Boston, MA},
  abstract	= {This paper examines some of the physical mechanisms and
		  remote linkages associated with extreme wintertime
		  precipitation in the Balkans. The analysis is assessed on
		  daily timescales to determine the role of the circulation
		  and atmospheric moisture on extreme events, and also at
		  intraseasonal and interannual timescales to find possible
		  linkages with the North Atlantic Oscillation (NAO) and the
		  Arctic Oscillation (AO) patterns. A nonlinear
		  classification known as the self-organizing map (SOM) is
		  employed to obtain the climate modes and anomalies that
		  dominated during the 1980--93 period. An artificial neural
		  network (ANN) is also used to derive daily precipitation at
		  gridpoint scale and at local scale in Bucharest, Romania.
		  Of the predictors used, 500--1000-hPa thickness, 700-hPa
		  geopotential heights, and 700-hPa moisture are the most
		  important controls of daily precipitation. These results
		  are substantiated with the climate states from the SOM
		  classification, which show strong meridional flow over
		  central and eastern Europe coupled to increased winter
		  disturbances in the central Mediterranean and a tongue of
		  moisture at the 700-hPa level from the eastern
		  Mediterranean and the Black Sea during anomalously wet
		  events in the Bulgarian region. Dry events are almost an
		  inverse of these conditions. Extreme events are further
		  modulated by changes in the circulation associated with the
		  AO. In contrast, the NAO does not play a role on wintertime
		  precipitation over the region. The ANN captures well
		  synoptic events and dry spells, but tends to overestimate
		  (underestimate) small (large) events. This suggests a
		  problem for area-averaged precipitation, which is already
		  biased by its spatial resolution. However, comparison
		  between precipitation at Bucharest station and at its
		  nearest grid point shows that the performance of the ANN is
		  slightly better at gridpoint scale.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  caviglia90a,
  author	= {D. D. Caviglia and G. M. Bisio and F. Curatelli and L.
		  Giovannacci and L. Raffo},
  title		= {Pre-placement of {VLSI} blocks through learning neural
		  networks},
  booktitle	= {Proc. EDAC, European Design Automation Conf. , Glasgow,
		  Scotland},
  year		= {1990},
  pages		= {650--654},
  organization	= {IEEE; EDAC},
  publisher	= {IEEE Computer Society Press},
  address	= {Washington, DC},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cawley93a,
  author	= {G. C. Cawley and P. D. Noakes},
  title		= {The Use of Vector Quantization in Neural Speech
		  Synthesis},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2227--2230},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {Our previous work has indicated that multi-layer
		  perceptrons (MLPs) trained using the backpropagation (BP)
		  algorithm, have great difficulty in learning continuous
		  mappings with sufficient accuracy for speech synthesis. The
		  use of vector quantization allows networks to be trained to
		  select a sequence of entries from a codebook of speech
		  parameter vectors. For the network to be able to generalize
		  meaningfully some correlation must exist between codebook
		  vectors and the indices by which they are recalled
		  (otherwise the network will be attempting to learn an
		  essentially random mapping). This paper describes the use
		  of the Hamming learning vector quantizer (H-LVQ), which is
		  used to generate a codebook of speech vectors in which such
		  a correlation exists.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cawley96a,
  author	= {G. C. Cawley},
  title		= {An improved vector quantisation algorithm for speech
		  transmission over noisy channels},
  booktitle	= {Proceedings ICSLP 96. Fourth International Conference on
		  Spoken Language Processing},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  editor	= {H. T. Bunnell and W. Idsardi},
  address	= {New York, NY, USA},
  pages		= {299--301},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cazuguel98a,
  author	= {G. Cazuguel and A. Cziho and B. Solaiman and C. Roux},
  title		= {Medical image compression and characterization using
		  vector quantization: an application of
		  \mbox{self-organizing} maps and quadtree decomposition},
  booktitle	= {Proceedings. 1998 IEEE International Conference on
		  Information Technology Applications in Biomedicine, ITAB
		  '98},
  publisher	= {IEEE},
  year		= {1998},
  editor	= {S. Laxminarayan and E. Micheli-Tzanakou},
  address	= {New York, NY, USA},
  pages		= {127--32},
  dbinsdate	= {oldtimer}
}

@Article{	  cereghino01a,
  author	= {Cereghino, R. and Giraudel, J. L. and Compin, A.},
  title		= {Spatial analysis of stream invertebrates distribution in
		  the Adour-Garonne drainage basin (France), using Kohonen
		  self organizing maps},
  journal	= {ECOLOGICAL MODELLING},
  year		= {2001},
  volume	= {146},
  number	= {1--3},
  month		= {DEC 1},
  pages		= {167--180},
  abstract	= {We analysed the regional distribution of 283 lotic
		  macroinvertebrate species from four insect orders
		  (Ephemeroptera, Plecoptera, Trichoptera, Coleoptera = EPTC)
		  in the Adour-Garonne drainage basin (South-Western France,
		  surface = 116 000 km(2)). The aim of this work was to
		  provide a stream classification based on characteristic
		  species assemblages. The faunistic data corresponded to the
		  occurrence (presence or absence) of 283 species at 252
		  sampling sites. These data were computed with the Kohonen
		  self organised map algorithm (SOM) (Kohonen,
		  Self-Organizing Maps, volume30 of Springer Series in
		  Information Sciences. Springer, Berlin, Heidelberg. (Second
		  Extended Edition 1997)). This neural network algorithm has
		  already been successfully used in ecology (Giraudel et al.,
		  Artificial neural networks, applications to ecology and
		  evolution. Springer-Verlag, (in press); Chon et al., Ecol.
		  Model., 90, 1996, 69--78) for communities patternizing. SOM
		  enable visualisation of the complex species assemblage in a
		  two-dimensional space, preserving the topology of the input
		  data. Then, using the U-matrix method, it was possible to
		  classify the data without prior knowledge. Four major EPTC
		  regions were characterised within the drainage basin
		  (Massif Central mountains, Pyrenees mountains, Piedmont and
		  plains, Toulouse city agglomeration), along with their
		  theoretical species assemblage. The number of species
		  characterising each region ranged from 45 to 159,
		  underlining the spatial (i.e. longitudinal and
		  geographical) differences in EPTC assemblages. The main
		  interest of our results is that the stability of these
		  theoretical assemblages may be used to define
		  representative and/or reference sites for biological
		  surveillance, as any change in species composition within a
		  given EPTC region can be considered as a biological
		  indicator of environmental changes. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  cervera95a,
  author	= {Enrique Cervera and Angel P. {del Pobil}},
  title		= {Perception-based Qualitative Reasoning in Manipulation
		  with Uncertainty},
  booktitle	= {Proc. CAEPIA'95, VI Conference of the Spanish Association
		  for Artificial Intelligence},
  year		= {1995},
  pages		= {129--139},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cervera95b,
  author	= {Enrique Cervera and Angel P. {del Pobil} and Edward Marta
		  and Miguel A. Serna},
  title		= {Interpreting Tactile Information with Neural Networks in
		  Robot Tasks},
  booktitle	= {Proc. CAEPIA'95, VI Conference of the Spanish Association
		  for Artificial Intelligence},
  year		= {1995},
  pages		= {415--423},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cervera95c,
  author	= {Enrique Cervera and Angel P. {del Pobil}},
  title		= {A Supervised Learning Method with Multiple Self-Organizing
		  Maps},
  booktitle	= {Proc. CAEPIA'95, VI Conference of the Spanish Association
		  for Artificial Intelligence},
  year		= {1995},
  pages		= {471--479},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cervera95d,
  author	= {Enrique Cervera and Angel P. {del Pobil} and Edward Marta
		  and Miguel A. Serna},
  title		= {Monitoring Robotic Tasks in a Floxible Manufacturing
		  System},
  booktitle	= {Proc. TTIA'95, Transferencia Tecnol{\'{o}}gica de
		  Inteligencia Artificial a Industria, Medicina y
		  Aplicaciones Sociales},
  year		= {1995},
  editor	= {Ram{\'{o}}n Rizo Aldeguer and Juan Manuel Gar{\'{c}}ia
		  Chamizo},
  pages		= {3--12},
  dbinsdate	= {oldtimer}
}

@Article{	  cervera95e,
  author	= {Cervera, E. and {del Pobil}, A. P. },
  title		= {On the integration of sensors and neural networks in
		  intelligent robotic systems},
  journal	= {Systems Analysis Modelling Simulation},
  year		= {1995},
  volume	= {18--19},
  pages		= {297--300},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  cervera95f,
  author	= {Cervera, E. and {del Pobil}, A. P. and Marta, E. and
		  Serna, M. A. },
  title		= {Use of sensors to deal with uncertainty in realistic
		  robotic environments},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2492},
  number	= {pt. 2},
  pages		= {740--7},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cervera95g,
  author	= {E. Cervera and A. P. {del Pobil}},
  title		= {Multiple \mbox{self-organizing} maps for supervised
		  learning},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {345--52},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cervera95h,
  author	= {E. Cervera and A. P. {del Pobil} and E. Marta and M. A.
		  Serna},
  title		= {Dealing with uncertainty in fine motion: a neural
		  approach},
  booktitle	= {Industrial and Engineering Applications of Artificial
		  Intelligence and Expert Systems. Proceedings of the Eighth
		  International Conference},
  publisher	= {Gordon \& Breach},
  year		= {1995},
  editor	= {G. F. Forsyth and M. Ali},
  address	= {Newark, NJ, USA},
  pages		= {119--26},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cervera95i,
  author	= {E. Cervera and A. P. {del Pobil}},
  title		= {Self-organizing maps for supervision in robot pick-and-
		  place operations},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {D. W. Pearson and N. C. Steele and R. F. Albrecht},
  address	= {Vienna, Austria},
  pages		= {372--5},
  dbinsdate	= {oldtimer}
}

@Article{	  cervera96a,
  author	= {E. Cervera and A. P. {del Pobil} and E. Marta and M. A.
		  Serna},
  title		= {Perception-based learning for motion in contact in task
		  planning},
  journal	= {Journal of Intelligent and Robotic Systems: Theory and
		  Applications},
  year		= {1996},
  volume	= {17},
  number	= {3},
  pages		= {283--308},
  dbinsdate	= {oldtimer}
}

@Article{	  cervera97a,
  author	= {E. Cervera and A. P. {del Pobil}},
  title		= {Multiple \mbox{self-organizing} maps: a hybrid learning
		  scheme},
  journal	= {Neurocomputing},
  year		= {1997},
  volume	= {16},
  number	= {4},
  pages		= {309--18},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cervera99a,
  author	= {E. Cervera and A. P. {del Pobil}},
  title		= {A SOM-based sensing approach to robotic manipulation
		  tasks},
  booktitle	= {Kohonen Maps},
  pages		= {207--218},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: robots, manipulation, force sensing, learning},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cetin95a,
  author	= {D. {\c{C}}etin and F. Yildirim and D. Demirekler and B.
		  Nakibo{\u{g}}lu and B. T{\"{u}}z{\"{u}}n},
  title		= {Text-Independent Speaker Identification Using Learning
		  Vector Quantization},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {267--269},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@Article{	  cha01a,
  author	= {Cha, Y. Y. and Oh, J. H.},
  title		= {A field-of-view generation algorithm using neural
		  network},
  journal	= {Mechatronics},
  year		= {2001},
  volume	= {11},
  number	= {6},
  month		= {September },
  pages		= {731--744},
  organization	= {Dept. of Mech. Design Engineering, College of Engineering,
		  Won-Kwang University},
  publisher	= {},
  address	= {},
  abstract	= {In various works using vision system, if a field of view
		  (FOV) of CCD camera cannot cover overall working area on a
		  table or a floor, the overall working area must be divided
		  into several areas whose sizes equal that of FOV. In this
		  study, an effective FOV generation algorithm in which FOV
		  is automatically made is proposed for vision system.
		  Instead of existing sequential FOV generation method, we
		  propose a new FOV generation method by using newly modified
		  self-organizing map (SOM) which has multiple structure
		  based on SOM of neural network and uses new training rule
		  by introducing the movement, creation and deletion terms.
		  In consequence of real experiment, we show that the
		  proposed algorithm is able to decrease the number of
		  created FOVs about 10% more than the existing sequential
		  algorithm. This result is acquired by which the proposed
		  algorithm minimizes overlaps between FOVs, assigns optimal
		  positions of FOVs and eliminates the unnecessary FOVs.
		  Consequently, as the number of FOVs decreases, we have been
		  able to diminish the total working time using vision
		  system.},
  dbinsdate	= {2002/1}
}

@Article{	  cha97a,
  author	= {Jihun Cha and L. V. Fausett},
  title		= {Comparison of three clustering algorithms and an
		  application to color image compression},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3077},
  pages		= {225--35},
  note		= {(Applications and Science of Artificial Neural Networks
		  III Conf. Date: 21--24 April 1997 Conf. Loc: Orlando, FL,
		  USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cha98a,
  author	= {Eui Young Cha and Myung Ho Kang},
  title		= {Multiple target tracking in clutter backgrounds using
		  \mbox{self-organizing} feature map},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1162--6},
  dbinsdate	= {oldtimer}
}

@Article{	  chakraborty00a,
  author	= {Chakraborty, G. and Chakraborty, B.},
  title		= {A novel normalization technique for unsupervised learning
		  in {ANN}},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  volume	= {11},
  pages		= {253--7},
  abstract	= {Unsupervised learning is used to categorize
		  multidimensional data into a number of meaningful classes
		  on the basis of the similarity or correlation between
		  individual samples. In neural-network implementation of
		  various unsupervised algorithms such as principal component
		  analysis, competitive learning or self-organizing map,
		  sample vectors are normalized to equal lengths so that
		  similarity could be easily and efficiently obtained by
		  their dot products. In general, sample vectors span the
		  whole multidimensional feature space and existing
		  normalization methods distort the intrinsic patterns
		  present in the sample set. In this work, a novel method of
		  normalization by mapping the samples to a new space of one
		  more dimension is proposed. The original distribution of
		  the samples in the feature space is shown to be almost
		  preserved in the transformed space. Simple rules are given
		  to map from original space to the normalized space and vice
		  versa.},
  dbinsdate	= {oldtimer}
}

@Article{	  chakraborty93a,
  author	= {K. Chakraborty and U. Roy},
  title		= {Connectionist models for part-family classifications},
  journal	= {Computers \& Industrial Engineering},
  year		= {1993},
  volume	= {24},
  number	= {2},
  pages		= {189--198},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chan00a,
  author	= {Chan, A. and Spracklen, T.},
  title		= {Feature indicators: a self-organizing map approach to
		  legacy code},
  booktitle	= {Proceedings of the International Conference on Artificial
		  Intelligence. IC-AI'2000. CSREA Press, Athens, GA, USA},
  year		= {2000},
  volume	= {3},
  pages		= {1449--54},
  abstract	= {The self-organizing map's unsupervised clustering
		  property, is known for classifying high dimensional data
		  sets into clusters that have similar features. Based on
		  this property, it is demonstrated that a self-organizing
		  map is capable of identifying features within software code
		  by grouping procedures with similar properties together.
		  This permits us to identify potential objects, abstract
		  data types or classes. To further enhance the visualization
		  of clusters by providing a form of justification for the
		  groupings, we introduce "feature indicators" that labels
		  the map with the elements that most contributed to each
		  fired self-organizing map's unit.},
  dbinsdate	= {2002/1}
}

@Article{	  chan01a,
  author	= {Chan, C. W. and Jin, H. and Cheung, K. C. and Zhang, H.
		  Y.},
  title		= {Fault detection of systems with redundant sensors using
		  constrained Kohonen networks},
  journal	= {Automatica},
  year		= {2001},
  volume	= {37},
  number	= {10},
  month		= {October },
  pages		= {1671--1676},
  organization	= {Department of Mechanical Engineering, University of Hong
		  Kong},
  publisher	= {},
  address	= {},
  abstract	= {The Kohonen self-organizing map (KN) was developed for
		  pattern recognition, and has been extended to fault
		  classification. However, the KN cannot be applied to
		  classify faults from the system output if it contains other
		  factors, such as system state and sensor mounting errors.
		  To overcome this problem, a constrained KN (CKN) is
		  proposed. To eliminate the effect of the system state and
		  the mounting errors, it is proposed that the weight vectors
		  of the CKN are constrained in the parity space. The
		  training algorithm of the CKN is derived, and its
		  convergence discussed. Application of the CKN to fault
		  classification is presented, and its performance is
		  illustrated by an example involving a redundant sensor
		  system with six sensors.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chan01b,
  author	= {Chan, J. C. W. and DeFries R. S. and Townshend, J. R. G.},
  title		= {Improved recognition of spectrally mixed land cover
		  classes using spatial textures and voting classifications},
  booktitle	= {Computer Analysis of Images and Patterns. 9th
		  International Conference, CAIP 2001. Proceedings (Lecture
		  Notes in Computer Science Vol.2124). Springer-Verlag,
		  Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {217--27},
  abstract	= {Forest regeneration is important to account for carbon
		  sinks. Mapping regenerating forests from satellite data is
		  difficult because they are spectrally mixed with natural
		  forests. This paper investigates the combined use of
		  texture features and voting classifications to enhance the
		  recognition of these two classes. Bagging and boosting were
		  applied to learning vector quantization (LVQ) and decision
		  trees. Our results showed that spatial textures improved
		  the class separability. After applying voting
		  classifications, the class accuracy of the decision tree
		  increased by 5--7% and that of LVQ by approximately 3%. A
		  substantial reduction (between 23% to 40%) of confusions
		  between regenerating forests and natural forests was
		  recorded. Comparatively, bagging is more consistent than
		  boosting. An interesting observation is that even LVQ, a
		  stable learner, was able to benefit from both voting
		  classification algorithms.},
  dbinsdate	= {2002/1}
}

@Article{	  chan01d,
  author	= {Chan, J. C. W. and Chan, K. P. and Yeh, A. G. O.},
  title		= {Detecting the nature of change in an urban environment: A
		  comparison of machine learning algorithms},
  journal	= {PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING},
  year		= {2001},
  volume	= {67},
  number	= {2},
  month		= {FEB},
  pages		= {213--225},
  abstract	= {The performance of difference machine learning algorithms
		  for detecting nature of change was compared. To alleviate
		  the problem of obtaining enough training data, simulated
		  training data were generated from single-date images. A
		  one-pass classification with four machine learning
		  algorithms, namely, Multi-Layer Perceptrons (MLP), Learning
		  Vector Quantization (LVQ), Decision Tree Classifiers (DTC),
		  and the Maximum- Likelihood Classifier (MLC), were tested.
		  Recognition rates, ease of use, and degree of automation of
		  the four algorithms were assessed. The results showed that
		  the incorporation of cross-combined simulated training data
		  enhanced the detection of nature of change. Compared to
		  conventional post- classification comparison methods, LVQ
		  and ore did better in terms of overall accuracy In terms of
		  average accuracy of the change classes, LVQ was the best
		  performer. DTC was the easiest to use and the most robust
		  in training. MLP procedures were the most difficult to
		  replicate.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chan01e,
  author	= {Chan, A. T. S. and Shiu, A. and Jiannong Cao and Hong Va
		  Leong},
  title		= {Reactive web policing based on self-organizing maps},
  booktitle	= {Proceedings of IEEE Region 10 International Conference on
		  Electrical and Electronic Technology. TENCON 2001. IEEE,
		  Piscataway, NJ, USA},
  year		= {2001},
  volume	= {1},
  pages		= {160--4},
  abstract	= {Almost without any doubt, the Internet and Web in
		  particular, have brought about radical changes in
		  information retrieval with unparallel benefits to
		  organizations and individuals to gain access to vast amount
		  of articles and documents. The focus on restricting
		  inappropriate materials at their source is not well suited
		  to the nature and open architecture of Internet, where
		  information source maybe in a different legal jurisdiction
		  than the recipient. This paper proposes a reactive approach
		  based on SOM (Self-Organising Maps) neural network to deal
		  with dynamic blocking and filtering of Internet contents.
		  We describe the design and implementation of a web policing
		  proxy (WebPolice) based on a Kohonen's neural network model
		  that attempts to classify the web contents dynamically
		  using competitive learning. The parameter setting of the
		  network has been experimented to obtain the optimal
		  classification rate and performance for the model.},
  dbinsdate	= {2002/1}
}

@Article{	  chan93a,
  author	= {Chan, L. S. -C. and Hean-Lee Poh and Teo Jasic},
  title		= {Neural networks and their applications},
  journal	= {Computer Processing of Chinese \& Oriental Languages},
  year		= {1993},
  volume	= {7},
  number	= {2},
  pages		= {133--66},
  month		= {Dec},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chan94a,
  author	= {Mike V. Chan and Xin Feng and James A. Heinen and Russell
		  J. Niederjohn},
  title		= {Classification of Speech Accents with Neural Networks},
  pages		= {4483--4486},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, speech recognition, comparison},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chan95a,
  author	= {L. A. Chan and N. M. Nasrabadi and V. Mirelli},
  title		= {Automatic target recognition using modularly cascaded
		  vector quantizers and multilayer perceptrons},
  booktitle	= {1996 IEEE International Conference on Acoustics, Speech,
		  and Signal Processing Conference Proceedings},
  publisher	= {ASME Press},
  year		= {1995},
  volume	= {6},
  editor	= {C. H. Dagli and M. Akay and C. L. P. Chen and B. R.
		  Fernandez and J. Ghosh},
  address	= {New York, NY, USA},
  pages		= {3386--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chan95b,
  author	= {Samuel W. K. Chan and James Franklin},
  title		= {A Neurosymbolic Integrated Model for Semantic Ambiguation
		  Resolution},
  volume	= {VI},
  pages		= {2965--2970},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chan95c,
  author	= {K. W. Chan and K. L. Chan},
  title		= {Multi-Reference Neighborhood Search for Vector
		  Quantization by Neural Network Prediction and
		  Self-Organizing Feature Map},
  volume	= {IV},
  pages		= {1898--1902},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chan95d,
  author	= {Chan, K. W. and Chan, K. L. },
  title		= {Multi-reference neighborhood search for vector
		  quantization by self-organized featured map},
  booktitle	= {Fifth International Conference on Image Processing and its
		  Applications},
  year		= {1995},
  pages		= {579--83},
  organization	= {City Polytech. of Hong Kong, Kowloon, Hong Kong},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chan95e,
  author	= {Lai-Wan Chan and Man-Wai Chau and Wing-Chung Chung},
  title		= {GlobalOR: a parallel implementation of the
		  \mbox{self-organizing} map},
  booktitle	= {Proceedings of International Conference on Neural
		  Information Processing (ICONIP `95)},
  year		= {1995},
  editor	= {Zhong, Y. and Yang, Y. and Wang, M. },
  volume	= {2},
  pages		= {625--8},
  organization	= {Dept. of Comput. Sci. , Chinese Univ. of Hong Kong,
		  Shatin, Hong Kong},
  publisher	= {Publishing House of Electron. Ind},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@Article{	  chan96a,
  author	= {L. A. Chan and N. H. Nasrabadi and V. Mirelli},
  title		= {Wavelet-based learning vector quantization for automatic
		  target recognition},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2755},
  pages		= {82--93},
  note		= {(Signal Processing, Sensor Fusion, and Target Recognition
		  V Conf. Date: 8--10 April 1996 Conf. Loc: Orlando, FL, USA
		  Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chan96b,
  author	= {L. A. Chan and N. M. Nasrabadi and V. Mirelli},
  title		= {Multi-stage target recognition using modular vector
		  quantizers and multilayer perceptrons},
  booktitle	= {Proceedings 1996 IEEE Computer Society Conference on
		  Computer Vision and Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  address	= {Los Alamitos, CA, USA},
  pages		= {114--19},
  abstract	= {An automatic target recognition (ATR) classifier is
		  proposed that uses modularly cascaded vector quantizers
		  (VQs) and multilayer perceptrons (MLPs). A dedicated VQ
		  codebook is constructed for each target class at a specific
		  range of aspects, which is trained with the K-means
		  algorithm and a modified learning vector quantization (LVQ)
		  algorithm. Each final codebook is expected to give the
		  lowest mean squared error (MSE) for its correct target
		  class at a given range of aspects. These MSEs are then
		  processed by an array of window MLPs and a target MLP
		  consecutively. In the spatial domain target recognition
		  rates of 90.3 and 65.3 percent are achieved for moderately
		  and highly cluttered test sets, respectively. Using the
		  wavelet decomposition with an adaptive and independent
		  codebook per subband, the VQs alone have produced
		  recognition rates of 98.7 and 69.0 percent on more
		  challenging training and test sets, respectively.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chan96c,
  author	= {L. A. Chan and N. M. Nasrabadi},
  title		= {Modular wavelet-based vector quantization for automatic
		  target recognition},
  booktitle	= {1996 IEEE/SICE/RSJ International Conference on Multisensor
		  Fusion and Integration for Intelligent Systems},
  publisher	= {IEEE},
  year		= {1996},
  address	= {New York, NY, USA},
  pages		= {462--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chan96d,
  author	= {L. A. Chan and N. M. Nasrabadi},
  title		= {An application of wavelet-based vector quantization in
		  target recognition},
  booktitle	= {Proceedings of the IEEE International Joint Symposia on
		  Intelligence and Systems},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  address	= {Los Alamitos, CA, USA},
  pages		= {274--81},
  abstract	= {An automatic target recognition (ATR) classifier is
		  constructed that uses a set of dedicated vector quantizers
		  (VQs). The background pixels in each input image are
		  properly clipped out by a set of aspect windows. The
		  extracted target area for each aspect window is then
		  enlarged to a fixed size, after which a wavelet
		  decomposition splits the enlarged extraction into several
		  subbands. A dedicated VQ codebook is generated for each
		  subband of a particular target class at a specific range of
		  aspects. Thus, each codebook consists of a set of feature
		  templates that are iteratively adapted to represent a
		  particular subband of a given target class at a specific
		  range of aspects. These templates are then further trained
		  by a modified learning vector quantization (LVQ) algorithm
		  that enhances their discriminatory characteristics.},
  dbinsdate	= {oldtimer}
}

@Article{	  chan97a,
  author	= {L. A. Chan and N. M. Nasrabadi},
  title		= {An application of wavelet-based vector quantization in
		  target recognition},
  journal	= {International Journal on Artificial Intelligence Tools
		  [Architectures, Languages, Algorithms]},
  year		= {1997},
  volume	= {6},
  number	= {2},
  pages		= {165--78},
  dbinsdate	= {oldtimer}
}

@Book{		  chan97b,
  author	= {Chan, L. A. and Nasrabadi, N. M.},
  title		= {Automatic Target Recognition Using Wavelet-Based Vector
		  Quantization. Interim rept. Dec 96-Jul 97.},
  year		= {1997},
  abstract	= {An automatic target recognition classifier is described
		  that uses a set of dedicated vector quantizers (VQs) in the
		  wavelet domain. The background pixels in each input image
		  are properly clipped out by a set of aspect windows. The
		  extracted target area for each aspect window is then
		  enlarged to a fixed size, after which a wavelet
		  decomposition is used to split this region into several
		  subbands. A dedicated VQ codebook is then generated for
		  each subband of a particular target class at a specific
		  range of aspects. Thus, each codebook consists of a set of
		  feature templates that are iteratively adapted to represent
		  a particular subband of a given target class at a specific
		  range of aspects. These templates are then further trained
		  by a modified learning vector quantization (LVQ) algorithm
		  that enhances their discriminatory characteristics.
		  Finally, a path selector was designed to speed up the
		  recognition process at the expense of a tolerable
		  degradation in the recognition rate.},
  dbinsdate	= {oldtimer}
}

@Article{	  chan99a,
  author	= {Chan, L. A. and Nasrabadi, N. M.},
  title		= {Automatic target recognition using vector quantization and
		  neural networks},
  journal	= {Optical-Engineering},
  year		= {1999},
  volume	= {38},
  pages		= {2147--61},
  abstract	= {We propose an automatic target recognition (ATR) algorithm
		  that uses a set of dedicated vector quantizers (VQs) and
		  multilayer perceptrons (MLPs). For each target class at a
		  specific range of aspects, the background pixels of an
		  input image are first removed. The extracted target area is
		  then subdivided into several subimages. A dedicated VQ
		  codebook is constructed for each of the resulting
		  subimages. Using the K-means algorithm, each VQ codebook
		  learns a set of patterns representing the local features of
		  a particular target for a specific range of aspects. The
		  resulting codebooks are further trained by a modified
		  learning vector quantization (LVQ) algorithm, which
		  enhances the discriminatory power of the codebooks. Each
		  final codebook is expected to give the lowest mean squared
		  error (MSE) for its correct target class and range of
		  aspects. These MSEs are then input to an array of
		  window-level MLPs (WMLPs), where each WMLP is specialized
		  in recognizing its intended target class for a specific
		  range of aspects. The outputs of these WMLPs are
		  manipulated and passed to a target-level MLP, which
		  produces the final recognition results. We trained and
		  tested the proposed ATR algorithm on large and realistic
		  data sets and obtained impressive results using the
		  wavelet-based adaptive product VQs configuration.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chan99b,
  author	= {Chan, C. W. and Cheung, K. C. and Hong Jin and Zhang, H.
		  Y.},
  title		= {A constrained Kohonen network and its application to
		  sensor fault detection},
  booktitle	= {Automatic Control in Aerospace 1998. Proceedings volume
		  from the 14th IFAC Symposium. Elsevier Science, Kidlington,
		  UK},
  year		= {1999},
  volume	= {},
  pages		= {249--54},
  abstract	= {Kohonen network (KN) has a good capability for pattern
		  classification. However when measurements used for this
		  purpose consist not only of the pattern to be classified
		  but also of other information, the original KN can not be
		  used successfully. For example, the sensor measurement data
		  not only consist of fault information we want to classify
		  but also information of system state variable and other
		  errors and noises. In order to detect and isolate faults in
		  the sensor measurement a constrained Kohonen network (CKN)
		  is constructed and its algorithm is developed in this
		  paper. The main idea of CKY is to constrain the weight
		  vector of the network in some subspace such that the
		  unwanted information (e.g., that of the system state
		  variable) is zeroed by the weight vector while the wanted
		  information (e.g., faults) is classified. The CKN can be
		  successfully used to detect and isolate faults using sensor
		  measurements.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chance01a,
  author	= {Chance, D. and Lebby, G. L. and King, M. C. and Cooke, M.
		  J.},
  title		= {Unsupervised mapping of biometric signatures for enhanced
		  computer security},
  booktitle	= {Proceedings of the IASTED International Conference.
		  Artificial Intelligence and Applications. ACTA Press,
		  Anaheim, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {86--8},
  abstract	= {Describes a method of performing biometric security to
		  enhance the ability to authenticate a user given a known
		  reliable biometric database. We propose a clinical
		  technique called nerve conduction and apply artificial
		  neural networking technology in the form of the Kohonen
		  paradigm to enhance automated biometric security. The nerve
		  conduction study is applied to measure the nerve response
		  to electrical stimulation across a given pathway. By
		  applying this procedure we obtain a biological signal of a
		  user, which is interpreted as a biometric print of user
		  uniqueness. The use of the Kohonen method assures that the
		  high level of distinct classification desired is achieved.
		  Experimental results imply that this approach is highly
		  effective and the implementation of real-time data
		  extraction and analysis lend this method to application in
		  continuous high traffic secure environments.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chandrasekaran93a,
  author	= {V. Chandrasekaran and M. Palaniswami and Terry M. Caelli},
  title		= {Performance Evaluation of Spatio-Temporal Feature Maps
		  with gated Neuronal Architecture},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {IV},
  pages		= {112--118},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chandrasekaran93b,
  author	= {V. Chandrasekaran and M. Palaniswami and Terry M. Caelli},
  title		= {An Extended Self-Organizing Map with Gated Neurons},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1474--1479},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chandrasekaran95a,
  author	= {Chandrasekaran, V. and Palaniswami, M. and Caelli, T. M.
		  },
  title		= {Pattern recognition by topology free spatio-temporal
		  feature map},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {2},
  pages		= {1136--41},
  organization	= {Sch. of Electr. Eng. \& Comput. Sci. , Melbourne Univ. ,
		  Parkville, Vic. , Australia},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  chandrasekaran95b,
  author	= {Chandrasekaran, V. and Palaniswami, M. and Caelli, T. M.},
  title		= {Spatio-temporal feature maps using gated neuronal
		  architecture},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1995},
  volume	= {6},
  number	= {5},
  pages		= {1119--31},
  month		= {Sept},
  abstract	= {In this paper, Kohonen's self-organizing feature map is
		  modified by a novel technique of allowing the neurons in
		  the feature map to compete in a selective manner. The
		  selective competition is achieved by grating the
		  N-dimensional feature space using a spatial frequency and
		  setting a criterion for the neurons to compete based on the
		  region in which the input pattern resides. The spatial
		  grating and selective competition are achieved by
		  introducing a gated neuronal architecture in the feature
		  map. As the selection criterion changes with time, it
		  generates a time sequence of winning node indexes providing
		  more input information and potentially allowing higher
		  classification performance. These time sequences are then
		  used to predict the class label of the input pattern more
		  accurately. Three possible class label prediction
		  algorithms are formulated based on evidential reasoning
		  method and Bayes conditional probability theorem. These are
		  tested on real world 8-class texture and a synthetic
		  12-class three-dimensional object recognition problems. The
		  classification performance is then compared with the
		  results obtained by using a standard statistical linear
		  discriminant analysis.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chandrasekaran97a,
  author	= {V. Chandrasekaran and Zhi-Qiang Liu},
  title		= {Projection pursuits in {SOM} classifiers},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {100--105},
  dbinsdate	= {oldtimer}
}

@Article{	  chandrasekaran98a,
  author	= {Chandrasekaran, V. and Liu, Zhi Qiang},
  title		= {Topology constraint free fuzzy gated neural networks for
		  pattern recognition},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1998},
  number	= {3},
  volume	= {9},
  pages		= {483--502},
  abstract	= {In this paper, a novel topology constraint free neural
		  network architecture using a generalized fuzzy gated neuron
		  model is presented for pattern recognition task. The main
		  feature is that the network does not require weight
		  adaptation at its input and the weights are initialized
		  directly from the training pattern set. The elimination of
		  the need for iterative weight adaptation schemes
		  facilitates quick network set up times which make the fuzzy
		  gated neural networks very attractive. The performance of
		  the proposed network is found to be functionally equivalent
		  to spatio-temporal feature maps under a mild technical
		  condition. The classification performance of fuzzy gated
		  neural network is demonstrated on a 12-class synthetic
		  three-dimensional (3-D) object data set, real-world
		  eight-class texture data set, and real-world 12-class 3-D
		  object data set. The performance results are compared with
		  the classification accuracies obtained from spatio-temporal
		  feature map, adaptive subspace self-organizing map,
		  multilayer feedforward neural networks, radial basis
		  function neural networks, and linear discriminant analysis.
		  Despite the network's ability to accurately classify seen
		  data and adequately generalize validation data, its
		  performance is found to be sensitive to noise perturbations
		  due to fine fragmentation of the feature space. This paper
		  also provides partial solutions to the above robustness
		  issue by proposing certain improvements to various modules
		  of the proposed fuzzy gated neural network.},
  dbinsdate	= {oldtimer}
}

@Article{	  chang00a,
  author	= {Chang, Jyh-Shan and Chiueh, Tzi-Dar},
  title		= {Image vector quantization using classified
		  binary-tree-structured self-organizing feature maps},
  journal	= {IEICE Transactions on Information and Systems},
  year		= {2000},
  volume	= {E-83-D},
  number	= {10},
  month		= {Oct},
  pages		= {1898--1907},
  organization	= {Natl Taiwan Univ},
  publisher	= {IEICE of Japan},
  address	= {Tokyo},
  abstract	= {With the continuing growth of the World Wide Web (WWW)
		  services over the Internet, the demands for rapid image
		  transmission over a network link of limited bandwidth and
		  economical image storage of a large image database are
		  increasing rapidly. In this paper, a classified
		  binary-tree-structured Self Organizing Feature Map neural
		  network is proposed to design image vector codebooks for
		  quantizing images. Simulations show that the algorithm not
		  only produces codebooks with lower distortion than the
		  well-known CVQ algorithm but also can minimize the edge
		  degradation. Because the adjacent codewords in the proposed
		  algorithm are updated concurrently, the codewords in the
		  obtained codebooks tend to be ordered according to their
		  mutual similarity which means more compression can be
		  achieved with this algorithm. It should also be noticed
		  that the obtained codebook is particularly well suited for
		  progressive image transmission because it always forms a
		  binary tree in the input space.},
  dbinsdate	= {2002/1}
}

@Article{	  chang01a,
  author	= {Chang, Kui-yu and Ghosh, Joydeep},
  title		= {Unified model for probabilistic principal surfaces},
  journal	= {IEEE Transactions on Pattern Analysis and Machine
		  Intelligence},
  year		= {2001},
  volume	= {23},
  number	= {1},
  month		= {Jan},
  pages		= {22--41},
  organization	= {Interwoven, Inc},
  publisher	= {IEEE},
  address	= {Los Alamitos, CA},
  abstract	= {Principal curves and surfaces are nonlinear
		  generalizations of principal components and subspaces,
		  respectively. They can provide insightful summary of
		  high-dimensional data not typically attainable by classical
		  linear methods. Solutions to several problems, such as
		  proof of existence and convergence, faced by the original
		  principal curve formulation have been proposed in the past
		  few years. Nevertheless, these solutions are not generally
		  extensible to principal surfaces, the mere computation of
		  which presents a formidable obstacle. Consequently,
		  relatively few studies of principal surfaces are available.
		  Recently, we proposed the probabilistic principal surface
		  (PPS) to address a number of issues associated with current
		  principal surface algorithms. PPS uses a manifold oriented
		  covariance noise model, based on the generative
		  topographical mapping (GTM), which can be viewed as a
		  parametric formulation of Kohonen's self-organizing map.
		  Building on the PPS, we introduce a unified covariance
		  model that implements PPS (0 < \alpha < 1), GTM (\alpha =
		  1), and the manifold-aligned GTM (\alpha > 1) by varying
		  the clamping parameter \alpha. Then, we comprehensively
		  evaluate the empirical performance (reconstruction error)
		  of PPS, GTM, and the manifold-aligned GTM on three popular
		  benchmark data sets. It is shown in two different
		  comparisons that the PPS outperforms the GTM under
		  identical parameter settings. Convergence of the PPS is
		  found to be identical to that of the GTM and the
		  computational overhead incurred by the PPS decreases to 40
		  percent or less for more complex manifolds. These results
		  show that the generalized PPS provides a flexible and
		  effective way of obtaining principal surfaces.},
  dbinsdate	= {2002/1}
}

@Article{	  chang02a,
  author	= {Chang, H. C. and Kopaska-Merkel, D. C. and Chen, H. C.},
  title		= {Identification of lithofacies using Kohonen
		  self-organizing maps},
  journal	= {COMPUTERS \& GEOSCIENCES},
  year		= {2002},
  volume	= {28},
  number	= {2},
  month		= {MAR},
  pages		= {223--229},
  abstract	= {Lithofacies identification is a primary task in reservoir
		  characterization. Traditional techniques of lithofacies
		  identification from core data are costly, and it is
		  difficult to extrapolate to non-cored wells. We present a
		  low-cost automated technique using Kohonen self-organizing
		  maps (SOMs) to identify systematically and objectively
		  lithofacies from well log data, SOMs are unsupervised
		  artificial neural networks that map the input space into
		  clusters in a topological form whose organization is
		  related to trends in the input data. A case study used five
		  wells located in Appleton Field. Escambia County, Alabama
		  (Smackover Formation, limestone and dolomite. Oxfordian,
		  Jurassic). A five-input. one-dimensional output approach is
		  employed, assuming the lithofacies are in
		  ascending/descending order with respect to
		  paleoenvironmental energy levels. To consider the possible
		  appearance of new logfacies not seen in training mode,
		  which may potentially appear in test wells, the maximum
		  number of outputs is set to 20 instead of four, the
		  designated number of lithofacies in the study area. This
		  study found eleven major clusters. The clusters were
		  compared to depositional lithofacies identified by manual
		  core examination. The clusters were ordered by the SOM in a
		  pattern consistent with environmental gradients inferred
		  from core examination: bind/boundstone, grainstone,
		  packstone. and wackestone. This new approach predicted
		  lithofacies identity from well log data with 78.8% accuracy
		  which is more accurate than using a backpropagation neural
		  network (57.3%). The clusters produced by the SOM are
		  ordered with respect to paleoenvironmental energy levels.
		  This energy- related clustering provides geologists and
		  petroleum engineers with valuable geologic information
		  about the logfacies and their interrelationships. This
		  advantage is not obtained in backpropagation neural
		  networks and adaptive resonance theory neural networks.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chang92a,
  author	= {C. -C. Chang and C. -H. Chang and S. -Y. Hwang},
  title		= {A connectionist approach for thresholding},
  booktitle	= {Proc. 11ICPR, International Conference on Pattern
		  Recognition},
  year		= {1992},
  volume	= {III},
  pages		= {522--525},
  organization	= {Int. Assoc Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang92b,
  author	= {Chang, W. and Soliman, H. S. and Sung, A. H. },
  title		= {Image data compression using counterpropagation network},
  booktitle	= {1992 IEEE International Conference on Systems, Man and
		  Cybernetics},
  year		= {1992},
  volume	= {1},
  pages		= {405--9},
  organization	= {Dept. of Comput. Sci. , New Mexico Inst. of Min. \&
		  Technol. , Socorro, NM, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang93a,
  author	= {Ray-I Chang and Pei-Yung Hsiao},
  title		= {Force Directed Self-Organizing Map and its Application to
		  {VLSI} Cell Placement},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {I},
  pages		= {103--109},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {This paper describes a new self-organizing map called
		  force directed self-organizing map (FDSOM) which can be
		  used in VLSI cell placement with various constraints on
		  their connection and dimension such that the total wire
		  length and area of the resulting placement are minimized.
		  This procedure combines ideas from a force directed
		  relaxation and the self-organization algorithm proposed by
		  Kohonen. It is specially suited for such a
		  self-organization problem that those (input) sample vectors
		  are not easily available. A proof of convergence for this
		  model is given in this paper. With this property, it can
		  therefore be used in CAM or any other computational task.
		  The experimental results obtained in this paper are quite
		  encouraging. All these processes are convergent in a
		  reasonable number of iterations. It was found that the
		  proposed approach is competitive with the state-of-the-art
		  algorithms and uses more fewer nodes and connection
		  weights.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang93b,
  author	= {Ray-I Chang and Pei-Yung Hsiao},
  title		= {Circuit placement in arbitrarily shaped regions using
		  neural network},
  booktitle	= {Proceedings TENCON '93. 1993 IEEE Region 10 Conference on
		  'Computer, Communication, Control and Power Engineering'},
  year		= {1993},
  editor	= {Yuan Baozong},
  volume	= {2},
  pages		= {1150--3},
  organization	= {Dept. of Comput. \& Inf. Sci. , Nat. Chiao Tung Univ. ,
		  Hsinchu, Taiwan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang93c,
  author	= {Chang, W. and Soliman, H. S. and Sung, A. H. },
  title		= {Preserving visual perception by learning natural
		  clustering},
  booktitle	= {1993 IEEE International Conference on Neural Networks},
  year		= {1993},
  volume	= {2},
  pages		= {661--6},
  organization	= {Dept. of Comput. Sci. , New Mexico Inst. of Min. \&
		  Technol. , Socorro, NM, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang93d,
  author	= {Chang, R. -I. and Hsiao, P. -Y. },
  title		= {Arbitrarily sized cell placement by \mbox{self-organizing}
		  neural networks},
  booktitle	= {Proceedings of the 1993 IEEE International Symposium on
		  Circuits and Systems},
  year		= {1993},
  volume	= {3},
  pages		= {2043--6},
  organization	= {Dept. of Comput. \& Inf. Sci. , Nat. Chiao Tung Univ. ,
		  Hsinchu, Taiwan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang94a,
  author	= {Ray-I Chang and Pei-Yung Hsiao},
  title		= {Force Directed Self-Organizing Maps for {L}-Shaped Cell
		  Placement using Delta Learning Rule},
  pages		= {3381--3386},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, cell placement, modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang94b,
  author	= {Ray-I Chang and Pei-Yung Hsiao},
  title		= {Artificial Texture Generation using Force Directed
		  Self-Organizing Maps},
  pages		= {4123--4128},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, texture generation},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang94c,
  author	= {W. Chang and H. S. Soliman and A. H. Sung},
  title		= {A Vector Quantization Neural Network to Compress Still
		  Monochromatic Images},
  pages		= {4163--4168},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, vector quantization, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang94d,
  author	= {Ray-I Chang and Pei-Yung Hsiao},
  title		= {Fast {S}elf-{O}rganization by Query-Based Algorithm and
		  its Applications},
  booktitle	= {Proc. 1994 Int. Symp. on Speech, Image Processing and
		  Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {85--88},
  organization	= {{IEEE} Hong Kong Chapt. of Signal Processing},
  address	= {Hong Kong},
  annote	= {modification},
  dbinsdate	= {oldtimer}
}

@Article{	  chang94e,
  author	= {Chen-Huei Chang and Shu-Yuen Hwang},
  title		= {{2-D} curve partitioning by {K}ohonen feature maps},
  journal	= {Journal of Visual Communication and Image Representation},
  year		= {1994},
  volume	= {5},
  number	= {2},
  pages		= {148--55},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang94f,
  author	= {Ray-I Chang and Pei-Yung Hsiao},
  title		= {Rectangular {VLSI} cell placement using force directed
		  \mbox{self-organizing} maps and delta learning rules},
  booktitle	= {Proceedings of 1994 IEEE Region 10's Ninth Annual
		  International Conference. Theme: Frontiers of Computer
		  Technology},
  year		= {1994},
  editor	= {Chan, T. K. },
  volume	= {2},
  pages		= {1020--4},
  organization	= {Dept. of Comput. \& Inf. Sci. , Nat. Chiao Tung Univ. ,
		  Hsinchu, Taiwan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang94g,
  author	= {Chang, W. and Soliman, H. S. and Sung, A. H. },
  title		= {Fingerprint image compression by a natural clustering
		  neural network},
  booktitle	= {Proceedings ICIP-94},
  year		= {1994},
  volume	= {2},
  pages		= {341--5},
  organization	= {New Mexico Inst. of Min. \& Technol. , Socorro, NM, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang94h,
  author	= {Kuo-Chu Chang and Yi-Chuan Lu},
  title		= {Feedback learning: a hybrid {SOFM}/{LVQ} approach for
		  radar target classification},
  booktitle	= {1994 International Symposium on Artificial Neural
		  Networks. ISANN '94. Proceedings},
  year		= {1994},
  pages		= {465--70},
  organization	= {Center of Excellence in Command, Control, Commun. \&
		  Intelligence, George Mason Univ. , Fairfax, VA, USA},
  publisher	= {Nat. Cheng Kung Univ},
  address	= {Tainan, Taiwan},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chang94i,
  author	= {W. Chang and H. S. Soliman and A. H. Sung},
  title		= {Fingerprint image compression by a clustering learning
		  network},
  booktitle	= {Industrial and Engineering Applications of Artificial
		  Intelligence and Expert Systems. Proceedings of the Seventh
		  International Conference},
  publisher	= {Gordon \& Breach},
  year		= {1994},
  editor	= {F. D. Anger and R. V. Rodriguez and M. Ali},
  address	= {Yverdon les Bains, Switzerland},
  pages		= {51--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chang95a,
  author	= {Ray-I Chang and Pei-Yung Hsiao},
  title		= {Unsupervised Query-Based Learning Algorithm and It's
		  Application to {K}ohonen's Self-Organizing Maps},
  volume	= {V},
  pages		= {2610--2614},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {Query-based learning (QBL) algorithms have been introduced
		  to supervised multilayer perceptrons to provide good
		  training results for correct classification. Since their
		  queried data are produced by external supervisors,
		  conventional QBL methods can not be directly applied to
		  unsupervised learning models which have no external
		  supervisor exist. In this paper, a novel unsupervised QBL
		  (UQBL) algorithm is proposed. In which, network's training
		  samples are further improved by both the goal-oriented
		  selective attention and the self-regulation property. The
		  proposed UQBL method considers not only the external
		  stimulus but also the internal desire. It is not an
		  anthropomorphic style that overestimates the importance of
		  internal desire. We just try to combine two different
		  system parameters for network training. This method is
		  different from the conventional supervised/unsupervised
		  learning algorithms. Our experiments show that the proposed
		  UQBL method can be successfully applied for Kohonen's self
		  organizing maps (SOM). It can provide faster convergence
		  and is more insensitive to network initialization than the
		  standard {SOM}.},
  dbinsdate	= {oldtimer}
}

@Article{	  chang95b,
  author	= {Chang, Kuo-Chu and Lu, Yi-Chuan},
  title		= {High resolution polarimetric {SAR} target classification
		  with neural network},
  journal	= {IEEE International Conference on Fuzzy Systems},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1995},
  number	= {},
  volume	= {3},
  pages		= {1681--1688},
  abstract	= {An improved version of the SOFM/LVQ classifier currently
		  used in an ATR system for {SAR} imagery is presented. This
		  classifier was originally designed to construct a few
		  number of templates to represent a set of targets with
		  different orientations. The classifier accepts an input
		  target data, computes distances of this data with those
		  representative templates, and then classifies this data to
		  the target class with the shortest distance. with this
		  distance discriminator, a good classification performance
		  was obtained when only target data were tested. However,
		  the simple distance measure produces poor classification
		  results when unknown targets such as natural or manmade
		  clutters are present and when each target is represented by
		  a small number of templates. We correct this deficiency by
		  incorporating an entropy measure into the original
		  classifier. With this entropy discriminator, our system
		  rejects a majority of the false alarms while maintaining a
		  high correct classification rate with a relatively few
		  templates for each target.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chang96a,
  author	= {Chir-Ho Chang and Hsien-Hui Tseng and Bor-Yao Huang},
  title		= {Noise immunization of a neural fuzzy intelligent
		  recognition system by the use of feature and rule
		  extraction technique},
  booktitle	= {Soft Computing in Intelligent Systems and Information
		  Processing. Proceedings of the 1996 Asian Fuzzy Systems
		  Symposium},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {Y-Y Chen and K. Hirota and J-J Yen},
  address	= {New York, NY, USA},
  pages		= {73--8},
  dbinsdate	= {oldtimer}
}

@Article{	  chang97a,
  author	= {W. Chang and H. S. Soliman},
  title		= {Image coding by a neural net classification process},
  journal	= {Applied Artificial Intelligence},
  year		= {1997},
  volume	= {11},
  number	= {1},
  pages		= {33--57},
  abstract	= {A self-organizing neural network performing learning
		  vector quantization (LVQ) to compress image data is
		  proposed. By using unsupervised learning, our LVQ neural
		  model approximates optimal pattern clustering from training
		  images through a memory adaptation process, and builds a
		  compression codebook in the synaptic weight matrix. The
		  neural codebook, trained by example pictures, can be used
		  as a codec to compress and decompress other pictures in a
		  speedy fashion. Special image types, such as fingerprints,
		  verify this property in our experiments. Our approach is
		  compared with other recently developed neural VQ models
		  (neural gas, growing cell structures, and conscious
		  competitive learning) and methodological premises are
		  discussed. The performance of our model is also compared
		  with JPEG and wavelet methods. Other advantages of our
		  neural codec model are its low training time, high
		  utilization of neurons, robust clustering capability, and
		  simple computation. Further, our model has some filtering
		  effects through special training methods and learning
		  enhancement techniques for obtaining a compact neural
		  codebook to yield both high compression and high picture
		  quality.},
  dbinsdate	= {oldtimer}
}

@Article{	  chang97b,
  author	= {Ray-I Chang and Pei-Yung Hsiao},
  title		= {{VLSI} Circuit Placement with Rectilinear Modules using
		  Three-Layer Force-Directed Self-Organizing Maps},
  journal	= {IEEE Transactions on Neural Networks},
  year		= 1997,
  volume	= 8,
  pages		= {1049--1064},
  dbinsdate	= {oldtimer}
}

@Article{	  chang97c,
  author	= {Ray-I Chang and Pei-Yung Hsiao},
  title		= {Unsupervised Query-Based Learning of Neural Networks Using
		  Selective Attention and Self-Regulation},
  journal	= {IEEE Transactions on Neural Networks},
  year		= 1997,
  volume	= 8,
  pages		= {205--217},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chang98a,
  author	= {Jyh-Shan Chang and J. -H. J. Lin and Tzi-Dar Chiueh},
  title		= {Color image vector quantization using binary tree
		  structured \mbox{self-organizing} feature maps},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1428--32},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chang98b,
  author	= {Maiga Chang and Horng-Jyh Yu and Jia-Sheng Heh},
  title		= {Evolutionary \mbox{self-organizing} map},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {680--5},
  dbinsdate	= {oldtimer}
}

@Article{	  changchien01a,
  author	= {Changchien, S. W. and Lu, T. -C.},
  title		= {Mining association rules procedure to support on-line
		  recommendation by customers and products fragmentation},
  journal	= {Expert Systems with Applications},
  year		= {2001},
  volume	= {20},
  number	= {4},
  month		= {May 2001},
  pages		= {325--335},
  organization	= {Department of Information Management, Chaoyang University
		  of Technology},
  publisher	= {},
  address	= {},
  abstract	= {Electronic Commerce (EC) has offered a new channel for
		  instant on-line shopping. However, there are too many
		  various products available from a great number of virtual
		  stores on the Internet for Internet shoppers to select.
		  On-line one-to-one marketing therefore becomes a great
		  assistance to Internet shoppers. One of the most important
		  marketing resources is the prior daily transaction records
		  in the database. The great amount of data not only gives
		  the statistics, but also offers the resource of experiences
		  and knowledge. It is quite natural that marketing managers
		  can perform data mining on the daily transactions and treat
		  the shoppers the way they prefer. However, the data mining
		  on a significant amount of transaction records requires
		  efficient tools. Data mining from automatic or
		  semi-automatic exploration and analysis on a large amount
		  of data items set in a database can discover significant
		  patterns and rules underlying the database. The knowledge
		  can be equipped in the on-line marketing system to promote
		  Internet sales. The purpose of this paper is to develop a
		  mining association rules procedure from a database to
		  support on-line recommendation. By customers and products
		  fragmentation, product recommendation based on the hidden
		  habits of customers in the database is therefore very
		  meaningful. The proposed data mining procedure consists of
		  two essential modules. One is a clustering module based on
		  a neural network, Self-Organization Map (SOM), which
		  performs affinity grouping tasks on a large amount of
		  database records. The other rule is extraction module
		  employing rough set theory that can extract association
		  rules for each homogeneous cluster of data records and the
		  relationships between different clusters. The implemented
		  system was applied to a sample of sales records from a
		  database for illustration. },
  dbinsdate	= {2002/1}
}

@InCollection{	  chantelou96a,
  author	= {D. Chantelou and G. Hebrail and C. Muller},
  title		= {Visualizing 2665 electric power load curves an a single
		  {A4} sheet of paper},
  booktitle	= {ISAP `96. International Conference on Intelligent Systems
		  Applications to Power Systems Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {O. A. Mohammed and K. Tomsovic},
  address	= {New York, NY, USA},
  pages		= {126--32},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chao92a,
  author	= {Chao, J. and Minowa, K. and Tsujii, S. },
  title		= {Unsupervised learning of {3D} objects conserving global
		  topological order},
  booktitle	= {IEEE International Conference on Systems Engineering},
  year		= {1992},
  pages		= {24--7},
  organization	= {Dept. of Electr. \& Electron. Eng. , Chuo Univ. , Toyko,
		  Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  chao93a,
  author	= {Chao, J. and Minowa, K. and Tsujii, S. },
  title		= {Unsupervised learning of {3D} objects conserving global
		  topological order},
  journal	= {IEICE Transactions on Fundamentals of Electronics,
		  Communications and Computer Sciences},
  year		= {1993},
  volume	= {E76-A},
  number	= {5},
  pages		= {749--53},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chao94a,
  author	= {Jinhui Chao and Kenji Minowa and Shigeo Tsujii},
  title		= {Acquistion of Global Topology for {3D} Objects with Local
		  Competition},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1460--1463},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, extension, internal representations},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chao94b,
  author	= {Chao, J. and Nakayama, J. and Tsujii, S. },
  title		= {Acquisition of global topology for {3D} objects with local
		  competition},
  booktitle	= {APCCAS `94. 1994 IEEE Asia-Pacific Conference on Circuits
		  and Systems},
  year		= {1994},
  pages		= {673--7},
  organization	= {Dept. of Electr. \& Electron. Eng. , Chuo Univ. , Tokyo,
		  Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chao96a,
  author	= {Jinhui Chao and J. Nakayama},
  title		= {Cubical singular simplex model for {3D} objects and fast
		  computation of homology groups},
  booktitle	= {Proceedings of the 13th International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  volume	= {4},
  address	= {Los Alamitos, CA, USA},
  pages		= {190--4},
  dbinsdate	= {oldtimer}
}

@Article{	  chapline97a,
  author	= {George Chapline},
  title		= {Spontaneous origin of topological complexity in
		  \mbox{self-organizing} neural networks},
  journal	= {Network: Computation in Neural Systems},
  year		= 1997,
  volume	= 8,
  pages		= {185--194},
  dbinsdate	= {oldtimer}
}

@Article{	  chappelier01a,
  author	= {Chappelier, J. C and Gori, M. and Grumbach, A.},
  title		= {Time in connectionist models},
  journal	= {Sequence learning. Paradigms, algorithms, and applications
		  (Lecture Notes in Artificial Intelligence Vol.1828).
		  Springer-Verlag, Berlin, Germany; 2001; xiii+387
		  pp.p.105--34},
  year		= {2001},
  volume	= {},
  pages		= {105--34},
  abstract	= {The prototypical use of "classical" connectionist models
		  (including the multilayer perceptron (MLP), the Hopfield
		  network and the Kohonen self-organizing map) concerns
		  static data processing. These classical models are not well
		  suited to working with data varying over time. In response
		  to this, temporal connectionist models have appeared and
		  constitute a continuously growing research field. The
		  purpose of this chapter is to present the main aspects of
		  this research area and to review the key connectionist
		  architectures that have been designed for solving temporal
		  problems. We present the fundamentals of temporal
		  processing with neural networks. Several temporal
		  connectionist models are then detailed. As a matter of
		  illustration, important applications are reviewed. We
		  conclude with the presentation of a promising future issue:
		  the extension of temporal processing to even more complex
		  structured data.},
  dbinsdate	= {2002/1}
}

@InCollection{	  chappelier96a,
  author	= {J. -C. Chappelier and A. Grumbach},
  title		= {A {K}ohonen map for temporal sequences},
  booktitle	= {Neural Networks and Their Applications. Conference
		  Proceedings},
  publisher	= {Domaine Univ. Saint-Jerome},
  year		= {1996},
  address	= {Marseille, France},
  pages		= {104--10},
  dbinsdate	= {oldtimer}
}

@Article{	  chappell93a,
  author	= {Geoffrey J. Chappell and John G. Taylor},
  title		= {The Temporal {K}ohonen Map},
  journal	= {Neural Networks},
  year		= {1993},
  volume	= {6},
  pages		= {441--445},
  dbinsdate	= {oldtimer}
}

@Article{	  charalambous00a,
  author	= {Charalambous, C. and Charitou, A. and Kaourou, F.},
  title		= {Comparative analysis of artificial neural network models:
		  Application in bankruptcy prediction},
  journal	= {ANNALS OF OPERATIONS RESEARCH},
  year		= {2000},
  volume	= {99},
  pages		= {403--425},
  abstract	= {This study compares the predictive performance of three
		  neural network methods, namely the learning vector
		  quantization, the radial basis function, and the
		  feedforward network that uses the conjugate gradient
		  optimization algorithm, with the performance of the
		  logistic regression and the backpropagation algorithm. All
		  these methods are applied to a dataset of 139 matched-pairs
		  of bankrupt and non-bankrupt US firms for the period
		  1983--1994. The results of this study indicate that the
		  contemporary neural network methods applied in the present
		  study provide superior results to those obtained from the
		  logistic regression method and the backpropagation
		  algorithm.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  charalambous01a,
  author	= {Charalambous, C. and Hadjinicola, G. C. and Muller, E.},
  title		= {Product positioning using principles from the
		  self-organizing map},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {457--463},
  abstract	= {This paper presents a methodology that identifies the
		  position of a new product in the attribute space. The
		  methodology uses principles from Kohonen's self-organizing
		  feature map. The algorithm presented is robust and can be
		  used for a number of objective functions commonly used in
		  the product positioning problem. The method can also be
		  used in competitive environments where other competing
		  products are already present in the market. Furthermore,
		  the algorithm can accommodate single-choice models (the
		  consumer purchases the product "closest" to his/her
		  preferences) and probabilistic-choice models (the consumer
		  assigns to each product a probability for purchasing it).},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chaudhary92a,
  author	= {Chaudhary, S. D. and Kalra, P. K. and Srivastava, S. C. },
  title		= {Short term electric load forecasting using artificial
		  neural network},
  booktitle	= {Expert System Application to Power Systems IV
		  Proceedings},
  year		= {1992},
  editor	= {Dillon, T. S. },
  pages		= {159--63},
  organization	= {Dept. of Electr. Eng. , Indian Inst. of Technol. , India},
  publisher	= {CRL Publishing},
  address	= {Aldershot, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chaudhuri95a,
  author	= {Chaudhuri, T. R. and Yeh, J. C. -H. and Hamey, L. G. C.
		  and Westcott, C. T. },
  title		= {Baked product classification with the use of a
		  \mbox{self-organising} map},
  booktitle	= {Proceedings of the Sixth Australian Conference on Neural
		  Networks (ACNN`95)},
  year		= {1995},
  editor	= {Charles, M. and Latimer, C. },
  pages		= {152--5},
  organization	= {Dept. of Comput. , Macquarie Univ. , North Ryde, NSW,
		  Australia},
  publisher	= {Univ. Sydney},
  address	= {Sydney, NSW, Australia},
  dbinsdate	= {oldtimer}
}

@Article{	  chauhan97a,
  author	= {S. Chauhan and M. P. Dave},
  title		= {{K}ohonen neural network classifier for voltage collapse
		  margin estimation},
  journal	= {Electric Machines and Power Systems},
  year		= {1997},
  volume	= {25},
  number	= {6},
  pages		= {607--19},
  dbinsdate	= {oldtimer}
}

@Article{	  chebira97a,
  author	= {A. Chebira and K. Madani and G. Mercier},
  title		= {Various ways for building a multi-neural network system:
		  application to a control process},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3077},
  pages		= {148--59},
  note		= {(Applications and Science of Artificial Neural Networks
		  III Conf. Date: 21--24 April 1997 Conf. Loc: Orlando, FL,
		  USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chedid94a,
  author	= {R. Chedid and N. Najjar and F. Chedid},
  title		= {A neural network approach for finite element software},
  booktitle	= {Proceedings of the IASTED International Conference:
		  Modelling, Simulation and Identification},
  publisher	= {IASTED},
  year		= {1994},
  editor	= {Y. Kagawa},
  address	= {Calgary, Alta. , Canada},
  pages		= {232--6},
  dbinsdate	= {oldtimer}
}

@Article{	  chedid96a,
  author	= {R. Chedid and N. Najjar},
  title		= {Automatic finite-element mesh generation using artificial
		  neural networks---Part {I}: Prediction of mesh density},
  journal	= {IEEE Transactions on Magnetics},
  year		= {1996},
  volume	= {32},
  number	= {5, pt. 3},
  pages		= {5173--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chella01a,
  author	= {Chella, A. and Guarino, M. D. and Pirrone, R.},
  title		= {A {SOM}/{ARSOM} hierarchy for the description of dynamic
		  scenes},
  booktitle	= {AI*IA 2001: Advances in Artificial Intelligence. 7th
		  Congress of the Italian Association for Artificial
		  Intelligence. Proceedings (Lecture Notes in Artificial
		  Intelligence Vol.2175). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {362--8},
  abstract	= {A neural architecture is presented, aimed at describing
		  the dynamic evolution of complex structures inside a video
		  sequence. The proposed system is arranged as a tree of
		  self-organizing maps. Leaf nodes are implemented by ARSOM
		  networks as a way to code dynamic inputs, while classical
		  SOMs are used to implement the upper levels of the
		  hierarchy. Depending on the application domain, inputs are
		  made by suitable low level features extracted frame by
		  frame of the sequence. Theoretical foundations of the
		  architecture are reported along with a detailed outline of
		  its structure, and encouraging experimental results.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chella01b,
  author	= {Chella, A. and Guarino, M. D. and Pirrone, R.},
  title		= {Description of dynamic structured scenes by a
		  {SOM}/{ARSOM} hierarchy},
  booktitle	= {Artificial Neural Networks---ICANN 2001. International
		  Conference. Proceedings (Lecture Notes in Computer Science
		  Vol.2130). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {1034--41},
  abstract	= {A neural architecture is presented whose intention is to
		  describe the dynamic evolution of complex structures inside
		  a video sequence. The proposed system is arranged as a tree
		  of self-organizing maps (SOMs). Leaf nodes are implemented
		  by ARSOM (autoregressive SOM) networks as a way to code
		  dynamic inputs, while classical SOMs are used to implement
		  the upper levels of the hierarchy. Depending on the
		  application domain, inputs are made by suitable low-level
		  features extracted frame-by-frame from the sequence.
		  Theoretical foundations of the architecture are reported,
		  along with a detailed outline of its structure, and
		  encouraging experimental results are obtained.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chella91a,
  author	= {Chella, A. and Gioiello, M. and Sorbello, F. },
  title		= {A new digital architecture implementing the {K}ohonen
		  maps},
  booktitle	= {Digital Signal Processing---91. Proceedings of the
		  International Conference},
  year		= {1991},
  editor	= {Cappellini, V. and Constantinides, A. G. },
  pages		= {514--19},
  organization	= {Dipartimento di Ingegneria Elettrica, Palermo Univ. ,
		  Italy},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chella97a,
  author	= {A. Chella and S. Gaglio and V. Mulia and G. Sajeva},
  title		= {An {ASSOM} neural network to represent actions performed
		  by an autonomous agent},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {799--804},
  dbinsdate	= {oldtimer}
}

@Article{	  chen00a,
  author	= {Dar Ren Chen and Ruey Feng Chang and Yu Len Huang},
  title		= {Breast cancer diagnosis using \mbox{self-organizing} map
		  for sonography},
  journal	= {Ultrasound in Medicine and Biology},
  year		= {2000},
  volume	= {26},
  pages		= {405--11},
  abstract	= {The purpose of this study was to evaluate the performance
		  of neural network model self-organizing maps (SOM) in the
		  classification of benign and malignant sonographic breast
		  lesions. A total of 243 breast tumors (82 malignant and 161
		  benign) were retrospectively evaluated. When a sonogram was
		  performed, the analog video signal was captured to obtain a
		  digitized sonographic image. The physician selected the
		  region of interest in the sonography. An {SOM} model using
		  24 autocorrelation texture features classified the tumor as
		  benign or malignant. In the experiment, cases were sampled
		  with k-fold cross-validation (k=10) to evaluate the
		  performance using receiver operating characteristic (ROC)
		  curves. The ROC area index for the proposed {SOM} system is
		  0.9357+or-0.0152, the accuracy is 85.6%, the sensitivity is
		  97.6%, the specificity is 79.5%, the positive predictive
		  value is 70.8%, and the negative predictive value is 98.5%.
		  This computer-aided diagnosis system can provide a useful
		  tool and its high negative predictive value could
		  potentially help avert benign biopsies.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chen00b,
  author	= {Chen, C. H. and Shrestha, Binesh},
  title		= {Classification of multi-sensor remote sensing images using
		  Self-Organizing Feature Maps and radial basis function
		  networks},
  booktitle	= {International Geoscience and Remote Sensing Symposium
		  (IGARSS)},
  year		= {2000},
  editor	= {},
  volume	= {2},
  pages		= {711--713},
  organization	= {Univ of Massachusetts Dartmouth},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Neural networks have been known for its classification
		  capability and have been used quite extensively in pixel
		  classification for remote sensing in recent years. In this
		  paper we propose the use of Self-Organizing Feature Map
		  (SOFM) neural networks to evaluate the centers for the
		  hidden neurons in RBF neural networks for pixel
		  classification. The combined use of both neural networks
		  leads to a much better classification than the use of only
		  one of the two networks. For the experimental study the
		  image data considered is from both SAR and ATM sensors and
		  for each pixel the combined data form a 15 dimensional
		  feature vector. Five pattern classes are defined for 5
		  crops. The use of SOFM alone provides only 62.7% correct at
		  best. If only RBF network is used the best reported
		  performance is 89.5% correct. However by combining SOFM and
		  RBF, the best average performance is 95.15% correct, a
		  dramatic improvement over the use of either network.
		  Performance degradation is about 5% if we use only the SAR
		  data. This points to the needs for the data level fusion
		  for most effective pixel classification. Performance
		  comparison is also made with the traditional k-nearest
		  neighbor decision rule which provides 86.5 correct. The
		  results clearly demonstrate the advantage of multi-sensor
		  remote sensing using neural networks.},
  dbinsdate	= {2002/1}
}

@Article{	  chen00c,
  author	= {Chen, Fei-Long and Liu, Shu-Fan},
  title		= {Neural-network approach to recognize defect spatial
		  pattern in semiconductor fabrication},
  journal	= {IEEE Transactions on Semiconductor Manufacturing},
  year		= {2000},
  volume	= {13},
  number	= {3},
  month		= {Aug},
  pages		= {366--373},
  organization	= {Natl Tsing Hua Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Yield enhancement in semiconductor fabrication is
		  important. Even though IC yield loss may be attributed to
		  many problems, the existence of defects on the wafer is one
		  of the main causes. When the defects on the wafer form
		  spatial patterns, it is usually a clue for the
		  identification of equipment problems or process variations.
		  This research intends to develop an intelligent system,
		  which will recognize defect spatial patterns to aid in the
		  diagnosis of failure causes. The neural-network
		  architecture named adaptive resonance theory network 1
		  (ART1) was adopted for this purpose. Actual data obtained
		  from a semiconductor manufacturing company in Taiwan were
		  used in experiments with the proposed system. Comparison
		  between ART1 and another unsupervised neural network,
		  self-organizing map (SOM), was also conducted. The results
		  show that ART1 architecture can recognize the similar
		  defect spatial patterns more easily and correctly.},
  dbinsdate	= {2002/1}
}

@Article{	  chen00d,
  author	= {Yinyong Chen and Reggia, J. A.},
  title		= {The temporal correlation hypothesis for self-organizing
		  feature maps},
  journal	= {International-Journal-of-Systems-Science},
  year		= {2000},
  volume	= {31},
  pages		= {911--21},
  abstract	= {Feature maps, in which one or more aspects of the
		  environment are systematically represented over the surface
		  of the cerebral cortex, are often found in primary sensory
		  and motor cortical regions of the vertebrate brain. They
		  have inspired a great deal of computational modelling, and
		  this has provided evidence that such maps are emergent
		  properties of the interactions of numerous cortical neurons
		  and their adaptive, nonlinear connections. We address the
		  issue of how multiple feature maps that coexist in the same
		  region of the cerebral cortex align with each other. We
		  hypothesize that such alignment is governed by temporal
		  correlations: features in one map that are temporally
		  correlated with those in another come to occupy the same
		  spatial locations over time. To examine the feasibility of
		  this hypothesis and to establish some of its detailed
		  implications, we initially studied a computational model of
		  the primary sensori-motor cortex. Coexisting sensory and
		  motor maps formed and generally aligned in a fashion
		  consistent with the temporal correlation hypothesis. We
		  summarize these results, and then mathematically analyse a
		  simplified model of self-organization during unsupervised
		  learning. We show that the properties observed
		  computationally are quite general: that temporally
		  correlated inputs become spatially correlated (i.e.
		  aligned), while input patterns that are temporally
		  anti-correlated tend to result in mutually exclusive (i.e.
		  unaligned) spatial distributions. This work provides a
		  framework in which to interpret and understand future
		  experimental studies of map relationships.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from internet}
}

@Article{	  chen01a,
  author	= {Chen, H. B. and Grant-Muller, S. and Mussone, L. and
		  Montgomery, F.},
  title		= {A study of hybrid neural network approaches and the
		  effects of missing data on traffic forecasting},
  journal	= {NEURAL COMPUTING \& APPLICATIONS},
  year		= {2001},
  volume	= {10},
  number	= {3},
  pages		= {277--286},
  abstract	= {In this pal)er we present an application of hybrid neural
		  network approaches and an assessment of the effects of
		  missing data on motorway traffic flow forecasting. Two 1
		  ybrid aj)13roaches are developed using a Self-Organising
		  Map (SOM) to initially, classify traffic into different
		  states. The first hybrid approach includes four
		  Auto-Regressive Integrated Moving Average (ARIMA) models,
		  whilst the second uses two Multi-Layer Perception (MLP)
		  models. It was found that the SOM/ARIMA hybrid approach
		  out-performs all individual ARIMA models, whilst the
		  SOM/MLP hybrid approach achieves superior forecasting
		  performance to all models used in this study, including
		  three naive models. The effects of different proportions of
		  missing data on Neural Network (NN) performance when
		  forecasting traffic flow are assessed and several initial
		  substitution options to replace missing data are discussed.
		  Overall, it is shown that ARIMA models are more sensitive
		  to the percentage of missing data than neural networks in
		  this context.},
  dbinsdate	= {2002/1}
}

@Article{	  chen01b,
  author	= {Chen, J. J. W. and Peck, K. and Hong, T. M. and Yang, S.
		  C. and Sher, YP and Shih, JY and Wu, R and Cheng, JL and
		  Roffler, SR and Wu, CW and Yang, PC},
  title		= {Global analysis of gene expression in invasion by a lung
		  cancer model},
  journal	= {CANCER RESEARCH},
  year		= {2001},
  volume	= {61},
  number	= {13},
  month		= {JUL 1},
  pages		= {5223--5230},
  abstract	= {Metastasis is a complicated multistep process that
		  involves interactions between cancer cells and their
		  surrounding microenvironments. Previously, we have
		  established a series of lung adenocarcinoma cell lines with
		  varying degrees of invasiveness. Tracheal graft assay
		  confirmed that cell lines with higher in vitro invasiveness
		  had greater in vivo invasive potential. In this study, me
		  used these model cell lines to identify invasion associated
		  genes using cDNA microarray with colorimetric detection. A
		  more invasive subline, CL 1--5-F 4, derived from metastatic
		  lung tumor of severe combined immunodeficient mice
		  inoculated with CL 1--5 cells, was combined with CL 1--0,
		  CL 1--1, and CL 1--5 in cDNA microarray screening. cDNA
		  microarray membranes, each containing 9600 nonredundant
		  expressed sequence tag clones, mere used to identify
		  differentially expressed genes in these cell lines. For
		  statistical analysis, self-organizing map algorithm was
		  performed to identify the expression patterns. Positive
		  correlation between gene expression levels and cell line
		  invasiveness was found in 2.9% of the 9600 putative genes,
		  On the other hand, negative correlation was found in 3.3%
		  of the genes. The trends of expression of some of the genes
		  were also confirmed by Northern hybridization and flow
		  cytometry, Our data demonstrated that genes related to cell
		  adhesion, motility, angiogenesis, signal transduction, and
		  some other expressed sequence tag genes may play
		  significant roles in the metastasis process. These results
		  substantiate the model system with which one can identify
		  invasion-associated genes by using cDNA microarray and
		  cancer cell lines of different invasiveness. This technique
		  may allow us to explore complex interactions between
		  multiple genes that orchestrate the process of cancer
		  metastasis.},
  dbinsdate	= {2002/1}
}

@Article{	  chen02a,
  author	= {Chen, J. and Hagiwara, I. and Su, X. and Shi, Q. Z.},
  title		= {A bispectrum feature extraction enhanced structure damage
		  detection approach},
  journal	= {JSME INTERNATIONAL JOURNAL SERIES C-MECHANICAL SYSTEMS
		  MACHINE ELEMENTS AND MANUFACTURING},
  year		= {2002},
  volume	= {45},
  number	= {1},
  month		= {MAR},
  pages		= {121--126},
  abstract	= {The subject of structure defect diagnosis has been
		  extensively investigated in the field of nondestructive
		  testing (NDT). In this paper, a new approach for detecting
		  structure damage is proposed, which is based on the
		  combination of the bispectrum feature extraction method and
		  the learning vector quantization (LVQ) identification
		  method. Because bispectrum analysis possesses the
		  capability of restraining Gaussian noise, it may be
		  employed to enhance the performance of the feature
		  extraction method. In simulation, by using the proposed
		  method, it has been shown that very high accuracy of
		  structure damage identification can be obtained compared
		  with the modal assurance criterion (MAC) formed from the
		  modal parameters method, especially in the case of low
		  signal-to-noise ratio environment.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chen90a,
  author	= {Daowen Chen and Yuqing Gao},
  title		= {Classification and trajectory for {C}hinese speech by
		  self-organization feature maps},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conference},
  year		= {1990},
  volume	= {I},
  pages		= {195},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  chen92a,
  author	= {M. -S. Chen and Hsiao-Chuan Wang},
  title		= {A decision enhanced pattern classifier based on neural
		  network approach},
  journal	= {Pattern Recognition Letters},
  year		= {1992},
  volume	= {13},
  number	= {5},
  pages		= {315--323},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chen92b,
  author	= {Oscal T. -C. Chen and Bing J. Sheu and Wai-Chi Fang},
  title		= {Adaptive Vector Quantization for Image Compression Using
		  Self-Organization Approach},
  booktitle	= {Proc. ICASSP-92, International Conference on Acoustics,
		  Speech and Signal processing},
  year		= {1992},
  volume	= {II},
  pages		= {385--388},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  chen93a,
  title		= {Artificial neural networks and their applications in
		  control and system engineering: an introduction of neural
		  networks},
  author	= {Yunping Chen},
  journal	= {Power System Technology},
  year		= {1993},
  colume	= {1},
  pages		= {56--58},
  month		= {January},
  note		= {(in Chinese)},
  annote	= {General review},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chen93b,
  author	= {X. Chen and R. Kothari and P. Klinkhachorn},
  title		= {Reduced Color Image Based on Adaptive Palette Color
		  Selection Using Neural Networks},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {I},
  pages		= {555--558},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale},
  dbinsdate	= {oldtimer}
}

@Article{	  chen94a,
  author	= {Yung-Sheng Chen and Yu-Chang Hsu},
  title		= {Image segmentation of a color-blindness plate},
  journal	= {Applied Optics},
  year		= {1994},
  volume	= {33},
  number	= {29},
  pages		= {6818--22},
  month		= {Oct},
  abstract	= {Segmentation of a color-blindness plate (CBP) image is
		  discussed and its method is developed. The method uses
		  self-organizing feature map algorithm and labeling process
		  as well as computation of spatial distance. Experimental
		  results show that the method can classify the CBP image and
		  background successfully.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chen95a,
  author	= {Yifeng Chen and Yuanda Cao},
  title		= {A Hybrid Neural Network for Spatio-Temporal Pattern
		  Recognition},
  volume	= {III},
  pages		= {1414--1417},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chen95b,
  author	= {Yifeng Chen and Zhuoqun Xu},
  title		= {A High-Dimensional {SOFM} Vector Quantizer With Weightless
		  Neural Prediction},
  volume	= {III},
  pages		= {1418--1421},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chen95c,
  author	= {Yifeng Chen},
  title		= {A high-dimensional {SOFM} neural vector quantizer for
		  image compression},
  booktitle	= {Proceedings of International Conference on Neural
		  Information Processing (ICONIP `95)},
  year		= {1995},
  editor	= {Zhong, Y. and Yang, Y. and Wang, M. },
  volume	= {2},
  pages		= {698--702},
  organization	= {Comput. Sci. Dept. , Peking Univ. , Beijing, China},
  publisher	= {Publishing House of Electron. Ind},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chen95d,
  author	= {Ting-Yu Chen and Jih-Chang Wang and Hsin-Li Chang},
  title		= {Applying habitual domains to modify the
		  \mbox{self-organizing} map},
  booktitle	= {Fuzzy Logic for the Applications to Complex Systems.
		  Proceedings of the International Joint Conference of
		  CFSA/IFIS/SOFT '95 on Fuzzy Theory and Applications},
  publisher	= {World Scientific},
  year		= {1995},
  editor	= {W. L. Chiang and J. Lee},
  address	= {Singapore},
  pages		= {302--7},
  dbinsdate	= {oldtimer}
}

@Article{	  chen96a,
  author	= {Hsinchun Chen and C. Schuffels and R. Orwig},
  title		= {Internet categorization and search: a
		  \mbox{self-organizing} approach},
  journal	= {Journal of Visual Communication and Image Representation},
  year		= {1996},
  volume	= {7},
  number	= {1},
  pages		= {88--102},
  abstract	= {The problems of information overload and vocabulary
		  differences have become more pressing with the emergence of
		  increasingly popular Internet services. The main
		  information retrieval mechanisms provided by the prevailing
		  Internet WWW software are based on either keyword search
		  (e.g., the Lycos server at CMU, the Yahoo server at
		  Stanford) or hypertext browsing (e.g., Mosaic and
		  Netscape). This research aims to provide an alternative
		  concept-based categorization and search capability for WWW
		  servers based on selected machine learning algorithms. Our
		  proposed approach, which is grounded on automatic textual
		  analysis of Internet documents (homepages), attempts to
		  address the Internet search problem by first categorizing
		  the content of Internet documents. We report results of our
		  recent testing of a multilayered neural network clustering
		  algorithm employing the Kohonen self-organizing feature map
		  to categorize (classify) Internet homepages according to
		  their content. The category hierarchies created could serve
		  to partition the vast Internet services into
		  subject-specific categories and databases and improve
		  Internet keyword searching and/or browsing.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chen96b,
  author	= {O. T. -C. Chen and Chih-Yung Chen and Hwai-Tsu Cheng and
		  Fang-Ru Hsu and Huang-Lin Yang and Youn-Gwo Lee},
  title		= {A multi-lingual speech recognition system using a neural
		  network approach},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1576--81},
  dbinsdate	= {oldtimer}
}

@Article{	  chen97a,
  author	= {Yuhai Chen and A. T. Chwang},
  title		= {Self-organized feature map of particle image for flow
		  measurement},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3172},
  pages		= {142--52},
  dbinsdate	= {oldtimer}
}

@Article{	  chen98a,
  author	= {Hsinchun Chen and Andrea L. Houston and Robin R. Sewell
		  and Bruce R. Schatz},
  title		= {Internet Browsing and Searching: User Evaluations of
		  Category Map and Concept Space Techniques},
  journal	= {Journal of the American Society for Information Science},
  year		= 1998,
  volume	= {49},
  pages		= {582--603},
  abstract	= {The Internet provides an exceptional testbed for
		  developing algorithms that can improve browsing and
		  searching large information spaces. Browsing and searching
		  tasks are susceptible to problems of information overload
		  and vocabulary differences. Two of these algorithms, a
		  Kohonen algorithm category map for browsing and an
		  automatically generated concept space algorithm for
		  searching, are investigated if they can help improve
		  browsing and/or searching the Internet.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chen99a,
  author	= {Chen, Z. and Feng, T. J. and Houkes, Z.},
  title		= {Texture segmentation based on wavelet and {K}ohonen
		  network for remotely sensed images},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and Cybernetics.
		  },
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {6},
  pages		= {816--21},
  abstract	= {In this paper, an approach based on wavelet decomposition
		  and Kohonen's self-organizing map is developed for image
		  segmentation. After performing the 2D wavelet transform of
		  image, some features are extracted for texture
		  segmentation, and the Kohonen neural network is used to
		  accomplish feature clustering. The experimental results
		  demonstrated the satisfactory effect of the proposed
		  approach both for simulated textured image and
		  multi-spectral remotely sensed images.},
  dbinsdate	= {oldtimer}
}

@Article{	  chen99b,
  author	= {Chen, Zengqiang and He, Jiangfeng and Yuan, Zhuzhi},
  title		= {An adaptive identification and control scheme using radial
		  basis function networks},
  journal	= {Journal of Systems Engineering and Electronics},
  year		= {1999},
  volume	= {10},
  number	= {1},
  pages		= {54--61},
  abstract	= {Adaptive identification and control of nonlinear dynamical
		  systems are investigated using radial basis function
		  networks (RBF). Firstly, a novel approach to train the RBF
		  is introduced, which employs an adaptive fuzzy generalized
		  learning vector quantization (AFGLVQ) technique and
		  recursive least squares algorithm with variable forgetting
		  factor (VRLS). The AFGLVQ adjusts the centers of the RBF
		  while the VRLS updates the connection weights of the
		  network. The identification algorithm has the properties of
		  rapid convergence and persistent adaptability that make it
		  suitable for real-time control. Secondly, on the basis of
		  the one-step ahead RBF predictor, the control law is
		  optimized iteratively through a numerical stable Davidon's
		  least squares-based (SDLS) minimization approach. Four
		  nonlinear examples are simulated to demonstrate the
		  effectiveness of the identification and control
		  algorithms.},
  dbinsdate	= {oldtimer}
}

@Article{	  chen99c,
  author	= {Chen, T. and Chen, L. H. and Ma, K. K.},
  title		= {Colour image indexing using {SOM} for region-of-interest
		  retrieval},
  journal	= {Pattern Analysis and Applications},
  year		= {1999},
  volume	= {2},
  pages		= {164--71},
  abstract	= {We present a novel approach to image indexing by
		  incorporating the Kohonen's self-organising map (SOM) for
		  content-based image retrieval. An important and unique
		  aspect of our interactive scheme is to allow the user to
		  select a region-of-interest from the sample image, and
		  subsequent query concentrates on matching the regional
		  colour features to find images containing similar regions
		  as indicated by the user. The {SOM} algorithm is capable of
		  adaptively partitioning each image into several homogeneous
		  regions for representing and indexing the image. This is
		  achieved by unsupervised clustering and classification of
		  pixel-level features, called local neighbourhood
		  histograms, without a priori knowledge about the data
		  distribution in the feature space. The indexes generated
		  from the resultant prototypes of {SOM} learning demonstrate
		  fairly good performance over an experimental image
		  database, and therefore suggest the effectiveness and
		  significant potential of our proposed indexing and
		  retrieval strategy for application to content-based image
		  retrieval.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cheneval95a,
  author	= {Y. Cheneval},
  title		= {Packlib, an interactive environment to develop modular
		  software for data processing},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {673--82},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cheng00a,
  author	= {Guojian Cheng and Andreas Zell},
  title		= {Externally Growing Cell Structures for Pattern
		  Classification},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {University Tubingen; Wilhelm-Schickard-Institut for
		  Computer Science, Dept. Computer Architecture},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}



@InProceedings{	  cheng92a,
  author	= {Yizong Cheng},
  title		= {Clustering with Competing Self-Organizing Maps},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {785--790},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cheng92b,
  author	= {Y. M. Cheng and others},
  title		= {Hybrid Segmental-{LVQ} for Large Vocabulary Speech
		  Recognition},
  booktitle	= {Proc. ICASSP-92, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1992},
  volume	= {I},
  pages		= {593--596},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cheng92c,
  author	= {Qiming Cheng and Shujing Zhang},
  title		= {A neural network for spectrum estimation of
		  quasi-stationary signal},
  booktitle	= {Proceedings of the IEEE International Symposium on
		  Industrial Electronics},
  year		= {1992},
  volume	= {1},
  pages		= {419--22},
  organization	= {Inst. of Inf. Sci. , Northern Jiaotong Univ. , Beijing,
		  China},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cheng94a,
  author	= {G. Cheng and X. Liu and J. X. Wu},
  title		= {Interactive Knowledge Discovery through
		  {S}elf-{O}rganizing {F}eature {M}aps},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {IV},
  pages		= {430--434},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, medical analysis},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cheng96a,
  author	= {Gongxian Cheng and Xiaohui Liu and J. Wu and B. Jones and
		  R. Hitchings},
  title		= {Discovering knowledge from visual field data: results in
		  optic nerve diseases},
  booktitle	= {Medical Informatics Europe '96: Human Facets in
		  Information Technologies},
  publisher	= {IOS Press},
  year		= {1996},
  editor	= {J. Brender and J. P. Christensen and J. -R. Scherrer and
		  P. McNair},
  address	= {Amsterdam, Netherlands},
  pages		= {629--33},
  dbinsdate	= {oldtimer}
}

@Article{	  cheng97a,
  author	= {Yizong Cheng},
  title		= {Convergence and ordering of {K}ohonen's batch map},
  journal	= {Neural Computation},
  year		= {1997},
  volume	= {9},
  number	= {8},
  pages		= {1667--76},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cheng98a,
  author	= {Yizong Cheng},
  title		= {Batch \mbox{self-organizing} maps on a unit sphere},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {2273--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cheng99a,
  author	= {Cheng, Lin Liu and Nakagawa, M.},
  title		= {Prototype learning algorithms for nearest neighbor
		  classifier with application to handwritten character
		  recognition},
  booktitle	= {Proceedings of the Fifth International Conference on
		  Document Analysis and Recognition. ICDAR '99},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {378--81},
  abstract	= {This paper reviews some prototype learning algorithms for
		  nearest neighbor (NN) classifier design land evaluates
		  their performances in handwritten character recognition.
		  The algorithms include the well-known LVQ and those that
		  globally optimize an objective function, as well as some
		  newly derived variants. Experimental results of handwritten
		  numeral recognition and Chinese character recognition show
		  that the global optimization algorithms generally
		  outperform LVQ. Particularly, the generalized LVQ of Sato
		  and Yamada (1998) and a new algorithm MAXP2 yield best
		  results.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cherkassky90a,
  author	= {V. Cherkassky and H. Lari-Najafi},
  title		= {Self-organizing neural network for nonparametric
		  regression analysis},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  volume	= {I},
  pages		= {370--374},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  cherkassky91a,
  author	= {V. Cherkassky and H. Lari-Najafi},
  title		= {Constrained topological mapping for nonparametric
		  regression analysis},
  journal	= {Neural Networks},
  year		= {1991},
  volume	= {4},
  number	= {1},
  pages		= {27--40},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cherkassky91b,
  author	= {V. Cherkassky and Y. Lee and H. Lari-Najafi},
  title		= {Self-organizing network for regression: efficient
		  implementation and comparative evaluation},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {79--84},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {A new method called Constrained Topological Mapping (CTM)
		  has been recently proposed for nonparametric regression
		  analysis (Cherkassky and Lari-najafi, 1990). The CTM
		  algorithm is a modification of Kohonen's self-organizing
		  maps suitable for regression problems. We discuss efficient
		  software implementations of the algorithm that may be
		  especially attractive for multivariate problems which
		  require the large number of units in a map. We also present
		  experimental comparisons with alternative neural network
		  approaches (backpropagation) and conventional approaches
		  (Projection Pursuit) to regression.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cherkassky92a,
  author	= {Vladimir Cherkassky},
  title		= {Neural Networks and Nonparametric Regression},
  booktitle	= {Workshop on Neural Networks for Signal Processing},
  year		= {1992},
  editor	= {S. Y. Kung and F. Fallside and J. Aa. Sorensen and C. A.
		  Kamm},
  pages		= {511--521},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  cherkassky92b,
  author	= {Vladimir Cherkassky and Hossein Lari-Najafi},
  title		= {Data Representation for Diagnostic Neural Networks},
  journal	= {{IEEE} Expert},
  year		= {1992},
  volume	= {7},
  number	= {5},
  pages		= {43--53},
  month		= {October},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cherkassky92c,
  author	= {Vladimir Cherkassky and Filip Mulier},
  title		= {Conventional and neural approaches to regression},
  booktitle	= {Proc. SPIE Conf. on Appl. of Artificial Neural Networks},
  year		= {1992},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cherkassky97a,
  author	= {Vladimir Cherkassky and Younggyun Kim and Filip Mulier},
  title		= {Constrained Topological Maps for Regression and
		  Classification},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {1},
  address	= {Singapore},
  pages		= {330--333},
  dbinsdate	= {oldtimer}
}

@Article{	  cherubini92a,
  author	= {A. Cherubini and R. Odorico},
  title		= {Discrimination of pp to tt events by a neural network
		  classifier},
  journal	= {Z. Physik C [Particles and Fields]},
  year		= {1992},
  volume	= {53},
  number	= {1},
  pages		= {139--148},
  x		= {. . . Performance of the NN as a tt event classifier is
		  found to be less satisfactory than that achievable by
		  statistical methods. },
  dbinsdate	= {oldtimer}
}

@Article{	  cherubini92b,
  author	= {A. Cherubini and R. Odorico},
  title		= {{LVQ NET 1.10}---a program for neural net and statistical
		  pattern recognition},
  journal	= {Computer Phys. Communications},
  year		= {1992},
  volume	= {72},
  number	= {2--3},
  pages		= {249--264},
  month		= {November},
  x		= {A neural net program for pattern classification is
		  presented. The neural net architecture is based on an
		  improved version of Kohonen's (1988) learning vector
		  organization: learning vector quantization with training
		  count. . . . },
  dbinsdate	= {oldtimer}
}

@Article{	  cheu95a,
  author	= {R. L. Cheu and S. G. Ritchie},
  title		= {Automated detection of lane-blocking freeway incidents
		  using artificial neural networks},
  journal	= {Transportation Research Part C [Emerging Technologies]},
  year		= {1995},
  volume	= {3C},
  number	= {6},
  pages		= {371--88},
  dbinsdate	= {oldtimer}
}

@Article{	  cheu95b,
  author	= {R. L. Cheu and S. G. Ritchie},
  title		= {Loop-based freeway incident detection using neural
		  networks},
  journal	= {IES Journal},
  year		= {1995},
  volume	= {35},
  number	= {2},
  pages		= {26--32},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cheung93a,
  author	= {Cheung, E. S. H. and Constantinides, A. G. },
  title		= {Fast nearest neighbour search algorithms for
		  \mbox{self-organising} map and vector quantisation},
  booktitle	= {Conference Record of The Twenty-Seventh Asilomar
		  Conference on Signals, Systems and Computers},
  year		= {1993},
  editor	= {Singh, A. },
  volume	= {2},
  pages		= {946--50},
  organization	= {Dept. of Electr. \& Electron. Eng. , Imperial Coll. of
		  Sci. , Technol. \& Med. , London, UK},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chi00a,
  author	= {Chi, Sheng-Chai and Kuo, Ren-Jien and Teng, Po-Wen},
  title		= {Fuzzy self-organizing map neural network for market
		  segmentation of credit card},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {3617--3622},
  organization	= {I-Shou Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Up to date, the proposed clustering analysis methods are
		  tremendous. In most of the methods, however, human-made
		  determination like the number of clustering groups should
		  be decided previously. Not only will the result be affected
		  by the subjective point of view of the decision-maker, but
		  also clustering efficiency is not good enough. To overcome
		  these drawbacks, this research attempts to combine fuzzy
		  sets theory with the unsupervised learning network model to
		  create an unsupervised Fuzzy Self-Organizing Map (FSOM)
		  model. This model integrates artificial neural network with
		  fuzzy sets theory to take respective advantages of learning
		  function and the capability of handling uncertainty problem
		  in human recognition process. Generally, the fuzzy
		  clustering analysis model developed in the research can
		  completely explain the results from the experiments.
		  Besides, this model seems more useful and practical than
		  other clustering methods. The integration of FSOM and BPN
		  networks to establish an intelligent decision support
		  system can improve the problem of being unable to quickly
		  analyze a new customer information and effectively response
		  a suggestion to the decision maker.},
  dbinsdate	= {2002/1}
}

@Article{	  chi95a,
  author	= {Zheru Chi and Hong Yan},
  title		= {Handwritten Numeral Recognition Using a Small Number of
		  Fuzzy Rules With Optimized Defuzzification Parameters},
  journal	= {Neural Networks},
  year		= {1995},
  volume	= {8},
  number	= {5},
  pages		= {821--827},
  publisher	= {Elsevier Science Ltd},
  dbinsdate	= {oldtimer}
}

@Article{	  chi95b,
  author	= {Chi, Z. and Wu, J. and Yan, H. },
  title		= {Handwritten numeral recognition using
		  \mbox{self-organizing} maps and fuzzy rules},
  journal	= {Pattern Recognition},
  year		= {1995},
  volume	= {28},
  number	= {1},
  pages		= {59--66},
  month		= {Jan},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chialvo97a,
  author	= {Dante R. Chialvo},
  title		= {Mapping {Sameness{ into }Neighborness}},
  booktitle	= {Fractals in the Natural and Applied Sciences},
  publisher	= {World Scientific},
  year		= 1997,
  editor	= {Novak},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chiang95a,
  author	= {Jung-Hsien Chiang and P. Gader},
  title		= {Improving digit recognition reliability by a hybrid neural
		  model},
  booktitle	= {Fuzzy Logic for the Applications to Complex Systems.
		  Proceedings of the International Joint Conference of
		  CFSA/IFIS/SOFT '95 on Fuzzy Theory and Applications},
  publisher	= {World Scientific},
  year		= {1995},
  editor	= {W. L. Chiang and J. Lee},
  address	= {Singapore},
  pages		= {182--7},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chiang96a,
  author	= {Jung-Hsien Chiang and P. Gader},
  title		= {A hybrid feature extraction framework for handwritten
		  numeric fields recognition},
  booktitle	= {Proceedings of the 13th International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  volume	= {4},
  address	= {Los Alamitos, CA, USA},
  pages		= {436--40},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chiang96b,
  author	= {Jung-Hsien Chiang and P. Gader},
  title		= {A hybrid fuzzy feature extraction framework for
		  handwritten numeric fields recognition},
  booktitle	= {Proceedings of the Fifth IEEE International Conference on
		  Fuzzy Systems. FUZZ-IEEE '96},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1881--5},
  dbinsdate	= {oldtimer}
}

@Article{	  chiang97a,
  author	= {Jung-Hsien Chiang and P. D. Gader},
  title		= {Hybrid fuzzy-neural systems in handwritten word
		  recognition},
  journal	= {IEEE Transactions on Fuzzy Systems},
  year		= {1997},
  volume	= {5},
  number	= {4},
  pages		= {497--510},
  dbinsdate	= {oldtimer}
}

@Article{	  chiang97b,
  author	= {Jung-Saien Chiang and P. D. Gader},
  title		= {Recognition of handprinted numerals in VISA(R) card
		  application forms},
  journal	= {Machine Vision and Applications},
  year		= {1997},
  volume	= {10},
  number	= {3},
  pages		= {144--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chicco01a,
  author	= {Chicco, G. and Napoli, R. and Piglione, F.},
  title		= {Load pattern clustering for short-term load forecasting of
		  anomalous days},
  booktitle	= {2001 IEEE Porto Power Tech Proceedings. IEEE, Piscataway,
		  NJ, USA},
  year		= {2001},
  volume	= {2},
  pages		= {6},
  abstract	= {Load forecasting algorithms try to capture regular
		  behaviours in historic load time series in order to perform
		  an accurate forecast. The presence of anomalous days
		  (holidays, working days between holidays, social events) is
		  a serious drawback and requires a dedicated forecast. The
		  successful application of artificial neural networks (ANN)
		  in this field suggested the use of the Kohonen
		  Self-Organising Map for clustering the similar load
		  patterns and classifying day typologies. In order to
		  evaluate the benefits of this choice, this work compares
		  the Kohonen map with a classic clustering algorithm, both
		  applied to grouping the daily load patterns in homogeneous
		  sets. The information gathered by the clustered data is
		  then applied to the 24-hour ahead load forecasting of
		  anomalous days, by means of an ANN-based approach. The
		  results show that the combined use of both clustering
		  techniques allows better understanding of the anomalous
		  load patterns.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chicco01b,
  author	= {Chicco, G. and Napoli, R. and Piglione, F.},
  title		= {Neural networks for fast voltage prediction in power
		  systems},
  booktitle	= {2001 IEEE Porto Power Tech Proceedings. IEEE, Piscataway,
		  NJ, USA},
  year		= {2001},
  volume	= {2},
  pages		= {5},
  abstract	= {In power system security assessment, the prediction
		  methods allow fast local approximation of the numeric load
		  flow algorithm. A preliminary contingency screening is then
		  obtained by quick estimation of the postfault bus voltages
		  and line power flows. In last decades artificial neural
		  networks (ANN) proved to be very suited for approximation
		  of complex input-output relationships, learnt from a set of
		  samples. However, most proposed methods employ the classic
		  multi-layer perceptron trained with the back-propagation
		  algorithm, which lacks fast learning capabilities. In this
		  paper, we present some different approaches to the fast
		  voltage prediction task. For this purpose, we compare the
		  capabilities of three fast learning ANNs (the radial basis
		  function network, the progressive learning network, and the
		  self-organising map used as associative memory). Simulation
		  tests, referred to normal and post-fault conditions, have
		  been carried out in a wide range of operating scenarios.},
  dbinsdate	= {2002/1}
}

@InCollection{	  chin91a,
  author	= {V. H. Chin},
  title		= {Performance of selected speech features for isolated digit
		  recognition of speech by a neural network model},
  booktitle	= {C-CORE Publication no. 91--15},
  publisher	= {C-CORE},
  year		= {1991},
  x		= {nt9093b---AN ACCESSION NUMBER: MIC9201900XSP Julkaisun
		  tyyppi on epaselvahko; naista NTIS-tietueista ei oikein saa
		  selvaa siita, etta mista oikein on kyse. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ching00a,
  author	= {Ching Tang Hsieh and Mu Chun Su and Uei Jyh Chen and Horng
		  Jae Lee},
  title		= {A new generalized learning vector quantization algorithm},
  booktitle	= {IEEE APCCAS 2000. 2000 IEEE Asia-Pacific Conference on
		  Circuits and Systems. Electronic Communication Systems.
		  IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {},
  pages		= {339--44},
  abstract	= {A new approach to data clustering which is capable of
		  detecting clusters of different shapes is proposed. In
		  classical clustering approaches, it is a great challenge to
		  separate clusters if the cluster prototypes are difficult
		  to represent by a mathematical formula. In this paper, we
		  propose an improved learning vector quantization (LVQ)
		  algorithm using the concept of symmetry. Through several
		  computer simulations, the results show that the proposed
		  method with random initialization is effective in detecting
		  linear, spherical and ellipsoidal clusters. Besides, this
		  method can also solve the crossed question.},
  dbinsdate	= {2002/1}
}

@Article{	  chinnam99a,
  author	= {Chinnam, Ratna Babu},
  title		= {On-line reliability estimation of individual components,
		  using degradation signals},
  journal	= {IEEE Transactions on Reliability, ional Society for
		  Optical Engineering},
  year		= {1999},
  number	= {4},
  volume	= {48},
  pages		= {403--412},
  abstract	= {This paper provides a unique approach that allows
		  'determination of a component's reliability as it degrades
		  with time' by monitoring its degradation measures. The
		  concepts have been implemented using: finite-duration
		  impulse response multi-layer perceptron neural networks for
		  modeling degradation measures, and self-organizing maps for
		  modeling degradation variation. The specific application
		  considered is in-process monitoring of the condition of the
		  drill-bit in a drilling process, using the torque and
		  thrust signals. An approach to compute prediction limits
		  for any feedforward neural network, critical for on-line
		  performance reliability monitoring of systems using neural
		  networks, is introduced by combining the network with a
		  self-organizing map. Experimental results show that neural
		  networks are effective in: modeling the degradation
		  characteristics of the monitored drill-bits, and predicting
		  conditional and unconditional performance reliabilities as
		  they degrade with time or usage. In contrast to traditional
		  approaches, this approach to on-line performance
		  reliability monitoring opens new avenues for better
		  understanding and monitoring systems that exhibit failures
		  through degradation. Essentially, implementation of this
		  'performance reliability monitoring' reduces overall
		  operations costs by facilitating optimal
		  component-replacement and maintenance strategies.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chiou93a,
  author	= {Chiou, Y. -S. P. and Lure, Y. M. F. and Freedman, M. T.
		  and Fritz, S. },
  title		= {Application of neural network based hybrid system for lung
		  nodule detection},
  booktitle	= {Proceedings of Sixth Annual IEEE Symposium on
		  Computer-Based Medical Systems},
  year		= {1993},
  pages		= {211--16},
  organization	= {Caelum Res. Corp. , Silver Spring, MD, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  chiou93b,
  author	= {Chiou, Y. S. and Lure, Yuan Ming F. and Ligomenides, Panos
		  A. and Freedman, Matthew T. M. D. and Fritz, Steven L.},
  title		= {Shape feature analysis using artificial neural networks
		  for improvements of hybrid lung nodule detection system.},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering, Society of photo-optical instrumentation
		  engineers.},
  year		= {1993},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  number	= {},
  volume	= {1898},
  pages		= {609--617},
  abstract	= {A neural network-based Hybrid Lung Nodule Detection (HLND)
		  system is developed for improving diagnostic accuracy and
		  speed for lung cancerous pulmonary radiology. The
		  configuration of the HLND system includes the following
		  processing phases: (1) data acquisition and pre-processing,
		  in order to reduce and to enhance the figure-background
		  contrast; (2) quick selection of nodule suspects based upon
		  the most prominent feature of nodules, the disc shape; and
		  (3) complete feature space determination and neural
		  classification of nodules. Nodule suspects are captured and
		  stored in 32 x 32 images after first two processing phases.
		  Eight categories are identified for further neural analysis
		  and classification. A gradient operation for edge
		  enhancement is applied to the suspected image block to
		  obtain two parameters for description of shape features. A
		  self-organized Kohonen feature map is developed for
		  analysis and classification of the derived parameters.
		  Histogram equalization technique is then applied to the
		  weights of trained Kohonen nets for further information
		  enhancement. Analysis of weights of trained neural net and
		  physics of each anatomic class is able to provide the
		  insights of each shape feature as well as to accurately
		  detect the nodules.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chiou94a,
  author	= {Lih-Yih Chiou and Jimmy Limqueco and Jun Tian and
		  Chidchanok Lirsinsap and Henry Chu},
  title		= {Modified Frequency Sensitive {S}elf-{O}rganization
		  {N}eural {N}etwork for Image Data Compression},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {342--347},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, modification, image compression},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chitralekha99a,
  author	= {Chitralekha, R. and Iftekharuddin, K. M.},
  title		= {Detection of targets varying in fine details, rotation,
		  and translation},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.3805},
  year		= {1999},
  volume	= {3805},
  pages		= {146--55},
  abstract	= {Automatic target recognition (ATR) has been a topic of
		  interest for many researchers because of its applications
		  in the fields of defense, manufacturing, health sciences
		  etc. The ability of massive parallelism and high-speed
		  classification of neural network (NN) makes it a good
		  choice for ATR. In this paper, we present a novel ATR
		  approach for targets varying in fine details, rotation and
		  translation using a learning vector quantization (LVQ) NN.
		  The algorithm includes two phases such as the feature
		  extraction and the NN discrimination. The feature
		  extraction algorithm obtains the features of the original
		  and distorted targets in the Fourier-log-polar domain and
		  clusters them into a set of centers. These centers are then
		  applied as inputs to an LVQ NN for training. We explore two
		  distinct discrimination algorithms. In the first algorithm,
		  unrotated target features are applied as training vectors
		  and the network is tested with features of rotated targets.
		  In the second algorithm, the LVQ NN is trained using the
		  rotated images and tested for unknown rotated target
		  features. The algorithm is also applied for the cases of
		  targets varying in fine details and translation and a
		  combination of rotation, fine details and translation.},
  dbinsdate	= {2002/1}
}

@Article{	  chiu01a,
  author	= {Chiu, M. -J. and Lin, C. -C. and Chuang, K. -H. and Chen,
		  J.-H. and Huang, K.-M.},
  title		= {Tissue segmentation-assisted analysis of f{MRI} for human
		  motor response: An approach combining artificial neural
		  network and fuzzy C means},
  journal	= {Journal of Digital Imaging},
  year		= {2001},
  volume	= {14},
  number	= {1},
  month		= {March 2001},
  pages		= {38--47},
  organization	= {Institute of Electrical Engineering, College of Electrical
		  Engineering, National Taiwan University},
  publisher	= {},
  address	= {},
  abstract	= {The authors have developed an automated algorithm for
		  segmentation of magnetic resonance images (MRI) of the
		  human brain. They investigated the quantitative analysis of
		  tissue-specific human motor response through an approach
		  combining gradient echo functional MRI and automated
		  segmentation analysis. Fifteen healthy volunteers, placed
		  in a 1.5 T clinical MR imager, performed a self-paced
		  finger opposition throughout the activation periods.
		  T<sub>1</sub>-weighted images (WI), T<sub>2</sub>WI, and
		  proton density WI were acquired for segmentation analysis.
		  Single-slice axial T<sub>2</sub><sup>*</sup> fast low-angle
		  shot (FLASH) images were obtained during the functional
		  study. Pixelwise cross-correlation analysis was performed
		  to obtain an activation map. A cascaded algorithm,
		  combining Kohonen feature maps and fuzzy C means, was
		  applied for segmentation. After processing, masks for gray
		  matter, white matter, small vessels, and large vessels were
		  generated. Tissue-specific analysis showed a signal change
		  rate of 4.53% in gray matter, 2.98% in white matter, 5.79%
		  in small vessels, and 7.24% in large vessels. Different
		  temporal patterns as well as different levels of activation
		  were identified in the functional response from various
		  types of tissue. High correlation exists between cortical
		  gray matter and subcortical white matter (r = 0.957), while
		  the vessel behaves somewhat different temporally. The
		  cortical gray matter fits best to the assumed input
		  function (r = 0.957) followed by subcortical white matter
		  (r = 0.829) and vessels (r = 0.726). The automated
		  algorithm of tissue-specific analysis thus can assist
		  functional MRI studies with different modalities of
		  response in different brain regions.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  chiuderi94a,
  author	= {Chiuderi, A. and Fini, S. and Cappellini, V. },
  title		= {An application of data fusion to landcover classification
		  of remote sensed imagery: a neural network approach},
  booktitle	= {1994 IEEE International Conference on MFI '94. Multisensor
		  Fusion and Integration for Intelligent Systems},
  year		= {1994},
  pages		= {756--62},
  organization	= {Dipartimento di Ingegneria Elettronica, Florence, Italy},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chiueh93a,
  author	= {Tzi-Dar Chiueh and Tser-Tzi Tang and Lian-Gee Chen},
  title		= {Vector Quantization Using Tree-Structured
		  {S}elf-{O}rganizing {F}eature {M}aps},
  booktitle	= {Proc. Int. Workshop on Application of Neural Networks to
		  Telecommunications},
  year		= {1993},
  editor	= {Joshua Alspector and Rodney Goodman and Timothy X. Brown},
  pages		= {259--265},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, modification, image coding},
  dbinsdate	= {oldtimer}
}

@Article{	  chiueh94a,
  author	= {Tzi-Dar Chiueh and Tser-Tzi Tang and Lian-Gee Chen},
  title		= {Vector Quantization Using Tree-Structured
		  {S}elf-{O}rganizing {F}eature {M}aps},
  journal	= {IEEE Journal on Selected Areas in Communications},
  year		= {1994},
  volume	= {12},
  number	= {9},
  pages		= {1594--1599},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@Article{	  cho00a,
  author	= {Cho, Sung Bae},
  title		= {Ensemble of structure-adaptive \mbox{self-organizing} maps
		  for high performance classification},
  journal	= {Information Sciences},
  year		= {2000},
  number	= {1},
  volume	= {123},
  pages		= {103--114},
  abstract	= {Combining multiple models has been recently exploited for
		  the development of reliable neural networks. This paper
		  introduces a structure-adaptive self-organizing map (SOM)
		  which can adapt the structure as well as the weights, and
		  presents a method to improve the performance by combining
		  the multiple maps. The structure-adaptive {SOM} places the
		  nodes of prototype vectors into the pattern space properly
		  so as to make the decision boundaries as close to the class
		  boundaries as possible. In order to show the performance of
		  the proposed method, experiments with the unconstrained
		  handwritten digit database of Concordia University in
		  Canada have been conducted.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cho01a,
  author	= {Sung-Bae Cho},
  title		= {Self-Organizing Map Ensembles with Structure Adaptation},
  booktitle	= {International Joint Conference on Neural Networks,
		  Washington, DC, USA, July 15--19},
  pages		= {},
  year		= {2001},
  month		= {July},
  note		= {CD-ROM},
  dbinsdate	= {2002/1}
}

@Article{	  cho02a,
  author	= {Sung Bae Cho},
  title		= {Applying {WEBSOM} to automatic {FAQ} e-mail
		  classification},
  journal	= {International-Journal-of-Knowledge-Based-Intelligent-Engineering-Systems}
		  ,
  year		= {2002},
  volume	= {6},
  pages		= {17--22},
  abstract	= {Recently, several services to facilitate fluent
		  utilization of the information in Internet have appeared
		  due to the proliferation of computer and Internet. However,
		  many computer users are not so familiar with such services
		  that they need assistance to use them easily. In this paper
		  we propose a two-level self-organizing map (SOM) to
		  automatically respond to the users' e-mail questions on
		  Internet portal service, and help them to find their answer
		  for themselves by browsing the map hierarchically. The
		  system consists of keyword clustering SOM to reduce a
		  variable length e-mail to a normalized vector and document
		  classification SOM to classify the question into an answer
		  class. The final map is also used for browsing. Experiments
		  with real world data from Hanmail net, the biggest portal
		  service in Korea, show that the SOM is useful and the
		  browsing based on the map is conceptual and efficient.},
  dbinsdate	= {2002/1},
  merjnote      = {last name chechked from internet}
}

@InProceedings{	  cho94a,
  author	= {Kwang Bo Cho and Cheol Hoon Park and Soo-Young Lee},
  title		= {Image Compression using Multi-layer Perceptron with Block
		  Classification and {SOFM} Coding},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {III},
  pages		= {26--31},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, image compression, hybrid},
  dbinsdate	= {oldtimer}
}

@Article{	  cho95a,
  author	= {Seongwon Cho and Jinwuk Seok},
  title		= {Self-organizing feature map with constant learning rate
		  and binary reinforcement},
  journal	= {Journal of the Korean Institute of Telematics and
		  Electronics},
  year		= {1995},
  volume	= {32B},
  number	= {1},
  pages		= {180--8},
  month		= {Jan},
  dbinsdate	= {oldtimer}
}

@Article{	  cho96a,
  author	= {Hyun-Chul Cho and Kee-Seong Lee and Geon Sa-Gong},
  title		= {3-D object recognition independent of the translation and
		  rotation using an ultrasonic sensor array and invariant
		  moments},
  journal	= {Transactions of the Korean Institute of Electrical
		  Engineers},
  year		= {1996},
  volume	= {45},
  number	= {10},
  pages		= {1494--9},
  dbinsdate	= {oldtimer}
}

@Article{	  cho96b,
  author	= {Sungzoon Cho and Min Jang and J. A. Reggia},
  title		= {Effects of varying parameters on properties of
		  \mbox{self-organizing} feature maps},
  journal	= {Neural Processing Letters},
  year		= {1996},
  volume	= {4},
  number	= {1},
  pages		= {53--9},
  abstract	= {The behavior of self-organizing feature maps is critically
		  dependent on parameters such as lateral connection radius,
		  lateral inhibition intensity, and network size. With no
		  theoretical guidelines for the choice of these parameters,
		  they are usually selected through a trial-and-error
		  process. In order to provide heuristic guidelines for
		  future model designers as well as to give insight into
		  which model features are responsible for specific aspects
		  of maps, we systematically varied these parameters and
		  studied their effects on the properties of a
		  self-organizing feature map. The connectivity radius was
		  found to determine the size of activation clusters
		  quadratically. As the intensity of lateral inhibition was
		  varied, feature patterns varied from stripe-like to
		  clusters in the map, with other intermediate patterns also
		  occurring. The number of clusters of each feature increase
		  nonlinearly as the network size increased.},
  dbinsdate	= {oldtimer}
}

@Article{	  cho97a,
  author	= {Sung-Bae Cho},
  title		= {Neural-network classifiers for recognizing totally
		  unconstrained handwritten numerals},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  volume	= {8},
  number	= {1},
  pages		= {43--53},
  dbinsdate	= {oldtimer}
}

@Article{	  cho97b,
  author	= {Sung-Bae Cho},
  title		= {Self-Organizing Map with Dynamical Node Splitting:
		  Application to Handwritten Digit Recognition},
  journal	= {Neural Computation},
  year		= 1997,
  volume	= 9,
  pages		= {1345--1355},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cho97c,
  author	= {Sung-Bae Cho},
  title		= {Handwritten Digit Recognition by Combining
		  Structure-Adaptive Self-Organzing Maps},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {1231--1234},
  dbinsdate	= {oldtimer}
}

@Article{	  cho98a,
  author	= {Seongwon Cho},
  title		= {Self-organizing map with time-invariant learning rate and
		  its exponential stability analysis},
  journal	= {Neurocomputing},
  year		= 1998,
  volume	= 19,
  pages		= {1--11},
  abstract	= {In this paper a new self-organizing map (SOM) is developed
		  that is suitable for digital hardware implementation. In
		  the proposed neural model, a time-invariant learning rate
		  is used, whereas the original Kohonen {SOM} uses a
		  time-varying learning rate. There is a binary
		  re-inforcement term in order to compensate for the lowered
		  learning ability due to the constant learning rate. The
		  proposed {SOM} is exponentially stable. The experimental
		  results conducted with two different types of data show
		  that the proposed method has better learning ability than
		  the original {SOM}.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cho99a,
  author	= {Hyun Chul Cho and Keeseong Lee},
  title		= {3-{D} object recognition using an ultrasonic sensor array
		  and neural networks},
  booktitle	= {Proceedings 1999 IEEE/RSJ International Conference on
		  Intelligent Robots and Systems. Human and Environment
		  Friendly Robots with High Intelligence and Emotional
		  Quotients.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1181--4},
  abstract	= {3-D object recognition independent of translation and
		  rotation is presented using an ultrasonic sensor array,
		  invariant moment vectors and neural networks. Using
		  invariant moment vectors of the acquired 16*8 pixel data of
		  square, rectangular, cylindric and regular triangular
		  blocks, 3-D objects can be classified by self organizing
		  feature map neural networks. Invariant moment vectors are
		  constant independent of translation and rotation. The
		  recognition rates for the training and testing data were
		  96.2% and 92.3%, respectively.},
  dbinsdate	= {oldtimer}
}

@Article{	  cho99b,
  author	= {Hong Shik Cho and Jong Keun Park and Gwang Won Kim},
  title		= {Power system transient stability analysis using {K}ohonen
		  neural networks},
  journal	= {Engineering Intelligent Systems for Electrical Engineering
		  and Communications},
  year		= {1999},
  volume	= {7},
  pages		= {209--14},
  abstract	= {This paper proposes two effective learning algorithms for
		  the Kohonen neural network (KNN) called boundary search
		  algorithm (BSA) and iterative condensed nearest neighbor
		  (ICNN) rule. Compared with conventional learning algorithms
		  for KNN, for example, the learning vector quantization
		  (LVQ) method, the proposed learning algorithms place the
		  codebook vectors near the decision boundaries. They are
		  suitable especially for problems in which the decision
		  boundary is clear such as power system stability
		  evaluation. The effectiveness of the proposed algorithms is
		  shown with the transient stability evaluation problem of a
		  4-generator, 6-bus sample power system.},
  dbinsdate	= {oldtimer}
}

@Article{	  cho99c,
  author	= {Cho, H. S. and Park, J. K. and Kim, G. W.},
  title		= {Power system transient stability analysis using Kohonen
		  neural networks},
  journal	= {ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING
		  AND COMMUNICATIONS},
  year		= {1999},
  volume	= {7},
  number	= {4},
  month		= {DEC},
  pages		= {209--214},
  abstract	= {This paper proposes two effective learning algorithms of
		  Kohonen Neural Network (KNN) called Boundary Search
		  Algorithm (BSA) and Iterative Condensed Nearest Neighbor
		  (ICNN) rule. Compared with conventional learning algorithms
		  of KNN, for example, Learning Vector Quantization (LVQ)
		  method, the proposed learning algorithms place the codebook
		  vectors near the decision boundaries. They are suitable
		  especially for the problems where the decision boundary is
		  clear such as power system stability evaluation. The
		  effectiveness of the proposed algorithms is shown with the
		  transient stability evaluation problem of a 4-generator,
		  6-bus sample power system.},
  dbinsdate	= {2002/1}
}

@InCollection{	  choe96a,
  author	= {Yoonsuck Choe and J. Sirosh and R. Miikkulainen},
  title		= {Laterally interconnected \mbox{self-organizing} maps in
		  handwritten digit recognition},
  booktitle	= {Advances in Neural Information Processing 8. Proceedings
		  of the 1995 Conference},
  publisher	= {{MIT} Press},
  year		= {1996},
  editor	= {D. S. Touretzky and M. C. Mozer and M. E. Hasselmo},
  address	= {Cambridge, MA, USA},
  pages		= {736--42},
  dbinsdate	= {oldtimer}
}

@InCollection{	  choe97a,
  author	= {Yoonsuck Choe and Risto Miikkulainen},
  title		= {Self-organization and segmentation with laterally
		  connected maps of spiking neurons},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {26--31},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  choe97b,
  author	= {Y. Choe and R. Miikkulainen},
  title		= {Self-Organization and Segmentation with Laterally
		  Connected Spiking Neurons},
  booktitle	= {Proceedings of the International Joint Conference on
		  Artificial Intelligence (IJCAI-97)},
  year		= {1997},
  publisher	= {Kaufmann},
  address	= {San Francisco},
  pages		= {1120--1125},
  dbinsdate	= {oldtimer}
}

@Article{	  choe98a,
  author	= {Choe, Yoonsuck and Miikkulainen, Risto},
  title		= {Self-organization and segmentation in a laterally
		  connected orientation map of spiking neurons},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {139--157},
  abstract	= {The RF-SLISSOM model integrates two separate lines of
		  research on computational modeling of the visual cortex.
		  Laterally connected self-organizing maps have been used to
		  model how afferent structures such as orientation columns
		  and patterned lateral connections can simultaneously
		  self-organize through input-driven Hebbian adaptation.
		  Spiking neurons with leaky integrator synapses have been
		  used to model image segmentation and binding by
		  synchronization and desynchronization of neuronal group
		  activity. Although these approaches differ in how they
		  model the neuron and what they explain, they share the same
		  overall layout of a laterally connected two-dimensional
		  network. This paper shows how both self-organization and
		  segmentation can be achieved in such an integrated network,
		  thus presenting a unified model of development and
		  functional dynamics in the primary visual cortex.},
  dbinsdate	= {oldtimer}
}

@Article{	  choi91a,
  author	= {Dong Hyuk Choi and Seong Won Ryu and Hyun Chul Kang and
		  Kyu Tae Park},
  title		= {Hangul recognition using a hierarchical neural network},
  journal	= {J. Korean Inst. of Telematics and Electronics},
  year		= {1991},
  volume	= {28B},
  number	= {11},
  pages		= {1--7},
  month		= {November},
  note		= {(in Korean)},
  dbinsdate	= {oldtimer}
}

@Article{	  choi92a,
  author	= {Doo-Il Choi and Sang-Hui Park},
  title		= {A self creating and organizing neural network},
  journal	= {Trans. Korean Inst. of Electrical Engineers},
  year		= {1992},
  volume	= {41},
  number	= {5},
  pages		= {533--540},
  month		= {May},
  note		= {(in Korean)},
  dbinsdate	= {oldtimer}
}

@Article{	  choi93a,
  author	= {J. Choi and B. J. Sheu},
  title		= {A high precision {VLSI} winner-take-all circuit for
		  \mbox{self-organizing} neural networks},
  journal	= {IEEE J. Solid-State Circuits},
  year		= {1993},
  volume	= {28},
  number	= {5},
  pages		= {579--584},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  choi94a,
  author	= {Kwan-Seon Choi and Min-Hong Han},
  title		= {Self-organization feature maps and dynamic vector
		  quantization hierarchical neural network for recognition of
		  keywords in Korean continuous speech},
  journal	= {Journal of the Korea Information Science Society},
  year		= {1994},
  volume	= {21},
  number	= {10},
  pages		= {1927--36},
  month		= {Oct},
  dbinsdate	= {oldtimer}
}

@Article{	  choi96a,
  author	= {Su-An Choi and Seung-Ryeol Kim and Jong-Duk Kim and Myeong
		  Seok Park and Young Keun Chang and Sang Mok Chang},
  title		= {The characteristics of quartz crystal microbalance coated
		  with lipid Langmuir-Blodgett films as an olfactory sensing
		  system},
  journal	= {Sensors and Materials},
  year		= {1996},
  volume	= {8},
  number	= {8},
  pages		= {513--21},
  dbinsdate	= {oldtimer}
}

@Article{	  chou97a,
  author	= {C. -H. Chou},
  title		= {A necessary modification for groove tracking method},
  journal	= {Physica B},
  year		= {1997},
  volume	= {233},
  number	= {2--3},
  pages		= {130--3},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chou97b,
  author	= {Wen-Kuang Chou},
  title		= {Classification of Program Behavior Based on
		  Self-Organizing Maps},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {1},
  address	= {Singapore},
  pages		= {346--350},
  dbinsdate	= {oldtimer}
}

@Article{	  chow92a,
  author	= {Mo-Yuen Chow and Chew, A. V. and Sui-Oi Yee},
  title		= {Performance of an fault detector artificial neural network
		  using different paradigms},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 2},
  pages		= {973--81},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chow94a,
  author	= {Mo-Yuen Chow and Menozzi, A. },
  title		= {A self-organized {CMAC} controller},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Industrial Technology},
  year		= {1994},
  pages		= {68--72},
  organization	= {Dept. of Electr. \& Comput. Eng. , North Carolina State
		  Univ. , Raleigh, NC, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  chowdhury96a,
  author	= {B. H. Chowdhury and Kunyu Wang},
  title		= {Fault classification using {K}ohonen feature mapping},
  booktitle	= {ISAP `96. International Conference on Intelligent Systems
		  Applications to Power Systems Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {O. A. Mohammed and K. Tomsovic},
  address	= {New York, NY, USA},
  pages		= {194--8},
  dbinsdate	= {oldtimer}
}

@Article{	  chowdhury96b,
  author	= {B. H. Chowdhury and Kunyu Wang},
  title		= {Fault classification in power systems using artificial
		  neural networks},
  journal	= {Engineering Intelligent Systems for Electrical Engineering
		  and Communications},
  year		= {1996},
  volume	= {4},
  number	= {2},
  pages		= {101--12},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  choy95a,
  author	= {Clifford Sze-Tsan Choy and Wan-Chi Siu},
  title		= {New Approach for Solving the Travelling Salesman Problem
		  using Self-Organizing Learning},
  volume	= {V},
  pages		= {2632--2635},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  choy95b,
  author	= {Choy, C. S. -T. and Ser, P. -K. and Siu, W. -C. },
  title		= {Peak detection in {H}ough transform via
		  \mbox{self-organizing} learning},
  booktitle	= {1995 IEEE Symposium on Circuits and Systems},
  year		= {1995},
  volume	= {1},
  pages		= {139--42},
  organization	= {Dept. of Electron. Eng. , Hong Kong Polytech. , Kowloon,
		  Hong Kong},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  choy95c,
  author	= {Choy, C. S. -T. and Wan-Chi Siu},
  title		= {Algorithm for solving bipartite subgraph problem with
		  probabilistic \mbox{self-organizing} learning},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  year		= {1995},
  volume	= {5},
  pages		= {3351--4},
  organization	= {Dept. of Electron. Eng. , Hong Kong Polytech. Univ,
		  Kowloon, Hong Kong},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  christodoulou01a,
  author	= {Christodoulou, C. I. and Michaelides, S. C. and Pattichis,
		  C.S. and Kyriakou, K.},
  title		= {Classification of satellite cloud imagery based on
		  multi-feature texture analysis and neural networks},
  booktitle	= {IEEE International Conference on Image Processing},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {497--500},
  organization	= {Department of Computer Science, University of Cyprus},
  publisher	= {},
  address	= {},
  abstract	= {The aim of this work was to develop a system based on
		  modular neural networks and multi-feature texture analysis
		  that will facilitate the automated interpretation of cloud
		  images. This will speed up the interpretation process and
		  provide continuity in the application of satellite imagery
		  for weather forecasting. A series of infrared satellite
		  images from the Geostationary satellite METEOSAT7 were
		  employed in this research. Nine different texture feature
		  sets (a total of 55 features) were extracted from the
		  segmented cloud images using the following algorithms:
		  first order statistics, spatial gray level dependence
		  matrices, gray level difference statistics, neighborhood
		  gray tone difference matrix, statistical feature matrix,
		  Laws texture energy measures, fractals, and Fourier power
		  spectrum The neural network SOFM classifier and the
		  statistical KNN classifier were used for the classification
		  of the cloud images. Furthermore, the classification
		  results of the different feature sets were combined
		  improving the classification yield to 91%.},
  dbinsdate	= {2002/1}
}

@InCollection{	  christodoulou95a,
  author	= {C. I. Christodoulou and C. S. Pattichis},
  title		= {A new technique for the classification and decomposition
		  of {EMG} signals},
  booktitle	= {1995 IEEE International Conference on Neural Networks
		  Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {2303--8},
  abstract	= {The shapes and firing rates of motor unit action
		  potentials (MUAPs) in an electromyographic (EMG) signal
		  provide an important source of information for the
		  diagnosis of neuromuscular disorders. In order to extract
		  this information from EMG signals recorded at force levels
		  up to 20% of maximum voluntary contraction (MVC) it is
		  required: i) To identify the MUAPs composing the EMG
		  signal, ii) To classify MUAPs with similar shape and iii)
		  To decompose the superimposed MUAP waveforms into their
		  constituent MUAPs. For the classification of MUAPs two
		  different pattern recognition techniques are presented: i)
		  An artificial neural network (ANN) technique based on
		  unsupervised learning, using the self-organizing feature
		  maps (SOFM) algorithm and learning vector quantization
		  (LVQ) and ii) A statistical pattern recognition technique
		  based on the euclidian distance. The success rate on real
		  data for the ANN technique is about 96% and for the
		  statistical one about 94%. For the decomposition of the
		  superimposed waveforms the following technique is used: i)
		  Crosscorrelation of each of the unique MUAP waveforms,
		  obtained by the classification process, with the
		  superimposed waveforms in order to find the best matching
		  point and ii) A combination of euclidian distance and area
		  measures in order to classify the components of the
		  decomposed waveform. The success rate for the decomposition
		  procedure is about 90%.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  christodoulou98a,
  author	= {Christodoulou, C. and Pattichis, C. S.},
  title		= {Combining neural classifiers in {EMG} diagnosis},
  booktitle	= {6th European Congress on Intelligent Techniques and Soft
		  Computing. EUFIT '98},
  publisher	= {Verlag Mainz},
  address	= {Aachen, Germany},
  year		= {1998},
  volume	= {3},
  pages		= {1837--41},
  abstract	= {In the case of difficult pattern recognition problems, the
		  combination of the outputs of multiple classifiers used for
		  input multiple feature sets, can improve the overall
		  classification performance. In this work a modular neural
		  network decision support system was developed for the
		  assessment of the electromyographic (EMG) signal recorded
		  from normal subjects and subjects suffering with myopathy
		  and motor neuron disease. Six different feature sets were
		  computed from the motor unit action potentials (MUAPs)
		  composing the EMG signal, as follows: (i) time domain
		  parameters, (ii) frequency domain parameters, (iii)
		  cepstral coefficients and (iv) three different wavelet
		  coefficients (Daubechies, Chui, and Battle-Lemarie). The
		  multiple feature sets were inputted into multiple
		  self-organising feature map (SOFM) classifiers and the
		  classification results were combined using majority voting.
		  Furthermore, a confidence measure was computed which
		  weighted the contribution of each feature set to the final
		  diagnostic yield. The average diagnostic yield for the
		  individual feature sets was 69.1% whereas when combining
		  the six classification results was 76.9%. The use of the
		  confidence measure enhanced further the diagnostic yield to
		  79.6%.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  christodoulou99a,
  author	= {Christodoulou, C. I. and Pattichis, C. S. and Pantziaris,
		  M. and Tegos T. and Nicolaides A. and Elatrozy T. and
		  Sabetai M. and Dhanjil S.},
  title		= {Multi-feature texture analysis for the classification of
		  carotid plaques},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3591--6},
  abstract	= {We develop a computer aided system which will facilitate
		  the automated characterisation of carotid plaques recorded
		  from high resolution ultrasound images for the
		  identification of individuals with asymptomatic carotid
		  stenosis at risk of stroke. The plaques were classified
		  into: symptomatic or asymptomatic. Ten different texture
		  feature sets were extracted from the segmented plaque
		  image. Although the statistics of all features extracted
		  for the two classes indicated a high degree of overlap, a
		  classification of the plaques was possible using the
		  unsupervised self-organizing feature map (SOFM) classifier
		  and combining techniques. The classification results of the
		  different feature sets were combined using the majority
		  voting and weighted averaging based on a confidence measure
		  derived from the SOFM. Combining the classification results
		  of the ten different feature sets improved significantly
		  the classification results obtained by the individual
		  feature sets, reaching an average diagnostic yield of
		  75%.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  christodoulou99b,
  author	= {Christodoulou, C. I. and Pattichis, C. S.},
  title		= {Medical diagnostic systems using ensembles of neural
		  {SOFM} classifiers},
  booktitle	= {ICECS'99. Proceedings of ICECS '99. 6th IEEE International
		  Conference on Electronics, Circuits and Systems. },
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {1},
  pages		= {121--4},
  abstract	= {A design for medical diagnostic systems composed of
		  ensembles of neural self organizing feature map (SOFM)
		  classifiers is presented. Each SOFM classifier was fed with
		  a different feature set extracted from the raw data and
		  their results were combined using (i) majority voting and
		  (ii) a confidence measure derived from the SOFM which
		  weighted the contribution of each feature set to the final
		  classification result. The following two diagnostic systems
		  were developed: (i) a decision support system for the
		  assessment of electromyographic (EMG) signals, and (ii) a
		  system for the characterisation of carotid plaques from
		  ultrasound images. The results in this work shows that
		  combining the classification results of multiple
		  classifiers using as input multiple feature sets, in
		  conjunction with the use of a confidence measure can
		  improve the overall classification performance of the
		  system.},
  dbinsdate	= {oldtimer}
}

@Article{	  chuang99a,
  author	= {Chuang, K.~H. and Chiu, M.~J. and Lin, C.~C. and Chen,
		  J.~H.},
  title		= {Model-Free Functional {MRI} Analysis Using {K}ohonen
		  Clustering Neural-Network and Fuzzy C-Means},
  journal	= {IEEE Transactions on Medical Imaging},
  year		= {1999},
  volume	= {18},
  number	= {12},
  pages		= {1117--1128},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chung00a,
  author	= {Hwang Chung and Tae Wan Ryu},
  title		= {Recognition of frequency modulated pulse signals},
  booktitle	= {Proceedings of the International Conference on Parallel
		  and Distributed Processing Techniques and Applications.
		  PDPTA'2000. CSREA Press, Athens, GA, USA},
  year		= {2000},
  volume	= {1},
  pages		= {357--61},
  abstract	= {A promising result is obtained in recognizing frequency
		  change as well as the base frequency from a frequency
		  modulated pulse signal (a pulse train signal carrying many
		  different frequencies.) The pulse train signal is a common
		  form of the output of typical biological visual receptors,
		  in which the instantaneous frequency change reflects the
		  change of the input signal intensity into the receptor
		  caused by the movement of an object. Using the N-tap delay
		  time signal as an input model for a neural network
		  processor, we have obtained promising results showing that
		  we can detect the instantaneous frequency change caused
		  when an object appears in sight of the receptor and when
		  the object leaves the receptor, and recognize the base
		  frequency as well. These two properties are similar to the
		  main characteristics of the biological visual recognition
		  system, since the frequency change gives movement
		  information on an object, while the base frequency is
		  related to the object visual signal intensity. In the
		  simulation, binary pulses having different periods are
		  mixed in the time domain, and the weight matrix is trained
		  by a self-organizing Kohonen algorithm, which is considered
		  to be similar to the actual visual recognition process.},
  dbinsdate	= {2002/1},
  merjanote     = {last name chechked from internet}
}

@InProceedings{	  chung93a,
  author	= {Fu-Lai Chung and Tong Lee},
  title		= {Fuzzy Learning Vector Quantization},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2739--2742},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {In this paper, a new supervised competitive learning
		  network model called fuzzy learning vector quantization
		  (FLVQ) which incorporates fuzzy concepts into the learning
		  vector quantization (LVQ) networks is proposed. Unlike the
		  original algorithm, the FLVQ's learning algorithm is
		  derived from optimizing an appropriate fuzzy objective
		  function which takes into accounts of two goals, namely,
		  minimizing the network output error which is the class
		  membership differences of target and actual values and
		  minimizing the distances between training patterns and
		  competing neurons. As compared with the LVQ network, the
		  proposed one consists of several distinctive features: i)
		  stand-alone operation, i.e. employing preprocessing
		  algorithm to obtain a good initial network state is not
		  required; ii) superior classification performance,
		  particularly in overlapping data sets; and iii) avoiding
		  neuron underutilization. These advantages are demonstrated
		  through an artificially generated data set and a vowel
		  recognition data set.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  chung93b,
  author	= {Fu-Lai Chung and Tong Lee},
  title		= {Unsupervised Fuzzy Competitive Learning with Monotonically
		  Decreasing Fuzziness},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2929--2932},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  chung94a,
  author	= {Chung, Fu Lai and Lee, Tong},
  title		= {Fuzzy learning model for membership function estimation
		  and pattern classification},
  journal	= {IEEE International Conference on Fuzzy Systems},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1994},
  number	= {},
  volume	= {1},
  pages		= {426--431},
  abstract	= {In this paper, a new fuzzy learning model called fuzzy
		  learning vector quantization (FLVQ) which incorporates
		  fuzzy clustering concept with Kohonen's learning vector
		  quantization (LVQ) model is proposed. The new learning
		  algorithm is derived from optimizing an appropriate fuzzy
		  objective function which takes into accounts of two goals,
		  namely, minimizing the differences between target and
		  actual class membership outputs, and minimizing the
		  distances between training patterns and neuron's parametric
		  vectors. It retains the LVQ's reinforce-or-punish learning
		  principle and more importantly introduces graded
		  corrections. As compared with the LVQ algorithm, the
		  proposed one is characterized by several distinctive
		  features: i) avoiding neuron underutilization; ii) superior
		  classification and generalization performances; and iii)
		  insensitive to initial conditions. Since the outputs of the
		  model have been formulated as fuzzy class membership
		  functions, it can be readily used to estimate the
		  membership functions of fuzzy systems. Furthermore, through
		  the concept of 'clusters as rules', the trained network can
		  be interpreted in the form of fuzzy IF-THEN rules. All
		  these features of the proposed model are demonstrated
		  through numerical examples.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  churcher91a,
  author	= {S. Churcher and D. J. Baxter and A. Hamilton and A. F.
		  Murray and H. Reekie},
  title		= {Towards a generic analogue {VLSI} neurocomputing
		  architecture},
  booktitle	= {Proc. 2nd International Conference on Microelectronics for
		  Neural Networks},
  year		= {1991},
  editor	= {U. Ramacher and U. Ruckert and J. A. Nossek},
  pages		= {127--133},
  publisher	= {Kyrill \& Method Verlag},
  address	= {Munich, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ciampi00a,
  author	= {Ciampi, A. and Lechevallier, Y.},
  title		= {Clustering large, multi-level data sets: an approach based
		  on Kohonen self-organizing maps},
  booktitle	= {Principles of Data Mining and Knowledge Discovery. 4th
		  European Conference, PKDD 2000. Proceedings (Lecture Notes
		  in Artificial Intelligence Vol.1910). Springer-Verlag,
		  Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {353--8},
  abstract	= {Standard clustering methods do not handle truly large data
		  sets well, and fail to take into account multi-level data
		  structures. This paper outlines an approach to clustering
		  that integrates a Kohonen self-organizing map (SOM) with
		  other clustering methods. Moreover, in order to take into
		  account multi-level structures, a statistical model is
		  proposed in which a mixture of distributions may have
		  mixing coefficients depending on higher-level variables.
		  Thus, in a first step, the SOM provides a substantial data
		  reduction, whereby a variety of ascending and divisive
		  clustering algorithms becomes accessible. As a second step,
		  statistical modelling provides both a direct means to treat
		  multi-level structures and a framework for model-based
		  clustering. The interplay of these two steps is illustrated
		  on an example of nutritional data from a multi-center study
		  on nutrition and cancer, known as EPIC.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  cinque01a,
  author	= {Cinque, L. and Foresti, G. L. and Gumina, A. and Levialdi,
		  S.},
  title		= {A modified fuzzy {ART} for image segmentation},
  booktitle	= {Proceedings 11th International Conference on Image
		  Analysis and Processing. IEEE Comput. Soc, Los Alamitos,
		  CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {102--7},
  abstract	= {This paper presents a clustering approach for image
		  segmentation based on a modified fuzzy ART model. The goal
		  of the proposed approach is to find a simple model able to
		  instance a prototype for each cluster in order to avoid
		  complex post-processing phases. Some results and
		  comparisons with other models present in the literature,
		  like SOM and original fuzzy ART are presented. Qualitative
		  and quantitative evaluations confirm the validity of our
		  approach.},
  dbinsdate	= {2002/1}
}

@InCollection{	  cinque98a,
  author	= {L. Cinque and R. Romangnoli and S. Levialdi and P. T. A.
		  Nguyen and L. Guan},
  title		= {\mbox{Self-organizing} map for segmenting {3D} biological
		  images},
  booktitle	= {Proceedings. Fourteenth International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society},
  year		= {1998},
  volume	= {1},
  editor	= {A. K. Jain and S. Venkatesh and B. C. Lovell},
  address	= {Los Alamitos, CA, USA},
  pages		= {471--3},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cios90a,
  author	= {K. J. Cios and L. S. Goodenday and M. Merhi and R. A.
		  Langenderfer},
  title		= {Neural networks in detection of coronary artery disease},
  booktitle	= {Proc. Computers in Cardiology},
  year		= {1990},
  pages		= {33--37},
  organization	= {IEEE},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cires96a,
  author	= {Cires, J. and Zufiria, P. J.},
  title		= {The \mbox{self-organizing} map as a perceptual level for
		  mobile robot control},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {183--9},
  abstract	= {The use of the self-organizing map as a perceptual level
		  in the control system for a mobile robot is introduced. The
		  self-organizing map is used to perform dimensionality
		  reduction on the space of sensor signals. Using the {SOM}
		  in this manner, the system must learn the effects of its
		  actions in terms of the activation of the self-organizing
		  map in order to plan. The general architecture of the
		  system is introduced, and some issues that must be
		  carefully studied are indicated. Finally, some concluding
		  remarks are presented.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cires97a,
  author	= {Cires, J. and Zufiria, P. J.},
  title		= {Mobile robot motion control through topological space},
  booktitle	= {Neural Networks in Engineering Systems. Proceedings of the
		  1997 International Conference on Engineering Applications
		  of Neural Networks},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1997},
  volume	= {1},
  pages		= {103--6},
  abstract	= {This paper discusses the control of a mobile robot that
		  uses a self-organizing map as a perceptual level by motion
		  planning in topological space. The self-organizing map
		  processes sensor information and produces a representation
		  of the robot's perception of the world. The control scheme
		  presented learns the dynamics of the robot in terms of the
		  activation of the self-organizing map and plans motions
		  through the topological space defined by this map instead
		  of using a representation of the real world.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cires98a,
  author	= {Cires, J. and Bertrand, F. and Zufiria, P. J.},
  title		= {Two modeling approaches for navigation control of a
		  {N}omad 200 mobile robot},
  booktitle	= {Intelligent Autonomous Vehicles 1998 (IAV'98). Proceedings
		  volume from 3rd IFAC Symposium},
  year		= {1998},
  publisher	= {Elsevier Sci},
  address	= {Kidlington, UK},
  volume	= {},
  pages		= {363--8},
  abstract	= {Mobile robots can use sensor information to create a
		  geometrical model of their environment. Alternatively, they
		  can use sensor information directly without a geometrical
		  interpretation. The paper presents two architectures, each
		  implementing one of these approaches. The first part of the
		  paper presents a sensor space representation architecture
		  using a self-organizing map as a perceptual level. The
		  second part of the article describes a system for
		  autonomous navigation based on geometrical environment
		  models.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cirrincione94a,
  author	= {Cirrincione, G. and Cirrincione, M. and Vitale, G. },
  title		= {A {K}ohonen neural network for the diagnosis of incipient
		  faults in induction motors},
  booktitle	= {ICEM 94. International Conference on Electrical Machines},
  year		= {1994},
  volume	= {2},
  pages		= {369--73},
  organization	= {INPG, Grenoble, France},
  publisher	= {Soc. Electr. Electron},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cirrincione96a,
  author	= {G. Cirrincione and M. Cirrincione and F. Piglione},
  title		= {A neural network architecture for static security mapping
		  in power systems},
  booktitle	= {MELECON '96. 8th Mediterranean Electrotechnical
		  Conference. Industrial Applications in Power Systems,
		  Computer Science and Telecommunications. Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  editor	= {M. {de Sario} and B. Maione and P. Pugliese and M.
		  Savino},
  address	= {New York, NY, USA},
  pages		= {1611--14},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cirrinclone94a,
  author	= {Cirrinclone, G. and Cirrincione, M. and Vitale, G. },
  title		= {Fault diagnosis in three-phase converters using the
		  {K}ohonen neural network classifier},
  booktitle	= {Symposium on Power Electronics, Electrical Drives,
		  Advanced Electrical Motors Proceedings},
  year		= {1994},
  volume	= {1},
  pages		= {359--63},
  organization	= {INPG, Grenoble, France},
  publisher	= {ANSALDO Trasporti},
  address	= {Italy},
  dbinsdate	= {oldtimer}
}

@Book{		  cislab95a,
  author	= {},
  title		= {Neural Networks Research Centre and Laboratory of Computer
		  and Information Science. Annual Report, 1995.},
  year		= {1995},
  abstract	= {The report covers the activities of the laboratory during
		  1995. Personnel, courses given, abstracts of research
		  projects, visits and conferences, theses and publications,
		  and other activities are listed. The research projects
		  included: The adaptive-subspace SOM (ASSOM); Statistical
		  data analysis by the self-organizing map; Improving the
		  edge preservation and compression efficiency in image
		  vector quantization by weighting the input samples during
		  the SOM training; Using LVQ and SOM in speech recognition
		  with multiple feature streams; Assessing the probability of
		  bankruptcy; Texture analysis by co-occurrence maps;
		  SOM-based interference cancellation; Adaptation in
		  telecommunication systems using neural networks; and Cloud
		  classification.},
  dbinsdate	= {oldtimer}
}

@Book{		  cislab98a,
  author	= {},
  title		= {Laboratory of Computer and Information Science, Helsinki
		  University of Technology. Annual Report, 1997.},
  year		= {1998},
  abstract	= {The Workshop on Self-Organizing Maps (WSOM'97) was the
		  first international meeting solely dedicated to the theory
		  and applications of the SOM. The WSOM-97 was a satellite
		  workshop of the 10th Scandinavian Conference on Image
		  Analysis (SCIA) that was held June 9--11 in Lappeenranta
		  arranged by the Pattern Recognition Society of Finland.
		  Other co-operating societies in the workshop were the
		  European Neural Network Society (ENNS), IEEE Finland
		  Section, and the Finnish Artificial Intelligence Society.
		  The NEuroNet sponsored 10 EU students to attend the
		  workshop.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  clark91a,
  author	= {G. A. Clark and J. E. Hernandez and N. K. DelGrande and R.
		  J. Sherwood and S. -Y. Lu and P. C. Schaich and P. F.
		  Durbin},
  title		= {Computer vision for locating buried objects},
  booktitle	= {Conf. Record of the Twenty-Fifth Asilomar Conf. on
		  Signals, Systems and Computers},
  year		= {1991},
  volume	= {II},
  pages		= {1235--1239},
  organization	= {EEE; Naval Postgraduate School; San Jose State Univ},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  cleva99a,
  author	= {Cleva, C. and Cachet, C. and Cabrolbass, D.},
  title		= {Clustering of Infrared-Spectra with {K}ohonen Networks},
  journal	= {Analusis},
  year		= {1999},
  volume	= {27},
  number	= {1},
  pages		= {81--90},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  clifton94a,
  author	= {Clifton, D. B. and Myler, H. R. and Weeks, A. R. },
  title		= {An approach to the acquisition of a world frame using a
		  visual associative memory},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {2},
  pages		= {1121--4},
  organization	= {Gov. Aerosp. Syst. Div. , Harris Corp. , Melbourne, FL,
		  USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  clippingdale93a,
  author	= {Simon Clippingdale and Roland Wilson},
  title		= {Self-Organization in Neural Networks subject to Random
		  Transformations},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2504--2507},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  clippingdale96a,
  author	= {S. Clippingdale and R. Wilson},
  title		= {Self-similar neural networks based on a {K}ohonen learning
		  rule},
  journal	= {Neural Networks},
  year		= {1996},
  volume	= {9},
  number	= {5},
  pages		= {747--63},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  coccorese94a,
  author	= {Coccorese, E. and Morabito, C. and Martone, R. },
  title		= {Classification of plasma equilibria in a tokamak using a
		  three-layer back propagation network},
  booktitle	= {Neural Nets Wirn Vietri 93---Proceedings of the 5th
		  Italian Workshop on Neural Nets},
  year		= {1994},
  editor	= {Caianiello, E. R. },
  pages		= {},
  organization	= {Istituto di Ing. Elettronica, Univ. di Reggio Calabria,
		  Italy},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cochrane99a,
  author	= {Cochrane, E. M. and Cochrane, J. C.},
  title		= {Exploring competition and co-operation for solving the
		  Euclidean Travelling Salesman Problem by using
		  Self-Organizing Map},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  year		= {1999},
  publisher	= {IEE},
  address	= {London, UK},
  volume	= {1},
  pages		= {180--185},
  abstract	= {In the last few years, several new results on
		  Self-Organizing Map algorithms applied to the Euclidean
		  Travelling Salesman Problem (ETSP) have emerged. All of
		  them have attempted to find quasi-optimal solutions for
		  this NP-complete combinatorial problem based on the
		  functional role of the learning process mapping understood
		  to occur in the brain. This paper brings an innovative
		  computational investigation based on a new model of lateral
		  interactions between neurons that can be thought of as
		  introducing co-operation between them. Such interactions
		  are now being observed in biological neural nets. Its
		  application to ETSP obtained results never before achieved.
		  Simulations using a sequential machine for well-known and
		  difficult TSP library instances as well as for problems
		  with over two thousands cities will be discussed in this
		  paper.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cohen94a,
  author	= {A. J. D. Cohen and M. J. Bishop},
  title		= {{S}elf-organizing maps in synthetic speech},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {IV},
  pages		= {544--549},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, speech syntesis},
  dbinsdate	= {oldtimer}
}

@InCollection{	  coimbra95a,
  author	= {A. J. F. Coimbra and J. Marino-Neto and F. M. {de Azevedo}
		  and C. G. Freitas and J. M. Barreto},
  title		= {Brain electrographic state detection using combined
		  unsupervised and supervised neural networks},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {D. W. Pearson and N. C. Steele and R. F. Albrecht},
  address	= {Vienna, Austria},
  pages		= {76--9},
  dbinsdate	= {oldtimer}
}

@Article{	  coiton91a,
  author	= {Y. Coiton and J. C. Gilhodes and J. L. Velay and J. P.
		  Roll},
  title		= {A neural network model for the intersensory coordination
		  involved in goal-directed movements},
  journal	= {Biol. Cyb. },
  year		= {1991},
  volume	= {66},
  number	= {2},
  pages		= {167--176},
  x		= {A neural network model for a sensorimotor system, which
		  was developed to simulate oriented movements in man, is
		  presented. . . . The sensory layer is an extension of the
		  topological network previously proposed by Kohonen (1984).
		  },
  dbinsdate	= {oldtimer}
}

@Article{	  coleman92a,
  author	= {K. G. Coleman and S. Watenpool},
  title		= {Neural networks in knowledge acquisition},
  journal	= {AI Expert},
  year		= {1992},
  volume	= {7},
  number	= {1},
  pages		= {36--39},
  month		= {January},
  x		= {Ilmeisesti introduktio aiheeseen. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  colla94a,
  author	= {A. M. Colla and N. Longo and G. Morgavi and S. Ridella},
  title		= {Learning in Hybrid Neural Models},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {230--233},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, pattern recognition, hybrid},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  colla94b,
  author	= {A. M. Colla and P. Pedrazzi},
  title		= {Single and Coupled Neural Handprinted Character
		  Classifiers},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {969--972},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, pattern recognition, comprarison},
  dbinsdate	= {oldtimer}
}

@Article{	  collica95a,
  author	= {Collica, R. S. and Card, J. P. and Martin, W. },
  title		= {SRAM bitmap shape recognition and sorting using neural
		  networks},
  journal	= {IEEE Transactions on Semiconductor Manufacturing},
  year		= {1995},
  volume	= {8},
  number	= {3},
  pages		= {326--32},
  month		= {Aug},
  dbinsdate	= {oldtimer}
}

@Article{	  collings93a,
  author	= {Collings, N. and Sumi, R. and Weible, K. J. and Acklin, B.
		  and Xue, W. },
  title		= {The use of optical hardware to find good solutions of the
		  travelling salesman problem ({TSP})},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1993},
  volume	= {1806},
  pages		= {637--41},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  collins94a,
  author	= {Collins, P. and Yu, S. and Eckersall, K. R. and Jervis, B.
		  W. and Bell, I. M. and Taylor, G. E. },
  title		= {Application of {K}ohonen and supervised forced
		  organisation maps to fault diagnosis in {CMOS} opamps},
  journal	= {Electronics Letters},
  year		= {1994},
  volume	= {30},
  number	= {22},
  pages		= {1846--7},
  month		= {Oct},
  abstract	= {Transistors with gate oxide shorts have been identified to
		  100% fault coverage in a CMOS opamp by monitoring supply
		  current changes first using multilayer perceptrons and then
		  Kohonen maps to resolve any ambiguities. A supervised
		  forced organisation map allows the location and resistance
		  of the short to be determined.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  collobert95a,
  author	= {M. Collobert and D. Collobert},
  title		= {A Neural System to Detect Faulty Components on Complex
		  Boards in Digital Switches},
  booktitle	= {Proc. Int. Workshop on Applications of Neural Networks to
		  Telecommunications 2},
  year		= {1995},
  editor	= {Joshua Alspector and Rodney Goodman and Timothy X. Brown},
  pages		= {334--338},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  colombi92a,
  author	= {J. M. Colombi},
  title		= {Cepstral and Auditory Model Features for Speaker
		  Recognition},
  school	= {Air Force Inst. of Tech. , School of Engineering},
  year		= {1992},
  address	= {Wright-Patterson AFB, OH},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  colombi93a,
  author	= {John M. Colombi and Steven K. Rogers and Dennis W. Ruck},
  title		= {Auditory Model Representation for Speaker Recognition},
  booktitle	= {Proc. ICASSP-93, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1993},
  volume	= {II},
  pages		= {700--703},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  colombi93b,
  author	= {Colombi, J. M. and Anderson, T. R. and Rogers, S. K. and
		  Ruck, D. W. and Warhola, G. T. },
  title		= {Auditory model representation and comparison for speaker
		  recognition},
  booktitle	= {1993 IEEE International Conference on Neural Networks},
  year		= {1993},
  volume	= {3},
  pages		= {1914--19},
  organization	= {AFIT, Wright-Patterson AFB, OH, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  comtat95a,
  author	= {C. Comtat and C. Morel},
  title		= {Approximate Reconstruction of {PET} Data with a
		  Self-Organizing Neural Network},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1995},
  volume	= {6},
  number	= {3},
  pages		= {783--789},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  conde94a,
  author	= {Toni Conde},
  title		= {Automatic Neural Detection of anomalies in
		  Electrocardiogram {ECG} Signals},
  pages		= {3552--3558},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, pattern recognition, hybrid},
  dbinsdate	= {oldtimer}
}

@InCollection{	  congorto97a,
  author	= {S. Congorto and S. D. Penna and S. N. Erne},
  title		= {Tissue segmentation of {MRI} of the head by means of a
		  {K}ohonen map},
  booktitle	= {Proceedings of the 18th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  `Bridging Disciplines for Biomedicine'},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {3},
  editor	= {H. Boom and C. Robinson and W. Rutten and M. Neuman and H.
		  Wijkstra},
  address	= {New York, NY, USA},
  pages		= {1087--8},
  dbinsdate	= {oldtimer}
}

@Article{	  congxing98a,
  author	= {Ouyang Congxing and Fang Zhenghu and Chen Kangsheng and
		  Yue Guangxin},
  title		= {A novel feedforward artificial neural network based on
		  \mbox{self-organizing} feature map: model and algorithms},
  journal	= {Acta Electronica Sinica},
  year		= {1998},
  volume	= {26},
  number	= {7},
  pages		= {165--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  conti91a,
  author	= {P. Conti and L. {De Giovanni}},
  title		= {On the mathematical treatment of self-organization:
		  extension of {SOM} classical results},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1809--1812},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  conway90a,
  author	= {B. A. Conway and M. Kabrisky and S. K. Rogers and G. B.
		  Lamon},
  title		= {Multi-dimensional {K}ohonen net on a {H}yper{C}ube},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1990},
  volume	= {1294},
  pages		= {269--275},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {Conf. paper in journal},
  abstract	= {This report details the implementation of the Kohonen
		  Self-Organizing Net on a 32-node Intel iPSC/1 HyperCube,
		  and the 25% performance improvement gained by increasing
		  the dimensionality of the net without increasing processing
		  requirements.},
  dbinsdate	= {oldtimer}
}

@Article{	  coolen92a,
  author	= {A. C. C. Coolen and L. G. V. M. Lenders},
  title		= {Dual processes in neural network models {I}. Neural
		  dynamics versus dynamics of learning},
  journal	= {J. Physics A [Mathematical and General]},
  year		= {1992},
  volume	= {25},
  number	= {9},
  pages		= {2577--2592},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  copland95a,
  author	= {Howard Copland and Tim Hendtlass},
  title		= {Engram Decay in Artificial Neural Networks},
  volume	= {I},
  pages		= {669--673},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  coppini95a,
  author	= {G. Coppini and E. Tamburini and A. L`Abbate and G. Valli},
  title		= {Assessment of regions at risk from coronary {X}-ray
		  imaging by {K}ohonen`s map},
  booktitle	= {Computers in Cardiology 1995},
  publisher	= {IEEE},
  year		= {1995},
  address	= {New York, NY, USA},
  pages		= {757--60},
  dbinsdate	= {oldtimer}
}

@InCollection{	  coppini97a,
  author	= {G. Coppini and E. Tamburini and A. L'Abbate and G. Valli},
  title		= {A model of the topology of coronary perfusion based on
		  \mbox{self-organizing} maps},
  booktitle	= {Computers in Cardiology 1997},
  publisher	= {IEEE},
  year		= {1997},
  address	= {New York, NY, USA},
  pages		= {737--40},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  corcoran95a,
  author	= {Corcoran, P. and Lowery, P. },
  title		= {Neural processing in an electronic odour sensing system},
  booktitle	= {Fourth International Conference on `Artificial Neural
		  Networks`},
  year		= {1995},
  pages		= {415--20},
  organization	= {Derby Univ. , UK},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  corcoran95b,
  author	= {Corcoran, P. and Lowery, P.},
  title		= {Neural network applications in multisensor systems},
  journal	= {Sensor Review},
  year		= {1995},
  number	= {4},
  volume	= {15},
  pages		= {15--18},
  abstract	= {The combination of artificial neural network processing
		  tools with multisensor arrays is becoming more widely
		  accepted as one that can provide a mechanism for the
		  enhancement of the quality of information derived from the
		  arrays. To date, much of the work has been based on the use
		  of the supervised multi-layer perceptrons in well-defined
		  problem areas. If the true potential of multisensor system
		  is to be realized, then alternative supervised and
		  unsurpervised architectures need to be investigated. The
		  greatest potential for solving real-world problems may be
		  found in the use of unsupervised self-organizing maps to
		  extract features that are qualitatively representative of
		  application environments.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  corne96a,
  author	= {S. Corne and T. Murray and S. Openshaw and L. See and I.
		  Turton},
  title		= {Using artificial intelligence techniques to model
		  subglacial water systems},
  booktitle	= {GeoComputation 96. 1st International Conference on
		  GeoComputation},
  publisher	= {Univ. Leeds},
  year		= {1996},
  volume	= {1},
  editor	= {R. J. Abrahart},
  address	= {Leeds, UK},
  pages		= {135--55},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cornu94a,
  author	= {Cornu, T. and Ienne, P. },
  title		= {Performance of digital neuro-computers},
  booktitle	= {Proceedings of the Fourth International Conference on
		  Microelectronics for Neural Networks and Fuzzy Systems},
  year		= {1994},
  pages		= {87--93},
  organization	= {MANTRA Centre for Neuro-Mimetic Syst. , Swiss Federal
		  Inst. of Technol. , Lausanne, Switzerland},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cornu94b,
  author	= {Cornu, T. and Ienne, P. and Niebur, D. and Viredaz, M. A.
		  },
  title		= {A systolic accelerator for power system security
		  assessment},
  booktitle	= {ISAP '94. International Conference on Intelligent System
		  Application to Power Systems},
  year		= {1994},
  editor	= {Hertz, A. and Holen, A. T. and Rault, J. -C. },
  volume	= {1},
  pages		= {431--8},
  organization	= {Centre for Neuro-Mimetic Systs. , Swiss Federal Inst. of
		  Technol. , Lausanne, Switzerland},
  publisher	= {EC2},
  address	= {Nanterre Cedex, France},
  dbinsdate	= {oldtimer}
}

@Article{	  cornu96a,
  author	= {Cornu, T. and Ienne, P. and Niebur, D. and Thiran, P. and
		  Viredaz, M. A. },
  title		= {Design, implementation, and test of a multi-model systolic
		  neural-network accelerator},
  journal	= {Scientific Programming},
  year		= {1996},
  volume	= {5},
  number	= {1},
  pages		= {47--61},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  corr01a,
  author	= {Corr, P. H. and Milligan, P. and Purnell, V.},
  title		= {A neural network based tool for semi-automatic code
		  transformation},
  booktitle	= {Vector and Parallel Processing---VECPAR 2000. 4th
		  International Conference. Selected Papers, and Invited
		  Talks (Lecture Notes in Computer Science Vol.1981).
		  Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {142--53},
  abstract	= {A neural network based tool has been developed to assist
		  in the process of code transformation. The tool offers
		  advice on appropriate transformations within a
		  knowledge-driven, semi-automatic parallelisation
		  environment. We have identified the essential
		  characteristics of codes relevant to loop transformations.
		  A Kohonen network is used to discover structure in the
		  characterised codes thus revealing new knowledge that may
		  be brought to bear on the mapping between codes and
		  transformations or transformation sequences. A transform
		  selector based on this process has been developed and
		  successfully applied to the parallelisation of sequential
		  codes.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  corradini00a,
  author	= {Corradini, Andrea and Gross, Horst-Michael},
  title		= {Implementation and comparison of three architectures for
		  gesture recognition},
  booktitle	= {ICASSP, IEEE International Conference on Acoustics, Speech
		  and Signal Processing---Proceedings},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {2361--2364},
  organization	= {Technical Univ of Ilmenau},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Several systems for automatic gesture recognition have
		  been developed using different strategies and approaches.
		  In these systems the recognition engine is mainly based on
		  three algorithms: dynamic pattern matching, statistical
		  classification, and neural networks (NN). In that paper
		  three architectures for the recognition of dynamic gestures
		  using the above mentioned techniques or a hybrid
		  combination of them are presented and compared. For all
		  architectures a common preprocessor receives as input a
		  sequence of color images, and produces as output a sequence
		  of feature vectors of continuous parameters. The first two
		  systems are hybrid architectures consisting of a
		  combination of neural networks and hidden Markov models
		  (HMM). NNs are used for the classification of single
		  feature vectors while HMMs for the modeling of sequences of
		  them with the aim to exploit the properties of both these
		  tools. More precisely, in the first system a Kohonen
		  feature map (SOM) clusters the input space. Further, each
		  code-book is transformed into a symbol from a discrete
		  alphabet and fed into a discrete HMM for classification. In
		  the second approach a Radial Basis Function (RBF) network
		  is directly used to compute the HMM state observation
		  probabilities. In the last system only dynamic programming
		  techniques are employed. An input sequence of feature
		  vectors is matched by some predefined templates by using
		  the dynamic time warping (DTW) algorithm. Preliminary
		  experiments with our baseline systems achieved a
		  recognition accuracy up to 92%. All systems use input from
		  a monocular color video camera, are user-independent but so
		  far, they are not yet real-time.},
  dbinsdate	= {2002/1}
}

@Article{	  corradini00b,
  author	= {Corradini, A. and Boehme, H. J and Gross, H. M.},
  title		= {A hybrid stochastic-connectionist approach to gesture
		  recognition},
  journal	= {International-Journal-on-Artificial-Intelligence-Tools-(Architectures,-Languages,-Algorithms)}
		  ,
  year		= {2000},
  volume	= {9},
  pages		= {177--203},
  abstract	= {In this paper a person-specific saliency system and
		  subsequently two architectures for the recognition of
		  dynamic gestures are described. The systems implemented are
		  designed to take a sequence of images and to assign it to
		  one of a number of discrete classes where each of them
		  corresponds to a gesture from a predefined small
		  vocabulary. Since we think that for a human-computer
		  interaction the localization of the user is essential for
		  any further step regarding the recognition and the
		  interpretation of gestures, in the first part, we begin
		  with describing our saliency system dedicated to the person
		  localization task in cluttered environments. Successively,
		  the intrinsic gesture recognition process is broken down
		  into an initial preprocessing stage followed by a mapping
		  from the preprocessed input variables to an output variable
		  representing the class label. Subsequently, we utilize two
		  different classifiers for mapping the ordered sequence of
		  feature vectors to one gesture category. The first
		  classifier utilizes a hybrid combination of Kohonen
		  self-organizing map (SOM) and discrete hidden Markov models
		  (DHMM). As a second recognizer a system of continuous
		  hidden Markov models (CHMM) is used. Preliminary
		  experiments with our baseline systems are demonstrated.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  corradini99a,
  author	= {Corradini, A. and Gross, H. M.},
  title		= {A hybrid stochastic-connectionist architecture for gesture
		  recognition},
  booktitle	= {Proceedings 1999 International Conference on Information
		  Intelligence and Systems},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {336--41},
  abstract	= {An architecture for the recognition of dynamic gestures is
		  described. The system implemented is designed to take a
		  sequence of images and to assign it to one of a number of
		  discrete classes where each of them corresponds to a
		  gesture from a predefined vocabulary. The classification
		  task is broken down into an initial preprocessing stage
		  following by a mapping from the preprocessed input
		  variables to an output variable representing the class
		  label. The preprocessing stage consists of the extraction
		  of one translation and scale invariant feature vector from
		  each image of the sequence. Further we utilize a hybrid
		  combination of a Kohonen self-organizing map (SOM) and
		  discrete hidden {M}arkov models (DHMM) for mapping an
		  ordered sequence of feature vectors to one gesture
		  category. We create one DHMM for each movement to be
		  detected. In the learning phase the {SOM} is used to
		  cluster the feature vector space. After the self-organizing
		  process each codebook is quantized into a symbol. Every
		  symbol sequence underlying a given movement is finally used
		  to train the corresponding {M}arkov model by means of the
		  nondiscriminative Baum-Welch algorithm, aiming at
		  maximizing the probability of the samples given the model
		  at hand. In the recognition phase the {SOM} transforms any
		  input image sequence into one symbol sequence which is
		  subsequently fed into a system of DHMMs. The gesture
		  associated with the model which best matches the observed
		  symbol sequence is chosen as the recognized movement.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  corradini99b,
  author	= {Corradini, A. and Gross, H. M.},
  title		= {Gesture recognition using hybrid {SOM}/{DHMM}},
  booktitle	= {Neural Nets WIRN Vietri-99. Proceedings of the 11th
		  Italian Workshop on Neural Nets. Springer-Verlag London,
		  London, UK},
  year		= {1999},
  volume	= {},
  pages		= {317--22},
  abstract	= {This paper describes a method for the recognition of
		  dynamic gestures using a combination neural
		  network/discrete hidden Markov model. First a reliable and
		  robust person localization task is presented. Then we focus
		  on the view-based recognition of the user's static gestural
		  instructions from a predefined vocabulary based on both a
		  skin color model and statistical normalized moment
		  invariants. Further, a Kohonen self organizing map (SOM) is
		  used to cluster the feature space. After the
		  self-organizing process we modify the SOM weight vectors
		  using the learning vector quantization method causing the
		  weights to approach the decision boundaries and we quantize
		  each of them into a symbol. Finally, the symbol sequence
		  extracted from time-sequential images is used as input for
		  a system of discrete hidden Markov models. To train and
		  test the system we gathered the data five movements from
		  our vocabulary (stop, go to left, go to right, hello-waving
		  left and hello-waving right). The system uses input from a
		  color video camera and is user-independent.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  corral94a,
  author	= {Juan A. Corral and Miguel Guerrero and Pedro J. Zufiria},
  title		= {Image Compression via Optimal Vector Quantization: A
		  Comparison Between {SOM}, {LBQ} and {K}-means Algorithms},
  pages		= {4113--4118},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, vector quantization, comparison},
  dbinsdate	= {oldtimer}
}

@Article{	  corridoni96a,
  author	= {J. M. Corridoni and A. {Del Bimbo} and L. Landi},
  title		= {{3D} object classification using multi-object {K}ohonen
		  networks},
  journal	= {Pattern Recognition},
  year		= {1996},
  volume	= {29},
  number	= {6},
  pages		= {919--35},
  dbinsdate	= {oldtimer}
}

@Article{	  cortiglioni01a,
  author	= {Cortiglioni, F. and Mahonen, P. and Hakala, P. and
		  Frantti, T.},
  title		= {Automated star-galaxy discrimination for large surveys},
  journal	= {ASTROPHYSICAL JOURNAL},
  year		= {2001},
  volume	= {556},
  number	= {2},
  month		= {AUG 1},
  pages		= {937--943},
  abstract	= {The size of survey data is increasing rapidly, and the
		  automatic classification of objects is becoming more
		  important. The classification is traditionally based, e.g.,
		  on point- spread function (PSF) fitting. Recently, several
		  different neural network approaches have been introduced
		  for classification. In this paper we use both
		  self-organized map and learning vector quantization based
		  neural networks for star-galaxy separation. Finally, we
		  test a hybrid algorithm using fuzzy classifier and
		  back-propagation neural networks. We show that different
		  methods give relatively similar results. The classification
		  accuracy is good enough for real data analysis, and
		  selection between different methods must be done based on
		  algorithmic complexity and availability of preclassified
		  training sets.},
  dbinsdate	= {2002/1}
}

@InCollection{	  cortijo96a,
  author	= {F. J. Cortijo and N. Perez {de la Blanca}},
  title		= {Automatic estimation of the {LVQ}-1 parameters.
		  Applications to multispectral image classification},
  booktitle	= {Proceedings of the 13th International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  volume	= {4},
  address	= {Los Alamitos, CA, USA},
  pages		= {346--50},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cosculluela93a,
  author	= {Cosculluela, M. J. and Dominguez, M. J. and Montes, R. and
		  Garcia-Tajedor, A. },
  title		= {Day type identification for electric hourly load demand
		  forecasting using \mbox{self-organizing} maps},
  booktitle	= {Sixth International Conference. Neural Networks and their
		  Industrial and Cognitive Applications. NEURO-NIMES 93
		  Conference Proceedings and Exhibition Catalog},
  year		= {1993},
  pages		= {129--37},
  organization	= {Knowledge Eng. Dept. , Eritel, Madrid, Spain},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Article{	  cosi00a,
  author	= {Cosi, P. and Frasconi, P. and Gori, M. and Lastrucci, L.
		  and Soda, G.},
  title		= {Competitive radial basis functions training for phone
		  classification},
  journal	= {NEUROCOMPUTING},
  year		= {2000},
  volume	= {34},
  month		= {SEP},
  pages		= {117--129},
  abstract	= {In this paper we describe the design of a phoneme
		  classifier that is based on AIDA, a speech database that
		  has been recently proposed as a standard for Italian
		  concerning the phonetic level. We present experimental
		  results using LVQ and show that the proper selection of
		  Kohonen's learning parameter alpha, based on some
		  intriguing links with Backpropagation learning, contributes
		  to improve the performance with respect to standard
		  heuristics proposed in the literature [Konen, Proc. IEEE 78
		  (9) (1990) 1464--1480], },
  dbinsdate	= {2002/1}
}

@InProceedings{	  cosi94a,
  author	= {P. Cosi and G. {De Poli} and G. Lauzzana},
  title		= {Timbre Classification by {NN} and Auditory Modeling},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {925--928},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, sound classification, pattern recognition},
  dbinsdate	= {oldtimer}
}

@Article{	  cosi94b,
  author	= {Cosi, P. and {De Poli}, G. and Lauzzana, G. },
  title		= {Auditory modelling and \mbox{self-organizing} neural
		  networks for timbre classification},
  journal	= {Journal of New Music Research},
  year		= {1994},
  volume	= {23},
  number	= {1},
  pages		= {71--98},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  cosmo93a,
  author	= {Cosmo, G. and {De Angelis}, A. },
  title		= {A hybrid neural network architecture for the
		  classification of the hadronic decays of the Z/sup 0/},
  journal	= {International Journal of Modern Physics C [Physics and
		  Computers]},
  year		= {1993},
  volume	= {4},
  number	= {5},
  pages		= {977--81},
  month		= {Oct},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  costa01a,
  author	= {Costa, J. A. F. and {De Andrade Netto}, M. L.},
  title		= {Clustering of complex shaped data sets via kohonen maps
		  and mathematical morphology},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Dasarathy, B. V.},
  volume	= {4384},
  pages		= {16--27},
  organization	= {Dept. of Comp. Eng./Indust. Automat., Sch. of Elec. and
		  Comp. Engineering, Universidade Estadual de Campinas},
  publisher	= {},
  address	= {},
  abstract	= {Clustering is the process of discovering groups within the
		  data, based on similarities, with a minimal, if any,
		  knowledge of their structure. The self-organizing (or
		  Kohonen) map (SOM) is one of the best known neural network
		  algorithms. It has been widely studied as a software tool
		  for visualization of high-dimensional data. Important
		  features include information compression while preserving
		  topological and metric relationship of the primary data
		  items. Although Kohonen maps had been applied for
		  clustering data, usually the researcher sets the number of
		  neurons equal to the expected number of clusters, or
		  manually segments a two-dimensional map using some a priori
		  knowledge of the data. This paper proposes techniques for
		  automatic partitioning and labeling SOM networks in
		  clusters of neurons that may be used to represent the data
		  clusters. Mathematical morphology operations, such as
		  watershed, are performed on the U-matrix, which is a
		  neuron-distance image. The direct application of watershed
		  leads to an oversegmented image. It is used markers to
		  identify significant clusters and homotopy modification to
		  suppress the others. Markers are automatically found by
		  performing a multi-level scan of connected regions of the
		  U-matrix. Each cluster of neurons is a sub-graph that
		  defines, in the input space, complex and nonparametric
		  geometries which approximately describes the shape of the
		  clusters. The process of map partitioning is extended
		  recursively. Each cluster of neurons gives rise to a new
		  map, which are trained with the subset of data that were
		  classified to it. The algorithm produces dynamically a
		  hierarchical tree of maps, which explains the cluster's
		  structure in levels of granularity. The distributed and
		  multiple prototypes cluster representation enables the
		  discoveries of clusters even in the case when we have two
		  or more non-separable pattern classes.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  costa01b,
  author	= {Costa, J. A. F. and {De Andrade Netto}, M. L.},
  title		= {A new tree-structured self-organizing map for data
		  analysis},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {1931--1936},
  organization	= {Dept. of Comp. Eng. Indust. Automat., Sch. of Elec. and
		  Comp. Engineering, Universidade Estadual de Campinas},
  publisher	= {},
  address	= {},
  abstract	= {Self-organizing map has been applied to a variety of tasks
		  including data visualization and clustering. Once the point
		  density of the neurons approximates the density of data, it
		  is possible to miner clustering information from the data
		  set after its unsupervised learning by using the neuron's
		  relations. This paper presents a new algorithm for
		  dynamical generation of a hierarchical structure of
		  self-organizing maps with applications to data analysis.
		  Differently from other tree-structured SOM approaches,
		  which nodes are neurons, in this case the tree nodes are
		  actually maps. From top to down, maps are automatically
		  segmented by using the U-matrix information, which presents
		  relations between neighboring neurons. The automatic map
		  partitioning algorithm is based on mathematical morphology
		  segmentation and it is applied to each map in each level of
		  the hierarchy. Clusters of neurons are automatically
		  identified and labeled and generate new sub-maps. Data are
		  partitioned accordingly the label of its best match unit in
		  each level of the tree. The algorithm may be seen as a
		  recursive partition clustering method with multiple
		  prototypes cluster representation, which enables the
		  discoveries of clusters in a variety of geometrical
		  shapes.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  costa99a,
  author	= {Costa, J. A. F. and {de Andrade Netto}, M. L.},
  title		= {Automatic data classification by a hierarchy of
		  \mbox{self-organizing} maps},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {419--24},
  abstract	= {Clustering is the process by which discrete objects are
		  assigned to groups that have similar characteristics.
		  Self-organizing maps (SOM) have been widely used as a data
		  visualization tool. Some of their advantages include
		  information compression and density estimation while trying
		  to preserve the topological and metric relationships of the
		  primary data items. For using {SOM} as a clustering tool
		  additional procedures are required to interpret the mapping
		  obtained through unsupervised learning. Costa and Netto
		  (1999) described the usage of image analysis and
		  mathematical morphology to find automatically regions of
		  similar neurons and their borders. The purpose of this
		  paper is to enhance the clustering process in order to
		  detail the underlying structure obtained in a first trial.
		  Groups of neurons associated to clusters are further
		  subdivided in new sub-networks, generating a tree-like
		  structure of {SOM}s. Differently to other hierarchical
		  {SOM} approaches, the number of sub-nets for a given {SOM}
		  in a given height of the tree is not specified in advance.
		  The process can be seen as a dynamic strategy for cluster
		  discovery.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  costa99b,
  author	= {Costa, J. A. F. and {de Andrade Netto}, M. L.},
  title		= {Cluster analysis using \mbox{self-organizing} maps and
		  image processing techniques},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and Cybernetics.
		  },
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {367--72},
  abstract	= {Cluster analysis methods are used to classify R unlabeled
		  objects in a P-dimensional space into groups based on their
		  similarities. This paper focuses on the use of self
		  organising maps (SOM) as a clustering tool and some of the
		  additional procedures required to enable a meaningful
		  cluster's interpretation in the trained map. Topics
		  discussed here include the usage of mathematical morphology
		  segmentation method watershed to segment the neuron's
		  distance image (u-matrix). Finding good watershed markers
		  and the modification of the u-matrix homotopy are
		  discussed. The algorithm automatically produces labeled
		  sets of neurons that are related to the clusters in the
		  P-dimensional space. An example of non-spherical, complex
		  shaped and nonlinearly separable clusters illustrate the
		  capabilities of the method.},
  dbinsdate	= {oldtimer}
}

@Article{	  costa99c,
  author	= {Costa, J. A. F. and Netto, M. L. A.},
  title		= {Estimating the number of clusters in multivariate data by
		  \mbox{self-organizing} maps},
  journal	= {International Journal of Neural Systems},
  year		= {1999},
  volume	= {9},
  pages		= {195--202},
  abstract	= {Determining the structure of data without prior knowledge
		  of the number of clusters of any information about their
		  composition is a problem of interest in many fields, such
		  as image analysis, astrophysics, biology, etc. Partitioning
		  a set of n patterns in a p-dimensional feature space must
		  be done such that those in a given cluster are more similar
		  to each other than the rest. As there are approximately
		  (K/sup n/)/(K/sup l/) possible ways of partitioning the
		  patterns among K clusters, finding the best solution is
		  very hard when n is large. The search space is increased
		  when we have no a priori number of partitions. Although the
		  self-organizing feature map (SOM) can be used to visualize
		  clusters, the automation of knowledge discovery by {SOM} is
		  a difficult task. The paper proposes region-based image
		  processing methods for post-processing the U-matrix
		  obtained after the unsupervised learning performed by
		  {SOM}. Mathematical morphology is applied to identify
		  regions of neurons that are similar. The number of regions
		  and their labels are automatically found and they are
		  related to the number of clusters in a multivariate data
		  set. New data can be classified by labeling it according to
		  the best match neuron. Simulations using data sets drawn
		  from finite mixtures of p-variate normal densities are
		  presented as well as related advantages and drawbacks of
		  the method.},
  dbinsdate	= {oldtimer}
}

@Booklet{	  costa99d,
  title		= {Classifica{\,c}{\~a}o Autom{\'a}tica e An{\'a}lise de
		  Dados por Redes Neurais Auto-Ograniz{\'a}veis},
  key		= {},
  author	= {Jos{\'e} Alfredo Ferreira Costa},
  howpublished	= {},
  address	= {},
  month		= {},
  year		= {1999},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  cotter88a,
  author	= {N. E. Cotter and K. Smith and M. Gaspar},
  title		= {A pulse-width modulation desing approach and
		  path-programmable logic for artificial neural networks},
  booktitle	= {Advanced Res. in VLSI. Proc. of the Fifth MIT Conf. },
  year		= {1988},
  editor	= {J. Allen and F. T. Leighton},
  pages		= {1--17},
  publisher	= {MIT Press},
  address	= {Cambridge, MA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cottrell01a,
  author	= {M. Cottrell and E. {De Bodt} and M. Verleysen},
  title		= {A statistical tool to assess the reliability of
		  self-organising maps},
  booktitle	= {Advances in Self-Organising Maps},
  crossref	= {},
  key		= {},
  pages		= {7--14},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {Springer},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  cottrell86a,
  author	= {M. Cottrell and J. -C. Fort},
  journal	= {Biol. Cyb. },
  volume	= 53,
  year		= 1986,
  pages		= {405--411},
  title		= {A Stochastic Model of Retinotopy: A Self-Organizing
		  Process},
  dbinsdate	= {oldtimer}
}

@Article{	  cottrell87a,
  author	= {Marie Cottrell and Jean-Claude Fort},
  title		= {{\'{E}}tude d'un processus d'auto-organisation},
  journal	= {Annales de l'Institut Henri Poincar{\'{e}}},
  year		= {1987},
  volume	= {23},
  number	= {1},
  pages		= {1--20},
  note		= {(in French)},
  annote	= {Proof of the convergence of the Self-organizing process in
		  one-dimensional case,},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  cottrell88a,
  author	= {Marie Cottrell},
  title		= {Mod{\'{e}}lisation de r{\'{e}}seaux de neurones par des
		  chaines de {M}arkov et autres applications},
  school	= {Universit{\'{e}} Paris Sud, Centre d'Orsay},
  address	= {Orsay, France},
  year		= {1988},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cottrell93a,
  author	= {Marie Cottrell and Patrick Letr{\'{e}}my and Elizabeth
		  Roy},
  title		= {Analysing a contingency table with {K}ohonen maps: a
		  factorial correspondence analysis},
  editor	= {Mira, J. and Cabestany, J. and Prieto, A. },
  pages		= {305--11},
  booktitle	= {New Trends in Neural Computation. International Workshop
		  on Artificial Neural Networks. IWANN '93 Proceedings},
  year		= {1993},
  organization	= {Paris Univ. , France},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@TechReport{	  cottrell93b,
  author	= {Marie Cottrell and Patrick Letr{\'{e}}my and Elizabeth
		  Roy},
  title		= {Analysing a contingency table with {K}ohonen maps: a
		  factorial correspondence analysis},
  institution	= {Universit\'{e} Paris 1},
  year		= {1993},
  number	= {19},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@TechReport{	  cottrell94a,
  author	= {M. Cottrell and J. C. Fort and G. Pag{\`{e}}s},
  title		= {Two or three things that we know about the {K}ohonen
		  algorithm},
  institution	= {Universit\'{e} Paris 1},
  year		= {1994},
  number	= {31},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cottrell94b,
  author	= {M. Cottrell and J. C. Fort and G. Pag{\`{e}}s},
  title		= {Two or three things that we know about the {K}ohonen
		  algorithm},
  booktitle	= {Proc. ESANN'94, European Symp. on Artificial Neural
		  Networks},
  year		= {1994},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {235--244},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cottrell94c,
  author	= {M. Cottrell and P. Letremy},
  title		= {Classification et analyse des correspondances au moyen de
		  l'algorithme de {K}ohonen: application à l'étude de données
		  socio-économiques},
  booktitle	= {Proceedings of Neuro-Nîmes 94},
  year		= {1994},
  pages		= {74--83},
  dbinsdate	= {oldtimer}
}

@Article{	  cottrell95a,
  author	= {M. Cottrell and J. C. Fort and G. Pag{\`{e}}s},
  title		= {Comment about 'Analysis of the Convergence Properties of
		  Topology Preserving Neural Networks'},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1995},
  volume	= {6},
  number	= {3},
  pages		= {797--799},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell95b,
  author	= {M. Cottrell and B. Girard and Y. Girard and C. Muller and
		  P. Rousset},
  title		= {Daily electrical power curves: classification and
		  forecasting using a {K}ohonen map},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {1107--13},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cottrell96a,
  author	= {Marie Cottrell and Eric {de Bodt}},
  title		= {A {K}ohonen Map Representation to Avoid Misleading
		  Interpretations},
  booktitle	= {Proc. ESANN'96, European Symp. on Artificial Neural
		  Networks},
  year		= {1996},
  publisher	= {D facto conference services},
  address	= {Bruges, Belgium},
  editor	= {Michel Verleysen},
  pages		= {103--110},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell96b,
  author	= {M. Cottrell and E. {de Bodt} and E. -F. Henrion},
  title		= {Understanding the leasing decision with the help of a
		  {K}ohonen map. An empirical study of the Belgian market},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {4},
  address	= {New York, NY, USA},
  pages		= {2027--32},
  abstract	= {Classical models which compare leasing and bank loans stay
		  on the hypothesis that lease and debt are substitutes: a
		  dollar of lease obligation replaces a dollar of debt
		  obligation. If leasing and debts are really substitutes,
		  how can we justify the leasing existence? The main
		  difficulty to overcome in order to empirically test the
		  complementary hypothesis is the impossibility to define a
		  practical measure of debts capacity. The only way to find
		  some empirical support to one of those hypothesis is to
		  study the financial profile of the lessees. The proposed
		  empirical study, which covers all the 1991 annuals accounts
		  of the Belgian corporations having more than 15 millions
		  BEF of assets (10.133 firms), put into light the existence
		  of subgroups in the lessees population and a clear
		  relationship between the use of leasing and the financial
		  health of the lessee. To obtain these results, we use
		  Kohonen maps as a nonlinear data analysis method which
		  combines a projection task in the sense of the reduction of
		  the number of dimensions and a quantification task (the
		  research of homogeneous subsets of firms). Key features of
		  the Kohonen algorithm are also discussed in regards of the
		  Principal Components Analysis.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell96c,
  author	= {M. Cottrell and E. {de Bodt} and P. Gregoire},
  title		= {Analyzing shocks on the interest rate structure with
		  {K}ohonen map},
  booktitle	= {Proceedings of the IEEE/IAFE 1996 Conference on
		  Computational Intelligence for Financial Engineering
		  (CIFEr)},
  publisher	= {IEEE},
  year		= {1996},
  address	= {New York, NY, USA},
  pages		= {162--7},
  abstract	= {Both the academics and practitioners consider modeling
		  between zero-coupon rates of different maturity to be an
		  important issue. This problem not only has implications for
		  the valuation of interest rate contingent claims, but also
		  for the management of financial institutions as well as for
		  monetary policy. This work attempts to classify shocks on
		  the interest rate term structure and to verify that these
		  classes are compatible with the theoretical shocks
		  predicted by the general equilibrium models and,
		  consequently, respect the no-arbitrage condition.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cottrell96d,
  author	= {M. Cottrell and E. {de Bodt} and P. Grégoire},
  title		= {Une application des cartes de {K}ohonen et de la procédure
		  de {M}onte-{C}arlo en vue de simuler l'évolution de la
		  structure des taux d'intérêts à long terme},
  booktitle	= {Troisiéme Rencontre Internationale ACSEG},
  year		= {1996},
  pages		= {27--36},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell96e,
  author	= {M. Cottrell and E. {de Bodt} and P. Grégoire},
  title		= {The relation between Interest Rate Shocks and the Initial
		  Interest Rate Structure : An Empirical Study using a
		  {K}ohonen Map},
  booktitle	= {Congress of the Association française de Finance
		  Internationale},
  year		= {1996},
  address	= {Genève},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell97a,
  author	= {Marie Cottrell},
  title		= {Theoretical aspects of the {SOM} algorithm},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {246---267},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell97b,
  author	= {M. Cottrell and E. {de Bodt} and E. F. Henrion and P.
		  Gregoire},
  title		= {Simulating Interest Rate Structure Evolution on a Long
		  Term Horizon},
  booktitle	= {Progress in Neural Processing---Decision Technologies for
		  Financial Engineering},
  publisher	= {World Scientific},
  year		= {1997},
  editor	= {A. S. Weigend and Y. Abu-Mostafa and A. P. N. Refenes},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell97c,
  author	= {Marie Cottrell and Bernard Girard and Patrick Rousset},
  title		= {Long Term Forecasting by Combining {K}ohonen Algorithm and
		  Standard Prevision},
  booktitle	= {Proc. ICANN'97, 7th International Conference on Artificial
		  Neural Networks},
  publisher	= {Springer},
  year		= 1997,
  volume	= 1327,
  series	= {Lecture Notes in Computer Science},
  address	= {Berlin},
  pages		= {993--998},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell97d,
  author	= {M. Cottrell and P. Rousset},
  title		= {The {K}ohonen Algorithm: A Powerful Tool for Analysing and
		  Representing Multidimensional Quantitative and Qualitative
		  Data},
  booktitle	= {Lecture Notes in Computer Science},
  publisher	= {Springer},
  year		= {1997},
  editor	= {J. Mira and R. Moreno-Diaz and J. Cabestany},
  volume	= {1240},
  pages		= {861--871},
  dbinsdate	= {oldtimer}
}

@TechReport{	  cottrell97e,
  author	= {M. Cottrell and P. Rousset},
  title		= {The {K}ohonen algorithm: a powerful tool for analyzing and
		  representing multidimensional quantitative and qualitative
		  data},
  institution	= {Universite Paris 1},
  year		= 1997,
  type		= {Pr{\'e}publication du SAMOS},
  number	= 76,
  address	= {Paris},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell97f,
  author	= {M. Cottrell and E. {de Bodt} and E. F. Henrion and Ph.
		  Gregoire},
  title		= {Simulating interest rate structure evolution on a long
		  term horizon. {A} {K}ohonen map application},
  booktitle	= {Decision Technologies for Financial Engineering.
		  Proceedings of the Fourth International Conference on
		  Neural Networks in the Capital Markets (NNCM'96)},
  publisher	= {World Scientific},
  year		= {1997},
  editor	= {A. S. Weigend and Y. Abu-Mostafa and A. -P. N. Refenes},
  address	= {Singapore},
  pages		= {162--74},
  dbinsdate	= {oldtimer}
}

@TechReport{	  cottrell97g,
  author	= {M. Cottrell and P. Gaubert and P. Letremy and P. Rousset},
  title		= {Analyzing and representing multidimensional quantitative
		  and qualitative data: {D}emographic study of the Rh{\^o}ne
		  valley. The domestic consumption of the {C}anadian
		  families},
  institution	= {Universite Paris 1},
  year		= 1997,
  type		= {Pr{\'e}publication du SAMOS},
  number	= 79,
  address	= {Paris},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell98a,
  author	= {Marie Cottrell and Eric {de Bodt} and Philippe
		  Gr{\'e}goire},
  title		= {Financial Applications of the Self-Organizing Map},
  booktitle	= {Proc. EUFIT'98, 6th European Congress on Intelligent
		  Techniques \& Soft Computing},
  publisher	= {ELITE Foundation},
  year		= 1998,
  volume	= 1,
  address	= {Aachen, Germany},
  pages		= {205--209},
  abstract	= {We present a financial application of the {SOM} algorithm.
		  We try to model in a nonparametric way the long term
		  evolution of interest rates in order to simulate the
		  distribution of future paths and to choose a risk
		  management policy. Our methodology is based on a double
		  Kohonen classification (for the initial interest rates
		  structure, and for the interest rates shocks, i.e. the
		  deformations of the structures). These classifications are
		  used to approximate the conditional distributions of the
		  shocks given the initial structure. Without assuming any
		  hypotheses on the functional form of the process generating
		  the interest rate structure and its dynamics, we can
		  reproduce long-term evolution compatible with the
		  historical observations.},
  dbinsdate	= {oldtimer}
}

@Article{	  cottrell98b,
  author	= {Cottrell, M. and Fort, J. C. and Pages, G.},
  title		= {Theoretical aspects of the {SOM} algorithm},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {119--138},
  abstract	= {The {SOM} algorithm is very astonishing. On the one hand,
		  it is very simple to write down and to simulate, its
		  practical properties are clear and easy to observe.
		  However, on the other hand, its theoretical properties
		  still remain without proof in the general case, despite the
		  tremendous efforts of several authors. In this paper, we
		  briefly review the previous results and provide some
		  conjectures for future work.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  cottrell98c,
  author	= {M. Cottrell},
  title		= {Nouvelles techniques neuronales en analyse des
		  donn{\'e}es. {A}pplication {\`a} la classification, {\`a}
		  la recherche de typologie et {\`a} la pr{\'e}vision.
		  {C}onf{\'e}rence Invit{\'e}e, Journ{\'e}es {ACSEG'97} Tours},
  institution	= {Universit{\'e} Paris 1},
  year		= {1998},
  type		= {Pr{\'e}publication du SAMOS},
  number	= {91},
  address	= {Paris},
  dbinsdate	= {oldtimer}
}

@Article{	  cottrell98d,
  author	= {M. Cottrell and B. Girard and P. Rousset},
  title		= {Forecasting of curves using a {K}ohonen classification},
  journal	= {Journal of Forecasting},
  year		= {1998},
  volume	= {17},
  pages		= {429--439},
  dbinsdate	= {oldtimer}
}

@Article{	  cottrell99a,
  author	= {M. Cottrell and P. Gaubert},
  title		= {Classification of recurring unemployed workers and
		  unemployment exits},
  journal	= {European Journal of Economics and Social Systems},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cottrell99b,
  author	= {M. Cottrell and P. Gaubert and P. Letremy and P. Rousset},
  title		= {Analyzing and representing multidimensional quantitative
		  and qualitative data : Demographic study of the Rh{\^o}ne
		  valley. The domestic consumption of the Canadian families.},
  booktitle	= {Kohonen Maps},
  pages		= {1--14},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: {SOM} algorithm, Data Analysis, Data Mining,
		  Visualisation,Economic Data},
  dbinsdate	= {oldtimer}
}

@Article{	  cottrell99c,
  author	= {M. Cottrell and P. Gaubert},
  title		= {Neural network and segmented labour market},
  journal	= {European Journal of Economics and Social Systems},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  couquin00a,
  author	= {Couquin, D. and Bolon, P. and Onea, A.},
  title		= {Objective metric for colour image comparison},
  booktitle	= {Signal Processing X Theories and Applications. Proceedings
		  of EUSIPCO 2000. Tenth European Signal Processing
		  Conference. Tampere Univ. Technology, Tampere, Finland},
  year		= {2000},
  volume	= {1},
  pages		= {119--22},
  abstract	= {A method for colour image comparison is proposed. An
		  indexation step is performed using a Kohonen neural
		  network, with the SOM (self organizing map) algorithm.
		  Therefore, a colour image in the red green blue (RGB) space
		  is interpreted as a 256 colour image. The comparison step,
		  between two indexed images, is based on a global
		  dissimilarity measure, which is an extension of Baddeley's
		  distance, adapted to this colour point set. Experimental
		  results obtained with real images are presented.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  coventry00a,
  author	= {Timothy E. J. Coventry and Johan Gouws},
  title		= {Coal Segregation by a Self-Organising Feature Map},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Department of Electrical \& Electronic Engineering, Rand
		  Afrikaans University},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  cox00a,
  author	= {Cox, Earl},
  title		= {Distributed intelligence in the B2B universe: Fuzzy and
		  neural connections in Web-centric knowledge management},
  journal	= {PC AI},
  year		= {2000},
  volume	= {14},
  number	= {3},
  month		= {},
  pages		= {16--19},
  organization	= {Research and Development at Panacya},
  publisher	= {Knowledge Technology Inc.},
  address	= {Phoenix, AZ},
  abstract	= {Business-to-Business (B2B) opportunities in the Internet
		  world establish lines of communication between business
		  peers on the Web. Capitalizing on the opportunities in this
		  world means leveraging knowledge and marketplace
		  intelligence at ever accelerating rates. Combined into a
		  set of analytical tools, fuzzy systems and self-organizing
		  neural networks provide the knowledge engineer and business
		  analyst with a rich set of modeling tools. In particular,
		  Kohonen nets provide data warehouse and data mart
		  designers, builders, and users with the technology for
		  exploring non-numeric data in a manner that can illuminate
		  deeply buried as well as sparce patterns.},
  dbinsdate	= {2002/1}
}

@Article{	  cox00b,
  author	= {Cox, E.},
  title		= {Free-form text data mining integrating fuzzy systems,
		  self-organizing neural nets and rule-based knowledge
		  bases},
  journal	= {PC-AI},
  year		= {2000},
  volume	= {14},
  pages		= {22--6},
  abstract	= {The use of automated tools to discover relationships
		  within large databases is very much on the increase. The
		  general techniques, lumped under the ubiquitous umbrella of
		  data mining or knowledge discovery, are now successful in a
		  wide spectrum of industries. This article examines a free
		  form text mining approach, based on systems already fielded
		  and in production, combines semantic analysis with ambient
		  analytical techniques including fuzzy rule induction and
		  self-organizing (Kohonen) networks. Using the experience
		  gained from these projects in the insurance and managed
		  health care industry, a fast and effective methodology
		  exists to find, expose and rank important patterns in large
		  databases of free form text.},
  dbinsdate	= {2002/1}
}

@InCollection{	  cramer96a,
  author	= {T. Cramer and J. Goppert and W. Rosenstiel},
  title		= {Modeling psychological stereotypes in
		  \mbox{self-organizing} maps},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {905--10},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cristea99a,
  author	= {Cristea, P. and Valsan, Z.},
  title		= {New cepstrum frequency scale for neural network speaker
		  verification},
  booktitle	= {ICECS'99. Proceedings of ICECS '99. 6th IEEE International
		  Conference on Electronics, Circuits and Systems. },
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1573--6},
  abstract	= {The influence of cepstrum parameters on text-dependent
		  speaker verification and speech recognition is
		  investigated. Experiments are performed to establish the
		  relevance of various resonant frequencies and frequency
		  bands in terms of their speech and speaker recognition
		  ability. A Romanian database of eighteen isolated words has
		  been used. The study of the filter bank analysis suggests a
		  new frequency scale instead of the currently used mel-scale
		  to extract from the speech signal cepstrum coefficients.
		  The proposed scale results in better performance in speaker
		  verification. The processes of speech recognition and
		  speaker verification are carried out by using a neural
		  network system comprising a self-organizing feature map
		  (SOFM) and a multilayer perceptron (MLP).},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  critchley92a,
  author	= {D. A. Critchley},
  title		= {Stable States, Transitions and Convergence in {K}ohonen
		  Self Organizing Maps},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {281--284},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  csabai91a,
  author	= {I. Csabai and F. Czako and Z. Fodor},
  title		= {Quark-and gluon-jet separation using neural networks},
  journal	= {Phys. Rev. D},
  year		= {1991},
  volume	= {44},
  number	= {7},
  pages		= {R1905-R1908},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  csabai92a,
  author	= {I. Csabai and T. Geszti and G. Vattay},
  title		= {Criticality in the \mbox{\mbox{one-dimensional}} {K}ohonen
		  neural map},
  journal	= {Phys. Rev. A [Statistical Physics, Plasmas, Fluids, and
		  Related Interdisciplinary Topics]},
  year		= {1992},
  volume	= {46},
  number	= {10},
  pages		= {6181--6184},
  dbinsdate	= {oldtimer}
}

@InCollection{	  cser98a,
  author	= {L. Cser and A. Korhonen and O. Simula and J. Larkiola and
		  J. Ahola},
  title		= {The {SOM} Based Data Mining in Hot Rolling},
  booktitle	= {Proceedings of 4th International Symposium on Measurement
		  Technology and Intelligent Instruments (ISMTII'98)},
  year		= {1998},
  address	= {Miskolc, Hungary},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  cser99a,
  author	= {Cser, L. and Korhonen, A. S. and Gulyas, J. and Mantyla,
		  P. and Simula, O. and Reiss, G. and Ruha, P.},
  title		= {Data mining and state monitoring in hot rolling},
  booktitle	= {Proceedings of the Second International Conference on
		  Intelligent Processing and Manufacturing of Materials.
		  IPMM'99. },
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {1},
  pages		= {529--36},
  abstract	= {An overview of state monitoring in hot rolling is
		  reviewed, and a new concept of state monitoring is shown.
		  Based on a detailed analysis of all factors a state monitor
		  is proposed. A system state corresponds to the proper
		  product quality. If the system is leaving the area of
		  required quality in the state space, a signal is given with
		  the evaluation of situation. Self-organising maps (SOM) are
		  especially suitable in analysing the very complex process
		  of hot rolling. Application of {SOM} helps to discover
		  hidden dependencies influencing the quality parameters,
		  such as flatness, profile, thickness and width deviation as
		  well as wedge and surface quality. Results from the
		  analysis of more than 70 parameters in 16,000 strips gave
		  the state space used in state monitoring based on online
		  data sampling. The coloured visualisation map shows the
		  state space enabling prediction of product quality.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  csillaghy95a,
  author	= {Csillaghy, A. },
  title		= {Retrieving information from digital solar radio
		  spectrograms},
  booktitle	= {Coronal Magnetic Energy Releases. Proceedings of the CESRA
		  Workshop},
  year		= {1995},
  editor	= {Benz, A. O. and Kruger, A. },
  pages		= {83--92},
  organization	= {Inst. fuer Astron. , Eidgenoessische Tech. Hochschule,
		  Zurich, Switzerland},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  csillaghy97a,
  author	= {Andr{\'e} Csillaghy},
  title		= {Information extraction by local density analysis: a
		  contribution to content-based management of scientific
		  data},
  school	= {Swiss Federal Institute of Technology Zurich},
  year		= 1997,
  address	= {Zurich, Switzerland},
  dbinsdate	= {oldtimer}
}

@Article{	  cumming93a,
  author	= {Simon Cumming},
  title		= {Neural Networks for Monitorig of Engine Condition Data},
  journal	= {Neural Computing {\&} Applications},
  year		= {1993},
  volume	= {1},
  number	= {1},
  pages		= {96--102},
  dbinsdate	= {oldtimer}
}

@Article{	  curry01a,
  author	= {Curry, B. and Davies, F. and Phillips, P. and Evans, M.
		  and Moutinho, L.},
  title		= {The Kohonen self-organizing map: an application to the
		  study of strategic groups in the {UK} hotel industry},
  journal	= {EXPERT SYSTEMS},
  year		= {2001},
  volume	= {18},
  number	= {1},
  month		= {FEB},
  pages		= {19--31},
  abstract	= {This paper examines a neural network method known as the
		  self- organizing map (SOM). The motivation behind the SOM
		  is to transform the data to a two-dimensional grid of nodes
		  while preserving its 'topological' structure. In neural
		  network terminology this involves unsupervised learning.
		  The nearest related statistical technique is cluster
		  analysis. We employ the SOM in the task of identifying
		  strategic groups of companies, using data which relate to
		  the generic strategies suggested by Porter. Following
		  identification of different groups of hotels with certain
		  strategic emphases, the study investigates correlations
		  between strategies followed and hotel performance. We
		  compare and contrast the 'feature map' generated by the SOM
		  with the results of a standard cluster analysis using the
		  k-means method. The data also cover performance indicators
		  and the results indicate that performance varies between
		  strategic groups.},
  dbinsdate	= {2002/1}
}

@Article{	  czyewski01a,
  author	= {Czyewski, A. and Krolikowski, R.},
  title		= {Neuro-rough control of masking thresholds for audio signal
		  enhancement},
  journal	= {Neurocomputing},
  year		= {2001},
  volume	= {36},
  pages		= {5--27},
  abstract	= {Addresses the problem of neuro-rough hybridisation applied
		  to the digital processing of audio signals. Moreover, the
		  application of some selected soft computing techniques to
		  non-stationary noise reduction is described. Some attention
		  is also placed on a discussion of the intelligent decision
		  algorithm's performance. The noise reduction algorithm is
		  based on a new perceptual approach exploiting some of the
		  properties of the human auditory system. Furthermore, this
		  paper introduces an engineered perceptual filter driven by
		  an intelligent controller employing rules generated with
		  the use of a rough set-based algorithm supported by a
		  neural network. The goal of the intelligent controller is
		  to estimate the current statistics of corrupting noise on
		  the basis of analysing the signals received from a
		  telecommunication channel. Thereafter, the noise estimate
		  enables one to determine the masking threshold levels which
		  allow one to make the noise inaudible in the audio signals.
		  Since the implemented decision algorithm requires quantised
		  data, Kohonen's self-organising maps (SOM) extended by
		  various distance metrics were used as data quantisers. Some
		  results of experiments in the domain of non-stationary
		  noise reduction in speech are discussed.},
  dbinsdate	= {2002/1}
}

@Article{	  dadios00a,
  author	= {Dadios, E. P. and Gunay, N. S.},
  title		= {A fuzzy logic based neural network controller for highly
		  nonlinear systems},
  journal	= {International-Journal-of-Knowledge-Based-Intelligent-Engineering-Systems}
		  ,
  year		= {2000},
  volume	= {4},
  pages		= {254--62},
  abstract	= {This paper investigates the applicability of developing a
		  learning controller based on a fuzzy logic reasoning that
		  is implemented by neural networks to control complex and
		  highly nonlinear systems. The flexible inverted pendulum
		  problem (FIPP) is used as a test case for this application
		  as it was shown by recent studies to exhibit more
		  nonlinearity compared to the conventional inverted pendulum
		  problem. The neural networks (feedforward and Kohonen self
		  organizing map) are used to determine the appropriate fuzzy
		  rules (antecedent and consequent), to generate the
		  membership functions, and to implement the fuzzy logic
		  based controller. The use of this method in building the
		  controller eliminates heuristic knowledge needed from a
		  human expert.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  dadios95a,
  author	= {Dadios, E. P. and Williams, D. J. },
  title		= {Application of neural networks to the flexible pole-cart
		  balancing problem},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {3},
  pages		= {2506--11},
  organization	= {Dept. of Manuf. Eng. , Loughborough Univ. of Technol. ,
		  UK},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  dae00a,
  author	= {Dae Seong Kang and Seok Bae Seo and Daijin Kim},
  title		= {Fast {VQ} codebook design by successively bisectioning of
		  principle axis},
  journal	= {Journal-of-KISS:-Software-and-Applications},
  year		= {2000},
  volume	= {27},
  pages		= {422--31},
  abstract	= {The paper proposes a new codebook generation method,
		  called a PCA-based VQ, that incorporates the PCA (principal
		  component analysis) technique into VQ (vector quantization)
		  codebook design. The PCA technique reduces the data
		  dimensions by transforming input image vectors into the
		  feature vectors. The cluster of feature vectors in the
		  transformed domain is bisectioned into two subclusters by
		  an optimally chosen partitioning hyperplane. We expedite
		  the searching of the optimal partitioning hyperplane that
		  is the most time consuming process by considering that: (1)
		  the optimal partitioning hyperplane is perpendicular to the
		  first principal axis of the feature vectors, (2) it is
		  located on the equilibrium point of the left and right
		  cluster's distortions, and (3) the left and right cluster's
		  distortions can be adjusted incrementally. This principal
		  axis bisectioning is successively performed on the cluster
		  whose difference of distortion between before and after
		  bisection is the maximum among the existing clusters until
		  the total distortion of clusters becomes as small as the
		  desired level. Simulation results show that the proposed
		  PCA based VQ method is promising because its reconstruction
		  performance is as good as that of the SOFM (self-organizing
		  feature maps) method and its codebook generation is as fast
		  as that of the K-means method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  dagitan90a,
  author	= {U. Dagitan and N. Yalabik},
  title		= {Connected word recognition using neural networks},
  booktitle	= {Neurocomputing, Algorithms, Architectures and
		  Applications. Proc. NATO Advanced Res. Workshop},
  year		= {1990},
  editor	= {F. Fogelman-Souli{\'{e}} and J. Herault},
  pages		= {297--300},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  x		= {A connected word recognition system which makes use of two
		  neural network models, namely, Kohonen's network and a
		  multilayer perceptron is implemented. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dahbur01a,
  author	= {Dahbur, K. and Muscarello, T.},
  title		= {Hybrid Kohonen neural network in data mining},
  booktitle	= {Proceedings of the IASTED International Conference.
		  Artificial Intelligence and Applications. ACTA Press,
		  Anaheim, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {30--3},
  abstract	= {O'Shea et al. (1995) presented the problem of discovering
		  patterns of potential serial criminals in criminal
		  databases. Instead of the manual system of distributing the
		  records at random to many police experts to visually
		  examine patterns, which has proven to be cumbersome, time
		  consuming and inefficient, they searched for an automated
		  system that best simulated the experts in the field. They
		  employed a back-propagation network (BPNN) to solve the
		  problem, but this approach failed, mainly because BPNN is a
		  supervised neural network that expects a comprehensive
		  training set before it can classify correctly. Such
		  comprehensive training set is impossible to obtain in a
		  continuously changing domain like the one in this problem.
		  We report on the use of a hybrid neural network made up of
		  several Kohonen networks that are integrated to cluster
		  multiple groups of attributes. We also report on the
		  advantages of this approach over both the traditional
		  approach of using a single neural network to accommodate
		  all the attributes, and that of applying a single
		  clustering algorithm on all the data attributes.},
  dbinsdate	= {2002/1}
}

@InCollection{	  daigremont96a,
  author	= {P. Daigremont and H. {De Lassus} and F. Badran and S.
		  Thiria},
  title		= {Regression by topological map: application on real data},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {185--90},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dalsgaard91a,
  author	= {P. Dalsgaard and O. Andersen and W. Barry},
  title		= {Multi-lingual acoustic-phonetic features for a number of
		  {E}uropean languages},
  booktitle	= {Proc. EUROSPEECH-91, 2nd European Conf. on Speech
		  Communication and Technology Proceedings},
  year		= {1991},
  volume	= {II},
  pages		= {685--688},
  organization	= {Assoc. Belge Acoust. ; Assoc. Italiana di Acustica; CEC;
		  et al},
  publisher	= {Istituto Int. Comunicazioni},
  address	= {Genova, Italy},
  dbinsdate	= {oldtimer}
}

@Article{	  dalsgaard92a,
  author	= {P. Dalsgaard},
  title		= {Phoneme label alignment using acoustic-phonetic features
		  and {G}aussian probability density functions},
  journal	= {Computer Speech and Language},
  year		= {1992},
  volume	= {6},
  number	= {4},
  pages		= {303--329},
  month		= {October},
  dbinsdate	= {oldtimer}
}

@Article{	  damarla92a,
  author	= {T. Raju Damarla and P. Karpur and P. K. Bhagat{'}},
  title		= {A self-learning neural net for ultrasonic signal
		  analysis},
  journal	= {Ultrasonics},
  year		= {1992},
  volume	= {30},
  number	= {5},
  pages		= {317--324},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  damore94a,
  author	= {Dario D'Amore and Vincenzo Piuri},
  title		= {Behavioral Simulation of Artificial Neural Networks: the
		  Case of Unsupervised Learning},
  booktitle	= {Proc. IMACS Int. Symp. on Signal Processing, Robotics and
		  Neural Networks},
  year		= {1994},
  pages		= {534--539},
  publisher	= {IMACS},
  address	= {Lille, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  damper98a,
  author	= {Damper, R. I. and Gilson, S. J.},
  title		= {Neural techniques for path linking, with applications to
		  image processing},
  booktitle	= {Proceedings of NC 1998. International ICSC/IFAC Symposium
		  on Neural Computation. ICSC Academic Press, Zurich,
		  Switzerland},
  year		= {1998},
  volume	= {},
  pages		= {488--94},
  abstract	= {Edge linking is a fundamental computer-vision task, yet
		  presents difficulties arising from the lack of information
		  in the image. Viewed as a constrained optimisation problem,
		  it is NP hard-being isomorphic to the classical travelling
		  salesman problem. Self-learning neural techniques boast the
		  ability to solve hard, ill-defined problems and, hence,
		  offer promise for such an application. This paper examines
		  the suitability of four well-known unsupervised techniques
		  for the task of edge linking, by applying them to a test
		  bed of edge point images and then evaluating their
		  performance. Techniques studied are the elastic net, active
		  contours (`snakes'), Kohonen map and Burr's modified
		  elastic net. Of these, only the elastic net and the Kohonen
		  map are realistic contenders for general edge-linking
		  tasks, although special treatment of noise in the image is
		  required.},
  dbinsdate	= {2002/1}
}

@InCollection{	  danforth94a,
  author	= {S. Danforth and I. Forman},
  title		= {Derived metaclasses in {SOM}},
  booktitle	= {Technology of Object-Oriented Languages and Systems, TOOLS
		  13. Proceedings of the Thirteenth International Conference
		  TOOLS Europe `94},
  publisher	= {Prentice Hall},
  year		= {1994},
  editor	= {B. Magnusson and B. Meyer and J. -M. Nerson and J. -F.
		  Perrot},
  address	= {Hemel Hempstead, UK},
  pages		= {63--73},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  danielson90a,
  author	= {S. Danielson},
  title		= {Recognition of {D}anish phonemes using an artificial
		  neural network},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {III},
  pages		= {677--682},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  daryoush97a,
  author	= {A. S. Daryoush and K. Kamogawa and K. Horikawa and T.
		  Tokumitsu and H. Ogawa},
  title		= {MMIC based {SOM} in optically fed phased array antennas
		  for Ka-band communication satellites},
  booktitle	= {1997 IEEE MTT-S International Microwave Symposium Digest},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {1},
  editor	= {G. A. Koepf},
  address	= {New York, NY, USA},
  pages		= {351--4},
  dbinsdate	= {oldtimer}
}

@InCollection{	  datta96a,
  author	= {A. Datta and S. K. Parui and B. B. Chaudhuri},
  title		= {Skeletal shape extraction from dot patterns by
		  self-organization},
  booktitle	= {Proceedings of the 13th International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  volume	= {4},
  address	= {Los Alamitos, CA, USA},
  pages		= {80--4},
  dbinsdate	= {oldtimer}
}

@Article{	  datta97a,
  author	= {A. Datta and S. K. Parui},
  title		= {Skeletons from dot patterns: a neural network approach},
  journal	= {Pattern Recognition Letters},
  year		= {1997},
  volume	= {18},
  number	= {4},
  pages		= {335--42},
  dbinsdate	= {oldtimer}
}

@Article{	  datta97b,
  author	= {A. Datta and T. Pal and S. K. Parui},
  title		= {A modified \mbox{self-organizing} neural net for shape
		  extraction},
  journal	= {Neurocomputing},
  year		= {1997},
  volume	= {14},
  number	= {1},
  pages		= {3--14},
  abstract	= {Some modifications on Kohonen's self-organizing feature
		  map are discussed to make it suitable for finding skeletons
		  of binary images. In Kohonen's feature map, the set of
		  processors and their neighbourhoods are fixed and do not
		  change in the learning process. This may pose problems when
		  the set of input vectors represents a prominent shape. The
		  reference vectors or weight vectors lying in zero-density
		  areas are affected by input vectors from all the
		  surrounding parts of the non-zero distribution [5]. Hence a
		  shape extraction problem requires a dynamic change in the
		  network topology. In the present paper, to overcome the
		  limitations of Kohonen's feature maps, we propose a
		  mechanism in which the set of processors and their
		  neighbourhoods change adaptively during learning, to
		  extract the shape of a binary object in the form of a
		  skeleton.},
  dbinsdate	= {oldtimer}
}

@Article{	  dave96a,
  author	= {M. P. Dave and S. Chauhan},
  title		= {A robust artificial neural network technique for dynamic
		  stability assessment},
  journal	= {Electric Machines and Power Systems},
  year		= {1996},
  volume	= {24},
  number	= {7},
  pages		= {733--44},
  dbinsdate	= {oldtimer}
}

@Article{	  davey95a,
  author	= {N. Davey and P. C. Barson and S. D. H. Field and R. J.
		  Frank and D. S. W. Tansley},
  title		= {The development of a software clone detector},
  journal	= {International Journal of Applied Software Technology},
  year		= {1995},
  volume	= {1},
  number	= {3--4},
  pages		= {219--36},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  davide94a,
  author	= {Fabrizio Davide and Corrado {Di Natale} and Arnaldo
		  D'Amico},
  title		= {Sensor Arrays and {S}elf-{O}rganizing {M}aps for Odour
		  Analysis in Artificial Olfactory Systems},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {354--357},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, pattern recognition},
  dbinsdate	= {oldtimer}
}

@Article{	  davide94b,
  author	= {Davide, F. A. M. and {Di Natale}, C. and D'Amico, A. },
  title		= {Self-organizing multisensor systems for odour
		  classification: internal categorization, adaptation and
		  drift rejection},
  journal	= {Sensors and Actuators B [Chemical]},
  year		= {1994},
  volume	= {B18},
  number	= {1--3},
  pages		= {244--58},
  month		= {March},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  de01a,
  author	= {De, A. and Chatterjee, N.},
  title		= {Impulse fault diagnosis in power transformers using
		  self-organising map and learning vector quantisation},
  journal	= {IEE Proceedings: Generation, Transmission and
		  Distribution},
  year		= {2001},
  volume	= {148},
  number	= {5},
  month		= {September },
  pages		= {397--405},
  organization	= {Electrical Engineering Department, Jadavpur University},
  publisher	= {},
  address	= {},
  abstract	= {An artificial intelligence approach is proposed to an
		  impulse fault diagnosis problem in oil-filled power
		  transformers. The experiment focuses on the distinction
		  between the effects caused by faults of a different nature
		  and the different physical location of occurrences in a
		  transformer winding. The proposed method involves an
		  artificial neural network-based pattern recognition
		  technique, to recognise the frequency responses of the
		  winding admittance of a typical high-voltage transformer
		  under healthy and different faulty conditions of winding
		  insulation. It attempts to establish a correlation between
		  the nature and site of the internal insulation fault and
		  its associated frequency response. A self-organising neural
		  network model has been employed as the basic pattern
		  recogniser, to discover the significant patterns and to
		  extract the hidden information from a set of frequency
		  response patterns obtained from an EMTP model of the
		  transformer with artificially simulated faults. A learning
		  vector quantisation-based classification technique has been
		  applied to efficiently classify visually indistinguishable
		  response patterns. The method applied to a winding model of
		  a high-voltage transformer, with tap changer winding,
		  exhibited high diagnostic accuracy by successful detection
		  and discrimination of faults of a different nature and site
		  of occurrence.},
  dbinsdate	= {2002/1}
}

@Article{	  de02a,
  author	= {De, A. and Chatterjee, N.},
  title		= {Recognition of impulse fault patterns in transformers
		  using Kohonen's self-organizing feature map},
  journal	= {IEEE TRANSACTIONS ON POWER DELIVERY},
  year		= {2002},
  volume	= {17},
  number	= {2},
  month		= {APR},
  pages		= {489--494},
  abstract	= {Determination of exact nature and location of faults
		  during impulse testing of transformers is of practical
		  importance to the manufacturer as well as designers. The
		  presently available diagnostic techniques more or less
		  depend on expertized knowledge of the test personnel, and
		  in many cases are not beyond ambiguity and controversy.
		  This paper presents an artificial neural network (ANN)
		  approach for detection and diagnosis of fault nature and
		  fault location in oil-filled power transformers during
		  impulse testing. This new approach relies on high
		  discrimination power and excellent generalization ability
		  of ANNs in a complex pattern classification problem, and
		  overcomes the limitations of conventional expert or
		  knowledge-based systems in this field. In the present work
		  the "self-organizing feature map" (SOFM) algorithm with
		  Kohonen's learning has been successfully applied to the
		  problem with good diagnostic accuracy.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  de_argandona95a,
  author	= {{de Argandona}, I. R. and Gu, Y. -H. and Carrasco, R. A.
		  },
  title		= {Image compression via multiresolution feature-based {VQ}
		  of {H}ermite-binomial transform coefficients using
		  {K}ohonen neural network},
  booktitle	= {Fifth International Conference on Image Processing and its
		  Applications},
  year		= {1995},
  pages		= {549--53},
  organization	= {Staffordshire Polytech. , Stafford, UK},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_bodt00b,
  author	= {{de Bodt}, E. and Cottrell, M.},
  title		= {Bootstrapping self-organising maps to assess the
		  statistical significance of local proximity},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {245--54},
  abstract	= {One of the attractive features of self-organising maps
		  (SOM) is the so-called "topological preservation property":
		  observations that are close to each other in the input
		  space (at least locally) remain close to each other in the
		  SOM. We propose the use of a bootstrap scheme to construct
		  a statistical significance test of the observed proximity
		  among individuals in the SOM. While computer intensive at
		  this stage, this work represents a first step in the
		  exploration of the sampling distribution of proximities in
		  the framework of the SOM algorithm.},
  dbinsdate	= {2002/1}
}

@Article{	  de_bodt95a,
  author	= {E. {de Bodt} and E. Henrion and S. Ibbou and A. Wolfs and
		  M. Cottrell and C. Van Wymeersch},
  title		= {Comprendre la décision de leasing à l'aide d'une carte de
		  {K}ohonen Une étude empirique},
  journal	= {Deuxième rencontre ANSEG},
  year		= {1995},
  pages		= {19--32},
  dbinsdate	= {oldtimer}
}

@Article{	  de_bodt96a,
  author	= {E. {de Bodt} and E. Henrion and S. Ibbou and A. Wolfs and
		  C. Van Wymeersch},
  title		= {Comprendre la décision de leasing à l'aide d'une carte de
		  {K}ohonen. Une étude empirique},
  journal	= {Cahiers Economiques de Bruxelles},
  year		= {1996},
  volume	= {151},
  number	= {3},
  pages		= {279--297},
  dbinsdate	= {oldtimer}
}

@InCollection{	  de_bodt97a,
  author	= {Eric {de Bodt} and Philippe Gr{\'e}goire and Marie
		  Cottrell},
  title		= {A Poverful Tool for Fitting and Forecasting Deterministic
		  and Stochastic Processes: The {K}ohonen Classification},
  booktitle	= {Proc. ICANN'97, 7th International Conference on Artificial
		  Neural Networks},
  publisher	= {Springer},
  year		= 1997,
  volume	= 1327,
  series	= {Lecture Notes in Computer Science},
  address	= {Berlin},
  pages		= {981--986},
  dbinsdate	= {oldtimer}
}

@InCollection{	  de_bodt97b,
  author	= {Eric {de Bodt} and Michel Verleysen and Marie Cottrell},
  title		= {{K}ohonen maps versus vector quantization for data
		  analysis},
  booktitle	= {Proc. ESANN'97, 5th European Symposium on Artificial
		  Neural Networks},
  publisher	= {D facto},
  year		= 1997,
  editor	= {Michel Verleysen},
  pages		= {211--218},
  address	= {Brussels, Belgium},
  dbinsdate	= {oldtimer}
}

@Article{	  de_bodt98a,
  author	= {E. {de Bodt} and E. Henrion and M. Cottrell and C. Van
		  Wymeersch},
  title		= {Self-Organizing Maps for Data Analysis: An Application to
		  the Belgian Leasing Market},
  journal	= {Journal of Computational Intelligence in Finance},
  year		= {1998},
  volume	= {6},
  number	= {6},
  pages		= {5--23},
  dbinsdate	= {oldtimer}
}

@InCollection{	  de_bodt98b,
  author	= {E. {de Bodt} and Ph. Gregoire and M. Cottrell},
  title		= {Projection of Long-term Interest Rates with Maps},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {24--38},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_bodt99a,
  author	= {{de Bodt}, E. and Cottrell, M. and Verleysen, M.},
  title		= {Using the {K}ohonen algorithm for quick initialization of
		  simple competitive learning algorithm},
  booktitle	= {7th European Symposium on Artificial Neural Networks.
		  ESANN'99. Proceedings. D-Facto, Brussels, Belgium},
  publisher	= {Editions D Facto},
  address	= {Bruxelles},
  year		= {1999},
  volume	= {},
  pages		= {19--26},
  editor	= {M. Verleysen},
  abstract	= {In a previous paper (1997), we compared the Kohonen
		  algorithm (SOM) to a simple competitive learning algorithm
		  (SCL) when the goal is to reconstruct an unknown density.
		  We showed that for that purpose, the {SOM} algorithm
		  quickly provides an excellent approximation of the initial
		  density, when the frequencies of each class are taken into
		  account to weight the quantifiers of the classes. Another
		  important property of the {SOM} is the well known topology
		  conservation, which implies that neighbor data are
		  classified into the same class (as usual) or into neighbor
		  classes. In this paper, we study another interesting
		  property of the {SOM} algorithm, that holds for any fixed
		  number of quantifiers. We show that even if we use those
		  approaches only for quantization, the {SOM} algorithm can
		  be successfully used to accelerate in a very large
		  proportion the speed of convergence of the classical SCL.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_bollivier90a,
  author	= {M. {de Bollivier} and P. Gallinari and S. Thiria},
  title		= {Cooperation of neural nets for robust classification},
  booktitle	= {Proc. IJCNN'90, International Joint Conference on Neural
		  Networks},
  year		= {1990},
  volume	= {I},
  pages		= {113--120},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_bollivier90b,
  author	= {M. {de Bollivier} and P. Gallinari and S. Thiria},
  title		= {Multi-module neural networks for classification},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  volume	= {II},
  pages		= {777--780},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InCollection{	  de_carli94a,
  author	= {F. {De Carli}},
  title		= {Neural networks for pattern recognition and classification
		  in the analysis of electrophysiologic signals},
  booktitle	= {Neural Networks in Biomedicine. Proceedings of the
		  Advanced School of the Italian Biomedical Physics
		  Association},
  publisher	= {World Scientific},
  year		= {1994},
  editor	= {F. Masulli and P. G. Morasso and A. Schenone},
  address	= {Singapore},
  pages		= {287--302},
  dbinsdate	= {oldtimer}
}

@Article{	  de_castro01a,
  author	= {{De Castro}, L. N. and {Von Zuben}, F. J.},
  title		= {Immune and neural network models: theoretical and
		  empirical comparisons},
  journal	= {International-Journal-of-Computational-Intelligence-and-Applications}
		  ,
  year		= {2001},
  volume	= {1},
  pages		= {239--57},
  abstract	= {This paper brings a detailed mathematical description of
		  an artificial immune network model, named aiNet. The model
		  is implemented in association with graph concepts and
		  hierarchical clustering techniques, and is proposed to
		  perform machine learning, data compression and cluster
		  analysis. Pictorial representations for the aiNet basic
		  units and typical architectures are introduced. The
		  proposed immune network was primarily compared on a
		  theoretical basis with well-known artificial neural
		  networks. Then, the aiNet was applied to a non-linearly
		  separable benchmark and a real-world problem, and the
		  results were compared with that of the self-organizing
		  feature map and with others already presented in the
		  literature.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  de_castro_santa_rosa00a,
  author	= {{de Castro Santa Rosa}, Antonio Nuno and Weigang, Li and
		  {Correia da Silva}, Nilton and Meneses, Paulo Roberto},
  title		= {Filtering and classification of {SAR} images using
		  Parallel-{SOM}},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {4055},
  pages		= {469--475},
  organization	= {Univ of Brasilia},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {},
  abstract	= {Map Gauss filter is a linear adaptive filter commonly used
		  to reduce speckle noise present in synthetic aperture radar
		  images of remote sensing satellites. In this study was
		  incorporating some modifications that allow us to maximize
		  the signal-to-noise ratio at the same time almost total
		  features of the image are preserved. To evaluate the
		  performance of the new filter, both original and filtered
		  images were classified by a unsupervised technique known as
		  Parallel Self-Organizing Map (P-SOM) and the results of
		  this classification were compared. The P-SOM is an
		  algorithm with its own-organization mapping that is
		  specific for parallel computing environment. As examples of
		  applications, are presented the results of the
		  classification for preprocessed original RADARSAT images
		  using the Map gauss, Frost and Gamma filters.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  de_dominicis94a,
  author	= {{De Dominicis}, R. and Bocchi, L. and Coppini, G. and
		  Valli, G. },
  title		= {Computer aided analysis of lung-parenchyma lesions in
		  standard chest radiography},
  booktitle	= {Proceedings of the International Conference on Neural
		  Networks and Expert Systems in Medicine and Healthcare},
  year		= {1994},
  editor	= {Ifeachor, E. C. and Rosen, K. G. },
  pages		= {174--80},
  organization	= {Dept. of Clinical Pathophysiology, Florence Univ. ,
		  Italy},
  publisher	= {Univ. Plymouth},
  address	= {Plymouth, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_giovanni90a,
  author	= {Livia {De Giovanni} and Stefano Montesi},
  title		= {Experimental Studies on Speech Recognition in Telecom
		  Environment},
  editor	= {Andrea Paoloni},
  pages		= {75--84},
  booktitle	= {Proc. 1st Workshop on Neural Networks and Speech
		  Processing, November 89, Roma},
  address	= {Roma, Italy},
  year		= {1990},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_giovanni91a,
  author	= {L. {De Giovanni} and M. Fedeli and S. Montesi},
  title		= {{'}{S}hift-tolerant{'} {LVQ2}---based digits recognition},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1803--1807},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_giovanni91b,
  author	= {L. {De Giovanni} and R. Lanuti and S. Montesi},
  title		= {Isolated word recognition by integration of {MLP} and
		  {LVQ2} networks},
  booktitle	= {Proc. Fourth Italian Workshop. Parallel Architectures and
		  Neural Networks},
  year		= {1991},
  editor	= {E. R. Caianiello},
  pages		= {238--243},
  organization	= {Univ. Salerno; Inst. Italiano di Studi Filosofici},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_haan91a,
  author	= {G. R. {De Haan} and {\"{O}}. E{\~{g}}ecio{\~{g}}lu},
  title		= {Links between \mbox{self-organizing} feature maps and
		  weighted vector quantization},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {887--892},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {A novel learning algorithm for self-organizing feature
		  maps (SOFMs) is presented. The learning algorithm is based
		  on an extension of vector quantization called weighted
		  vector quantization (WVQ). WVQ distortion is a weighted sum
		  of the distortion between an input vector and each of the
		  codevectors in the codebook. A formulation of WVQ is given,
		  as well as two optimality conditions which are analogous to
		  the nearest neighbor and centroid conditions of vector
		  quantization. The authors then incorporate the SOFM
		  neighborhood mechanism into WVQ, and use the WVQ optimality
		  conditions to derive the algorithm.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_haan91b,
  author	= {G. R. {De Haan} and O. Egecioglu},
  title		= {Neighborhood distortion functions and
		  \mbox{self-organizing} feature maps},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  pages		= {964},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  de_ketelaere97a,
  author	= {Bart {De Ketelaere} and Dimitrios Moshou and Peter
		  Coucke},
  title		= {A hierarchical \mbox{self-organizing} map for
		  classification problems},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {86--90},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_lima94a,
  author	= {{de Lima}, A. F. M. M. and Alden, R. T. H. },
  title		= {Neural network assessment of small signal stability},
  booktitle	= {1994 Canadian Conference on Electrical and Computer
		  Engineering. Conference Proceedings},
  year		= {1994},
  editor	= {Baird, C. R. and El-Hawary, M. E. },
  volume	= {2},
  pages		= {730--3},
  organization	= {Power Res. Lab. , McMaster Univ. , Hamilton, Ont. ,
		  Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  de_rajat02a,
  author	= {{De Rajat}, K. and Basak, Jayanta and {Pal Sankar}, K.},
  title		= {Unsupervised feature extraction using neuro-fuzzy
		  approach},
  journal	= {Fuzzy Sets and Systems},
  year		= {2002},
  volume	= {126},
  number	= {3},
  month		= {Mar 16 },
  pages		= {277--291},
  organization	= {Machine Intelligence Unit, Indian Statistical Institute},
  publisher	= {},
  address	= {},
  abstract	= {The present article demonstrates a way of formulating a
		  neuro-fuzzy approach for feature extraction under
		  unsupervised training. A fuzzy feature evaluation index for
		  a set of features is newly defined in terms of degree of
		  similarity between two patterns in both the original and
		  transformed feature spaces. A concept of flexible
		  membership function incorporating weighted distance is
		  introduced for computing membership values in the
		  transformed space that is obtained by a set of linear
		  transformation on the original space. A layered network is
		  designed for performing the task of minimization of the
		  evaluation index through unsupervised learning process.
		  This extracts a set of optimum transformed features, by
		  projecting n-dimensional original space directly to n prime
		  -dimensional (n prime less than or equal n) transformed
		  space, along with their relative importance. The extracted
		  features are found to provide better classification
		  performance than the original ones for different real life
		  data with dimensions 3, 4, 9, 18 and 34. The superiority of
		  the method over principal component analysis network,
		  nonlinear discriminant analysis network and Kohonen
		  self-organizing feature map is also established. },
  dbinsdate	= {2002/1}
}

@InCollection{	  de_sa93a,
  title		= {A Note on Learning Vector Quantization},
  author	= {Virginia R. {de Sa} and Dana H. Ballard},
  pages		= {220--227},
  booktitle	= {Advances in Neural Information Processing Systems 5},
  editor	= {L. Giles and S. Hanson and J. Cowan},
  address	= {San Mateo, CA},
  year		= {1993},
  publisher	= {Morgan Kaufmann },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  de_sa93b,
  author	= {Virginia R. {de Sa}},
  title		= {Learning Classification with Unlabeled Data},
  booktitle	= {Proc. NIPS'93, Neural Information Processing Systems},
  year		= {1993},
  editor	= {Jack D. Cowan and Gerald Tesauro and Joshua Alspector},
  pages		= {112--119},
  publisher	= {Morgan Kaufmann Publishers},
  address	= {San Francisco, CA},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  de_sa94a,
  author	= {Virginia Ruth {de Sa}},
  title		= {Unsupervised Classification Learning from Cross-Modal
		  Environmental Structure},
  school	= {University of Rochester, Department of Computer Science},
  address	= {Rochester, New York},
  year		= {1994},
  dbinsdate	= {oldtimer}
}

@InCollection{	  de_souza96a,
  author	= {Jr. P. A. {de Souza} and E. O. T. Salles and V. K. Garg},
  title		= {Artificial neural network in Mossbauer mineralogy},
  booktitle	= {38th Midwest Symposium on Circuits and Systems.
		  Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  editor	= {L. P. Caloba and P. S. R. Diniz and A. C. M. {de Querioz}
		  and E. H. Watanabe},
  address	= {New York, NY, USA},
  pages		= {558--61},
  dbinsdate	= {oldtimer}
}

@Article{	  de_stefano00a,
  author	= {{De Stefano}, C. and Sansone, C. and Vento, M.},
  title		= {To reject or not to reject: that is the question---an
		  answer in case of neural classifiers},
  journal	= {IEEE Transactions on Systems, Man and Cybernetics, Part C
		  (Applications and Reviews)},
  year		= {2000},
  volume	= {30},
  pages		= {84--94},
  abstract	= {A method defining a reject option that is applicable to a
		  given 0-reject classifier is proposed. The reject option is
		  based on an estimate of the classification reliability,
		  measured by a reliability evaluator Psi . Trivially, once a
		  reject threshold sigma has been fixed, a sample is rejected
		  if the corresponding value of Psi is below sigma .
		  Obviously, as sigma represents the least tolerable
		  classification reliability level, when its value varies the
		  reject option becomes more or less severe. In order to
		  adapt the behavior of the reject option to the requirements
		  of the considered application domain, a function P
		  characterizing the reject option's adequacy to the domain
		  has been introduced. It is shown that P can be expressed as
		  a function of sigma and, consequently, the optimal value
		  for sigma is defined as the one which maximizes the
		  function P. The method for determining the optimal
		  threshold value is independent of the specific 0-reject
		  classifier, while the definition of the reliability
		  evaluators is related to the classifier's architecture.
		  General criteria for defining appropriate reliability
		  evaluators within a classification paradigm are illustrated
		  in the paper and are based on the localization, in the
		  feature space, of the samples that could be classified with
		  a low reliability. The definition of the reliability
		  evaluators for three popular architectures of neural
		  networks (backpropagation, learning vector quantization and
		  probabilistic network) is presented. Finally, the method
		  has been tested with reference to a complex classification
		  problem with data generated according to a
		  distribution-of-distributions model.},
  dbinsdate	= {oldtimer}
}

@Article{	  de_stefano94a,
  author	= {{De Stefano}, C. and Sansone, C. and Vento, M.},
  title		= {Evaluating competitive learning strategies for handwritten
		  character recognition},
  journal	= {Proceedings of IEEE International Conference on Systems,
		  Man, and Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1994},
  number	= {},
  volume	= {1},
  pages		= {759--764},
  abstract	= {In this paper, with reference to the supervised Learning
		  Vector Quantization paradigm (LVQ), different competitive
		  learning strategies are experimentally examined; the
		  characterization of the obtainable results in terms of
		  classification rate and generalization capability is
		  carried out by considering, as a case study, the
		  recognition of handwritten numerals. Experimental results
		  have been obtained with reference to a standard database of
		  handwritten characters and using a structural description
		  method [1].},
  dbinsdate	= {oldtimer}
}

@InCollection{	  de_vel95a,
  author	= {O. {de Vel} and S. Li and D. Coomans},
  title		= {Performance analysis of {K}ohonen \mbox{self-organising}
		  feature maps compared with linear and nonlinear
		  dimensionality reduction techniques},
  booktitle	= {Proceedings of the Sixth Australian Conference on Neural
		  Networks (ACNN`95)},
  publisher	= {Univ. Sydney},
  year		= {1995},
  editor	= {M. Charles and C. Latimer},
  address	= {Sydney, NSW, Australia},
  pages		= {276--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  dean97a,
  author	= {D. Dean and K. Subramanyan and J. Kamath and F. Bookstein
		  and D. Wilson and D. Kwon and P. Buckley},
  title		= {Comparison of traditional brain segmentation tools with
		  {{3D}} \mbox{self-organizing} maps},
  booktitle	= {Information Processing in Medical Imaging. 15th
		  International Conference, IPMI'97. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {J. Duncan and G. Gindi},
  address	= {Berlin, Germany},
  pages		= {393--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  deaton94a,
  author	= {Deaton, R. and Sun, J. and Reddick, W. E. },
  title		= {Self-organized feature detection and segmentation of
		  magnetic resonance images},
  booktitle	= {Proceedings of the 16th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  Engineering Advances: New Opportunities for Biomedical
		  Engineers},
  year		= {1994},
  editor	= {Sheppard, N. F. , Jr. and Eden, M. and Kantor, G. },
  volume	= {1},
  pages		= {602--3},
  organization	= {Dept. of Electr. Eng. , Memphis Univ. , TN, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  deaton95a,
  author	= {R. Deaton and J. Sun and W. E. Reddick},
  title		= {Two-Layer {S}elf-{O}rganizing {M}aps for Segmentation of
		  Magnetic Resonance Images of the Human Brain},
  volume	= {II},
  pages		= {815--818},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@Article{	  debeljak01a,
  author	= {Debeljak, Z. and Strapac, M. and Medic-Saric, M.},
  title		= {Application of self-organizing maps for the classification
		  of chromatographic systems and prediction of values of
		  chromatographic quantities},
  journal	= {JOURNAL OF CHROMATOGRAPHY A},
  year		= {2001},
  volume	= {925},
  number	= {1--2},
  month		= {AUG 3},
  pages		= {31--40},
  abstract	= {The applicability of self-organizing maps (SOM) for the
		  classification of chromatographic systems or components of
		  chromatographic systems based on data taken from literature
		  is shown. The SOM approach is compared to dendrogram and
		  principal components analysis (PCA) approaches. It has been
		  shown that the distance between classified objects could
		  reveal linear correspondence with quantity to be optimized,
		  e.g. resolution, so it can be applied in the
		  chromatographic method development. SOMs can also be
		  applied for prediction of chromatographic quantities. It is
		  shown that SOM-based response surface modeling is
		  comparable to triangular presentation of mobile phase
		  composition response surfaces. },
  dbinsdate	= {2002/1}
}

@Article{	  deboeck00a,
  author	= {Deboeck, G. J.},
  title		= {Modeling non-linear market dynamics for intra-day
		  trading},
  journal	= {Neural Network World},
  year		= {2000},
  volume	= {10},
  pages		= {3--27},
  abstract	= {At the end of the 20th century, speculation on short-term
		  price movements of stocks is on the rise. Electronic direct
		  access trading often simply called day trading has exploded
		  in popularity. The paper focuses on short-term trading. The
		  main objectives are to review what caused the increased
		  interest in short-term trading, demonstrate various
		  approaches, discuss intra-day trading strategies, show how
		  they can be deployed in practice, and illustrate what kinds
		  of results can be achieve by day trading. As a concrete
		  example we apply various intra-day trading strategies to an
		  Internet stock, in particular CMGI Inc. CMGI is a venture
		  capital fund that invests in attractive Internet companies.
		  The paper starts by discussing the major changes that have
		  occurred in the past couple of years in the functioning of
		  markets and in the access to market information for
		  individuals. It then compares strategies for short-term
		  trading based on different frequencies (from fixed time
		  periods to trading in variable time horizons), and various
		  technical indicators. After reviewing some technical
		  analysis approaches, the study shows how NASDAQ Level II
		  information can be used for intra-day trading. To detect
		  patterns in the price formation process, the study applied
		  the self-organizing map method to hourly samples of Level
		  II NASDAQ information. This produced two-dimensional
		  representations of market-maker behaviors. The same
		  self-organizing map method is also used to detect turns in
		  price movements based on a digital codification of the old
		  Japanese candlestick approach.},
  dbinsdate	= {oldtimer}
}

@Article{	  deboeck00b,
  author	= {Deboeck, Guido},
  title		= {Self-organizing Patterns in World Poverty Using Multiple
		  Indicators of Poverty, Repression and Corruption},
  journal	= {Neural Network World},
  year		= {2000},
  volume	= {10},
  number	= {1--2},
  pages		= {239--254},
  dbinsdate	= {oldtimer}
}

@Book{		  deboeck01a,
  author	= {G. Deboeck and T. Kohonen},
  title		= {Visual Explorations in Finance with Self-Organizing Maps
		  (Translated into Russian)},
  publisher	= {Albina, Moscow},
  year		= {2001},
  dbinsdate	= {2002/1}
}

@Book{		  deboeck94a,
  author	= {G. Deboeck},
  title		= {Trading on the Edge: Neural Genetic and Fuzzy Systems for
		  Chaotic Financial Markets},
  publisher	= {John Wiley and Sons},
  year		= {1994},
  address	= {New York},
  month		= {April},
  note		= {377 p.},
  dbinsdate	= {oldtimer}
}

@Article{	  deboeck98a,
  author	= {G. Deboeck},
  title		= {Financial Applications of Self-Organzing Maps},
  journal	= {Neural Network World},
  year		= {1998},
  volume	= {8},
  number	= {2},
  pages		= {213--241},
  dbinsdate	= {oldtimer}
}

@Book{		  deboeck98b,
  author	= {G. Deboeck and T. Kohonen},
  title		= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer-Verlag},
  year		= {1998},
  address	= {London},
  note		= {258 p.},
  dbinsdate	= {oldtimer}
}

@Article{	  deboeck98c,
  author	= {G. Deboeck},
  title		= {Financial Applications of Self-Organizing Maps},
  journal	= {Electronic newsletter American Heuristics Inc},
  year		= {1998},
  month		= {February},
  dbinsdate	= {oldtimer}
}

@Article{	  deboeck98d,
  author	= {G. Deboeck},
  title		= {Pattern Recognition and Prediction made simpler with
		  Viscovery({TM})},
  journal	= {Neural Network World},
  year		= {1998},
  volume	= {8},
  number	= {4},
  pages		= {463--470},
  dbinsdate	= {oldtimer}
}

@InCollection{	  deboeck98e,
  author	= {G. Deboeck},
  title		= {Picking Mutual Funds with Self-Organizing Maps},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {39--58},
  dbinsdate	= {oldtimer}
}

@InCollection{	  deboeck98f,
  author	= {G. Deboeck},
  title		= {Investment Maps of Emerging Markets},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {83--105},
  dbinsdate	= {oldtimer}
}

@InCollection{	  deboeck98g,
  author	= {G. Deboeck},
  title		= {Software Tools for Self-Organizing Maps},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {179--194},
  dbinsdate	= {oldtimer}
}

@InCollection{	  deboeck98h,
  author	= {G. Deboeck},
  title		= {Best Practices in Data Mining using Self-Organizing Maps},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {203--229},
  dbinsdate	= {oldtimer}
}

@Article{	  deboeck99a,
  author	= {G. Deboeck},
  title		= {Self-Organizing Maps facilitate knowledge discovery in
		  finance},
  journal	= {Financial Engineering News},
  year		= {1999},
  volume	= {1},
  pages		= {1--6},
  dbinsdate	= {oldtimer}
}

@Article{	  deboeck99b,
  author	= {Deboeck, G.},
  title		= {Data mining with \mbox{self-organizing} maps: Best
		  practices in finance, economics, and modeling},
  journal	= {PC AI},
  year		= {1999},
  volume	= {13},
  pages		= {33--6},
  abstract	= {Many articles and courses outline principles and
		  algorithms for data mining discussing techniques rather
		  than practice. This article examines the "best practices"
		  of applying data mining to finance, economics or marketing
		  applications. The process described includes data analysis,
		  clustering, visualization, and the use of Self-Organizing
		  Maps (SOM), a technique based on unsupervised neural
		  networks that uses competitive learning in order to create
		  a reduced two-dimensional representation of large
		  multidimensional data sets. The first part of this article
		  provides the main steps; the remainder of the article
		  applies the steps to the problem of assessing country
		  credit risks based on economic, financial and stock market
		  data.},
  dbinsdate	= {oldtimer}
}

@Article{	  deboeck99c,
  author	= {G. Deboeck},
  title		= {Public Domain vs Commercial Tools for Creating Neural
		  Self-Organizing Maps},
  journal	= {PC AI Magazine},
  year		= {1999},
  month		= {January-February},
  pages		= {27--30},
  abstract	= {Common statistical methods such as clustering and
		  projection do not perform certain tasks well. These include
		  handling high-dimensional data, reducing the dimensionality
		  of a large data set, or producing effective visualizations
		  of multidimensional data. Classic methods often make
		  assumptions regarding linearity, normal distribution, or
		  inherent clustering tendencies in the data. Hence there is
		  the need for a different approach, such as neural networks.
		  The article focuses on one class of neural net tools, and
		  within that class on a single approach: the self-organizing
		  map (SOM). Invented by Teuvo Kohonen in the early '80s,
		  {SOM}s have been used extensively in engineering and other
		  technical fields. Recently, self-organizing maps have been
		  used in business applications such as finance, economics,
		  and marketing analyses. The article examines existing
		  public domain and commercial {SOM} software.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  deboeck99d,
  author	= {G. J. Deboeck},
  title		= {For Finding Value in Markets that are Expensive},
  booktitle	= {Kohonen Maps},
  pages		= {15--32},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: data mining, finance, stock markets, visual
		  exploration},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  decaestecker93a,
  author	= {Christine Decaestecker},
  title		= {{NNP}: A Neural Net Classifier using Prototypes},
  booktitle	= {Proc. of {IEEE} International Conference on Neural
		  Networks, San Francisco},
  year		= {1993},
  volume	= {II},
  pages		= {822--824},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dedieu92a,
  author	= {E. Dedieu and E. Mazer},
  title		= {An approach to sensorimotor relevance},
  booktitle	= {Toward a Practice of Autonomous Systems. Proc. First
		  European Conf. on Artificial Life},
  year		= {1992},
  editor	= {F. J. Varela and P. Bourgine},
  pages		= {88--95},
  organization	= {Cite des Sci. Ind. ; Banque de France; Fondation de
		  France; Electr. France; CEMAGREF; CNR; AFCET; CREA;
		  OFFILIB; Sun Microsyst},
  publisher	= {MIT Press},
  address	= {Cambridge, MA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  deffontaines92a,
  author	= {Deffontaines, F. and Ungering, A. and Tryba, V. and Goser,
		  K. },
  title		= {The concept of a {RISC} architecture for combining fuzzy
		  logic and a {K}ohonen map on an integrated circuit},
  booktitle	= {Fifth International Conference. Neural Networks and their
		  Applications. NEURO NIMES 92},
  year		= {1992},
  pages		= {555--64},
  organization	= {Lehrstuhl Bauelemente der Elektrotech. , Dortmund Univ. ,
		  Germany},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dehghan00a,
  author	= {Dehghan, M. and Faez, K. and Ahmadi, M.},
  title		= {Hybrid handwritten word recognition using self-organizing
		  feature map, discrete {HMM}, and evolutionary programming},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {515--520},
  organization	= {Amirkabir Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A hybrid system for the recognition of handwritten Farsi
		  words using self-organizing feature map, right-left
		  discrete hidden Markov models, and evolutionary programming
		  is presented. The histogram of chain-code directions of the
		  image strips, scanned from right to left by a sliding
		  window, is used as feature vectors. The self-organizing
		  feature map is used for constructing the codebook and also
		  smoothing the observation probability distributions. A
		  population based approach using evolutionary programming
		  with a self-adaptive Cauchy mutation operator is used to
		  find an appropriate initial model as starting point for the
		  classical Baum-Welch algorithm. Experimental results were
		  found to be promising.},
  dbinsdate	= {2002/1}
}

@Article{	  dehghan00b,
  author	= {Dehghan, M. and Faez, K. and Ahmadi, M. and Shridhar, M.},
  title		= {Holistic handwritten word recognition using discrete {HMM}
		  and self-organizing feature map},
  journal	= {PROC IEEE INT CONF SYST MAN CYBERN, IEEE, PISCATAWAY, NJ},
  year		= {2000},
  number	= {},
  volume	= {4},
  pages		= {2735--2739},
  abstract	= {A holistic system for the recognition of handwritten
		  Farsi/Arabic words using right-left discrete hidden Markov
		  models (HMM) and Kohonen self-organizing vector
		  quantization is presented. The histogram of chain-code
		  directions of the image strips, scanned from right to left
		  by a sliding window, is used as feature vectors. The
		  neighborhood information preserved in the self-organizing
		  feature map (SOFM), was used for smoothing the observation
		  probability distributions of trained HMMs. Experiments
		  carried out on test samples show promising performance
		  results.},
  dbinsdate	= {2002/1}
}

@Article{	  dehghan01a,
  author	= {Dehghan, M. and Faez, K. and Ahmadi, M. and Shridhar, M.},
  title		= {Handwritten Farsi (Arabic) word recognition: a holistic
		  approach using discrete {HMM}},
  journal	= {PATTERN RECOGNITION},
  year		= {2001},
  volume	= {34},
  number	= {5},
  month		= {MAY},
  pages		= {1057--1065},
  abstract	= {A holistic system for the recognition of handwritten
		  Farsi/Arabic words using right-left discrete hidden Markov
		  models (HMM) and Kohonen self-organizing vector
		  quantizatiun is presented. The histogram of chain-code
		  directions of the image strips, scanned from right to left
		  by a sliding window, is used as feature vectors. The
		  neighborhood information preserved in the self-organizing
		  feature map (SOFM), is used for smoothing the observation
		  probability distributions of trained HMMs. Experiments
		  carried out on test samples show promising performance
		  results. },
  dbinsdate	= {2002/1}
}

@TechReport{	  dekker93a,
  author	= {Anthony H. Dekker},
  title		= {Optimal Colour Quantization using {K}ohonen Neural
		  Networks},
  institution	= {Department of Information and Computer Science, National
		  University of Singapore},
  year		= {1993},
  number	= {TR10},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@Article{	  dekker94a,
  author	= {Anthony H. Dekker},
  title		= {{K}ohonen neural networks for optimal colour
		  quantization},
  journal	= {Network: Computation in Neural Systems},
  year		= {1994},
  volume	= {5},
  pages		= {351--367},
  dbinsdate	= {oldtimer}
}

@InCollection{	  dekker94b,
  author	= {Anthony Dekker and Paul Farrow},
  title		= {Creativity, Chaos and Artificial Intelligence},
  booktitle	= {Artificial Intelligence and Creativity},
  publisher	= {Kluwer Academic Publisher},
  year		= {1994},
  editor	= {T. Dartnall},
  address	= {Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dekker95a,
  author	= {Anthony H. Dekker and Pushkar K. Piggott},
  title		= {Robot Learning with Neural Self-Organization},
  booktitle	= {Proc. of Robots for Australian Industries, National
		  Conference of the Australian Robot Association},
  year		= {1995},
  pages		= {369--381},
  organization	= {Australian Robot Association},
  dbinsdate	= {oldtimer}
}

@Article{	  del_bimbo93a,
  author	= {Del Bimbo, A. and Landi, L. and Santini, S. },
  title		= {Three-dimensional planar-faced object classification with
		  {K}ohonen maps},
  journal	= {Optical Engineering},
  year		= {1993},
  volume	= {32},
  number	= {6},
  pages		= {1222--34},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  del_coz99a,
  author	= {{del Coz}, J. J. and Luaces, O. and Quevedo, J. R. and
		  Alonso, J. and Ranilla, J. and Bahamonde, A.},
  title		= {Self-organizing cases to find paradigms},
  booktitle	= {FOUNDATIONS AND TOOLS FOR NEURAL MODELING, PROCEEDINGS,
		  VOL I},
  year		= {1999},
  pages		= {527--536},
  abstract	= {Case-based information systems can be seen as lazy machine
		  learning algorithms; they select a number of training
		  instances and then classify unseen cases as the most
		  similar stored instance. One of the main disadvantages of
		  these systems is the high number of patterns retained. In
		  this paper, a new method for extracting just a small set of
		  paradigms from a set of training examples is presented.
		  Additionally, we provide the set of attributes describing
		  the representative examples that are relevant for
		  classification purposes. Our algorithm computes the Kohonen
		  self-organizing maps attached to the training set to then
		  compute the coverage of each map node. Finally, a heuristic
		  procedure selects both the paradigms and the dimensions (or
		  attributes) to be considered when measuring similarity in
		  future classification tasks.},
  dbinsdate	= {2002/1}
}

@Article{	  delgado00a,
  author	= {Delgado, A.},
  title		= {Control of nonlinear systems using a self-organising
		  neural network},
  journal	= {NEURAL COMPUTING \& APPLICATIONS},
  year		= {2000},
  volume	= {9},
  number	= {2},
  pages		= {113--123},
  abstract	= {Two applications of Self Organizing Map (SOM) networks in
		  the context of nonlinear control are introduced, one in
		  approximate feedback linearisation and the second in
		  optimal control. It is shown that a modified SOM can be
		  used to approximately Input/Output (I/O) linearise and to
		  control nonlinear systems using a combination of the SOM
		  learning algorithm, and a biologically inspired
		  optimisation algorithm known as chemotaxis. A proof to
		  guarantee the stability of the closed loop during the
		  training of the network and the operation of the whole
		  system is included. The results are illustrated with
		  simulations of a single link manipulator.},
  dbinsdate	= {2002/1}
}

@Article{	  delibasis99a,
  author	= {Delibasis, K. K. and Mouravliansky, N. and Matsopoulos, G.
		  K. and Nikita, K. S. and Marsh, A.},
  title		= {{MR} functional cardiac imaging: Segmentation, measurement
		  and {WWW} based visualization of {4D} data},
  journal	= {Future Generation Computer Systems},
  year		= {1999},
  number	= {2},
  volume	= {15},
  pages		= {185--193},
  abstract	= {This paper considers the problem of ventricular
		  segmentation and visualization from dynamic (4D) {MR}
		  cardiac data covering an entire patient cardiac cycle, in a
		  format that is compatible with the web. Four different
		  methods are evaluated for the process of segmentation of
		  the objects of interest: The K-means clustering algorithm,
		  the fuzzy K-means (FKM) algorithm, self-organizing maps
		  (SOMs) and seeded region growing algorithm. The technique
		  of active surface is then subsequently applied to refine
		  the segmentation results, employing a deformable
		  generalized cylinder as geometric primitive. The final
		  ventricular models are presented in VRML 2.0 format. The
		  same process is repeated for all the 3D volumes of the
		  cardiac cycle. The radial displacement between end systole
		  and end diastole is calculated for each point of the active
		  surface and is encoded in colour on the VRML vertex, using
		  the RGB colour model. Using the VRML 2.0 specifications,
		  morphing is performed showing all cardiac phases in real
		  time. The expert has the ability to view the objects and
		  interact with them using a simple internet browser.
		  Preliminary results of normal and abnormal cases indicate
		  that very important pathological situations (such as
		  infarction) can be visualized and thus easily diagnosed and
		  localized with the assistance of the proposed technique.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dellacasa93a,
  author	= {Dellacasa, R. and Morasso, P. and Repetto, S. and
		  Vercelli, G. and Zaccaria, R. },
  title		= {Self-organizing navigation: From neural maps to navigation
		  situations},
  booktitle	= {Proceedings of the Fifth International Conference on Tools
		  with Artificial Intelligence TAI '93},
  year		= {1993},
  pages		= {458--9},
  organization	= {Dept. of Comput. , Commun. \& Syst. Sci. , Genova Univ. ,
		  Italy},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  delopoulos96a,
  author	= {A. Delopoulos and M. Rangoussi and J. Anderson},
  title		= {Recognition of voiced speech from the bispectrum},
  booktitle	= {Signal Processing VIII, Theories and Applications.
		  Proceedings of EUSIPCO-96, Eighth European Signal
		  Processing Conference},
  publisher	= {Edizioni LINT Trieste},
  year		= {1996},
  volume	= {1},
  editor	= {G. Ramponi and G. L. Sicuranza and S. Carrato and S.
		  Marsi},
  address	= {Trieste, Italy},
  pages		= {117--20},
  dbinsdate	= {oldtimer}
}

@Article{	  demartines92a,
  author	= {P. Demartines and F. Blayo},
  title		= {{K}ohonen \mbox{self-organizing} maps: Is the
		  normalization necessary?},
  journal	= {Complex Systems},
  year		= {1992},
  volume	= {6},
  number	= {2},
  pages		= {105--123},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  demartines93a,
  author	= {Demartines, P. and Herault, J. },
  title		= {Representation of nonlinear data structures through a fast
		  {VQP} neural network},
  booktitle	= {Sixth International Conference. Neural Networks and their
		  Industrial and Cognitive Applications. NEURO-NIMES 93
		  Conference Proceedings and Exhibition Catalog},
  year		= {1993},
  pages		= {411--24},
  organization	= {Lab. TIRF, INPG, Grenoble, France},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  demartines95a,
  author	= {Pierre Demartines},
  title		= {Data Analysis Through Self-Organized Neural Networks},
  school	= {Grenoble University},
  year		= {1995},
  address	= {Grenoble, France},
  note		= {(in french)},
  dbinsdate	= {oldtimer}
}

@Article{	  demartines97a,
  author	= {P. Demartines and J. H{\'e}rault},
  title		= {Curvilinear Component Analysis: A Self-Organizing Neural
		  Network for Nonlinear Mapping of Data Sets},
  journal	= {IEEE Transactions on Neural Networks},
  type		= {Brief Paper},
  year		= 1997,
  volume	= 8,
  number	= 1,
  month		= {January},
  pages		= {148--154},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  demers94a,
  author	= {David DeMers and Kenneth Kreutz-Delgado},
  title		= {Good Fibrations: Canonical Parametrization of Fibre
		  Bundles with {S}elf-{O}rganizing {M}aps},
  booktitle	= {Proc. WCNN'94 World Congress on Neural Networks},
  year		= {1994},
  volume	= {II},
  pages		= {54--59},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, robot control},
  dbinsdate	= {oldtimer}
}

@Article{	  demers96a,
  author	= {David DeMers and Kenneth Kreutz-Delgado},
  title		= {Canonical Parametrization of Excess Motor Degrees of
		  Freedom with Self-Organizing Maps},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1996},
  volume	= {7},
  number	= {1},
  pages		= {43--55},
  month		= {January},
  abstract	= {The problem of sensorimotor control is underdetermined due
		  to excess (or 'redundant') degrees of freedom when there
		  are more joint variables than the minimum needed for
		  positioning an end-effector. A method is presented for
		  solving the nonlinear inverse kinematics problem for a
		  redundant manipulator by learning a natural
		  parameterization of the inverse solution manifolds with
		  self-organizing maps. The parameterization approximates the
		  topological structure of the joint space, which is that of
		  a fiber bundle. The fibers represent the 'self-motion
		  manifolds' along which the manipulator can change
		  configuration while keeping the end-effector at a fixed
		  location. The method is demonstrated for the case of the
		  redundant planar manipulator. Data samples along the
		  self-motion manifolds are selected from a large set of
		  measured input-output data. This is done by taking points
		  in the joint space corresponding to end-effector locations
		  near 'query points,' which define small neighborhoods in
		  the end-effector work space. Self-organizing maps are used
		  to construct an approximate parameterization of each
		  manifold which is consistent for all of the query points.
		  The resulting parameterization is used to augment the
		  overall kinematics map so that it is locally invertible.
		  Joint-angle and end-effector position data, along with the
		  learned parameterizations, are used to train neural
		  networks to approximate direct inverse functions.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  demian92a,
  author	= {V. Demian and J. -C. Mignot},
  title		= {Implementation of the \mbox{self-organizing} feature map
		  on parallel computers},
  booktitle	= {Parallel Processing: CONPAR 92-VAPP V. Second Joint
		  International Conference on Vector and Parallel
		  Processing},
  year		= {1992},
  editor	= {L. Bouge and M. Cosnard and Y. Robert and D. Trystram},
  pages		= {775--776},
  organization	= {LIP; CNRS; DEC; AFCET; INRIA; IEEE; ACM; et al},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  demian93a,
  author	= {V. Demian and J. -C. Mignot},
  title		= {Optimization of the Self-Organizing Feature Map on
		  Parallel Computers},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {483--486},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {In this paper, we propose two implementations of the SOFM
		  on parallel computers. One is for a MIMD computer, the
		  other one is for a SIMD computer. We propose a new mapping
		  of the neurons onto the processors which permits to obtain
		  an optimal load balancing. We propose a new learning method
		  for the SOFM using a block strategy. This allows to exploit
		  the high performance level of the new generation of
		  parallel computers. We show that the block strategy
		  performs well on several examples outperforming classical
		  implementations.},
  dbinsdate	= {oldtimer}
}

@Article{	  demian96a,
  author	= {V. Demian and J. -C. Mignot},
  title		= {Implementation of the \mbox{self-organizing} feature map
		  on parallel computers},
  journal	= {Computers and Artificial Intelligence},
  year		= {1996},
  volume	= {15},
  number	= {1},
  pages		= {63--80},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  demiros01a,
  author	= {Demiros, Iason and Antonopoulos, Vassilios and
		  Georgantopoulos, Byron and Triantafyllou, Yannis and
		  Piperidis, Stelios},
  title		= {Connectionist models for sentence-based text extracts},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2648--2653},
  organization	= {Inst. for Language and Speech Proc.},
  publisher	= {},
  address	= {},
  abstract	= {This paper addresses the problem of creating a summary by
		  extracting a set of sentences that are likely to represent
		  the content of a document. A small scale experiment is
		  conducted leading to the compilation of an evaluation
		  corpus for the Greek language. Two models of sentence
		  extraction are then described, along the lines of shallow
		  linguistic analysis, feature combination and machine
		  learning. Both models are based on term extraction and
		  statistical filtering. After extracting the individual
		  features of the text, we apply them to two neural networks
		  that classify each sentence depending on its feature
		  vector, the term weight being the feature with the best
		  discriminant capacity. A three-layer feedforward network
		  trained with the highly popular backpropagation algorithm
		  and a competitive learning self-organizing map
		  characterized by the formation of a topographic map, both
		  trained on a small manually annotated corpus of summaries,
		  perform the sentence extraction task. Both methods could-be
		  used for rapid light information retrieval-oriented
		  summarization.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  deng00a,
  author	= {Deng, Da and Kasabov, Nikola},
  title		= {{ESOM}: An algorithm to evolve self-organizing maps from
		  on-line data streams},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {3--8},
  organization	= {Univ of Otago},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {An algorithm of evolving self-organizing map (ESOM) is
		  proposed as a dynamic version of the Kohonen
		  self-organizing map, where network structure is evolved in
		  an on-line adaptive mode. Experiments have been carried out
		  on some benchmark data sets as well as on macroeconomic
		  data. Results show that ESOM is a good tool for clustering,
		  data analysis, and visualization.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  deng94a,
  author	= {Da Deng and Chan, K. P. and Yinglin Yu},
  title		= {Handwritten {C}hinese character recognition using spatial
		  {G}abor filters and \mbox{self-organizing} feature maps},
  booktitle	= {Proceedings ICIP-94},
  year		= {1994},
  volume	= {3},
  pages		= {940--4},
  organization	= {Dept. of Comput. Sci. , Hong Kong Univ. , Hong Kong},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  denoeux95a,
  author	= {Denoeux, Thierry},
  title		= {Evidence-theoretic neural network classifier},
  journal	= {Proceedings of IEEE International Conference on Systems,
		  Man, and Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1995},
  number	= {},
  volume	= {1},
  pages		= {712--717},
  abstract	= {A new classifier based on the Dempster-Shafer theory of
		  evidence is presented. The approach consists in considering
		  the similarity to prototype vectors as evidence supporting
		  certain hypotheses concerning the class membership of a
		  pattern to be classified. The different items of evidence
		  are represented by basic belief assignments over the set of
		  classes and combined by Dempster's rule of combination. An
		  implementation of this procedure in a neural network with
		  specific architecture and learning procedure is presented.
		  A comparison with LVQ and RBF neural network classifiers is
		  performed.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  der93a,
  author	= {R. Der and Th. Villmann},
  title		= {Dynamics of {S}elf {O}rganized {F}eature {M}apping},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {457--460},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  der93b,
  author	= {R. Der and M. Herrmann},
  title		= {Phase Transitions in Self-Organized Maps},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {597--600},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  der93c,
  author	= {R. Der and M. Herrmann and Th. Villmann},
  title		= {Spontaneous Symmetry-Breaking Effects in
		  {S}elf-{O}rganized {F}eature {M}aps: A {G}inzburg-{L}andau
		  Approach},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {461--464},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  der93d,
  author	= {Der, R. and Villmann, T. },
  title		= {Dynamics of self-organized feature mapping},
  booktitle	= {New Trends in Neural Computation. International Workshop
		  on Artificial Neural Networks. IWANN '93 Proceedings},
  year		= {1993},
  editor	= {Mira, J. and Cabestany, J. and Prieto, A. },
  pages		= {312--15},
  organization	= {Inst. fur Inf. Leipzig Univ. , Germany},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  der94a,
  author	= {R. Der and M. Herrmann},
  title		= {Reordering Transitions in {S}elf-{O}rganized {F}eature
		  {M}aps with Short-Range Neighbourhood},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {322--325},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {analysis, reordering, convergence values},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  der94b,
  author	= {R. Der and M. Herrmann},
  title		= {Instabiliries in Self-Organized Feature Maps with Short
		  Neighborhood Range},
  booktitle	= {Proc. ESANN'94, European Symp. on Artificial Neural
		  Networks},
  year		= {1994},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {271--276},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  der94c,
  author	= {R. Der and M. Herrmann},
  title		= {Nonlinear Chaos Control by Neural Nets},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1227--1230},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, control, chaos},
  dbinsdate	= {oldtimer}
}

@Article{	  der94d,
  author	= {Der, R. and Herrmann, M. },
  title		= {Critical phenomena in \mbox{self-organizing} feature maps:
		  {G}inzburg-{L}andau approach},
  journal	= {Physical Review E [Statistical Physics, Plasmas, Fluids,
		  and Related Interdisciplinary Topics]},
  year		= {1994},
  volume	= {49},
  number	= {6},
  pages		= {pt. B},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@InCollection{	  der96a,
  author	= {Ralf Der and Gerd Balzuweit and Michael Herrmann},
  title		= {Constructing Principal Manifolds in Sparse Data Sets by
		  Self-Organizing Maps with Self-Regulating Neighborhood
		  Width},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {480--483},
  dbinsdate	= {oldtimer}
}

@Article{	  der97a,
  author	= {R. Der and M. Herrmann and T. Villmann},
  title		= {Time behavior of topological ordering in
		  \mbox{self-organizing} feature mapping},
  journal	= {Biological Cybernetics},
  year		= {1997},
  volume	= {77},
  number	= {6},
  pages		= {419--27},
  dbinsdate	= {oldtimer}
}

@InCollection{	  der99a,
  author	= {R. Der and M. Herrmann},
  title		= {Second-Order Learning in Self-Organizing Maps},
  booktitle	= {Kohonen Maps},
  pages		= {293--302},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: self-organising maps, second-order learning,
		  local learning parameters, over-fitting, phase
		  transitions},
  dbinsdate	= {oldtimer}
}

@Article{	  dersch95a,
  author	= {Dominik Dersch and Paul Tavan},
  title		= {Asymptotic Level Density in Topological Feature Maps},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1995},
  volume	= {6},
  number	= {1},
  pages		= {230--236},
  month		= {January},
  dbinsdate	= {oldtimer}
}

@Article{	  deschenes95a,
  author	= {Deschenes, C. J. and Noonan, J. },
  title		= {Fuzzy {K}ohonen network for the classification of
		  transients using the wavelet transform for feature
		  extraction},
  journal	= {Information Sciences},
  year		= {1995},
  volume	= {87},
  number	= {4},
  pages		= {247--66},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  desieno88a,
  author	= {Duane DeSieno},
  title		= {Adding a conscience to competitive learning},
  booktitle	= {Proc. ICNN'88, International Conference on Neural
		  Networks},
  year		= {1988},
  pages		= {117--124},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  desimio93a,
  author	= {Martin P. DeSimio and Timothy R. Anderson},
  title		= {Phoneme Recognition with Binaural Cochlear Models and The
		  Stereausis Representation},
  booktitle	= {Proc. ICASSP-93, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1993},
  volume	= {I},
  pages		= {521--524},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  desmet01a,
  author	= {Desmet, P.},
  title		= {Buying behavior study with basket analysis: pre-clustering
		  with a Kohonen map},
  journal	= {European-Journal-of-Economic-and-Social-Systems},
  year		= {2001},
  volume	= {15},
  pages		= {17--30},
  abstract	= {From the products bought, by basket analysis we seek to
		  infer interest, values and choice criteria and predict
		  purchase probabilities for other products. This statistical
		  approach relies on the existence of a few general
		  underlying clusters which enables the prediction of general
		  and specific buying behavior. Compared to traditional
		  clustering methods, a Kohonen map, a neural network, allows
		  the projection and clustering of data for which the
		  proximity presents a meaning or interest. Beyond the
		  interest of these neural networks for graphic
		  representation, the article suggests different ways of
		  articulating general and product-specific typologies which
		  are illustrated on a real database of buyers' behavior in a
		  book club. The results clearly show a significant
		  improvement with regards to the results obtained with
		  current models using either RFM segmentation or logistic
		  regression.},
  dbinsdate	= {2002/1}
}

@Article{	  dewen95a,
  author	= {Hu Dewen and Shen Shi and Wang Zhengzhi and Wen Xisen},
  title		= {Probability distribution of {K}ohonen neural network in
		  the post training phase},
  journal	= {Acta Electronica Sinica},
  year		= {1995},
  volume	= {23},
  number	= {8},
  pages		= {52--6},
  dbinsdate	= {oldtimer}
}

@Article{	  dewen95b,
  author	= {Hu Dewen and Wen Xisheng and Shen Shi and Wang Zhengzhi},
  title		= {Probability distribution of {K}ohonen neural network in
		  the post-training phase},
  journal	= {Chinese Journal of Electronics},
  year		= {1995},
  volume	= {4},
  number	= {4},
  pages		= {53--7},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dhawan93a,
  author	= {Atam P. Dhawan and Louis Arata},
  title		= {Segmentation of Medical Images Through Competitive
		  Learning},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1277--1282},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {The method learns useful features and regions through the
		  use of a local measure and competitive learning ({SOM}).
		  The presented approach sounds familiar. },
  dbinsdate	= {oldtimer}
}

@Article{	  dhawan93b,
  author	= {Dhawan, A. P. and Arata, L. },
  title		= {Segmentation of medical images through competitive
		  learning},
  journal	= {Computer Methods and Programs in Biomedicine},
  year		= {1993},
  volume	= {40},
  number	= {3},
  pages		= {203--15},
  month		= {July},
  abstract	= {In image analysis applications, segmentation of gray-level
		  images into meaningful regions is an important low-level
		  processing step. Various approaches to segmentation
		  investigated in the literature, in general, use either
		  local information of gray-level values of pixels (region
		  growing based methods, for example) or the global
		  information (histogram thresholding based methods, for
		  example). Application of these approaches for segmenting
		  medical images often does not provide satisfactory results.
		  Medical images are usually characterized by low local
		  contrast and noisy or faded features causing unacceptable
		  performance of local information based segmentation
		  methods. In addition, because of a large amount of
		  structural information found in medical images, global
		  information based segmentation methods yield inadequate
		  results in region extraction. We present a novel approach
		  to image segmentation that combines local contrast as well
		  as global gray-level information. The presented method
		  adaptively learns useful features and regions through the
		  use of a normalized contrast function as a measure of local
		  information and a competitive learning based method to
		  update region segmentation incorporating global information
		  about the gray-level distribution of the image. In this
		  paper, we present the framework of such a self organizing
		  feature map, and show the results on simulated as well as
		  real medical images.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  di00a,
  author	= {Di, Li and Yonglun, Song and Feng, Ye},
  title		= {On line monitoring of weld defects for short-circuit gas
		  metal arc welding based on the Self-Organize feature Map
		  neural networks},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {239--244},
  organization	= {South-China Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {In this paper a method for automatic detection of weld
		  defects of short-circuit gas metal arc welding has been
		  presented. It is based on the extraction of arc signal
		  features as well as classification of the obtained features
		  using Self-Organize feature Map (SOM) neural networks in
		  order to get the weld quality information, for example, to
		  determine if there is defect in the product. This is
		  important for the on-line monitoring of weld quality
		  especially in Robotic welding and lay the foundation for
		  the further real-time control of weld quality.},
  dbinsdate	= {2002/1}
}

@Article{	  di_bona99a,
  author	= {{Di Bona}, S. and Huwer, S. and Niemann, H. and Salvetti,
		  O.},
  title		= {Nonlinear neural enhancement of anatomical differences in
		  deformed brain {MR} images},
  journal	= {Pattern Recognition and Image Analysis},
  year		= {1999},
  volume	= {9},
  pages		= {554--65},
  abstract	= {Presents an approach for a data-driven comparison and
		  registration of digital images, the BrainMatcher algorithm.
		  The approach proposes an innovative neural network model
		  based on the extension of the self organizing map idea
		  introduced by Teuvo Kohonen. This method is extended in
		  order to match homologous image pairs with big disparities,
		  considering both their morphometric and densitometric
		  characteristics. The model is applied to a real case in the
		  field of medical image analysis: tomographic scan pairs are
		  processed in order to find the law able to model a growing
		  intracranial lesion inside the brain.},
  dbinsdate	= {oldtimer}
}

@Article{	  di_martino94a,
  author	= {{Di Martino}, J. C. and Colnet, B. },
  title		= {Image segmentation by non supervised neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1994},
  volume	= {2182},
  pages		= {350--6},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  di_martino94b,
  author	= {{Di Martino}, J. C. and Colnet, B. and {Di Martino}, M.},
  title		= {The use of non-supervised neural networks to detect lines
		  in lofargram},
  booktitle	= {ICASSP-94. 1994 IEEE International Conference on
		  Acoustics, Speech and Signal Processing},
  year		= {1994},
  volume	= {2},
  pages		= {II/293--6},
  organization	= {CRIN-INRIA Lorraine, Vandoeuvre-les-Nancy, France},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  di_natale00a,
  author	= {{Di Natale}, Corrado and Salimbeni, Danio and Paolesse,
		  Roberto and Macagnano, Antonella and D'Amico, Arnaldo},
  title		= {Porphyrins-based opto-electronic nose for volatile
		  compounds detection},
  journal	= {Sensors and Actuators, B: Chemical},
  year		= {2000},
  volume	= {65},
  number	= {1},
  month		= {},
  pages		= {220--226},
  organization	= {Univ of Rome},
  publisher	= {Elsevier Sequoia SA},
  address	= {Lausanne},
  abstract	= {Thin films of different metalloporphyrins have been used
		  as sensing materials for the development of optical sensors
		  for the detection of different volatile organic compounds
		  (VOC). Absorption spectra of thin films showed changes
		  after interactions with analytes. The development of a
		  measurement set-up is discussed and the results obtained in
		  the context of some VOC's detection are reported. Data have
		  been analyzed considering both stand alone sensors (looking
		  at the response isotherm and sensitivity comparison) and
		  each sensor as a component of an opto-electronic nose. In
		  this last case the capability in distinguishing different
		  volatile compounds and the contribution of each sensor has
		  been investigated by means of a self organizing map.},
  dbinsdate	= {2002/1}
}

@Article{	  di_natale94a,
  author	= {{Di Natale}, C. and Davide, F. A. M. and D'Amico, A. and
		  Gopel, W. and Weimar, U.},
  title		= {Sensor arrays calibration with enhanced neural networks},
  journal	= {Sensors and Actuators B [Chemical]},
  year		= {1994},
  volume	= {B19},
  number	= {1--3},
  pages		= {654--7},
  month		= {April},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  di_natale95a,
  author	= {{Di Natale}, G. and Davide, F. and D'Amico, A.},
  title		= {Pattern recognition in gas sensing: well-stated techniques
		  and advances},
  journal	= {Sensors and Actuators B [Chemical]},
  year		= {1995},
  volume	= {B23},
  number	= {2--3},
  pages		= {111--18},
  month		= {Feb},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  di_natale95b,
  author	= {{Di Natale}, C. and Davide, F. A. M. and D'Amico, A. and
		  Hierlemann, A. and Mitrovics, J. and Schweizer, M. and
		  Weimar, U. and Gopel, W. },
  title		= {A composed neural network for the recognition of gas
		  mixtures},
  journal	= {Sensors and Actuators B [Chemical]},
  year		= {1995},
  volume	= {B25},
  number	= {1--3},
  pages		= {808--12},
  month		= {April},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  di_natale95c,
  author	= {{Di Natale}, C. and Davide, F. A. M. and D'Amico, A. },
  title		= {A \mbox{self-organizing} system for pattern
		  classification: time varying statistics and sensor drift
		  effects},
  journal	= {Sensors and Actuators B [Chemical]},
  year		= {1995},
  volume	= {B27},
  number	= {1--3},
  pages		= {237--41},
  month		= {June},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  di_natale97a,
  author	= {{Di Natale}, C. and Macagnano, A. and Damico, A. and
		  Davide, F.},
  title		= {Electronic Nose Modeling and Data Analysis Using a Self
		  Organizing Map},
  journal	= {IEE Proceedings-Science, Measurement and Technology},
  year		= {1997},
  pages		= {1236--43},
  volume	= {8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  di_natale97b,
  author	= {Corrado {Di Natale} and Arnaldo D'Amico},
  title		= {Modelling and data analysis of multisensor systems with
		  the \mbox{self-organizing} map: application to the
		  electronic nose},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {14--19},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  di_natale98a,
  author	= {{di Natale}, C. and Paolesse, R. and Macagnano, A. and
		  Mantini, A. and d'Amico, A.},
  title		= {Self-organizing map analysis of the selectivity properties
		  of {QMB} sensors coated by porphyrin films},
  booktitle	= {Eurosensors XII. Proceedings of the 12th European
		  Conference on Solid-State Tranducers and the 9th UK
		  Conference on Sensors and their Applications},
  publisher	= {IOP Publishing},
  address	= {Bristol, UK},
  year		= {1998},
  volume	= {2},
  pages		= {1145--8},
  abstract	= {In recent years metalloporphyrins and their derivatives
		  have been proposed as sensitive layers for mass variation
		  based transducers for the detection of volatile compounds.
		  Despite the positive result obtained so far a systematic
		  study of the kinds of interactions ruling the sensing
		  mechanism has not yet been conducted. This paper reports on
		  a study oriented towards a deeper comprehension of such
		  mechanisms and it introduces a self-organizing map based
		  methodology for the analysis of selectivities.},
  dbinsdate	= {oldtimer}
}

@Article{	  di_pietro91a,
  author	= {G. N. {di Pietro}},
  title		= {How artificial neurons recognise natural speech},
  journal	= {Bull. des Schweizerischen Elektrotechnischen Vereins \&
		  des Verbandes Schweizerischer Elektrizit{\"{a}}tswerke},
  year		= {1991},
  volume	= {82},
  number	= {21},
  pages		= {17--22},
  note		= {(in German)},
  x		= {Introduktio aiheeseen. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  di_stefano91a,
  author	= {A. {Di Stefano} and O. Mirabella and G. {Di Cataldo} and
		  G. Palumbo},
  title		= {On the use of neural networks for {H}amming coding},
  booktitle	= {Proc. ISCAS'91, Int. Symp. on Circuits and Systems},
  year		= {1991},
  volume	= {III},
  pages		= {1601--1604},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {Kohonen slab;},
  dbinsdate	= {oldtimer}
}

@Article{	  diab97a,
  author	= {S. L. Diab and M. A. Karim and K. M. Iftekharuddin},
  title		= {Scale and translation invariant detection of targets
		  varying in fine details},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3069},
  pages		= {269--80},
  note		= {(Automatic Target Recognition VII Conf. Date: 22--24 April
		  1997 Conf. Loc: Orlando, FL, USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  diamantini94a,
  author	= {Claudia Diamantini and Arnaldo Spalvieri},
  title		= {Vector Quantization for Minimum Error Probability},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1091--1094},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  diamantini94b,
  author	= {Diamantini, C. and Spalvieri, A. },
  title		= {Certain facts about {K}ohonen's {LVQ}1 algorithm},
  booktitle	= {1994 IEEE International Symposium on Circuits and
		  Systems},
  year		= {1994},
  volume	= {6},
  pages		= {427--30},
  organization	= {Instituto di Inf. , Ancona Univ. , Italy},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  diamantini96a,
  author	= {C. Diamantini and A. Spalvieri},
  title		= {Certain facts about {K}ohonen`s {LVQ}1 algorithm},
  journal	= {IEEE Transactions on Circuits and Systems I: Fundamental
		  Theory and Applications},
  year		= {1996},
  volume	= {43},
  number	= {5},
  pages		= {425--7},
  dbinsdate	= {oldtimer}
}

@Article{	  diao00a,
  author	= {Diao, Qian and Wang, Yongcheng and Zhang, Huihui},
  title		= {A neural network approach to constructing concept
		  association of Chinese information},
  journal	= {Journal of the China Society for Scientific and Technical
		  Information},
  year		= {2000},
  volume	= {19},
  pages		= {170--5},
  abstract	= {A neural network approach to constructing concept
		  association for Chinese information is shown. It is based
		  on the Kohonen {SOM}. The result can be used for the
		  concept indexing and automatic classification of Chinese
		  information and can improve the efficiency of search
		  engines on the Internet. Some experimental and test results
		  are included.},
  dbinsdate	= {oldtimer}
}

@Article{	  dias99a,
  author	= {J. M. Dias and A. Dourado},
  title		= {A Self Organizing Fuzzy Controller with a Fixed Maximum
		  Number of Rules and an Adaptive Similarity Factor},
  journal	= {Fuzzy Sets and Systems},
  volume	= {103},
  pages		= {27--48},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  diaz01a,
  author	= {Diaz, I. and Diez, A. B. and Vega, A. A. C.},
  title		= {Complex process visualization through continuous feature
		  maps using radial basis functions},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {443--449},
  abstract	= {In this paper we propose a method for complex process
		  visualization using a continuous mapping from the space of
		  measurements or features of the process onto a continuous
		  visualization space. To construct this mapping we suggest a
		  continuous extension of the self organizing map using a
		  kernel regression approach. We also describe a method for
		  continuous condition monitoring based on the proposed
		  continous mapping. We finally illustrate the proposed
		  method with experimental data from an induction motor
		  working in different fault conditions.},
  dbinsdate	= {2002/1}
}

@InCollection{	  diaz97a,
  author	= {F. Diaz and J. M. Ferrandez and P. Gomez and V. Rodellar
		  and V. Nieto},
  title		= {Spoken-digit recognition using \mbox{self-organizing} maps
		  with perceptual pre-processing},
  booktitle	= {Biological and Artificial Computation: From Neuroscience
		  to Technology. International Work Conference on Artificial
		  and Natural Neural Networks, IWANN'97. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {J. Mira and R. Moreno-Diaz and J. Cabestany},
  address	= {Berlin, Germany},
  pages		= {1203--12},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dimakov00a,
  author	= {Valentin Dimakov and Vladimir Golovko},
  title		= {Self-Organizing Path Planning Control System for a
		  Vehicle},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Department of Computers and Mechanics, Brest polytechnic
		  institute},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dingle93a,
  author	= {Dingle, A. A. and Andreae, J. H. and Jones, R. D. },
  title		= {A chaotic neural unit},
  booktitle	= {1993 IEEE International Conference on Neural Networks},
  year		= {1993},
  volume	= {1},
  pages		= {335--40},
  organization	= {Dept. of Electr. \& Electron. Eng. , Canterbury Univ. ,
		  Christchurch, New Zealand},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dingle93b,
  author	= {Dingle, A. A. and Andreae, J. H. and Jones, R. D. },
  title		= {The chaotic \mbox{self-organizing} map},
  booktitle	= {Proceedings 1993 The First New Zealand International
		  Two-Stream Conference on Artificial Neural Networks and
		  Expert Systems},
  year		= {1993},
  editor	= {Kasabov, N. K. },
  pages		= {15--18},
  organization	= {Christchurch Hospital, New Zealand},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dirk00a,
  author	= {Dirk, Krechel and Rainer, Maximini and von Wangenheim,
		  Aldo},
  title		= {Parallel implementation of a {MR}-mammography matching
		  algorithm},
  booktitle	= {Proceedings of the IEEE Symposium on Computer-Based
		  Medical Systems},
  year		= {2000},
  editor	= {},
  volume	= {},
  pages		= {135--140},
  organization	= {Univ Kaiserslautern},
  publisher	= {IEEE},
  address	= {Los Alamitos, CA},
  abstract	= {We present a parallel matching component of an integrated
		  system for the automatic analysis of MRI-breast images
		  towards the early detection of breast cancer. The system
		  operates on taken images using the method of dynamic
		  contrast-enhanced MRI. Suspicious breast lesions are
		  automatically marked with colours, thus directing the
		  physician's attention towards the critical regions. A
		  proper and careful decision procedure is needed to
		  differentiate between increases of signal intensity
		  triggered by noise and tissue dislocations (motion
		  artifacts) and increases that are triggered by an
		  accumulation of contrast agent in the related breast
		  region. We present our component for image matching using
		  self organizing maps (SOM), which enables the system to
		  work properly even with image sequences that are strongly
		  deformed by the patients breathing movements. To reach the
		  time constraint of 15 minutes in medical practice we decide
		  to implement a parallel architecture for the neural network
		  matcher, which works on all computers in the heterogeneous
		  network of our medical partners. The system is tested on
		  real patient data and is now being refined in cooperation
		  with our partner hospital for Radiology and Nuclear
		  Medicine in Mainz.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  dittenbach00b,
  author	= {Dittenbach, Michael and Merkl, Dieter and Rauber,
		  Andreas},
  title		= {Growing hierarchical self-organizing map},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {15--19},
  organization	= {Technische Universitat Wien},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {In this paper we present the growing hierarchical
		  self-organizing map. This dynamically growing neural
		  network model evolves into a hierarchical structure
		  according to the requirements of the input data during an
		  unsupervised training process. We demonstrate the benefits
		  of this novel neural network model by organizing a
		  real-world document collection according to their
		  similarities.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  dittenbach01a,
  author	= {M. Dittenbach and A. Rauber and D. Merkl},
  title		= {Recent advances with the growing hierarchical
		  self-organising Map},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {140--5},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  dittenbach01b,
  author	= {Dittenbach, M. and Merkl, D. and Rauber, A.},
  title		= {Hierarchical clustering of document archives with the
		  Growing Hierarchical Self-Organizing Map},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {500--505},
  abstract	= {With the increasing amount of information available in
		  electronic document collections, methods for organizing
		  these collections to allow topic-oriented browsing and
		  orientation gain increasing importance. In this paper, we
		  present the Growing Hierarchical Self-Organizing Map, which
		  allows an automatic hierarchical decomposition and
		  organization of documents. We present a case study based on
		  a 3-month article collection from an Austrian daily
		  newspaper.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  dittenbach01c,
  author	= {Dittenbach, M. and Rauber, A. and Merkl, D.},
  title		= {Business, culture, politics, and sports---how to find your
		  way through a bulk of news? On content-based hierarchical
		  structuring and organization of large document archives},
  booktitle	= {Database and Expert Systems Applications. 12th
		  International Conference, DEXA 2001. Proceedings (Lecture
		  Notes in Computer Science Vol.2113). Springer-Verlag,
		  Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {200--10},
  abstract	= {With the increasing amount of information available in
		  electronic document collections, methods for organizing
		  these collections to allow topic-oriented browsing and
		  orientation gain increasing importance. The SOMLib digital
		  library system provides such an organization based on the
		  self-organizing map, a popular neural network model by
		  producing a map of the document space. However,
		  hierarchical relations between documents are hidden in the
		  display. Moreover, with increasing size of document
		  archives the required maps grow larger, thus leading to
		  problems for the user in finding proper orientation within
		  the map. In this case, a hierarchically structured
		  representation of the document space would be highly
		  preferable. We present the growing hierarchical
		  self-organizing map, a dynamically growing neural network
		  model, providing a content-based hierarchical decomposition
		  and organization of document spaces. This architecture
		  evolves into a hierarchical structure according to the
		  requisites of the input data during an unsupervised
		  training process. A recent enhancement of the training
		  process further ensures proper orientation of the various
		  topical partitions. This facilitates intuitive navigation
		  between neighboring topical branches. The benefits of this
		  approach are shown by organizing a real-world document
		  collection according to semantic similarities.},
  dbinsdate	= {2002/1}
}

@InCollection{	  dobrzewski97a,
  author	= {B. Dobrzewski and D. Ruwisch and M. Bode},
  title		= {Wave propagation in \mbox{self-organizing} feature maps as
		  a means for the representation of temporal sequences},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {661--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dogaru96a,
  author	= {R. Dogaru and A. T. Murgan and C. Cumaniciu},
  title		= {Fast Signal Recognition and Detection using {ART1} Neural
		  Networks and Nonlinear Preprocessing Units based on Time
		  Delay Embeddings},
  booktitle	= {Proc. ESANN'96, European Symp. on Artificial Neural
		  Networks},
  year		= {1996},
  publisher	= {D facto conference services},
  address	= {Bruges, Belgium},
  editor	= {Michel Verleysen},
  pages		= {309--314},
  dbinsdate	= {oldtimer}
}

@Article{	  doi96a,
  author	= {T. Doi and T. Namba and A. Uehara and N. Nagata and S.
		  Miyazaki and K. Shibahara and S. Yokoyama and A. Iwata and
		  T. Ae and M. Hirose},
  title		= {Optically interconnected {K}ohonen net for pattern
		  recognition},
  journal	= {Japanese Journal of Applied Physics, Part 1 [Regular
		  Papers \& Short Notes]},
  year		= {1996},
  volume	= {35},
  number	= {2B},
  pages		= {1405--9},
  note		= {(1995 International Conference on Solid State Devices and
		  Materials (SSDM `95) Conf. Date: 21--24 Aug. 1995 Conf.
		  Loc: Osaka, Japan)},
  dbinsdate	= {oldtimer}
}

@Article{	  dokur97a,
  author	= {Z. Dokur and T. Olmez and E. Yazgan and O. K. Ersoy},
  title		= {Detection of {ECG} waveforms by neural networks},
  journal	= {Medical Engineering \& Physics},
  year		= {1997},
  volume	= {19},
  number	= {8},
  pages		= {738--41},
  dbinsdate	= {oldtimer}
}

@Article{	  dollhopf01a,
  author	= {Dollhopf, S. L. and Hashsham, S. A. and Tiedje, J. M.},
  title		= {Interpreting 16S r{DNA} T-{RFLP} data: Application of
		  self- organizing maps and principal component analysis to
		  describe community dynamics and convergence},
  journal	= {MICROBIAL ECOLOGY},
  year		= {2001},
  volume	= {42},
  number	= {4},
  month		= {NOV},
  pages		= {495--505},
  abstract	= {Interpreting the large amount of data generated by rapid
		  profiling techniques, such as T-RFLP, DGGE, and DNA arrays,
		  is a difficult problem facing microbial ecologists. This
		  study compares the ability of two very different ordination
		  methods, principal component analysis (PCA) and
		  self-organizing map neural networks (SOMs), to analyze
		  16S-DNA terminal restriction-fragment length polymorphism
		  (T-RFLP) profiles from microbial communities in glucose-fed
		  methanogenic bioreactors during startup and changes in
		  operational parameters. Our goal was not only to identify
		  which samples were similar, but also to decipher community
		  dynamics and describe specific phylotypes, i.e.,
		  phylogenetically similar organisms, that behaved similarly
		  in different reactors. Fifteen samples were taken over 56
		  volume changes from each of two bioreactors inoculated from
		  river sediment (S2) and anaerobic digester sludge (M3) and
		  from a well-established control reactor (RI). PCA of
		  bacterial T-RFLP profiles indicated that both the S2 and M3
		  communities changed rapidly during the first nine volume
		  changes, and then became relatively stable. PCA also showed
		  that an HRT of 8 or 6 days had no effect on either reactor
		  community, while an HRT of 2 days changed community
		  structure significantly in both reactors. The SOM clustered
		  the terminal restriction fragments according to when each
		  fragment was most abundant in a reactor community,
		  resulting in four clearly discernible groups. Thirteen
		  fragments behaved similarly in both reactors, eight of
		  which composed a significant proportion of the microbial
		  community as judged by the relative abundance of the
		  fragment in the T-RFLP profiles. Six Bacteria terminal
		  restriction fragments shared between the two communities
		  matched cloned 16S rDNA sequences from the reactors related
		  to Spirochaeta, Aminobacterium, Thermotoga, and Clostridium
		  species. Convergence also occurred within the acetoclastic
		  methanogen community, resulting in a predominance of
		  Methanosarcina siciliae-related organisms. The results
		  demonstrate that both PCA and SOM analysis are useful in
		  the analysis of T-RFLP data; however, the SOM was better at
		  resolving patterns in more complex and variable data than
		  PCA ordination.},
  dbinsdate	= {2002/1}
}

@Article{	  dolmatova97a,
  author	= {L. Dolmatova and C. Ruckebusch and N. Dupuy and J. -P.
		  Huvenne and P. Legrand},
  title		= {Quantitative analysis of paper coatings using artificial
		  neural networks},
  journal	= {Chemometrics and Intelligent Laboratory Systems},
  year		= {1997},
  volume	= {36},
  number	= {2},
  pages		= {125--40},
  dbinsdate	= {oldtimer}
}

@InCollection{	  dolnicar97a,
  author	= {Sara Dolnicar},
  title		= {The use of neural networks in marketing: market
		  segmentation with self organising feature maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {38--43},
  dbinsdate	= {oldtimer}
}

@Article{	  domanski95a,
  author	= {Domanski, A. W. and Buczynski, R. and Sierakowski, M. },
  title		= {Liquid crystal cells and optical fibers in neural network
		  implementation},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2372},
  pages		= {354--9},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Book{		  domany96,
  title		= {Models of neural networks I (2. rev. ed)},
  year		= {1995},
  editor	= {Domany, E. and van Hemmen, J. L. and Schulten, K. },
  publisher	= {Springer},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  dominique96a,
  author	= {Dominique, F. and Subramanian, T. P. },
  title		= {Combined \mbox{self-organising} feature map-{LMS} adaptive
		  filter for digital co-channel interference suppression},
  journal	= {Electronics Letters},
  year		= {1996},
  volume	= {32},
  number	= {3},
  pages		= {168--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dong01a,
  author	= {Jian-Xiong Dong and Krzyzak, A. and Suen, C. Y.},
  title		= {A multi-net local learning framework for pattern
		  recognition},
  booktitle	= {Proceedings of Sixth International Conference on Document
		  Analysis and Recognition. IEEE Comput. Soc, Los Aalmitos,
		  CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {328--32},
  abstract	= {This paper proposes a general local learning framework to
		  effectively alleviate the complexities of classifier design
		  by means of "divide and conquer" principle and ensemble
		  method. The learning framework consists of quantization
		  layer and ensemble layer. After GLVQ and MLP are applied to
		  the framework, the proposed method is tested on MNIST
		  handwritten digit database. The obtained performance is
		  very promising, an error rate with 0.99%, which is
		  comparable to that of LeNet5, one of the best classifiers
		  on this database. Further, in contrast to LeNet5, our
		  method is especially suitable for a large-scale real-world
		  classification problem.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from internet.}
}

@Article{	  doniere94a,
  author	= {Doniere, Timothy F. and Dhawan, Atam P.},
  title		= {Space shuttle main engine sensor modeling using vector
		  quantization},
  journal	= {AIAA Journal},
  year		= {1994},
  number	= {10},
  volume	= {32},
  pages		= {2126--2128},
  abstract	= {In this study, the issue of decreasing the training time
		  of a feedforward neural network with backpropagation was
		  considered. The data reduction was achieved by learning
		  vector quantization (LVQ) algorithm. It should be noted
		  that LVQ employed as a neural network model can furnish a
		  more expeditious estimation. Therefore, if estimation time
		  is a critical factor, the LVQ neural network model could be
		  incorporated as an estimator. The improvement in
		  proficiency by decreasing training periods may be extremely
		  beneficial for the very large data sets, such as the data
		  compiled during the Space Shuttle main engine mainstage
		  operation.},
  dbinsdate	= {oldtimer}
}

@Article{	  dony95a,
  author	= {Robert D. Dony and Simon Haykin},
  title		= {Neural Network Approaches to Image Compression},
  journal	= {Proc. of the IEEE},
  year		= {1995},
  volume	= {83},
  number	= {2},
  pages		= {288--303},
  annote	= {image compression},
  dbinsdate	= {oldtimer}
}

@Article{	  dony97a,
  author	= {R. D. Dony and S. Haykin},
  title		= {Image Segmentation Using a Mixture of Principal Components
		  Representation},
  journal	= {IEE Proc. -Vis. Image Signal Process. },
  year		= 1997,
  volume	= 144,
  pages		= {73--80},
  dbinsdate	= {oldtimer}
}

@Article{	  dopazo97a,
  author	= {J. Dopazo and W. Huaichun and J. M. Carazo},
  title		= {A new type of unsupervised growing neural network for
		  biological sequence classification that adopts the topology
		  of a phylogenetic tree},
  journal	= {Lecture Notes in Computer Science},
  year		= {1997},
  volume	= {1240},
  pages		= {932--941},
  dbinsdate	= {oldtimer}
}

@Article{	  dopazo97b,
  author	= {J. Dopazo and J. M. Carazo},
  title		= {Phylogenetic reconstruction using an unsupervised growing
		  neural network that adopts the topology of a phylogenetic
		  tree},
  journal	= {Journal of Molecular Evolution},
  year		= {1997},
  volume	= {44},
  number	= {2},
  pages		= {226--233},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dorffner93a,
  author	= {Georg Dorffner and Peter Rappelsberger and Arthur Flexer},
  title		= {Using Self-Organizing Feature Maps to Classify {EEG}
		  Coherence Maps},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {882--887},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dorizzi90a,
  author	= {B. Dorizzi and J. -M. Auger},
  title		= {Parallel implementation of the {K}ohonen self-organization
		  algorithm},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conference},
  year		= {1990},
  volume	= {II},
  pages		= {681},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dormanns93a,
  author	= {Dormanns, M. and Heiss, H. U. },
  title		= {A solution for the processor allocation problem: topology
		  conserving graph mapping by self-organization},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  year		= {1993},
  editor	= {Albrecht, R. F. and Reeves, C. R. and Steele, N. C. },
  pages		= {198--205},
  organization	= {Dept. of Inf. , Karlsruhe Univ. , Germany},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dormanns95a,
  author	= {Dormanns, M. and Hans-Ulrich Heiss},
  title		= {Partitioning and mapping of large {FEM}-graphs by
		  self-organization},
  booktitle	= {Proceedings Euromicro Workshop on Parallel and Distributed
		  Processing},
  year		= {1995},
  pages		= {227--35},
  organization	= {Dept. of Inf. , Karlsruhe Univ. , Germany},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  doschner98a,
  author	= {Doschner, C.},
  title		= {Neural network for nonlinear dynamic system modeling based
		  on experimental data},
  booktitle	= {ISMCR '98. Proceedings of the Eighth International
		  Symposium on Measurement and Control in Robotics. Czech
		  Tech. Univ. Prague, Prague, Czech Republic},
  year		= {1998},
  volume	= {},
  pages		= {213--18},
  abstract	= {A procedure for the creation of a robot model is derived
		  from experimental data by means of a self-organizing
		  feature map, thereby avoiding extensive analytical
		  modeling. An algorithm for determination of the optimum
		  basic points needed for approximation of nonlinear robot
		  behaviour is defined as a function of the input signal
		  presentation frequency. Dynamic parameters of the robot are
		  learnt by evaluation of the measuring data and subsequently
		  used for creation of linearizing state feedback and for
		  synthesis of the overall control system.},
  dbinsdate	= {oldtimer}
}

@Article{	  doucette01a,
  author	= {Doucette, P. and Agouris, P. and Stefanidis, A. and
		  Musavi, M.},
  title		= {Self-organised clustering for road extraction in
		  classified imagery},
  journal	= {ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING},
  year		= {2001},
  volume	= {55},
  number	= {5--6},
  month		= {MAR},
  pages		= {347--358},
  abstract	= {The extraction of road networks from digital imagery is a
		  fundamental image analysis operation. Common problems
		  encountered in automated road extraction include high
		  sensitivity to typical scene clutter in high-resolution
		  imagery, and inefficiency to meaningfully exploit
		  multispectral imagery (MSI). With a ground sample distance
		  (GSD) of less than 2 m per pixel, roads can be broadly
		  described as elongated regions. We propose an approach of
		  elongated region-based analysis for 2D road extraction from
		  high-resolution imagery, which is suitable for MSI, and is
		  insensitive to conventional edge definition. A
		  self-organising road map (SORM) algorithm is presented.
		  inspired from a specialised variation of Kohonens
		  self-organising map (SOM) neural network algorithm. A
		  spectrally classified high-resolution image is assumed to
		  be the input for our analysis. Our approach proceeds by
		  performing spatial cluster analysis as a mid-level
		  processing technique. This allows us to improve tolerance
		  to road clutter in high- resolution images, and to minimise
		  the effect on road extraction of common classification
		  errors. This approach is designed in consideration of the
		  emerging trend towards high- resolution multispectral
		  sensors. Preliminary results demonstrate robust road
		  extraction ability due to the non-local approach, when
		  presented with noisy input. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  doucette99a,
  author	= {Doucette, P. and Agouris, P. and Musavi, M. and
		  Stefanidis, A.},
  title		= {Automated extraction of linear features from aerial
		  imagery using Kohonen learning and {GIS} data},
  booktitle	= {INTEGRATED SPATIAL DATABASES: DIGITAL IMAGES AND GIS},
  year		= {1999},
  pages		= {20--33},
  abstract	= {An approach to semi-automated linear feature extraction
		  from aerial imagery is introduced in which Kohonen's
		  self-organizing map (SOM) algorithm is integrated with
		  existing GIS data. The SOM belongs to a distinct class of
		  neural networks which is characterized by competitive and
		  unsupervised learning. Using radiometrically classified
		  image pixels as input, appropriate SOM network topologies
		  are modeled to extract underlying spatial structures
		  contained in the input patterns. Coarse- resolution GIS
		  vector data is used for network weight and topology
		  initialization when extracting specific feature components.
		  The Kohonen learning rule updates the synaptic weight
		  vectors of winning neural units that represent 2-D vector
		  shape vertices. Experiments with high-resolution
		  hyperspectral imagery demonstrate a robust ability to
		  extract centerline information when presented with coarse
		  input.},
  dbinsdate	= {2002/1}
}

@Article{	  doumas95a,
  author	= {Doumas, Anastasia and Mavroudakis, Konstantinos and
		  Gritzalis, Dimitris and Katsikas, Sokratis},
  title		= {Design of a neural network for recognition and
		  classification of computer viruses},
  journal	= {Computers \& Security},
  year		= {1995},
  number	= {5},
  volume	= {14},
  pages		= {435--448},
  abstract	= {A sufficient number of experiments has been conducted to
		  ascertain that a neural network can be used as a component
		  of a computer security system for the recognition and
		  classification of computer virus attack. A set of
		  attributes that describe the system activity and the
		  behaviour of computer viruses has been identified. The
		  error back propagation training algorithm and the
		  self-organizing feature map have been studied. Several
		  experiments were conducted using both algorithms, different
		  learning parameters, and two different training sets. For
		  each architecture, the size of the network with the best
		  performance was estimated experimentally. Results indicate
		  that both neural networks can discriminate input patterns,
		  at almost the same level of accuracy. The number of neurons
		  required for the solution of the specific problem using a
		  multilayer perceptron network was smaller than the
		  respective number for a self-organizing feature map
		  network. Therefore, using back propagation, the training
		  and the recall process were faster. In conclusion, neural
		  networks were proved to be efficient and practical devices
		  for computer virus recognition and classification, in
		  certain environments.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  douzono00a,
  author	= {Douzono, Hiroshi and Hara, Shigeomi and Noguchi, Yoshio},
  title		= {Clustering method of chromosome fluorescence profiles
		  using modified self organizing map controlled by simulated
		  annealing},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {103--106},
  organization	= {Saga Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The clustering method by the self-organizing map algorithm
		  of chromosome profiles measured by slit-scan flow-cytometer
		  is proposed. Moreover, the physical models of chromosomes
		  have been introduced in order to take into account the
		  rotation of chromosomes in the flowcytometer. By this
		  modification, the lengths of chromosomes and the intensity
		  distribution of chromosome fluorescence can be estimated
		  from chromosome profile data measured by the flowcytometer.
		  But the clustering results did not converged identically in
		  some experiments and the distribution of the rotation
		  angles was unnatural. So, we introduced simulated annealing
		  to improve the convergence of our SOM algorithm. We
		  compared the clustering results of this method with those
		  of K-means method and SOM method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  douzono01a,
  author	= {H. Douzono and S. Hara and Y. Noguchi},
  title		= {A design method of {DNA} chips using self-organising
		  maps},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {152--160},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  douzono01b,
  author	= {Douzono, H. and Hara, S. and Noguchi, Y.},
  title		= {A design method of {DNA} chips for {SNP} analysis using
		  self organizing maps},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2467--2471},
  organization	= {Faculty of Science and Engineering, Saga University},
  publisher	= {},
  address	= {},
  abstract	= {In this paper, we introduce a design method of DNA chips
		  using Self-Organizing Maps (SOM). DNA chips are powerful
		  tools for sequencings and SNP (Single Nucleotide
		  Polymorphism) analyses of DNA sequences. A DNA chip is an
		  array of DNA probes which are hybridized with the
		  compliment sub-sequences in the target sequence. However,
		  conventional DNA chips are showing tendency to be comprised
		  of longer probes and get larger in size to achieve a higher
		  resolution. To shrink the size of DNA chips, the design is
		  considered to be important. To solve this problem, we
		  applied SOM to obtain common features of DNA sequences with
		  small number of probes which efficiently cover the target
		  sequence with sufficient resolution for finding the correct
		  position of SNPs. We evaluated the DNA chips designed by
		  SOM with computer simulations of SNP analyses changing the
		  length of probes and size of the maps.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  douzono99a,
  author	= {Douzono, H. and Hara, S. and Eishima, S. and Noguchi, Y.},
  title		= {A clustering method of chromosome fluorescence profiles by
		  modified self organizing map},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3614--17},
  abstract	= {The clustering by the self-organizing map algorithm of
		  chromosome profiles measured by slit-scan flowcytometer is
		  proposed. Moreover, the physical models of chromosomes have
		  been introduced in order to take into account the rotation
		  of chromosomes in the flowcytometer. The self-organizing
		  map algorithm has been improved so that it can modify the
		  characteristic parameters of chromosome physical models. By
		  this modification, the lengths of chromosomes and the
		  intensity distribution of chromosome fluorescence can be
		  estimated from chromosome profile data measured by the
		  flowcytometer. The estimated lengths of chromosomes are
		  almost equal to known values of the lengths of chromosomes.
		  The clustering results by the above method are compared
		  with the clustering results of the same data by the K-mean
		  method and agglomerative hierarchical clustering.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  draghici00a,
  author	= {Draghici, S. and Cumberland, L. and Kovari, L. C.},
  title		= {Correlation of {HIV} protease structure with Indinavir
		  resistance: a data mining and neural networks approach},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.4057},
  year		= {2000},
  volume	= {4057},
  pages		= {319--29},
  abstract	= {This paper presents some results of data mining HIV
		  genotypic and structural data. Our aim is to try to relate
		  structural features of HIV enzymes essential to its
		  reproductive abilities to the drug resistance phenomenon.
		  This paper concentrates on the HIV protease enzyme and
		  Indinavir which is one of the FDA approved protease
		  inhibitors. Our starting point was the current list of HIV
		  mutations related to drug resistance. We used the fact that
		  some molecular structures determined through high
		  resolution X-ray crystallography were available for the
		  protease-Indinavir complex. Starting with these structures
		  and the known mutations, we modelled the mutant proteases
		  and studied the pattern of atomic contacts between the
		  protease and the drug. After suitable pre-processing, these
		  patterns have been used as the input of our data mining
		  process. We have used both supervised and unsupervised
		  learning techniques with the aim of understanding the
		  relationship between structural features at a molecular
		  level and resistance to Indinavir. The supervised learning
		  was aimed at predicting IC90 values for arbitrary mutants.
		  The SOFM was aimed at identifying those structural features
		  that are important for drug resistance and discovering a
		  classifier based on such features. We have used validation
		  and cross validation to test the generalization abilities
		  of the learning paradigm we have designed. The
		  straightforward supervised learning was able to learn very
		  successfully but validation results are less than
		  satisfactory. This is due to the insufficient number of
		  patterns in the training set which in turn is due to the
		  scarcity of the available data. The data mining using SOFM
		  was very successful. We have managed to distinguish between
		  resistant and non-resistant mutants using structural
		  features. We have been able to divide all reported HIV
		  mutants into several categories based on their
		  3-dimensional molecular structures and the pattern of
		  contacts between the mutant protease and Indinavir. Our
		  classifier shows reasonably good prediction performance
		  being able to predict the drug resistance of previously
		  unseen mutants with an accuracy of between 60% and 70%. We
		  believe that this performance can be greatly improved once
		  more data becomes available. The results presented here
		  support the hypothesis that structural features of the
		  molecular structure can be used in antiviral drug treatment
		  selection and drug design.},
  dbinsdate	= {2002/1}
}

@Article{	  draghici01a,
  author	= {Draghici, S.},
  title		= {The constraint based decomposition ({CBD}) training
		  architecture},
  journal	= {Neural Networks},
  year		= {2001},
  volume	= {14},
  number	= {4--5},
  month		= {},
  pages		= {527--550},
  organization	= {Wayne State University, Department of Computer Science},
  publisher	= {},
  address	= {},
  abstract	= {The Constraint Based Decomposition (CBD) is a constructive
		  neural network technique that builds a three or four layer
		  network, has guaranteed convergence and can deal with
		  binary, n-ary, class labeled and real-value problems. CBD
		  is shown to be able to solve complicated problems in a
		  simple, fast and reliable manner. The technique is further
		  enhanced by two modifications (locking detection and
		  redundancy elimination) which address the training speed
		  and the efficiency of the internal representation built by
		  the network. The redundancy elimination aims at building
		  more compact architectures while the locking detection aims
		  at improving the training speed. The computational cost of
		  the redundancy elimination is negligible and this
		  enhancement can be used for any problem. However, the
		  computational cost of the locking detection is exponential
		  in the number of dimensions and should only be used in low
		  dimensional spaces. The experimental results show the
		  performance of the algorithm presented in a series of
		  classical benchmark problems including the 2-spiral problem
		  and the Iris, Wine, Glass, Lenses, Ionosphere, Lung cancer,
		  Pima Indians, Bupa, TicTacToe, Balance and Zoo data sets
		  from the UCI machine learning repository. CBD's
		  generalization accuracy is compared with that of C4.5, C4.5
		  with rules, incremental decision trees, oblique
		  classifiers, linear machine decision trees, CN2, learning
		  vector quantization (LVQ), backpropagation, nearest
		  neighbor, Q* and radial basis functions (RBFs). CBD
		  provides the second best average accuracy on the problems
		  tested as well as the best reliability (the lowest standard
		  deviation). },
  dbinsdate	= {2002/1}
}

@InProceedings{	  dragutoiu00a,
  author	= {Dragutoiu, D. D. and Vasilievici, A.},
  title		= {Diagnosing active systems using neural networks},
  booktitle	= {OPTIM 2000. Proceedings of the 7th International
		  Conference on Optimization of Electrical and Electronic
		  Equipments. Transilvania Univ. Press, Brasov, Romania},
  year		= {2000},
  volume	= {3},
  pages		= {637--40},
  abstract	= {Diagnosis of an electrical system is a very difficult task
		  because of the large variety of error generator causes. The
		  diagnosis technique consists in reconstruction of the
		  behavior of an active system from a set of observable
		  events. The diagnosis process involves three steps:
		  interpretation, merging and diagnosing generation. The
		  interpretation generates a representation of the behavior
		  of the system based on observable events, the merging
		  combines the several results into a new interpretation and
		  the diagnosis information is generated on the basis of
		  fault events incorporated within the reconstructed
		  behavior. This technique is opposed to a model-based
		  diagnosis and does not reacquire the generation of the
		  whole system model. An advantage of this approach is that a
		  neural network can do the steps involved in diagnosing the
		  system. A good choice of a neural network is a
		  self-organizing map, because of its ease of implementation
		  and the good results achieved.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  driancourt91a,
  author	= {X. Driancourt and L. Bottou and P. Gallinari},
  title		= {Learning vector quantization, multi layer perceptron and
		  dynamic programming: comparison and cooperation},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {II},
  pages		= {815--819},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {This paper presents the comparison of several methods (DP,
		  MLP, TDNN, Shift-Tolerant LVQ, K-Means) on a multi-speaker
		  isolated word small vocabulary problem. A sub-optimal
		  cooperation between TDNN and other algorithms is proposed
		  and successfully tested on the problem. The combination of
		  TDNN and DP performs especially well. An optimal
		  cooperation method between DP and some other algorithms is
		  proposed.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  driancourt91b,
  author	= {X. Driancourt and L. Bottou and P. Gallinari},
  title		= {Comparison and cooperation of several classifiers (for
		  speech recognition)},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1649--1653},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  x		= {Presents the comparison of several methods on a
		  multi-speaker, isolated word, small vocabulary problem. },
  dbinsdate	= {oldtimer}
}

@Article{	  drimbarean01a,
  author	= {Drimbarean, A. and Whelan, P. F.},
  title		= {Experiments in colour texture analysis},
  journal	= {Pattern Recognition Letters},
  year		= {2001},
  volume	= {22},
  number	= {10},
  month		= {August },
  pages		= {1161--1167},
  organization	= {Vision Systems Laboratory, School of Electronic
		  Engineering, Dublin City University},
  publisher	= {},
  address	= {},
  abstract	= {In this paper we focus on the classification of colour
		  texture images. The main objective is to determine the
		  contribution of colour information to the overall
		  classification performance. Three relevant approaches to
		  grey scale texture analysis, namely local linear
		  transforms, Gabor filtering and the co-occurrence approach
		  are extended to colour images. They are evaluated in a
		  quantitative manner by means of a comparative experiment on
		  a set of colour images. We also investigate the effect of
		  using different colour spaces and the contribution of
		  colour and texture features separately and collectively.
		  The evaluation criteria is the classification accuracy
		  using a neural network classifier based on Learning Vector
		  Quantization. Experimental results indicate that the
		  incorporation of colour information enhances the
		  performance of the texture analysis techniques examined. },
  dbinsdate	= {2002/1}
}

@InCollection{	  driscoll95a,
  author	= {M. Driscoll and J. Mazumdar and I. Pilowsky and M.
		  Katsikitis},
  title		= {Application of neural networks to the categorisation of
		  facial expressions and its clinical significance},
  booktitle	= {Proceedings of the First Regional Conference, IEEE
		  Engineering in Medicine and Biology Society and 14th
		  Conference of the Biomedical Engineering Society of India.
		  An International Meet},
  publisher	= {IEEE},
  year		= {1995},
  address	= {New York, NY, USA},
  pages		= {4/37--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  drobics00a,
  author	= {Drobics, M. and Winiwater, W. and Bodenhofer, U.},
  title		= {Interpretation of self-organizing maps with fuzzy rules},
  booktitle	= {Proceedings 12th IEEE Internationals Conference on Tools
		  with Artificial Intelligence. ICTAI 2000. IEEE Comput. Soc,
		  Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {304--11},
  abstract	= {Exploration of large and high-dimensional data sets is one
		  of the main problems in data analysis. Self-organizing maps
		  (SOMs) can be used to map large data sets to a simpler;
		  usually two-dimensional topological structure. This mapping
		  is able to illustrate dependencies in the data in a very
		  intuitive manner and allows fast location of clusters.
		  However because of the black-box design of neural networks,
		  it is difficult to get qualitative descriptions of the
		  data. In our approach, we identify regions of interest in
		  SOMs by using unsupervised clustering methods. Then we
		  apply inductive learning methods to find fuzzy descriptions
		  of these clusters. Through the combination of these
		  methods, it is possible to use supervised machine learning
		  methods to find simple and accurate linguistic descriptions
		  of previously unknown clusters in the data.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  drobics01a,
  author	= {Drobics, M. and Bodenhofer, U. and Winiwarter, W. and
		  Klement EP},
  title		= {Data mining using synergies between self-organizing maps
		  and inductive learning of fuzzy rules},
  booktitle	= {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS
		  International Conference. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {3},
  pages		= {1780--5},
  abstract	= {Identifying structures in large data sets raises a number
		  of problems. On the one hand, many Methods cannot be
		  applied to larger data sets, while, on the other hand, the
		  results are often hard to interpret. We address these
		  problems by a novel three-stage approach. First, we compute
		  a small representation of the input data using a
		  self-organizing map. This reduces the amount of data and
		  allows us to create two-dimensional plots of the data. Then
		  we use this preprocessed information to identify clusters
		  of similarity. Finally, inductive learning methods are
		  applied to generate sets of fuzzy descriptions of these
		  clusters. This approach is applied to three case studies,
		  including image data and real-world data sets. The results
		  illustrate the generality and intuitiveness of the proposed
		  method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  du00a,
  author	= {Hong Du and M. Inui and M. Ohkita and K. Obu-Cann and
		  K.Fujimura and H. Tokutaka},
  title		= {Prediction of Oil Temperature Change of Substation
		  Transformer by Application of Self-Organizing Map ({SOM})},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  crossref	= {},
  key		= {},
  pages		= {293--98},
  year		= {2000},
  editor	= {},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  dubrovin00a,
  author	= {Dubrovin, V. I. and Subbotin, S. A.},
  title		= {Neural network diagnosis software},
  journal	= {Programmnye-Produkty-i-Sistemy. no.3; 2000; p.21--3},
  year		= {2000},
  volume	= {},
  pages		= {21--3},
  abstract	= {The NeuroDiag software system is presented; it realises
		  processes and diagnosis on the basis of a neural network
		  technique. Kohonen's self-organising map (SOM) with lateral
		  braking has been chosen as the basic model of neural
		  network in the software. The use of SOM is very promising
		  for classification problem solution, when the size of
		  teaching sample is small.},
  dbinsdate	= {2002/1}
}

@Article{	  duch00a,
  author	= {Duch, W. and Adamczak, R. and Diercksen, G. H. F.},
  title		= {Classification, association and pattern completion using
		  neural similarity based methods},
  journal	= {International-Journal-of-Applied-Mathematics-and-Computer-Science}
		  ,
  year		= {2000},
  volume	= {10},
  pages		= {747--66},
  abstract	= {A framework for Similarity-Based Methods (SBMs) includes
		  many classification models as special cases: neural
		  networks of the Radial Basis Function type, Feature Space
		  Mapping neurofuzzy networks based on separable transfer
		  functions, Learning Vector Quantization, variants of the k
		  nearest neighbor methods and several new models that may be
		  presented in a network form. Multilayer Perceptrons (MLPs)
		  use scalar products to compute a weighted activation of
		  neurons, combining soft hyperplanes to provide decision
		  borders. Distance-based multilayer perceptrons (DMLPs)
		  evaluate the similarity of inputs to weights offering a
		  natural generalization of standard MLPs. A cluster-based
		  initialization procedure determining the architecture and
		  values of all adaptive parameters is described. Networks
		  implementing SBM methods are useful not only for
		  classification and approximation, but also as associative
		  memories, in problems requiring pattern completion,
		  offering an efficient way to deal with missing values.
		  Non-Euclidean distance functions may also be introduced by
		  normalization of the input vectors in an extended feature
		  space. Both the approaches dramatically influence the
		  shapes of decision borders. An illustrative example showing
		  these changes is provided.},
  dbinsdate	= {2002/1}
}

@Article{	  duch94a,
  author	= {W{\l}odzis{\l}aw Duch},
  title		= {Quantitative Measures for Self-Organizing Topographic
		  Maps},
  journal	= {Open Systems {\&} Information Dynamics},
  year		= {1994},
  volume	= {2},
  number	= {3},
  pages		= {295--302},
  dbinsdate	= {oldtimer}
}

@InCollection{	  duch96a,
  author	= {W. Duch and A. Naud},
  title		= {Simplexes, multi-dimensional scaling and self-organized
		  mapping},
  booktitle	= {Proceedings of the 8th Joint EPS-APS International
		  Conference on Physics Computing, PC '96},
  publisher	= {Acad. Comput. Centre CYFRONET-KRAKOW},
  year		= {1996},
  editor	= {P. Borcherds and M. Bubak and A. Maksymowicz},
  address	= {Krakow, Poland},
  pages		= {367--70},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  duch96b,
  author	= {W{\l}odzis{\l}aw Duch and Antoine Naud},
  title		= {On Global Self-Organizing Maps},
  booktitle	= {Proc. ESANN'96, European Symp. on Artificial Neural
		  Networks},
  year		= {1996},
  publisher	= {D facto conference services},
  address	= {Bruges, Belgium},
  editor	= {Michel Verleysen},
  pages		= {91--96},
  dbinsdate	= {oldtimer}
}

@Article{	  duchon93a,
  author	= {A. Duchon and S. Katagiri},
  title		= {A minimum-distortion segmentation/{LVQ} hybrid algorithm
		  for speech recognition},
  journal	= {J. Acoust. Soc. of Japan},
  year		= {1993},
  volume	= {14},
  number	= {1},
  pages		= {37--42},
  month		= {January},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  duin95a,
  author	= {Duin, R. P. W. and Hoek, E. T. G. },
  title		= {{SMD} position measurement by a {K}ohonen network compared
		  with image processing},
  booktitle	= {Computer Analysis of Images and Patterns. 6th
		  International Conference, CAIP'95. Proceedings},
  year		= {1995},
  editor	= {Hlavac, V. and Sara, R. },
  pages		= {606--11},
  organization	= {Dept. of Appl. Phys. , Delft Univ. of Technol. ,
		  Netherlands},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dujardin99a,
  author	= {Dujardin, A. S. and Amarger, V. and Madani, K.},
  title		= {Multi-neural networks approaches for biomedical
		  applications: classification of brainstem auditory evoked
		  potentials},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3609--13},
  abstract	= {Auditory evoked potentials (AEPs) are electrical response
		  caused by the brief stimulation of the auditory sensing
		  system. AEP based techniques are important tools to
		  diagnosis many of auditory pathologies. Especially to
		  suspect the presence of auditory tumors called `acoustic
		  neuromas'. We investigate the design of a neural based
		  biomedical diagnosis aide tool. We use three models of
		  artificial neural networks: learning vector quantization,
		  radial basis function and backpropagation ones. In our
		  approach these three neural networks are used to achieve
		  the classification in two multi-neural network
		  configurations. A case study and experimental results are
		  reported and discussed.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dujardin99b,
  author	= {Dujardin, A. S. and Amarger, V. and Madani, K. and Adam,
		  O. and Motsch, J. F.},
  title		= {Multi-neural network approach for classification of
		  brainstem evoked response auditory},
  booktitle	= {Engineering Applications of Bio-Inspired Artificial Neural
		  Networks. International Work-Conference on Artificial and
		  Natural Neural Networks, IWANN'99. Proceedings, (Lecture
		  Notes in Computer Science Vol.1607)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {2},
  pages		= {255--64},
  abstract	= {In the field of otoneurology functional exploration there
		  are experimental techniques to analyze objectively the
		  state of nerve conduction in the auditory pathway. It
		  concerns brainstem auditory evoked response. In this paper,
		  we present a new classification approach based on a hybrid
		  neural network technique focusing this biomedical
		  application for developing a diagnostic tool. We have used
		  two models of artificial neural networks: learning vector
		  quantization and radial basis function ones. In our
		  approach, these two neural networks are used to achieve the
		  classification in a serial multineural network
		  configuration. Case study and experimental results have
		  bean reported and discussed.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  duller97a,
  author	= {A. W. G. Duller},
  title		= {Self-organizing neural networks: their application to
		  {real-world} problems},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {1},
  address	= {Singapore},
  pages		= {666--669},
  dbinsdate	= {oldtimer}
}

@Article{	  duller98a,
  author	= {Duller, A. W. G.},
  title		= {Self-organising neural networks: their application to
		  "real-world" problems},
  journal	= {Australian Journal of Intelligent Information Processing
		  Systems},
  year		= {1998},
  volume	= {5},
  pages		= {175--80},
  abstract	= {This paper discusses the use of a variety of self
		  organising neural networks to solve industrial and other
		  "real-world" problems. In particular an application within
		  the oil industry dealing with the classification of
		  fluorescence spectrograms from crude oil samples, an image
		  processing application in which microscopic pollen is
		  classified for use in climate change studies and oil
		  exploration analysis and finally an application related to
		  aiding the early diagnosis of skin cancer. All of these
		  tasks are currently performed manually but results are the
		  consuming to obtain and are often inaccurate. Three self
		  organising neural networks are considered, the
		  Self-organising Feature Map, an ART network and a
		  self-training, deformable template matching neural network
		  called P/sub A/RADISE. The operational characteristics of
		  the networks are discussed with particular reference to the
		  "real-world" applications.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dumitrache94a,
  author	= {Ion Dumitrache and Catalin Buiu},
  title		= {Evolutionary synthesis of unsupervised learning
		  algorithms},
  booktitle	= {Proc. IMACS Int. Symp. on Signal Processing, Robotics and
		  Neural Networks},
  year		= {1994},
  pages		= {530--533},
  publisher	= {IMACS},
  address	= {Lille, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dupaguntla89a,
  author	= {Narasimha Rao Dupaguntla and V. Vemuri},
  title		= {Neural network architecture for texture segmentation and
		  labelling},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {I},
  pages		= {127--133},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  duqueanton97a,
  author	= {Duqueanton,M. and Ruber,B. and Killat,U. },
  title		= { Extending {K}ohonen Self Organizing Mapping for Adaptive
		  Resource Management in Cellular Radio Networks},
  journal	= {IEEE Trans. on Veh. Technol. },
  year		= {1997},
  pages		= {560--8},
  volume	= {46},
  dbinsdate	= {oldtimer}
}

@InCollection{	  durand97a,
  author	= {S. Durand and F. Alexandre},
  title		= {Learning speech as acoustic sequences with the
		  unsupervised model, TOM},
  booktitle	= {Neural Networks and Their Applications. Conference
		  Proceedings},
  publisher	= {World Scientific},
  year		= {1997},
  editor	= {A. Goscinski and M. Hobbs and W. Zhou},
  address	= {Singapore},
  pages		= {267--73},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  duranton89a,
  author	= {M. Duranton and N. Mauduit},
  title		= {A general purpose digital architecture for neural network
		  simulations},
  booktitle	= {First IEE International Conference on Artificial Neural
		  Networks},
  year		= {1989},
  pages		= {62--66},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  durbin87a,
  author	= {R. Durbin and D. Willshaw},
  title		= {An analogue approach to the travelling salesman problem
		  using an elastic net method},
  journal	= {Nature},
  year		= {1987},
  volume	= {326},
  pages		= {689--691},
  dbinsdate	= {oldtimer}
}

@Article{	  durbin90a,
  author	= {R. Durbin and G. Mitchison},
  title		= {A Dimension Reduction Framework for Understanding Cortical
		  Maps},
  journal	= {Nature},
  year		= {1990},
  volume	= {343},
  pages		= {644--647},
  dbinsdate	= {oldtimer}
}

@Article{	  dushuang95a,
  author	= {Huang Dushuang},
  title		= {An analysis of the statistical properties on the
		  self-supervised learning subspaces for pattern
		  recognition},
  journal	= {Acta Electronica Sinica},
  year		= {1995},
  volume	= {23},
  number	= {9},
  pages		= {99--102},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  dutt99a,
  author	= {Dutt, T. and Kuan Cheung Sin and Marshall, C. L.},
  title		= {Determination of a superior set of k-factors for a
		  multi-factor stock returns model through feature selection
		  and clustering using neural networks},
  booktitle	= {Proceedings of the International ICSC Congress on
		  Computational Intelligence Methods and Applications. ICSC
		  Academic Press, Zurich, Switzerland},
  year		= {1999},
  volume	= {},
  pages		= {},
  abstract	= {In this paper, we demonstrate the effectiveness of neural
		  networks in determining a superior set of K-factors for a
		  multi-factor stock returns model. We propose a two-stage
		  methodology involving classification and subsequent feature
		  selection. From our experiments done on a dataset of about
		  4,000 companies listed on the New York Stock Exchange
		  during 1992--96, we derive the top ten features that
		  possessed the highest explanatory power in discriminating
		  between the returns of the different classes of stocks.
		  While our results and subsequent discussion is on a
		  specific factor selection problem for a multi-factor model,
		  we emphasise that since the methodology is generic, the
		  approach can be used effectively in a variety of cases. We
		  tackle the first part of the problem with Kohonen's
		  self-organising maps (SOM), which are a competitive-based
		  network paradigm for data clustering. A SOM is a
		  feedforward neural network that uses an unsupervised
		  training algorithm, and through a process called
		  self-organisation, configures the output units into a
		  topological representation of the original data. SOM
		  belongs to a general class of neural network methods that
		  can be trained to learn or find relationships between
		  inputs and outputs or to organise data so as to disclose so
		  far unknown patterns or structures. For feature selection,
		  we use a 3-layer feedforward, back-propagation network with
		  a special penalty term. For performance measurement, we
		  look at classification accuracy and quality of selected
		  factors as well as at statistical tests and comparisons
		  with a control portfolio.},
  dbinsdate	= {2002/1}
}

@Article{	  duvillier94a,
  author	= {Duvillier, J. and Killinger, M. and Heggarty, K. and Yao,
		  K. and {de Bougrenet de la Tocnaye}, J. L. },
  title		= {All-optical implementation of a \mbox{self-organizing}
		  map: a preliminary approach},
  journal	= {Applied Optics},
  year		= {1994},
  volume	= {33},
  number	= {2},
  pages		= {258--66},
  month		= {Jan},
  dbinsdate	= {oldtimer}
}

@Article{	  dyck99a,
  author	= {Dyck, D. and Lowther, D. A. and Mai, W. and Henneberger,
		  G.},
  title		= {Three-dimensional mesh improvement using self organizing
		  feature maps},
  journal	= {IEEE Transactions on Magnetics},
  year		= {1999},
  volume	= {35},
  pages		= {1334--7},
  abstract	= {A method of adaptation in the solution of three
		  dimensional electromagnetic field problems is described.
		  The approach is based on using a self organizing feature
		  map to redistribute the nodes rather than generating new
		  ones on each adaptive pass.},
  dbinsdate	= {oldtimer}
}

@Article{	  dyvig92a,
  author	= {Dyvig, J. R. },
  title		= {Object discrimination using neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 1},
  pages		= {191--9},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  dzemyda01a,
  author	= {Dzemyda, G.},
  title		= {Visualization of a set of parameters characterized by
		  their correlation matrix},
  journal	= {Computational Statistics and Data Analysis},
  year		= {2001},
  volume	= {36},
  number	= {1},
  month		= {Mar 28 2001},
  pages		= {15--30},
  organization	= {Inst. of Mathematics and Informatics},
  publisher	= {},
  address	= {},
  abstract	= {An approach to visualization of a set of parameters
		  characterized by their correlation matrix has been
		  proposed. It integrates two methods for data mapping:
		  Sammon's mapping and self-organizing map (SOM). They are
		  based on different principles, and, therefore, supplement
		  each other when they are used jointly. It is shown
		  experimentally that some (sometimes sufficient) knowledge
		  on a set of parameters may be obtained by using individual
		  methods. However, in most cases the necessity and quality
		  of their joint use is unquestionable---this allows us to
		  observe the same data set from various standpoints and to
		  extend our knowledge on the object of investigation. },
  dbinsdate	= {2002/1}
}

@Article{	  dzemyda01b,
  author	= {Dzemyda, G. and Tiesis, V.},
  title		= {Visualisation of multidimensional objects and the
		  socio-economical impact to activity in {EC} {RTD}
		  databases},
  journal	= {Informatica},
  year		= {2001},
  volume	= {12},
  pages		= {239--62},
  abstract	= {The paper deals with the analysis of Research and
		  Technology Development (RTD) in the Central European
		  countries and the relation of RTD with economic and social
		  parameters of countries in this region. A methodology has
		  been developed for quantitative and qualitative ranking and
		  estimates of relationship among multidimensional objects on
		  the base of such analysis. The knowledge has been
		  discovered in four databases: two databases of European
		  Commission (EC) containing data on the RTD activities,
		  databases of USA CIA and The World Bank containing economic
		  and social data. Data mining has been performed by means of
		  visual cluster analysis (using the non-linear Sammon's
		  mapping and Kohonen's artificial neural network---the
		  self-organising map), regression analysis and non-linear
		  ranking (using graphs of domination). The results on
		  clustering of the Central European countries and on the
		  relations among RTD parameters with economic and social
		  parameters are obtained. In addition, the data served for
		  testing various features of realisation of the
		  self-organising map. The integration of non-classical
		  methods (the self-organising map and graphs of domination)
		  with classical ones (regress analysis and Sammon' mapping)
		  increases the capacity of visual analysis and allows making
		  more complete conclusions.},
  dbinsdate	= {2002/1}
}

@Book{		  dzung97a,
  author	= {Dzung, T. P.},
  title		= {Applications of Unsupervised Clustering Algorithms to
		  Aircraft Identification Using High Range Resolution
		  Radar.},
  year		= {1997},
  abstract	= {Identification of aircraft from high range resolution
		  (HRR) radar range profiles requires a database of
		  information capturing the variability of the individual
		  range profiles as a function of viewing aspect. This
		  database can be a collection of individual signatures or a
		  collection of average signatures distributed over the
		  region of viewing aspect of interest. An efficient database
		  is one which captures the intrinsic variability of the HRR
		  signatures without either excessive redundancy typical of
		  single-signature databases, or without the loss of
		  information common when averaging arbitrary groups of
		  signatures. The identification of 'natural' clustering of
		  similar HRR signatures provides a means for creating
		  efficient databases of either individual signatures, or of
		  signature templates. Using a k-means and the Kohonen self
		  organizing feature net, we identify the natural clustering
		  of the HRR radar range profiles into groups of similar
		  signatures based on the match quality metric used within a
		  Vector Quantizer classification algorithm. This greatly
		  reduces the redundancy in such databases while retaining
		  classification performance. Such clusters can be useful in
		  template-based algorithms where groups of signatures are
		  averaged to produce a template. Instead of basing the group
		  of signatures to be averaged on arbitrary regions of
		  viewing aspect, the averages are taken over the signatures
		  containing intake natural clusters which have been
		  identified.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  edmonson93a,
  author	= {Edmonson, P. J. and Smith, P. M. and Campbell, C. K. },
  title		= {SAW injection locked oscillators: dynamic behaviour and
		  application to neural networks},
  booktitle	= {IEEE 1993 Ultrasonics Symposium Proceedings},
  year		= {1993},
  editor	= {Levy, M. and McAvoy, B. R. },
  volume	= {1},
  pages		= {131--5},
  organization	= {Dept. of Electr. \& Comput. Eng. , McMaster Univ. ,
		  Hamilton, Ont. , Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {The characteristics of an injection-locked oscillator are
		  examined in two parts. Part A covers the dynamic behaviour
		  of a surface acoustic wave injection-locked oscillator and
		  its similarity near lock-in to that of an acousto-optic
		  modulator. It is shown that a reflective wave component
		  travels between the injected source and the oscillator
		  resulting in periodic locking and unlocking of the system.
		  These reflected waves are responsible for the classical
		  modulation sidebands near lock-in. In part B a SAW based
		  neural network that can associate, categorize and store
		  input stimuli is presented. The network can be modelled as
		  a modified Kohonen self-organizing feature map.},
  dbinsdate	= {oldtimer}
}

@Article{	  eglen92a,
  author	= {S. J. Eglen and G. Hill and F. J. Lazare and N. P.
		  Walker},
  title		= {Using neural networks},
  journal	= {GEC Review},
  year		= {1992},
  volume	= {7},
  number	= {3},
  pages		= {146--155},
  x		= {. . . The authors describe experiments which illustrate
		  some of the potential of neural networks. They discuss . .
		  . Kohonen networks,},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  eikens98a,
  author	= {Eikens, B. and Karim, M. N.},
  title		= {Identification of a fermentation with {SOM}},
  booktitle	= {Computer Applications in Biotechnology 1998. (CAB7)
		  Horizon of BioProcess Systems Engineering in 21st Century.
		  Proceedings volume from the 7th IFAC International
		  Conference},
  year		= {1998},
  publisher	= {Elsevier Sci},
  address	= {Kidlington, UK},
  volume	= {},
  pages		= {239--46},
  abstract	= {The paper demonstrates the applicability of unsupervised
		  neural networks in the form of self-organizing maps (SOM)
		  to process visualization and modeling. The structures of
		  {SOM}s and learning algorithms are summarized. Unsupervised
		  methods are applied to identify the different metabolic
		  pathways which exist during a yeast fermentation. The
		  neural network model was able to predict the different
		  metabolic pathways.},
  dbinsdate	= {oldtimer}
}

@Article{	  eklund01a,
  author	= {Eklund, T. and Back, B. and Vanharanta, H. and Visa, A.},
  title		= {Benchmarking global pulp and paper companies using
		  self-organizing maps},
  journal	= {Paperi ja Puu/Paper and Timber},
  year		= {2001},
  volume	= {83},
  number	= {4},
  month		= {},
  pages		= {304--316},
  organization	= {Abo Akademi University},
  publisher	= {},
  address	= {},
  abstract	= {Performing financial benchmarks in today's
		  information-rich society can be a daunting task. With the
		  evolution of the Internet, access to massive amounts of
		  financial data, typically in the form of financial
		  statements, is widespread. Managers and stakeholders are in
		  need of a tool allowing them to quickly and accurately
		  analyze this data. An emerging technique that may be suited
		  for this application is the self-organizing map. The
		  purpose of this study was to evaluate the performance of
		  self-organizing maps for the purpose of financial
		  benchmarking of international pulp and paper companies. For
		  the study, financial data, in the form of seven financial
		  ratios, was collected, using the Internet as the primary
		  source of information. A total of 76 companies, and six
		  regional averages, were included in the study. The time
		  frame of the study was the period 1995--99. A number of
		  benchmarks were performed, and the results analyzed based
		  on information contained in the annual reports. The results
		  of the study indicate that self-organizing maps can be
		  feasible tools for the financial benchmarking of large
		  amounts of financial data.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  el-beltagy99a,
  author	= {El-Beltagy, M. and Keane, A.},
  title		= {Using self organizing maps and genetic algorithms for
		  model selection in multilevel optimization},
  booktitle	= {MULTIPLE APPROACHES TO INTELLIGENT SYSTEMS, PROCEEDINGS},
  year		= {1999},
  pages		= {137--144},
  abstract	= {In Multilevel Optimization there is usually a choice to be
		  made between different models when carrying out design
		  evaluations. The choice is between accurate /
		  computationally expensive evaluations and
		  approximate/computational cheap ones. Here, a strategy is
		  sought for selecting between different models during the
		  search. The focus of the paper is on preliminary work
		  carried out using a self organizing map (SOM) for model
		  selection.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  el-sharkawi93a,
  author	= {El-Sharkawi, M. A. and Atteri, R. },
  title		= {Static security assessment of power system using {K}ohonen
		  neural network},
  booktitle	= {ANNPS '93. Proceedings of the Second International Forum
		  on Applications of Neural Networks to Power Systems},
  year		= {1993},
  editor	= {Tamura, Y. and Suzuki, H. and Mori, H. },
  pages		= {373--7},
  organization	= {Dept. of Electr. Eng. , Washington Univ. , Seattle, WA,
		  USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  el-sharkawi96a,
  author	= {M. A. El-Sharkawi and S. J. Huang},
  title		= {Development of genetic algorithm embedded {K}ohonen neural
		  network for dynamic security assessment},
  booktitle	= {ISAP `96. International Conference on Intelligent Systems
		  Applications to Power Systems Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {O. A. Mohammed and K. Tomsovic},
  address	= {New York, NY, USA},
  pages		= {44--9},
  dbinsdate	= {oldtimer}
}

@Article{	  el-sharkawi96b,
  author	= {M. A. El-Sharkawi},
  title		= {Neural networks' power},
  journal	= {IEEE Potentials},
  year		= {1996},
  volume	= {15},
  number	= {5},
  pages		= {12--15},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  el-tobely00a,
  author	= {Tarek {El Tobely} and Yuichiro Yoshiki and Ryuichi Tsuda
		  and Naoyuki Tsuruta and Makoto Amamiya},
  title		= {Randomized Self-Organizing Maps and Itse Application},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  crossref	= {},
  key		= {},
  pages		= {207--14},
  year		= {2000},
  editor	= {},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  el-tobely00b,
  author	= {{El Tobely}, T. and Yoshiki, Y. and Tsuda, R. and Tsuruta,
		  N. and Amamiy, M},
  title		= {Dynamic hand gesture recognition based on randomized Self-
		  Organizing Map algorithm},
  booktitle	= {ALGORITHMIC LEARNING THEORY, PROCEEDINGS},
  year		= {2000},
  pages		= {252--263},
  abstract	= {Gesture recognition is an appealing tool for natural
		  interface with computers especially for physically impaired
		  persons. In this paper, it is proposed to use
		  Self-Organized Map (SOM) to recognize the posture images of
		  hand gestures. Since the competition algorithm of SOM
		  allows alleviating many difficulties associated with
		  gesture recognition. However, it is required to reduce the
		  recognition time of one image in SOM network to the range
		  of normal video camera rates, this permits the network to
		  accept dynamic input. images and to perform on- line
		  recognition for hand gestures. To achieve this, the
		  Randomized Self-Organizing Map algorithm (RSOM) is proposed
		  as a new recognition algorithm for SOM. With RSOM
		  algorithm, the recognition time of one image reduced to
		  12.4 % of the normal SOM competition algorithm with 100 %
		  accuracy and allowed the network to recognize images within
		  the range of normal video rates. The experimental results
		  to recognize six dynamic hand gestures using RSOM algorithm
		  is presented.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  el-tobely01c,
  author	= {{El Tobely}, T. and Yoshiki, Y. and Tsuda, R.},
  title		= {{SOM} competition for complex image scene with variant
		  object position},
  booktitle	= {9th European Symposium on Artificial Neural Networks.
		  ESANN'2001. Proceedings. D-Facto, Evere, Belgium},
  year		= {2001},
  volume	= {},
  pages		= {353--8},
  abstract	= {A new self-organizing map (SOM) competition algorithm is
		  proposed for image recognition applications. The
		  competition in this algorithm depends on a subset of the
		  most discriminate weights of the network codebooks. This
		  can indeed reduce the required recognition time for one
		  image. In addition, the competition is applied only to
		  those pixels corresponding to the object's gray levels;
		  this allows the recognition of complex images with
		  different lighting conditions. Furthermore, to allow shift
		  variations in the position of the input object,
		  window-based competition is proposed. Where different
		  subset windows are selected from the input image, then the
		  competition is applied between each window of the same size
		  in the center of the codebook of all feature-map neurons.
		  The experimental results with the new algorithm showed good
		  performance in recognizing gestures in complex images with
		  variant object positions, while the normal SOM competition
		  algorithm completely failed.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  el_beltagy99b,
  author	= {{El Beltagy}, M. and Keane, A.},
  title		= {Using self organizing maps and genetic algorithms for
		  model selection in multilevel optimization},
  booktitle	= {Multiple Approaches to Intelligent Systems. 12th
		  International Conference on Industrial and Engineering
		  Applications of Artificial Intelligence and Expert Systems.
		  IEA/AIE-99. Proceedings},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {137--44},
  abstract	= {In multilevel optimization there is usually a choice to be
		  made between different models when carrying out design
		  evaluations. The choice is between accurate/computationally
		  expensive evaluations and approximate/computationally cheap
		  ones. A strategy is sought for selecting between different
		  models during the search. The focus of the paper is on
		  preliminary work carried out using a self organizing map
		  (SOM) for model selection.},
  dbinsdate	= {oldtimer}
}

@Article{	  el_gamal99a,
  author	= {{El Gamal}, M. A. and {El Yazeedt}, M. F. A.},
  title		= {A combined clustering and neural network approach for
		  analog multiple hard fault classification},
  journal	= {Journal of Electronic Testing: Theory and Applications},
  year		= {1999},
  volume	= {14},
  pages		= {207--17},
  abstract	= {A new neural network-based fault classification strategy
		  for hard multiple faults in analog circuits is proposed.
		  The magnitude of the harmonics of the Fourier components of
		  the circuit response at different test nodes due to a
		  sinusoidal input signal are first measured or simulated. A
		  selection criterion for determining the best components
		  that describe the circuit behaviour under fault-free
		  (nominal) and fault situations is presented, An algorithm
		  that estimates the overlap between different faults in the
		  measurement space is also introduced. The learning vector
		  quantization neural network is then effectively trained to
		  classify circuit faults. Performance measures reveal very
		  high classification accuracy in both training and testing
		  stages. Two different examples, which demonstrate the
		  proposed strategy, are described.},
  dbinsdate	= {oldtimer}
}

@Article{	  el_gamal99c,
  author	= {{El Gamal}, M. A. and {Abu El Yazeed}, M. F.},
  title		= {A new classification technique for neural network-based
		  analog hard fault diagnosis},
  journal	= {Journal of Engineering and Applied Science},
  year		= {1999},
  volume	= {46},
  pages		= {537--53},
  abstract	= {A new neural network-based fault classification strategy
		  for hard multiple faults in analog circuits is proposed.
		  The magnitude of the harmonics of the Fourier components of
		  the circuit response at different test nodes due to a
		  sinusoidal input signal are first measured or simulated. A
		  selection criterion for determining the best components
		  that describe the circuit behaviour under fault-free
		  (nominal) and fault situations is presented. An algorithm
		  that estimates the overlap between different faults in the
		  measurement space is also introduced. The learning vector
		  quantization neural network is then effectively trained to
		  classify circuit faults. Performance measures reveal very
		  high classification accuracy in both training and testing
		  stages. A detailed example, which demonstrates the proposed
		  strategy, is described.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  el_ghaziri91a,
  author	= {H. {El Ghaziri}},
  title		= {Solving routing problems by a \mbox{self-organizing} map},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {829--834},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  eldracher94a,
  author	= {Martin Eldracher and Hans Geiger},
  title		= {Adaptive Topologically Distributed Encoding},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {771--774},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  elfadil01a,
  author	= {Elfadil, N. and Hani, M. K. and Nor, S. M. and Hussein,
		  S.},
  title		= {Kohonen self organizing maps and expert system for blood
		  classification},
  booktitle	= {Proceedings of IEEE Region 10 International Conference on
		  Electrical and Electronic Technology. TENCON 2001. IEEE,
		  Piscataway, NJ, USA},
  year		= {2001},
  volume	= {1},
  pages		= {174--80},
  abstract	= {Information gathering in medicine generally follows a set
		  of sequence: an interview with the patient, an examination,
		  and one or more laboratory tests to support the working
		  diagnosis. Building a knowledge base from observing a
		  medical examination, however, is risky. Medical
		  decision-making relies on imprecise information gathered in
		  a variety of ways and interpreted in a largely intuitive
		  fashion. This paper proposes a novel method that integrates
		  neural network and expert system paradigms to produce an
		  automated knowledge acquisition system. This system will
		  produce symbolic knowledge from medical data
		  automatically.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  elfadil01b,
  author	= {Elfadil, N. and Hani, M. K. and Nor, S. M. and Hussein,
		  S.},
  title		= {An approach for automating knowledge acquisition process
		  by using Kohonen self-organizing neural networks and expert
		  system},
  booktitle	= {First International Conference on Mechatronics.
		  Mechatronics---An Integrated Engineering for the New
		  Millennium. Conference Proceedings. Int. Islamic Univ.
		  Malaysia, Kuala Lumpar, Malaysia},
  year		= {2001},
  volume	= {2},
  pages		= {550--61},
  abstract	= {Self-organizing maps are the unsupervised neural network
		  model that lends itself to the cluster analysis of high
		  dimensional input data. However, interpreting a trained map
		  proves to be difficult because the features responsible for
		  specific cluster assignment are not evident from resulting
		  map representation. The paper presents an approach for
		  automated knowledge acquisition system using Kohonen
		  self-organizing maps and k-means clustering. For the sake
		  of illustrating the system overall architecture and
		  validating it, a data set containing facts about certain
		  animals is used as training data set.},
  dbinsdate	= {2002/1}
}

@InCollection{	  ellmer96a,
  author	= {E. Ellmer and D. Merkl and G. Quirchmayr and A. M. Tjoa},
  title		= {Process model reuse to promote organizational learning in
		  software development},
  booktitle	= {Proceedings of The Twentieth Annual International Computer
		  Software and Applications Conference (COMPSAC '96)},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  address	= {Los Alamitos, CA, USA},
  pages		= {21--6},
  abstract	= {Software development often suffers from well-known
		  problems as for example wrong schedules and cost
		  estimations, low productivity, and low product quality. In
		  order to overcome these problems we suggest in this paper
		  to adapt the concepts of 'organizational memory' and
		  'organizational learning' and we argue in favor of
		  establishing a reuse culture of software process models. We
		  introduce an approach based on the process
		  definition/instantiation/enaction paradigm and on the reuse
		  of explicit software process descriptions (process models).
		  The key features of our approach are the division of
		  process descriptions into a goal-oriented process
		  definition document and a formal implementation-oriented
		  process model on the one hand, and the use of an artificial
		  neural network, more precisely a self-organizing map, for
		  classification and retrieval purposes on the other hand. In
		  this paper, we present an exposition of our approach and
		  discuss the promising results of an experiment in
		  structuring a software process library and retrieving reuse
		  candidates for upcoming projects.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ellmer96b,
  author	= {Ernst Ellmer and Dieter Merkl},
  title		= {Defining a Set of Criteria for the Assessment of Tool
		  Support for {CMM}-based Software Process Improvement},
  booktitle	= {Proc. SAST'96, 4th International IEEE Symposium on
		  Assessment of Software Tools},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= 1996,
  dbinsdate	= {oldtimer}
}

@InProceedings{	  elmaraghy93a,
  author	= {ElMaraghy, H. and Syed, A. and Chu, H. },
  title		= {Applications of mapping concepts to multi-robot collision
		  avoidance and task plan execution},
  booktitle	= {IEEE Pacific Rim Conference on Communications, Computers
		  and Signal Processing},
  year		= {1993},
  volume	= {2},
  pages		= {466--9},
  organization	= {McMaster Univ. , Hamilton, Ont. , Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {Assembly task planning is concerned with generating the
		  sequence of operations and the required detailed execution
		  instructions. Realtime monitoring and diagnosing of
		  uncertain events, based on the latest feedback from sensors
		  (vision, tactile, force, etc.), during robot assembly tasks
		  execution plays a vital role in ensuring a reliable and
		  robust assembly. This paper presents techniques developed
		  for realtime monitoring of assembly operations, detection
		  of errors due to parts mishandling such as incorrect parts
		  manipulation, insertion and placement in the workspace or
		  due to interference between robots sharing the workspace.
		  Two-dimensional maps are created by adaptively projecting
		  the typically three or more dimensional assembly problems
		  to a simpler two-dimensional problem space which provides
		  the flexible and efficient associative properties needed
		  for effective realtime monitoring and diagnosis. A
		  self-organizing neural network (Kohonen map) is used for
		  organizing the various inputs regarding parts, tools and
		  sensors feedback during robotic assembly and diagnosing the
		  sources of errors. Replanning of the assembly to recover
		  from errors would then proceed based on this feedback.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  elo92a,
  author	= {Pekka Elo and Jukka Saarinen and Alpo V{\"{a}}rri and
		  Hannu Nieminen and Kimmo Kaski},
  title		= {Classification of Epileptic {EEG} by Using Self-Organizing
		  Maps},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1147--1150},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  elo92b,
  author	= {Pekka Elo},
  title		= {Itseorganisoituva neuraaliverkko {EEG}-signaalin
		  luokittelussa},
  institution	= {Tampere University of Technology, Electronics Laboratory},
  number	= {1--92},
  address	= {Tampere, Finland},
  year		= {1992 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  elsherif93a,
  author	= {Elsherif, H. and Hambaba, M. },
  title		= {A modular neural network architecture for pattern
		  classification},
  booktitle	= {Neural Networks for Processing III Proceedings of the 1993
		  IEEE-SP Workshop},
  year		= {1993},
  editor	= {Kamm, C. A. and Kuhn, G. M. and Yoon, B. and Chellappa, R.
		  and Kung, S. Y. },
  pages		= {232--8},
  organization	= {Electr. Eng. \& Comput. Sci. Dept. , Stevens Inst. of
		  Technol. , Hoboken, NJ, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  elsherif94a,
  author	= {Elsherif, H. and Hambaba, M. },
  title		= {On modifying the weights in a modular recurrent
		  connectionist system},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {1},
  pages		= {535--9},
  organization	= {Intelligent Syst. Lab. , Stevens Inst. of Technol. ,
		  Hoboken, NJ, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  elsherif94b,
  author	= {Elsherif, H. and Hambaba, M. },
  title		= {On modifying the weights in a modular recurrent
		  connectionist system},
  booktitle	= {World Congress on Neural Networks-San Diego. 1994
		  International Neural Network Society Annual Meeting},
  year		= {1994},
  volume	= {3},
  pages		= {III/243--7},
  organization	= {Dept. of Electr. Eng. \& Comput. Sci. , Stevens Inst. of
		  Technol. , Hoboken, NJ, USA},
  publisher	= {Lawrence Erlbaum Associates},
  address	= {Hillsdale, NJ, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  eltn98a,
  author	= {Eltn, S. D.},
  title		= {An adaptive learning vector quantisation algorithm for
		  cluster analysis and radar pulse train deinterleaving},
  booktitle	= {Proceedings of the Ninth Australian Conference on Neural
		  Networks (ACNN'98). Univ. Queensland, Brisbane,
		  Qld.,Australia},
  year		= {1998},
  volume	= {},
  pages		= {31--5},
  abstract	= {We examine the use of an unsupervised learning vector
		  quantisation (LVQ) algorithm for cluster analysis and radar
		  pulse train deinterleaving. A modification to the
		  conventional LVQ technique is proposed and results in a
		  neural network with improved performance and which adapts
		  itself to the signal environment. The new method provides
		  an enhanced degree of robustness to cluster initialisation
		  and to the order in which the data are presented to the
		  clustering network. It also has the potential to offer
		  computational savings and is general enough to be
		  applicable to other areas of signal recognition and signal
		  classification.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  emamian00a,
  author	= {Emamian, Vahid and Kaveh, Mostafa and Tewfik, Ahmed H.},
  title		= {Robust clustering of acoustic emission signals using the
		  Kohonen network},
  booktitle	= {ICASSP, IEEE International Conference on Acoustics, Speech
		  and Signal Processing---Proceedings},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {3891--3894},
  organization	= {Univ of Minnesota},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Acoustic emission-based techniques are promising for
		  non-destructive inspection of mechanical systems. For
		  reliable automatic fault monitoring, it is important to
		  identify the transient crack-related signals in the
		  presence of strong time-varying noise and other
		  interference. In this paper we propose the application of
		  the Kohonen network for this purpose. The principal
		  components of the short-time Fourier transforms of the data
		  were applied to the input of the network. The clustering
		  results confirm the capability of the Kohonen network for
		  reliable source identification of acoustic emission
		  signals, assuming enough care has been taken in
		  implementing the training algorithm of the network.},
  dbinsdate	= {2002/1}
}

@Article{	  emamian00b,
  author	= {Emamian, Vahid and Kaveh, Mostafa and Tewfik, Ahmed H.},
  title		= {Acoustic emission classification for failure prediction
		  due to mechanical fatigue},
  journal	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  volume	= {3986},
  number	= {},
  month		= {},
  pages		= {78--84},
  organization	= {Univ of Minnesota},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {Acoustic Emission signals (AE), generated by the formation
		  and growth of micro-cracks in metal components, have the
		  potential for use in mechanical fault detection in
		  monitoring complex-shaped components in machinery including
		  helicopters and aircraft [2]. A major challenge for an
		  AE-based fault detection algorithm is to distinguish
		  crack-related AE signals from other interfering transient
		  signals, such as fretting-related AE signals and
		  electromagnetic transients. Although under a controlled
		  laboratory environment we have fewer interference sources,
		  there are other undesired sources which have to be
		  considered. In this paper, we present some methods, which
		  make their decision based on the features extracted from
		  time-delay and joint time-frequency components by means of
		  a Self-Organizing Map (SOM) neural network using
		  experimental data collected in a laboratory by colleagues
		  at the Georgia Institute of Technology.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  emamian01a,
  author	= {Emamian, V. and Shi, Z. and Kaveh, M. and Tewfik, A. H.},
  title		= {Acoustic emission classification using signal subspace
		  projections},
  booktitle	= {ICASSP, IEEE International Conference on Acoustics, Speech
		  and Signal Processing---Proceedings},
  year		= {2001},
  editor	= {},
  volume	= {5},
  pages		= {3321--3324},
  organization	= {Dept. of Elec. and Computer Eng., University of
		  Minnesota},
  publisher	= {},
  address	= {},
  abstract	= {In using acoustic emissions (AE) for mechanical
		  diagnostics, one major problem is the differentiation of
		  events due to crack growth in a component from noise of
		  various origins. This work presents two algorithms for
		  automatic clustering and separation of AE events based on
		  multiple features extracted from experimental data. The
		  first algorithm consists of two steps. In the first step,
		  the noise is separated from the events of interest and
		  subsequently removed using a combination of covariance
		  analysis, principal component analysis (PCA), and
		  differential time delay estimates. The second step
		  processes the remaining data using a self-organizing map
		  (SOM), which outputs the noise and AE signals into separate
		  neurons. The algorithm is verified with two sets of data,
		  and a correct classification ratio of over 95% is achieved.
		  The second algorithm characterizes the AE signal subspace
		  based on the principal eigenvectors of the covariance
		  matrix of an ensemble of the AE signals. The latter
		  algorithm has a correct classification ratio over 90%.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  embrechts93a,
  author	= {Embrechts, M. and Yapo, T. C. and Lahey, R. T. , Jr. },
  title		= {The application of neural networks to flow regime
		  identification},
  booktitle	= {Proceedings of the American Power Conference},
  year		= {1993},
  volume	= {1},
  pages		= {860--4},
  organization	= {Dept. of Nucl. Eng. , Rensselaer Polytech. Inst. , Troy,
		  NY, USA},
  publisher	= {Illinois Inst. Technol},
  address	= {Chicago, IL, USA},
  abstract	= {This paper deals with the application of a Kohonen map for
		  the identification of two-phase flow regimes where a
		  mixture of gas and fluid flows through a horizontal tube.
		  Depending on the relative flow velocities of the gas and
		  the liquid phase, four distinct flow regimes can be
		  identified: Wavy flow, pug flow, slug flow and annular
		  flow. Figure 1 is a schematic of these flow regimes. The
		  objective identification of two-phase flow regimes
		  constitutes an important and challenging problem for the
		  design of safe and reliable nuclear power plants. Previous
		  attempts to classify these flow regimes are reviewed by
		  Franca \& Lahey. We will describe how a Kohonen map can be
		  applied to distinguish between flow regimes based on the
		  Fourier power spectra and wavelet transforms of pressure
		  drop fluctuations. The Fourier power spectra allowed the
		  Kohonen map to identify the flow regimes successfully. In
		  contrast, the Kohonen maps based between wavy and annular
		  flows.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  encarnacao92a,
  author	= {L. Miguel Encarnacao and Markus H. Gross},
  title		= {An Adaptive Classification Scheme to Approximate Decision
		  Boundaries Using Local {B}ayes Criteria---The {Melting
		  Octree} Network},
  address	= {Berkeley, CA},
  year		= {1992},
  institution	= {International Computer Science Institute},
  number	= {ICSI TR-92--047 / ZGDV-Report 60/92},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  endo00a,
  author	= {Masahiro Endo and Masahiro Ueno and Takaya Tanabe and
		  Manabu Yamamoto},
  title		= {Experimental Analysis of Image {CL}ustering Method using
		  Self-Organizing Maps},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {272--7},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@Article{	  endo00b,
  author	= {Endo, Masahiro and Ueno, Masahiro and Tanabe, Takaya and
		  Yamamoto, Manabu},
  title		= {Clustering method using self-organizing map},
  journal	= {Neural Networks for Signal Processing---Proceedings of the
		  IEEE Workshop},
  year		= {2000},
  volume	= {1},
  number	= {},
  month		= {},
  pages		= {261--270},
  organization	= {NTT Cyber Space Lab},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {As a step towards developing a high-performance image
		  retrieval system, we propose a clustering method that
		  efficiently classifies image objects having an unknown
		  probability distribution, without requiring the
		  determination of complicated parameters, through the use of
		  self-organizing map (SOM) and a method of image processing.
		  To ensure that this clustering method is fast and highly
		  reliable, we defined a hierarchical SOM and used it to
		  construct the clustering method. Experiments using
		  artificial image data confirmed the basic performance and
		  adaptability of the SOM for clustering images. We also
		  confirmed experimentally and theoretically that our
		  clustering method using the hierarchical SOM is faster than
		  one using a non-hierarchical SOM for the objects used in
		  these experiments.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  engel95a,
  author	= {Engel, K. and Peier, D. },
  title		= {Influence of {PD} fault development on fault type
		  recognition using an artificial neural network},
  booktitle	= {Ninth International Symposium on High Voltage
		  Engineering},
  year		= {1995},
  volume	= {5},
  pages		= {5861/1--4},
  organization	= {Inst. of High Voltage Eng. , Dortmund Univ. , Germany},
  publisher	= {Inst. High Voltage Eng},
  address	= {Graz, Austria},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  english90a,
  author	= {T. M. English and L. C. Boggess},
  title		= {Compact input coding for speech recognition by neural
		  net},
  booktitle	= {Proc. Cooperation, ACM Eighteenth Annual Computer Science
		  Conf. },
  year		= {1990},
  pages		= {444},
  publisher	= {ACM},
  address	= {New York, NY},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  english92a,
  author	= {Thomas M. English and Lois C. Boggess},
  title		= {Back-Propagation Training of a Neural Network for Word
		  Spotting},
  booktitle	= {Proc. ICASSP-92, International Conference on Acoustics,
		  Speech, and Signal Processing},
  year		= {1992},
  volume	= {III},
  pages		= {357--360},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  eppler00a,
  author	= {Eppler, W. and Fischer, T. and Gemmeke, H. and
		  Chilingarian, A. and Vardanyan, A.},
  title		= {Neural chip {SAND} in online data processing of extensive
		  air showers},
  journal	= {Computer Physics Communications},
  year		= {2000},
  volume	= {126},
  number	= {1},
  month		= {},
  pages		= {63--66},
  organization	= {Forschungszentrum Karlsruhe},
  publisher	= {Elsevier Science Publishers B.V.},
  address	= {Amsterdam},
  abstract	= {The neural chip SAND (Simple Applicable Neural Device) was
		  designed to accelerate computations of neural networks at a
		  very low cost basis, due to the fact that only few
		  peripheral chips are necessary to use the neural network
		  chip in applications. Four SAND-chips were implemented on
		  one PCI-board. The board is highly usable for hardware
		  triggers in particle physics. The performance of a
		  SAND-PCI-board is 800 Mega Connections per Second due to
		  four neuro-chips, each with four parallel 16 bit
		  multipliers and 40 bit adders. SAND is able to implement
		  feedforward neural networks with a maximum of 512 input
		  neurons and three hidden layers. Kohonen feature maps and
		  radial basis function networks may be also calculated. The
		  application of the SAND-PCI-board is proposed for cosmic
		  ray physics to allow online analysis of extensive air
		  showers.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  eppler01a,
  author	= {Eppler, W. and Bottger, T.},
  title		= {Optimization of piecewise linear networks ({PLN}) by
		  pruning},
  booktitle	= {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS
		  International Conference. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {1},
  pages		= {185--90},
  abstract	= {PLNs are neural networks with linear and metric neurons
		  that separate a non-linear input space into several linear
		  regions. The separation is done by LVQ-like metric neurons
		  (Kohonen, 1989). Linear output neurons provide a linear
		  mapping from input to output space. Unlike other neural
		  networks there is a three-dimensional weight matrix between
		  the input, hidden and output layer rather than a
		  two-dimensional weight matrix between the hidden and output
		  layer. For training of PLNs different training strategies
		  like gradient descent or linear regression exist that are
		  combined with a constructive method producing hidden units.
		  The fastest method is an incremental regression with only 2
		  to 5 cycles of the complete training set. Incremental
		  training means that the network weights are refreshed after
		  each presentation of a pattern. One drawback of this method
		  is the nonsmooth approximation of the objective function,
		  especially for those spots corresponding to the first few
		  patterns of a linear region. One bad effect is an
		  insufficient generalization in this region. Pruning solves
		  this problem. Results are presented. Applications are seen
		  mainly in approximation tasks and especially, in control
		  tasks and system identification.},
  dbinsdate	= {2002/1}
}

@Article{	  erberich00a,
  author	= {Erberich, Stephan G. and Dietrich, Thomas and Kemeny,
		  Stefan and Krings, Timo and Willmes, Klaus and Thron, Armin
		  and Oberschelp, Walter},
  title		= {Analysis of short single rest/activation epoch f{MRI} by
		  self-organizing map neural network},
  journal	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  volume	= {3978},
  number	= {},
  month		= {},
  pages		= {258--264},
  organization	= {Univ of Technology RWTH},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {A technique based on a self-organizing neural network is
		  proposed for analyzing short single rest/activation epoch
		  functional magnetic resonance images. The method reduces
		  scan and analysis time, and the probability of possible
		  motion artifacts from the relaxation of the patients head.
		  The results were compared using a Kolmogorov-Smirnov
		  statistical test. To remove non-periodical time courses
		  from training, an auto-correlation function and bandwidth
		  limiting Fourier filtering in combination with Gauss
		  temporal smoothing was used.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  erberich00b,
  author	= {Erberich, S. G. and Dietrich, T. and Kemeny, S. and
		  Krings, T. and Willmes K. and Thron A. and Oberschelp W.},
  title		= {Analysis of short single rest/activation epoch f{MRI} by
		  self-organizing map neural network},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.3978},
  year		= {2000},
  volume	= {3978},
  pages		= {258--64},
  abstract	= {Functional magnet resonance imaging (fMRI) has become a
		  standard non invasive brain imaging technique delivering
		  high spatial resolution. Brain activation is determined by
		  magnetic susceptibility of the blood oxygen level (BOLD
		  effect) during an activation task, e.g. motor, auditory and
		  visual tasks. Usually box-car paradigms have 2--4
		  rest/activation epochs with at least an overall of 50
		  volumes per scan in the time domain. Statistical test based
		  analysis methods need a large amount of repetitively
		  acquired brain volumes to gain statistical power, like
		  Student's t-test. The introduced technique based on a
		  self-organizing neural network (SOM) makes use of the
		  intrinsic features of the condition change between rest and
		  activation epoch and demonstrated to differentiate between
		  the conditions with less time points having only one rest
		  and one activation epoch. The method reduces scan and
		  analysis time and the probability of possible motion
		  artifacts from the relaxation of the patient's head.
		  Functional magnet resonance imaging (fMRI) of patients for
		  pre-surgical evaluation and volunteers were acquired with
		  motor (hand clenching and finger tapping), sensory (ice
		  application), auditory (phonological and semantic word
		  recognition task) and visual paradigms (mental rotation).},
  dbinsdate	= {2002/1}
}

@InProceedings{	  erberich01a,
  author	= {Erberich, S. G. and Liebert, M. and Willmes, K. and Thron,
		  A. and Oberschelp, W.},
  title		= {Analysis of event-related f{MRI} using a non-linear
		  regression self-organizing map neural network},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Chen, C. and Clough, A. V.},
  volume	= {4321},
  pages		= {327--335},
  organization	= {Neurofunctional Imaging Laboratory, Univ. of Technology
		  RWTH, Univ. Hospital},
  publisher	= {},
  address	= {},
  abstract	= {Functional magnetic resonance imaging (fMRI) becomes a
		  common method to study task induced brain activation. Using
		  rapid Echo Planar Imaging (EPI) sequences one can obtain a
		  higher MR-Signal under a task condition close by activated
		  areas as a result of susceptibility changes in blood
		  oxygenation (BOLD effect). Beside the commonly used blocked
		  task designs, event-related paradigms gain more importance
		  for activation of higher cognitive functions enabling more
		  sophisticated and complex paradigms. For the analysis of
		  event-related fMRI data one can use statistical tests, in
		  example t-test used by SPM Software. The introduced
		  analysis method based on an artificial neural network
		  algorithm, a self-organizing map (SOM), is capable to
		  distinguish between task related activation, deactivation
		  and baseline patterns from the time series. This is
		  achieved by temporal sorting and projection of all events
		  from one condition into one combined hemodynamic response
		  sampling for each voxel. These responses, having individual
		  patterns can be separated by their pattern features and is
		  done by training of the neural network. After training the
		  SOM consists of a pattern-to-voxel mapping which is
		  superimposed onto either an anatomical or EPI image of the
		  subject for the task evaluation.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  erberich99a,
  author	= {Erberich, Stephan G. and Fellenberg, Matthias and Krings,
		  Timo and Kemeny, Stefan and Reith, Wolfgang and Willmes,
		  Klaus and Oberschelp, Walter},
  title		= {Unsupervised time course analysis of functional magnetic
		  resonance imaging ({fMRI}) using \mbox{self-organizing}
		  maps ({SOM})},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3660},
  pages		= {19--26},
  abstract	= {A method separating brain voxels based on their features
		  in the time domain using a self-organizing map (SOM) neural
		  network technique without modeling the hemodynamic response
		  function (HRF) is introduced for the functional
		  differentiation of brain voxels. Voxels are selected which
		  are candidates of being activated using the autocorrelation
		  function. Voxel time courses are labeled by the neuron
		  having the smallest Euclidean distance to the presented
		  voxel time course using the trained {SOM}. The result of
		  the labeling and the learned feature time course vectors
		  are compared visually with the p-value map of the
		  Kolmogorov-Smirnov statistics.},
  dbinsdate	= {oldtimer}
}

@Article{	  erdi84a,
  author	= {P. {\'{E}}rdi and Gy. Barna},
  title		= {Self-organizing mechanism for the formation of ordered
		  neural mappings},
  journal	= {Biol. Cyb. },
  year		= {1984},
  volume	= {51},
  number	= {2},
  pages		= {93--101},
  dbinsdate	= {oldtimer}
}

@InCollection{	  eriksson98a,
  author	= {Lars Eriksson and Fredrik Sebelius and Christian
		  Balkenius},
  title		= {Neural Control of a Virtual Prosthesis},
  booktitle	= {Proceedings of ICANN98, the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer},
  year		= 1998,
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= 2,
  address	= {London},
  pages		= {905--910},
  dbinsdate	= {oldtimer}
}

@InCollection{	  erkmen97a,
  author	= {I. Erkmen and A. Ozdogan},
  title		= {Short term load forecasting using genetically optimized
		  neural network cascaded with a modified {K}ohonen
		  clustering process},
  booktitle	= {Proceedings of the 1997 IEEE International Symposium on
		  Intelligent Control},
  publisher	= {IEEE},
  year		= {1997},
  editor	= {K. Ciliz and Y. Istefanopulos},
  address	= {New York, NY, USA},
  pages		= {107--12},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  erwin91a,
  author	= {E. Erwin and K. Obermeyer and K. Schulten},
  title		= {Convergence Properties of Self-Organizing Maps},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {Kohonen, Teuvo and M{\"{a}}kisara, Kai and Simula, Olli
		  and Kangas, Jari},
  pages		= {409--414},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  annote	= {Convergence time and the number and characteristics of
		  metastable states is studied. },
  dbinsdate	= {oldtimer}
}

@InCollection{	  erwin92a,
  author	= {Edgar Erwin and Klaus Obermayer and Klaus Schulten},
  title		= {Formation of Dimension-Reducing {SOM} totopic Maps},
  booktitle	= {Proc. Fourth Conf. on Neural Networks},
  editor	= {Samir I. Sayegh},
  publisher	= {Indiana University at Fort Wayne},
  address	= {Fort Wayne, IN},
  year		= 1992,
  pages		= {115--126},
  dbinsdate	= {oldtimer}
}

@Article{	  erwin92b,
  author	= {Ed Erwin and Klaus Obermayer and Klaus Schulten},
  title		= {Self-organizing maps: Ordering, convergence properties and
		  energy functions},
  journal	= {Biol. Cyb. },
  year		= {1992},
  volume	= {67},
  number	= {1},
  pages		= {47--55},
  dbinsdate	= {oldtimer}
}

@Article{	  erwin92c,
  author	= {Ed Erwin and Klaus Obermayer and Klaus Schulten},
  title		= {Self-organizing maps: Stationary states, metastability and
		  convergence rate},
  journal	= {Biol. Cyb. },
  year		= {1992},
  volume	= {67},
  number	= {1},
  pages		= {35--45},
  dbinsdate	= {oldtimer}
}

@InCollection{	  erwin93a,
  author	= {Edgar Erwin and Klaus Obermayer and Klaus Schulten},
  title		= {A Comparison of Models of Visual Cortical Map Formation},
  booktitle	= {Computation and Neural Systems},
  editor	= {Frank Eeckman and James Bower},
  publisher	= {Kluwer Academic Publishers},
  chapter	= {60},
  pages		= {395--402},
  year		= 1993,
  dbinsdate	= {oldtimer}
}

@InProceedings{	  esogbue93a,
  author	= {Augustine O. Esogbue and James A. Murrell},
  title		= {A Fuzzy Adaptive Controller Using Reinforcement Learning
		  Neural Networks},
  booktitle	= {Proc. International Conference on Fuzzy Systems},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1993},
  pages		= {178--183},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  esp00a,
  author	= {Esp, D. G. and McGrail, A. J.},
  title		= {Advances in data mining for dissolved gas analysis},
  booktitle	= {Conference Record of IEEE International Symposium on
		  Electrical Insulation},
  year		= {2000},
  editor	= {},
  volume	= {},
  pages		= {456--459},
  organization	= {Natl Grid Co plc},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper reports NGC's continued application and
		  refinement of a data mining technique based on the Kohonen
		  neural network. The technique has been applied to NGC's
		  database of transformer dissolved gas-in-oil analysis (DGA)
		  measurements for high voltage transformers. The technique
		  has proven able to highlight bad data and 'blind test'
		  data, and has been optimized to reveal the early stages of
		  potential plant problems. A number of key types of
		  transformer condition have been distinguished by it,
		  including for example three kinds of partial discharge. The
		  Kohonen technique has been successfully applied to
		  transmission, distribution and generator transformers. In
		  addition a practical tool for DGA interpretation is being
		  developed. We are now looking to expand the use of the
		  technique to other monitored parameters.},
  dbinsdate	= {2002/1}
}

@Article{	  espinosa02a,
  author	= {Espinosa, G. and Arenas, A. and Giralt, F.},
  title		= {An integrated {SOM}-fuzzy {ARTMAP} neural system for the
		  evaluation of toxicity},
  journal	= {JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES},
  year		= {2002},
  volume	= {42},
  number	= {2},
  month		= {MAR-APR},
  pages		= {343--359},
  abstract	= {Self-organized maps (SOM) have been applied to analyze the
		  similarities of chemical compounds and to select from a
		  given pool of descriptors the smallest and more relevant
		  subset needed to build robust QSAR models based on fuzzy
		  ARTMAP. First, the category maps for each molecular
		  descriptor and for the target activity variable were
		  created with SOM and then classified on the basis of
		  topology and nonlinear distribution. The best subset of
		  descriptors was obtained by choosing from each cluster the
		  index with the highest correlation with the target variable
		  and then in order of decreasing correlation. This process
		  was terminated when a dissimilarity measure increased,
		  indicating that the inclusion of more molecular indices
		  would not add supplementary information. The optimal subset
		  of descriptors was used as input to a fuzzy ARTMAP
		  architecture modified to effect predictive capabilities.
		  The performance of the integrated SOM-fuzzy ARTMAP approach
		  was evaluated with the prediction of the acute toxicity
		  LC50 of a homogeneous set of 69 benzene derivatives in the
		  fathead minnow and the oral rat toxicity LD50 of a
		  heterogeneous set of 155 organic compounds. The proposed
		  methodology minimized the problem of misclassification of
		  similar compounds and significantly enhanced the predictive
		  capabilities of a properly trained fuzzy ARTMAP network.},
  dbinsdate	= {2002/1}
}

@Article{	  essenreiter01a,
  author	= {Essenreiter, R. and Karrenbach, M. and Treitel, S.},
  title		= {Identification and classification of multiple reflections
		  with self-organizing maps},
  journal	= {Geophysical Prospecting},
  year		= {2001},
  volume	= {49},
  number	= {3},
  month		= {April 2001},
  pages		= {341--352},
  organization	= {Geophysical Institute, University of Karlsruhe},
  publisher	= {},
  address	= {},
  abstract	= {Artificial neural networks can be used effectively to
		  identify and classify multiple events in a seismic data
		  set. We use a specialized neural network, a self-organizing
		  map (SOM), that tries to establish rules for the
		  characterization of the physical problem. Selected seismic
		  data attributes from CMP gathers are used as input
		  patterns, such that the SOM arranges the data to form
		  clusters in an abstract space. We show with synthetic and
		  real data how the SOM can identify and classify primaries
		  and multiples, and how it can classify the various types of
		  multiple corresponding to a certain generating mechanism in
		  the subsurface.},
  dbinsdate	= {2002/1}
}

@Article{	  esteves01a,
  author	= {Esteves, S. R. R. and Wilcox, S. J. and Hawkes, D. L. and
		  O'Neill C. and Hawkes F. R.},
  title		= {The development of a neural network based monitoring and
		  control system for biological wastewater treatment
		  systems},
  journal	= {International-Journal-of-COMADEM},
  year		= {2001},
  volume	= {4},
  pages		= {22--8},
  abstract	= {A series of experiments under varying loading conditions,
		  using a 30 l Up-flow Anaerobic Sludge Blanket (UASB)
		  reactor and a 20 l aerobic vessel, was performed using a
		  simulated textile effluent (STE). The acquired data was
		  used in the training and testing of three control
		  strategies. The systematic experiments varied both the
		  organic load and dye concentration in a series of step
		  changes, with the objective of the control schemes being to
		  ensure optimal operation of the reactor during changes in
		  load. The results proved that a hybrid structure containing
		  a Kohonen or Self Organising Map (SOM) followed by a series
		  of backpropagation networks was the most efficient at
		  dealing with different load conditions whilst being least
		  influenced by sensor loss.},
  dbinsdate	= {2002/1}
}

@InCollection{	  estevez98a,
  author	= {Pablo A. Est{\'e}vez and Rodrigo E. Caballero},
  title		= {A Niching Genetic Algorithm for Selecting Features for
		  Neural Network Classifiers},
  booktitle	= {Proceedings of ICANN98, the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer},
  year		= 1998,
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= 1,
  address	= {London},
  pages		= {311--316},
  dbinsdate	= {oldtimer}
}

@InCollection{	  euliano96a,
  author	= {N. R. Euliano and J. C. Principe},
  title		= {Spatio-temporal \mbox{self-organizing} feature maps},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {4},
  address	= {New York, NY, USA},
  pages		= {1900--5},
  abstract	= {Thus far, the success of capturing and classifying
		  temporal information with neural networks has been limited.
		  Our methodology adds a spatio-temporal coupling to the
		  Self-Organizing Feature Map (SOFM) which creates temporally
		  and spatially localized neighborhoods in the map. The
		  spatio-temporal coupling is based on traveling waves of
		  activity which start at each winning node and are naturally
		  attenuated over time. When these traveling waves reinforce
		  each other, temporal activity wavefronts are created which
		  are then used to enhance a node's possibility of winning
		  the next competition. The spatio-temporal coupling is
		  easily implemented with only local connectivity and
		  calculations. Once trained, the spatio-temporal SOFM can be
		  used for detection or for partial pattern recall. The
		  methodology gracefully handles time-warping and multiple
		  patterns with overlapping input vectors.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  euliano99a,
  author	= {N. R. Euliano and J. C. Principe},
  title		= {A Spatio-Temporal Memory Based on {SOM}s with Activity
		  Diffusion},
  booktitle	= {Kohonen Maps},
  pages		= {253--266},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: reaction-diffusion, spatio-temporal memory, time
		  dependent vector quantization},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  euyseok00a,
  author	= {Euyseok Hong and Myunghee Jung},
  title		= {Criticality prediction with unsupervised neural networks},
  booktitle	= {Proceedings of the International Conference on Artificial
		  Intelligence. IC-AI'2000. CSREA Press, Athens, GA, USA},
  year		= {2000},
  volume	= {2},
  pages		= {899--903},
  abstract	= {Criticality prediction models are used for identifying
		  trouble spots of software system in analysis or design
		  phases. Many criticality prediction models for identifying
		  fault-prone modules using complexity metrics have been
		  suggested. But most of them need training data set.
		  Unfortunately very few organizations have their own
		  training data. To solve this problem, we build a new
		  prediction model, KSM, based on Kohonen SOM neural
		  networks. We compare KSM and a well-known prediction model,
		  backpropagation neural network model (BPM), considering
		  internal characteristics, utilization cost and accuracy of
		  prediction. As a result, we show that KSM has similar
		  performance with BPM.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  evans94a,
  author	= {Evans, W. and Musgrove, P. B. and Davies, J. and Phillips,
		  J. D. },
  title		= {Use of a neural network to differentiate {IR}on deficiency
		  anaemia from beta thalassaemia minor},
  booktitle	= {Proceedings of the International Conference on Neural
		  Networks and Expert Systems in Medicine and Healthcare},
  year		= {1994},
  editor	= {Ifeachor, E. C. and Rosen, K. G. },
  pages		= {59--66},
  organization	= {Wolverhampton Univ. , UK},
  publisher	= {Univ. Plymouth},
  address	= {Plymouth, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fakhr94a,
  author	= {Waleed Fakhr and M. Kamel and M. I. Elmasry},
  title		= {{MMI} Training of Minimum Complexity Adaptive Nearest
		  Neighbor Classifiers},
  pages		= {401--406},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {vector quantization, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fakhr94b,
  author	= {Waleed Fakhr and M. Kamel and M. I. Elmasry},
  title		= {The Adaptive Feature Extraction Nearest Neighbor
		  Classifier},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {III},
  pages		= {123--128},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {comparison},
  dbinsdate	= {oldtimer}
}

@Article{	  faldella97a,
  author	= {E. Faldella and B. Fringuelli and D. Passeri and L. Rosi},
  title		= {A neural approach to robotic haptic recognition of 3-D
		  objects based on a {K}ohonen \mbox{self-organizing} feature
		  map},
  journal	= {IEEE Transactions on Industrial Electronics},
  year		= {1997},
  volume	= {44},
  number	= {2},
  pages		= {267--9},
  dbinsdate	= {oldtimer}
}

@Article{	  fan02a,
  author	= {Fan, Yun and Wang, Run-sheng},
  title		= {Self-organizing technique for categorizing image
		  database},
  journal	= {Mini-Micro-Systems},
  year		= {2002},
  volume	= {23},
  pages		= {482--5},
  abstract	= {A cluster technique based on a Kohonen map neural network
		  is used to categorize images in a database. An image
		  database browsing technique is realized based on image
		  category, and an index structure similar to the SS-tree is
		  also proposed. Query by example is carried out on the image
		  category, which is more efficient than an exhaustive search
		  method, and the comparison time don't increase as database
		  size increases. The experiments demonstrate the efficiency
		  of our cluster algorithm and the proposed query by example
		  method.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name assumed, similarly to other papers in 
                   Mini-Micro-Systems. sheng is in small-> it is part of 
                   first name.}
}

@Article{	  fang02a,
  author	= {Fang, Gao Lin and Gao, Wen and Wang, Chun Li and Chen, Yi
		  Qiang},
  title		= {A signer-independent sign language recognition system
		  based on {SOFM}/{HMM}},
  journal	= {Chinese-Journal-of-Computers},
  year		= {2002},
  volume	= {25},
  pages		= {16--21},
  abstract	= {Sign language recognition has emerged as one of the most
		  important research areas in the field of human-computer
		  interaction. The aim of sign language recognition is to
		  provide an efficient and accurate mechanism to transcribe
		  sign language into text or speech so that communication
		  between deaf and hearing society becomes more convenient.
		  State-of-the-art sign language recognition should be able
		  to solve the signer-independent problem for practical
		  applications. This paper analyzes the features of
		  signer-independent sign language: (1) the convergence
		  difficulty caused by mass data and noticeable distinctions
		  between different people data. (2) the urgent need to
		  extract common features from different people data. Aimed
		  at these features, the SOFM/HMM model presented in this
		  paper combines the powerful feature extraction performances
		  of self-organizing feature maps (SOFM) with excellent
		  temporal processing properties of hidden Markov models
		  (HMM) within a novel scheme. Each SOFM eigenvector centroid
		  is regarded as one of the components in the state of HMM
		  which construct the state probability density function in
		  terms of the weighted sum. The model parameters can be
		  re-estimated through the Expectation-Maximization (EM)
		  algorithm. When the proposed model is applied to
		  signer-independent Chinese Sign Language (CSL) recognition
		  with a vocabulary of 208 signs, 95.3% recognition rate is
		  obtained in the registered test (Reg.) and 88.2% in the
		  unregistered test (Reg.). Meanwhile, results from the
		  conventional HMM system are provided as comparison.
		  Experimental results show the SOFM/HMM system increases the
		  recognition accuracy by 5% than conventional HMM one.},
  dbinsdate	= {2002/1},
  merjanote     = {last name guessed, no data on internet}
}

@Article{	  fang92a,
  author	= {Wai-Chi Fang and Bing J. Sheu and Oscal T. -{C}. Chen and
		  Joongho Choi},
  title		= {A {VLSI} neural processor for image data compression using
		  self-organization networks},
  journal	= {{IEEE} Trans. Neural Networks},
  year		= {1992},
  volume	= {3},
  pages		= {506--518},
  annote	= {An implementation of a 'neural' VQ algorithm. },
  dbinsdate	= {oldtimer}
}

@InCollection{	  fang96a,
  author	= {Xiang Fang and P. Thole and J. Goppert and W. Rosenstiel},
  title		= {A hardware supported system for a special online
		  application of \mbox{self-organizing} map},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {956--61},
  abstract	= {In this paper we present a hardware supported system which
		  enables the parallel computing of euclidean distance with
		  Kohonen's self-organizing map (SOM) for a special online
		  application. The hardware is implemented as neural
		  coprocessors connected to a Sun workstation via a parallel
		  interface. For the aim of the computing time requirement, a
		  four step pipeline and parallelism are used. The
		  algorithmic description of the hardware and a simulation
		  environment have been written in VHDL. Field programmable
		  gate arrays (FPGAs) are used to implement these neural
		  coprocessors. The system can be used in various
		  applications of neural networks.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  farkas93a,
  author	= {Farkas, J. },
  title		= {Neural networks and document classification},
  booktitle	= {1993 Canadian Conference on Electrical and Computer
		  Engineering},
  year		= {1993},
  editor	= {Bhargava, V. K. },
  volume	= {1},
  pages		= {1--4},
  organization	= {Centre for Inf. Technol. Innovation, Commun. Canada,
		  Laval, Que. , Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  farkas94a,
  author	= {Farkas, J. },
  title		= {Generating document clusters using thesauri and neural
		  networks},
  booktitle	= {1994 Canadian Conference on Electrical and Computer
		  Engineering. Conference Proceedings},
  year		= {1994},
  editor	= {Baird, C. R. and El-Hawary, M. E. },
  volume	= {2},
  pages		= {710--13},
  organization	= {Centre for Inf. Technol. Innovation, Laval, Que. ,
		  Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  farkas97a,
  author	= {I. Farkas},
  title		= {Invariance of Gaussian-vector mapping using a
		  \mbox{self-organizing} map},
  journal	= {Neural Network World},
  year		= {1997},
  volume	= {7},
  number	= {2},
  pages		= {153--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  farkas97b,
  author	= {Igor Farkas and Lucius Chudy},
  title		= {Application of a growing \mbox{self-organizing} map to
		  thinning of binary characters with noise},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {215--219},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  farkas99a,
  author	= {I. Farkas and R. Miikkulainen},
  title		= {Modeling the self-organization of directional selectivity
		  in the primary visual cortex},
  booktitle	= {Proceedings of the International Conference on Artificial
		  Neural Networks (ICANN-99)},
  year		= {1999},
  publisher	= {Springer},
  address	= {New York},
  pages		= {251--256},
  abstract	= {A model is proposed to demonstrate how neurons in the
		  primary visual cortex could self-organize to represent the
		  direction of motion. The model is based on a temporal
		  extension of the self-organizing map where neurons act as
		  leaky integrators. The map is trained with moving Gaussian
		  inputs, and it develops a retinotopic map with orientation
		  columns that divide into areas of opposite direction
		  selectivity, as found in the visual cortex.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fatehi99a,
  author	= {Fatehi, A. and Abe, K.},
  title		= {Convergence of {SOM} multiple models identifier},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {1074--7},
  abstract	= {An algorithm to derive a multiple models set for a plant
		  by the use of the self-organising map (SOM) were introduced
		  by the authors (1999). The statistical properties of the
		  models are investigated in this paper. As a plant, we
		  consider a linear time invariant one. The parameters of the
		  plant at each step are selected randomly with a specified
		  distribution. Based on this distribution, the point
		  distribution of the parameters of the multiple models is
		  derived for this plant and compared with the plant
		  parameters distribution.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fatehi99b,
  author	= {Fatehi, A. and Abe, K.},
  title		= {Multilinear modeling of nonlinear continuously variable
		  transmission ({CVT}) gear-box plant by {SOM} neural
		  networks},
  booktitle	= {Proceedings of International Workshop on Soft Computing in
		  Industry '99 (IWSCI'99). Muroran Inst. of Technol, Muroran,
		  Japan},
  year		= {1999},
  volume	= {},
  pages		= {31--5},
  abstract	= {In this paper the method of multiple (multilinear)
		  identification based on self-organizing map neural networks
		  (SOMNN) is developed for a nonlinear plants. Inputs to the
		  NN are parameters of the instantaneous model evaluated in a
		  narrow window. {SOM}NN learns these neurons. Also the
		  reference vector of the winner neuron is supposed to be the
		  best model parameters. The irregular lattice type {SOM} is
		  used in this study. This structure lets to increase the
		  number of neurons during the learning. This method is
		  applied to the nonlinear continuously variable transmission
		  (CVT) gearbox plant. The identification starts with a
		  single model but the number of models increase
		  automatically for more accuracy.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  favalli96a,
  author	= {L. Favalli and A. Mecocci and R. Pizzi},
  title		= {Non-linear adaptive filtering for channel equalization},
  booktitle	= {Symposium on Control, Optimization and Supervision. CESA
		  '96 IMACS Multiconference. Computational Engineering in
		  Systems Applications},
  publisher	= {World Scientific},
  year		= {1996},
  volume	= {2},
  editor	= {E. Binaghi and P. A. Brivio and A. Rampini},
  address	= {Singapore},
  pages		= {860--5},
  dbinsdate	= {oldtimer}
}

@Article{	  favalli97a,
  author	= {Favalli, Lorenzo and Pizzi, Rita and Mecocci, Alessandro},
  title		= {Non linear mobile-radio channel estimation using neural
		  networks},
  journal	= {International Conference on Digital Signal Processing.
		  DSP},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1997},
  number	= {},
  volume	= {1},
  pages		= {287--290},
  abstract	= {Channel estimation is a crucial problem in any
		  equalization algorithm in time varying environments such as
		  those encountered in mobile radio systems. Referring to the
		  European GSM mobile radio system, in this paper an approach
		  based on automatic tracking of the channel variations based
		  on the use of neural networks (NN) is presented which
		  exhibits good bit error rate (BER) performances is
		  presented. The NN architecture employed is a modification
		  if Kohonen's Self Organizing Map (SOM) with recursive
		  structure (output folded to the input). This dynamic
		  network performs a classification by vector quantization of
		  the input stream using the information contained in the
		  middamble of the GSM burst with an unsupervised control
		  scheme. The neural receiver is demonstrated to converge
		  very quickly to stability and to have a lower computational
		  complexity than the Viterbi algorithm.},
  dbinsdate	= {oldtimer}
}

@Article{	  favata91a,
  author	= {Favio Favata and Richard Walker},
  title		= {A study of the application of {K}ohonen-type neural
		  networks to the {T}ravelling {S}alesman {P}roblem},
  journal	= {Biol. Cyb. },
  year		= {1991},
  volume	= {64},
  number	= {6},
  pages		= {463--468},
  annote	= {Application, Motivation of the topology-preserving
		  mappings},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fechner94a,
  author	= {Thomas Fechner and Ralf Tanger},
  title		= {A Hybrid Neural Network Architecture for Automatic Object
		  Recognition},
  booktitle	= {Proc. NNSP'94, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1994},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {187--194},
  annote	= {application, pattern recognition},
  abstract	= {This paper describes the application of a hybrid neural
		  network architecture for automatic object recognition in
		  inverse synthetic aperture radar ({ISAR}) imagery. The
		  architecture employs a cascaded combination of an
		  unsupervised and a supervised trained Neural Network. The
		  unsupervised trained Self-Organizing Feature Map is used
		  for object segmentation and the supervised trained
		  Multi-Layer Perceptron classifies the segmented objects.
		  The classification result is fed back to the Feature Map
		  Segmentor in order to improve segmentation and
		  classification. The functionality of this approach is
		  demonstrated by the use of simulated noisy ISAR images from
		  different objects.},
  dbinsdate	= {oldtimer}
}

@Article{	  fechner96a,
  author	= {T. Fechner and R. Hantsche and R. Tanger},
  title		= {Classification of objects in {ISAR}-imagery using
		  artifical neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2760},
  pages		= {339--45},
  note		= {(Applications and Science of Artificial Neural Networks II
		  Conf. Date: 9--12 April 1996 Conf. Loc: Orlando, FL, USA
		  Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@Article{	  feiten93a,
  author	= {B. Feiten and S. G{\"u}nzel},
  title		= {Distance Measure for the Organization of Sounds},
  journal	= {Acustica},
  year		= 1993,
  volume	= 78,
  pages		= {181--184},
  dbinsdate	= {oldtimer}
}

@Article{	  feiten94a,
  author	= {Feiten, B. and Gunzel, S. },
  title		= {Automatic indexing of a sound database using
		  \mbox{self-organizing} neural nets},
  journal	= {Computer Music Journal},
  year		= {1994},
  volume	= {18},
  number	= {3},
  pages		= {53--65},
  month		= {Fall},
  dbinsdate	= {oldtimer}
}

@Article{	  feng01a,
  author	= {Feng, Y. and Kubo, M. and Aghbari, Z. and Makinouchi, A.},
  title		= {A new {SOM}-based r*-tree: Building and retrieving},
  journal	= {Research Reports on Information Science and Electrical
		  Engineering of Kyushu University},
  year		= {2001},
  volume	= {6},
  number	= {2},
  month		= {September },
  pages		= {209--214},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {R-trees are widely used in spatial and multi-dimensional
		  databases, However, according to our investigation, the
		  overlap among the leaf nodes of R-trees is serious and the
		  objects are not well-clustered in the leaf nodes, which
		  greatly affect the effect of the pruning strategics when
		  nearest neighbour searching is performed and also affect
		  the other search performance of R-trees. The forced
		  reinsertion introduced in R*-tree can improve this problem
		  to some extent, but can not completely solve this problem.
		  In this study, we try to combine SOM (Self Organizing Map)
		  technology and R*-tree technology to lessen the overlap
		  among the leaf nodes of R*-tree and to improve the
		  clustering degree of the objects in the leaf nodes. The
		  experimental result shows that the SOM-based R*-tree
		  proposed in this paper has a much better search performance
		  than R*-tree.},
  dbinsdate	= {2002/1}
}

@Article{	  feng02a,
  author	= {Feng, Run-Ming and Tia, Xin-Hua and Huan, Ke-Di},
  title		= {Modifying the legacy simulation/simulation facility to be
		  a system compliant with High Level Architecture},
  journal	= {Journal-of-System-Simulation},
  year		= {2002},
  volume	= {14},
  pages		= {293--6},
  abstract	= {To modify the legacy simulation/simulation facility to be
		  a system compliant with HLA is to enable it to become a
		  reusable unit in the development of High Level Architecture
		  (HLA) federation, to participate in a new simulation to
		  realize a new simulation objective with high cost-effect.
		  This paper discusses the general theory and method to
		  modify the legacy simulation/simulation facility to be
		  compliant with HLA and focuses on two essential activities
		  in modification: to develop SOM and to develop the HLA
		  interface. Finally it provides an example of how to modify
		  a Matlab/Simulink simulation to be compliant with HLA.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name guessed from small capitals in names (ming, hua, di)}
}

@InProceedings{	  feng92a,
  author	= {Feng, T. -J. and Boite, R. and Leich, H. },
  title		= {Feature extract by \mbox{self-organizing} maps:
		  application to filter},
  booktitle	= {ISSPA 92. Third International Symposium on Signal
		  Processing and its Applications. Proceedings},
  year		= {1992},
  editor	= {Gray, D. A. },
  volume	= {2},
  pages		= {487--92},
  organization	= {Dept. of Phys. , Ocean Univ. of Qindao, China},
  publisher	= {IREE Australia},
  address	= {Edgecliff, NSW, Australia},
  dbinsdate	= {oldtimer}
}

@Article{	  feng97a,
  author	= {J. F. Feng and B. Tirozzi},
  title		= {Convergence theorems for the {K}ohonen feature mapping
		  algorithms with VLRPs},
  journal	= {Computers \& Mathematics with Applications},
  year		= {1997},
  volume	= {33},
  number	= {3},
  pages		= {45--63},
  dbinsdate	= {oldtimer}
}

@Article{	  feng98a,
  author	= {Ma Feng and Xia Shao-Wei and Tong Xin and Tang Ze-Sheng},
  title		= {A {SOM}-{PNN} classifier for probabilistic segmentation
		  and visualization of volume data},
  journal	= {Chinese Journal of Computers},
  year		= {1998},
  volume	= {21},
  number	= {9},
  pages		= {819--24},
  dbinsdate	= {oldtimer}
}

@InCollection{	  feng98b,
  author	= {Feng, Ma and Wenping, Wang and Wai, Wan Tsang and Zesheng,
		  Tang and Shaowei, Xia and Xin, Tong},
  title		= {Probabilistic segmentation of volume data for
		  visualization using {SOM}-{PNN} classifier},
  booktitle	= {IEEE Symposium on Volume Visualization},
  publisher	= {IEEE},
  year		= {1998},
  address	= {New York, NY, USA},
  pages		= {71--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  feng98c,
  author	= {Feng, Ma and Shaowei, Xia and Zesheng, Tang and Wenping,
		  Wang and Wai Wan Tsang},
  title		= {Probabilistic classification of medical images using a
		  hybrid neural network},
  booktitle	= {Joint Conference on Intelligent Systems 1999 (JCIS'98)},
  publisher	= {Association for Intelligent Machinery, Inc},
  year		= {1998},
  volume	= {4},
  pages		= {222--5},
  abstract	= {We present a new hybrid neural network to perform the
		  probabilistic classification of medical images. The new
		  classifier produces probabilistic classification with
		  Bayesian confidence measure. Based on the {SOM} map trained
		  with a large training data set, the proposed hybrid neural
		  network performs the probabilistic classification using the
		  probabilistic neural net (PNN) algorithm. This hybrid
		  neural network overcomes the shortcomings of the parametric
		  methods, the nonparametric methods, and the {SOM} method in
		  medical image classification. The proposed classifier has
		  been used to segment the CT sloth data resulting in better
		  numerical results and segmented images.},
  dbinsdate	= {oldtimer}
}

@Article{	  feng98d,
  author	= {Ma Feng and Xia Shaowei},
  title		= {A multiscale approach to automatic medical image
		  segmentation using \mbox{self-organizing} map},
  journal	= {Journal of Computer Science and Technology (English
		  Language Edition)},
  year		= {1998},
  volume	= {13},
  number	= {5},
  pages		= {402--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ferens93a,
  author	= {Ferens, K. and Lehn, W. and Kinsner, W. },
  title		= {Image compression using learning vector quantization},
  booktitle	= {IEEE WESCANEX 93. Communications, Computers and Power in
		  the Modern Environment Conference Proceedings},
  year		= {1993},
  pages		= {299--312},
  organization	= {Dept. of Electr. \& Comput. Eng. , Manitoba Univ. ,
		  Winnipeg, Man. , Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  ferguson99a,
  author	= {Ferguson, Keith L. and Allinson, Nigel M.},
  title		= {Rate-constrained \mbox{self-organizing} neural maps and
		  efficient psychovisual methods for low bit rate video
		  coding},
  journal	= {Neural Networks Signal Process Proc IEEE},
  year		= {1999},
  number	= {},
  volume	= {},
  pages		= {390--399},
  abstract	= {The video coding problem is essentially an operational
		  distortion-rate issue where the underlying input pixel
		  data, probability distributions and dimensions are
		  discrete, unknown and not smooth. In the low bit rate case
		  the high resolution assumptions for vector quantization are
		  not strictly valid and the problem is exacerbated. However,
		  by considering the rate-constrained operational points on
		  sets of self-organizing neural maps (SOMs), provides a
		  methodology for selecting locally optimal vector
		  quantizers. The learning process of the standard {SOM}
		  algorithm is modified to minimize the distortion subject to
		  a constraint of entropy approximation. The applied training
		  set is adapted to suit the proposed coding environment.
		  Operating in the discrete wavelet transform (DWT) domain is
		  well suited to the inclusion of a psychovisual model. The
		  spatial frequency response, the multiresolution scene
		  analysis and the central focusing aspects of the visual
		  cortex are incorporated into the model. The resulting video
		  coding algorithm is bit rate scalable from 10k bits per
		  second (bits/s) and provides subjectively acceptable video
		  at a fixed frame rate of 10 frames per second (fps) with a
		  QCIF pixel resolution.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fernandez00a,
  author	= {Fernandez, Elsa and Echave, Imanol and Grana, Manuel},
  title		= {Increased robustness in visual processing with {SOM}-based
		  filtering},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {131--134},
  organization	= {UPV/EHU},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {To increase the robustness of visual processing in the
		  context of mobile robotics, we introduce an image filtering
		  process based on the codebooks computed by the SOM. The
		  Self Organizing Map and the Simple Competitive Learning are
		  used to compute adaptively the vector quantizers of color
		  image sequences. The codebook computed for each image in
		  the sequence is then used as a smoothing filter, the VQ
		  Bayesian Filter (VQ-BF), for the preprocessing of the
		  images in the sequence. This filter is applied to the
		  computation of optical flow at the single pixel level.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  fernandez00c,
  author	= {Fernandez, E. and Echave, I. and Grana, M.},
  title		= {Competitive neural networks for robust computation of
		  optical flow},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {89--94},
  abstract	= {The self organizing map and the simple competitive
		  learning are used to compute adaptively the vector
		  quantizers of color image sequences. The codebook computed
		  for each image in the sequence is then used as a smoothing
		  filter, the VQ Bayesian filter (VQ-BF), for the
		  preprocessing of the images in the sequence. The optical
		  flow is then robustly and efficiently computed over the
		  filtered images applying a correlation method on the
		  isolated pixels.},
  dbinsdate	= {2002/1}
}

@Article{	  fernandez01b,
  author	= {Fernandez, E. A. and Willshaw, P. and Perazzo, C. A. and
		  Presedo, R. J. and Barro, S.},
  title		= {Detection of abnormality in the electrocardiogram without
		  prior knowledge by using the quantisation error of a
		  self-organising map, tested on the European ischaemia
		  database},
  journal	= {MEDICAL \& BIOLOGICAL ENGINEERING \& COMPUTING},
  year		= {2001},
  volume	= {39},
  number	= {3},
  month		= {MAY},
  pages		= {330--337},
  abstract	= {Most systems for the automatic detection of abnormalities
		  in the ECG require prior knowledge of normal and abnormal
		  ECG morphology from pre-existing databases. An automated
		  system for abnormality detection has been developed based
		  on learning normal ECG morphology directly from the patient
		  The quantisation error from a self-organising map 'learns'
		  the form of the patient's ECG and detects any change in its
		  morphology. The system does not require prior knowledge of
		  normal and abnormal morphologies. It was tested on 76
		  records from the European Society of Cardiology database
		  and detected 40.5% of those first abnormalities declared by
		  the database to be ischaemic. The system also responded to
		  abnormalities arising from ECG axis changes and slow
		  baseline drifts and revealed that ischaemic episodes are
		  often followed by long-term changes in ECG morphology.},
  dbinsdate	= {2002/1}
}

@Article{	  fernandez96a,
  author	= {J. -J. Fernandez and J. -M. Carazo},
  title		= {Analysis of structural variability within two-dimensional
		  biological crystals by a combination of patch averaging
		  techniques and self organizing maps},
  journal	= {Ultramicroscopy},
  year		= {1996},
  volume	= {65},
  number	= {1--2},
  pages		= {81--93},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fernandez99a,
  author	= {Fernandez, E. A. and Presedo, J. and Barro, S.},
  title		= {An {ECG} ischemic detection system based on
		  \mbox{self-organizing} maps and a sigmoid function
		  pre-processing stage},
  booktitle	= {Artificial Intelligence in Medicine. Joint European
		  Conference on Artificial Intelligence in Medicine and
		  Medical Decision Making, AIMDM'99. Proceedings (Lecture
		  Notes in Artificial Intelligence Vol. 1620)},
  year		= {1999},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  volume	= {1620},
  pages		= {207--16},
  abstract	= {The detection of ischemia from the electrocardiogram is a
		  time-consuming visualization task requiring the full
		  attention of the physician. This task may become unviable
		  when many patients have to be monitored, for example in an
		  Intensive Coronary Care Unit. In order to automate the
		  detection process and minimize the number of misclassified
		  episodes we propose the use of an Artificial Neural Net
		  (the Self-Organizing Map-SOM) along with the use of extra
		  parameters (not only ST segment and T wave deviation)
		  measured from the ECG record. The {SOM} is a widely used
		  Neural Network which has the ability to handle a large
		  number of attributes per case and to represent these cases
		  in clusters defined by possession of similar
		  characteristics. In this work we propose a three-block
		  ischemic detection system. It consists of a pre-processing
		  block, a {SOM} block and a timing block. For the
		  pre-processing block we use the sigmoid function as a
		  smoothing stage in order to eliminate the intrinsic
		  oscillation of the signals. The {SOM} block is the ischemic
		  detector and the timing block is used to decide if the
		  {SOM} output meets the ischemic duration criteria. With
		  this strategy, 83.33% of the ischemic episodes (over 7
		  records) tested were successfully detected, and the timing
		  block proved to be robust to noisy signals, providing
		  reliability in the detection of an ischemic episode. The
		  system could be useful in an Intensive Coronary Care Unit
		  because it will allow a large number of patients to be
		  monitored simultaneously detecting episodes when they
		  actually occur. It will also permit visualisation of the
		  evolution of the patient's response to therapy.},
  dbinsdate	= {oldtimer}
}

@Article{	  ferran91a,
  author	= {E. A. Ferr{\'{a}}n and P. Ferrara},
  title		= {Topological maps of protein sequences},
  journal	= {Biol. Cyb. },
  year		= {1991},
  volume	= {65},
  number	= {6},
  pages		= {451--458},
  annotate	= {Application},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ferran91b,
  author	= {Edgardo A. Ferr{\'{a}}n and Pascual Ferrara},
  title		= {Unsupervised clustering of proteins},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1341--1344},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  ferran92a,
  author	= {E. A. Ferr{\'{a}}n and P. Ferrara},
  title		= {Clustering proteins into families using artificial neural
		  networks},
  journal	= {Computer Applications in the Biosciences},
  year		= {1992},
  volume	= {8},
  number	= {1},
  pages		= {39--44},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ferran92b,
  author	= {Edgardo A. Ferr{\'{a}}n and Bernard Pflugfelder and
		  Pascual Ferrara},
  title		= {Large scale application of neural networks to protein
		  classification},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1521--1524},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ferran92c,
  author	= {Edgardo A. Ferr{\'{a}}n},
  title		= {An Ordering Theorem that Allows for Ordering Changes},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {165--169},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  ferran92d,
  author	= {E. A. Ferr{\'{a}}n and P. Ferrara},
  title		= {A neural network dynamics that resembles protein
		  evolution},
  journal	= {Physica A},
  year		= {1992},
  volume	= {185},
  number	= {1--4},
  pages		= {395--401},
  annotate	= {Application},
  dbinsdate	= {oldtimer}
}

@Article{	  ferran92e,
  author	= {Edgardo A. Ferr{\'{a}}n and Pascual Ferrara},
  title		= {A Fast Method to Search for Protein Homologies Using
		  Neural Networks},
  journal	= {Int. J. Neural Networks},
  year		= {1992},
  volume	= {3},
  pages		= {221--226},
  dbinsdate	= {oldtimer}
}

@Article{	  ferran93a,
  author	= {E. A. Ferr{\'{a}}n},
  title		= {On {K}ohonen's ordering theorem for
		  \mbox{\mbox{one-dimensional}} self-organized mappings},
  journal	= {Network},
  year		= {1993},
  volume	= {4},
  pages		= {337--354},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ferran93b,
  author	= {Edgardo A. Ferr{\'{a}}n and Pascual Ferrara and Bernard
		  Pflugfelder},
  title		= {Protein Classification Using Neural Networks},
  booktitle	= {Proc. First International Conference on Intelligent
		  Systems for Molecular Biology},
  year		= {1993},
  editor	= {Lawrence Hunter and David Searls and Jude Shavlik},
  pages		= {127--135},
  publisher	= {AAAI Press},
  address	= {Menlo Park, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  ferran93c,
  author	= {Edgardo A. Ferr{\'{a}}n and Bernard Pflugfelder},
  title		= {A hybrid method to cluster protein sequences based on
		  statistics and artificial neural networks},
  journal	= {Computer Applications in the Biosciences},
  year		= {1993},
  volume	= {9},
  number	= {6},
  pages		= {671--680},
  dbinsdate	= {oldtimer}
}

@Article{	  ferran94a,
  author	= {Ferran, E. A.},
  title		= {Self-organized neural maps of human protein sequences},
  journal	= {Protein Science},
  year		= {1994},
  volume	= {3},
  number	= {3},
  month		= {Mar},
  pages		= {507--521},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ferrara92a,
  author	= {Ferrara, P. and Ferscha, A. and Haring, G. },
  title		= {A collision avoiding six legged walking machine based on
		  {K}ohonen feature maps},
  booktitle	= {ECAI 92. 10th European Conference on Artificial
		  Intelligence Proceedings},
  year		= {1992},
  pages		= {216--18},
  organization	= {Inst. fur Stat. und Inf. , Wien, Austria},
  publisher	= {Wiley},
  address	= {Chichester, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fessant01a,
  author	= {Fessant, F. and Aknin, P. and Oukhellou, L. and Midenet,
		  S.},
  title		= {Comparison of supervised self-organizing maps using
		  Euclidian or Mahalanobis distance in classification
		  context},
  booktitle	= {Connectionist Models of Neurons, Learning Processes, and
		  Artificial Intelligence. 6th International Work-Conference
		  on Artificial and Natural Neural Networks, IWANN 2001.
		  Proceedings, Part I (Lecture Notes in Computer Science Vol.
		  2084). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {637--44},
  abstract	= {The supervised self-organizing map consists in associating
		  output vectors to input vectors through a map, after
		  self-organizing it on the basis of both input and desired
		  output given altogether. The paper compares the use of
		  Euclidian distance and Mahalanobis distance for this model.
		  The distance comparison is made on a data classification
		  application with either global approach or partitioning
		  approach. The Mahalanobis distance in conjunction with the
		  partitioning approach leads to interesting classification
		  results.},
  dbinsdate	= {2002/1}
}

@InCollection{	  fialho94a,
  author	= {F. A. P. Fialho and N. D. Santos},
  title		= {A general architecture for simulating complex systems able
		  of auto-organization},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {57--62},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ficola95a,
  author	= {A. Ficola and M. La Cava and F. Magnino},
  title		= {An approach to fault diagnosis in dynamic systems using
		  {K}ohonen neural networks},
  booktitle	= {ISIE `95. Proceedings of the IEEE International Symposium
		  on Industrial Electronics},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {166--71},
  dbinsdate	= {oldtimer}
}

@InCollection{	  fidalgo97a,
  author	= {J. N. Fidalgo and M. A. Matos and M. T. {Ponce de Leao}},
  title		= {Assessing error bars in distribution load curve
		  estimation},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {1017--22},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  field95a,
  author	= {Simon Field and Neil Davey and Ray Frank},
  title		= {A Complexity Analysis of Telecommunication Software Using
		  Neural Networks},
  booktitle	= {Proc. Int. Workshop on Applications of Neural Networks to
		  Telecommunications 2},
  year		= {1995},
  editor	= {Joshua Alspector and Rodney Goodman and Timothy X. Brown},
  pages		= {226--233},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fiesler94a,
  author	= {E. Fiesler},
  title		= {Comparative Bibliography of Ontogenic Neural Networks},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {793--796},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {bibliography study},
  dbinsdate	= {oldtimer}
}

@InCollection{	  figueiredo98a,
  author	= {F. L. Figueiredo and F. Violaro},
  title		= {An isolated word speech recognition system based on
		  {K}ohonen neural network},
  booktitle	= {Proceedings 5th Brazilian Symposium on Neural Networks},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {1998},
  editor	= {A. {de Padua Braga} and T. B. Ludermir},
  pages		= {151--6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  filho98a,
  author	= {T. E. F. Filho and R. O. Messina and Jr. E. F. Cabral},
  title		= {Learning vector quantization in text-independent automatic
		  speaker recognition},
  booktitle	= {Proceedings 5th Brazilian Symposium on Neural Networks},
  publisher	= {IEEE Computer Society},
  year		= {1998},
  editor	= {A. {de Padua Braga} and T. B. Ludermir},
  address	= {Los Alamitos, CA, USA},
  pages		= {135--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  filippetti96a,
  author	= {F. Filippetti and G. Franceschini and A. Ometto and S.
		  Meo},
  title		= {A survey of neural network approach for induction machine
		  on-line diagnosis},
  booktitle	= {31st Universities Power Engineering Conference. Conference
		  Proceedings},
  publisher	= {Technol. Educ. Inst. Iraklio},
  year		= {1996},
  volume	= {1},
  address	= {Iraklio, Greece},
  pages		= {17--20},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  filippi93a,
  author	= {Filippi, E. and Lawson, J. C. },
  title		= {A parallel implementation of {K}ohonen's
		  \mbox{self-organizing} maps on the SMART neurocomputer},
  booktitle	= {New Trends in Neural Computation. International Workshop
		  on Artificial Neural Networks. IWANN '93 Proceedings},
  year		= {1993},
  editor	= {Mira, J. and Cabestany, J. and Prieto, A. },
  pages		= {388--93},
  organization	= {INPG, Grenoble, France},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  finch92a,
  author	= {S. Finch and N. Chater},
  title		= {Unsupervised methods for finding linguistic categories},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1365--1368},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  finch94a,
  author	= {A. Finch and J. Austin},
  title		= {A Neural Network for Dimension Reduction and its
		  Application to Image Segmentation},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1141--1144},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, image segmentation},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fiorentini94a,
  author	= {G. Fiorentini and G. Pasquariello and G. Satalino and F.
		  Spilotros},
  title		= {Hybrid System for Ship Detection in Radar Images},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {276--279},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, image processing, pattern recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  firenze94a,
  author	= {F. Firenze and L. Ricciardiello and S. Pagliano},
  title		= {Self-Organizing Networks: A Challenging Approach to Fault
		  Diagnosis of Industrial Processes},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1239--1242},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, process monitoring},
  dbinsdate	= {oldtimer}
}

@InCollection{	  firenze94b,
  author	= {Firenze, F. and Morasso, P.},
  title		= {Adaptive modulation of receptive fields in
		  \mbox{self-organizing} networks},
  booktitle	= {International Conference on Artificial Neural Networks},
  publisher	= {Springer-Verlag},
  year		= {1994},
  editor	= {M. Marinaro and Morasso, P.},
  volume	= {1},
  address	= {London},
  pages		= {314--317},
  abstract	= {Most self-organizing neural networks involve units which,
		  similarly to biological neurons, have receptive fields
		  (RFs) of finite sizes, defined as the regions in the
		  feature-space where they achieve non-vanishing activation.
		  In some models, the widths of the RFs are slowly restricted
		  during learning in order to achieve global convergence
		  through weights "freezing"; in other cases, they are
		  adjusted, either in supervised mode or heuristically, with
		  the aim to favour development of "locally tuned" units. We
		  propose a new mechanism of adaptive modulation of the RFs,
		  driven by the local density of the input data distribution,
		  which can be coupled with many self-organizing models,
		  making them more robust and flexible. In particular, we
		  focus our attention on two interesting aspects:
		  "self-stabilization" of learning parameters during "online"
		  learning, and function approximation with "adaptive
		  resolution".},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  firenze95a,
  author	= {Firenze, F. and Morasso, P. and Ottonello, G. and and A.
		  Morandi},
  title		= {A {RBF}-based neural network which implements adaptive
		  resolution function approximation},
  booktitle	= {Proceedings of the International Conference on Artificial
		  Neural Networks (ICANN'95---Paris October 9--13)},
  editor	= {F. Fogelman},
  volume	= {2},
  year		= {1995},
  pages		= {93--97},
  abstract	= {A RBF-based Neural Network is presented where both the
		  number of units and the width parameter of Gaussian RBFs
		  are adjusted by means of self-organizing mechanisms which
		  are driven by the local density of the training samples in
		  the function domain. The model is based on the assumption
		  that the focal density of training samples in the function
		  domain is related to the focal bandwidth of the function
		  itself in the Nyquist sense. In this way. function
		  approximation is achieved with a degree of accuracy which
		  is related to the local bandwidth of the function itself.
		  Such a property has been called "adaptive resolution"
		  function approximation. A simulation experiment is shown
		  where the properties of our model are highlighted by
		  comparison with the standard Moody \& Darken model.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  firenze95b,
  author	= {Firenze, F. and Schenone, A. and Acquarone, F. and P.
		  Morasso},
  title		= {An interactive neural network based approach to the
		  segmentation of multimodal medical images},
  booktitle	= {Proceedings of the VII Italian Workshop on Neural Nets
		  (WIRN'95, Vietri sul Mare, Italy, MAY 18--20)},
  year		= {1995},
  pages		= {251--259},
  abstract	= {A system for the segmentation of multi-modal image volumes
		  in the field of medical imaging is presented. It is
		  composed of two subsystems: (1) the multi-modal volume is
		  first processed by means of a clustering approach based on
		  the capture-effect neural network (CENN) and a 2D
		  projection image is produced which can be interpreted as a
		  2D segmentation map of the original multidimensional image
		  data; (2) the system graphic interface is provided with a
		  software tool by means of which the user can further
		  analyze the problem interactively with the system. The
		  choice of the CENN clustering procedure has been motivated
		  by the fact that it is able to produce a 2D segmentation
		  map which can be considered as a "comfortable" starting
		  point for further detailed investigation of the expert
		  user. In particular, the user can refine and even slightly
		  modify the segmentation map, according to its expertize and
		  knowledge of the problem at hand, by using a number of
		  powerful facilities provided by the system graphic
		  interface. A real data experiment is presented which shows
		  the potentialities of the system.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fischer00a,
  author	= {Igor Fischer and Andreas Zell},
  title		= {String averages and self-organizing maps for strings},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Universit\"{a}t T\"{u}bingen, Wilhelm-Schickard-Institut
		  f\"{u}r Informatik},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InCollection{	  fischer97a,
  author	= {T. Fischer and W. Eppler and H. Gemmeke and G. Kock and T.
		  Becher},
  title		= {The SAND neurochip and its embedding in the MiND system},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {1235--40},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fischl94a,
  author	= {R. Fischl},
  title		= {Application of Neural Networks to Power System Security:
		  Technology and Trends},
  pages		= {3719--3723},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {review, diagnostics, monitoring},
  dbinsdate	= {oldtimer}
}

@Article{	  fisher96a,
  author	= {III J. W. Fisher and J. C. Principe},
  title		= {Unsupervised learning for nonlinear synthetic discriminant
		  functions},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2752},
  pages		= {2--13},
  note		= {(Optical Pattern Recognition VII Conf. Date: 9--10 April
		  1996 Conf. Loc: Orlando, FL, USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  flanagan00a,
  author	= {Flanagan, John A.},
  title		= {Neuron weight dynamics in the {SOM} and Self-Organized
		  Criticality},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {39--44},
  organization	= {Helsinki Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The Self-Organizing Map (SOM) was developed as a heuristic
		  model of a global self-organizing process. Since then it
		  has been used in different applications as an unsupervised
		  learning algorithm for large scale data processing. Part of
		  the reason for its widespread use is its robustness and
		  ability to form ordered maps in diverse situations. The
		  dynamics of the SOM are compared to those of a system
		  operating in Self-Organized Criticality (SOC). An SOC
		  system should exhibit a robust convergence to a critical
		  state independent of the initial conditions, and the
		  dynamics of the system should follow power laws, with the
		  formation of self-similar structures. It is shown that
		  instead of considering the SOM as a learning algorithm but
		  rather as a non linear dynamical system, driven by
		  perturbations (i.e. the input to be learned), which
		  converges to an attractor (i.e. the organized
		  configuration), the SOM can be seen as a system operating
		  at SOC. This approach could lead to an understanding of the
		  organized configuration in the SOM.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  flanagan00b,
  author	= {Flanagan, J. A.},
  title		= {Self-organisation in the {SOM} with a finite number of
		  possible inputs},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {261--6},
  abstract	= {Given a one dimensional SOM with a monotonically
		  decreasing neighbourhood and an input distribution which is
		  not Lebesque continuous, a set of sufficient conditions and
		  a theorem are stated which ensure probability one
		  organisation of the neuron weights. This leads to a rule
		  for choosing the number of neurons and width of the
		  neighbourhood to improve the chances of reaching an
		  organised state in a practical implementation of the SOM.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  flanagan00c,
  author	= {Flanagan, John A.},
  title		= {Self-organization in the {SOM} and Lebesque continuity of
		  the input distribution},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {26--31},
  organization	= {Helsinki Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Given a one dimensional SOM with a monotonically
		  decreasing neighborhood and an input distribution which can
		  be Lebesque continuous or not, a set of sufficient
		  conditions and a Theorem are stated which ensure
		  probability one organization of the neuron weights. The
		  implication of the Theorem in the case of an input
		  distribution not Lebesque continuous is a rule for choosing
		  the number of neurons and width of the neighborhood to
		  improve the chances of reaching an organized state in a
		  practical implementation of the SOM. In the case of a
		  Lebesque continuous input self-organization in the standard
		  SOM is proved without modifying the winner definition.
		  Possibilities of extending the analysis to the
		  multi-dimensional case and to a decreasing gain function
		  are discussed.},
  dbinsdate	= {2002/1}
}

@InBook{	  flanagan01a,
  author	= {J. A. Flanagan},
  editor	= {F. Moss and S. Gielen},
  title		= {Topologically Ordered Neural Networks},
  chapter	= {16},
  publisher	= {Elsevier Science},
  year		= {2001},
  key		= {},
  volume	= {4},
  number	= {},
  series	= {Handbook of Biological Physics},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {685--729},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  flanagan01b,
  author	= {Flanagan, J. A.},
  title		= {Self-organization in the one-dimensional {SOM} with a
		  decreasing neighborhood},
  journal	= {NEURAL NETWORKS},
  year		= {2001},
  volume	= {14},
  number	= {10},
  month		= {DEC},
  pages		= {1405--1417},
  abstract	= {A proof of self-organization for a general, standard, one-
		  dimensional SOM and a monotonically decreasing neighborhood
		  function such that W less than or equal toN, is given,
		  where N is the total number of neurons and W the width of
		  the neighborhood function. It is the generalization of
		  already existing proofs for two specific cases with W
		  greater than or equal toN/2. Lebesgue continuity of the
		  distribution of the input is not a requirement. The order p
		  of the SOM, where p = [N/W] + 1, if N mod W not equal 0 or
		  else p = N/W, is fundamental to the proof. A total of 2(p)
		  basic intervals of non-zero probability on the support of
		  the input are sufficient for self-organization. These basic
		  intervals are separated by minimum distances which depend
		  on parameters of the SOM (e.g. gain, neighborhood
		  function). The result gives a relationship which in a
		  practical situation can be used to determine, for a given
		  number of neurons and neighborhood function, the minimum
		  number of discrete data points which can guarantee self-
		  organization. },
  dbinsdate	= {2002/1}
}

@Article{	  flanagan01c,
  author	= {Flanagan, J. A.},
  title		= {Self-organized criticality and the self-organizing map},
  journal	= {Physical Review E. Statistical Physics, Plasmas, Fluids,
		  and Related Interdisciplinary Topics},
  year		= {2001},
  volume	= {63},
  number	= {3 II},
  month		= {March 2001},
  pages		= {361301--361306},
  organization	= {Neural Networks Research Center, Helsinki University of
		  Technology},
  publisher	= {},
  address	= {},
  abstract	= {The 1D self-organizing map (SOM) was presented as an
		  algorithm that exhibits both spatial and temporal
		  correlations and can be characterized by power laws. These
		  correlations were demonstrated by simulation and partly by
		  analysis. The SOM can be characterized as an self-organized
		  critical (SOC) system and more specifically as belonging to
		  the extremal dynamics family of SOC systems. This
		  observation in itself is interesting given that the SOM is
		  a widely used learning algorithm from the area of
		  artificial neural network.},
  dbinsdate	= {2002/1}
}

@Article{	  flanagan01d,
  author	= {Flanagan, J. A.},
  title		= {Self-organized criticality and the self-organizing map},
  journal	= {PHYSICAL REVIEW E},
  year		= {2001},
  volume	= {6303},
  number	= {3},
  month		= {MAR},
  pages		= {art. no.--036130},
  abstract	= {The self-organizing map (SOM), a biologically inspired,
		  learning algorithm from the field of artificial neural
		  networks, is presented as a self-organized critical (SOC)
		  model of the extremal dynamics family. The SOM's ability to
		  converge to an ordered configuration, independent of the
		  initial state, is known and has been demonstrated, in the
		  one-dimensional case. In this ordered configuration it is
		  now indicated by analysis and shown by simulation that the
		  dynamics of the SOM are critical. By viewing the SOM as a
		  SOC system, alternative interpretations of learning, the
		  organized configuration, and the formation of topograhic
		  maps can be made.},
  dbinsdate	= {2002/1}
}

@PhDThesis{	  flanagan94a,
  author	= {John Adrian Flanagan},
  title		= {Self-Organizing Neural Networks},
  school	= {{\'E}cole Polytechnique F{\'e}d{\'e}rale de Lausanne},
  year		= 1994,
  address	= {Lausanne},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  flanagan94b,
  author	= {John A. Flanagan and Martin Hasler},
  title		= {Classification Properties of the {K}ohonen Neural Network:
		  Are the Independent of the Parametric Representation of
		  Iput},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conf. Proc. of Finnish Artificial Intelligence Society},
  pages		= {13--21},
  booktitle	= {Proc. Conf. on Artificial Intelligence Res. in Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {analysis, pattern recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  flanagan95a,
  author	= {John A. Flanagan and Martin Hasler},
  title		= {Self-Organization, Metastable States and the {ODE} Method
		  in the {K}ohonen Neural Network},
  booktitle	= {Proc. ESANN'95, European Symp. on Artificial Neural
		  Networks},
  year		= {1995},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {1--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  flanagan95b,
  author	= {J. A. Flanagan and M. Hasler},
  title		= {Self-organising artifical neural networks},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {322--9},
  dbinsdate	= {oldtimer}
}

@Article{	  flanagan96a,
  author	= {John A. Flanagan},
  title		= {Self-Organization in {K}ohonen's {SOM}},
  journal	= {Neural Networks},
  year		= 1996,
  volume	= 9,
  pages		= {1185--1197},
  annote	= {Analyzes special cases: 1D {SOM} in 1D space, general type
		  of probablity distribution: the ordered configuration is
		  (the only) absorbing one. ND {SOM} in ND space: the system
		  enters an 'organized' state in finite time with probability
		  one, but this state is not an absorbing one. This also
		  shows that there is no 'disorganized' absorbing state. },
  abstract	= {Self-organisation in Kohonen's self-organising map (SOM)
		  is analysed by considering the neuron weights to be a
		  {M}arkov process. While many works exist which analyse the
		  one-dimensional {SOM}, the aim of the study is to
		  demonstrate probability one convergence of the neuron
		  weights to an organised configuration in one- and also in
		  higher-dimensional {SOM}s. A proof of self-organisation is
		  given for the one-dimensional case for a general type of
		  probability distribution satisfying conditions given in
		  terms of the parameters of the network. A modified version
		  of the {SOM} algorithm is described which has an absorbing
		  organised configuration, even in higher dimensions.
		  Probability one convergence to this configuration is
		  demonstrated. The higher-dimensional {SOM} is also analysed
		  and it is shown for certain conditions that the first entry
		  time of the neuron weights into a predefined organised
		  state is finite with probability one.},
  dbinsdate	= {oldtimer}
}

@Article{	  flanagan97a,
  author	= {John A. Flanagan},
  title		= {Analysing a Self-Organizing Algorithm},
  journal	= {Neural Networks},
  year		= 1997,
  volume	= 10,
  pages		= {875--883},
  dbinsdate	= {oldtimer}
}

@InCollection{	  flanagan97b,
  author	= {John A. Flanagan},
  title		= {Self-organisation in the \mbox{\mbox{one-dimensional}}
		  {SOM} with a reduced width neighbourhood},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {268--273},
  dbinsdate	= {oldtimer}
}

@Article{	  flanagan98a,
  author	= {Flanagan, John A},
  title		= {Sufficient conditions for self-organization in the
		  \mbox{one-dimensional} {SOM} with a reduced width
		  neighbourhood},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1--3},
  volume	= {21},
  pages		= {51--60},
  abstract	= {Self-organization in the one-dimensional self-organizing
		  map (SOM) is analyzed in the case of a strictly decreasing
		  neighbourhood function whose width is such that at any
		  iteration more than half the neurons are updated.
		  Sufficient conditions for probability one self-organization
		  are given in terms of the parameters of the network and the
		  support of the input signal. The main result is that three
		  intervals of non-zero probability separated by a minimum
		  distance are required to obtain the result. This result
		  compares with a previous analysis for the case of a
		  neighbourhood function with non-zero update at each
		  iteration for each neuron, where two intervals of non-zero
		  probability were required.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  flanagan98b,
  author	= {J. A. Flanagan},
  title		= {The Self-Organising Map, Robustness, Self-Organising
		  Criticality and Power Laws},
  booktitle	= {Proceedings of the European Symposium on Artificial Neural
		  Networks (ESANN'98)},
  year		= {1998},
  address	= {Bruges, Belgium},
  pages		= {209--214},
  abstract	= {Observations of complex dynamical systems operating at
		  self-organising criticality (SOC) have shown them to be
		  inherently robust to fluctuations in their environment.
		  This SOC has the signature spectrum S(f) varies as 1/f/sup
		  beta /, beta approximately=1 for some variable f in the
		  system. The observation of power laws in the spectrum of
		  the updates of the neuron weights in the {SOM} are
		  reported. Such a signature is shown in the {SOM} for
		  certain types of neighbourhood functions, which are
		  intuitively robust. Other neighbourhood functions have
		  different spectrums which are presented, but their meaning
		  remains to be explained.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  flanagan99a,
  author	= {Flanagan, John A.},
  title		= {Self-organization in the {SOM} with a decreasing
		  neighborhood function of any width},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  year		= {1999},
  publisher	= {IEE},
  address	= {London, UK},
  volume	= {1},
  pages		= {156--161},
  abstract	= {A proof of self-organization for a general one dimensional
		  {SOM} (i.e. one dimensional array of neurons, one
		  dimensional input) with a strictly monotonically decreasing
		  neighborhood function of any width W is given. The proof is
		  not dependent on any particular type of probability
		  distribution of the input but rather minimum conditions
		  that the distribution must satisfy are specified. For a
		  total of N neurons the degree, n, of the {SOM} is defined
		  here as n = N div W+1 when N mod W does not equal 0 or else
		  n = N/W. It is shown that a total of 2n intervals of
		  non-zero probability on the support of the input, separated
		  by distances which depend on parameters of the {SOM} are
		  sufficient for self-organization.},
  dbinsdate	= {oldtimer}
}

@Article{	  flexer01a,
  author	= {Flexer, A.},
  title		= {On the use of self-organizing maps for clustering and
		  visualization},
  journal	= {Intelligent-Data-Analysis},
  year		= {2001},
  volume	= {5},
  pages		= {373--84},
  abstract	= {We show that the number of output units used in a
		  self-organizing map (SOM) influences its applicability for
		  either clustering or visualization. By reviewing the
		  appropriate literature and theory and own empirical
		  results, we demonstrate that SOMs can be used for
		  clustering or visualization separately, for simultaneous
		  clustering and visualization, and even for clustering via
		  visualization. For all these different kinds of
		  application, SOM is compared to other statistical
		  approaches., This will show SOM to be a flexible tool which
		  can be used for various forms of explorative data analysis
		  but it will also be made obvious that this flexibility
		  comes with a price in terms of impaired performance.},
  dbinsdate	= {2002/1}
}

@InCollection{	  flexer97a,
  author	= {A. Flexer},
  title		= {Limitations of \mbox{self-organizing} maps for vector
		  quantization and multidimensional scaling},
  booktitle	= {Advances in Neural Information Processing Systems 9.
		  Proceedings of the 1996 Conference},
  publisher	= {MIT Press},
  year		= {1997},
  editor	= {M. C. Mozer and M. I. Jordan and T. Petsche},
  address	= {London, UK},
  pages		= {445--51},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  flexer99a,
  author	= {Flexer, A.},
  title		= {On the use of self-organizing maps for clustering and
		  visualization},
  booktitle	= {PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY},
  year		= {1999},
  pages		= {80--88},
  abstract	= {We show that the number of output units used in a self-
		  organizing map (SOM) influences its applicability for
		  either clustering or visualization. By reviewing the
		  appropriate literature and theory and own empirical
		  results, we demonstrate that SOMs can be used for
		  clustering or visualization separately, for simultaneous
		  clustering and visualization, and even for clustering via
		  visualization. For all these different kinds of
		  application, SOM is compared to other statistical
		  approaches. This will show SOM to be a flexible tool which
		  can be used for various forms of explorative data analysis
		  but it will also be made obvious that this flexibility
		  comes with a price in terms of impaired performance. The
		  usage of SOM in the data mining community is covered by
		  discussing its application in the data mining tools
		  CLEMENTINE and WEBSOM.},
  dbinsdate	= {2002/1}
}

@Article{	  flotzinger92a,
  author	= {D. Flotzinger and J. Kalcher and G. Pfurtscheller},
  title		= {{EEG} classification by learning vector quantization},
  journal	= {Biomed. Tech. (Berlin)},
  year		= {1992},
  volume	= {37},
  number	= {12},
  pages		= {303--309},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  flotzinger93a,
  author	= {D. Flotzinger and J. Kalcher and G. Pfurtscheller},
  title		= {Suitability of Learning Vector Quatization for On-line
		  Learning: A Case Study of {EEG} Classification},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {I},
  pages		= {224--227},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  flotzinger93b,
  author	= {D. Flotzinger},
  title		= {On-Line Learning With Learning Vector Quantization: A Case
		  Study of {EEG} Classification},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {1019},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  flotzinger94a,
  author	= {D. Flotzinger and M. Pregenzer and G. Pfurtscheller},
  title		= {Feature Selection with Distinction Sensitive Learning
		  Vector Quantization and Genetic Algorithms},
  pages		= {3448--3451},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, feature selection, comparison},
  dbinsdate	= {oldtimer}
}

@Article{	  fogelman-soulie89a,
  author	= {F. {Fogelman-Souli{\'{e}}} and P. Gallinari},
  title		= {Connexionist approaches in learning},
  journal	= {Bull. de liaison de la recherche en informatique et en
		  automatique},
  year		= {1989},
  volume	= {124},
  pages		= {19--21},
  note		= {(in French)},
  dbinsdate	= {oldtimer}
}

@Article{	  fogelman-soulie91a,
  author	= {F. Fogelman-Souli{\'{e}}},
  title		= {Neural networks and computing},
  journal	= {Future Generation Computer Systems},
  year		= {1991},
  volume	= {7},
  number	= {1},
  pages		= {69--77},
  month		= {October},
  annote	= {Conf. paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  folk94a,
  author	= {Folk, R. and Kartashov, A. },
  title		= {A simple elastic model for \mbox{self-organizing}
		  topological mappings},
  journal	= {Network: Computation in Neural Systems},
  year		= {1994},
  volume	= {5},
  number	= {3},
  pages		= {369--87},
  month		= {Aug},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  folk94b,
  author	= {Folk, R. and Kartashov, A. },
  title		= {Dynamics of ordering for \mbox{\mbox{one-dimensional}}
		  topological mappings},
  booktitle	= {Cybernetics and Systems '94. Proceedings of the Twelfth
		  European Meeting on Cybernetics and Systems Research},
  year		= {1994},
  editor	= {Trappl, R. },
  volume	= {2},
  pages		= {1695--702},
  organization	= {Inst. fur Theor. Phys. , Linz Univ. , Austria},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fomin94a,
  author	= {T. Fomin and Cs. Szepesv{\'{a}}ri and A. L{\"{o}}rincz},
  title		= {Self-Organizing Neurocontrol},
  pages		= {2777--2780},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {control application, modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fontaine94a,
  author	= {Fontaine, V. and Leich, H. and Hennebert, J. },
  title		= {Influence of vector quantization on isolated word
		  recognition},
  booktitle	= {Signal Processing VII, Theories and Applications.
		  Proceedings of EUSIPCO-94. Seventh European Signal
		  Processing Conference},
  year		= {1994},
  editor	= {Holt, M. J. J. and Cowan, C. F. N. and Grant, P. M. and
		  Sandham, W. A. },
  volume	= {1},
  pages		= {115--18},
  organization	= {Faculte Polytech. de Mons, Belgium},
  publisher	= {Eur. Assoc. Signal Process},
  address	= {Lausanne, Switzerland},
  dbinsdate	= {oldtimer}
}

@Article{	  foody99a,
  author	= {Foody, G.~M.},
  title		= {Applications of the Self-Organizing Feature Map
		  Neural-Network in Community Data-Analysis},
  journal	= {Ecological Modelling},
  year		= {1999},
  volume	= {120},
  number	= {2--3},
  pages		= {97--107},
  dbinsdate	= {oldtimer}
}

@InCollection{	  forkheim95a,
  author	= {K. E. Forkheim and D. Scuse and H. Pasterkamp},
  title		= {A comparison of neural network models for wheeze
		  detection},
  booktitle	= {IEEE WESCANEX 95. Communications, Power, and Computing.
		  Conference Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {214--19},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fort01a,
  author	= {Fort, J. C. and Cottrell, M. and Letremy, P.},
  title		= {Stochastic on-line algorithm versus batch algorithm for
		  quantization and self organizing maps},
  booktitle	= {Neural Networks for Signal Processing XI: Proceedings of
		  the 2001 IEEE Signal Processing Society Workshop. IEEE,
		  Piscataway, NJ, USA},
  year		= {2001},
  volume	= {},
  pages		= {43--52},
  abstract	= {The Kohonen algorithm (SOM) was originally designed as a
		  stochastic algorithm which works in an on-line way and
		  which was designed to model some adaptative features of the
		  human brain. In fact it is nowadays extensively used for
		  data mining, data visualization, and exploratory data
		  analysis. Some users are tempted to use the batch version
		  of the Kohonen algorithm since it is a deterministic
		  algorithm which can be convenient if one needs to get
		  reproducible results and which can go faster in some cases.
		  In this paper, we try to elucidate the mathematical nature
		  of this batch variant and give some elements of comparison
		  of both algorithms. Then we compare both versions on a real
		  data set.},
  dbinsdate	= {2002/1}
}

@Article{	  fort88a,
  author	= {J. -C. Fort},
  title		= {Solving a combinatorial problem via \mbox{self-organizing}
		  process: an application of the {K}ohonen algorithm to the
		  {T}raveling {S}alesman {P}roblem},
  journal	= {Biol. Cyb. },
  year		= {1988},
  volume	= {59},
  number	= {1},
  pages		= {33--40},
  dbinsdate	= {oldtimer}
}

@Article{	  fort93a,
  author	= {Jean-Claude Fort and Gilles Pag{\`e}s},
  title		= {Sur la convergence p. s. de l'algorithme de {K}ohonen
		  g{\'{e}}n{\'{e}}ralis{\'{e}}},
  journal	= {note aux C. R. Acad. Sci. Paris},
  year		= {1993},
  volume	= {S{\'{e}}rie I},
  number	= {317},
  pages		= {389--394},
  note		= {(in French)},
  dbinsdate	= {oldtimer}
}

@TechReport{	  fort93c,
  author	= {Jean-Claude Fort and Gilles Pag{\`{e}}s},
  title		= {Sur la convergence p. s. de l'algorithme de {K}ohonen
		  g{\'{e}}n{\'{e}}ralis{\'{e}}},
  institution	= {Universit\'{e} Paris 1 Pantheon Sorbonne, Samos},
  year		= {1993},
  number	= {10},
  address	= {90, rue de Tolbiac---75634 Paris Cedex 13},
  note		= {(in french)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fort94a,
  author	= {Jean-Claude Fort and Gilles Pag{\`{e}}s},
  title		= {About the convergence of the generalized {K}ohonen
		  algorithm},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  pages		= {318--321},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {analysis, convergence},
  dbinsdate	= {oldtimer}
}

@TechReport{	  fort94b,
  author	= {Jean-Claude Fort and Gilles Pag{\`{e}}s},
  title		= {About the a. s. convergence of the {K}ohonen algorithm
		  with a generalized neighborhood function},
  institution	= {Universit\'{e} Paris 1},
  year		= {1994},
  number	= {29},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fort94c,
  author	= {Jean-Claude Fort and Gilles Pag{\`{e}}s},
  title		= {A non linear {K}ohonen algorithm},
  booktitle	= {Proc. ESANN'94, European Symp. on Artificial Neural
		  Networks},
  year		= {1994},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {257--262},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fort95a,
  author	= {Jean-Claude Fort and Gilles Pag{\`{e}}s},
  title		= {About the {K}ohonen algorithm: Strong or weak
		  self-organization},
  booktitle	= {Proc. ESANN'95, European Symp. on Artificial Neural
		  Networks},
  year		= {1995},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {9--14},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fort96a,
  author	= {Jean-Claude Fort and Gilles Pag{\`{e}}s},
  title		= {Quantization vs. \ Organization in the {K}ohonen {S. O. M.
		  }},
  booktitle	= {Proc. ESANN'96, European Symp. on Artificial Neural
		  Networks},
  year		= {1996},
  publisher	= {D facto conference services},
  address	= {Bruges, Belgium},
  editor	= {Michel Verleysen},
  pages		= {85--89},
  dbinsdate	= {oldtimer}
}

@Article{	  fort96b,
  author	= {J. -C. Fort and G. Pages},
  title		= {About the {K}ohonen algorithm: strong or weak
		  self-organization?},
  journal	= {Neural Networks},
  year		= {1996},
  volume	= {9},
  number	= {5},
  pages		= {773--85},
  dbinsdate	= {oldtimer}
}

@InCollection{	  fort97a,
  author	= {J. -C. Fort and G. Pages},
  title		= {Convergences of the {K}ohonen maps: a dynamical system
		  approach},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {631--6},
  dbinsdate	= {oldtimer}
}

@Article{	  foulkes97a,
  author	= {S. B. Foulkes and D. M. Booth},
  title		= {Improved object segmentation using {M}arkov random fields,
		  artificial neural networks, and parallel processing
		  techniques},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3068},
  pages		= {443--54},
  note		= {(Signal Processing, Sensor Fusion, and Target Recognition
		  VI Conf. Date: 21--24 April 1997 Conf. Loc: Orlando, FL,
		  USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fox90a,
  author	= {K. L. Fox and R. R. Henning and J. H. Reed and R. P.
		  Simonian},
  title		= {A neural network approach towards intrusion detection},
  booktitle	= {Proc. 13th National Computer Security Conference.
		  Information Systems Security. Standards---the Key to the
		  Future},
  year		= {1990},
  volume	= {I},
  pages		= {124--134},
  organization	= {NIST},
  publisher	= {NIST},
  address	= {Gaithersburg, MD},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fox91a,
  author	= {Dieter Fox and Volker Heinze and Knut M{\"{o}}ller and
		  Sebastian Thrun and Gerd Veenker},
  title		= {Learning by Error-Driven Decomposition},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {207--212},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  franca95a,
  author	= {R. M. Vilar Fran{\c{c}}a and B. G. Aguiar Neto},
  title		= {Comparing Self-Organizing Algorithms For Vector
		  Quantization},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {481--484},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  franchi94a,
  author	= {P. Franchi and P. Morasso and G. Vercelli},
  title		= {A hybrid \mbox{self-organizing} architecture for
		  autonomous mobile robots},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1287--1290},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, control, robots},
  dbinsdate	= {oldtimer}
}

@Article{	  francois92a,
  author	= {Olivier Francois and Jacques Demongeot and Thierry Herve},
  title		= {Convergence of a Self-Organizing Stochastic Neural
		  Network},
  journal	= {Neural Networks},
  volume	= {5},
  year		= {1992},
  pages		= {277--282},
  dbinsdate	= {oldtimer}
}

@Article{	  frasconi97a,
  author	= {P. Frasconi and M. Gori and G. Soda},
  title		= {Links between {LVQ} and backpropagation},
  journal	= {Pattern Recognition Letters},
  year		= {1997},
  volume	= {18},
  number	= {4},
  pages		= {303--10},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  frauel00a,
  author	= {Frauel, Y. and Pauliat, G. and Villing, A. and Roosen,
		  G.},
  title		= {Photorefractive neural network performing a topological
		  map},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {4089},
  pages		= {668--675},
  organization	= {Cent Natl de la Recherche Scientifique},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {In this paper we describe the realization and the
		  operation of a high capacity optoelectronic neural network
		  implementing a classification of vectors through a Kohonen
		  topological map. The setup uses volume holographic
		  interconnects inside a photorefractive crystal to implement
		  the neurons. We show that the system work and is able to
		  classify several tens of vectors.},
  dbinsdate	= {2002/1}
}

@Article{	  frauel01a,
  author	= {Frauel, Y. and Pauliat, G. and Villing, A. and Roosen,
		  G.},
  title		= {High-capacity photorefractive neural network implementing
		  a Kohonen topological map},
  journal	= {Applied-Optics},
  year		= {2001},
  volume	= {40},
  pages		= {5162--9},
  abstract	= {We designed and built a high-capacity neural network based
		  on volume holographic interconnections in a photorefractive
		  crystal. We used this system to implement a Kohonen
		  topological map. We describe and justify our optical setup
		  and present some experimental results of self-organization
		  in the learning database.},
  dbinsdate	= {2002/1}
}

@Article{	  frauel99a,
  author	= {Frauel, Y. and Villing, A. and Pauliat, G. and Roosen,
		  G.},
  title		= {Automatic classification of images with a photorefractive
		  crystal},
  journal	= {Advances in Photorefractive Materials, Effects, and
		  Devices. Seventh Topical Meeting. OSA Trends in Optics and
		  Photonics Series Vol.27. Opt. Soc. America, Washington, DC,
		  USA; 1999; xii+673 pp.p.631--6},
  year		= {1999},
  volume	= {},
  pages		= {631--6},
  abstract	= {We propose a system that takes advantage of volume
		  holographic interconnections and of the particular kinetics
		  of photorefractive crystals. This system is a neural
		  network that implements a Kohonen self-organizing map. It
		  is devoted to the automatic classification of a set of
		  vectors according to their mutual correlations. We present
		  in the paper the problems we encountered while defining and
		  building the experimental setup as well as some solutions
		  we found to improve the system.},
  dbinsdate	= {2002/1}
}

@Article{	  frey93a,
  author	= {Frey, J. and Scheppelmann, D. and Glombitza, G. and
		  Meinzer, H. -P. },
  title		= {A parallel topological feature map in APL},
  journal	= {APL Quote Quad},
  year		= {1993},
  volume	= {24},
  number	= {1},
  pages		= {97--103},
  month		= {Aug},
  annote	= {A conference paper in journal},
  abstract	= {One can distinguish two different approaches of neural
		  networks the supervised networks and the self organizing or
		  unsupervised neural networks. The first type of neural nets
		  is supplied with an ideal result regarding the input.
		  During the learning procedure, the neural net adjusts
		  weighting factors of the links between neurons so that the
		  input feature vectors map to the ideal output. Those nets
		  are used for example in robotics, where the ideal result is
		  well known: it is the position the robot should be placed
		  in. For the cases where no ideal result is known, the
		  second type of neural nets, the so called self-learning
		  Topological Feature Map (TFM) is appropriate. This paper
		  will introduce such a neural net based on the idea of
		  Kohonen's TFM. The original algorithm was extremely
		  sequential and therefore not suitable for an APL
		  implementation. The parallelization of the algorithm led to
		  important improvements in speed and convergence to the
		  global optimum.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  frick00a,
  author	= {Frick, K. L. and Starrett, S. K.},
  title		= {Classification of disturbance characteristics using a
		  Kohonen neural network},
  booktitle	= {LESCOPE'00. 2000 Large Engineering Systems Conference on
		  Power Engineering Conference Proceedings. Theme: Navigating
		  the New Power Challenge. Large Engineering Systems,
		  Halifax, NS, Canada},
  year		= {2000},
  volume	= {},
  pages		= {124--8},
  abstract	= {The paper presents an artificial neural network (ANN) used
		  to classify power system disturbances according to power
		  system response characteristics. The ANN used was a
		  competitive layer architecture that was trained with the
		  Kohonen learning rule. The resulting classifications are
		  examined with regard to disturbance location. Prony
		  analysis was used to represent the original time series
		  data as a sum of exponential terms defined by frequency,
		  phase, amplitude and damping coefficients. The frequency
		  and damping coefficients were held constant for all cases,
		  and the amplitude coefficients were used as inputs for the
		  ANN. The method was tested with measured disturbances from
		  the WSCC.},
  dbinsdate	= {2002/1}
}

@Article{	  frietman01a,
  author	= {E. E. E. Frietman and M. T. Hill and G. D. Khoe},
  title		= {A Kohonen Neural Network Controlled All-Optical Router
		  System},
  journal	= {International Journal of Computer Research, Special Issue:
		  Past, Present and Future of Neural Networks},
  year		= {2001},
  key		= {},
  volume	= {10},
  number	= {2},
  pages		= {251--67},
  month		= {},
  note		= {Guest Editors: P.G. Anderson and G. Antoniou and V.
		  Mladenov and E. Oja and M. Paprzycki and N. C. Steele},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  frisone95a,
  author	= {Frisone, F. and Firenze, F. and Morasso, P. and L.
		  Ricciardiello},
  title		= {Application of topology representing networks to the
		  estimation of the intrinsic dimensionality of data},
  booktitle	= {Proceedings of the International Conference on Artificial
		  Neural Networks (ICANN'95---Paris October 9--13)},
  editor	= {F. Fogelman},
  volume	= {1},
  year		= {1995},
  pages		= {323--327},
  abstract	= {The problem of estimating the intrinsic dimensionality of
		  a distribution of data by means of an approach based on
		  topology-representing networks (TRN) is investigated and an
		  algorithm is developed. The proposed methodology is based
		  on the conjecture that the number of cross-connections that
		  each neuron establishes with other neurons at the end of a
		  learning phase is considered as representative of the
		  intrinsic dimensionality of input data close to the neuron
		  itself. In this paper, the heuristic validity of the
		  conjecture is tested in parallel with the concept of
		  kissing number in the sphere packing problem. Results of
		  simulation experiments are reported.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  frisone97a,
  author	= {Francesco Frisone and Pietro G. Morasso and Vittorio
		  Sanguineti},
  title		= {Coordinate-free representation of sensorimotor spaces},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {163--168},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  frisone97b,
  author	= {Frisone, F. and Perico, L. and P. Morasso},
  title		= {Extending the {TRN} model in a biologically plausible
		  way.},
  booktitle	= {Artificial Neural Networks---ICANN'97},
  editor	= {W. Gerstner, M. Hasler and J. D. Nicoud},
  year		= {1997},
  publisher	= {Springer Verlag},
  address	= {Berlin, Germany},
  pages		= {201--206},
  abstract	= {The topology representing network (TRN) model is extended
		  by using an activation dynamics which implicitly orders the
		  neurons according to the distance from the input pattern.
		  This allows one to apply the same Hebbian learning method
		  to the thalamo-cortical and cortico-cortical connections.
		  The model proposed combines a process of diffusion (via the
		  excitatory topologically organized connections) and a
		  process of competitive distribution of activation which
		  tends to sharpen the active map region. The dynamics is
		  analyzed taking into account the excitatory nature of the
		  majority of cortical synapses and the puzzling presence of
		  long-range competition without long-range inhibition. The
		  model is shown to be more consistent than TRN or other
		  self-organizing paradigms with a number of
		  neurophysiological facts.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  frisone97c,
  author	= {Frisone, F. and Sanguineti, V. and P. G. Morasso},
  title		= {A Novel Hypothesis on Cortical Map: Topological
		  Continuity.},
  booktitle	= {Neural Net---WIRN96. Proceedings of the 8th Italian
		  Workshop on Neural Nets. },
  publisher	= {Springer},
  year		= {1997},
  editor	= {M. Marinaro and R. Tagliaferri},
  address	= {London, UK},
  pages		= {194--198},
  abstract	= {The paper proposes that some cortical maps are indeed
		  topologically continuous, distributed representations, i.e.
		  representations that also contain and make use of
		  topological information. It is shown that the hypothesis is
		  consistent with experimental observations and might suggest
		  how the cortex can carry out computations that involve
		  spatial or topological reasoning, like trajectory
		  generation, visuomotor transformations.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  frisone98a,
  author	= {Frisone, F. and Vitali, P. and Morasso, P.},
  title		= {Cortical activity pattern in complex tasks.},
  booktitle	= {Computational Neuroscience: Trends in Research},
  publisher	= {Plenum Press},
  year		= {1998},
  editor	= {J. M. Bower},
  pages		= {13--18},
  dbinsdate	= {oldtimer}
}

@Article{	  frisone98b,
  author	= {Frisone, F. and Morasso, P. and Perico, L.},
  title		= {Self-organization in cortical maps \& {EM} learning.},
  journal	= {Journal of Advanced Computational Intelligence},
  year		= {1998},
  volume	= {2},
  pages		= {178--184},
  dbinsdate	= {oldtimer}
}

@InCollection{	  frisone99a,
  author	= {Frisone, F. and Morasso, P. and Iann\`{o}, G. and
		  Marongiu, M. and Vitali, P. and Rodriguez, G.},
  title		= {Spatio-temporal cortical activity patterns in cognitive
		  tasks using {fMRI}.},
  booktitle	= {Neural Nets},
  publisher	= {Springer-Verlag},
  year		= {1999},
  editor	= {M. Marinaro and R. Tagliaferri},
  address	= {London},
  pages		= {126--131},
  abstract	= {In this study we investigate the spatio-temporal
		  organization of the human cortical activity in complex
		  tasks. Such complex interactions are crucial, for instance,
		  in the case of voluntary actions, memory and are probably
		  impaired in neurological disorders (e.g. Epilepsy) or
		  psychiatric disorders (e.g. schizophrenia). The aim of the
		  performed {fMRI} experiments was to evaluate multiple
		  cortical areas involved in verbal output and input during
		  two different covert language tasks, namely "verbal
		  fluency" and "verbal understanding". We discuss the
		  relevance of such data for a theory of cortex dynamics
		  which emphasizes the role of long-range lateral
		  connections. The future experimental investigation will be
		  aimed at the analysis of the spatio-temporal structure of
		  cortical activity in pathological conditions, such as
		  epilepsy.},
  dbinsdate	= {oldtimer}
}

@Article{	  frisone99b,
  author	= {Frisone, F. and Vitali, P. and Iann\`{o}, G. and Marongiu,
		  M. and Morasso, P. and Pilot, A. and Rodriguez, G. and
		  Rosa, M. and Sardanelli, F.},
  title		= {Can the synchronization of cortical areas be evidenced by
		  {fMRI}?},
  journal	= {Journal of Neurocomputing},
  year		= {1999},
  volume	= {26},
  pages		= {1019--24},
  abstract	= {The goal of this study is to investigate the possibility
		  of analyzing spatio-temporal organization of the human
		  cortical activity during different complex tasks, by means
		  of {fMRI}. To evidence cortical areas synchronization we
		  propose a computational approach based on a self-organizing
		  neural network ("neural gas") that detects time-dependent
		  alterations in the regional intensity of the functional
		  signal. Results of the application of such an approach are
		  reported and are compared with the results obtained with a
		  standard statistical package.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  frisone99c,
  author	= {Frisone, F. and Morasso, P. G.},
  title		= {Soft-competitive versus {EM} learning in cortical map
		  modeling},
  booktitle	= {Neural Nets WIRN VIETRI-98. Proceedings of the 10th
		  Italian Workshop on Neural Nets},
  editor	= {M. Marinaro and R. Tagliaferri},
  year		= {1999},
  publisher	= {Springer-Verlag London},
  address	= {London, UK},
  volume	= {},
  pages		= {120--5},
  abstract	= {Starting from the problem of density estimation, it is
		  shown that EM learning can be considered as a Hebbian
		  mechanism. From this it is possible to outline a theory of
		  self-organization of cortical maps which is based on a well
		  defined optimization process and still preserves
		  biologically desirable characteristics: local computation
		  and uniform treatment of input and lateral connections.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritsch91a,
  author	= {T. Fritsch and W. Mandel},
  title		= {Communication network routing using neural nets-numerical
		  aspects and alternative approaches},
  booktitle	= {Proc. IJCNN'91 International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {752--757},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {The authors discuss various approaches of using Hopfield
		  networks in routing problems in computer communication
		  networks. It is shown that the classical approach using the
		  original Hopfield network leads to evident numerical
		  problems, and hence is not practicable. The heuristic
		  choice of the Lagrange parameters, as presented in the
		  literature, can result in incorrect solutions for variable
		  dimensions, or is very time consuming, in order to search
		  the correct parameter sets. The modified method using
		  eigenvalue analysis using predetermined parameters yields
		  recognizable improvements. On the other hand, it is not
		  able to produce correct solutions for different topologies
		  with higher dimensions. From a numerical viewpoint,
		  determining the eigenvalues of the connection matrix
		  involves severe problems, such as stiffness, and shows
		  evident instability of the simulated differential
		  equations. The authors present possible alternative
		  approaches such as the self-organizing feature map and
		  modifications of the Hopfield net, e.g., mean field
		  annealing, and the Pottglas model.},
  dbinsdate	= {oldtimer}
}

@Article{	  fritsch93a,
  author	= {T. Fritsch and M. Mittler and P. Tran-Gia},
  title		= {Artificial Neural Net Applications in Telecommunication
		  Systems},
  journal	= {Neural Computing {\&} Applications},
  year		= {1993},
  volume	= {1},
  number	= {2},
  pages		= {124--146},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritsch93b,
  author	= {T. Fritsch and P. H. Kraus and H. Przuntek and P.
		  Tran-Gia},
  title		= {Classification of {P}arkinson Rating-Scale-Data Using a
		  Self-Organizing Neural Net},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {I},
  pages		= {93--98},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritsch93c,
  author	= {Thomas Fritsch and Stefan Hanshans},
  title		= {An Integrated Approach to Cellular Mobile Communication
		  Planning Using Traffic Data Prestructured by a
		  Self-Organizing Feature Map},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {II},
  pages		= {822D-822I},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritsch94a,
  author	= {Thomas Fritsch},
  booktitle	= {Neural Networks in Telecommunications},
  chapter	= {Cellular Mobile Communication Design Using Self-Organizing
		  Feature Maps},
  title		= {Cellular Mobile Communication Design Using Self-Organizing
		  Feature Maps},
  publisher	= {Kluwer},
  year		= {1994},
  editor	= {Ben Yuhas and Nirwan Ansari},
  pages		= {211--232},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritsch95a,
  author	= {Fritsch, Th. and Neuner, B. and Klotz, P. and Kraus, P. H.
		  },
  title		= {A \mbox{self-organizing} neural net clustering {P}arkinson
		  patients and control persons using motor data},
  booktitle	= {Proceedings of the Eighth IEEE Symposium on Computer-Based
		  Medical Systems},
  year		= {1995},
  pages		= {118--24},
  organization	= {Inst. of Comput. Sci. , Wurzburg Univ. , Germany},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritzke91a,
  author	= {B. Fritzke and P. Wilke},
  title		= {{FLEXMAP}---A neural network with linear time and space
		  complexity for the traveling salesman problem},
  booktitle	= {Proc. IJCNN-90, Int. Joint Conference on Neural Networks,
		  Singapore},
  pages		= {929--934},
  year		= {1991},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritzke91b,
  author	= {B. Fritzke},
  title		= {Unsupervised clustering with growing cell structures},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  volume	= {2},
  pages		= {531--536},
  year		= {1991},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritzke91c,
  author	= {B. Fritzke and C. Nasahl},
  title		= {A neural network that learns to do hyphenation},
  booktitle	= {Artificial Neural Networks},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  year		= {1991},
  address	= {Amsterdam, Netherlands},
  publisher	= {North-Holland},
  pages		= {1375--1378 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritzke91d,
  author	= {Bernd Fritzke},
  title		= {Let It Grow---Self-Organizing Feature Maps with Problem
		  Dependent Cell Structure},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {403--408},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritzke92b,
  author	= {B. Fritzke},
  title		= {Using a library of efficient data structures and
		  algorithms as a neural network research tool},
  booktitle	= {Artificial Neural Networks 2},
  editor	= {I. Aleksander and J. Taylor},
  address	= {Amsterdam, Netherlands},
  publisher	= {North-Holland},
  pages		= {1273--1276},
  year		= {1992 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritzke92c,
  author	= {Bernd Fritzke},
  title		= {Growing Cell Structures---a Self-Organizing Network in
		  {\it {k}} Dimensions},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1051--1056},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  fritzke92d,
  author	= {Bernd Fritzke},
  title		= {Wachsende Zellstrukturen--ein selbstorganisierendes
		  neuronales Netzwerkmodell},
  school	= {Technische Fakult{\"{a}}t, Universit{\"{a}}t
		  Erlangen-N{\"{u}}rnberg},
  address	= {Erlangen, Germany},
  year		= {1992},
  dbinsdate	= {oldtimer}
}

@InCollection{	  fritzke93a,
  title		= {{K}ohonen feature maps and growing cell structures---a
		  performance comparison},
  author	= {Bernd Fritzke},
  pages		= {123--130},
  booktitle	= {Advances in Neural Information Processing Systems 5},
  editor	= {L. Giles and S. Hanson and J. Cowan},
  address	= {San Mateo, CA},
  year		= {1993},
  publisher	= {Morgan Kaufmann},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritzke93b,
  author	= {B. Fritzke},
  title		= {A growing and splitting elastic network for vector
		  quantization},
  booktitle	= {Proc. 1993 IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1993},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@TechReport{	  fritzke93c,
  author	= {B. Fritzke},
  title		= {Growing Cell Structures---a \mbox{self-organizing} network
		  for unsupervised and supervised learning},
  institution	= {Int. Computer Science Institute},
  number	= {TR-93--026},
  address	= {Berkeley, CA},
  year		= {1993 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritzke93d,
  author	= {Bernd Fritzke},
  title		= {Vector Quantization with a Growing and Splitting Elastic
		  Net},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {580--585},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritzke93e,
  author	= {Bernd Fritzke},
  title		= {A Growing and Splitting Elastic Network for Vector
		  Quantization},
  booktitle	= {Neural Networks for Signal Processing 3---Proceedings of
		  the 1993 IEEE Workshop},
  year		= {1993},
  editor	= {Kamm, C. A. and Kung, S. Y. and Yoon, B. and Chellappa, R.
		  and Kung, S. Y. },
  pages		= {281--290},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, New Jersey, USA},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@Article{	  fritzke94a,
  author	= {Fritzke, Bernd},
  title		= {Growing cell structures---a \mbox{self-organizing} network
		  for unsupervised and supervised learning},
  journal	= {Neural Networks},
  year		= {1994},
  number	= {9},
  volume	= {7},
  pages		= {1441--1460},
  abstract	= {We present a new self-organizing neural network model that
		  has two variants. The first variant performs unsupervised
		  learning and can be used for data visualization,
		  clustering, and vector quantization. The main advantage
		  over existing approaches (e.g., the Kohonen feature map) is
		  the ability of the model to automatically find a suitable
		  network structure and size. This is achieved through a
		  controlled growth process that also includes occasional
		  removal of units. The second variant of the model is a
		  supervised learning method that results from the
		  combination of the above-mentioned self-organizing network
		  with the radial basis function (RBF) approach. In this
		  model it is possible---in contrast to earlier approaches -
		  to perform the positioning of the RBF units and the
		  supervised training of the weights in parallel. Therefore,
		  the current classification error can be used to determine
		  where to insert new RBF units. This leads to small networks
		  that generalize very well. Results on the two-spirals
		  benchmark and a vowel classification problem are presented
		  that are better than any results previously published.},
  dbinsdate	= {oldtimer}
}

@Article{	  fritzke95a,
  author	= {Fritzke, B. },
  title		= {Growing grid---a \mbox{self-organizing} network with
		  constant neighbourhood range and adaptation strength},
  journal	= {Neural Processing Letters},
  year		= {1995},
  volume	= {2},
  number	= {5},
  pages		= {9--13},
  month		= {Sept},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fritzke96a,
  author	= {Bernd Fritzke},
  title		= {Growing Self-Organizing Maps---Why?},
  booktitle	= {Proc. ESANN'96, European Symp. on Artificial Neural
		  Networks},
  year		= {1996},
  publisher	= {D facto conference services},
  address	= {Bruges, Belgium},
  editor	= {Michel Verleysen},
  pages		= {61--72},
  dbinsdate	= {oldtimer}
}

@InCollection{	  fritzke99a,
  author	= {B. Fritzke},
  title		= {Kohonen Self-Organizing Map with quantized weights},
  booktitle	= {Kohonen Maps},
  pages		= {131--44},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InCollection{	  frotschnig95a,
  author	= {A. Frotschnig and Man-Wook Han},
  title		= {Control of autonomous mobile robots using artificial
		  neural networks},
  booktitle	= {The First World Congress on Intelligent Manufacturing
		  Processes and Systems. Proceedings},
  publisher	= {Univ. Puerto Rico},
  year		= {1995},
  volume	= {1},
  address	= {San Juan, Puerto Rico},
  pages		= {621--30},
  dbinsdate	= {oldtimer}
}

@Article{	  fu93a,
  author	= {Hsin Chia Fu and Lin, Y. Y. and Hsiao-Tien Pao},
  title		= {Neural nets for radio Morse code recognizing},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1993},
  volume	= {1965},
  pages		= {334--45},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fujimura00a,
  author	= {K. Fujimura and H. Tokutaka and K. Obu-Cann and S.
		  Hatano},
  title		= {Visualizing Winning Frequencies and Color Classification
		  Map using Modified Counter-Propagation},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {278--85},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@InProceedings{	  fujimura01a,
  author	= {K. Fujimura and S. -I. Fujiwaki and O. -C. Kwaw and H.
		  Tokutaka},
  title		= {Optimisation of electronic parts mounting machines usin
		  {{SOM}-{TSP}} method with 5 dimensional data},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {246--52},
  year		= {2001},
  editor	= {Nigel Allison and Hujun Yin and Lesley Allison and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  fujimura01b,
  author	= {Fujimura, K. and Fujiwaki, S. and Tokutaka, H.},
  title		= {Optimization of high speed Chip-mounter using {SOM}-{TSP}
		  method},
  journal	= {Transactions-of-the-Institute-of-Electronics,-Information-and-Communication-Engineers-D-II}
		  ,
  year		= {2001},
  volume	= {},
  pages		= {1194--202},
  abstract	= {In this paper, we propose the application of SOM-TSP
		  method in optimizing the efficiency of surface mounting of
		  electronic parts on the printed circuit board by the
		  machine called as "Chip-mounter." It was found that the
		  required time for mounting electronic parts will be
		  decreased by our proposed method as compared to the
		  built-in method on the Chip-mounter, through our numerical
		  experiment. The output of the Chip-mounter will be
		  increased using our proposed method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  fujimura93a,
  author	= {Kikuo Fujimura and Heizo Tokutaka and Satoru Kishida and
		  Katsumi Nishimori and Naganori Ishihara and Koh Yamane and
		  Makoto Ishihara},
  title		= {Application of {K}ohonen's Self-Organizing Feature Maps
		  into the Problem of Selecting the Buttons},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2472--2475},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {We applied Kohonen's Self-Organizing Feature Maps (SOM)
		  into the problem of selecting the buttons. 3-dimensional
		  (3D) data spaces are represented by two maps of
		  2-dimensional (2D) {SOM} which are formed on the planes
		  that are projected from 3D objects. Assuming the ability of
		  {SOM} to approximate the shape of input-data spaces, we
		  select the best matching color-series when reference-data
		  point exists in the {SOM}. Using this method, very good
		  recognition (96%) was obtained. This method was better than
		  the neural network with Back Propagation learning on the
		  points that adjustments of parameters are easy and
		  paradoxical data-relations are permitted.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  fujimura93b,
  author	= {Kikuo Fujimura and Heizo Tokutaka and Satoru Kishida and
		  Katsumi Nishimori and Naganori Ishihara and Koh Yamane and
		  Makoto Ishihara},
  title		= {Application of {K}ohonen's Self-Organizing Feature Maps
		  into the Problem of Selecting the Buttons},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1993},
  number	= {NC92--141},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fujimura93c,
  author	= {Kikuo Fujimura and Heizo Tokutaka and Satoru Kishida and
		  Katsumi Nishimori and Naganori Ishihara},
  title		= {Ability of generalization into the problem of selecting
		  the buttons},
  booktitle	= {Proc. JNNS-93, Annual Conf. of Japanese Neural Network
		  Society},
  year		= {1993},
  pages		= {197--198},
  organization	= {JNNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fujimura94a,
  author	= {Kikuo Fujimura and Tomoya Yamagishi and Heizo Tokutaka and
		  Tetsuya Fujiwara and Satoru Kishida},
  title		= {Lateral Interaction in the {K}ohonen's Learning Model},
  pages		= {71--72},
  booktitle	= {Proc. 3rd International Conference on Fuzzy Logic, Neural
		  Nets and Soft Computing},
  year		= {1994},
  publisher	= {Fuzzy Logic Systems Institute},
  address	= {Iizuka, Japan},
  annote	= {analysis},
  dbinsdate	= {oldtimer}
}

@TechReport{	  fujimura94b,
  author	= {Kikuo Fujimura and Heizo Tokutaka and Satoru Kishida},
  title		= {Application of {K}ohonen's Self-Organizing Feature Maps
		  into the Problem of Selecting the Color Combination of
		  Fifteen Buttons and Infinite Cloths},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1994},
  number	= {NC93--146},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@TechReport{	  fujimura94c,
  author	= {Kikuo Fujimura and Heizo Tokutaka and Yasuhiro Ohshima and
		  Satoru Kishida},
  title		= {The Traveling Salesman Problem Applied to the
		  Self-Organizing Feature Map},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1994},
  number	= {NC93--147},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fujimura94d,
  author	= {Kikuo Fujimura and Heizo Tokutaka and Yasuhiro Ohshima and
		  Satoru Kishida},
  title		= {The Traveling Salesman Problem Applied to the
		  Self-Organizing Feature Map},
  booktitle	= {Proc. ICONIP'94},
  year		= {1994},
  dbinsdate	= {oldtimer}
}

@Article{	  fujimura95a,
  author	= {Kikuo Fujimura and Heizo Tokutaka and Satoru Kishida},
  title		= {A method of classification using {K}ohonen's
		  Self-Organizing Feature Maps---Application of the color
		  matching problem in the combination of fifteen buttons and
		  cloths},
  journal	= {Trans. IEE of Japan},
  year		= {1995},
  volume	= {115-C},
  number	= {5},
  pages		= {736--743},
  dbinsdate	= {oldtimer}
}

@Article{	  fujimura96a,
  author	= {Kikuo Fujimura and Heizo Tokutaka and Yasuhiro Ohshima and
		  Schi-Ichi Tanaka and Satoru Kishida},
  title		= {An improvement of algorithm using {K}ohonen's
		  Self-Organizing Feature Map for the traveling salesman
		  problem},
  journal	= {Trans. IEE of Japan},
  year		= {1996},
  volume	= {116-C},
  number	= {3},
  pages		= {350--358},
  dbinsdate	= {oldtimer}
}

@InCollection{	  fujimura96b,
  author	= {K. Fujimura and S. Tanaka and H. Tokutaka and S. Kishida},
  title		= {The automatic button-color matching system using
		  {K}ohonen's \mbox{self-organizing} feature maps in the
		  textile field},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {4},
  address	= {New York, NY, USA},
  pages		= {2055--9},
  abstract	= {We introduce the automatic button-color matching system
		  using Kohonen's Self-Organizing Feature Maps (SOMs). The
		  system consisted of two processes; (1) self-organizing
		  feature mapping and (2) {SOM} analyzing. The recognition
		  test of the system was performed using an actual data set.
		  From the results, the total recognition rate of 78% as
		  almost the same as LVQ (80%) was obtained. Furthermore,
		  ranking information of 'best button', 'next best button',
		  can be obtained.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  fujimura97a,
  author	= {Kikuo Fujimura and Heizo Tokutaka and Shin-Ichi Tanaka and
		  Satoru Kishida},
  title		= {The optimization for {TSP} using {SOM} method of many
		  cities, for example 532 cities in {USA}},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {80--85},
  dbinsdate	= {oldtimer}
}

@TechReport{	  fujimura97b,
  author	= {K. Fujimura and H. Tokutaka and S. Kishida and M.
		  Ishikawa},
  title		= {Visualization for Probability Density Function of
		  Multi-Dimensional Data by Self-Organizing Map (in
		  Japanese)},
  institution	= {IEICE},
  year		= {1997},
  key		= {NC96--163},
  dbinsdate	= {oldtimer}
}

@Article{	  fujimura99a,
  author	= {K. Fujimura and H. Tokutaka and I. Masumi},
  title		= {Performance of Improved {SOM}-{TSP} Algorithm for
		  Traveling Salesman Problem of Many Cities},
  journal	= {Transactions of IEE Japan},
  year		= {1999},
  volume	= {119},
  note		= {(in Japanese)},
  number	= {7},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fujimura99b,
  author	= {Fujimura, Kikuo and Obu-Cann, Kwaw and Tokutaka, Heizo},
  title		= {Optimization of surface component mounting on the printed
		  circuit board using {SOM}-{TSP} method},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  year		= {1999},
  publisher	= {IEE},
  address	= {London, UK},
  volume	= {2},
  pages		= {643--648},
  abstract	= {We propose the application of {SOM}-TSP method in
		  optimizing the efficiency of surface mounting of electronic
		  parts on the printed circuit board. It was found that the
		  required time for mounting electronic parts will be
		  decreased by our proposed method as compared to the
		  built-in method on the mounting-system, through our
		  numerical experiment. The output of the factory will be
		  increased using our proposed method.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  fujinaga97a,
  author	= {S. Fujinaga and M. Hagiwara},
  title		= {Procedural Knowledge Processing based on Area
		  Representation using a Neural Networks},
  booktitle	= {1997 IEEE International Conference on Systems, Man, and
		  Cybernetics},
  year		= 1997,
  volume	= 5,
  pages		= {4519--4524},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fujita91a,
  author	= {M. Fujita and B. Bavarian},
  title		= {An {ART2-TPM} neural network for automatic pattern
		  classification},
  booktitle	= {Proc. IJCNN-91, International Joint Conference on Neural
		  Networks, Seattle},
  year		= {1991},
  volume	= {II},
  pages		= {479--484},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fujita93a,
  author	= {Hideko Fujita and Makoto Yamamoto and Shigeki Kobayashi
		  and Xu Youheng},
  title		= {Pattern Classification of Waveforms Using {LVQ1}},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {951--954},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@TechReport{	  fujiwara94a,
  author	= {Tetsuya Fujiwara and Kikuo Fujimura and Heizo Tokutaka and
		  Satoru Kishida},
  title		= {Consideration for lateral interaction and neighborhood
		  shape in the {K}ohonen's model},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1994},
  number	= {NC94--49},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@TechReport{	  fujiwara95a,
  author	= {Tetsuya Fujiwara and Kikuo Fujimura and Heizo Tokutaka and
		  Satoru Kishida},
  title		= {The lateral interaction in the {K}ohonen's model---the
		  lateral interaction of physical type},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1995},
  number	= {NC94--100},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fukayama99a,
  author	= {Fukayama, A. and Ida, M. and Katai, O.},
  title		= {Behavior-based fuzzy control system for a mobile robot
		  with environment recognition by sensory-motor
		  coordination},
  booktitle	= {FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems.
		  Conference Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {1},
  pages		= {105--10},
  abstract	= {An idea to form symbolic representations which can be
		  coupled with behavior-based robot systems is introduced.
		  Our behavior-based control system is implemented with
		  Subsumption Architecture (SA), which is a class of
		  behavior-based systems, and the modules of SA are realized
		  with fuzzy control. Our fundamental idea in applying a
		  symbolic environmental model to this behavior-based system
		  is that the categories attached to the symbols are to be
		  extracted from the sensory-motor coordination of the robot.
		  We realized this idea by the use of self-organizing map
		  (SOM) which categorizes behavior sequence of the robot. By
		  categorizing the resulting behaviors, the concept about the
		  categories of the environmental structures, which is
		  embedded in the sensory-motor coordination, can be
		  extracted. In the last part of this paper, we introduce a
		  hybrid system which is composed of a lower-level
		  behavior-based system and a higher-level symbol system, and
		  then is applied to a path-planning problem as a
		  verification of our proposed idea.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fulantelli00a,
  author	= {Fulantelli, G. and Rizzo, R. and Arrigo, M. and Corrao,
		  R.},
  title		= {An adaptive open hypermedia system on the Web},
  booktitle	= {Adaptive Hypermedia and Adaptive Web-Based Systems.
		  International Conference, AH 2000. Proceedings (Lecture
		  Notes in Computer Science Vol.1892). Springer-Verlag,
		  Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {305--10},
  abstract	= {A prototype of an open and adaptive hypertext learning
		  environment on the Web is presented. The nodes of the
		  hypertext system are sorted in clusters which are ordered
		  on a map by a self-organizing neural network. This map
		  represents the information domain model on which an overlay
		  model of the learning goal and of the user knowledge is
		  built up. The user model is made up of a user knowledge
		  model and a preference model that takes into account the
		  user's attitude to approaching the information necessary to
		  achieve the learning goal. The information domain model
		  allows users to add new documents to the system that are
		  ordered by the neural network in the appropriate clusters.
		  This maintains the consistency of the user model and of the
		  learning goal when the number of documents grows.},
  dbinsdate	= {2002/1}
}

@InCollection{	  fung95a,
  author	= {Chun Che Fung and Kok Wai Wong and H. Eren and R.
		  Charlebois},
  title		= {Lithology classification using \mbox{self-organising} map},
  booktitle	= {1995 IEEE International Conference on Neural Networks
		  Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {526--31},
  dbinsdate	= {oldtimer}
}

@InCollection{	  fung97a,
  author	= {Chun Che Fung and Kok Wai Wong and H. Eren},
  title		= {Determination of a generalised BPNN using {SOM} data-
		  splitting and early stopping validation approach},
  booktitle	= {Proceedings of the Eighth Australian Conference on Neural
		  Networks (ACNN'97)},
  publisher	= {Telstra Res. Lab},
  year		= {1997},
  editor	= {M. Dale and A. Kowalczyk and R. Slaviero and J.
		  Szymanski},
  address	= {Clayton, Vic. , Australia},
  pages		= {129--33},
  dbinsdate	= {oldtimer}
}

@Article{	  furukawa02a,
  author	= {Furukawa, S. and Middlebrooks, J. C.},
  title		= {Cortical representation of auditory space:
		  Information-bearing features of spike patterns},
  journal	= {JOURNAL OF NEUROPHYSIOLOGY},
  year		= {2002},
  volume	= {87},
  number	= {4},
  month		= {APR},
  pages		= {1749--1762},
  abstract	= {Previous studies have demonstrated that the spike patterns
		  of cortical neurons vary systematically as a function of
		  sound- source location such that the response of a single
		  neuron can signal the location of a sound source throughout
		  360degrees of azimuth. The present study examined specific
		  features of spike patterns that might transmit information
		  related to sound- source location. Analysis was based on
		  responses of well- isolated single units recorded from
		  cortical area A2 in alpha- chloralose-anesthetized cats.
		  Stimuli were 80-ms noise bursts presented from loudspeakers
		  in the horizontal plane; source azimuths ranged through
		  360degrees in 20degrees steps. Spike patterns were averaged
		  across samples of eight trials. A competitive artificial
		  neural network (ANN) identified sound- source locations by
		  recognizing spike patterns; the ANN was trained using the
		  learning vector quantization learning rule. The information
		  about stimulus location that was transmitted by spike
		  patterns was computed from joint stimulus-response
		  probability matrices. Spike patterns were manipulated in
		  various ways to isolate particular features. Full-spike
		  patterns, which contained all spike-count information and
		  spike timing with 100-mus precision, transmitted the most
		  stimulus- related information. Transmitted information was
		  sensitive to disruption of spike timing on a scale of more
		  than similar to 4 ms and was reduced by an average of
		  similar to 35% when spike--timing information was
		  obliterated entirely. In a condition in which all but the
		  first spike in each pattern were eliminated, transmitted
		  information decreased by an average of only similar to11%.
		  In many cases, that condition showed essentially no loss of
		  transmitted information. Three unidimensional features were
		  extracted from spike patterns. Of those features, spike
		  latency transmitted similar to60% more information than
		  that transmitted either by spike count or by a measure of
		  latency dispersion. Information transmission by spike
		  patterns recorded on single trials was substantially
		  reduced compared with the information transmitted by
		  averages of eight trials. In a comparison of averaged and
		  nonaveraged responses, however, the information transmitted
		  by latencies was reduced by only similar to29%, whereas
		  information transmitted by spike counts was reduced by 79%.
		  Spike counts clearly are sensitive to sound-source location
		  and could transmit information about sound-source
		  locations. Nevertheless, the present results demonstrate
		  that the timing of the first poststimulus spike carries a
		  substantial amount, probably the majority, of the
		  location-related information present in spike patterns. The
		  results indicate that any complete model of the cortical
		  representation of auditory space must incorporate the
		  temporal characteristics of neuronal response patterns.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  furukawa94a,
  author	= {Hiroshi Furukawa and Tohru Ueda and Masaharu Kitamura},
  title		= {A systematic method for rational definition of plant
		  diagnostic symptoms by \mbox{self-organizing} neural
		  networks},
  pages		= {555--556},
  booktitle	= {Proc. 3rd International Conference on Fuzzy Logic, Neural
		  Nets and Soft Computing},
  year		= {1994},
  publisher	= {Fuzzy Logic Systems Institute},
  address	= {Iizuka, Japan},
  annote	= {application, diagnostic system},
  dbinsdate	= {oldtimer}
}

@InCollection{	  furukawa94b,
  author	= {H. Furukawa and T. Ueda and M. Kitamura},
  title		= {A rational method for definition of plant diagnostic
		  symptoms by \mbox{self-organizing} neural networks},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {897--902},
  dbinsdate	= {oldtimer}
}

@InCollection{	  furukawa95a,
  author	= {H. Furukawa and T. Uedu and M. Kitamura},
  title		= {Use of \mbox{self-organizing} neural networks for rational
		  definition of plant diagnostic symptoms},
  booktitle	= {Proceedings of the Topical Meeting on Computer-Based Human
		  Support Systems: Technology, Methods, and Future},
  publisher	= {IASTED-ACTA Press},
  year		= {1995},
  editor	= {M. H. Hamza},
  address	= {Calgary, Alta. , Canada},
  pages		= {441--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  furukawa95b,
  author	= {Akinori Furukawa and Naohiro Ishii},
  title		= {Unsupervised Learning of Consept for Action Planning},
  volume	= {III},
  pages		= {1316--1321},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  furukawa96a,
  author	= {H. Furukawa and T. Ueda and M. Kitamura},
  title		= {A systematic method for rational definition of plant
		  diagnostic symptoms by \mbox{self-organizing} neural
		  networks},
  journal	= {Neurocomputing},
  year		= {1996},
  volume	= {13},
  number	= {2--4},
  pages		= {171--83},
  note		= {(3rd International Conference on Fuzzy Logic, Neural Nets
		  and Soft Computing (IIZUKA'94) Conf. Date: 1--7 Aug. 1994
		  Conf. Loc: Iizuka, Japan Conf. Sponsor: Int. Fuzzy Syst.
		  Assoc. ; Int. Neural Network Soc. ; Japan Soc. Fuzzy Theory
		  \& Syst. ; et al)},
  dbinsdate	= {oldtimer}
}

@Article{	  futagami97a,
  author	= {N. Futagami and N. Okino},
  title		= {A study of bionic autonomous distributed {CAD} system},
  journal	= {Journal of the Japan Society of Precision Engineering},
  year		= {1997},
  volume	= {63},
  number	= {10},
  pages		= {1385--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  futami97a,
  author	= {R. Futami and H. Tanno and N. Hoshimiya},
  title		= {A Model for {M}c{G}urk Effect Based on Feature Maps and
		  Reciprocal Connections},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {1},
  address	= {Singapore},
  pages		= {99--102},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  fyfe95a,
  author	= {Fyfe, C. },
  title		= {Radial feature mapping},
  booktitle	= {ICANN `95. International Conference on Artificial Neural
		  Networks. Neuronimes `95 Scientific Conference},
  year		= {1995},
  editor	= {Fogelman-Soulie, F. and Gallinari, P. },
  volume	= {2},
  pages		= {27--32},
  organization	= {Dept. of Comput. \& Inf. Syst. , Paisley Univ. , UK},
  publisher	= {EC2 \& Cie},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@Article{	  gabor96a,
  author	= {A. J. Gabor and R. R. Leach and F. U. Dowla},
  title		= {Automated seizure detection using a \mbox{self-organizing}
		  neural network},
  journal	= {Electroencephalography and Clinical Neurophysiology},
  year		= {1996},
  volume	= {99},
  number	= {3},
  pages		= {257--66},
  dbinsdate	= {oldtimer}
}

@Article{	  gabor98a,
  author	= {A. J. Gabor},
  journal	= {EEG Clinical Neurophysiology},
  title		= {Seizure detection using a \mbox{self-organizing} neural
		  network: validation and comparison with other detection
		  strategies},
  year		= {1998},
  number	= {1},
  pages		= {27--32},
  volume	= {107},
  abstract-url	= {ftp://nova.ucdavis.edu/pub/valid_abs},
  url		= {ftp://nova.ucdavis.edu/pub/valid.ps.gz},
  keywords	= {eeg, seizure detection, neural networks, self-organizing
		  map, wavelet filter},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gabriel93a,
  author	= {Gabriel Gabriel and Christos N. Schizas and Constantinos
		  S. Pattichis and Renos Constantinou and Annie
		  Hadjianastasiou and Akis Schizas},
  title		= {Qualitative Morphological Analysis of Muscle Biopsies
		  Using Neural Networks},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {943--946},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {Qualitative data from human muscle biopsies have been
		  extracted and analyzed by artificial neural network (ANN)
		  models trained with the Kohonen's self-organizing feature
		  maps algorithm to provide an automated medical diagnosis.
		  Data from 6 distinct groups of neuromuscular disorders were
		  examined. Training and evaluation were carried out on 80
		  and 25 cases respectively. The diagnostic performance of
		  models investigated varied form 87 to 95%, and 88 to 92%
		  for the training and evaluation. Furthermore, the
		  diagnostic usefulness of the self-organizing feature map
		  models was tested on 11 muscle biopsies with no specific
		  diagnostic findings that gave encouraging results.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gader95a,
  author	= {Gader, P. and Jung-Hsien Chiang},
  title		= {Robust handwritten word recognition with fuzzy sets},
  booktitle	= {Proceedings of ISUMA---NAFIPS '95 The Third International
		  Symposium on Uncertainty Modeling and Analysis and Annual
		  Conference of the North American Fuzzy Information
		  Processing Society},
  year		= {1995},
  pages		= {198--203},
  organization	= {Missouri Univ. , Columbia, MO, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  abstract	= {A hybrid fuzzy neural system is used to improve a
		  handwritten word recognition algorithm. The word
		  recognition algorithm matches digital images of handwritten
		  words to strings in a lexicon. This algorithm requires a
		  module to assign character class membership values to
		  images of segments of handwritten words. Many of these
		  images are not characters. It is shown that a hybrid neural
		  system consisting of a cascade of a Kohonen Self-Organizing
		  Feature Map (SOFM) followed by Choquet fuzzy integrals can
		  yield improved performance over a multi-layer feedforward
		  network (MLFN). The hybrid method scored a word recognition
		  rate of 85% compared to 77% for the MLFN method.},
  dbinsdate	= {oldtimer}
}

@Article{	  gader97a,
  author	= {Paul D. Gader and James M. Keller and Raghu Krishnapuram
		  and Jung-Hsien Chiang and Magdi A. Mohamed},
  title		= {Neural and Fuzzy Methods in Handwriting Recognition},
  journal	= {IEEE Computer},
  year		= 1997,
  volume	= 30,
  number	= 2,
  month		= {February},
  pages		= {79--86},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gailliat00a,
  author	= {T. Galliat and W. Huisinga and P. Deuflhard},
  title		= {Self-Organizing Maps Combined with Eigenmode Analysis for
		  Automated Cluster Identication},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Konrad-Zuse-Zentrum Berlin; Freie Universi at Berlin,
		  Fachbereich Mathematik und Informatik},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gajewski01a,
  author	= {Gajewski, P.},
  title		= {Channel assignment in {CDMA} systems with walsh and {PN}
		  coding},
  booktitle	= {IEEE Vehicular Technology Conference},
  year		= {2001},
  editor	= {},
  volume	= {2},
  pages		= {982--986},
  organization	= {Military University of Technology},
  publisher	= {},
  address	= {},
  abstract	= {Many channels assignment procedures and allocation
		  strategies for cellular network have been discussed
		  recently for various multi-access methods. A channel
		  allocation both for new calls and for handovered ones
		  should minimise co-channel interference. In CDMA, channels
		  are creating by spreading and accessing codes.
		  Signal-to-noise ratio (SNR) is generally dependent on
		  number of users N and correlation coefficients
		  r<sub>k,i</sub> between sequences used for transmission.
		  The codes assignment method based on Hopfield neural
		  network (HNN) with Kohonen self-organised future maps
		  (KSOFM) is presented here. Two-stage algorithm is proposed
		  for DS-CDMA systems with heterogeneous cells. The
		  cross-correlation coefficients are the main factors of
		  minimised cost function that is calculated for the various
		  traffic and multi-access interference parameters. The
		  simulation model was examined for a number of PN sequences
		  as well as for orthogonal Walsh mapping.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  gale00a,
  author	= {Tim Gale and Lorna Peters and Ray Frank and Neil Davey},
  title		= {Visual Categorisation in a modified self organising map:
		  implications for category-spesific agnosia},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Department of Computer Science; Department of Psychology,
		  University of Hertfordshire; Hertfordshire Neurosciences
		  Research Group (HNRG), QEII Hospital, Howlands,
		  Welwyn-Garden-City, Herts, UK},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InCollection{	  galindo95a,
  author	= {P. L. Galindo},
  title		= {The competitive forward-backward algorithm ({CFB})},
  booktitle	= {Fourth International Conference on `Artificial Neural
		  Networks`},
  publisher	= {IEE},
  year		= {1995},
  address	= {London, UK},
  pages		= {82--5},
  dbinsdate	= {oldtimer}
}

@Article{	  galli94a,
  author	= {Galli, I. and Mecocci, A. and Cappellini, V. },
  title		= {Improved colour image vector quantisation by means of
		  \mbox{self-organising} neural networks},
  journal	= {Electronics Letters},
  year		= {1994},
  volume	= {30},
  number	= {4},
  pages		= {333--4},
  month		= {Feb},
  dbinsdate	= {oldtimer}
}

@Article{	  ganesh_murthy98a,
  author	= {C. N. S. {Ganesh Murthy} and Y. V. Venkatesh},
  title		= {Encoded pattern classification using constructive learning
		  algorithms based on learning vector quantization},
  journal	= {Neural Networks},
  year		= {1998},
  volume	= {11},
  number	= {2},
  pages		= {315--22},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ganslmeier00a,
  author	= {Ganslmeier, B. and Schels, A. and Lang, E. W.},
  title		= {{PCA} and {ICA} analysis of process control data obtained
		  during Si-wafer manufacturing},
  booktitle	= {Proceedings of the IASTED International Conference. Signal
		  Processing and Communications. IASTED/ACTA Press, Anaheim,
		  CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {72--7},
  abstract	= {Process control data obtained during silicon wafer
		  manufacturing were analysed using self organizing map
		  (SOM), principal component analysis (PCA) and independent
		  component analysis (ICA) neural network algorithms. During
		  preprocessing of the raw data the Kohonen network (SOM) was
		  used as a filter to approximate the underlying probability
		  density of the data and to have an indication of possible
		  outliers. PCA was used to effect a large-dimensionality
		  reduction. These reduced data sets were further analysed
		  with various ICA network implementations to extract
		  statistically independent features in the input data
		  ensemble which may be used for further classification
		  tasks.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  gao99a,
  author	= {Gao, X. Z. and Ovaska, S. J.},
  title		= {A new fuzzy filter with application in motion control
		  systems},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {280--5},
  abstract	= {We propose a fuzzy logic-based filter. Different from
		  existing fuzzy filters, our model applies the
		  self-organizing map algorithm together with the least
		  squares method to get the optimal membership functions, as
		  well as the consequent inference parameters. This approach
		  results in a delayless fuzzy filter that has more efficient
		  filtering capability than conventional fuzzy filters. A DC
		  servo motor system is employed as a testbed for this fuzzy
		  filter. Simulation shows that our fuzzy filter can
		  efficiently filter out the harmful velocity noise produced
		  by the low-cost tachometer in the feedback loop.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gaolin01a,
  author	= {Gaolin Fang and Wen Gao and Jiyong Ma},
  title		= {Signer-independent sign language recognition based on
		  {SOFM}/{HMM}},
  booktitle	= {Proceedings IEEE ICCV Workshop on Recognition, Analysis,
		  and Tracking of Faces and Gestures in Real-Time Systems.
		  IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {90--5},
  abstract	= {The aim of sign language recognition is to provide an
		  efficient and accurate mechanism to transcribe sign
		  language into text or speech. State-of-the-art sign
		  language recognition should be able to solve the
		  signer-independent problem for practical application. In
		  this paper, a hybrid SOFM/HMM system, which combines
		  self-organizing feature maps (SOFMs) with hidden Markov
		  models (HMMs), is presented for signer-independent Chinese
		  sign language recognition. We implement the SOFM/HMM sign
		  recognition system. Meanwhile, results from the HMM-based
		  system are provided as comparison. Experimental results
		  show the SOFM/HMM system increases the recognition accuracy
		  by 5% than the HMM-based one. Furthermore, a self-adjusting
		  recognition algorithm is also proposed for improving the
		  SOFM/HMM discrimination. When it is applied to the SOFM/HMM
		  system it can improve the recognition accuracy by 1.9%. All
		  experiments were performed in real-time with the dictionary
		  size 208.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  garani01a,
  author	= {S. Garani and J. C. Principe},
  title		= {Dynamic vector quantisation of speech},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {238--45},
  year		= {2001},
  editor	= {Nigel Allison and Hujun Yin and Lesley Allison and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  garavaglia00a,
  author	= {Garavaglia, Susan B.},
  title		= {Health care customer satisfaction survey analysis using
		  self-organizing maps and `exponentially smeared' data
		  vectors},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {119--124},
  organization	= {Schering-Plough Corp},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The Tanimoto Coefficient Self-Organizing Map (TCSOM) is
		  combined with a data transformation that `smears' binary
		  vectors with an exponential decay transformation according
		  to a subjective assessment of how close in meaning adjacent
		  vector elements are. The methodology is demonstrated using
		  managed health care plan satisfaction survey data
		  originally in ordinal integer values. Five different SOMs
		  were trained and tested using ordinal, binary, and
		  `smeared' vectors. The result is that the TCSOM provides a
		  useful visualization of the complexity of responses and
		  highlights specific areas for health plan quality and
		  service improvements that might be missed if only simple
		  average total scores were considered.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  garavaglia01a,
  author	= {Garavaglia, S. B.},
  title		= {Statistical analysis of the Tanimoto coefficient
		  self-organizing map ({TCSOM}) applied to health behavioral
		  survey data},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2483--2488},
  organization	= {Integrated Therapeutics Group, Schering-Plough
		  Corporation},
  publisher	= {},
  address	= {},
  abstract	= {The Tanimoto Coefficient Self-Organizing Map (TCSOM)
		  introduced by Garavaglia [4] is applied to self-reported
		  data on dairy milk consumption to identify milk consumption
		  patterns throughout a person's life. The data are a cohort
		  of U. S. residents aged 66 and older. These results are
		  merged with outcomes on self-reported osteoporosis and
		  examination data on bone mineral density (BMD). For this
		  cohort, lack of daily milk consumption as a child and
		  irregular patterns of milk consumption appear to contribute
		  to osteoporosis and low BMD later in life. Simple
		  statistical analysis, including a z-test and several
		  empirical distribution function (EDF) tests validate some
		  of the self-organizing properties of the TCSOM. Density
		  plots of also illustrate the non-linear grouping and
		  self-organizing characteristics.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  garavaglia93a,
  author	= {Susan Garavaglia},
  title		= {A {S}elf-{O}rganizing {M}ap Applied to Macro and Micro
		  Analysis of Data with Dummy Variables},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {I},
  pages		= {362--368},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  garavaglia94a,
  author	= {Susan Garavaglia},
  title		= {An Information Theoretic Re-interpretation of the {S}elf
		  {O}rganizing {M}ap With Standard Scaled Dummy Variables},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {502--509},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {analysis, data analysis},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  garavaglia95a,
  author	= {Susan Garavaglia},
  title		= {A Case Study in the Design of {S}elf-{O}rganizing {M}aps
		  Using {S}ammon's Map},
  volume	= {I},
  pages		= {203--211},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InCollection{	  garavaglia96a,
  author	= {S. Garavaglia},
  title		= {Determination of systematic risk in {US} businesses using
		  {S}ammon's mapping and \mbox{self-organizing} maps},
  booktitle	= {WCNN'96. World Congress on Neural Networks. International
		  Neural Network Society 1996 Annual Meeting},
  publisher	= {Lawrence Erlbaum Associates},
  year		= {1996},
  address	= {Mahwah, NJ, USA},
  pages		= {831--40},
  dbinsdate	= {oldtimer}
}

@InCollection{	  garavaglia98a,
  author	= {S. Garavaglia},
  title		= {A heuristic \mbox{self-organizing} map trained using the
		  {T}animoto coefficient},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {289--94},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  garcia-tejedor94a,
  author	= {Garcia-Tejedor, A. and Cosculluela, M. J. and Bermejo, C.
		  and Montes, R. },
  title		= {A neural system for short-term load forecasting based on
		  day-type classification},
  booktitle	= {ISAP '94. International Conference on Intelligent System
		  Application to Power Systems},
  year		= {1994},
  editor	= {Hertz, A. and Holen, A. T. and Rault, J. -C. },
  volume	= {1},
  pages		= {353--60},
  organization	= {Dept. of Knowledge Eng. , Eritel, Madrid, Spain},
  publisher	= {EC2},
  address	= {Nanterre Cedex, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  garcia01a,
  author	= {Garcia Lagos, F. and Joya, G. and Marin, F. J. and
		  Sandoval, F.},
  title		= {Neural networks for contingency evaluation and monitoring
		  in power systems},
  booktitle	= {Bio-Inspired Applications of Connectionism. 6th
		  International Work-Conference on Artificial and Natural
		  Neural Networks, IWANN 2001. Proceedings, Part II. (Lecture
		  Notes in Computer Science Vol.2085). Springer-Verlag,
		  Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {711--18},
  abstract	= {In this paper an analysis of the applicability of
		  different neural paradigms to contingency analysis in power
		  systems is presented. On one hand, unsupervised
		  self-organizing maps by Kohonen have been implemented for
		  visualization and graphic monitoring of contingency
		  severity. On the other hand, supervised feedforward neural
		  paradigms such as multilayer perceptron and radial basis
		  function, are implemented for severity numerical evaluation
		  and contingency ranking. Experiments have been performed
		  with successfully result in the case of Kohonen and
		  multilayer perceptron paradigms.},
  dbinsdate	= {2002/1}
}

@Article{	  gardner00a,
  author	= {Gardner, Julian W. and Shin, Hyun Woo and Hines, Evor L.
		  and Dow, Crawford S.},
  title		= {Electronic nose system for monitoring the quality of
		  potable water},
  journal	= {Sensors and Actuators, B: Chemical},
  year		= {2000},
  volume	= {69},
  number	= {3},
  month		= {Oct},
  pages		= {336--341},
  organization	= {Univ of Warwick},
  publisher	= {Elsevier Sequoia SA},
  address	= {Lausanne},
  abstract	= {A measurement system has been developed for the testing of
		  cyanobacteria in water, and it consists of three main
		  stages: the odour sampling system, an electronic nose
		  (e-nose) and a CellFacts instrument that analyses liquid
		  samples. The e-nose system, which employs an array of six
		  commercial odour sensors, has been used to monitor not only
		  different strains but also the growth phase of
		  cyanobacteria (i.e. blue-green algae) in water over a
		  40-day period. Principal components analysis (PCA),
		  multi-layer perceptron (MLP), learning vector quantisation
		  (LVQ) and Fuzzy ARTMAP were used to analyse the response of
		  the sensors. The optimal MLP network was found to classify
		  correctly 97.1% of the unknown nontoxic and 100% of the
		  unknown toxic cyanobacteria. The optimal LVQ and Fuzzy
		  ARTMAP algorithms were able to classify 100% of both
		  strains of cyanobacteria samples. The accuracy of MLP, LVQ
		  and Fuzzy ARTMAP in terms of predicting four different
		  growth phases of toxic cyanobacteria was 92.3%, 95.1% and
		  92.3%, respectively. These results show the potential
		  application of neural network based e-noses to test the
		  quality of potable water as an alternative to instruments,
		  such as liquid chromatography or optical microscopy.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  gardner00b,
  author	= {Gardner, M. and Bieker, J.},
  title		= {Data mining solves tough semiconductor manufacturing
		  problems},
  booktitle	= {Proceedings. KDD-2000. Sixth ACM SIGKDD International
		  Conference on Knowledge Discovery and Data Mining. ACM, New
		  York, NY, USA},
  year		= {2000},
  volume	= {},
  pages		= {376--83},
  abstract	= {Quickly solving product yield and quality problems in a
		  complex manufacturing process is becoming increasingly more
		  difficult. The easier problems have been solved using
		  process control, statistical analysis and design of
		  experiments, which have established a solid base for a
		  well-tuned manufacturing process. However, the dynamic
		  "higher-tier" problems, coupled with quicker time-to-market
		  expectations, is making finding and resolving problems
		  quickly an overwhelming task. These dynamic "higher-tier"
		  problems include: multi-factor and nonlinear interactions,
		  intermittent problems, dynamically changing processes,
		  installing new processes, multiple products and, of course,
		  the increasing volumes of data. Data mining technology can
		  increase product yield and quality to the next higher level
		  by quickly finding and solving these tougher problems. Case
		  studies of semiconductor wafer manufacturing problems are
		  presented. A combination of self-organizing neural networks
		  and rule induction is used to identify the critical
		  poor-yield factors from normally collected wafer
		  manufacturing data. Subsequent controlled experiments and
		  process changes confirmed the solutions. Wafer yield
		  problems were solved 10* faster than standard approaches,
		  yield increases ranged from 3% to 15%, and endangered
		  customer product deliveries were saved. This approach is
		  flexible and can be appropriate for a number of complex
		  manufacturing processes.},
  dbinsdate	= {2002/1}
}

@Article{	  gardner96a,
  author	= {J. W. Gardner and P. N. Bartlett},
  title		= {Performance definition and standardization of electronic
		  noses},
  journal	= {Sensors and Actuators B [Chemical]},
  year		= {1996},
  volume	= {B33},
  number	= {1--3},
  pages		= {60--7},
  note		= {(International Solid-State Sensors and Actuators
		  Conference---TRANSDUCERS '95 Conf. Date: 25--29 June 1995
		  Conf. Loc: Stockholm, Sweden)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  gardner97a,
  author	= {R. D. Gardner and D. A. Harle},
  title		= {Alarm correlation and network fault resolution using the
		  {K}ohonen \mbox{self-organising} map},
  booktitle	= {GLOBECOM 97. IEEE Global Telecommunications Conference.
		  Conference Record},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1398--402},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gardon99a,
  author	= {Gardon, A. P. and Vazquez, C. R. and Illarramendi, A. A.},
  title		= {Spanish phoneme classification by means of a hierarchy of
		  Kohonen Self-Organizing Maps},
  booktitle	= {TEXT, SPEECH AND DIALOGUE},
  year		= {1999},
  pages		= {181--186},
  abstract	= {In this paper, some results of the classification of
		  spanish phonemes by means of Kohonen Self-Organizing Maps
		  (SOM) are presented. These results show that SOM may be
		  very useful in the previous steps of a continous speech
		  recognizer, as well as a valuable aid in the signal
		  phonetic segmentation process. This intermediate
		  classification provides a new representation of the signal
		  such that the "phonetic" gap between inputs and outputs of
		  the recognizer is drastically reduced, so simplifying the
		  task of the recognizer.},
  dbinsdate	= {2002/1}
}

@Proceedings{	  garrido90a,
  title		= {Statistical Mechanics of Neural Networks. Proc. XI Sitges
		  Conference},
  year		= {1990},
  editor	= {L. Garrido},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  garside92a,
  author	= {Jeffrey J. Garside and Ronald H. Brown and Timothy L.
		  Ruchti and Xin Feng},
  title		= {Nonlinear Estimation of Torque in Switched Reluctance
		  Motor Using Grid Locking and Preferential Training
		  Techniques on Self-Organizing Neural Networks},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {II},
  pages		= {811--816},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  garside92b,
  author	= {Garside, J. J. and Ruchti, T. L. and Brown, R. H. },
  title		= {Using \mbox{self-organizing} artificial neural networks
		  for solving uncertain dynamic nonlinear system
		  identification and function modeling problems},
  booktitle	= {Proceedings of the 31st IEEE Conference on Decision and
		  Control},
  year		= {1992},
  volume	= {3},
  pages		= {2716--21},
  organization	= {Marquette Univ. , Milwaukee, WI, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Proceedings{	  gassilloud91a,
  title		= {Computing with Parallel Architectures: T. Node},
  year		= {1991},
  editor	= {D. Gassilloud and J. C. Grossetie},
  publisher	= {Kluwer},
  organization	= {Inst. Syst. Eng. Inf},
  address	= {Dordrecht, Netherlands},
  x		= {The following topics were dealt with: T. Node, Supernode
		  architecture; communicating processes design; domain
		  decomposition on transputer arrays; programming distributed
		  computers; Petri net tool for designing transputer network
		  applications; software tools for programs development on
		  reconfigurable parallel architecture; imagesynthesis on T.
		  Node; parallel image processing algorithms and
		  architectures; parallel implementation of Kohonen's
		  algorithm on transputers; and learning on VLSI: a general
		  purpose digital neurochip. Sis. mm. Auger91},
  dbinsdate	= {oldtimer}
}

@Article{	  gasteiger93a,
  author	= {Johann Gasteiger and Jure Zupan},
  title		= {Neural Networks in Chemistry},
  journal	= {Angewandte Chemie, Intrenational Edition in English},
  year		= {1993},
  volume	= {32},
  number	= {4},
  pages		= {503--527},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@Article{	  gasteiger94a,
  author	= {Johann Gasteiger and Xinzhi Li and Anders Uschold},
  title		= {The Beauty of Molecular Surfaces as Revealed by
		  Self-Organizing Neural Networks},
  journal	= {Journal of Molecular Graphics},
  year		= 1994,
  volume	= 12,
  month		= {June},
  pages		= {90--97},
  abstract	= {The Kohonen neural network is a self-organizing network
		  that can be used for the projection of the surface
		  properties of molecules. This allows one to view properties
		  on a molecular surface, like the electrostatic potential in
		  a single picture. These maps are useful for the comparison
		  of molecules and provide a new definition of molecular
		  similarity.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  gaubert98a,
  author	= {P. Gaubert and M. Cottrell and P. Rousset},
  title		= {Neural network and segmented labour market.
		  {C}onf{\'e}rence {ACSEG'97} Tours 97},
  institution	= {Universit{\'e} Paris 1},
  year		= {1998},
  type		= {Pr{\'e}publication du SAMOS},
  number	= {84},
  address	= {Paris},
  dbinsdate	= {oldtimer}
}

@Article{	  gautama99a,
  author	= {Temujin Gautama and Marc M. {van Hulle}},
  title		= {Self-Organized Feature Extraction Achieved with a
		  Parametrized Filterbank},
  journal	= {Neural Processing Letters},
  year		= {1999},
  volume	= {10},
  number	= {2},
  month		= {October},
  pages		= {131--137},
  dbinsdate	= {oldtimer}
}

@Book{		  gautreaux95a,
  author	= {Gautreaux, M. M.},
  title		= {Hyperspectral Imagery Analysis Using Neural Network
		  Techniques. Master's thesis.},
  year		= {1995},
  abstract	= {Every material has a unique electromagnetic
		  reflectance/emission signature which can be used to
		  identify it. Hyperspectral imagers, by collecting high
		  spectral resolution data, provide the ability to identify
		  these spectral signatures. Utilization and exploitation of
		  hyperspectral data is challenging because of the enormous
		  data volume produced by these imagers. Most current
		  processing and analyzation techniques involve
		  dimensionality reduction, during which some information is
		  lost. This thesis demonstrates the ability of neural
		  networks and the Kohonen Self-Organizing Map to classify
		  hyperspectral data. The possibility of real time processing
		  is addressed. (AN).},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gavrilas01a,
  author	= {Gavrilas, Mihai and Sfintes, Viorel Calin and Filimon,
		  Marius Nelu},
  title		= {Identifying typical load profiles using neural-fuzzy
		  models},
  booktitle	= {Proceedings of the IEEE Power Engineering Society
		  Transmission and Distribution Conference},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {421--426},
  organization	= {Department of Power Engineering, Gh. Asachi Technical
		  Univ. of Iasi},
  publisher	= {},
  address	= {},
  abstract	= {This paper describes a modified self-organizing algorithm,
		  which addresses the problem of consumer classification in
		  distribution networks according to the shape of the load
		  profiles and the automatic extraction of the typical load
		  profiles for each consumer category. The algorithm is a
		  modified/weighted form of the fuzzy implementation of the
		  Kohonen algorithm. The performances of the algorithm were
		  studied using a set of 96 load profiles metered in the
		  distribution network of a public utility in Romania. The
		  algorithm produced 9 typical load profiles. The proposed
		  approach was able to capture the quantitative and/or
		  qualitative differences between load profiles of different
		  consumers with same activities.},
  dbinsdate	= {2002/1}
}

@Article{	  ge00a,
  author	= {Ge, Ming and Chiu, Min-Sen and Wang, Qing-Guo},
  title		= {Extended self-organizing map for nonlinear system
		  identification},
  journal	= {Industrial and Engineering Chemistry Research},
  year		= {2000},
  volume	= {39},
  number	= {10},
  month		= {Oct},
  pages		= {3778--3788},
  organization	= {Natl Univ of Singapore},
  publisher	= {ACS},
  address	= {Washington, DC},
  abstract	= {Local model networks (LMN) are recently proposed for
		  modeling a nonlinear dynamical system with a set of locally
		  valid submodels across the operating space. Despite the
		  recent advances of LMN, a priori knowledge of the processes
		  has to be exploited for the determination of the LMN
		  structure and the weighting functions. However, in most
		  practical cases, a priori knowledge may not be readily
		  accessible for the construction of LMN. In this paper, an
		  extended self-organizing map (ESOM) network, which can
		  overcome the aforementioned difficulties, is developed to
		  construct the LMN. The ESOM is a multilayered network that
		  integrates the basic elements of a traditional
		  self-organizing map and a feed-forward network into a
		  connectionist structure. A two-phase learning algorithm is
		  introduced for constructing the ESOM from the plant
		  input-output data, with which the structure is determined
		  through the self-organizing phase and the model parameters
		  are obtained by the linear least-squares optimization
		  method. Literature examples are used to demonstrate the
		  effectiveness of the proposed scheme.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ge99a,
  author	= {Ge, Ming and Chiu, Min Sen and Wang, Qing Guo},
  title		= {Extended \mbox{self-organizing} map for nonlinear system
		  identification},
  booktitle	= {Proceedings of the IEEE Conference on Decision and
		  Control},
  year		= {1999},
  volume	= {1},
  pages		= {1065--1070},
  abstract	= {Local model networks (LMN) are employed to represent a
		  nonlinear dynamical system with a set of locally valid
		  sub-models across the operating range. A new extended
		  self-organizing map network (ESOM) is developed in this
		  paper for the identification of the LMN. The ESOM is a
		  multi-layered network that integrates the basic elements of
		  traditional self-organizing maps and a feed-forward network
		  into a connectionist structure which distribute the
		  learning tasks. A novel two-phase learning algorithm is
		  introduced for constructing the ESOM from plant
		  input-output data, with which the structure is determined
		  through the self-organizing and the parameters are obtained
		  with the linear least square optimization method. The
		  predictive performance of the model derived from the ESOM
		  is evaluated in three case studies. Simulation results
		  demonstrate the effectiveness of the proposed scheme in
		  comparison with other methods.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gelli94a,
  author	= {Kiran Gelli and Robert A. McLaughlan and Rajab Challoo and
		  Syed Iqbal Omar},
  title		= {Multible Sensor Target Classification Using an
		  Unsupervised Hybrid Neural Network},
  pages		= {4028--4032},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, pattern recognition, hybrid},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gelli94b,
  author	= {Kiran Gelli and Robert McLauchlan and Rajab Challoo and
		  Syed Iqbal Omar},
  title		= {A Hybrid Neural Network Architecture for Sensor Fusion},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {679--685},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, data analysis, target detection},
  dbinsdate	= {oldtimer}
}

@InCollection{	  gelli94c,
  author	= {K. Gelli and R. A. McLauchlan and S. I. Omar and R.
		  Challoo},
  title		= {Multisensor fusion/integration using an unsupervised
		  hybrid neural network},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {433--40},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gemello91a,
  author	= {Roberto Gemello and Cataldo Lettera and Franco Mana and
		  Lorenzo Masera},
  title		= {Self Organizing Feature Maps for Contour Detection in
		  Videophone Images},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1305--1308},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  gemello92a,
  author	= {R. Gemello and C. Lettera and F. Mana and L. Masera},
  title		= {Self organizing feature maps for contour detection in
		  videophone images},
  journal	= {CSELT Technical Reports},
  year		= {1992},
  volume	= {20},
  number	= {2},
  pages		= {143--147},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InCollection{	  genc96a,
  author	= {I. Genc and C. Guzelis},
  title		= {One-dimensional signal recognition by two-dimensional
		  dynamical arrays},
  booktitle	= {Proceedings of the Eleventh International Symposium on
		  Computer and Information Sciences. ISCIS},
  publisher	= {Middle East Tech. Univ},
  year		= {1996},
  volume	= {2},
  editor	= {V. Atalay and U. Halici and K. Inan and N. Yalabik and A.
		  Yazici},
  address	= {Ankara, Turkey},
  pages		= {535--42},
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  gengo89a,
  author	= {J. T. Gengo},
  title		= {Application of Neural Networks to the {F}/{A}-18 {E}ngine
		  {C}ondition {M}onitoring {S}ystem},
  school	= {Naval Postgraduate School},
  year		= {1989},
  address	= {Monterey, CA},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@Article{	  george00a,
  author	= {George, S. E.},
  title		= {A visualization and design tool ({AVID}) for data mining
		  with the self-organizing feature map},
  journal	= {International-Journal-on-Artificial-Intelligence-Tools-(Architectures,-Languages,-Algorithms)}
		  ,
  year		= {2000},
  volume	= {9},
  pages		= {369--75},
  abstract	= {This paper presents the AVID software tool which is
		  particularly useful for data mining with an artificial
		  neural network known as the self-organising feature map
		  (SOM). AVID supports network training in both the: 1)
		  selection of network inputs, and 2) visualisation of the
		  trained SOM. Both these features are novel aids to SOM
		  network training and are particularly important when
		  consideration is given to using the SOM for data mining.
		  Once trained the SOM produces a 2D topological ordering of
		  the input training data and it is particularly useful for
		  representing the relationships within multidimensional
		  data. The main classes within the data can be identified
		  from the output map. AVID is an important software tool
		  which enables data mining with the SOM by the selection of
		  network inputs and the subsequent visualisation of the
		  classes within these input vectors.},
  dbinsdate	= {2002/1}
}

@Article{	  george00b,
  author	= {George, S. E.},
  title		= {Spatio-temporal analysis with the self-organizing feature
		  map},
  journal	= {Knowledge-and-Information-Systems},
  year		= {2000},
  volume	= {2},
  pages		= {359--72},
  abstract	= {Spatio-temporal pattern recognition problems are
		  particularly challenging. They typically involve detecting
		  change that occurs over time in two-dimensional patterns.
		  Analytic techniques devised for temporal data must take
		  into account the spatial relationships among data points.
		  An artificial neural network known as the self-organizing
		  feature map (SOM) has been used to analyze spatial data.
		  The paper further investigates the use of the SOM with
		  spatio-temporal pattern recognition. The principles of the
		  two-dimensional SOM are developed into a novel
		  three-dimensional network and experiments demonstrate that
		  (i) the three-dimensional network makes a better
		  topological ordering and (ii) there is a difference in
		  terms of the spatio-temporal analysis that can be made with
		  the three-dimensional network.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  georges95a,
  author	= {Georges, E. M. and Lai, L. L. and Ndeh-Che, F. and Braun,
		  H. },
  title		= {Neural networks implementation with {VLSI}},
  booktitle	= {Fourth International Conference on `Artificial Neural
		  Networks`},
  year		= {1995},
  pages		= {489--94},
  organization	= {City Univ. , London, UK},
  publisher	= {IEE},
  address	= {London, UK},
  abstract	= {This paper presents the design of a new and efficient
		  Winner-Take-All (WTA) cell for the Self-organizing Mapping
		  (SOM) neuron. This cell is implemented in VLSI that
		  provides both faster operation and a reduction in the
		  number of transistors per cell compared to existing
		  designs. The operation of the circuit is described and
		  results of SPICE simulation are presented.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gera92a,
  author	= {Michael Gera},
  title		= {Finding Multi-Faculty Structure},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1357--1360},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gera93a,
  author	= {Gera, M. H. },
  title		= {Learning with mappings and input-orderings using random
		  access memory based neural networks},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  year		= {1993},
  editor	= {Albrecht, R. F. and Reeves, C. R. and Steele, N. C. },
  pages		= {184--9},
  organization	= {Dept. of Comput. , Imperial Coll. , London, UK},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  geraci91a,
  author	= {M. Geraci and F. Sorbello and G. Vassallo},
  title		= {A new approach to the travelling salesman problem using
		  {K}ohonen maps},
  booktitle	= {Fourth Italian Workshop. Parallel Architectures and Neural
		  Networks},
  year		= {1991},
  editor	= {E. R. Caianiello},
  pages		= {344--350},
  organization	= {Univ. Salerno; Inst. Italiano di Studi Filosofici},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gerecke99a,
  author	= {Gerecke, U. and Sharkey, N.},
  title		= {Quick and dirty localization for a lost robot},
  booktitle	= {Proceedings 1999 IEEE International Symposium on
		  Computational Intelligence in Robotics and Automation.
		  CIRA'99.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {},
  pages		= {262--7},
  abstract	= {The lost robot problem is tackled here. The robot is
		  placed randomly in an environment and, when started up, has
		  to determine where it is. A new method is presented that
		  employs a {SOM} to provide a short-list of candidate
		  locations for the robot. A quick and dirty localization
		  method sits on top of the {SOM} and disambiguates its
		  output by moving the robot a small distance away from the
		  initial position and accumulating evidence. Two studies are
		  presented that evaluate the accuracy and reliability of the
		  method in worlds of different sizes. These yield favorable
		  results and illustrate the trade-off between accuracy and
		  reliability. The results show that the location of the
		  robot can be computed with a satisfactory degree of
		  reliability and accuracy within a fairly small radius of
		  uncertainty.},
  dbinsdate	= {oldtimer}
}

@Article{	  gerl97a,
  author	= {S. Gerl and P. Levi},
  title		= {3-D human face recognition by \mbox{self-organizing}
		  matching approach},
  journal	= {Pattern Recognition and Image Analysis},
  year		= {1997},
  volume	= {7},
  number	= {1},
  pages		= {38--46},
  note		= {(4th Open German-Russian Workshop on Pattern Recognition
		  and Image Understanding Conf. Date: 3--9 March 1996 Conf.
		  Loc: Valday, Russia)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  germen97a,
  author	= {Emin Germen and Semih Bilgen},
  title		= {A Statistical Approach to Determine the Neighborhood
		  FUnction and Learning Rule in Self-Organized Maps},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {1},
  address	= {Singapore},
  pages		= {334--337},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  germond93a,
  author	= {Germond, A. J. and Niebur, D. },
  title		= {Neural network applications in power systems},
  booktitle	= {PSCC. Eleventh Power Systems Computation Conference.
		  Tutorial Session Proceedings},
  year		= {1993},
  pages		= {61--70},
  organization	= {Lab. de Reseaux d'Energie Electron. , Ecole Polytech.
		  Federale de Lausanne, Switzerland},
  publisher	= {Power Systm. Comput. Conference},
  address	= {Zurich, Switzerland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gersho90a,
  author	= {M. Gersho and R. Reiter},
  title		= {Information Retrieval using Self-Organizing and
		  Heteroassociative Supervised Neural Network},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  year		= {1990},
  volume	= {1},
  pages		= {361--364},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gersho90b,
  author	= {M. Gersho and R. Reiter},
  title		= {Information Retrieval using a Hybrid Multi-Layer Neural
		  Network},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, San Diego},
  year		= {1990},
  volume	= {2},
  pages		= {111--117},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Book{		  geszti90a,
  author	= {T. Geszti},
  title		= {Physical Models of Neural Networks},
  publisher	= {World Scientific},
  address	= {Singapore},
  year		= 1990,
  dbinsdate	= {oldtimer}
}

@InProceedings{	  geszti90b,
  author	= {T. Geszti and I. Csabai and F. Czak{\'{o}} and T.
		  Szak{\'{a}}cs and R. Serneels and G. Vattay},
  title		= {Dynamics of the {K}ohonen Map},
  booktitle	= {Statistical Mechanics of Neural Networks: Sitges,
		  Barcelona, Spain},
  year		= 1990,
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  pages		= {341--349},
  dbinsdate	= {oldtimer}
}

@Article{	  geszti92a,
  author	= {T. Geszti and I. Csabai},
  title		= {Habituation in learning vector quantization},
  journal	= {Complex Systems},
  year		= {1992},
  volume	= {6},
  number	= {2},
  pages		= {179--191},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  geszti92b,
  author	= {Tamas Geszti},
  title		= {Hydrodynamics of Learning Vector Quantization},
  booktitle	= {From Phase Transitions to Chaos},
  year		= {1992},
  editor	= {G. Gy{\"o}rgyi and I. Kondor and L. Sasvari and T. Tel},
  pages		= {},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  geva00b,
  author	= {Geva, S. and Buckingham, L.},
  title		= {{VQT}ree: Vector quantization for decision tree
		  induction},
  booktitle	= {KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS},
  year		= {2000},
  pages		= {349--359},
  abstract	= {We describe a new oblique decision tree induction
		  algorithm. The VQTree algorithm uses Learning Vector
		  Quantization to form a nonparametric model of the training
		  set, and from that obtains a set of hyperplanes which are
		  used as oblique splits in the nodes of a decision tree. We
		  use a set of public data sets to compare VQTree with two
		  existing decision tree induction algorithms, C5.0 and OC1.
		  Our experiments show that VQTree produces compact decision
		  trees with higher accuracy than either C5.0 or OC1 on some
		  datasets.},
  dbinsdate	= {2002/1}
}

@Article{	  geva91a,
  author	= {S. Geva and J. Sitte},
  title		= {Adaptive nearest neighbor pattern classification},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1991},
  volume	= {2},
  number	= {2},
  pages		= {318--322},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  geva91b,
  author	= {S. Geva and J. Sitte},
  title		= {Adaptive pattern classification by decision surface
		  mapping},
  booktitle	= {Proc. ACNN'91, Second Australian Conf. on Neural
		  Networks},
  year		= {1991},
  editor	= {M. Jabri},
  pages		= {13--16},
  organization	= {Australian Neurosciences Soc. ; Inst. Eng. Australia;
		  IEEE; Univ. Sydney; et al},
  publisher	= {Sydney Univ. Electr. Eng},
  address	= {Sydney, Australia},
  dbinsdate	= {oldtimer}
}

@Article{	  geva91c,
  author	= {Shlomo Geva and Joaquin Sitte},
  title		= {An Exponential Response Neural Net},
  journal	= {Neural Computation},
  year		= {1991},
  volume	= {3},
  number	= {4},
  pages		= {623--632},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ghosal92a,
  author	= {Sugata Ghosal and Rajiv Mehrotra},
  title		= {Application of Neural Networks in Segmentation of Range
		  Images},
  booktitle	= {Proc. IJCNN'92, Int. Joint Conference on Neural Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {III},
  pages		= {297--302},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ghosal93a,
  author	= {S. Ghosal and R. Mehrotra},
  title		= {A Two-Stage Neural Net for Segmentation of Range Images},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {II},
  pages		= {721--726},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ghosal93b,
  author	= {Ghosal, S. and Mehrotra, R. },
  title		= {Integrated range image segmentation using connectionist
		  paradigm},
  booktitle	= {Proceedings of the IECON '93. International Conference on
		  Industrial Electronics, Control, and Instrumentation},
  year		= {1993},
  volume	= {3},
  pages		= {1690--5},
  organization	= {Center for Robotics \& Manuf. Syst. , Kentucky Univ. ,
		  Lexington, KY, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  ghosal95a,
  author	= {Ghosal, S. and Mehrotra, R. },
  title		= {Range surface characterization and segmentation using
		  neural networks},
  journal	= {Pattern Recognition},
  year		= {1995},
  volume	= {28},
  number	= {5},
  pages		= {711--27},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  ghosh92a,
  author	= {Ashish Ghosh and Sankar K. Pal},
  title		= {Neural network, self-organization and object extraction},
  journal	= {Pattern Recognition Letters},
  year		= {1992},
  volume	= {13},
  number	= {5},
  pages		= {387--397},
  month		= {May},
  annote	= {Picture segmentation with (quite original) {SOM}
		  modification. One unit for one pixel in picture, input from
		  only that pixel, all units (and their neighbours) are
		  taught. },
  dbinsdate	= {oldtimer}
}

@Article{	  ghosh93a,
  author	= {A. Ghosh and N. R. Pal and S. R. Pal},
  title		= {Self-organization for object extraction using a multilayer
		  neural network and fuzzines measures},
  journal	= {IEEE Trans. on Fuzzy Systems},
  year		= {1993},
  volume	= {1},
  number	= {1},
  pages		= {54--68},
  month		= {February},
  x		= {in9093---Citationeissa mainitaan {SOFM}, mutta muuten
		  niiden liittyminen aiheeseen on hiukan hamaraa},
  dbinsdate	= {oldtimer}
}

@Article{	  ghosh93b,
  author	= {Ghosh, J. and Gangishetti, N. V. and Chakravarthy, S. V.
		  },
  title		= {Robust classification of variable length sonar sequences},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1993},
  volume	= {1966},
  pages		= {96--107},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  ghosh94a,
  author	= {Ghosh, J. and Chakravarthy, S. V. },
  title		= {The rapid kernel classifier: a link between the
		  \mbox{self-organizing} feature map and the radial basis
		  function network},
  journal	= {Journal of Intelligent Material Systems and Structures},
  year		= {1994},
  volume	= {5},
  number	= {2},
  pages		= {211--19},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  giacomini00a,
  author	= {Giacomini, M. and Ruggiero, C. and Mansi, C.},
  title		= {Application of Kohonen neural network for the elaboration
		  of electrogastrograms},
  booktitle	= {Medical Infobahn for Europe. Proceedings of MIE2000 and
		  GMDS2000. IOS Press, Amsterdam, Netherlands},
  year		= {2000},
  volume	= {},
  pages		= {185--9},
  abstract	= {A method is presented for the processing and analysis of
		  electrogastrography (EGG)-a noninvasive technique by which
		  gastric myoelectrical activity is recorded using abdominal
		  surface electrodes. The analysis is based on fast Fourier
		  transforms (FFT) and on unsupervised artificial neural
		  networks. Three kinds of patterns can be identified on the
		  neurons of a Kohonen output map with 32*16 neurons: one
		  relating to noisy spectral profiles, one relating to
		  pre-prandial profiles and one relating post-prandial
		  profiles. It is concluded that the described method is
		  reliable and can be used for the objective automated
		  analysis of EGG and for the investigation of possible
		  relations of the EGG with gastric pathologies.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  giacomini00b,
  author	= {Giacomini, M. and Ruggiero, C. and Caneva, F. and Bertone,
		  S.},
  title		= {Application of artificial neural network for the
		  identification of fresh water bacteria},
  booktitle	= {Medical Infobahn for Europe. Proceedings of MIE2000 and
		  GMDS2000. IOS Press, Amsterdam, Netherlands},
  year		= {2000},
  volume	= {},
  pages		= {106--10},
  abstract	= {A method based on an artificial neural network (ANN) for
		  monitoring aquatic bacteria, which would be useful for
		  health care, is presented. Environmental micro-organisms
		  include a large number of taxa. Some species that normally
		  are not pathogenic can represent a risk under certain
		  conditions, such as in old people and immune-compromised
		  individuals. A system based on an unsupervised ANN has been
		  set up using the fatty acid profiles of standard strains,
		  obtained by gas chromatography, as learning data. The
		  Kohonen output map resulted in a powerful tool for the
		  identification of fresh isolates coming from a pipeline of
		  the major civil water system of Genoa (Italy).},
  dbinsdate	= {2002/1}
}

@Article{	  giacomini96a,
  author	= {M. Giacomini and C. Ruggiero and M. Maillard and F. B.
		  Lillo and O. E. Varnier},
  title		= {Objective evaluation of two markers of {HIV}-1 infection
		  (p24 antigen concentration and {CD4}+ cell counts) by a
		  self organizing neural network},
  journal	= {Medical Informatics},
  year		= {1996},
  volume	= {21},
  number	= {3},
  pages		= {215--28},
  dbinsdate	= {oldtimer}
}

@Article{	  giles01a,
  author	= {Giles, C. L. and Lawrence, S. and Tsoi, A. C.},
  title		= {Noisy time series prediction using recurrent neural
		  networks and grammatical inference},
  journal	= {Machine Learning},
  year		= {2001},
  volume	= {44},
  number	= {1--2},
  month		= {July/August 2001},
  pages		= {161--183},
  organization	= {Sch. of Info. Sci. and Technology, Dept. of Computer Sci.
		  and Eng., Pennsylvania State University},
  publisher	= {},
  address	= {},
  abstract	= {Financial forecasting is an example of a signal processing
		  problem which is challenging due to small sample sizes,
		  high noise, non-stationarily, and non-linearity. Neural
		  networks have been very successful in a number of signal
		  processing applications. We discuss fundamental limitations
		  and inherent difficulties when using neural networks for
		  the processing of high noise, small sample size signals. We
		  introduce a new intelligent signal processing method which
		  addresses the difficulties. The method proposed uses
		  conversion into a symbolic representation with a
		  self-organizing map, and grammatical inference with
		  recurrent neural networks. We apply the method to the
		  prediction of daily foreign exchange rates, addressing
		  difficulties with non-stationarily, overfitting, and
		  unequal a priori class probabilities, and we find
		  significant predictability in comprehensive experiments
		  covering 5 different foreign exchange rates. The method
		  correctly predicts the direction of change for the next day
		  with an error rate of 47.1%. The error rate reduces to
		  around 40% when rejecting examples where the system has low
		  confidence in its prediction. We show that the symbolic
		  representation aids the extraction of symbolic knowledge
		  from the trained recurrent neural networks in the form of
		  deterministic finite state automata. These automata explain
		  the operation of the system and are often relatively
		  simple. Automata rules related to well known behavior such
		  as trend following and mean reversal are extracted.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  gioiello91a,
  author	= {M. Gioiello and G. Vassallo and A. Chella and F.
		  Sorbello},
  title		= {Self-organizing maps: a new digital architecture},
  booktitle	= {Trends in Artificial Intelligence. 2nd Congress of the
		  Italian Association for Artificial Intelligence, AI IA
		  Proceedings},
  year		= {1991},
  editor	= {E. Ardizzone and S. Gaglio and F. Sorbello},
  pages		= { 385--398},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gioiello91b,
  author	= {M. Gioiello and G. Vassallo and A. Chella and F.
		  Sorbello},
  title		= {A digital implementation of \mbox{self-organizing} feature
		  maps},
  booktitle	= {Fourth Italian Workshop. Parallel Architectures and Neural
		  Networks},
  year		= {1991},
  editor	= {E. R. Caianiello},
  pages		= {191--198},
  organization	= {Univ. Salerno; Inst. Italiano di Studi Filosofici},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gioiello92a,
  author	= {M. Gioiello and G. Vassallo and F. Sorbello},
  title		= {A New Fully Digital Neural Network Hardware Architecture
		  for Binary Valued Pattern Recognition},
  booktitle	= {International Conference on Signal Processing Applications
		  and Technology},
  year		= {1992},
  pages		= {705--708},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gioiello92b,
  author	= {M. Gioiello and G. Vassallo and F. Sorbello},
  title		= {A New Approach to Pattern Recognition Using Digital
		  {K}ohonen Map and Its Application to Hand-written Digits
		  Recognition},
  booktitle	= {The V Italian Workshop on Parallel Architectures and
		  Neural Networks},
  publisher	= {World Scientific},
  address	= {Singapore},
  year		= {1992},
  pages		= {293--298},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  girardin99a,
  author	= {Girardin, L.},
  title		= {An eye on network intruder-administrator shootouts},
  booktitle	= {Proceedings of the Workshop on Intrusion Detection and
		  Network Monitoring (ID'99)},
  publisher	= {USENIX Association},
  address	= {Berkeley, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {19--28},
  abstract	= {Carefully logging of network activity is essential to meet
		  the requirements of high security and optimal resource
		  availability. However, detecting break-in attempts within
		  this activity is a difficult task. Making the distinction
		  between misuse and normal use is hard, and identifying
		  intrusions that use novel attacks is fundamentally
		  difficult. In this paper, we introduce a visual approach
		  for analyzing network activity. This approach differs from
		  anomaly and misuse detection because it considers human
		  factors to support the exploration of network traffic. Our
		  prototype application is based on an unsupervised neural
		  network, and consequently does not rely on any prior
		  knowledge of the data being analyzed. We use
		  self-organizing maps to project the network events on to a
		  space that is appropriate for visualization and achieve
		  their exploration using a map metaphor. The approach we
		  present can be used to analyze past and present activities,
		  as well as to show trends in the events. To demonstrate the
		  usability of our tools, we describe the investigation of a
		  data set containing common intrusion patterns. We also
		  discuss some weaknesses of current intrusion detection
		  systems and propose a new paradigm for monitoring network
		  activity that enables the discovery of new, sophisticated
		  and structured attacks.},
  dbinsdate	= {oldtimer}
}

@Article{	  giraudel01a,
  author	= {Giraudel, J. L. and Lek, S.},
  title		= {A comparison of self-organizing map algorithm and some
		  conventional statistical methods for ecological community
		  ordination},
  journal	= {ECOLOGICAL MODELLING},
  year		= {2001},
  volume	= {146},
  number	= {1--3},
  month		= {DEC 1},
  pages		= {329--339},
  abstract	= {In order to summarise the structure of ecological
		  communities some ordination techniques are well known and
		  widely-used, (e.g. Principal Component Analysis (PCA),
		  Correspondence Analysis (CoA). Inspired by the structure
		  and the mechanism of the human brain, the Artificial Neural
		  Networks should be a convenient alternative tool to
		  traditional statistical methods. The Kohonen
		  Self-Organizing Map (SOM) is one of the most well- known
		  neural network with unsupervised learning rules; it
		  performs a topology-p reserving projection of the data
		  space onto a regular two-dimensional space. Its achievement
		  has already been demonstrated in various areas, but this
		  approach is not yet widely known and used by ecologists.
		  The present work describes how SOM can be used for the
		  study of ecological communities. After the presentation of
		  SOM adapted to ecological data, SOM was trained on popular
		  example data; upland forest in Wisconsin (USA). The SOM
		  results were compared with classical statistical
		  techniques. Similarity between the results may be observed
		  and constitutes a validation of the SOM method. SOM
		  algorithm seems fully usable in ecology, it can perfectly
		  complete classical techniques for exploring data and for
		  achieving community ordination. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  girolami00a,
  author	= {Girolami, M.},
  title		= {A generative model for sparse discrete binary data with
		  non-uniform categorical priors},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {1--6},
  abstract	= {The generative topographic mapping (GTM) was developed and
		  introduced as a principled alternative to the
		  self-organising map for, principally, visualising
		  high-dimensional continuous data. There are many cases
		  where the observation data is ordinal and discrete and the
		  application of methods developed specifically for
		  continuous data is inappropriate. Based on the continuous
		  GTM data model a nonlinear latent variable model for
		  modeling sparse high-dimensional binary data is presented.
		  The primary motivation for this work is the requirement for
		  a dense and low-dimensional representation of sparse binary
		  vector space models of text documents based on the
		  multivariate Bernoulli event model. The method is however
		  applicable to binary data in general.},
  dbinsdate	= {2002/1}
}

@Article{	  girolami01a,
  author	= {Girolami, M.},
  title		= {The topographic organization and visualization of binary
		  data using multivariate-Bernoulli latent variable models},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2001},
  volume	= {12},
  number	= {6},
  month		= {November },
  pages		= {1367--1374},
  organization	= {Appl Computational Intell. Res. Unit, Div. of Computing
		  and Info. Systems, University of Paisley},
  publisher	= {},
  address	= {},
  abstract	= {A nonlinear latent variable model for the topographic
		  organization and subsequent visualization of multivariate
		  binary data is presented. The generative topographic
		  mapping (GTM) is a nonlinear factor analysis model for
		  continuous data which assumes an isotropic Gaussian noise
		  model and performs uniform sampling from a two-dimensional
		  (2-D) latent space. Despite the success of the GTM when
		  applied to continuous data the development of a similar
		  model for discrete binary data has been hindered due, in
		  part, to the nonlinear link function inherent in the
		  binomial distribution which yields a log-likelihood that is
		  nonlinear in the model parameters. This paper presents an
		  effective method for the parameter estimation of a binary
		  latent variable model-a binary version of the GTM-by
		  adopting a variational approximation to the binomial
		  likelihood. This approximation thus provides a
		  log-likelihood which is quadratic in the model parameters
		  and so obviates the necessity of an iterative M-step in the
		  expectation maximization (EM) algorithm. The power of this
		  method is demonstrated on two significant application
		  domains, handwritten digit recognition and the topographic
		  organization of semantically similar text-based
		  documents.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  giron99a,
  author	= {Giron, A. and Vilain, J. and Serruys, C. and Brahmi, D.
		  and Deschavanne, P. and Fertil, B.},
  title		= {Analysis of parametric images derived from genomic
		  sequences using neural network based approaches},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3604--8},
  abstract	= {The exploration of DNA genomic huge sequences (up to
		  several megabases) needs new kind of data representation
		  allowing robust analyses. With the help of the chaos game
		  representation method (CGR), fractal images can be
		  generated, which allow to observe, at a glance, frequencies
		  of words (small sequences of the four bases: G, A, T, C) in
		  DNA sequences. Classification of CGR images and extraction
		  of main features are the issues addressed in this work,
		  using a classical statistical analysis (principal component
		  analysis) and neural networks grounded on curvilinear
		  component analysis algorithm and Kohonen map.},
  dbinsdate	= {oldtimer}
}

@Article{	  giuliano93a,
  author	= {F. Giuliano and P. Arrigo and F. Scalia and P. P. Cardo
		  and G. Damiani},
  title		= {Potentially functional regions of nucleic acids recognized
		  by a {{K}ohonen's} \mbox{self-organizing} maps},
  journal	= {Comput. Applic. Biosci. },
  year		= {1993},
  volume	= {9},
  number	= {6},
  pages		= {687--693},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  giusto90a,
  author	= {Daniele D. Giusto and Gianni Vernazza},
  title		= {Color-Image Coding by an Advanced Vector-Quantizer},
  booktitle	= {Proc. ICASSP-90, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1990},
  volume	= {III},
  pages		= {2265--2268},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  gjerdingen89a,
  author	= {R. O. Gjerdingen},
  title		= {Using connectionist models to explore complex musical
		  patterns},
  journal	= {Computer Music J. },
  year		= {1989},
  volume	= {13},
  number	= {3},
  pages		= {67--75},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  glara-bengoechea91a,
  author	= {Antonio Glar{\'{i}}a-Bengoechea and Yves Burnod},
  title		= {Self-Organization of the Functional Characteristics of
		  Motor Cortex Neuron Distribution: A Modified {K}ohonen
		  network to Neutralize the Temporal Statistics of
		  Spontaneous Movements},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {Kohonen, Teuvo and M{\"{a}}kisara, Kai and Simula, Olli
		  and Kangas, Jari},
  pages		= {501--504},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  glass00a,
  author	= {Glass, J. O. and Reddick, W. E. and Goloubeva, O. and Yo,
		  V. and Steen, R. G.},
  title		= {Hybrid artificial neural network segmentation of precise
		  and accurate inversion recovery ({PAIR}) images from normal
		  human brain},
  journal	= {MAGNETIC RESONANCE IMAGING},
  year		= {2000},
  volume	= {18},
  number	= {10},
  month		= {DEC},
  pages		= {1245--1253},
  abstract	= {This paper presents a novel semi-automated segmentation
		  and classification method based on raw signal intensities
		  from a quantitative T1 relaxation technique with two novel
		  approaches for the removal of partial volume effects. The
		  segmentation used a Kohonen Self Organizing Map that
		  eliminated inter- and intra-operator variability. A
		  Multi-layered Backpropagation Neural Network was able to
		  classify the test data with a predicted accuracy of 87.2%
		  when compared to manual classification. A Linear
		  interpolation of the quantitative T1 information by region
		  and on a pixel-by-pixel basis was used to redistribute
		  voxels containing a partial volume of gray matter (GM) and
		  white matter (WM) or a partial volume of GM and
		  cerebrospinal fluid (CSF) into the principal components of
		  GM, WM, and CSF. The method presented was validated against
		  manual segmentation of the base images by three experienced
		  observers. Comparing segmented outputs directly to the
		  manual segmentation revealed a difference of less than 2%
		  in GM and less than 6% in WM for pure tissue estimations
		  for both the regional and pixel- by-pixel redistribution
		  techniques. This technique produced accurate estimates of
		  the amounts of GM and WM while providing a reliable means
		  of redistributing partial volume effects. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  glass01a,
  author	= {Glass, J. O. and Reddick, W. E. and Steen, R. G.},
  title		= {Validation of a semi-automated segmentation algorithm with
		  partial volume redistribution},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Sonka, M. and Hanson, K. M.},
  volume	= {4322},
  pages		= {226--235},
  organization	= {Department of Diagnostic Imaging, St. Jude Children's Res.
		  Hospital},
  publisher	= {},
  address	= {},
  abstract	= {To reduce partial volume contamination, we present a
		  linear interpolation combining quantitative T1 information
		  with segmented base images. In addition, manual
		  segmentation was completed for comparison to both of the
		  techniques. To quantitatively assess T1, a precise and
		  accurate inversion recovery (PAIR) sequence was acquired.
		  The Kohonen SOM segmentation algorithm used the four base
		  images as inputs and had nine output neurons. The segmented
		  regions were manually classified by an expert for training
		  a multi-layered backpropagation neural network to automate
		  this process. A linear interpolation based on mean T1
		  relaxivity for each segmented class (regional method) and a
		  pixel by pixel basis (pixel method) was performed. Manual
		  segmentation was performed directly on base images by three
		  observers. Differences between the techniques are reported
		  as percent errors of the mean difference divided by the
		  mean estimates of the manual segmentation. Within observer
		  variances for the manual segmentation were less than 5.6%
		  while between observer variances were 11.7% and 7.2% for
		  white and gray matter respectively. The regional method had
		  variances of 4.1% and 1.0% while the pixel method produced
		  variances of 5.8% and 1.5% for white and gray matter,
		  respectively, compared to the manual segmentation.},
  dbinsdate	= {2002/1}
}

@Article{	  glover94a,
  author	= {Glover, F. },
  title		= {Optimization by ghost image processes in neural networks},
  journal	= {Computers \& Operations Research},
  year		= {1994},
  volume	= {21},
  number	= {8},
  pages		= {801--22},
  month		= {Oct},
  dbinsdate	= {oldtimer}
}

@InCollection{	  godavarti95a,
  author	= {M. Godavarti and J. J. Rodriguez and T. A. Yopp and G. M.
		  Lambert and D. W. Galbraith},
  title		= {Neural network analysis of digital flow cytometric data},
  booktitle	= {1995 IEEE International Conference on Neural Networks
		  Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {2211--16},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goddard00a,
  author	= {Goddard, J. and Martinez, A. E. and Martinez, F. M. and
		  Aljama, T.},
  title		= {A comparison of different clustering algorithms for speech
		  recognition},
  booktitle	= {Proceedings of the 43rd IEEE Midwest Symposium on Circuits
		  and Systems. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {3},
  pages		= {1222--5},
  abstract	= {K-means and SOM have been frequently applied to clustering
		  problems in speech recognition. Recently, new clustering
		  algorithms have been introduced which present certain
		  advantages over both of them. The present paper compares
		  the performance of one of these, STVQ, to k-means and SOM
		  on two well-known speech data sets.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  godfrey93a,
  author	= {Kaith R. L. Godfrey},
  title		= {Self-Organized Color Image Quantization for Color Image
		  Data Compression},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1622--1626},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  godino-llorente00a,
  author	= {Godino-Llorente, Juan I. and Aguilera-Navarro, Santiago
		  and Gomez-Vilda, Pedro},
  title		= {Non supervised neural net applied to the detection of
		  voice impairment},
  booktitle	= {ICASSP, IEEE International Conference on Acoustics, Speech
		  and Signal Processing---Proceedings},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {3594--3597},
  organization	= {Universidad Politecnica de Madrid},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Most of vocal and voice diseases cause changes in the
		  voice. ENT clinicians use acoustic voice analysis to
		  characterise pathological voices. The authors have focused
		  their task in detection of impaired voices by means of
		  neural network technology (ANN) and acoustic analysis.
		  Former and actual works demonstrates that impaired voice
		  detection can be carried out by means of supervised neural
		  nets: MLP (Multilayer perceptron). This paper is focussed
		  in the task of detection of pathological voices by means of
		  non supervised neural nets (Kohonen Self Organising Maps),
		  comparing results with those obtained using supervised
		  neural nets (MLPs). The aim of this paper is to study and
		  compare two neural nets based methods to be used for the
		  detection of impaired voices: supervised (MLP ANNs) and
		  non-supervised Neural Nets (Kohonen ANNs). Voice registers
		  are parameterized by means of acoustic parameters.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  goerke01a,
  author	= {Goerke, N. and Kintzler, F. and Eckmiller, R.},
  title		= {Self organized classification of chaotic domains from a
		  nonlinear attractor},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {1637--1641},
  organization	= {Department of Computer Science VI, Neuroinformatik,
		  University of Bonn},
  publisher	= {},
  address	= {},
  abstract	= {We propose a method to use self organizing neural networks
		  to extract information out of nonlinear dynamic systems for
		  control. Nonlinear strange attractors are educed by these
		  systems or the attractors can be reconstructed. These
		  attractors are partitioned by a newly developed self
		  organizing neural network. Thus the stream of system states
		  is transformed into a stream of symbols, which can now
		  serve as basis for further investigation or control. We are
		  convinced, that controlling and understanding such
		  nonlinear or chaotic systems is easier, when using the
		  information within the stream of extracted symbols.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  goerke01b,
  author	= {Goerke, N. and Kintzler, F. and Eckmiller, R.},
  title		= {Self organized partitioning of chaotic attractors for
		  control},
  booktitle	= {Artificial Neural Networks---ICANN 2001. International
		  Conference. Proceedings (Lecture Notes in Computer Science
		  Vol.2130). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {851--6},
  abstract	= {We propose a method to use self organizing neural networks
		  to extract information out of nonlinear dynamic systems for
		  control. Nonlinear strange attractors are reduced by these
		  systems or the attractors can be reconstructed. These
		  attractors are partitioned by a newly developed self
		  organizing neural network. Thus the stream of system states
		  is transformed into a stream of symbols, which can now
		  serve as basis for further investigation or control. We are
		  convinced that controlling and understanding such nonlinear
		  or chaotic systems is easier, when using the information
		  within the stream of extracted symbols.},
  dbinsdate	= {2002/1}
}

@Article{	  goertzel96a,
  author	= {B. Goertzel},
  title		= {Mobile Activation Bubbles in Toroidal {K}ohonen Networks},
  journal	= {Applied Mathematics Letters},
  year		= {1996},
  volume	= {9},
  number	= {5},
  pages		= {79--82},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gokcay01a,
  author	= {Gokcay, D. and Harris, J. G. and Leonard, C. M. and
		  Briggs, R.W.},
  title		= {Self-organizing features for regularized standardization
		  of brain images},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Sonka, M. and Hanson, K. M.},
  volume	= {4322},
  pages		= {1645--1653},
  organization	= {University of Florida, Computer and Information Sciences},
  publisher	= {},
  address	= {},
  abstract	= {A semi-automatic, feature-based standardization technique
		  is proposed to complement the existing global image
		  standardization methods. The benefits of our method are
		  speed and accuracy in local alignment. The method consists
		  of three phases: In phase one, templates are generated from
		  the atlas structures, using Self-Organizing Maps (SOMs).
		  The parameters of each SOM are determined using a new
		  topology evaluation technique. In phase two, the atlas
		  templates are reconfigured using points from individual
		  features, to establish a one-to-one correspondence between
		  the atlas and individual structures. During training, a
		  regularization procedure can be optionally invoked to
		  guarantee smoothness in areas where the discrepancy between
		  the atlas and individual feature is high. In the final
		  phase, difference vectors are generated using the
		  corresponding points of the atlas and the individual
		  structure. The whole image is warped by interpolation of
		  the difference vectors through Gaussian radial basis
		  functions, whic h are determined by minimizing a membrane
		  energy. Results are demonstrated on selected sulci in brain
		  MRIs.},
  dbinsdate	= {2002/1}
}

@InCollection{	  goktepe96a,
  author	= {M. Goktepe and E. Yalabik and R. Atalay},
  title		= {Unsupervised segmentation of gray level {M}arkov model
		  textures with hierarchical self organizing maps},
  booktitle	= {Proceedings of the 13th International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  volume	= {4},
  address	= {Los Alamitos, CA, USA},
  pages		= {90--4},
  dbinsdate	= {oldtimer}
}

@Article{	  golbraikh00a,
  author	= {Golbraikh, A. and Bernard, P. and Chretien, J. R.},
  title		= {Validation of protein-based alignment in 3D quantitative
		  structure-activity relationships with Co{MFA} models},
  journal	= {EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY},
  year		= {2000},
  volume	= {35},
  number	= {1},
  month		= {JAN},
  pages		= {123--136},
  abstract	= {The predictive capabilities of protein-based alignment
		  (PBA) and structure-based alignment (SBA) comparative
		  molecular field analysis (CoMFA) models have been compared.
		  3D quantitative structure-activity relationship (3D QSAR)
		  models have been derived for a series of N-benzylpiperidine
		  derivatives which are potent acetylcholinesterase (AChE)
		  inhibitors interesting for Alzheimer's disease. To
		  establish a comparison with the classical SEA procedure,
		  different assay models were derived by superposing ligand
		  conformers that are docked to the AChE active site and by
		  using the most active compound as the reference one. A
		  Kohonen self organizing map (SOM) was applied to analyse
		  the molecular diversity of the test set relative to that of
		  the training set, in order to explain the influence of
		  molecular diversity on the predictive power of the
		  considered models. SEA 3D QSAR models have to be used to
		  predict the inhibitory activity only for compounds
		  belonging to subgroups included in the training set. The
		  PEA 3D QSAR models appeared to have a higher
		  predictability, even for compounds with a molecular
		  diversity greater than that of the training set. This
		  results from the fact that the protein helps to
		  automatically select the active conformation which is
		  fitting the 3D QSAR model. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  goldstein90a,
  author	= {M. Goldstein},
  title		= {Self-organizing feature maps for the multiple travelling
		  salesmen problem ({MTSP})},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  volume	= {I},
  pages		= {258--261},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  golshani97a,
  author	= {F. Golshani and Y. Park},
  title		= {Content-based image indexing and retrieval system in
		  ImageRoadMap},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3229},
  pages		= {194--205},
  note		= {(Multimedia Storage and Archiving Systems II Conf. Date:
		  3--4 Nov. 1997 Conf. Loc: Dallas, TX, USA Conf. Sponsor:
		  SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  golz98a,
  author	= {Golz, M. and Sommer, D. and Lembcke, T. and Kurella, B.},
  title		= {Classification of pre-stimulus {EEG} of K-complexes using
		  competitive learning networks},
  booktitle	= {6th European Congress on Intelligent Techniques and Soft
		  Computing. EUFIT '98},
  publisher	= {Verlag Mainz},
  address	= {Aachen, Germany},
  year		= {1998},
  volume	= {3},
  pages		= {1767--71},
  abstract	= {The spectral powers of the pre-stimulus EEG are used as
		  input feature vectors to learning vector quantization and
		  self-organizing map networks to classify these vectors into
		  four classes. The four classes are observed in the EEG as
		  eliciting patterns of K-complexes due to double-tone pip
		  stimulations during sleep. A modified learning vector
		  quantization algorithm was applied resulting to higher
		  classification rates and lower sensitivity to network
		  initialization. With both algorithms the classification
		  rates of over 90% were achievable.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gomez-ruiz00a,
  author	= {J. A. Gomez-Ruiz and E. Lopez-Rubio and J.
		  Mu\~{n}oz-Perez},
  title		= {Expansive Competitive Learning for Colour Image
		  Compression},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Dpto. de Lenguajes y Ciencias de la Computacion E.T.S.
		  Ingenieria Informatica. Universidad de Malaga},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gomolka96a,
  author	= {Gomolka, Z. and Pekala, R. and Pyzik, L.},
  title		= {The pre-processing of the diagnostics signals using {LVQ}
		  method},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {257--60},
  abstract	= {The global analysis of the LVQ network performance for
		  diagnostics of an aircraft piston engine was presented. The
		  network learned using the vibration signal of the engine.
		  Generally, the obtained results show that using signal
		  vibrations directly gives good effects even if the maximum
		  level of noise reaches 40%. It is easy to see that the LVQ
		  network gives better results with comparison to the types
		  of neural networks trained using signals obtained from
		  Fourier series.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gong91a,
  author	= {W. Gong and K. R. Rao and M. T. Manry},
  title		= {Vector quantization and progressive image transmission
		  using {K}ohonen \mbox{self-organizing} feature map},
  booktitle	= {Conf. Record of the Twenty-Fifth Asilomar Conf. on
		  Signals, Systems and Computers},
  year		= {1991},
  volume	= {I},
  pages		= {477--481},
  organization	= {IEEE; Naval Postgraduate School; San Jose State Univ},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  gong93a,
  author	= {Wei Gong and Rao, K. R. and Manry, M. T. },
  title		= {Progressive image transmission},
  journal	= {IEEE Transactions on Circuits and Systems for Video
		  Technology},
  year		= {1993},
  volume	= {3},
  number	= {5},
  pages		= {380--3},
  month		= {Oct},
  abstract	= {Progressive image transmission (PIT) is widely used in
		  many applications, since it generates the successively
		  improved reconstructions of an image. In spatial domain PIT
		  systems, perfectly reconstructed images can be obtained at
		  the final stage. However the intermediate images are not
		  good and there is no data compression. In this paper, a new
		  simple spatial domain progressive image transmission using
		  modified Kohonen self-organizing feature map algorithm,
		  which is called Kohonen vector quantizer (KVQ), is
		  presented. To alleviate edge distortion a classification
		  technique is applied to KVQ. Computer simulation results
		  show that very good intermediate images can be obtained at
		  reasonable bit rates using the PIT scheme introduced
		  here.},
  dbinsdate	= {oldtimer}
}

@Article{	  gonzalez95a,
  author	= {A. I. Gonzalez and M. Grana and A. D'Anjou},
  title		= {An Analysis of the {G {LVQ} } Algorithm},
  journal	= {IEEE Transactions on Neural Networks},
  type		= {Letter},
  year		= 1995,
  volume	= 6,
  number	= 4,
  pages		= {1012--1016},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@InCollection{	  gonzalez97a,
  author	= {A. I. Gonzalez and M. Grana and A. D'Anjou and F. X.
		  Albizuri and M. Cottrell},
  title		= {Self organizing map for adaptive nonstationary clustering:
		  {SOM} experimental results on color quantization of image
		  sequences},
  booktitle	= {5th European Symposium on Artificial Neural Networks ESANN
		  '97. Proceedings},
  publisher	= {D facto},
  year		= {1997},
  editor	= {M. Verleysen},
  address	= {Brussels, Belgium},
  pages		= {199--204},
  dbinsdate	= {oldtimer}
}

@Article{	  gonzalez97b,
  author	= {A. I. Gonzalez and M. Gra{\~n}a and A. D'Anjou and F. X.
		  Albizuri and M. Cottrell},
  title		= {A Sensitivity Analysis of the Self-Organizing Maps as an
		  Adaptive One-pass Non-stationary Clustering Algorithm: the
		  Case of Color Quantization of Image Sequences},
  journal	= {Neural Processing Letters},
  year		= 1997,
  volume	= 6,
  pages		= {77--89},
  dbinsdate	= {oldtimer}
}

@Article{	  goodacre94a,
  author	= {R. Goodacre and M. J. Neal and D. B. Kell and L. W.
		  Greenham and W. C. Noble and R. G. Harvey},
  title		= {Rabid identification using pyrolysis mass spectrometry and
		  artificial neural networks of {\it Propionibactreium acnes}
		  isolated from dogs},
  journal	= {J. Appl. Bacteriology},
  year		= {1994},
  volume	= {76},
  pages		= {124--134},
  annote	= {application, data analysis},
  dbinsdate	= {oldtimer}
}

@Article{	  goodacre94b,
  author	= {Royston Goodacre},
  title		= {Characterization and Quantification of Microbial Systems
		  Using Pyrolysis Mass Spectrometry: Introducing Neural
		  Networks to Analytical Pyrolysis},
  journal	= {Microbiology Europe},
  year		= {1994},
  volume	= {2},
  number	= {2},
  pages		= {16--22},
  annote	= {application, data analysis},
  dbinsdate	= {oldtimer}
}

@Article{	  goodacre96a,
  author	= {R. Goodacre and S. A. Howell and W. C. Noble and M. J.
		  Neal},
  title		= {Sub-species discrimination using pyrolysis mass
		  spectrometry and \mbox{self-organizing} neural networks of
		  {\it Propionibacterium acnes} isolated from human skin},
  journal	= {Zentralblatt für Bakteriologie---International Journal of
		  Medical Microbiology, Virology, Parasitology and Infectious
		  Diseases},
  year		= 1996,
  volume	= 284,
  pages		= {501--515},
  abstract	= {Curie-point pyrolysis mass spectra were obtained from 30
		  Propionibacterium acnes strains isolated from the foreheads
		  of six healthy humans. Multivariate analyses and Kohonen
		  artificial neural networks (KANNs), employing unsupervised
		  learning, were used successfully to discriminate between
		  the P. acnes isolates from different individual hosts. The
		  classification of the isolates by KANNs was compared with
		  the more classical multivariate techniques of canonical
		  variates analysis and hierarchical cluster analysis and
		  found to give similar groupings. The combination of
		  pyrolysis mass spectrometry with these numerical methods
		  also showed that more than one strain of P. acnes had been
		  isolated from three of the human hosts. },
  dbinsdate	= {oldtimer}
}

@Article{	  goodacre96b,
  author	= {R. Goodacre and J. Pygall and D. B. Kell},
  title		= {Plant seed classification using pyrolysis mass
		  spectrometry with unsupervised learning: the application of
		  auto-associative and {K}ohonen artificial neural networks},
  journal	= {Chemometrics and Intelligent Laboratory Systems},
  year		= {1996},
  volume	= {34},
  number	= {1},
  pages		= {69--83},
  dbinsdate	= {oldtimer}
}

@InCollection{	  goodacre99a,
  author	= {R. Goodacre and N. Kaderbhai and A. C. McGovern and E. A.
		  Goodacre},
  title		= {Chemometric analyses with Self Organising Feature Maps: A
		  worked example of the analysis of cosmetics using Raman
		  spectroscopy},
  booktitle	= {Kohonen Maps},
  pages		= {335--348},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  goodhill97a,
  author	= {Geoffrey J. Goodhill and Terrence J. Sejnowski},
  title		= {A Unifying Objective Function for Topographic Mappings},
  journal	= {Neural Computation},
  year		= 1997,
  volume	= 9,
  pages		= {1291--1303},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goppert92a,
  author	= {J. G{\"{o}}ppert and H. Speckmann and W. Rosenstiel and G.
		  Kraus and G. Gauglitz},
  title		= {Evaluation of Spectra in Chemistry and Physics with
		  {{K}ohonen's} {S}elforganizing {F}eature {M}ap},
  booktitle	= {Proc. Neuro-Nimes'92},
  year		= {1992},
  pages		= {405--416},
  publisher	= {EC2},
  address	= {Nanterre, France},
  annote	= {application, data analysis},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goppert93a,
  author	= {Josef G{\"{o}}ppert and Wolfgang Rosenstiel},
  title		= {{Self-Organizing Maps} vs. {Backpropagation}: An
		  Experimental Study},
  booktitle	= {Proc. Workshop on Desing Methodologies for
		  Microelectronics and Signal Processing},
  year		= {1993},
  pages		= {153--162},
  annote	= {application, data analysis, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goppert93b,
  author	= {Josef G{\"{o}}ppert and Wolfgang Rosenstiel},
  title		= {Topology-Preserving Interpolation in {S}elf-{O}rganizing
		  {M}aps},
  booktitle	= {Proc. Neuro-Nimes'93},
  year		= {1993},
  pages		= {425--434},
  publisher	= {EC2},
  address	= {Nanterre, France},
  annote	= {function approximation, modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goppert94a,
  author	= {Josef G{\"{o}}ppert and Wolfgang Rosenstiel},
  title		= {Dynamic Extensions of {S}elf-{O}rganizing {M}aps},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {330--333},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {modification, dynamical signals},
  dbinsdate	= {oldtimer}
}

@Article{	  goppert94b,
  author	= {Josef G{\"{o}}ppert and Wolfgang Rosenstiel},
  title		= {The Use of Neural Networks in the Online Analysis},
  journal	= {Fresenius J. Anal. Chem. },
  year		= {1994},
  volume	= {349},
  pages		= {367--371},
  annote	= {application, data analysis},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goppert94c,
  author	= {Josef G{\"{o}}ppert and Wolfgang Rosenstiel},
  title		= {Selective Attention and {S}elf-{O}rganizing {M}aps},
  booktitle	= {Proc. Neuro-Nimes'94},
  year		= {1994},
  publisher	= {EC2},
  address	= {Nanterre, France},
  annote	= {modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goppert95a,
  author	= {Josef G{\"{o}}ppert and Wolfgang Rosenstiel},
  title		= {Topological Interpolation in {SOM} by Affine
		  Transformations},
  booktitle	= {Proc. ESANN'95, European Symp. on Artificial Neural
		  Networks},
  year		= {1995},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {15--20},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goppert95b,
  author	= {Josef G{\"{o}}ppert and Wolfgang Rosenstiel},
  title		= {Interpolation in {SOM}: Improved Generalization by
		  Iterative Methods},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {69--74},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InCollection{	  goppert95c,
  author	= {J. Goppert and W. Rosenstiel},
  title		= {Neurons with continuous varying activation in
		  \mbox{self-organizing} maps},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {419--26},
  dbinsdate	= {oldtimer}
}

@InCollection{	  goppert96a,
  author	= {J. Goppert and W. Rosenstiel},
  title		= {Regularized {SOM}-training: a solution to the topology-
		  approximation dilemma?},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {38--43},
  dbinsdate	= {oldtimer}
}

@InCollection{	  goppert96b,
  author	= {J. Goppert and W. Rosenstiel},
  title		= {Varying cooperation in {SOM} for improved function
		  approximation},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {1--6},
  dbinsdate	= {oldtimer}
}

@Article{	  goppert97a,
  author	= {J. Goppert and W. Rosenstiel},
  title		= {The continuous interpolating \mbox{self-organizing} map},
  journal	= {Neural Processing Letters},
  year		= {1997},
  volume	= {5},
  number	= {3},
  pages		= {185--92},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goren-bar01a,
  author	= {Goren-Bar, D. and Kuflik, T. and Lev, D. and Shoval, P.},
  title		= {Automating personal categorization using artificial neural
		  networks},
  booktitle	= {USER MODELING 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {188--198},
  abstract	= {Organizations as well as personal users invest a great
		  deal of time in assigning documents they read or write to
		  categories. Automatic document classification that matches
		  user subjective classification is widely used, but much
		  challenging research still remain to be done. The
		  self-organizing map (SOM) is an artificial neural network
		  (ANN) that is mathematically characterized by transforming
		  high-dimensional data into two- dimensional representation.
		  This enables automatic clustering of the input, while
		  preserving higher order topology. A closely related method
		  is the Learning Vector Quantization (LVQ) algorithm, which
		  uses supervised learning to maximize correct data
		  classification, This study evaluates and compares the
		  application of SOM and LVQ to automatic document
		  classification, based on a subjectively predefined set of
		  clusters in a specific domain. A set of documents from an
		  organization, manually clustered by a domain expert, was
		  used in the experiment. Results show that in spite of the
		  subjective nature of human categorization, automatic
		  document clustering methods match with considerable success
		  subjective, personal clustering, the LVQ method being more
		  advantageous.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  gorinevsky93a,
  author	= {Gorinevsky, D. M. and Connolly, T. H. },
  title		= {Comparison of inverse manipulator kinematics
		  approximations from scattered input-output data using
		  {ANN}-like methods},
  booktitle	= {Proceedings of the 1993 American Control Conference},
  year		= {1993},
  volume	= {1},
  pages		= {751--5},
  organization	= {Lehrstuhl B fuer Mechanik, Tech. Univ. , Munchen,
		  Germany},
  publisher	= {American Autom. Control Council},
  address	= {Evanston, IL, USA},
  abstract	= {We compare the application of five different methods for
		  the approximation of the inverse kinematics of a robot arm
		  from a number of joint angle/Cartesian coordinate training
		  pairs. The first method is a standard feed-forward neural
		  network with error back-propagation learning. The next two
		  methods employ an extended Kohonen Map that we combine with
		  Shepard interpolation for the forward computation. We
		  consider learning of the Kohonen Map with the method of
		  Ritter et al. and compare it to our own method based on
		  steepest descent optimization. We also study two scattered
		  data approximation algorithms, namely Gaussian Radial Basis
		  Function interpolation and a Local Polynomial Fit method
		  that could be considered as a modification of McLain's
		  method. We propose extensions of the considered scattered
		  data approximation algorithms to make them suitable for
		  vector-valued multi-variable functions, such as the mapping
		  of Cartesian coordinates into joint angle coordinates.},
  dbinsdate	= {oldtimer}
}

@Article{	  gorinevsky94a,
  author	= {Gorinevsky, D. and Connolly, T. H. },
  title		= {Comparison of {SOM} neural network and scattered data
		  approximation: the inverse manipulator kinematics example},
  journal	= {Neural Computation},
  year		= {1994},
  volume	= {6},
  number	= {3},
  pages		= {521--42},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goser88a,
  author	= {Goser, Karl},
  title		= {Konzepte und schaltungen f{\"u}r lernende speicher in
		  {VLSI}-technik},
  booktitle	= {Tagungsband der ITG-Fachtagung {Digitale Speicher}},
  year		= {1988},
  pages		= {391--405},
  publisher	= {ITG},
  address	= {Darmstadt, Germany},
  month		= {September},
  note		= {In German},
  dbinsdate	= {oldtimer}
}

@Article{	  goser89a,
  author	= {Karl Goser and Ulrich Hilleringmann and Ulrich Rueckert
		  and Klaus Schumacher},
  title		= {{VLSI} Technologies for Artificial Neural Networks},
  journal	= {IEEE Micro},
  year		= {1989},
  volume	= {9},
  pages		= {28--42},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goser89b,
  author	= {K. Goser and K. M. Marks and U. Rueckert and V. Tryba},
  title		= {Selbstorganisierende Parameterkarten zur
		  Prozess{\"u}berwachung und -voraussage},
  booktitle	= {3. Internationaler GI-Kongress {\"u}ber Wissensbasierte
		  Systeme, M{\"u}nchen, October 16--17},
  year		= {1989},
  pages		= {225--237},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@Article{	  goser89c,
  author	= {K. Goser},
  title		= {Mikroelektronik neuronaler Netze},
  journal	= {Z. Mikroelektronik},
  year		= {1989},
  volume	= {3},
  pages		= {104--108},
  dbinsdate	= {oldtimer}
}

@Article{	  goser90a,
  author	= {K. Goser and I. Kreuzer and U. Rueckert and V. Tryba},
  title		= {Chip-Architekturen f{\"u}r k{\"u}nstliche neuronale
		  Netzwerke},
  journal	= {Z. Mikroelektronik},
  year		= {1990},
  volume	= {me4},
  pages		= {208--211},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  goser91a,
  author	= {Karl Goser},
  title		= {{K}ohonen's Map---Their Application and Implementation in
		  Microelectronics},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {703--708},
  publisher	= {North-Holland},
  address	= {Amsterdam, Nethderlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  goser92a,
  author	= {K. Goser and U. Ramacher},
  title		= {Mikroelektronische Realisierung von k{\"u}nstlichen
		  neuronalen Netzen},
  journal	= {Informationstechnik},
  year		= {1992},
  volume	= {4},
  dbinsdate	= {oldtimer}
}

@InCollection{	  goser96a,
  author	= {K. Goser and K. Schuhmacher and M. Hartung and K. Heesche
		  and B. Hesse and A. Kanstein},
  title		= {Neuro-fuzzy systems for engineering applications},
  booktitle	= {AFRICON '96. Incorporating AP-MTT-96 and COMSIG-96. 1996
		  IEEE AFRICON. 4th AFRICON Conference in Africa. Electrical
		  Energy Technology, Communication Systems, Human Resources},
  publisher	= {IASTED-Acta Press},
  year		= {1996},
  volume	= {2},
  editor	= {R. V. Mayorga},
  address	= {Anaheim, CA, USA},
  pages		= {759--64},
  dbinsdate	= {oldtimer}
}

@InCollection{	  goser97a,
  author	= {Karl Goser},
  title		= {Self-organizing map for intelligent process control},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {75--79},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  govekar92a,
  author	= {E. Govekar and E. Susi{\v{c}} and P. Mu{\v{z}}i{\v{c}} and
		  I. Grabec},
  title		= {Self-Organizing Neural Network Application to Technical
		  Process Parameters Estimation},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {579--582},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  grabec90a,
  author	= {I. Grabec},
  title		= {Self-Organization of Neurons Described by the
		  Maximum-Entropy Principle},
  journal	= {Biol. Cyb. },
  volume	= 63,
  pages		= {403--409},
  year		= 1990,
  dbinsdate	= {oldtimer}
}

@InProceedings{	  grabec91a,
  author	= {Igor Grabec},
  title		= {Modeling of Chaos by a Self-Organizing Neural Network},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {151--156},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  graepel97a,
  author	= {T. Graepel and M. Burger and K. Obermayer},
  title		= {Phase transitions in stochastic \mbox{self-organizing}
		  maps},
  journal	= {Physical Review E [Statistical Physics, Plasmas, Fluids,
		  and Related Interdisciplinary Topics]},
  year		= {1997},
  volume	= {56},
  number	= {4},
  pages		= {3876--90},
  dbinsdate	= {oldtimer}
}

@InCollection{	  graepel97b,
  author	= {Thore Graepel and Matthias Burger and Klaus Obermayer},
  title		= {Deterministic annealing for topographic vector
		  quantization and \mbox{self-organizing} maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {345--350},
  dbinsdate	= {oldtimer}
}

@Article{	  graepel98a,
  author	= {T. Graepel and M. Burger and K. Obermayer},
  title		= {\mbox{Self-organizing} maps: generalizations and new
		  optimization techniques},
  journal	= {Neurocomputing},
  year		= {1998},
  volume	= {21},
  number	= {1--3},
  pages		= {173--90},
  abstract	= {Three algorithms are used to generate topographic mappings
		  to the practitioner of unsupervised data analysis. Each
		  algorithms are based on the minimization of a cost
		  function. The soft topographic vector quantization
		  algorithm (STVQ) provides a tool for the creation of
		  self-organizing maps of Euclidean data. The kernel-based
		  soft topographic mapping (STMK) is a generalization of STVQ
		  and introduces a new distance measures in data space, based
		  on kernel function. The soft topographic mapping for
		  proximity data (STMP) is another generalization of STVQ
		  that enables the user to generate topographic maps for data
		  which are given in terms of pairwise proximities. Both STMK
		  and STMP share the robust optimization properties of
		  STVQ.},
  dbinsdate	= {oldtimer}
}

@Article{	  graepel99a,
  author	= {T. Graepel and K. Obermayer},
  title		= {A Stochastic Self Organizing Map for Proximity Data},
  journal	= {Neural Computation},
  volume	= {11},
  pages		= {139--155},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  graf88a,
  author	= {D. H. Graf and W. R. LaLonde},
  title		= {A Neural Controller for Collision-Free Movement of General
		  Robot Manipulators},
  booktitle	= {Proc. ICNN'88, International Conference on Neural
		  Networks},
  volume	= {I},
  pages		= {77--84},
  year		= {1988},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  graf89a,
  author	= {D. H. Graf and W. LaLonde},
  title		= {Neuroplanners for Hand-Eye Coordination},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  pages		= {543--548},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  graf95a,
  author	= {Graf, H. P. and Reyneri, L. M. and Burns, D. C. and
		  Underwood, I. and Murray, A. F. and Vass, D. G. and
		  Skinner, S. R. and Steck, J. E. and Behrman, E. C. and
		  Cairns, G. and Tarassenko, L. and Ruping, S. and Goser, K.
		  and Ruckert, U. },
  title		= {Neural networks-extraordinary variation},
  journal	= {IEEE Micro},
  year		= {1995},
  volume	= {15},
  number	= {3},
  pages		= {48--59},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  graham91a,
  author	= {D. P. W. Graham and G. M. T. D'Eleuterio},
  title		= {A hierarchy of self-organized multiresolution artificial
		  neural networks for robotic control},
  booktitle	= {Proc. IJCNN-91, International Joint Conference on Neural
		  Networks, Seattle},
  year		= {1991},
  volume	= {II},
  pages		= {1002},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {. . . The overlapping input domain cells of each of the
		  layers in the hierarchy are organized using a simple
		  Kohonen network. Using this novel approach,},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  grana99a,
  author	= {Grana, M. and Echave, I.},
  title		= {Real time optical flow computation based on adaptive color
		  quantization by competitive neural networks},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3837},
  pages		= {165--174},
  abstract	= {An image smoothing technique based on vector quantization
		  of the pixel neighborhoods is proposed. The smoothed images
		  are used to compute the optical flow based on the
		  correlation approach. The smoothing preserves the
		  boundaries, improving the estimation of the flow at the
		  boundaries while eliminating or reducing the spurious
		  detections due to noise effects in the homogeneous regions.
		  The codebook is computed once for the entire sequence,
		  introducing some additional artifacts that could be avoided
		  by the smooth adaptation of the codebook with the
		  self-organizing map. The adaptation could account for small
		  interframe variations of color distribution due to motion,
		  lighting variations and noise.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  graupe90a,
  author	= {D. Graupe and R. Liu},
  title		= {A neural network approach to decomposing surface {EMG}
		  signals},
  booktitle	= {Proc. 32nd Midwest Symp. on Circuits and Systems},
  year		= {1990},
  volume	= {II},
  pages		= {740--743},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {. . . Hopfield neural network; Kohonen network; . . . },
  dbinsdate	= {oldtimer}
}

@InCollection{	  graupe96a,
  author	= {D. Graupe and H. Kordylewski},
  title		= {Network based on {SOM} (Self-Organizing-Map) modules
		  combined with statistical decision tools},
  booktitle	= {Proceedings of the 39th Midwest Symposium on Circuits and
		  Systems},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  editor	= {G. Cameron and M. Hassoun and A. Jerdee and C. Melvin},
  address	= {New York, NY, USA},
  pages		= {471--4},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  graupe97a,
  author	= {Graupe, D. and Kordylewski, H.},
  title		= {A large scale memory ({LAMSTAR}) neural network for
		  medical diagnosis},
  booktitle	= {Proceedings of the 19th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  `Magnificent Milestones and Emerging Opportunities in
		  Medical Engineering'.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1997},
  volume	= {3},
  pages		= {1332--5},
  abstract	= {Discusses applications of the LAMSTAR network to a medical
		  diagnostic case; specifically, to a urologic medical
		  diagnosis. The LAMSTAR network is a self trained network
		  based on {SOM} (Self-Organizing-Map) modules. It employs
		  arrays of link-weight vectors to channel information
		  vertically and horizontally through the network to
		  facilitate fast memory retrieval. For diagnosis, the
		  LAMSTAR network displays the diagnosis with suggestions to
		  perform specific further tests. Also, the network
		  interpolate/extrapolate those subwords (states of car
		  systems), that were not present in the input word. As a
		  medical diagnostic tool, the LAMSTAR network evaluates
		  patients' conditions and long term forecasting after
		  removal of kidney stones. The LAMSTAR network attempts to
		  predict the treatment's results (failure/success) by
		  analyzing the correlations among 100 patients (input
		  words), each described by 17 subwords. The paper thus
		  illustrates the scope of applications of the LAMSTAR
		  network.},
  dbinsdate	= {oldtimer}
}

@Article{	  gray98a,
  author	= {Gray, Robert M. and Perlmutter, Keren O. and Olshen,
		  Richard A.},
  title		= {Quantization, classification, and density estimation for
		  {K}ohonen's Gaussian mixture},
  journal	= {Data Compression Conference},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  number	= {},
  volume	= {},
  pages		= {63--72},
  abstract	= {We consider the problem of joint quantization and
		  classification for the example of a simple Gaussian mixture
		  used by Kohonen to demonstrate the performance of his
		  'learning vector quantization' (LVQ). Implicit in the
		  problem is the issue of estimating the underlying
		  densities, which is accomplished by CART and by an inverse
		  halftoning method.},
  dbinsdate	= {oldtimer}
}

@Article{	  greaves01a,
  author	= {Greaves, A. J. and Gasteiger, J.},
  title		= {The use of self-organising neural networks in dye design},
  journal	= {Dyes and Pigments},
  year		= {2001},
  volume	= {49},
  number	= {1},
  month		= {April 2001},
  pages		= {51--63},
  organization	= {Department of Colour Chemistry, University Leeds},
  publisher	= {},
  address	= {},
  abstract	= {The mapping of molecular surfaces is of particular
		  interest to dye chemists for numerous reasons, none more so
		  than the prediction of dye-substrate binding.
		  Self-organising neural networks have been used to map the
		  hydrogen bonding, electrostatic and hydrophobic 3D
		  molecular surface potentials of a series of dyes. The
		  results indicate that the hydrogen bonding potential, the
		  molecular electrostatic potential and their combination are
		  useful in classifying the dyes and that the hydrogen
		  bonding potential is a useful molecular descriptor of
		  substantivity.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  greenspan91a,
  author	= {H. Greenspan and R. Goodman and R. Chellappa},
  title		= {Texture analysis via unsupervised and supervised
		  learning},
  booktitle	= {Proc. IJCNN-91, International Joint Conference on Neural
		  Networks, Seattle},
  year		= {1991},
  volume	= {I},
  pages		= {639--644},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  gregory99a,
  author	= {Gregory, J. S. and Junold, R. M. and Undrill, P. E. and
		  Aspen, R. M.},
  title		= {Analysis of trabecular bone structure using {F}ourier
		  transforms and neural networks},
  journal	= {IEEE Transactions on Information Technology in
		  Biomedicine},
  year		= {1999},
  volume	= {3},
  pages		= {289--94},
  abstract	= {Hip fracture due to osteoporosis (OP) and hip
		  osteoarthritis (OA) are both important causes of locomotor
		  morbidity in the elderly population. In osteoporosis, bone
		  mass gradually decreases until the skeleton is too fragile
		  to support the body and a fracture occurs, typically in the
		  femur, wrist or spine. In osteoarthritis, there is a
		  proliferation of bone, leading to a stiffening of the
		  tissue. Current clinical methods for assessment of bone
		  changes in these disorders largely depend on assessing bone
		  mineral density. However, this does not provide any
		  information about bone structure, which is considered to be
		  an equally important factor in assessing bone quality. This
		  paper presents a novel approach for computer analysis of
		  trabecular (or cancellous) bone structure. The technique
		  uses a Fourier transform to generate a "spectral
		  fingerprint" of an image. Principal components analysis is
		  then applied to identify key features from the Fourier
		  transform and this information is passed to a neural
		  network for classification. Testing this on a series of 100
		  histological sections of trabecular bone from patients with
		  OP and OA and a normal group correctly classified over 90%
		  of the OP group with an overall accuracy of 77%-84%. Such
		  high success rates on a small group suggest that this may
		  provide a simple, but powerful, method for identifying
		  alterations in bone structure.},
  dbinsdate	= {oldtimer}
}

@Article{	  griffith94a,
  author	= {Griffith, N. },
  title		= {Connectionist visualisation of tonal structure},
  journal	= {Artificial Intelligence Review},
  year		= {1994--1995},
  volume	= {8},
  number	= {5--6},
  pages		= {393--408},
  dbinsdate	= {oldtimer}
}

@Article{	  griffith94b,
  author	= {Griffith, N. },
  title		= {Development of tonal centres and abstract pitch as
		  categorizations of pitch use},
  journal	= {Connection Science},
  year		= {1994},
  volume	= {6},
  number	= {2--3},
  pages		= {155--75},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  grigore00a,
  author	= {Grigore, O. and Grigore, O. and Florescu, A.},
  title		= {The control of a nonlinear system using competitive neural
		  networks},
  booktitle	= {OPTIM 2000. Proceedings of the 7th International
		  Conference on Optimization of Electrical and Electronic
		  Equipments. Transilvania Univ. Press, Brasov, Romania},
  year		= {2000},
  volume	= {3},
  pages		= {671--4},
  abstract	= {This paper presents a controller based on Kohonen's
		  self-organizing map (SOM), used in commanding time varying
		  systems with uncertain tasks. First, a reduction procedure
		  of the initial set of parameters was applied using an
		  unsupervised pattern recognition technique. After this an
		  SOM was trained using the minimized set of data obtained
		  above. An application of a missile-target tracking was
		  implemented using the mentioned method, and the results are
		  compared with those obtained in a classical approach.},
  dbinsdate	= {2002/1}
}

@InCollection{	  grigore97a,
  author	= {O. Grigore},
  title		= {Syntactical \mbox{self-organizing} map},
  booktitle	= {Computational Intelligence Theory and Applications.
		  International Conference, 5th Fuzzy Days. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {B. Reusch},
  address	= {Berlin, Germany},
  pages		= {101--9},
  dbinsdate	= {oldtimer}
}

@Article{	  grigull01a,
  author	= {Grigull, J. and Alexandrova, R. and Paterson, A. D.},
  title		= {Clustering of pedigrees using marker allele frequencies:
		  Implact on linkage analysis},
  journal	= {GENETIC EPIDEMIOLOGY},
  year		= {2001},
  volume	= {21},
  pages		= {S61--S66},
  abstract	= {Ethnicity may form the basis for locus heterogeneity at
		  certain susceptibility loci for complex diseases.
		  Classification of pedigrees into ethnic groups is usually
		  based upon self-report, but this may not be sensitive or
		  specific. We investigated whether it is possible to cluster
		  families from an admixed population using pedigree-specific
		  marker allele frequencies. We used 323 autosomal
		  microsatellite markers from 216 pedigrees who described
		  themselves as either Caucasian or African American. First,
		  we compared the stated ethnicity of pedigrees with clusters
		  using pedigree-specific marker allele frequencies as input
		  for a self-organizing map, a type of neural network. Using
		  data from different chromosomes, nine pedigrees which were
		  self-reported as African American were clustered with the
		  Caucasian pedigrees. Removal of these nine pedigrees from
		  the African American group did not markedly affect linkage
		  results. We then proceeded to determine whether there was
		  further heterogeneity between pedigrees using I x 3 nodes.
		  Forty-four pedigrees were clustered in a group intermediate
		  to the African American or Caucasian clusters. This group
		  was composed of 36 and 8 pedigrees that described
		  themselves as African American and Caucasian, respectively.
		  Linkage analysis was performed in this group and results
		  compared with the groups based upon self-reported
		  ethnicity. Linkage to a region on chromosome 3 was observed
		  in this intermediate group, which was more significant than
		  any of the results obtained when pedigrees were grouped
		  using self-reported ethnicity. Use of marker data may
		  assist in clustering pedigrees with similar ethnic
		  backgrounds and may increase the power for genetic linkage
		  studies. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  grim00a,
  author	= {Jir Grim and Pavel Pudil and Petr Somol},
  title		= {Recognition of handwritten numerals by structural
		  probalistic neural networks},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Institute of Information Theory and Automation},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  grim00b,
  author	= {Grim, J.},
  title		= {Self-organizing maps and probabilistic neural networks},
  journal	= {Neural-Network-World},
  year		= {2000},
  volume	= {10},
  pages		= {407--15},
  abstract	= {The self-organizing map algorithm for training of
		  artificial neural networks is shown to be closely related
		  to a sequential modification of EM algorithm for
		  maximum-likelihood estimation of finite mixtures. The
		  established correspondence provides a helpful theoretical
		  basis for interpretation of the properties of the SOM
		  algorithm and for the choice of involved parameters.},
  dbinsdate	= {2002/1}
}

@Book{		  grimaldi94a,
  author	= {Grimaldi, V.},
  title		= {Perspective d'utilisation des reseaux de neurones non
		  supervises en discrimination des signatures courants de
		  Foucault. (Using unsupervised neural networks for eddy
		  currents signature discrimination: a prospective study).},
  year		= {1994},
  abstract	= {This report describes an application of unsupervised
		  neural networks for eddy currents Non Destructive Testing
		  (NDT) inspection of steam generator tubes. As a matter of
		  fact conventional neurocomputing techniques (multilayer
		  perceptions) fail to achieve desired results because of the
		  inherent lack of data and the slowly changing
		  characteristics involved. This is an original neural
		  approach to defect detection, characterized by two aspects:
		  diagnosis is expressed in architectural terms and the
		  potential advantages of using unsupervised neural
		  techniques are systematically discussed. After briefly
		  recalling the context and origin of the study, we present
		  the framework of the Kohonen self organizing maps within
		  the proposed diagnosis architecture. We then show how we
		  intend to use them for sound/unsound discrimination
		  compliant with eddy currents NDT requirements. Preliminary
		  results are presented in the last part of the report. They
		  seem confirm that this new approach is worth being more
		  deeply investigated. (author). 13 figs., 17 refs., 3
		  annexes. (Atomindex citation 27:019750)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  grimmer96a,
  author	= {Udo Grimmer},
  title		= {Clementine: Data Mining Software},
  booktitle	= {Classification and Multivariate Graphics: Models, Software
		  and Applications},
  year		= 1996,
  editor	= {Hans-Joachim Mucha and Hans-Hermann Bock},
  number	= {10},
  series	= {Weierstrass-Institut f{\"u}r Angewandte Analysis und
		  Stochastik},
  address	= {Berlin},
  pages		= {25--31},
  dbinsdate	= {oldtimer}
}

@Article{	  groenen01a,
  author	= {Groenen, P. J. F. and Jajuga, K.},
  title		= {Fuzzy clustering with squared Minkowski distances},
  journal	= {Fuzzy Sets and Systems},
  year		= {2001},
  volume	= {120},
  number	= {3},
  month		= {Jun 16 2001},
  pages		= {227--237},
  organization	= {Department of Education, Data Theory Group},
  publisher	= {},
  address	= {},
  abstract	= {This paper presents a new fuzzy clustering model based on
		  a root of the squared Minkowski distance which includes
		  squared and unsquared Euclidean distances and the
		  L<sub>1</sub>-distance. An algorithm is presented that is
		  based on iterative majorization and yields a convergent
		  series of monotone nonincreasing loss function values. This
		  algorithm coincides under some condition with the ISODATA
		  algorithm of Dunn (J. Cybernet. 3 (1974) 32--57) and the
		  fuzzy c-means algorithm of Bezdek (Ph.D. Thesis, Cornell
		  University, Ithaca, 1973) for squared Euclidean distance
		  and with an algorithm of Jajuga (Fuzzy Sets and Systems 39
		  (1991) 43--50) for L<sub>1</sub>-distances. To find a
		  global minimum we compare a special strategy called fuzzy
		  steps with fuzzy Kohonen clustering networks (FKCN)
		  (Pattern Recognition 27 (1994) 757--764) and multistart.
		  Fuzzy steps and FKCN are based on finding updates for a
		  decreasing weighting exponent, which seems to work
		  particularly well for hard clustering. To assess the
		  performance of the methods, two numerical experiments and a
		  simulation study are performed.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  grohn00a,
  author	= {Grohn, Matti and Nieminen, Marko and Haho, P{\"a}ivi and
		  Smeds, Riitta},
  title		= {Experiences in using DISCUS for visualizing human
		  communication},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  volume	= {3960},
  pages		= {302--311},
  abstract	= {In this paper, we present further improvements to the
		  DISCUS software that can be used to record and analyze the
		  flow and contents of business process simulation session
		  discussion. The tool was initially introduced in `Visual
		  Data Exploration and Analysis VI' conference. The initial
		  features of the tool enabled the visualization of
		  discussion flow in business process simulation sessions and
		  the creation of {SOM} (Self organizing map) analyses. The
		  improvements of the tool consist of additional
		  visualization possibilities that enable quick on-line
		  analyses and improved graphical statistics. We have also
		  created the very first interface to audio data and
		  implemented two ways to visualize it. We also outline
		  additional possibilities to use the tool in other
		  application areas: these include usability testing and the
		  possibility to use the tool for capturing design rationale
		  in a product development process. The data gathered with
		  DISCUS may be used in other applications, and further work
		  may be done with data mining techniques.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gronfors94a,
  author	= {Tapio Gr{\"{o}}nfors},
  title		= {Use of Self-Organizing Maps for Preliminary Classification
		  Tasks of Auditory Brainstem Responses},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conf. Proc. of Finnish Artificial Intelligence Society},
  pages		= {44--46},
  booktitle	= {Proc. Conf. on Artificial Intelligence Res. in Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {application, pattern recognition},
  dbinsdate	= {oldtimer}
}

@InCollection{	  gross91a,
  author	= {M. Gross and F. Seibert},
  title		= {Neural network image analysis for environmental
		  protection},
  booktitle	= {Visualisierung von Umweldtdaten},
  publisher	= {Springer},
  year		= {1991},
  editor	= {Gr{\"{u}}tzner},
  address	= {Berlin},
  dbinsdate	= {oldtimer}
}

@Article{	  gross93a,
  author	= {Markus H. Gross and F. Seibert},
  title		= {Visualization of multidimensional image data sets using a
		  neural network},
  journal	= {Visual Computer},
  year		= {1993},
  volume	= {10},
  pages		= {145--159},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gross94a,
  author	= {Gross, M. H. and Koch, R. and Lippert, L. and Dreger, A.
		  },
  title		= {Multiscale image texture analysis in wavelet spaces},
  booktitle	= {Proceedings ICIP-94},
  year		= {1994},
  volume	= {3},
  pages		= {412--16},
  organization	= {Dept. of Comput. Sci. , Eidgenossische Tech. Hochschule,
		  Zurich, Switzerland},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  gross95a,
  author	= {Markus H. Gross and Rolf Koch},
  title		= {Visualization of Multidimensional Shape and Texture
		  Features in Laser Range Data Using Complex-Valued {G}abor
		  Wavelets},
  journal	= {IEEE Transactions on Visualization and Computer Graphics},
  year		= 1995,
  volume	= 1,
  pages		= {44--59},
  dbinsdate	= {oldtimer}
}

@Article{	  grossberg99a,
  author	= {S. Grossberg and J. R. Williamson},
  title		= {A Self Organizing Neural System for Learning to Recognize
		  Textured Scenes},
  journal	= {Vision Research},
  volume	= {39},
  pages		= {1385--1406},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@Article{	  grossman91a,
  author	= {B. Grossman and Xing Gao and M. Thursby},
  title		= {Composite damage assessment employing an optical neural
		  network processor and an embedded fiber optic sensor
		  array},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1991},
  volume	= {1588},
  pages		= {64--75},
  annote	= {Conf. paper in journal},
  x		= {. . . The optical processor, a pre-trained Kohonen neural
		  network. },
  dbinsdate	= {oldtimer}
}

@InCollection{	  grozinger96a,
  author	= {M. Grozinger and T. Uhl and J. Roschke},
  title		= {{K}ohonen feature maps in the online detection of {REM}
		  sleep from single channel sleep {EEG} data},
  booktitle	= {WCNN'96. World Congress on Neural Networks. International
		  Neural Network Society 1996 Annual Meeting},
  publisher	= {Lawrence Erlbaum Associates},
  year		= {1996},
  address	= {Mahwah, NJ, USA},
  pages		= {885},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gruen02a,
  author	= {Gruen, R. and Kubota, T.},
  title		= {A neural network approach to system performance analysis},
  booktitle	= {Proceedings IEEE SoutheastCon 2002. IEEE, Piscataway, NJ,
		  USA},
  year		= {2002},
  volume	= {},
  pages		= {349--54},
  abstract	= {Neural networks are used in a wide variety of situations
		  to solve complex problems. Some of the categories for which
		  neural networks are used include: prediction software,
		  classification algorithms, data association environments,
		  data conceptualization environments, and data filtering
		  problems. This work described in this paper implements a
		  neural network that spans both the prediction and data
		  association problems. The neural network approach to system
		  performance analysis takes performance data from computer
		  systems and uses a Kohonen based neural network to analyze
		  the performance data and attempts to find bottlenecks in
		  the computer system. The data performance analysis results
		  are present as line graphs that can be interpreted by
		  computer experts to determine bottlenecks within the
		  computer system, and can intelligently suggest upgrades to
		  improve any subsystem that suffers from poor performance.
		  The aim of this work is to provide a "proof of concept" for
		  use in IT assessments, but can also be applied to any
		  situation involving computer performance analysis.},
  dbinsdate	= {2002/1}
}

@MastersThesis{	  gruner92a,
  author	= {J. S. Gruner},
  title		= {Comparison of Artificial Neural Networks with a
		  Conventional Heuristic Technique for Optimization
		  Problems},
  school	= {Air Force Inst. of Tech. },
  year		= {1992},
  address	= {Wright-Patterson AFB, OH},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@Article{	  grunewald92a,
  author	= {Grunewald, A. },
  title		= {Neighborhoods and trajectories in {K}ohonen maps},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1710},
  number	= {pt. 1},
  pages		= {670--9},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  guan94a,
  author	= {Cuntai Guan and Ce Zhu and Yongbin Chen and Zhenya He},
  title		= {Performance Comparison of Several Speech Recognition
		  Methods},
  booktitle	= {Proc. Int. Symp. on Speech, Image Processing and Neural
		  Networks},
  year		= {1994},
  volume	= {II},
  pages		= {710--713},
  organization	= {{IEEE} Hong Kong Chapter of Signal Processing},
  address	= {Hong Kong},
  annote	= {application, speech recognition, comparison},
  dbinsdate	= {oldtimer}
}

@InCollection{	  guan95a,
  author	= {Y. Guan and T. G. Clarkson and J. G. Taylor},
  title		= {Learning transformed prototypes ({LTP})-a statistical
		  pattern classification technique of neural networks},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {441--7},
  dbinsdate	= {oldtimer}
}

@InCollection{	  guan97a,
  author	= {Huiwei Guan and Chi-kwong Li and To-yat Cheung and
		  Songnian Yu},
  title		= {Parallel design and implementation of {SOM} neural
		  computing model in {PVM} environment of a distributed
		  system},
  booktitle	= {Proceedings of Advances in Parallel and Distributed
		  Computing},
  publisher	= {IEEE Computer Society Press},
  year		= {1997},
  address	= {Los Alamitos, CA, USA},
  pages		= {26--31},
  abstract	= {A parallel design and implementation of the
		  Self-Organizing Map (SOM) neural computing model is
		  proposed. The parallel design of {SOM} is implemented in a
		  parallel virtual machine (PVM) environment of a distributed
		  system. A practical realization of {SOM} algorithm is
		  investigated, the construction of computing module in
		  parallel virtual machine is discussed, the communication
		  methods and an optimization of messages passing between
		  multiple processes are proposed, and the parallel
		  programming technique and a PVM implementation of {SOM}
		  neural computing model are given and discussed in detail.},
  dbinsdate	= {oldtimer}
}

@Article{	  guerin-dugue94a,
  author	= {Guerin-Dugue, A. and Palagi, P. M. },
  title		= {Texture segmentation using pyramidal {G}abor functions and
		  \mbox{self-organising} feature maps},
  journal	= {Neural Processing Letters},
  year		= {1994},
  volume	= {1},
  number	= {1},
  pages		= {25--9},
  month		= {Sept},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  guerin-dugue96a,
  author	= {Anne Gu{\'{e}}rin-Dugu{\'{e}} and Carleos Aviles-Cruz and
		  Patricia M. Palagi},
  title		= {Interpreting Data Through Neural and Statistical Tools},
  booktitle	= {Proc. ESANN'96, European Symp. on Artificial Neural
		  Networks},
  year		= {1996},
  publisher	= {D facto conference services},
  address	= {Bruges, Belgium},
  editor	= {Michel Verleysen},
  pages		= {229--236},
  dbinsdate	= {oldtimer}
}

@Article{	  guerrero01a,
  author	= {Guerrero, V. P. and {De Moya Anegon}, F.},
  title		= {Reduction of the dimension of a document space using the
		  fuzzified output of a Kohonen network},
  journal	= {Journal of the American Society for Information Science
		  and Technology},
  year		= {2001},
  volume	= {52},
  number	= {14},
  month		= {December },
  pages		= {1234--1241},
  organization	= {Library and Information Sci. Faculty, University of
		  Extremadura},
  publisher	= {},
  address	= {},
  abstract	= {The vectors used in IR, whether to represent the documents
		  or the terms, are high dimensional, and their dimensions
		  increase as one approaches real problems. The algorithms
		  used to manipulate them, however, consume enormously
		  increasing amounts of computational capacity as the said
		  dimension grows. We used the Kohonen algorithm and a
		  fuzzification module to perform a fuzzy clustering of the
		  terms. The degrees of membership obtained were used to
		  represent the terms and, by extension, the documents,
		  yielding a smaller number of components but still endowed
		  with meaning. To test the results, we use a topological
		  classification of sets of transformed and untransformed
		  vectors to check that the same structure underlies both.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  guerrero95a,
  author	= {Joaqu{\'{\i}}n Carretero Guerrero},
  title		= {Clasificaci{\'{o}}n por Visi{\'{o}}n Artificial de Maderas},
  booktitle	= {Proc. TTIA'95, Transferencia Tecnol{\'{o}}gica de
		  Inteligencia Artificial a Industria, Medicina y
		  Aplicaciones Sociales},
  year		= {1995},
  editor	= {Ram{\'{o}}n Rizo Aldeguer and Juan Manuel Gar{\'{c}}ia
		  Chamizo},
  pages		= {189--197},
  note		= {(in spanish)},
  dbinsdate	= {oldtimer}
}

@Article{	  guerrero_bote02a,
  author	= {{Guerrero Bote}, V. P. and {De Moya Anegon}, F. and
		  Herrero Solana, V.},
  title		= {Document organization using Kohonen's algorithm},
  journal	= {Information Processing and Management},
  year		= {2002},
  volume	= {38},
  number	= {1},
  month		= {January },
  pages		= {79--89},
  organization	= {Library and Information Sci. Faculty, University of
		  Extremadura},
  publisher	= {},
  address	= {},
  abstract	= {The classification of documents from a bibliographic
		  database is a task that is linked to processes of
		  information retrieval based on partial matching. A method
		  is described of vectorizing reference documents from LISA
		  which permits their topological organization using
		  Kohonen's algorithm. As an example a map is generated of
		  202 documents from LISA, and an analysis is made of the
		  possibilities of this type of neural network with respect
		  to the development of information retrieval systems based
		  on graphical browsing. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  guillaume99a,
  author	= {Guillaume, D. and Murtagh, F.},
  title		= {An application of {XML} and {XL}ink using a
		  graph-partitioning method and a density map for information
		  retrieval and knowledge discovery},
  booktitle	= {Astronomical Society of the Pacific Conference Series},
  year		= {1999},
  volume	= {172},
  pages		= {278--82},
  abstract	= {We have defined an XML language for astronomy, called AML
		  (Astronomical Markup Language), able to represent
		  meta-information for astronomical objects, tables, articles
		  and authors. The various AML documents created have links
		  between them, and an innovative tool can cluster the
		  documents with a graph-partitioning algorithm using the
		  links. The result is displayed on a density map similar to
		  Kohonen self-organising maps. AML and its advantages are
		  briefly described, as well as the clustering program, which
		  is one of the many possible applications of AML.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  guillot94a,
  author	= {M. Guillot and R. Azouzi},
  title		= {Improving on-line adaptation in neurocontrol using a
		  combination of \mbox{self-organizing} map and multilayer
		  feedforward network},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {915--22},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  guimaraes00a,
  author	= {Guimaraes, G.},
  title		= {Temporal knowledge discovery for multivariate time series
		  with enhanced self-organizing maps},
  booktitle	= {Proceedings of the IEEE-INNS-ENNS International Joint
		  Conference on Neural Networks. IJCNN 2000. Neural
		  Computing: New Challenges and Perspectives for the New
		  Millennium. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {6},
  pages		= {165--70},
  abstract	= {This paper presents enhanced self-organizing maps (SOM)
		  for exploratory multivariate time series analysis in the
		  context of temporal data mining. The main idea lies in an
		  adequate combination of approaches with SOM for temporal
		  processing. It is part of a recently developed method that
		  introduces several abstraction levels for temporal
		  knowledge conversion. The method provides a conversion of
		  discovered temporal patterns in multivariate time series
		  with enhanced SOM into a linguistic knowledge
		  representation, in form of temporal grammatical rules. This
		  method was successfully applied to a problem in medicine.
		  Even some previously unknown knowledge was found.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  guimaraes01a,
  author	= {G. Guimar{\~a}es and F. Moura-Pires},
  title		= {An Essay in classifying self-organising maps for temporal
		  sequence processing},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {259--67},
  year		= {2001},
  editor	= {Nigel Allison and Hujun Yin and Lesley Allison and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  gulcur92a,
  author	= {Gulcur, H. O. and Buyukaksoy, G. },
  title		= {Identification of different types of leucocytes in dried
		  blood smears using neural networks},
  booktitle	= {Proceedings of the 1992 International Biomedical
		  Engineering Days},
  year		= {1992},
  editor	= {Ulgen, Y. },
  pages		= {203--6},
  organization	= {Inst. of Biomed. Eng. , Bogazici Univ. , Istanbul,
		  Turkey},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  gulski93a,
  author	= {Gulski, E. and Krivda, A. },
  title		= {Neural networks as a tool for recognition of partial
		  discharges},
  journal	= {IEEE Transactions on Electrical Insulation},
  year		= {1993},
  volume	= {28},
  number	= {6},
  pages		= {984--1001},
  month		= {Dec},
  dbinsdate	= {oldtimer}
}

@Article{	  gunter02a,
  author	= {Gunter, Simon and Bunke, Horst},
  title		= {Self-organizing map for clustering in the graph domain},
  journal	= {Pattern Recognition Letters},
  year		= {2002},
  volume	= {23},
  number	= {4},
  month		= {},
  pages		= {405--417},
  organization	= {Department of Computer Science, University of Bern},
  publisher	= {Elsevier Science B.V.},
  address	= {},
  abstract	= {Self-organizing map (som) is a flexible method that can be
		  applied to various tasks in pattern recognition. However it
		  is limited in the sense that it uses only pattern
		  representations in terms of feature vectors. It was only
		  recently that an extension to strings was proposed. In the
		  present paper we go a step further and present a version of
		  som that works in the domain of graphs. Graphs are a
		  powerful data structure that include pattern
		  representations based on strings and feature vectors as
		  special cases. After introducing the new method a number of
		  experiments will be described demonstrating its feasibility
		  in the context of a graph clustering task.},
  dbinsdate	= {2002/1}
}

@Article{	  guo94a,
  author	= {Y. Guo and B. Forster},
  title		= {Unsupervised classification of high spectral resolution
		  images using the {K}ohonen self-organization neural
		  network},
  journal	= {Journal of Infrared and Millimeter Waves},
  year		= {1994},
  volume	= {13},
  number	= {6},
  pages		= {409--417},
  dbinsdate	= {oldtimer}
}

@Article{	  gupta97a,
  author	= {V. K. Gupta and J. G. Chen and M. B. Murtaza},
  title		= {A learning vector quantization neural network model for
		  the classification of industrial construction projects},
  journal	= {Omega},
  year		= {1997},
  volume	= {25},
  number	= {6},
  pages		= {715--27},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  gursoy00a,
  author	= {Gursoy, A. and Atun, M.},
  title		= {Neighbourhood preserving load balancing: a self-organizing
		  approach},
  booktitle	= {Euro-Par 2000 Parallel Processing. 6th International
		  Euro-Par Conference. Proceedings (Lecture Notes in Computer
		  Science Vol.1900). Springer-Verlag, Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {234--41},
  abstract	= {Describes a static load-balancing algorithm based on
		  Kohonen self-organizing maps (SOMs) for a class of parallel
		  computations where the communication pattern exhibits
		  spatial locality, and we present initial results. The
		  topology-preserving mapping achieved by a SOM reduces the
		  communication load across processors, however, it does not
		  take load balancing into consideration. We introduce a
		  load-balancing mechanism into the SOM algorithm. We also
		  present a preliminary multi-level implementation which
		  resulted in significant execution time improvements. The
		  results are promising to further improve SOM-based load
		  balancing for geometric graphs.},
  dbinsdate	= {2002/1}
}

@TechReport{	  gustafsson96a,
  author	= {Lennart Gustafsson},
  title		= {Inadequate Cortical Feature Maps: A Neural Circuit Theory
		  of Autism},
  institution	= {Lule{\aa} University of Technology, Division of Industrial
		  Electronics},
  year		= 1996,
  number	= {TULEA 1996:08},
  dbinsdate	= {oldtimer}
}

@Article{	  guterman96a,
  author	= {H. Guterman and Y. Nehmadi and A. Christyakov and J. F.
		  Soustiel and M. Feinsod},
  title		= {A comparison of neural network and {B}ayes recognition
		  approaches in the evaluation of the brainstem trigeminal
		  evoked potentials in multiple sclerosis},
  journal	= {International Journal of Bio-Medical Computing},
  year		= {1996},
  volume	= {43},
  number	= {3},
  pages		= {203--13},
  dbinsdate	= {oldtimer}
}

@InCollection{	  gwiazda95a,
  author	= {A. Gwiazda and R. Knosala},
  title		= {Application of the {K}ohonen net for classification of the
		  constructional form of the {3D} objects},
  booktitle	= {Proceedings of the Second International Symposium on
		  Methods and Models in Automation and Robotics},
  publisher	= {Wydawnictwo Uczelniane Politech. Szczecinskiej},
  year		= {1995},
  volume	= {2},
  editor	= {S. Banka and S. Domek and Z. Emirsajlow},
  address	= {Szczecin, Poland},
  pages		= {715--18},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ha99a,
  author	= {Seong Wook Ha and Dae Seong Kang and Kee Hang Kwan and
		  Daijin Kim},
  title		= {N-rule genetic \mbox{self-organizing} map using genetic
		  algorithm},
  booktitle	= {FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems.
		  Conference Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1781--4},
  abstract	= {Proposes a method for n-rule representation and n-rule
		  inference. A network based on this method takes an
		  architecture of a genetic self organizing map, called an
		  n-rule genetic self-organizing map (n-RGSOM). The paper
		  also provides n-nodes maintenance rules for n-RGSOM and
		  utilizes some criteria for node overlapping prevention
		  generated by the genetic operation. By using these criteria
		  we can avoid critical area errors, and a node can take a
		  single operation per iteration. The simulation results show
		  that the genetic learning algorithm and n-nodes maintenance
		  method developed in the paper is effective.},
  dbinsdate	= {oldtimer}
}

@Article{	  haapanen96a,
  author	= {M. -L. Haapanen and L. Liu and T. Hiltunen and L. Leinonen
		  and J. Karhunen},
  title		= {Cul-de-sac Hypernasality Test with Pattern Recognition of
		  {LPC} Indices},
  journal	= {Folia Phoniatrica et Logopaedica},
  year		= {1996},
  volume	= {48},
  pages		= {35--43},
  dbinsdate	= {oldtimer}
}

@Article{	  habibi91a,
  author	= {A. Habibi},
  title		= {Neural networks in bandwidth compression},
  journal	= {Proc. SPIE---The Internatioanl Society for Optical
		  Engineering},
  year		= {1991},
  volume	= {1567},
  pages		= {334--340},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {Conf. paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  hadjitodorov00a,
  author	= {Hadjitodorov, Stefan and Nikolova, Nina},
  title		= {Generalized net model of the self-organizing map of
		  Kohonen classical training procedure},
  journal	= {Advances in Modeling and Analysis B},
  year		= {2000},
  volume	= {43},
  number	= {1--2},
  month		= {},
  pages		= {51--60},
  organization	= {Bulgarian Acad of Sciences},
  publisher	= {AMSE Press},
  address	= {Tassin-la-Demi-Lune},
  abstract	= {The Self-Organizing Map (SOM) has the special property to
		  create effectively a spatially organized internal
		  representations of various features of input vectors. SOM
		  is widely used for clustering and classification. In its
		  classical training procedure if there exists more than one
		  closest neuron the algorithm chooses the first one and
		  ignores the others. This leads to a loss of the
		  possibilities to train the network faster and to use more
		  effectively and completely the information contained in the
		  data. The idea of this paper is to cope with this problem
		  and to correct all neurons belonging to the neighborhoods
		  of all closest neurons. The correction of the placement of
		  the neighbor neuron is done once no matter how many times
		  it falls in a closest neuron neighborhoods. A first attempt
		  for generalized nets-modelling of the training procedure of
		  the well-known SOM is done. This model is universal for
		  such training algorithms and enables their better
		  understanding.},
  dbinsdate	= {2002/1}
}

@Article{	  hadjitodorov94a,
  author	= {S. Hadjitodorov and B. Boyanov and T. Ivanov and N.
		  Dalakchieva},
  title		= {Text-independent speaker identification using neural nets
		  and {AR}-vector models},
  journal	= {Electronics Letters},
  year		= {1994},
  volume	= {30},
  number	= {11},
  pages		= {838--840},
  annote	= {application, pattern recognition, speaker recognition},
  dbinsdate	= {oldtimer}
}

@Book{		  hadley97a,
  author	= {Hadley, R. F. and Cardei, V. C.},
  title		= {Acquisition of the active-passive distinction from sparse
		  input and no error feedback. Technical report no. CSS-IS
		  TR97--01.},
  year		= {1997},
  abstract	= {Describes a connectionist-inspired, parallel processing
		  network which learns, on the basis of relevantly sparse
		  input, to assign meaning interpretations to novel test
		  sentences in both active and passive voice. Training and
		  test sentences are generated from a simple recursive
		  grammar, but once trained, the network is set to process
		  thousands of sentences containing deeply embedded clauses.
		  Training is entirely unsupervised with regard to error
		  feedback, with only Kohonen and Hebbian forms of training
		  employed. The active-passive distinction is acquired
		  without any supervised provision of cues or flags that
		  indicate whether the input sentence is in active or passive
		  mode. The results show the degree of achievement of
		  criteria for syntactic and semantic systematicity.},
  dbinsdate	= {oldtimer}
}

@Article{	  haese01a,
  author	= {Haese, K. and Goodhill, G. J.},
  title		= {Auto-{SOM}: Recursive parameter estimation for guidance of
		  self- organizing feature maps},
  journal	= {NEURAL COMPUTATION},
  year		= {2001},
  volume	= {13},
  number	= {3},
  month		= {MAR},
  pages		= {595--619},
  abstract	= {An important technique for exploratory data analysis is to
		  forma mapping from the high-dimensional data space to a
		  low- dimensional representation space such that
		  neighborhoods are preserved. A popular method for achieving
		  this is Kohonen's self-organizing map (SOM) algorithm.
		  However, in its original form, this requires the user to
		  choose the values of several parameters heuristically to
		  achieve good performance. Here we present the Auto-SOM, an
		  algorithm that estimates the learning parameters during the
		  training of SOMs automatically. The application of Auto-SOM
		  provides the facility to avoid neighborhood violations up
		  to a user-defined degree in either mapping direction.
		  Auto-SOM consists of a Kalman filter implementation of the
		  SOM coupled with a recursive parameter estimation method.
		  The Kalman filter trains the neurons' weights with
		  estimated learning coefficients so as to minimize the
		  variance of the estimation error. The recursive parameter
		  estimation method estimates the width of the neighborhood
		  function by minimizing the prediction error variance of the
		  Kalman filter. In addition, the "topographic function" is
		  incorporated to measure neighborhood violations and prevent
		  the map's converging to configurations with neighborhood
		  violations. It is demonstrated that neighborhoods can be
		  preserved in both mapping directions as desired for
		  dimension- reducing applications. The development of
		  neighborhood- preserving maps and their convergence
		  behavior is demonstrated by three examples accounting for
		  the basic applications of self-organizing feature maps.},
  dbinsdate	= {2002/1}
}

@InCollection{	  haese96a,
  author	= {K. Haese and H.-D. {Vom Stein}},
  title		= {Fast \mbox{self-organising} of n-dimensional topology
		  maps},
  booktitle	= {Signal Processing VIII, Theories and Applications.
		  Proceedings of EUSIPCO-96, Eighth European Signal
		  Processing Conference},
  publisher	= {Edizioni LINT Trieste},
  year		= {1996},
  volume	= {2},
  editor	= {G. Ramponi and G. L. Sicuranza and S. Carrato and S.
		  Marsi},
  address	= {Trieste, Italy},
  pages		= {835--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  haese96b,
  author	= {Haese, K. and {Vom Stein}, H. D.},
  title		= {Neural recognition of flat objects from perspective images
		  based on the contour},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {367--70},
  abstract	= {This paper presents a new method of recognizing flat
		  objects from a single perspective projection image taken
		  from an unknown viewpoint. This method is based on the
		  extraction of the contour of the object. Characteristic
		  points on this contour are determined independent of the
		  viewpoint. Length and angles between these points can then
		  be modelled for all viewpoints. These are the features used
		  to train a self-organizing Kohonen map, whose necessary
		  dimension is deduced from the set of training data. The
		  method is tested on vessels even with blurred images.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  haese97a,
  author	= {K. Haese},
  title		= {Optimizing the \mbox{self-organizing}-process of topology
		  maps},
  booktitle	= {Computational Intelligence Theory and Applications.
		  International Conference, 5th Fuzzy Days. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {B. Reusch},
  address	= {Berlin, Germany},
  pages		= {92--100},
  dbinsdate	= {oldtimer}
}

@Article{	  haese98a,
  author	= {K. Haese},
  title		= {\mbox{Self-organizing} feature maps with self-adjusting
		  learning parameters},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1998},
  volume	= {9},
  number	= {6},
  pages		= {1270--8},
  abstract	= {This paper presents an extension of the self-organizing
		  learning algorithm of feature maps in order to improve its
		  convergence to neighborhood preserving maps. The Kohonen
		  learning algorithm is controlled by two learning
		  parameters, which have to be chosen empirically because
		  there exists neither rules nor a method for their
		  calculation. Consequently, often time consuming parameter
		  studies have to precede before a neighborhood preserving
		  feature map is obtained. To circumvent those lengthy
		  numerical studies, here, a method is presented and
		  incorporated into the learning algorithm which determines
		  the learning parameters automatically. Therefore, system
		  models of the learning and organizing process are developed
		  in order to be followed and predicted by linear and
		  extended Kalman filters. The learning parameters are
		  optimal within the system models, so that the
		  self-organizing process converges automatically to a
		  neighborhood preserving feature map of the learning data.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  haese98b,
  author	= {K. Haese},
  title		= {Automatic learning parameters for \mbox{self-organizing}
		  feature maps},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1007--12},
  dbinsdate	= {oldtimer}
}

@Article{	  haese99a,
  author	= {Haese, K.},
  title		= {{K}alman filter implementation of \mbox{self-organizing}
		  feature maps},
  journal	= {Neural Computation},
  year		= {1999},
  volume	= {11},
  pages		= {1211--33},
  abstract	= {The self-organizing learning algorithm of Kohonen and most
		  of its extensions are controlled by two learning
		  parameters, the learning coefficient and the width of the
		  neighborhood function, which have to be chosen empirically
		  because neither rules nor methods for their calculation
		  exist. Consequently, often time-consuming parameter studies
		  precede neighborhood-preserving feature maps of the
		  learning data. To circumvent those lengthy numerical
		  studies, this article describes the learning process by a
		  state-space model in order to use the linear Kalman filter
		  algorithm training the feature maps. Then the Kalman filter
		  equations calculate the learning coefficient online during
		  the training, while the width of the neighborhood function
		  needs to be estimated by a second extended Kalman filter
		  for the process of neighborhood preservation. The
		  performance of the Kalman filter implementation is
		  demonstrated on toy problems as well as on a crab
		  classification problem. The results of crab classification
		  are compared to those of generative topographic mapping, an
		  alternative method to the self-organizing feature map.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  haessly95a,
  author	= {A. Haessly and J. Sirosh and R. Miikkulainen},
  title		= {A Model of Visually Guided Plasticity of the Auditory
		  Spatial Map in the Barn Owl},
  booktitle	= {Proceedings of the Seventeenth Annual Meeting of the
		  Cognitive Science Society (COGSCI-95)},
  year		= {1995},
  publisher	= {Erlbaum},
  address	= {Hillsdale, NJ},
  pages		= {154--158},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hafliger97a,
  author	= {P. Hafliger and M. Mahowald and L. Watts},
  title		= {A spike based learning neuron in analog {VLSI}},
  booktitle	= {Advances in Neural Information Processing Systems 9.
		  Proceedings of the 1996 Conference},
  publisher	= {MIT Press},
  year		= {1997},
  editor	= {M. C. Mozer and M. I. Jordan and T. Petsche},
  address	= {London, UK},
  pages		= {692--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hagenbuchner01a,
  author	= {M. Hagenbuchner and A. C. Tsoi and A. Sperduti},
  title		= {A suprevised self-organising map for structured data},
  booktitle	= {Advances in Self-Organising Maps},
  crossref	= {},
  key		= {},
  pages		= {21--28},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {Springer},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hagiwara93a,
  author	= {Masafumi Hagiwara},
  title		= {Self-Organizing Feature Map with a Momentum Term},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {267--270},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {The objectives of this paper are to derive a momentum term
		  in the Kohonen's self-organizing feature map algorithm
		  theoretically and to show the effectiveness of the term by
		  computer simulations. We will derive a self-organizing
		  feature map algorithm having the momentum term through the
		  following assumptions: 1) The cost function is E super(n)
		  identical with sigma super(n) sub( arrow down )dk alpha
		  super(n- mu ) E sub( mu ), where E sub( mu ) is the
		  modified Lyapunov function originally proposed by Ritter
		  and Schulten at the mu th learning time and alpha is the
		  momentum coefficient. 2) The latest weights are assumed in
		  calculating the cost function E super(n). According to our
		  simulations, it has shown that the momentum term in the
		  self-organizing feature map can considerably contribute to
		  the acceleration of the convergence.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hagiwara95a,
  author	= {Hagiwara, M. },
  title		= {Self-organizing concept maps},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {1},
  pages		= {447--51},
  organization	= {Dept. of Electr. Eng. , Keio Univ. , Yokohama, Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {Self-Organizing Concept Maps (SOCOMs) based on a neural
		  network model have been proposed in this paper. They can
		  arrange concepts or words in a map space using Kohonen's
		  self-organizing map algorithm. One of the most important
		  advantages of the proposed maps is that they employ the
		  idea of k-nearest neighbor (k-NN): they do not require all
		  of the data among concepts or words. We propose two kinds
		  of SOCOMs: one is a metric SOCOM, another is a non-metric
		  one. The metric SOCOM uses the information about the metric
		  data such as similarity. The non-metric one uses the
		  information about the rank order of similarity among items.
		  The combination of the idea of k-NN and a non-metric SOCOM
		  is effective to relax the severe requirements on data: it
		  does not require all of the detailed metric information
		  among concepts or words. Computer simulation results have
		  shown the effectiveness of the proposed SOCOM.},
  dbinsdate	= {oldtimer}
}

@Article{	  hagiwara96a,
  author	= {Masafumi Hagiwara},
  title		= {Self-organizing feature map with a momentum term},
  journal	= {Neurocomputing},
  year		= {1996},
  volume	= {10},
  number	= {1},
  pages		= {71--81},
  month		= {January},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  haitao95a,
  author	= {Tang Haitao and Olli Simula},
  title		= {Neural Adaptation for Optimal Traffic Shaping in Telephone
		  Systems},
  volume	= {IV},
  pages		= {1561--1565},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  haitao96a,
  author	= {T. Haitao and O. Simula},
  title		= {The optimal utilization of multi-service {SCP}},
  booktitle	= {Intelligent Networks and New Technologies. Proceedings of
		  the IFIP TC6 Conference on Intelligent Networks and New
		  Technologies},
  publisher	= {Chapman \& Hall},
  year		= {1996},
  editor	= {J. Norgaard and V. B. Iversen},
  address	= {London, UK},
  pages		= {175--88},
  dbinsdate	= {oldtimer}
}

@Article{	  hakkinen00a,
  author	= {Hakkinen, P. M. H.},
  title		= {Neural network used to analyze multiple perspectives
		  concerning computer-based learning environments},
  journal	= {QUALITY \& QUANTITY},
  year		= {2000},
  volume	= {34},
  number	= {3},
  month		= {AUG},
  pages		= {237--258},
  abstract	= {The aim of this study is to explore the possibilities of
		  neural networks to support the analysis and representation
		  of the complex qualitative data in behavioral sciences. In
		  this study for testing the methodological possibilities we
		  analysed data of designers', teachers' and students'
		  interpretations of the same educational software. The
		  intentions of three designers concerning the interaction
		  with their own software were compared with the
		  interpretations of three teachers' anticipations of the
		  interaction, and with the actual learning situations of
		  three pairs of students. The particular kind of neural
		  network used for the data analysis was TS-SOM
		  (Koikkalainen, 1994), which is a variant of a
		  self-organizing map SOM algorithm (Kohonen, 1984). On the
		  basis of the results it can be concluded that the method
		  seems to be promising to handle and visualize the data
		  reduction in a systemic manner without oversimplifying the
		  complex data. Furthermore, the method supports the
		  researcher in finding the most essential places where to
		  focus more detailed qualitative analyses. The visualization
		  tools also allow us to verify the interpretations between
		  independent raters, which increases the reliability of
		  qualitative data analysis.},
  dbinsdate	= {2002/1}
}

@InCollection{	  hakkinen97a,
  author	= {Erkki H{\"a}kkinen and Pasi Koikkalainen},
  title		= {The neural data analysis environment},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {69--74},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hakkinen97b,
  author	= {Erkki H{\"a}kkinen and Pasi Koikkalainen},
  title		= {{SOM} Based Visualization in Data Analysis},
  booktitle	= {Proc. ICANN'97, 7th International Conference on Artificial
		  Neural Networks},
  publisher	= {Springer},
  year		= 1997,
  volume	= 1327,
  series	= {Lecture Notes in Computer Science},
  address	= {Berlin},
  pages		= {610--606},
  dbinsdate	= {oldtimer}
}

@Article{	  halgamuge97a,
  author	= {S. K. Halgamuge},
  title		= {Self-evolving neural networks for rule-based data
		  processing},
  journal	= {IEEE Transactions on Signal Processing},
  year		= {1997},
  volume	= {45},
  number	= {11},
  pages		= {2766--73},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  halici95a,
  author	= {Halici, U. and Erol, A. },
  title		= {A hierarchical neural network for optical character
		  recognition},
  booktitle	= {ICANN `95. International Conference on Artificial Neural
		  Networks. Neuronimes `95 Scientific Conference},
  year		= {1995},
  editor	= {Fogelman-Soulie, F. and Gallinari, P. },
  volume	= {2},
  pages		= {251--6},
  organization	= {Dept. of Electr. \& Electron. Eng. , Middle East Tech.
		  Univ. , Ankara, Turkey},
  publisher	= {EC2 \& Cie},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@Article{	  halici96a,
  author	= {U. Halici and G. Ongun},
  title		= {Fingerprint classification through \mbox{self-organizing}
		  feature maps modified to treat uncertainties},
  journal	= {Proceedings of the IEEE},
  year		= {1996},
  volume	= {84},
  number	= {10},
  pages		= {1497--512},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ham94a,
  author	= {F. M. Ham and L. V. Fausett and M. C. Gonzalez-Guirado and
		  I. Kostanic},
  title		= {Development and analysis of interpolating {ART} and {SOM}
		  networks},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {97--102},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hamad95a,
  author	= {Denis Hamad and Stephane Delsert},
  title		= {Nonlinear Mapping Procedures for Unsupervised Pattern
		  Classification},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {457--460},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@InBook{	  hamalainen02a,
  author	= {Timo D. H{\"a}m{\"a}lainen},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Parallel Implementations of Self-Organizing Maps},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {245--78},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Misc{		  hamalainen92a,
  author	= {Ari H{\"{a}}m{\"{a}}l{\"{a}}inen},
  title		= {Itseorganisoituvan piirrekartan k{\"{a}ytt\"o}
		  tiheysfunktion estimoimiseen},
  school	= {{University of Jyv\"askyl\"a}},
  year		= {1992},
  address	= {{Jyv\"askyl\"a}, Finland},
  note		= {Thesis for the degree of Licentiate of Technology,
		  University of Jyv{\"a}skyl{\"a}, Jyv{\"a}skyl{\"a}, Finland},
  annote	= {Connections of {SOM} to a kernel (Parzen) estimator. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hamalainen94a,
  author	= {Ari H{\"{a}}m{\"{a}}l{\"{a}}inen},
  title		= {A Measure of Disorder for the Self-Organizing Map},
  pages		= {659--664},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {analysis, topology measure},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  hamalainen95a,
  author	= {Ari H{\"{a}}m{\"{a}}l{\"{a}}inen},
  title		= {Self-Organizing Map and Reduced Kernel Density
		  Estimation},
  school	= {Jyv{\"{a}}skyl{\"{a}} University},
  year		= {1995},
  address	= {Jyv{\"{a}}skyl{\"{a}}, Finland},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hamalainen95b,
  author	= {A. Hamalainen},
  title		= {Using genetic algorithm in \mbox{self-organizing} map
		  design},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {D. W. Pearson and N. C. Steele and R. F. Albrecht},
  address	= {Vienna, Austria},
  pages		= {364--7},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hamalainen96a,
  author	= {T. Hamalainen and P. Kolinummi and K. Kaski},
  title		= {Linearly expandable partial tree shape architecture for
		  parallel neurocomputer},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {365--70},
  dbinsdate	= {oldtimer}
}

@Article{	  hamalainen97a,
  author	= {T. Hamalainen and H. Klapuri and J. Saarinen and K.
		  Kaski},
  title		= {Mapping of {SOM} and {LVQ} algorithms on a tree shape
		  parallel computer system},
  journal	= {Parallel Computing},
  year		= {1997},
  volume	= {23},
  number	= {3},
  pages		= {271--89},
  abstract	= {Parallel mappings of Kohonen's self organizing map (SOM)
		  and learning vector quantization (LVQ) algorithms are
		  presented for a tree shape parallel computer system called
		  TUTNC (Tampere University of Technology Neural Computer).
		  The lattice of neurons in {SOM} is partitioned columnwise
		  to parallel processors in a neuron parallel manner. In
		  addition, an efficient method is presented for the
		  neighborhood computation to make the computation time
		  independent of {SOM} size and processor count. The tree
		  shape architecture is shown to match well the requirements
		  of mapped algorithms and their relations in such a
		  prototype system TUTNC are studied. Performance has been
		  measured for sample configurations and estimated for a
		  larger system. Comparisons to other implementations on
		  various platforms show, that good performance per processor
		  has been achieved.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hambaba96a,
  author	= {M. L. Hambaba},
  title		= {Intelligent hybrid system for data mining},
  booktitle	= {Proceedings of the IEEE/IAFE 1996 Conference on
		  Computational Intelligence for Financial Engineering
		  (CIFEr)},
  publisher	= {IEEE},
  year		= {1996},
  address	= {New York, NY, USA},
  pages		= {111},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hamdi98a,
  author	= {Hamdi, M. S. and Kaiser, K.},
  title		= {Learning to coordinate behaviors},
  booktitle	= {ECAI 98. 13th European Conference on Artificial
		  Intelligence. Proceedings},
  publisher	= {Wiley},
  address	= {Chichester, UK},
  year		= {1998},
  volume	= {},
  pages		= {440--4},
  abstract	= {This paper presents a self-improving reactive control
		  system for autonomous agents. It relies on the emergence of
		  more global behavior from the interaction of smaller
		  behavioral units. To coordinate behaviors we use a dynamic
		  self-organizing feature map with output and reinforcement
		  learning. The dynamic self-organizing map is used to
		  partition the space of sequences of situations into
		  different regions. Reinforcement learning refines the
		  content of the regions based on the experience of the
		  agent. We show the effectiveness of the method and evaluate
		  it through several simulation studies.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hamdi98b,
  author	= {Hamdi, M. S. and Kaiser, K.},
  title		= {Learning intelligent behavior},
  booktitle	= {Advanced Topics in Artificial Intelligence. 11th
		  Australian Joint Conference on Artificial Intelligence,
		  AI'98},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1998},
  volume	= {},
  pages		= {143--54},
  abstract	= {We present a method for extending the capabilities of a
		  reactive agent using learning. The method relies on the
		  emergence of more global behavior from the interaction of
		  smaller behavioral units. To coordinate behaviors we use a
		  dynamic self-organizing feature map and reinforcement
		  learning. The dynamic self-organizing map is used to
		  partition the space of sequences of situations into
		  different regions. Reinforcement learning refines the
		  content of the regions based on the experience of the
		  agent. We show the effectiveness of the method and evaluate
		  it through several simulation studies.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hamey98a,
  author	= {L. G. C. Hamey and J. C. -H. Yeh and T. Westcott and S. K.
		  Y. Sung},
  title		= {Pre-processing colour images with a \mbox{self-organising}
		  map: baking curve identification and bake image
		  segmentation},
  booktitle	= {Proceedings. Fourteenth International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society},
  year		= {1998},
  volume	= {2},
  editor	= {A. K. Jain and S. Venkatesh and B. C. Lovell},
  address	= {Los Alamitos, CA, USA},
  pages		= {1771--5},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hammami97a,
  author	= {O. Hammami and D. Suzuki},
  title		= {A Pipelined Speculative {SIMD} Architecture for {SOM}
		  {ANN}},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {II},
  pages		= {985--990},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hammami98a,
  author	= {O. Hammami},
  title		= {Performance impacts of superscalar microarchitecture on
		  {SOM} execution},
  booktitle	= {Proceedings 31st Annual Simulation Symposium},
  publisher	= {IEEE Computer Society},
  year		= {1998},
  address	= {Los Alamitos, CA, USA},
  pages		= {202--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hammer01a,
  author	= {B. Hammer and T. Villmann},
  title		= {Estimationg relevant input dimensions for self-organising
		  algorithms},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {173--80},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hammerstrom91a,
  author	= {Dan Hammerstrom and Nguyen Nguyen},
  title		= {An Implementation of {K}ohonen's Self-Organizing Map on
		  the {A}daptive {S}olutions Neurocomputer},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {715--720},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  hammond01a,
  author	= {Hammond, P. and Hutton, T. J. and {Nelson Moon}, Z. L. and
		  Hunt, N. P. and Madgwick AJA},
  title		= {Classifying vertical facial deformity using supervised and
		  unsupervised learning},
  journal	= {Methods-of-Information-in-Medicine},
  year		= {2001},
  volume	= {40},
  pages		= {365--72},
  abstract	= {We evaluate the potential for machine learning techniques
		  to identify objective criteria for classifying vertical
		  facial deformity. Classifications were induced from raw
		  data with simple visualisation, C5.0 and Kohonen feature
		  maps; and using a point distribution model (PDM) of shape
		  templates comprising points taken from digitised
		  radiographs. The induced decision trees enable a direct
		  comparison of clinicians' idiosyncrasies in classification.
		  Unsupervised algorithms induce models that are potentially
		  more objective, but their blackbox nature makes them
		  unsuitable for clinical application. The PDM methodology
		  gives dramatic visualisations of two modes separating
		  horizontal and vertical facial growth. Kohonen feature maps
		  favour one clinician and PDM the other. Clinical response
		  suggests that while Clinician 1 places greater weight on 5
		  of 6 parameters, Clinician 2 relies on more parameters that
		  capture facial shape. While machine learning and
		  statistical analyses classify subjects for vertical facial
		  height, they have limited application in their present
		  form. The supervised learning algorithm C5.0 is effective
		  for generating rules for individual clinicians but its
		  inherent bias invalidates its use for objective
		  classification of facial form for research purposes. On the
		  other hand, promising results from unsupervised strategies
		  (especially the PDM) suggest a potential use for objective
		  classification and further identification and analysis of
		  ambiguous cases. At present, such methodologies may be
		  unsuitable for clinical application because of the
		  invisibility of their underlying processes. Further study
		  is required with additional patient data and a wider group
		  of clinicians.},
  dbinsdate	= {2002/1}
}

@Article{	  hamzaoui97a,
  author	= {R. Hamzaoui},
  title		= {Codebook clustering by \mbox{self-organizing} maps for
		  fractal image compression},
  journal	= {Fractals},
  year		= {1997},
  volume	= {5},
  number	= {suppl. issue},
  pages		= {27--38},
  note		= {(Fractal Image Encoding and Analysis Conf. Date: 8--17
		  July 1995 Conf. Loc: Trondheim, Norway Conf. Sponsor:
		  NATO)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  han94a,
  author	= {Han, M. -W. and Kolejka, T. },
  title		= {Artificial neural networks for control of autonomous
		  mobile robots},
  booktitle	= {Intelligent Manufacturing Systems 1994 (IMS`94). A
		  Postprint Volume from the IFAC Workshop},
  year		= {1994},
  editor	= {Kopacek, P. },
  pages		= {157--62},
  organization	= {Inst. of Handling Devices \& Robotics, Tech. Univ. of
		  Vienna, Austria},
  publisher	= {Pergamon},
  address	= {Oxford, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  han95a,
  author	= {Kyung Ah Han and Jong Chan Lee and Chi Jung Hwang},
  title		= {Image Clustering using \mbox{Self-organizing} feature map
		  with Refinement},
  volume	= {I},
  pages		= {465--469},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  han96a,
  author	= {Kyung-Ah Han and Sung-Hyun Myaeng},
  title		= {Image organization and retrieval with automatically
		  constructed feature vectors},
  journal	= {SIGIR Forum},
  year		= {1996},
  volume	= {spec. issue},
  pages		= {157--65},
  note		= {(19th Annual International ACM SIGIR Conference on
		  Research and Development in Information Retrieval Conf.
		  Date: 18--22 Aug. 1996 Conf. Loc: Zurich, Switzerland)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  han96b,
  author	= {Dong-Hoon Han and Hyo-Kyung Sung and Ki-Tae Park and
		  Yong-Hyon Cho and Heung-Moon Choi},
  title		= {Neural network approach to the nonlinear shape
		  restorations},
  booktitle	= {1996 IEEE International Conference on Systems, Man and
		  Cybernetics. Information Intelligence and Systems},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {504--9},
  abstract	= {We propose a neural network approach to the nonlinear
		  shape restoration which is efficient regardless of the
		  availability of the distortion models. Nonlinear mapping is
		  extracted from the distorted image by using a reinforced
		  learning SOFM (self-organizing feature map). For the exact
		  extraction of the mappings between the distorted image and
		  the original one, we define a disorder index in the SOFM,
		  and we used this index to reinforce the training of the
		  mappings selectively. Simulations are conducted on various
		  kinds of distorted images with or without distortion
		  models, and the results show that the proposed approach is
		  very efficient and practical in nonlinear shape
		  restorations.},
  dbinsdate	= {oldtimer}
}

@Article{	  han97a,
  author	= {D. -H. Han and H. -K. Sung and H. -M. Choi},
  title		= {Nonlinear shape restoration based on selective learning
		  {SOFM} approach},
  journal	= {Journal of the Korean Institute of Telematics and
		  Electronics},
  year		= {1997},
  volume	= {34C},
  number	= {1},
  pages		= {59--64},
  dbinsdate	= {oldtimer}
}

@Article{	  hanaki96a,
  author	= {S. Hanaki and T. Nakamoto and T. Moriizumi},
  title		= {Artificial odor-recognition system using neural network
		  for estimating sensory quantities of blended fragrance},
  journal	= {Sensors and Actuators A [Physical]},
  year		= {1996},
  volume	= {A57},
  number	= {1},
  pages		= {65--71},
  dbinsdate	= {oldtimer}
}

@Article{	  hanawa92a,
  author	= {M. Hanawa and T. Hasega-Wa},
  title		= {A pseudo-phoneme coding system of speech at very low bit
		  rate using \mbox{self-organizing} feature maps},
  journal	= {Trans. Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1992},
  volume	= {J75D-II},
  number	= {2},
  pages		= {426--428},
  month		= {February},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@Article{	  handmann97a,
  author	= {Handmann, Uwe and Kalinke, Thomas},
  title		= {Fusion of texture and contour based methods for object
		  recognition},
  journal	= {IEEE Intelligent Transport Systems Conference ITSC.
		  Proceedings},
  year		= {1997},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  number	= {},
  volume	= {},
  pages		= {876--881},
  abstract	= {We propose a new approach to object detection based on
		  data fusion of texture and edge information. A self
		  organizing Kohonen map is used as the coupling element of
		  the different representations. Therefore, an extension of
		  the proposed architecture incorporating other features,
		  even features not derived from vision modules, is straight
		  forward. It simplifies to a redefinition of the local
		  feature vectors and a retraining of the network structure.
		  The resulting hypotheses of object locations generated by
		  the detection process finally are inspected by a neural
		  network classifier based on cooccurrence matrices.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  handschin97a,
  author	= {E. Handschin and D. Kuhlmann and C. Rehtanz},
  title		= {Visualization and analysis of voltage stability using
		  \mbox{self-organizing} neural networks},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {1113--18},
  dbinsdate	= {oldtimer}
}

@InCollection{	  handschin97b,
  author	= {Edmund Handschin and Christian Rehtanz},
  title		= {{K}ohonen neural networks for visualization and analysis
		  of voltage stability},
  booktitle	= {Proceedings of PSAC'97, 10th International Conference on
		  Power System Automation and Control, Bred, Slovenien, 1.
		  --3. 10. },
  year		= 1997,
  dbinsdate	= {oldtimer}
}

@Article{	  hani01a,
  author	= {Hani, M. K. and Nor, S. M. and Hussein, S. and Elfadil,
		  N.},
  title		= {Machine learning: the automation of knowledge acquisition
		  using Kohonen self-organising map neural network},
  journal	= {Malaysian-Journal-of-Computer-Science},
  year		= {2001},
  volume	= {14},
  pages		= {68--82},
  abstract	= {In machine learning, a key aspect is the acquisition of
		  knowledge. As problems become more complex, and experts
		  become scarce, the manual extraction of knowledge becomes
		  very difficult. Hence, it is important that the task of
		  knowledge acquisition be automated. This paper proposes a
		  novel method that integrates neural network and expert
		  system paradigms to produce an automated knowledge
		  acquisition system. A rule-generation algorithm is
		  proposed, whereby symbolic rules are generated from a
		  neural network that has been trained by an unsupervised
		  Kohonen self-organising map (KSOM) learning algorithm. The
		  generated rules are evaluated and verified using an expert
		  system inference engine. To demonstrate the applicability
		  of the proposed method to real-world problems, a case study
		  in medical diagnosis is presented.},
  dbinsdate	= {2002/1}
}

@Article{	  hanke96a,
  author	= {J. Hanke and J. G. Reich},
  title		= {{K}ohonen Map as a Visualization Tool for the Analysis of
		  Protein Sequences---Multiple Alignments, Domains and
		  Segments of Secondary Structures},
  journal	= {Computer Applications in the Biosciences},
  year		= {1996},
  volume	= {12},
  number	= {6},
  pages		= {447--454},
  dbinsdate	= {oldtimer}
}

@Article{	  hanke96b,
  author	= {J. Hanke and G. Beckmann and P. Bork and J. G. Reich},
  title		= {Self organizing hierarchic networks for pattern
		  recognition in protein sequence},
  journal	= {Protein Science},
  year		= 1996,
  volume	= 3,
  pages		= {72--82},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hannah94a,
  author	= {Paul Hannah and Russel Stonier and Stephen Smith},
  title		= {Using the Recursive Least Squares {K}ohonen Map for
		  Improved Function Approximation},
  editor	= {A. C. Tsoi and T. Downs},
  pages		= {165--168},
  booktitle	= {Proc. 5th Australian Conf. on Neural Networks},
  year		= {1994},
  publisher	= {University of Queensland},
  address	= {St. Lucia, Australia},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hannah99a,
  author	= {Hannah, P. and Stonier, R. and Cole, C.},
  title		= {Approximating rail locomotive dynamics using the {SOCM}
		  network},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1934--8},
  abstract	= {We demonstrate the self-organising continuous map (SOCM),
		  a novel use for the self-organising map/learning vector
		  quantisation network that widens the scope of the {SOM}
		  architecture. We use the {SOM}/LVQ network as a
		  distribution service, apportioning an equal quantity of
		  work to a number of intelligent nodes. Advantages include
		  improved accuracy, effective and balanced multi-processing
		  for small cluster systems, and potentially large reductions
		  in training and recall times. The example problem chosen
		  uses neural networks to model force dynamics of a coal
		  train. The SOCM configuration used consists of a {SOM}
		  network where each node is a backpropagation (BP) network.
		  We show that the collection of as few as two BP networks
		  gives at least a 30% reduction in approximation error when
		  compared to the original BP network. We discuss how the
		  SOCM approach could be used in other areas of artificial
		  intelligence, including evolutionary systems, parallel
		  processing, error balancing, hybrid networks, and online
		  training.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hao94a,
  author	= {Gang Hao and Shang, J. S. and Vargas, L. G. },
  title		= {A neural network approach for the real time control of a
		  {FMS}},
  booktitle	= {Proceedings of the Twenty-Seventh Hawaii International
		  Conference on System Sciences. Vol. III: Information
		  Systems: Decision Support and Knowledge-Based Systems},
  year		= {1994},
  editor	= {Nunamaker, J. F. , Jr. and Sprague, R. H. , Jr. },
  pages		= {641--8},
  organization	= {Dept. of Appl. Stat. \& OR, City Polytech. of Hong Kong,
		  Kowloon, Hong Kong},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  hao96a,
  author	= {G. Hao and K. K. Lai},
  title		= {Solving the AGV problem via a \mbox{self-organizing}
		  neural network},
  journal	= {Journal of the Operational Research Society},
  year		= {1996},
  volume	= {47},
  number	= {12},
  pages		= {1477--93},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  haque94a,
  author	= {Haque, A. L. and Cheung, J. Y. },
  title		= {A continuous input heteroassociative neural network model
		  for perfect recall},
  booktitle	= {World Congress on Neural Networks-San Diego. 1994
		  International Neural Network Society Annual Meeting},
  year		= {1994},
  volume	= {4},
  pages		= {IV/85--90},
  organization	= {Sch. of Comput. Sci. , Oklahoma Univ. , Norman, OK, USA},
  publisher	= {Lawrence Erlbaum Associates},
  address	= {Hillsdale, NJ, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  hara93a,
  author	= {Hara, Yoshihisa and Ono, Makoto and Fujimura, Sadao},
  title		= {Analysis of j{ERS}-1 {SAR} imagery},
  journal	= {DIG International Geoscience and Remote Sensing Symposium
		  (IGARSS)},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1993},
  number	= {},
  volume	= {3},
  pages		= {1191--1193},
  abstract	= {Land type classification using {SAR} data is an area of
		  current interest and research. In this paper, a
		  quantitative analysis is made for JERS-1 {SAR} imagery, and
		  a new classification technique is applied to determination
		  of land types in the {SAR} images. We utilized an
		  unsupervised neural network to provide automatic
		  classification, and employed an iterative algorithm to
		  improve the performance. First, S/N and statistical
		  properties are evaluated for each land type, and it is
		  shown that the images have enough quality for
		  classification. Then Learning Vector Quantization (LVQ) is
		  applied to unsupervised classification of {SAR} images, and
		  the results are compared with those of the Migrating Means
		  method. Results show that LVQ outperforms Migrating
		  classes. To further improve the performance, an iterative
		  algorithm, where the {SAR} image is reclassified using
		  Maximum Likelihood (ML) classifier, is applied. It is
		  experimentally shown that this algorithm converges, and
		  significantly improves the performance of the unsupervised
		  LVQ method while preserving the advantages of automatic
		  operation inherent in unsupervised techniques.},
  dbinsdate	= {oldtimer}
}

@Article{	  hara95a,
  author	= {Hara, Yoshihisa and Atkins, Robert G. and Shin, Robert T.
		  and Kong, Jin Au and Yueh, Simon H. and Kwok, Ronald},
  title		= {Application of neural networks for sea ice classification
		  in polarimetric {SAR} images},
  journal	= {IEEE Transactions on Geoscience and Remote Sensing},
  year		= {1995},
  number	= {3},
  volume	= {33},
  pages		= {740--748},
  abstract	= {A new classification technique is applied to determine sea
		  ice types in polarimetric and multifrequency synthetic
		  aperture radar ({SAR}) images, utilizing an unsupervised
		  neural network to provide automatic reclassification, and
		  using an iterative algorithm to improve the performance.
		  The Learning Vector Quantization (LVQ) is first applied to
		  the unsupervised classification of {SAR} images and the
		  results are compared with those of a conventional
		  technique, the Migrating Means method. An iterative
		  algorithm is then applied where the {SAR} image is
		  reclassified using the Maximum Likelihood (ML) classifier.
		  The new algorithm successfully identifies first-year and
		  multiyear sea ice regions in the images at three
		  frequencies. The results show that L- and P- band images
		  have similar characteristics, while the C-band image is
		  substantially different.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hara98a,
  author	= {S. Hara and H. Douzono and S. Eishima and H. Tokushima and
		  Y. Noguchi},
  title		= {Clustering of chromosome fluorescence profiles by
		  \mbox{self-organising} map using chromosome physical
		  models},
  booktitle	= {Proceedings of the IEEE International Joint Symposia on
		  Intelligence and Systems},
  publisher	= {IEEE Computer Society},
  year		= {1998},
  address	= {Los Alamitos, CA, USA},
  pages		= {99--102},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hardam90a,
  author	= {E. Hardam and L. Schweizer and S. Tubaro},
  title		= {Study of learning rules for {S}elf-{O}rganizing {F}eature
		  {M}aps applied to Vector Quantization},
  booktitle	= {Proc. Third Italian Workshop on Parallel Architectures and
		  Neural Networks},
  year		= {1990},
  volume	= {},
  pages		= {413--416},
  organization	= {SIREN},
  publisher	= {World Scientific},
  address	= {Singapore},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  harger92a,
  author	= {R. O. Harger},
  title		= {Object detection in clutter with learning maps},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1992},
  volume	= {1630},
  pages		= {176--186},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {Conf. paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  haring93a,
  author	= {S. Haring and M. A. Viergever and J. N. Kok},
  title		= {Applying Scaled Differential Invariant Features to Image
		  Segmentation with {K}ohonen Feature Maps},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {193--196},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {An approach to image segmentation has been developed on
		  the basis of scaled differential geometrical invariant
		  features for describing pixels properties and utilizing
		  Kohonen networks for probabilistic pixel classification.
		  The invariant feature pattern representation of a training
		  image is input to a Kohonen network, of which the weight
		  vectors tend to form a so-called Kohonen Feature Map.
		  Supervised labeling of the weight vectors in the map is
		  accomplished using classes derived from an a priori
		  segmentation of the training image. Any image similar to
		  the training image can be segmented by presenting the
		  feature pattern representation of each pixel to the map and
		  interpreting the caused excitation pattern. The applied
		  features yield a mathematically thorough and complete
		  description of arbitrary image structures up to any desired
		  order. The Kohonen map has successfully been applied
		  although the classification problem is non-linear.
		  Furthermore, the map provides a means to obtain
		  probabilistic segmentations.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  haring93b,
  author	= {Haring, S. and Viergever, M. A. and Kok, J. N. },
  title		= {A multiscale approach to image segmentation using
		  {K}ohonen networks},
  booktitle	= {Information Processing in Medical Imaging. 13th
		  International Conference, IPMI '93 Proceedings},
  year		= {1993},
  editor	= {Barrett, H. H. and Gmitro, A. F. },
  pages		= {212--24},
  organization	= {Comput. Vision Res. Group, Univ. Hospital Utrecht,
		  Netherlands},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  haring94a,
  author	= {Haring, S. and Viergever, M. A. and Kok, J. N. },
  title		= {{K}ohonen networks for multiscale image segmentation},
  journal	= {Image and Vision Computing},
  year		= {1994},
  volume	= {12},
  number	= {6},
  pages		= {339--44},
  month		= {July-Aug},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  haritopoulos01a,
  author	= {M. Haritopoulos and H. Yin and N. M. Allinson},
  title		= {Nonlinear blind source separation using {SOM}s and
		  applications to image denoising},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {275--82},
  year		= {2001},
  editor	= {Nigel Allison and Hujun Yin and Lesley Allison and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  harp91a,
  author	= {S. A. Harp and T. Samad},
  title		= {Genetic optimization of \mbox{self-organizing} feature
		  maps},
  booktitle	= {Proc. IJCNN-91, International Joint Conference on Neural
		  Networks, Seattle},
  year		= {1991},
  volume	= {I},
  pages		= {341--346},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {We present an application of the genetic algorithm to the
		  design of Kohonen self-organizing feature maps. The genetic
		  algorithm is used to optimize various parameters of the
		  network model for a given problem. Performance criteria
		  relevant to clustering or vector quantization applications
		  are considered: RMS error and an information-theoretic "map
		  entropy" measure. Experimental results demonstrate the
		  effectiveness of the approach, and suggest some interesting
		  generalizations.},
  dbinsdate	= {oldtimer}
}

@Article{	  harp95a,
  author	= {Steven A. Harp and Tariq Samad and Michael Villano},
  title		= {Modeling Student Knowledge with Self-Organizing Feature
		  Maps},
  journal	= {{IEEE} Trans. on Systems, Man and Cypernetics},
  year		= {1995},
  volume	= {25},
  number	= {5},
  pages		= {727--737},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  harris93a,
  author	= {Tom Harris},
  title		= {A {K}ohonen {S. O. M. } Based, Machine Health Monitorin
		  System which Enables Diagnosis of Faults not Seen in the
		  Training Set},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {947--950},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  harris95a,
  author	= {T. Harris and L. Gamlyn and P. Smith and J. MacIntyre and
		  A. Brason and R. Palmer and H. Smith and A. Slater},
  title		= {{'NEURAL-MAINE'}: Intelligent On-Line Multiple Sensor
		  Diagnostics For Steam Turbines In Power Generation},
  volume	= {II},
  pages		= {686--691},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  harris95b,
  author	= {T. Harris},
  title		= {{K}ohonen neural networks for machine and process
		  condition monitoring},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {D. W. Pearson and N. C. Steele and R. F. Albrecht},
  address	= {Vienna, Austria},
  pages		= {3--4},
  dbinsdate	= {oldtimer}
}

@TechReport{	  hase96a,
  author	= {Hidemi Hase and Hisayoshi Matsuyama and Heizo Tokutaka and
		  Satoru Kishida},
  title		= {Speech signal processing using adaptive subspace {SOM}
		  ({ASSOM})},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1996},
  number	= {NC95--140},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@Article{	  hasenauer01a,
  author	= {Hasenauer, H. and Merkl, D. and Weingartner, M.},
  title		= {Estimating tree mortality of Norway spruce stands with
		  neural networks},
  journal	= {ADVANCES IN ENVIRONMENTAL RESEARCH},
  year		= {2001},
  volume	= {5},
  number	= {4},
  month		= {NOV},
  pages		= {405--414},
  abstract	= {Within forest growth modeling LOGIT models are used to
		  predict individual tree mortality. In this paper we
		  present, Multi- Layer Perceptron, Learning Vector
		  Quantization and Cascade Correlation networks as different
		  formalisms for mortality predictions. The data set for
		  parameterizing the LOGIT model and training the different
		  neural network types comes from the Austrian National
		  Forest Inventory. After training the different network
		  types, we evaluate the resulting mortality predictions
		  using an independent data set from the Litschau forest. The
		  results indicate that Multi-Layer Perceptron with the
		  learning algorithm resilient back-propagation and scaled
		  conjugate gradient and Cascade Correlation with learning
		  algorithm resilient back-propagation perform the best
		  predictions. This suggests that neural networks are a
		  viable alternative to the conventional LOGIT approach. },
  dbinsdate	= {2002/1}
}

@InCollection{	  hasenauer97a,
  author	= {Hubert Hasenauer and Dieter Merkl},
  title		= {Forest Tree Mortality Simulation in Uneven-Aged Stands
		  Using Connectionist Networks},
  booktitle	= {Proc. EANN'97, Int'l Conference on Engineering Application
		  of Neural Networks},
  year		= 1997,
  dbinsdate	= {oldtimer}
}

@InCollection{	  hasenjager99a,
  author	= {M. Hasenj{\"a}ger and H. Ritter and K. Obermayer},
  title		= {Active Learning in Self-Organizing Maps},
  booktitle	= {Kohonen Maps},
  pages		= {57--70},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: autoencoder, data selection, missing data,
		  pairwise data, topographic clustering},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hashemi95a,
  author	= {Hashemi, M. R. and Yeap, T. H. and Panchanathan, S. },
  title		= {Predictive vector quantization using neural networks},
  booktitle	= {1995 Canadian Conference on Electrical and Computer
		  Engineering},
  year		= {1995},
  editor	= {Gagnon, F. },
  volume	= {2},
  pages		= {834--7},
  organization	= {Dept. of Electr. Eng. , Ottawa Univ. , Ont. , Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {In this paper we propose a new predictive vector
		  quantization (PVQ) technique for image and video
		  compression. This technique has been implemented using
		  neural networks. A Kohonen self-organized feature map is
		  used to implement the vector quantizer, while a multi layer
		  perceptron implements the predictor. The proposed technique
		  provides a superior coding performance.},
  dbinsdate	= {oldtimer}
}

@Article{	  hashemi95b,
  author	= {R. R. Hashemi and T. M. Schafer and W. G. Hinson and Jr.
		  J. O. Lay},
  title		= {Identifying and testing of signatures for non-volatile
		  biomolecules using tandem mass spectra},
  journal	= {SIGBIO Newsletter},
  year		= {1995},
  volume	= {15},
  number	= {3},
  pages		= {11--19},
  dbinsdate	= {oldtimer}
}

@Article{	  hashemi97a,
  author	= {M. R. Hashemi and T. H. Yeap and S. Panchanathan},
  title		= {Predictive vector quantization using neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3030},
  pages		= {14--20},
  note		= {(Applications of Artificial Neural Networks in Image
		  Processing II Conf. Date: 12--13 Feb. 1997 Conf. Loc: San
		  Jose, CA, USA Conf. Sponsor: SPIE; Soc. Imaging Sci. \&
		  Technol)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hashemi98a,
  author	= {Ray R. Hashemi and James M. Danley and Alexander A. Tyler
		  and William Slikker and Merle Paule},
  title		= {Quality of Information Granulation: {K}ohonen
		  Self-Organizing Map vs. Neighborhood System},
  booktitle	= {Proc. JCIS'98},
  publisher	= {Association for Intelligent Machinery, Inc},
  year		= 1998,
  editor	= {Paul P. Wang},
  volume	= {II},
  pages		= {294--297},
  abstract	= {The quality of information granulation performed by the
		  restricted neighborhood system (RNS) and the Kohonen
		  self-organizing map (KSOM) are compared. This goal has been
		  met by classification of several sets of new objects by
		  super rules learned from the several sets of granules
		  generated by RNS and KSOM. The method with better
		  classification results has a superior capability of
		  information granulation.},
  dbinsdate	= {oldtimer}
}

@Article{	  hashemi99a,
  author	= {Hashemi, Ray R. and Slikker, William Jr. and Tyler,
		  Alexander A. and Paule, Merle G.},
  title		= {Profiling through {K}ohonen \mbox{self-organizing} map:
		  The effect of birth weight on the performance measures of
		  an operant test battery},
  journal	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  year		= {1999},
  number	= {},
  volume	= {9},
  pages		= {941--946},
  abstract	= {Two groups of 6--7 years old children at the Arkansas
		  Children's Hospital (ACH) are chosen for this study. The
		  first group is the control group made of children with
		  birth weight greater than or equal to 100 ounces. The
		  second group is the preemies group made of children with
		  birth weight less than or equal to 90 ounces. As a part of
		  an Operant Test Battery (OTB), a set of five behavioral
		  tasks is given to these two groups. Each task has several
		  performance measurements (OTB Variables). It was shown
		  previously that the OTB Variables may identify the children
		  with attention deficit disorder (ADD). Thus, in this paper
		  we try to investigate the birth weight effect on the OTB's
		  performance of the two groups of children. By doing so, we
		  try to determine whether the children with low birth weight
		  have a higher chance of having attention deficit disorder
		  (ADD) at later ages.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hatano97a,
  author	= {K. Hatano and Qing Qian and K. Tanaka},
  title		= {A {SOM}-based information organizer for text and video
		  data},
  booktitle	= {Database Systems for Advanced Applications '97.
		  Proceedings of the Fifth International Conference},
  publisher	= {World Scientific},
  year		= {1997},
  editor	= {R. Topor and K. Tanaka},
  address	= {Singapore},
  pages		= {205--14},
  dbinsdate	= {oldtimer}
}

@Article{	  hatano98a,
  author	= {K. Hatano and T. Kamei and K. Tanaka},
  title		= {Authoring and retrieval of video scenes by multi-level
		  \mbox{self-organizing} maps},
  journal	= {Transactions of the Information Processing Society of
		  Japan},
  year		= {1998},
  volume	= {39},
  number	= {4},
  pages		= {933--42},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hattori01a,
  author	= {Hattori, M. and Arisumi, H. and Ito, H.},
  title		= {Sequential learning for {SOM} associative memory with map
		  reconstruction},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {477--484},
  abstract	= {In this paper, we propose a sequential learning algorithm
		  for an associative memory based on Self-Organizing Map
		  (SOM). In order to store new information without retraining
		  weights on previously learned information, weights fixed
		  neurons and weights semi-fixed neurons are used in the
		  proposed algorithm. In addition, when a new input is
		  applied to the associative memory, a part of map is
		  reconstructed by using a small buffer. Owing to this
		  remapping, a topology preserving map is constructed and the
		  associative memory becomes structurally robust. Moreover,
		  it has much better noise reduction effect than the
		  conventional associative memory.},
  dbinsdate	= {2002/1}
}

@Article{	  hatzakis97a,
  author	= {E. J. Hatzakis and D. A. Karras and P. E. Tziannos and N.
		  Paritsis},
  title		= {Supervised and unsupervised neural and statistical methods
		  in psychiatric case categorisation},
  journal	= {Neural Network World},
  year		= {1997},
  volume	= {7},
  number	= {2},
  pages		= {161--75},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hatzipantelis95a,
  author	= {Hatzipantelis, E. and Murray, A. and Penman, J. },
  title		= {Comparing hidden {M}arkov models with artificial neural
		  network architectures for condition monitoring
		  applications},
  booktitle	= {Fourth International Conference on `Artificial Neural
		  Networks`},
  year		= {1995},
  pages		= {369--74},
  organization	= {Aberdeen Univ. , UK},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  hauck01a,
  author	= {Hauck, R. V. and Sewell, R. R. and Ng, T. D. and Chen,
		  H.},
  title		= {Concept-based searching and browsing: A geoscience
		  experiment},
  journal	= {Journal of Information Science},
  year		= {2001},
  volume	= {27},
  number	= {4},
  month		= {},
  pages		= {199--210},
  organization	= {Dept. of Mgmt. Information Systems, University of
		  Arizona},
  publisher	= {},
  address	= {},
  abstract	= {In the recent literature, we have seen the expansion of
		  information retrieval techniques to include a variety of
		  different collections of information. Collections can have
		  certain characteristics that can lead to different results
		  for the various classification techniques. In addition, the
		  ways and reasons that users explore each collection can
		  affect the success of the information retrieval technique.
		  The focus of this research was to extend the application of
		  our statistical and neural network techniques to the domain
		  of geological science information retrieval. For this
		  study, a test bed of 22,636 geoscience abstracts was
		  obtained through the NSF/DARPA/NASA funded Alexandria
		  Digital Library Initiative project at the University of
		  California at Santa Barbara. This collection was analyzed
		  using algorithms previously developed by our research
		  group: Concept space algorithm for searching and a Kohonen
		  self-organizing map (SOM) algorithm for browsing. Included
		  in this paper are discussions of our technique s, user
		  evaluations and lessons learned.},
  dbinsdate	= {2002/1}
}

@Article{	  hauske97a,
  author	= {G. Hauske},
  title		= {A Self-Organizing Map Approach to Image Quality},
  journal	= {Biosystems},
  year		= {1997},
  volume	= {40},
  number	= {1--2},
  pages		= {93--102},
  dbinsdate	= {oldtimer}
}

@Article{	  hauta-kasari99a,
  author	= {Hauta-Kasari, M. and Parkkinen, J. and J\"a\"askelainen,
		  T. and Lenz, R.},
  title		= {Multi-spectral texture segmentation based on the spectral
		  co-occurrence matrix},
  journal	= {Pattern Analysis and Applications},
  year		= {1999},
  volume	= {2},
  pages		= {275--84},
  abstract	= {Multi-spectral images are becoming more common in
		  industrial inspection tasks where the colour is used as a
		  quality measure. In this paper we propose a spectral
		  co-occurrence matrix-based method to analyse multi-spectral
		  texture images, in which every pixel contains a measured
		  colour spectrum. We first quantise the spectral domain of
		  the multi-spectral images using the self-organising map.
		  Next, we label the spectral domain according to the
		  quantised spectra. In the spatial domain, we represent a
		  multi-spectral texture using the spectral cooccurrence
		  matrix, which we calculate from the labelled image. In the
		  experimental part, we present the results of segmenting
		  natural multi-spectral textures. We compare the k-nearest
		  neighbour (k-NN) classifier and the multilayer perceptron
		  neural network-based segmentation results of the
		  multi-spectral and RGB colour textures.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hawickhorst95a,
  author	= {B. A. Hawickhorst and S. A. Zahorian and R. Rajagopal},
  title		= {A comparison of three neural network architectures for
		  automatic speech recognition},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks. Vol. 5. Fuzzy Logic and Evolutionary Programming.
		  Proceedings of the Artificial Neural Networks in
		  Engineering (ANNIE'95)},
  publisher	= {ASME Press},
  year		= {1995},
  editor	= {C. H. Dagli and M. Akay and C. L. P. Chen and B. R.
		  Fernandez and J. Ghosh},
  address	= {New York, NY, USA},
  pages		= {221--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hayakawa99a,
  author	= {Hayakawa, Y. and Kira, Y. and Ogata, T. and Sugano, S.},
  title		= {Extraction of human intention for human co-operating
		  systems-prototype assembling work support robot system
		  according to human intention},
  booktitle	= {Proceedings of the Ninth International Conference on
		  Advanced Robotics. 99 ICAR. Japan Robot Assoc, Tokyo,
		  Japan},
  year		= {1999},
  volume	= {},
  pages		= {199--204},
  abstract	= {In order to realize a system, which enables humans to use
		  their full potential, we aim to construct a design method
		  for systems that co-operate with humans by understanding
		  various human information. The paper deals with methods for
		  extracting human information such as intention, work
		  characteristics, mental strain, and a system which carries
		  out assembling work cooperation according to human
		  information. We present a self-organization approach for
		  extracting a human model that contains the human
		  information. As the human information, work characteristic
		  structure is extracted as the structure of a self-organized
		  map of the behavior information. The dynamic work
		  characteristic is extracted as the transition time between
		  the nodes in the self-organized work characteristic
		  structure. Mental strain is extracted from indexes
		  processed from respiration and pulse. Intention is
		  extracted by total judgement of the human information. An
		  assembling support robotic system, which carries out
		  physical support according to the judged "desire for
		  support" as human intention, was constructed. Experiments
		  were carried out with the robotic support system,
		  concerning subjects with no experience of assembling with
		  the system. Results show the validity of the basic motions
		  and functions of the system.},
  dbinsdate	= {2002/1}
}

@Book{		  haykin94a,
  author	= {Simon Haykin},
  title		= {Neural Networks. {A} Comprehensive Foundation},
  publisher	= {Macmillan},
  year		= 1994,
  address	= {New York},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  haynes94a,
  author	= {Haynes, J. D. },
  title		= {The guiding principle of form in the neural network
		  perspective},
  booktitle	= {1994 Proceedings Decision Sciences Institute. 1994 Annual
		  Meeting},
  year		= {1994},
  volume	= {2},
  pages		= {654--7},
  organization	= {Bond Univ. , Gold Coast, Qld. , Australia},
  publisher	= {Decision Sci. Inst},
  address	= {Atlanta, GA, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  haza-vandenpeereboom97a,
  author	= {Guillermo Haza-Vandenpeereboom and Luis N. Gray and Steve
		  J. Gill},
  title		= {Evolutionary Approach to the Development of Social
		  Structures by Individual Interaction in a Constrained
		  Environment},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {1},
  address	= {Singapore},
  pages		= {448--451},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  he00a,
  author	= {He, Yulan and Hui, Siu Cheung},
  title		= {Mining citation database for the retrieval of scientific
		  publications over the {WWW}},
  booktitle	= {16th World Computer Congress 2000. Proceedings of
		  Conference on Intelligent Information Processing.
		  Publishing House of Electron. Ind, Beijing, China},
  year		= {2000},
  volume	= {},
  pages		= {64--72},
  abstract	= {An enormous amount of publications and information are now
		  available on the World Wide Web (WWW). This paper discusses
		  an intelligent retrieval agent that is based on the mining
		  of a citation database for retrieving scientific
		  publications over the WWW. Two techniques, document and
		  author clustering, have been implemented. The document
		  clustering technique is based on the Kohonen's
		  Self-Organising Map (KSOM), while the author clustering
		  technique is based on author co-citation analysis. The
		  intelligent retrieval agent is part of the PubSearch system
		  which is a citation-based indexing, monitoring and
		  retrieval system to help researchers to search related
		  publications over the WWW.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from Internet}
}

@Article{	  he01a,
  author	= {He, Chao and Xu, Li-Xin and Dong, Ning and Zhang, Yu-He},
  title		= {New structural self-organizing fuzzy {CMAC} with basis
		  functions},
  journal	= {Journal of Beijing Institute of Technology (English
		  Edition)},
  year		= {2001},
  volume	= {10},
  number	= {3},
  month		= {September },
  pages		= {298--305},
  organization	= {Dept. of Automat. Control, Beijing Inst. of Technol.},
  publisher	= {},
  address	= {},
  abstract	= {To improve the nonlinear approximating ability of a
		  cerebellar model articulation controller (CMAC), a new kind
		  of fuzzy CMAC with Gauss basis functions (GFCMAC) was
		  presented by introducing the Gauss basis functions and the
		  similarity-measure-based addressing scheme. Moreover, based
		  upon the improvement of the self-organizing feature map
		  algorithm presented by Kohonen, the structural
		  self-organizing algorithm for GFCMAC (SOGFCMAC) was
		  proposed. Simulation results show that adopting the Gauss
		  basis functions and fuzzy techniques can remarkably improve
		  the nonlinear approximating capacity of CMAC. Compared with
		  the traditional CMAC, CMAC with general basis functions and
		  fuzzy CMAC (FCMAC), GOGFCMAC has the obvious advantages in
		  the aspects of the convergent speed, approximating accuracy
		  and structural self-organization.},
  dbinsdate	= {2002/1}
}

@Article{	  he02a,
  author	= {He, Y. and Hui, S. C. and Fong, A. C. M.},
  title		= {Mining a Web citation database for document clustering},
  journal	= {APPLIED ARTIFICIAL INTELLIGENCE},
  year		= {2002},
  volume	= {16},
  number	= {4},
  month		= {APR},
  pages		= {283--302},
  abstract	= {The World Wide Web has become an important medium for
		  disseminating scientific publications. Many publications
		  are now made available over the Web. However, existing
		  search engines are ineffective in searching these
		  publications, as they do not index Web publications that
		  normally appear in PDF (Portable Document Format) or
		  PostScript formats. One way to index Web publications is
		  through citation indices, which contain the references that
		  the publications cite. Web citation Database is a data
		  warehouse to store the citation indices. In this paper, we
		  propose a mining process to extract document cluster
		  knowledge from the Web Citation Database to support the
		  retrieval of Web publications. The mining techniques used
		  for document cluster generation are based on Kohonen's
		  Self- Organizing Map (KSOM) and Fuzzy Adaptive Resonance
		  Theory (Fuzzy ART). The proposed techniques have been
		  incorporated into a citation-based retrieval system known
		  as PubSearch for Web scientific publications.},
  dbinsdate	= {2002/1}
}

@Article{	  he93a,
  author	= {Yuping He and U{\v{g}}ur {\c{C}}ilingiro{\v{g}}lu},
  title		= {A Charge-Based On-Chip Adaptation {K}ohonen Neural
		  Network},
  journal	= {{IEEE} Trans. Neural Networks},
  year		= {1993},
  volume	= {4},
  number	= {3},
  pages		= {462--469},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  he94a,
  author	= {Jun He and Henri Leich},
  title		= {Speech Trajectory Recognition in {SOFM} by Using {B}ayes
		  Theorem},
  booktitle	= {Proc. Int. Symp. on Speech, Image Processing and Neural
		  Networks},
  year		= {1994},
  volume	= {I},
  pages		= {109--112},
  organization	= {{IEEE} Hong Kong Chapt. of Signal Processing},
  address	= {Hong Kong},
  annote	= {application, speech recognition, visualization},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  he94b,
  author	= {Zhenya He and Chenwu Wu and Jun Wang and Ce Zhu},
  title		= {A New Vector Quantization Algorithm Based on Simulated
		  Annealing},
  booktitle	= {Proc. of 1994 Int. Symp. on Speech, Image Processing and
		  Neural Networks},
  year		= {1994},
  volume	= {II},
  pages		= {654--657},
  organization	= {{IEEE} Hong Kong Chapt. of Signal Processing},
  address	= {Hong Kong},
  annote	= {application, vector quantization, optimization,
		  modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  he95a,
  author	= {Jialong He and Li Liu and G{\"{u}}nther Palm},
  title		= {Speaker identification using hybrid {LVQ---SLP} networks},
  volume	= {IV},
  pages		= {2052--2055},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  he97a,
  author	= {H. He and J. Wang and W. Graco and S. Hawkins},
  title		= {Application of neural networks to detection of medical
		  fraud},
  journal	= {Expert Systems with Applications},
  year		= {1997},
  volume	= {13},
  number	= {4},
  pages		= {329--36},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hebert99a,
  author	= {Hebert, J. F. and Marizeau, M. and Ghazzali, N.},
  title		= {An hybrid architecture for active and incremental
		  learning: the \mbox{self-organizing} perceptron ({SOP})
		  network},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1646--51},
  abstract	= {This paper describes a new hybrid architecture for an
		  artificial neural network classifier that enables
		  incremental learning. The learning algorithm of the
		  proposed architecture detects the occurrence of unknown
		  data and automatically adapts the structure of the network
		  to learn these new data, without degrading previous
		  knowledge. The architecture combines an unsupervised
		  self-organizing map with a supervised perceptron network to
		  form the self-organizing perceptron network.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hebert99b,
  author	= {Hebert, J. F. and Parizeau, M. and Ghazzali, N.},
  title		= {Cursive character detection using incremental learning},
  booktitle	= {Proceedings of the Fifth International Conference on
		  Document Analysis and Recognition. ICDAR '99},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {808--11},
  abstract	= {This paper describes a new hybrid architecture for an
		  artificial neural network classifier that enables
		  incremental learning. The learning algorithm of the
		  proposed architecture detects the occurrence of unknown
		  data and automatically adapts the structure of the network
		  to learn these new data, without degrading previous
		  knowledge. The architecture combines an unsupervised
		  self-organizing map with a supervised perceptron network to
		  form the hybrid self-organizing perceptron (SOP) network.
		  Recognition experiments conducted on isolated characters
		  taken in the context of cursive words show the promising
		  incremental capabilities of this SOP network.},
  dbinsdate	= {oldtimer}
}

@Article{	  hecht-nielsen87a,
  author	= {R. Hecht-Nielsen},
  title		= {Counterprogagation networks},
  journal	= {Appl. Opt. },
  year		= {1987},
  volume	= {26},
  number	= {23},
  pages		= {4979--4984},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hecht-nielsen87b,
  author	= {Robert Hecht-Nielsen},
  title		= {Counterpropagation Networks},
  booktitle	= {Proc. ICNN'87, International Conference on Neural
		  Networks},
  year		= {1987},
  volume	= {II},
  pages		= {19--32},
  organization	= {IEEE, San Diego Section, San Diego, CA; IEEE, Systems, Man
		  and Cybernetics Soc; IEEE, Control Systems Soc; IEEE,
		  Engineering in Medicine and Biology Soc},
  publisher	= {SOS Printing},
  address	= {San Diego, CA},
  note		= {Available from IEEE Service Cent, Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  hecht-nielsen88a,
  author	= {Robert Hecht-Nielsen},
  title		= {Applications of Counterpropagation networks},
  journal	= {Neural Networks},
  year		= {1988},
  volume	= {1},
  number	= {2},
  pages		= {131--139},
  x		= {By combining Kohonen learning and Grossberg learning a new
		  type of mapping neural network is obtained. . . . DIALOG
		  No: 02638467 EI Monthly No: EI8809091081},
  dbinsdate	= {oldtimer}
}

@Book{		  hecht-nielsen90a,
  author	= {Robert Hecht-Nielsen},
  title		= {Neurocomputing},
  publisher	= {Addison-Wesley},
  year		= {1990},
  address	= {Reading, MA},
  dbinsdate	= {oldtimer}
}

@Article{	  hecht-nielsen96a,
  author	= {R. Hecht-Nielsen},
  title		= {Review of `Self-Organizing Maps'},
  journal	= {IEEE Transactions on Neural Networks},
  type		= {Book Review},
  year		= 1996,
  volume	= 7,
  number	= 6,
  month		= {November},
  pages		= {1549--1550},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heggarty95a,
  author	= {Heggarty, K. and Duvillier, J. and Carpio Perez, E. and
		  {de Bougrenet de la Tocnaye}, J. -L. },
  title		= {All-optical \mbox{self-organizing} map applied to
		  character recognition},
  booktitle	= {Optical Computing. Proceedings of the International
		  Conference},
  year		= {1995},
  editor	= {Wherrett, B. S. and Chavel, P. },
  pages		= {411--14},
  organization	= {Dept. Opt. , ENST de Bretagne, Brest, France},
  publisher	= {IOP Publishing},
  address	= {Bristol, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  heggarty95b,
  author	= {Heggarty, K. and Duvillier, J. and Carpio Perez, E. and
		  {de Bougrenet de la Tocnaye}, J. L. },
  title		= {All-optical implementation of a \mbox{self-organizing}
		  map: learning and taxonomy capability assessment},
  journal	= {Applied Optics},
  year		= {1995},
  volume	= {34},
  number	= {35},
  pages		= {8167--75},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkila99a,
  author	= {Heikkil\"a, J. and Silven, O.},
  title		= {A real-time system for monitoring of cyclists and
		  pedestrians},
  booktitle	= {Proceedings Second IEEE Workshop on Visual Surveillance
		  (VS'99)},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {74--81},
  abstract	= {Camera based fixed systems are routinely used for
		  monitoring highway traffic. For this purpose inductive
		  loops and microwave sensors are mainly used. Both
		  techniques achieve very good counting accuracy and are
		  capable of discriminating trucks and cars. However
		  pedestrians and cyclists are mostly counted manually. In
		  this paper, we describe a new camera based automatic system
		  that utilizes Kalman filtering in tracking and Learning
		  Vector Quantization (LVQ) for classifying the observations
		  to pedestrians and cyclists. Both the requirements for such
		  systems and the algorithms used are described. The tests
		  performed show that the system achieves around 80%-90%
		  accuracy in counting and classification.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen93a,
  author	= {Jukka Heikkonen and Pasi Koikkalainen and Erkki Oja and
		  Jari Mononen},
  title		= {{S}elf-{O}rganizing {M}aps for Navigation and Collision
		  Free Movement},
  booktitle	= {Proc. Symp. on Neural Networks in Finland, {\AA}bo
		  Akademi, Turku, January 21. },
  year		= {1993},
  editor	= {Abhay Bulsari and Bj{\"{o}}rn Sax{\'{e}}n},
  pages		= {63--74},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen93b,
  author	= {Jukka Heikkonen and Pasi Koikkalainen},
  title		= {Object Motion Learning via Self-Organization},
  booktitle	= {Proc. 4th Int. Workshop: Time-Varying Image Processing and
		  Moving Object Recognition},
  year		= 1993,
  editor	= {Vito Cappellini},
  pages		= {327--334},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen93c,
  author	= {J. Heikkonen and P. Koikkalainen and E. Oja},
  title		= {From Situations to Actions: Motion Behavior Learning by
		  Self-Organization},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {262--267},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen93d,
  author	= {Jukka Heikkonen and Erkki Oja},
  title		= {Self-Organizing Maps for Visually Guided Collision-free
		  Navigation},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {669--672},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen93e,
  author	= {Jukka Heikkonen and Pasi Koikkalainen and Erkki Oja},
  title		= {{S}elf-{O}rganizing {M}aps for Collision-free Navigation},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {III},
  pages		= {141--144},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  heikkonen94a,
  author	= {Jukka Heikkonen},
  title		= {Subsymbolic Representations, Self-Organizing Maps, and
		  Object Motion Learning},
  school	= {Lappeenranta University of Technology},
  year		= {1994},
  address	= {Lappeenranta, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen94b,
  author	= {Heikkonen, J. and Koikkalainen, P. and Schnorr, C. },
  title		= {Learning motion trajectories via self-organization},
  booktitle	= {Proceedings of the 12th IAPR International Conference on
		  Pattern Recognition},
  year		= {1994},
  volume	= {2},
  pages		= {554--6},
  organization	= {Dept. of Inf. Technol. , Lappeenranta Univ. of Technol. ,
		  Finland},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen95a,
  author	= {Jukka Heikkonen},
  title		= {Computer Vision System for Analysing Air Flows},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {33--40},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen95b,
  author	= {Jukka Heikkonen and Martti Surakka and Jukka Riekki},
  title		= {Self-Organizing Controller for a Mobile Robot},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {53--56},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen95c,
  author	= {Jukka Heikkonen and Mika M{\"{a}}ntynen},
  title		= {Digit Recognition on Pulp Bales},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {75--78},
  organization	= {Finnish Artificial Intelligence Society},
  annote	= {LVQ comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen95d,
  author	= {Jukka Heikkonen and Jos{\'{e}} {del R. Mill{\'{a}}n} and
		  Enrique Cuesta},
  title		= {Incremental Learning from Basic Reflexes in an Autonomous
		  Mobile Robot},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {119--126},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@Article{	  heikkonen96a,
  author	= {J. Heikkonen},
  title		= {A computer vision approach to air flow analysis},
  journal	= {Pattern Recognition Letters},
  year		= {1996},
  volume	= {17},
  number	= {4},
  pages		= {369--84},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen96b,
  author	= {Heikkonen, J. and Varjo, J. and Varfis, A.},
  title		= {Applying \mbox{self-organizing} map to select {Landsat}
		  {TM} difference features for forest change analysis},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {329--32},
  abstract	= {Efficient use of multitemporal Landsat TM information in
		  forest change detection requires that the best features
		  from the huge amount of data can be selected for the task.
		  This paper shows how the self-organizing map (SOM) can be
		  used as a tool for evaluating the class discriminatory
		  potentials of different features and for selecting suitable
		  features for the forest change detection task.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  heikkonen97a,
  author	= {J. Heikkonen and I. Kanellopoulos and A. Varfis and A.
		  Steel and K. Fullerton},
  title		= {Urban land use mapping with multi-spectral and {SAR}
		  satellite data using neural networks},
  booktitle	= {IGARSS'97. 1997 International Geoscience and Remote
		  Sensing Symposium. Remote Sensing---A Scientific Vision for
		  Sustainable Development},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {4},
  editor	= {T. I. Stein},
  address	= {New York, NY, USA},
  pages		= {1660--2},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heikkonen97b,
  author	= {Heikkonen, J. and Varfis, A. and Wilkinson, G. and
		  Kanellopoulos, I. and Fullerton, K. and Steel, A.},
  title		= {Satellite image-based land cover/land use classification
		  of urban areas},
  booktitle	= {Neural Networks in Engineering Systems. Proceedings of the
		  1997 International Conference on Engineering Applications
		  of Neural Networks},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1997},
  volume	= {1},
  pages		= {9--16},
  abstract	= {A system for satellite image-based land cover/land use
		  classification of urban areas is described. The system
		  consists of the following main stages: feature extraction,
		  feature coding, feature selection and classification. In
		  feature extraction statistical, textural and Gabor features
		  are computed from satellite images. Next the features are
		  encoded and normalized by the self-organizing map algorithm
		  and a decision tree-based algorithm was developed to select
		  relevant features for the land cover/land use
		  classification scheme at hand. Finally a multilayer
		  perceptron is trained to map the selected features into the
		  classes. The proposed system is tested on a land cover/land
		  use classification task in the city of Lisbon with Landsat
		  TM and ERS-1 {SAR} images, and the results show the
		  potential of the proposed methodology.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  heikkonen97c,
  author	= {J. Heikkonen and P. Koikkalainen},
  title		= {Self-organization and autonomous robots},
  booktitle	= {Neural Systems for Robotics},
  publisher	= {Academic Press},
  year		= 1997,
  editor	= {O. Omidvar and P. {van der Smagt}},
  pages		= {297--337},
  address	= {San Diego, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  heim91a,
  author	= {P. Heim and B. Hochet and E. Vittoz},
  title		= {Generation of learning neighbourhood in {K}ohonen feature
		  maps by means of simple nonlinear network},
  journal	= {Electronics Letters},
  year		= {1991},
  volume	= {27},
  number	= {3},
  pages		= {275--277},
  annote	= {A grid of current inputs coupled together with nonlinear
		  resistors. },
  dbinsdate	= {oldtimer}
}

@Article{	  heim92a,
  author	= {P. Heim and X. Arregvit and E. Vittoz},
  title		= {Analogue {VLSI} implementation of {K}ohonen networks},
  journal	= {Bull. des Schweizerischen Elektrotechnischen Vereins {\&}
		  des Verbandes Schweizerischer Elektrizitaetswerke},
  year		= {1992},
  volume	= {83},
  number	= {5},
  pages		= {44--48},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heim93a,
  author	= {Heim, P. and Vittoz, E. A. },
  title		= {Precise analogue synapse for {K}ohonen feature maps},
  booktitle	= {ESSCIRC 93. Nineteenth European Solid-State Circuits
		  Conference. Proceedings},
  year		= {1993},
  pages		= {70--3},
  organization	= {Lab. d'Electron. Gen. , Ecole Polytech. Federale de
		  Lausanne, Switzerland},
  publisher	= {Editions Frontieres},
  address	= {Gif sur Yvette, France},
  dbinsdate	= {oldtimer}
}

@Article{	  heim94a,
  author	= {Heim, P. and Vittoz, E. A. },
  title		= {Precise analog synapse for {K}ohonen feature maps},
  journal	= {IEEE Journal of Solid-State Circuits},
  year		= {1994},
  volume	= {29},
  number	= {8},
  pages		= {982--5},
  month		= {Aug},
  dbinsdate	= {oldtimer}
}

@Article{	  heimel01a,
  author	= {Heimel, J. A. F. and Sompolinksy, H.},
  title		= {Stable orientation tuning in the visual cortex},
  journal	= {NEUROCOMPUTING},
  year		= {2001},
  volume	= {38},
  month		= {JUN},
  pages		= {1261--1266},
  abstract	= {We study the behaviour of the Ben-Yishai hypercolumn model
		  (Ben-Yishai et al., Proc. Natl. Acad. Sci. USA 92 (1995)
		  3844) under presentation of oriented stimuli, having
		  extended this model by including plastic afferent (LGN to
		  cortex) connections. We find that Hebbian plasticity
		  creates a self- organising map and show that constraining
		  or modifying the standard Hebb rule in a particular way
		  will lead to a contrast- insensitive tuning width, thus
		  giving an explanation for persistent orientation tuning as
		  observed in the Visual cortex. Our analytical results
		  confirm those of simulations done by {Von der Malsburg}
		  (Kybernetik 14 (1973) 85) and provide a starting point for
		  further analytical treatment of less restricted stimuli. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  heine93a,
  author	= {Steffen Heine and Ingo Neumann},
  title		= {Information Systems for Load-Data Analysis and Load
		  Forecast by Means of Specialised Neural Nets},
  booktitle	= {28th Universities Power Engineering Conf. 1993},
  year		= {1993},
  pages		= {},
  publisher	= {Staffordshire University},
  address	= {Stafford, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heine94a,
  author	= {Heine, S. and Neumann, I. },
  title		= {Data analysis by means of {K}ohonen feature maps for load
		  forecast in power systems},
  booktitle	= {IEE Colloquium on 'Advances in Neural Networks for Control
		  and Systems' (Digest No. 1994/136)},
  year		= {1994},
  pages		= {6/1--4},
  organization	= {Tech. Hochschule Leipzig, Germany},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Book{		  heine96a,
  author	= {Heine, S. and Land, T. and Neumann, I.},
  title		= {NEUPRO. Neuronale Systeme zur Analyse und Bedienerhilfe
		  von komplexen technischen Echtzeitprozessen. Teilprojekt:
		  Prognose elektrischer Belastungen. Abschlussbericht.
		  (NEUPRO. Neural networks for analysis and operation of
		  complex technological real time processes. Subproject:
		  electrical load forecast. Final report).},
  year		= {1996},
  abstract	= {Within control solutions predictive functions will be more
		  important in order to reach better operational conditions
		  (dispatching). The very important task 'electric load
		  forecast in power systems' belongs to such functionalities.
		  By means of artificial neural networks it is possible to
		  realize especially in short time ranges (hour intervalls,
		  weekly) powerfull forecast solutions with adaptive
		  behaviour---a prototyp was developed. The usable methods of
		  data analysis, classification of specialized neural
		  networks (typical daily behaviour), integration of network
		  specialists in a frame system of neural networks were
		  realized on the some technolological basis (here mostly be
		  means of Kohonen-Feature-Maps). The reached accuracy
		  depends strongly on the conditions of the application case
		  and the quality of available data materials. The acceptance
		  of users was reached and shown at an application case
		  (utility). (orig.). (Copyright (c) 1996 by FIZ. Citation
		  no. 96:004379.)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heinz00a,
  author	= {Gerd K. Heinz},
  title		= {Space-time Relations in Wave Interference Systems with
		  Attention to Nerve Networks},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Gesellschaft zur Förderung angewandter Informatik e.V.},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  heiss96a,
  author	= {H. Heiss and M. Dormanns},
  title		= {Partitioning and mapping of parallel programs by
		  self-organization},
  journal	= {Concurrency: Practice and Experience},
  year		= {1996},
  volume	= {8},
  number	= {9},
  pages		= {685--706},
  dbinsdate	= {oldtimer}
}

@InCollection{	  helbing97a,
  author	= {M. Helbing and L. Kahl and C. Rothlubbers and R.
		  Orglmeister},
  title		= {A reliable algorithm for automatic contour estimation in
		  medical ultrasonic images of the human heart},
  booktitle	= {4th International Workshop on Systems, Signals and Image
		  Processing. Proceedings},
  publisher	= {Poznan Univ. Technol},
  year		= {1997},
  editor	= {M. Domanski and R. Stasinski},
  address	= {Poznan, Poland},
  pages		= {141--4},
  dbinsdate	= {oldtimer}
}

@Article{	  hemani90a,
  author	= {A. Hemani and A. Postula},
  title		= {Cell placement by self-organisation},
  journal	= {Neural Networks},
  year		= {1990},
  volume	= {3},
  number	= {4},
  pages		= {337--338},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hemani90b,
  author	= {A. Hemani and A. Postula},
  title		= {A neural net based self organising scheduling algorithm},
  booktitle	= {Proc. EDAC, European Design Automation Conference},
  year		= {1990},
  pages		= {136--140},
  organization	= {IEEE; EDAC},
  publisher	= {IEEE Computer Society Press},
  address	= {Washington, DC},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hemani90c,
  author	= {A. Hemani and A. Postula},
  title		= {Scheduling by Self-Organization},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {2},
  pages		= {543--546},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  hemani92a,
  author	= {Ahmed Hemani},
  title		= {High-Level Synthesis of Synchronous Digital Systems using
		  Self-Organisation Algorithms for Scheduling and Binding},
  school	= {The Royal Inst. of Technology},
  year		= {1992},
  address	= {Stockholm, Sweden},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hemani93a,
  author	= {Ahmed Hemani},
  title		= {Self-organisation and its application to binding},
  booktitle	= {Proc. 6th International Conference on {VLSI} Design,
		  Bombay },
  year		= {1993},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hemani99a,
  author	= {A. Hemani and A. Postula},
  title		= {Self-Organising Maps in Computer Aided Design of
		  electronic circuits},
  booktitle	= {Kohonen Maps},
  pages		= {231--242},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: Scheduling by {SOM}, Electronic CAD, behavioural
		  synthesis},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hendtlass94a,
  author	= {Tim Hendtlass},
  title		= {A Dynamic Architecture for the Categorisation of
		  Information},
  editor	= {A. C. Tsoi and T. Downs},
  pages		= {169--172},
  booktitle	= {Proc. 5th Australian Conf. on Neural Networks},
  year		= {1994},
  publisher	= {University of Queensland},
  address	= {St. Lucia, Australia},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hendtlass95a,
  author	= {T. Hendtlass},
  title		= {A self organizing artificial neural network with problem
		  dependent structure},
  booktitle	= {1995 IEEE International Conference on Neural Networks
		  Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1111--15},
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  henseler87a,
  author	= {J. Henseler and J. C. Scholtes and C. R. J. Verhoest},
  title		= {The Design of a Parallel Knowledge-Based Optical-Character
		  Recognition System},
  school	= {Delft University},
  year		= {1987},
  address	= {Delft, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  henseler88a,
  author	= {J. Henseler and H. J. {van der Herik} and E. J. H.
		  Kerchhoffs and H. Koppelaar and J. C. Scholtes and C. R. J.
		  Verhoest},
  title		= {Knowledge-based Parallelism in Optical Character
		  Recognition},
  booktitle	= {Proc. Summer Comp. Simulation Conf. , Seattle},
  year		= {1988},
  pages		= {14--20},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  henseler92a,
  author	= {Henseler, J. and Braspenning, P. J.},
  title		= {Membrain: A cellular neural network model based on a
		  vibrating membrane.},
  journal	= {International Journal of Circuit Theory and Applications},
  year		= {1992},
  number	= {5},
  volume	= {20},
  pages		= {483--496},
  abstract	= {This paper introduces the Membrain model describing a
		  neural network architecture which is similar to the
		  architecture underlying the class of cellular neural
		  networks (CNNs). The main difference pertains to the
		  characteristic processing equation, which is based on a
		  wave equation instead of a heat equation. Within the CNN
		  framework, a cellular Membrain model may be obtained by
		  replacing the neuron output function by a first-order state
		  equation. Furthermore, the network-cloning templates are
		  chosen such that the CNN behaves like a system of coupled
		  harmonical oscillators. Since the energy of such a system
		  is bounded, the piecewise linear neuron characteristic
		  function may be chosen such that it always operates in the
		  linear regime. Our starting point is the analytical and
		  general solution for forced vibrations with damping. This
		  solution applies to a Membrain neural network whose
		  functional architecture is based on the specialized
		  solution for a network of coupled harmonic oscillators. In
		  particular, we present a Membrain CNN (MCNN) having a
		  toroidal connection structure such that the natural modes
		  of vibration of the net are translation-invariant.
		  Moreover, depending on the point group of the network, some
		  rotation invariance can also be obtained. Identifying the
		  input of such a network with the initial state of the
		  oscillators gives rise to an output which is in essence a
		  transversally travelling wave made up of components which
		  are coupled harmonic neuronal oscillators; that is, the
		  wave is a superposition of natural modes of vibration of
		  the network. The temporal wave pattern may be transformed
		  into a one-dimensional temporal signal which is invariant
		  under translation of the initial deflection pattern of the
		  MCNN. The amplitudes of the components in the temporal
		  signal correspond to the power spectrum of the natural
		  vibration modes in the MCNN. Interpreting the initial
		  deflection pattern as a grey-level image, the temporal
		  signal can be viewed as a modulation of a
		  translation-invariant 'fingerprint' of the image. The
		  signal may be sampled such that the modulated 'fingerprint'
		  can be classified using some of the traditional neural
		  network models. In particular we show that (1) a
		  self-organizing feature map clusters correlated images and
		  (2) a back-propagation neural network extracts
		  position-invariant features.},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  henseler93a,
  author	= {Johan Henseler},
  title		= {Connections, Neurons and Activation, The Organization of
		  Representation in Artificial Neural Networks},
  school	= {University of Limburg},
  year		= {1993},
  address	= {Maastricht, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  henson97a,
  author	= {D. B. Henson and S. E. Spenceley and D. R. Bull},
  title		= {Artificial neural network analysis of noisy visual field
		  data in glaucoma},
  journal	= {Artificial Intelligence in Medicine},
  year		= {1997},
  volume	= {10},
  number	= {2},
  pages		= {99--113},
  abstract	= {This paper reports on the application of an artificial
		  neural network to the clinical analysis of ophthalmological
		  data. In particular a 2-dimensional Kohonen self-organizing
		  feature map (SOM) is used to analyze visual field data from
		  glaucoma patients. Importantly, the paper addresses the
		  problem of how the {SOM} can be utilized to accommodate the
		  noise within the data. This is a particularly important
		  problem within longitudinal assessment, where detecting
		  significant change is the crux of the problem in clinical
		  diagnosis. Data from 737 glaucomatous visual field records
		  (Humphrey Visual Field Analyzer, program 24--2) are used to
		  train a {SOM} with 25 nodes organized on a square grid. The
		  SOM clusters the data organizing the output map such that
		  fields with early and advanced loss are at extreme
		  positions, with a continuum of change in place and extent
		  of loss represented by the intervening nodes. For each SOM
		  node 100 variants, generated by a computer simulation
		  modelling the variability that might be expected in a
		  glaucomatous eye, are also classified by the network to
		  establish the extent of noise upon classification. Field
		  change is then measured with respect to classification of a
		  subsequent field, outside the area defined by the original
		  field and its variants. The significant contribution of
		  this paper is that the spatial analysis of the field data,
		  which is provided by the SOM, has been augmented with noise
		  analysis enhancing the visual representation of
		  longitudinal data and enabling quantification of
		  significant class change.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  herbin95a,
  author	= {St{\'{e}}phane Herbin},
  title		= {Graph Matching by \mbox{Self-organizing} Feature Maps},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {57--62},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hernaez93a,
  author	= {I. Hern{\'{a}}ez and J. Barandiar{\'{a}}n and E. Monte and
		  B. Extebarria},
  title		= {A Segmentation Algorithm Based on Acoustical Features
		  Using a Self Organizing Neural Network},
  booktitle	= {Proc. EUROSPEECH-93, 3rd European Conf. on Speech,
		  Communication and Technology},
  year		= {1993},
  volume	= {I},
  pages		= {661--663},
  publisher	= {ECSA},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hernandez-gomez93a,
  author	= {Luis A. Hernandez-Gomez and Eduardo Lopez-Gonzalo},
  title		= {Phonetically-Driven {CELP} Coding Using
		  {S}elf-{O}rganizing {M}aps},
  booktitle	= {Proc. ICASSP-93, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1993},
  volume	= {II},
  pages		= {628--631},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hernandez-pajares91a,
  author	= {M. Hernandez-Pajares and E. Monte},
  title		= {Application of the {LVQ} neural method to a stellar
		  catalogue},
  booktitle	= {Proc. IWANN'91, Int. Workshop on Artificial Neural
		  Networks},
  year		= {1991},
  editor	= {A. Prieto},
  pages		= {422--429},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@Article{	  hernandez-pajares93a,
  author	= {M. Hernandez-Pajares and R. Cubarsi and E. Monte},
  title		= {The {S}elf-{O}rganizing {M}ap of Neighbour Stars and its
		  Kinematic Interpretation},
  journal	= {Neural Network World},
  year		= {1993},
  volume	= {3},
  pages		= {311--318},
  dbinsdate	= {oldtimer}
}

@Article{	  hernandez-pajares94a,
  author	= {Hernandez-Pajares, M. and Floris, J. },
  title		= {Classification of the Hipparcos Input Catalogue using the
		  {K}ohonen network},
  journal	= {Monthly Notices of the Royal Astronomical Society},
  year		= {1994},
  volume	= {268},
  number	= {2},
  pages		= {444--50},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  hernandez-pajares94b,
  author	= {Hernandez-Pajares, M. and Floris, J. and Murtagh, F. },
  title		= {How tracer objects can improve competitive learning
		  algorithms in astronomy},
  journal	= {Vistas in Astronomy},
  year		= {1994},
  volume	= {38},
  number	= {pt. 3},
  pages		= {317--30},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  herrmann95a,
  author	= {M. Herrmann and H. -U. Bauer and R. Der},
  title		= {Optimal Magnification Factors in Self-Organizing Feature
		  Maps},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {I},
  pages		= {75--80},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  herrmann95b,
  author	= {Michael Herrmann},
  title		= {Self-Organizing Feature Maps with Self-Organizing
		  Neighborhood Widths},
  volume	= {VI},
  pages		= {2998--3003},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  herrmann96a,
  author	= {M. Herrmann and H. H. Yang},
  title		= {Perspectives and limitations of \mbox{self-organizing}
		  maps in blind separation of source signals},
  booktitle	= {Progress in Neural Information Processing. Proceedings of
		  the International Conference on Neural Information
		  Processing},
  publisher	= {Springer-Verlag},
  year		= {1996},
  volume	= {2},
  editor	= {S. -I. Amari and L. Xu and L. -W. Chan and I. King and K.
		  -S. Leung},
  address	= {Singapore},
  pages		= {1211--16},
  dbinsdate	= {oldtimer}
}

@InCollection{	  herrmann96b,
  author	= {Michael Herrmann and Ralf Der and Gerd Balzuweit},
  title		= {Hierarchical Feature Maps and Non-Linear Component
		  Analysis},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1390--1394},
  dbinsdate	= {oldtimer}
}

@InCollection{	  herrmann97a,
  author	= {M. Herrmann and H. -U. Bauer and Th. Villmann},
  title		= {Measuring Topology Preservation in Maps of Real-World
		  Data},
  booktitle	= {Proc. ESANN'97, 5th European Symposium on Artificial
		  Neural Networks},
  publisher	= {D facto},
  year		= 1997,
  editor	= {Michel Verleysen},
  pages		= {205--210},
  address	= {Brussels, Belgium},
  dbinsdate	= {oldtimer}
}

@InCollection{	  herrmann97b,
  author	= {Michael Herrmann},
  title		= {On the merits of topography in neural maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {112--117},
  dbinsdate	= {oldtimer}
}

@InCollection{	  herrmann97c,
  author	= {M. Herrmann and H. -U. Bauer and Th. Villmann},
  title		= {A comparison of topography measures for neural maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {274--279},
  dbinsdate	= {oldtimer}
}

@InCollection{	  herrmann97d,
  author	= {M. Herrmann and T. Villmann},
  title		= {Vector quantization by optimal neural gas},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {625--30},
  dbinsdate	= {oldtimer}
}

@Book{		  hertz91a,
  author	= {John A. Hertz and Anders Krogh and Richard G. Palmer},
  title		= {Introduction to the Theory of Neural Computation},
  series	= {{S}anta {F}e {I}nstitute Studies in the Sciences of
		  Complexity: Lecture Notes},
  volume	= 1,
  publisher	= {Addison-Wesley},
  address	= {Redwood City, CA},
  year		= 1991,
  dbinsdate	= {oldtimer}
}

@InCollection{	  herzog96a,
  author	= {Andreas Herzog and Gerd Sommerkorn and Udo Seiffert and
		  Bernd Michaelis and Katharina Braun and Werner
		  Zuschratter},
  title		= {Rekonstruktion und Klassifikation dendritiscker Spines aus
		  konfokalen Bilddaten},
  booktitle	= {Bildverarbeitung f{\"u}r die Medizin. Tagungsband des
		  Aachener Workshops, Aachen, 8--9. Nov 1996},
  publisher	= {Augustinus Verlag},
  year		= 1996,
  address	= {Aachen},
  pages		= {65--70},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heskes00a,
  author	= {Heskes, Tom and Spanjers, Jan-Joost and Wiegerinck, Wim},
  title		= {{EM} algorithms for self-organizing maps},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {9--14},
  organization	= {SNN Univ of Nijmegen},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Self-organizing maps are popular algorithms for
		  unsupervised learning and data visualization. Exploiting
		  the link between vector quantization and mixture modeling,
		  we derive EM algorithms for self-organizing maps with and
		  without missing values. We compare self-organizing maps
		  with the elastic-net approach and explain why the former is
		  better suited for the visualization of high-dimensional
		  data. Several extensions and improvements are discussed.},
  dbinsdate	= {2002/1}
}

@Article{	  heskes01a,
  author	= {Heskes, T.},
  title		= {Self-organizing maps, vector quantization, and mixture
		  modeling},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2001},
  volume	= {12},
  number	= {6},
  month		= {November },
  pages		= {1299--1305},
  organization	= {RWCP Theoretical Foundation SNN, University of Nijmegen},
  publisher	= {},
  address	= {},
  abstract	= {Self-organizing maps are popular algorithms for
		  unsupervised learning and data visualization. Exploiting
		  the link between vector quantization and mixture modeling,
		  we derive expectation-maximization (EM) algorithms for
		  self-organizing maps with and without missing values. We
		  compare self-organizing maps with the elastic-net approach
		  and explain why the former is better suited for the
		  visualization of high-dimensional data. Several extensions
		  and improvements are discussed. As an illustration we apply
		  a self-organizing map based on a multinomial distribution
		  to market basket analysis.},
  dbinsdate	= {2002/1}
}

@Article{	  heskes91a,
  author	= {Thomas Heskes and Stan Gielen},
  title		= {Learning Processes in Neural Networks},
  journal	= {Phys. Rev. ~{A}},
  volume	= 44,
  pages		= {2718--2726},
  year		= 1991,
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heskes91b,
  author	= {Thomas Heskes and Bert Kappen and Stan Gielen},
  title		= {Neural Networks Learning in a Changing Environment},
  editor	= {T. Kohonen and K. M\"{a}kisara and O. Simula and J.
		  Kangas},
  booktitle	= {Artificial Neural Networks},
  volume	= 1,
  pages		= {15--20},
  address	= {Amsterdam, Netherlands},
  year		= 1991,
  publisher	= {North-Holland},
  dbinsdate	= {oldtimer}
}

@Article{	  heskes92a,
  author	= {Thomas Heskes and Bert Kappen},
  title		= {Learning-Parameter Adjustment in Neural Networks},
  journal	= {Physical Review~{A}},
  volume	= 45,
  pages		= {8885--8893},
  year		= 1992,
  dbinsdate	= {oldtimer}
}

@Article{	  heskes92b,
  author	= {Thomas Heskes and Eddy Slijpen and Bert Kappen},
  title		= {Learning in Neural Networks With Local Minima},
  journal	= {Physical Review~{A}},
  volume	= 46,
  pages		= {5221--5231},
  year		= 1992,
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heskes92c,
  author	= {Thomas Heskes and Eddy Slijpen},
  title		= {Global Performance of Learning Rules},
  editor	= {I. Aleksander and J. Taylor},
  booktitle	= {Artificial Neural Networks, 2},
  volume	= 1,
  pages		= {101--104},
  address	= {Amsterdam, Netherlands},
  year		= 1992,
  publisher	= {North-Holland},
  dbinsdate	= {oldtimer}
}

@Article{	  heskes93a,
  author	= {Thomas M. Heskes and Eddy T. P. Slijpen and Bert Kappen},
  title		= {Cooling Schedules for Learning in Neural Networks},
  journal	= {Physical Review~{E}},
  volume	= 47,
  pages		= {4457--4464},
  year		= 1993,
  dbinsdate	= {oldtimer}
}

@InCollection{	  heskes93b,
  author	= {Thomas Heskes and Bert Kappen},
  title		= {On-Line Learning Processes in Artificial Neural Networks},
  booktitle	= {Mathematical Foundations of Neural Networks},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  year		= 1993,
  editor	= {J. Taylor},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heskes93c,
  author	= {Thomas Heskes},
  title		= {Guaranteed Convergence of Learning Rules},
  booktitle	= {Proc. {ICANN'93}, International Conference on Artificial
		  Neural Networks},
  publisher	= {Springer},
  address	= {London, UK},
  pages		= {533--536},
  year		= 1993,
  dbinsdate	= {oldtimer}
}

@InProceedings{	  heskes93d,
  author	= {Tom M. Heskes and Bert Kappen},
  title		= {Error Potential for Self-Organization},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1219--1223},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  heskes93e,
  author	= {Tom Heskes},
  school	= {Katholieke Universiteit Nijmegen},
  title		= {Learning Processes in Neural Networks},
  address	= {Nijmegen, Netherlands},
  year		= {1993},
  dbinsdate	= {oldtimer}
}

@InCollection{	  heskes95a,
  author	= {T. Heskes and B. Kappen},
  title		= {Self-organization and nonparametric regression},
  booktitle	= {ICANN'95. International Conference on Artificial Neural
		  Networks},
  publisher	= {EC2 \& Cie},
  year		= {1995},
  volume	= {1},
  editor	= {F. Fogelman-Soulie and P. Gallinari},
  address	= {Paris, France},
  pages		= {81--6},
  dbinsdate	= {oldtimer}
}

@Article{	  heskes96a,
  author	= {T. M. Heskes},
  title		= {Transition times in \mbox{self-organizing} maps [central
		  nervous system application]},
  journal	= {Biological Cybernetics},
  year		= {1996},
  volume	= {75},
  number	= {1},
  pages		= {49--57},
  dbinsdate	= {oldtimer}
}

@InCollection{	  heskes99a,
  author	= {T. Heskes},
  title		= {Energy functions for \mbox{self-organizing} maps},
  booktitle	= {Kohonen Maps},
  pages		= {303--316},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: self-organising maps, energy functions, soft
		  assignments},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hessel94a,
  author	= {G. Hessel and W. Schmitt and F. -P. Weiss},
  title		= {A New Method for Acoustic Leak Detection at Complicated
		  Geometrical Structures},
  booktitle	= {Proc. SAFEPROCESS'94, IFAC Symp. on Fault Detection,
		  Supervision and Technical Processes},
  year		= {1994},
  volume	= {I},
  pages		= {153--158},
  dbinsdate	= {oldtimer}
}

@Article{	  hesselroth93a,
  author	= {Ted Hesselroth and Kakali Sarkar and P. Patrick {van der
		  Smagt} and Klaus Schulten},
  title		= {Neural Network Control of a Pneumatic Robot Arm},
  journal	= {{IEEE} Trans. on Syst. , Man and Cyb. },
  year		= 1993,
  volume	= 24,
  pages		= {28--37},
  dbinsdate	= {oldtimer}
}

@InCollection{	  heuser97a,
  author	= {U. Heuser and J. Goppert and W. Rosenstiel and A.
		  Stevens},
  title		= {Classification of human brain waves using
		  \mbox{self-organizing} maps},
  booktitle	= {Intelligent Data Analysis in Medicine and Pharmacology},
  publisher	= {Kluwer Academic Publishers},
  year		= {1997},
  editor	= {N. Lavrac and E. T. Keravnou and B. Zupan},
  address	= {Dordrecht, Netherlands},
  pages		= {279--94},
  dbinsdate	= {oldtimer}
}

@Article{	  higuchi98a,
  author	= {I. Higuchi and S. Eguchi},
  title		= {The Influence Function of Principal Component Analysis by
		  Self Organizing Rule},
  journal	= {Neural Computation},
  volume	= {10},
  pages		= {1434--1444},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hijikata97a,
  author	= {Y. Hijikata and H. Takeuchi and T. Yoshida and S.
		  Nishida},
  title		= {A dynamic linkage method for text data based on
		  \mbox{self-organizing} map},
  booktitle	= {Proceedings. 6th IEEE International Workshop on Robot and
		  Human Communication. RO-MAN '97 Sendai},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {S. C. Hirtle and A. U. Frank},
  address	= {Berlin, Germany},
  pages		= {420--5},
  abstract	= {In recent years, data base systems have become larger and
		  it is not so easy for users to get information which they
		  want. Hypermedia systems have become increasingly popular
		  as tools for information retrieval, however, links in
		  hypermedia are static and has to be defined by hands.
		  Furthermore there are problems in evaluation methods for
		  these data base systems. This paper describes a dynamic
		  linkage method for text data based on the self-organizing
		  map. The feature of our approach lies in the fact that this
		  method automatically groups texts and generates links among
		  them. We also propose an evaluation method for the
		  self-organizing map. A prototype system was developed and
		  some experiments were carried out to evaluate our system.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hiltunen93a,
  author	= {Tapio Hiltunen and Lea Leinonen and Jari Kangas},
  title		= {Visualization and Classification of Voice Quality with the
		  Self-Organizing Map},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {420},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hiltunen97a,
  author	= {Yrj{\"o} Hiltunen and Jouni Kaartinen and Mika
		  Ala-Korpela},
  title		= {Classification of human blood plasma lipid abnormalities
		  by 1H magnetic resonance spectroscopy and
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {159--162},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  himberg00a,
  author	= {Himberg, Johan},
  title		= {{SOM} based cluster visualization and its application for
		  false coloring},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {3},
  pages		= {587--592},
  organization	= {Helsinki Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The self-organizing map (SOM) is widely used as a data
		  visualization method in various engineering applications.
		  It performs a non-linear mapping from a high-dimensional
		  data space to a lower dimensional visualization space. In
		  this paper, a simple method for visualizing the cluster
		  structure of SOM model vectors is presented. The method may
		  be used to produce tree-like visualizations, but the main
		  application here is to get different color codings that
		  express the approximate cluster structure of the SOM model
		  vectors. This coloring may be exploited in making false
		  color (pseudo color) presentations of the original data.
		  The method is especially meant for making an easily
		  implementable, explorative cluster visualization tool.},
  dbinsdate	= {2002/1}
}

@InCollection{	  himberg98a,
  author	= {J. Himberg},
  title		= {Enhancing the {SOM}-based Data Visualization by Linking
		  Different Data Projections},
  booktitle	= {Proceedings of 1st International Symposium IDEAL'98,
		  Intelligent Data Engineering and Learning---Perspectives on
		  Financial Engineering and Data Mining},
  publisher	= {Springer},
  year		= {1998},
  address	= {Hong Kong},
  pages		= {427--434},
  abstract	= {The self-organizing map (SOM) is widely used as a data
		  visualization method especially in various engineering
		  applications. It performs a nonlinear mapping from a
		  high-dimensional data space to a lower dimensional
		  visualization space. The SOM can be used for example in
		  correlation detection and cluster visualization in
		  explorative manner. Two tools for refining the SOM-based
		  visualization are presented. The first one brings out a
		  sharper view to the correlation detection and the second
		  one brings additional information to the input space
		  distance visualization. Both tools are based on linking two
		  different data projections using color coding. The tools
		  are demonstrated using a real-world data example from a
		  queuing system.},
  dbinsdate	= {oldtimer}
}

@Article{	  hines99a,
  author	= {Hines, E. L. and Llobet, E. and Gardner, J. W.},
  title		= {Neural network based electronic nose for apple ripeness
		  determination},
  journal	= {Electronics Letters},
  year		= {1999},
  volume	= {35},
  pages		= {821--3},
  abstract	= {It is possible to non-destructively determine apple
		  ripeness using a simple electronic nose. The instrument
		  employs tin oxide resistive gas sensors and neural networks
		  (fuzzy ARTMAP, LVQ and MLP) to classify the samples into
		  three states of ripeness with 100% accuracy. Fuzzy ARTMAP
		  was found to be the best classifier in the presence of
		  simulated Gaussian noise.},
  dbinsdate	= {oldtimer}
}

@Article{	  hiotis93a,
  author	= {A. Hiotis},
  title		= {Inside a \mbox{self-organizing} map},
  journal	= {AI Expert},
  year		= {1993},
  volume	= {8},
  number	= {4},
  pages		= {38--43},
  month		= {April},
  annote	= {Author concentrates on practical issues of what a {SOM}
		  is, how to use it and how it works},
  dbinsdate	= {oldtimer}
}

@Article{	  hirano93a,
  author	= {T. Hirano and M. Sase and Y. Kosugi},
  title		= {Bidirectional feature map for robotic arm control},
  journal	= {Trans. Inst. Electronics, Information and Communication
		  Engineers},
  year		= {1993},
  volume	= {J76D-II},
  number	= {4},
  pages		= {881--888},
  month		= {April},
  note		= {(in Japanese)},
  annote	= {The authors propose a bidirectional feature map model},
  dbinsdate	= {oldtimer}
}

@Article{	  ho00a,
  author	= {Ho Lim Choi and Hee Jung Byun and Won Gyu Song and Jun Won
		  Son and Jong Tae Lim},
  title		= {On pattern classification of {EMG} signals for walking
		  motions},
  journal	= {Artificial-Life-and-Robotics},
  year		= {2000},
  volume	= {4},
  pages		= {193--7},
  abstract	= {We present a method to classify electromyogram (EMG)
		  signals which are utilized as control signals for a
		  patient-responsive walker-supported system for paraplegics.
		  Patterns of EMG signals for different walking motions are
		  classified via adequate filtering, real EMG signal
		  extraction, autoregressive (AR) modeling and a modified
		  self-organizing feature map (MSOFM). In particular, a
		  data-reducing extraction algorithm is employed for real EMG
		  signals. Moreover, the MSOFM classifies and determines the
		  results automatically using a fixed map. Finally,
		  experimental results are presented for validation.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ho93a,
  author	= {Ho, T. K. },
  title		= {Recognition of handwritten digits by combining independent
		  learning vector quantizations},
  booktitle	= {Proceedings of the Second International Conference on
		  Document Analysis and Recognition},
  year		= {1993},
  pages		= {818--21},
  organization	= {AT\&T Bell Lab. , Murray Hill, NJ, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hoang98a,
  author	= {Hoang, D. and Williamson, G.},
  title		= {A mixture of global and local gated experts for the
		  prediction of high frequency foreign exchange rates},
  booktitle	= {PRICAI'98: TOPICS IN ARTIFICIAL INTELLIGENCE},
  year		= {1998},
  pages		= {329--340},
  abstract	= {This paper presents a new mixture of experts neural
		  network architecture for the prediction of the US Dollar
		  Swiss Franc exchange rate. This architecture achieves
		  improved prediction results on noisy and non-stationary
		  data. In contrast to previous efforts the current system
		  was designed with a particular emphasis on solving the
		  problems of local overfitting \& underfitting caused by
		  non-stationarity and noise in the data. The cascade
		  correlation constructive neural network training algorithm
		  was used for the fast training of near optimal complexity
		  global \& local experts. The Kohonen Self Organizing Map
		  was used to find regions of the data on which to train
		  local experts. Improved results were obtained by using a
		  combination of the outputs of the global \& local experts.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hoare92a,
  author	= {Hoare, F. and {de Jager}, G. },
  title		= {Neural networks for extracting features of objects in
		  images as a pre-processing stage to pattern
		  classification},
  booktitle	= {Proceedings of the 1992 South African Symposium on
		  Communications and Signal Processing. COMSIG '92},
  year		= {1992},
  editor	= {Inggs, M. },
  pages		= {239--42},
  organization	= {Dept. of Electr. \& Electron. Eng. , Cape Town Univ. ,
		  Rondebosch, South Africa},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hochet90a,
  author	= {B. Hochet and V. Peiris and G. Corbaz and M. Declercq},
  title		= {Implementation of a neuron dedicated to {K}ohonen maps
		  with learning capabilities},
  booktitle	= {Proc. IEEE 1990 Custom Integrated Circuits Conf. },
  year		= {1990},
  pages		= {26. 1/1--4},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  hochet91a,
  author	= {Bertrand Hochet and Vincent Peiris and Samer Abdo and
		  Michel J. Declerq},
  title		= {Implementation of a learning {K}ohonen neuron based on a
		  new multilevel storage technique},
  journal	= {IEEE J. Solid-State Circuits},
  year		= {1991},
  volume	= {26},
  number	= {3},
  pages		= {262--266},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hodges90a,
  author	= {R. E. Hodges and C.-H. Wu and C. -J. Wang},
  title		= {A parallel implementation of the \mbox{self-organizing}
		  feature map using synchronous communication},
  booktitle	= {Proc. ISCAS'90, Int. Symp. on Circuits and Systems},
  year		= {1990},
  volume	= {I},
  pages		= {743--749},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hodges90b,
  author	= {R. E. Hodges and C.-H. Wu},
  title		= {The neural network self-healing process by using a
		  reconstructed sample space},
  booktitle	= {Proc. ISCAS'90, Int. Symp. on Circuits and Systems},
  year		= {1990},
  volume	= {I},
  pages		= {204--206},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {. . . self-healing property is shown to exist in networks
		  used for pattern recognition. The self-organizing feature
		  map (SOFM) is the network used to study this topic. . . .
		  },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hodges90c,
  author	= {R. E. Hodges and C.-H. Wu},
  title		= {A Method to Establish an Autonomous Self-Organizing
		  Feature Map},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  volume	= {I},
  pages		= {517--520 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hodges90d,
  author	= {R. E. Hodges and C.-H. Wu and C.-J. Wang},
  title		= {Parallelizing the Self-Organizing Feature Maps on
		  Multi-Processor Systems},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {II},
  pages		= {141--144 },
  dbinsdate	= {oldtimer}
}

@Article{	  hoehn02a,
  author	= {Hoehn, F. and Lindner, E. and Mayer, H. A. and Hermle, T.
		  and Rosenstiel, W.},
  title		= {Neural networks evaluating {NMR} data: An approach to
		  visualize similarities and relationships of sol-gel derived
		  inorganic- organic and organometallic hybrid polymers},
  journal	= {JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES},
  year		= {2002},
  volume	= {42},
  number	= {1},
  month		= {JAN-FEB},
  pages		= {36--45},
  abstract	= {An artificial neural network (ANN)-the Kohonen
		  Self-Organizing Feature Map (SOM)-is used to evaluate
		  solid-state NMR spectroscopic derived data of 72
		  siloxane-based phosphine and organometallic functionalized
		  hybrid polymers. The data set consists of parameters that
		  describe their structural features and their dynamic
		  behavior. The ANN visualizes similarities of the
		  investigated compounds by reducing the dimension of the
		  data set. This allows a comparison of these polymers that
		  was not possible beforehand because of their structural
		  diversity.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hoekstra93a,
  author	= {Aarnoud Hoekstra and Marc F. J. Drossaers},
  title		= {An Extended {K}ohonen Feature Map for Sentence
		  Recognition},
  booktitle	= {Proc. ICANN'93. International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {404--407},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hoffman01a,
  author	= {Hoffman, A. J. and {Van Der Merwe}, N. T. and Stander, C.
		  and Heyns S. P.},
  title		= {A comparative evaluation of neural classification
		  techniques for identifying multiple fault conditions},
  booktitle	= {Proceedings of the International Workshop on Intelligent
		  Data Acquisition and Advanced Computing Systems: Technology
		  and Applications. IDAACS'2001. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {},
  pages		= {16--20},
  abstract	= {The objective of this research is to compare different
		  neural network based classifiers for the accurate and
		  reliable assessment of the status of specific fault
		  conditions in a system with multiple fault conditions
		  present. The proposed strategy utilizes features extracted
		  from vibrational data and employs self-organising maps
		  (SOMs), radial basis function (RBF) and multilayer
		  perceptron (MLP) networks to model the status of fault
		  conditions, and to discriminate between different fault
		  conditions. Different combinations of vibrational features
		  are evaluated in terms of their ability to support the
		  reliable identification of and discrimination between
		  multiple fault conditions. The results indicate that both
		  SOM and RBF neural classifiers can be trained to reliably
		  identify specific faults in a system with multiple fault
		  conditions present. It is furthermore shown that neural
		  classifiers trained with data reflecting one type of fault
		  mechanism only cannot reliably distinguish between
		  observations with multiple fault conditions present.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hogden93a,
  author	= {John Hogden and Elliot Saltzman and Philip Rubin},
  title		= {Tracking Moving Objects with Unsupervised Neural
		  Networks},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {409--412},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hoglund00a,
  author	= {Hoglund, Albert J. and Hatonen, Kimmo and Sorvari, Antti
		  S.},
  title		= {Computer host-based user anomaly detection system using
		  the Self-Organizing Map},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {411--416},
  organization	= {Nokia Research Cent},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Computer systems are vulnerable to abuse by insiders and
		  to penetration by outsiders. The amount of monitoring data
		  generated in computer networks is enormous. Tools are
		  needed to ease the work of system operators. Anomaly
		  detection attempts to recognize abnormal behavior to detect
		  intrusions. A prototype UNIX Anomaly Detection System has
		  been constructed. The system is host-based and monitors
		  computer network host users. The system contains an
		  automatic anomaly detection component. This component uses
		  a test based on the Self-Organizing Map to test if user
		  behavior is anomalous. Both the test and the application
		  are presented in this paper.},
  dbinsdate	= {2002/1}
}

@InCollection{	  hoglund98a,
  author	= {Albert J. H{\"o}glund and Kimmo H{\"a}t{\"o}nen},
  title		= {Computer Network User Behaviour Visualization Using Self
		  Organizing Maps},
  booktitle	= {Proceedings of ICANN98, the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer},
  year		= 1998,
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= 2,
  address	= {London},
  pages		= {899--904},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  holdaway89a,
  author	= {R. M. Holdaway},
  title		= {Enhancing Supervised Learning Algorithms via
		  Self-Organization},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {II},
  pages		= {523--529 },
  dbinsdate	= {oldtimer}
}

@Article{	  holdaway90a,
  author	= {R. M. Holdaway and M. W. White},
  title		= {Enhancing supervised learning algorithms via
		  self-organization},
  journal	= {Int. J. Neural Networks---Res. \& Applications},
  year		= {1990},
  volume	= {1},
  number	= {4},
  pages		= {227--238},
  x		= {Ilmeisesti artikkeliversio Holdaway89:sta},
  dbinsdate	= {oldtimer}
}

@Article{	  holdaway90b,
  author	= {R. M. Holdaway and M. W. White},
  title		= {Computational neural networks: enhancing supervised
		  learning algorithms via self-organization},
  journal	= {Int. J. Bio-Medical Computing},
  year		= {1990},
  volume	= {25},
  number	= {2--3},
  pages		= {151--167},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hollmen00a,
  author	= {Hollmen, J. and Tresp, V. and Simula, O.},
  title		= {A learning vector quantization algorithm for probabilistic
		  models},
  booktitle	= {Signal Processing X Theories and Applications. Proceedings
		  of EUSIPCO 2000. Tenth European Signal Processing
		  Conference. Tampere Univ. Technology, Tampere, Finland},
  year		= {2000},
  volume	= {2},
  pages		= {721--4},
  abstract	= {In classification problems, it is preferred to attack the
		  discrimination problem directly rather than indirectly by
		  first estimating the class densities and then estimating
		  the discrimination function from the generative models
		  through Bayes' rule. Sometimes, however, it is convenient
		  to express the models as probabilistic models, since they
		  are generative in nature and can handle the representation
		  of high-dimensional data like time-series. In this paper,
		  we derive a discriminative training procedure based on
		  learning vector quantization (LVQ) where the codebook is
		  expressed in terms of probabilistic models. The
		  likelihood-based distance measure is justified using the
		  Kullback-Leibler distance. In updating the winner unit, a
		  gradient learning step is taken with regard to the
		  parameters of the probabilistic model. The method
		  essentially departs from a prototypical representation and
		  incorporates learning in the parameter space of generative
		  models. As an illustration, we present experiments in the
		  fraud detection domain, where models of calling behavior
		  are used to classify mobile phone subscribers to normal and
		  fraudulent users. This is an extension of our earlier work
		  in clustering probabilistic models with the self-organizing
		  map (SOM) algorithm to the classification domain.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hollmen96a,
  author	= {J. Hollm\'en and O. Simula},
  title		= {Prediction Models and Sensitivity Analysis of Industrial
		  Process Parameters by Using the Self-Organizing Map},
  booktitle	= {Proc. IEEE Nordic Signal Processing Symposium
		  (NORSIG'96)},
  year		= {1996},
  pages		= {79--82},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hollmen99a,
  author	= {Hollm\'en, Jaakko and Tresp, Volker and Simula, Olli},
  title		= {Self-Organizing Map for clustering probabilistic models},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  year		= {1999},
  publisher	= {IEE},
  address	= {London, UK},
  volume	= {2},
  pages		= {946--951},
  abstract	= {We present a general framework for Self-Organizing Maps,
		  which store probabilistic models in map units. We introduce
		  the negative log probability of the data sample as the
		  error function and motivate its use by showing its
		  correspondence to the Kullback-Leibler distance between the
		  unknown true distribution of data and our empirical models.
		  We present a general winner search procedure based on this
		  probability measure and an update step based on its
		  gradients. As an application, we derive the learning rules
		  for a particular probabilistic model that is used in user
		  profiling in mobile communications network. Due to the
		  constrained nature of the parameters of our probabilistic
		  model, we introduce a new parameter space, in which the
		  gradient update step is performed. In the experiments, we
		  show clustering of user profiles using calling data
		  involving normal users of mobile phones and users that are
		  known to be victims of fraud. In the summary, we discuss
		  further applications of the approach.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  holmstrom93a,
  author	= {Lasse Holmstr{\"{o}}m and Teuvo Kohonen},
  booktitle	= {Teko{\"{a}}lyn ensyklopedia},
  title		= {Neuraaliverkot},
  publisher	= {Gaudeamus},
  address	= {Helsinki, Finland},
  year		= {1993},
  editor	= {E. Hyv{\"{o}}nen and I. Karanta and M. Syrj{\"{a}}nen},
  pages		= {85--98},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  holmstrom93b,
  author	= {Lasse Holmstr{\"{o}}m and Ari H{\"{a}}m{\"{a}}l{\"{a}}inen},
  title		= {The Self-Organizing Reduced Kernel Density Estimator},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {I},
  pages		= {417--421},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  holmstrom95a,
  author	= {Lasse Holmstr{\"{o}}m and Ari Hottinen and Ari
		  H{\"{a}}m{\"{a}}l{\"{a}}inen},
  title		= {Using a {S}elf-{O}rganizing Kernel Density Estimator for
		  {CDMA} Communications},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {445--448},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  holmstrom96a,
  author	= {L. Holmstr\"om and P. Koistinen and J. Laaksonen and E.
		  Oja},
  title		= {Neural Network and Statistical Perspectives of
		  Classification},
  booktitle	= {Proc. 13th International Conference on Pattern
		  Recognition},
  year		= {1996},
  volume	= {IV},
  pages		= {286--290},
  dbinsdate	= {oldtimer}
}

@TechReport{	  holmstrom96b,
  author	= {L. Holmstr\"om and P. Koistinen and J. Laaksonen and E.
		  Oja},
  title		= {Comparison of Neural and Statistical Classifiers---Theory
		  and Practice},
  institution	= {University of Helsinki, Rolf Nevanlinna Institute},
  year		= {1996},
  number	= {A13},
  address	= {Helsinki, Finland},
  dbinsdate	= {oldtimer}
}

@Article{	  holmstrom97a,
  author	= {Lasse Holmstr{\"o}m and Petri Koistinen and Jorma
		  Laaksonen and Erkki Oja},
  title		= {Neural and Statistical Classifiers---Taxonomy and Two Case
		  Studies},
  journal	= {IEEE Transactions on Neural Networks},
  year		= 1997,
  volume	= 8,
  pages		= {5--17},
  dbinsdate	= {oldtimer}
}

@Article{	  holthausen97a,
  author	= {Klaus Holthausen and Olaf Breidbach},
  title		= {Self-organized feature maps and information theory},
  journal	= {Network: Computation in Neural Systems},
  year		= 1997,
  volume	= 8,
  pages		= {215--227},
  dbinsdate	= {oldtimer}
}

@Article{	  holubar00a,
  author	= {Holubar, P. and Zani, L. and Hager, M. and Froschl, W. and
		  Radak, Z. and Braun, R.},
  title		= {Modelling of anaerobic digestion using self-organizing
		  maps and artificial neural networks},
  journal	= {Water Science and Technology},
  year		= {2000},
  volume	= {41},
  number	= {12},
  month		= {},
  pages		= {149--156},
  organization	= {Univ of Agricultural Sciences},
  publisher	= {Int Water Assoc},
  address	= {},
  abstract	= {In this work the training of a self-organizing map and a
		  feed-forward back-propagation neural network was made. The
		  aim was to model the anaerobic digestion process. To
		  produce data for the training of the neural nets an
		  anaerobic digester was operated at steady state and
		  disturbed by pulsing the organic loading rate. Measured
		  parameters were: gas composition, gas production rate,
		  volatile fatty acid concentration, pH, redox potential,
		  volatile suspended solids and chemical oxygen demand of
		  feed and effluent. It could be shown that both types of
		  self-learning networks in principle could be used to model
		  the process of anaerobic digestion. Using the unsupervised
		  Kohonen self-organizing map, the model's predictions could
		  not follow the measurements in all details. This resulted
		  in an unsatisfactory regression coefficient of
		  R<sup>2</sup> = 0.69 for the gas composition and
		  R<sup>2</sup> = 0.76 for the gas production rate. When the
		  supervised FFBP neural net was used the training resulted
		  in more precise predictions. The regression coefficient was
		  found to be R<sup>2</sup> = 0.74 for the gas composition
		  and R<sup>2</sup> = 0.92 for the gas production rate.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  homayounpour95a,
  author	= {Homayounpour, M. M. and Chollet, G. },
  title		= {Neural net approaches to speaker verification: comparison
		  with second order statistic measures},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  year		= {1995},
  volume	= {1},
  pages		= {353--6},
  organization	= {URA, CNRS, Paris, France},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {The non-supervised Self Organizing Map of Kohonen (SOM),
		  the supervised Learning Vector Quantization algorithm
		  (LVQ3) [1], and a method based on Second-Order Statistical
		  Measures (SOSM) [2] were adapted, evaluated and compared
		  for speaker verification on 57 speakers of a POLYPHONE-like
		  data base. SOM and LVQ3 were trained by codebooks with 32
		  and 256 codes and two statistical measures; one without
		  weighting (SOSM1) and another with weighting (SOSM2) were
		  implemented. As decision criterion, the Equal Error Rate
		  (EER) and Best Match Decision Rule (BMDR) were employed and
		  evaluated. The weighted Linear Predictive Cepstrum
		  coefficients LPCC and the Delta LPCC were used jointly as
		  two kinds of spectral speech representations in a single
		  vector as distinctive features. LVQ3 demonstrates a
		  performance advantage over SOM. This is due to the fact
		  that LVQ3 allows the long-term fine-tuning of an interested
		  target codebook using speech data from a client and other
		  speakers, whereas SOM only uses data from the client. SOSM
		  performs better than SOM and LVQ3 for long test utterances,
		  while for short test utterances LVQ is the best method
		  among the methods studied here.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hong00a,
  author	= {Hong, Liu and Yulong, Mo},
  title		= {Modified sampling frequency-sensitive network based on
		  evolutionary programming for pattern clustering},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {4120},
  pages		= {229--237},
  organization	= {Shanghai Univ},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {In this paper, a modified evolutionary programming-based
		  sampling frequency-sensitive network is proposed for
		  pattern clustering. Many researchers study the neural
		  networks for pattern clustering recently. The Kohonen
		  Feature Maps (KFM) network and BP neural network are
		  examples. But there are some problems with these models.
		  For example, the network has a complicated structure and
		  large amount of neurons. The neural network usually gets in
		  unexpected local optimal solution. The results of pattern
		  classification often correlate with the initial conditions.
		  The fixed neural network structure is the major
		  disadvantage for pattern clustering where the optimal
		  number of patterns is unknown. Willie Chang presented a
		  sampling frequency-sensitive network in 1997. The model has
		  the advantages of simple structure and simple learning
		  rules. But it also has fixed architecture. The algorithm is
		  proposed in this paper which effectively uses the sampling
		  frequency-sensitive network and the powerful parallel
		  search optimization tool EP (evolutionary programming)
		  which is presented by Fogel,D.B.. The modified Hubert index
		  and cluster splitting and merging algorithm are used in
		  network architecture evolution. The rule of minimum mean
		  square error is used to get the optimal parameters. The
		  proposed method has an advantage of that the optimal
		  solution of neural network architecture and parameters can
		  be get simultaneously. So the classification network can
		  get the optimal number of clusters and the optimal vector
		  quantization. The results of the experiment are given to
		  prove that the neural network architecture can be changed
		  for real world problems and get the optimal results.},
  dbinsdate	= {2002/1}
}

@Article{	  hong01a,
  author	= {Hong, Liang Chen and Lin, Lin Shen},
  title		= {Auto-map reading based on Kohonen neural network},
  journal	= {Mini-Micro-Systems},
  year		= {2001},
  volume	= {22},
  pages		= {1464--6},
  abstract	= {A method of recognizing the color map and building the map
		  database by Neural Network is given. First train the NN by
		  color data, then recognize and extract the road through the
		  trained NN and filter the noise using a new algorithm, at
		  last build the map database. The result of experiment is
		  given at the end.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name assumed, similarly to other papers in 
                   Mini-Micro-Systems}
}

@InCollection{	  hong97a,
  author	= {G. S. Hong and M. Rahman and Q. Zhou},
  title		= {Tool condition monitoring using neural networks},
  booktitle	= {26th International Symposium on Industrial Robots.
		  Symposium Proceedings. Competitive Automation: New
		  Frontiers, New Opportunities},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {J. Komorowski and J. Zytkow},
  address	= {Berlin, Germany},
  pages		= {455--60},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  honkela00a,
  author	= {Honkela, T.},
  title		= {Adaptive and holistic knowledge representations using
		  self-organizing maps},
  booktitle	= {16th World Computer Congress 2000. Proceedings of
		  Conference on Intelligent Information Processing.
		  Publishing House of Electron. Ind, Beijing, China},
  year		= {2000},
  volume	= {},
  pages		= {81--6},
  abstract	= {This paper discusses the need for adaptivity and gradience
		  in knowledge representation systems. Problems related to
		  predetermined classification systems and to use of fixed
		  semantic primitives are presented. The qualities of the
		  Kohonen's self-organizing map algorithm are considered as
		  knowledge representation formalism useful especially in
		  complex and conceptually dynamic domains.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  honkela91a,
  author	= {Timo Honkela and Ari M. Veps{\"{a}}l{\"{a}}inen},
  title		= {Interpreting Imprecise Expressions: Experiments with
		  {K}ohonen's Self-Organizing Maps and Associative Memory},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {897--902},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  honkela93a,
  author	= {Timo Honkela},
  title		= {Neural Nets that Discuss: A General Model of Communication
		  Based on Self-Organizing Maps},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {408--411},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  honkela95a,
  author	= {Timo Honkela and Ville Pulkki and Teuvo Kohonen},
  title		= {Contextual Relations of Words in {G}rimm Tales, Analyzed
		  by Self-Organizing Map},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {3--7},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Book{		  honkela96a,
  author	= {Honkela, T. and Kaski, S. and Lagus, K. and Kohonen, T.},
  title		= {Newsgroup Exploration with {WEBSOM} Method and Browsing
		  Interface. Research rept.},
  year		= {1996},
  abstract	= {The current availability of large collections of full-text
		  documents in electronic form emphasizes the need for
		  intelligent information retrieval techniques. In the
		  report, we introduce the WEBSOM method for this task.
		  Self-Organizing Maps (SOMs) are used to position encoded
		  documents onto a map that provides a general view into the
		  text collection. The general view visualizes similarity
		  relations between the documents on a map display, which can
		  be utilized in exploring the material rather than having to
		  rely on traditional search expressions. Similar documents
		  become mapped close to each other. The potential of the
		  WEBSOM method is demonstrated in a case study where
		  articles from the Usenet newsgroup 'comp.ai.neural-nets'
		  are organized.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  honkela96b,
  author	= {Timo Honkela and Samuel Kaski and Krista Lagus and Teuvo
		  Kohonen},
  title		= {Newsgroup exploration with {WEBSOM} method and browsing
		  interface},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= 1996,
  number	= {A32},
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InCollection{	  honkela96c,
  author	= {Timo Honkela and Samuel Kaski and Krista Lagus and Teuvo
		  Kohonen},
  title		= {Exploration of full-text databases with
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings of the ICNN96, International Conference on
		  Neural Networks},
  publisher	= {IEEE Service Center},
  year		= 1996,
  volume	= {I},
  address	= {Piscataway, NJ},
  pages		= {56--61},
  dbinsdate	= {oldtimer}
}

@InCollection{	  honkela97a,
  author	= {Timo Honkela},
  title		= {Comparisons of self-organized word category maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {298--303},
  dbinsdate	= {oldtimer}
}

@InCollection{	  honkela97b,
  author	= {Timo Honkela and Samuel Kaski and Krista Lagus and Teuvo
		  Kohonen},
  title		= {{WEBSOM}-- \mbox{self-organizing} maps of document
		  collections},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {310--315},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  honkela97c,
  author	= {Timo Honkela},
  title		= {Self-Organizing Maps in Natural Language Processing},
  school	= {Helsinki University of Technology},
  year		= 1997,
  address	= {Espoo, Finland},
  abstract	= {Kohonen's Self-Organizing Map (SOM) is one of the most
		  popular artificial neural network algorithms. Word category
		  maps are SOMs that have been organized according to word
		  similarities, measured by the similarity of the short
		  contexts of the words. The central topic of the thesis is
		  the use of the SOM in natural language processing. The
		  approach based on the word category maps is compared with
		  the methods that are widely used in artificial intelligence
		  research. Modeling gradience, conceptual change, and
		  subjectivity of natural language interpretation are
		  considered. The main application area is information
		  retrieval and textual data mining for which a specific
		  SOM-based method called the WEBSOM method organizes a
		  document collection on a map considered. The main
		  application area is information retrieval and textural data
		  mining for which a specific SOM-based method called the
		  WEBSOM has been developed. The WEBSOM method organizes a
		  document collection on a map display that provides an
		  overview of the collection and facilitates interactive
		  browsing.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  honkela98a,
  author	= {T. Honkela and S. Kaski and T. Kohonen and K. Lagus},
  title		= {Self-Organizing Maps of Very Large Document Collections:
		  Justification for the {WEBSOM} method},
  booktitle	= {Classification, Data Analysis, and Data Highways},
  publisher	= {Springer},
  year		= 1998,
  editor	= {I. Balderjahn and R. Mathar and M. Schader},
  pages		= {245--252},
  address	= {Berlin},
  abstract	= {Powerful methods are needed for interactive exploration
		  and searching in the collections of miscellaneous textual
		  documents that are available in the electronic media.
		  Searching in text documents has traditionally been based on
		  keywords and Boolean expressions. With the WEBSOM (WWW
		  Exploration and Browsing Self-Organizing Map) method, a
		  document collection may be organized into a map display
		  that provides an overview of the collection and facilitates
		  interactive browsing. Interesting documents can be
		  retrieved by a content-addressable search. The WEBSOM
		  method is based on using the self-organizing map algorithm
		  for automatically learning relevant structures in the text
		  and for organizing the document collection.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  honkela98b,
  author	= {Honkela, T.},
  title		= {Learning to understand-general aspects of using
		  \mbox{self-organizing} maps in natural language processing},
  booktitle	= {AIP Conference Proceedings},
  year		= {1998},
  volume	= {},
  number	= {437},
  pages		= {563--76},
  abstract	= {The self-organizing map (SOM) is an artificial neural
		  network model based on unsupervised learning. In this
		  paper, the use of the SOM in natural language processing is
		  considered. The main emphasis is on natural features of
		  natural language including contextuality of interpretation,
		  and the communicative and social aspects of natural
		  language learning and usage. The SOM is introduced as a
		  general method for the analysis and visualization of
		  complex, multidimensional input data. The approach of how
		  to process natural language input is presented. Some
		  epistemological underpinnings are outlined, including the
		  creation of emergent and implicit categories by SOM,
		  intersubjectivity and relativity of interpretation, and the
		  relation between discrete symbols and continuous variables.
		  Finally, the use of SOM as a component in an anticipatory
		  system is presented, and the relation between anticipation
		  and self-organization is discussed.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  honkela98c,
  author	= {T. Honkela and K. Lagus and S. Kaski},
  title		= {Self-Organizing Maps of Large Document Collections},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {168--178},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  honkela99a,
  author	= {Honkela, T.},
  title		= {Connectionist analysis and creation of context for natural
		  language understanding and knowledge management},
  booktitle	= {Modeling and Using Context. Second International and
		  Interdisciplinary Conference, CONTEXT'99. Proceedings
		  (Lecture Notes in Artificial Intelligence Vol.1688)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {479--82},
  abstract	= {Context affects many aspects of behavior. Natural language
		  understanding is one of the prime examples. The paper
		  summarizes how an artificial neural network, the
		  self-organizing map, can be used in modeling contextuality
		  in data analysis and natural language processing. Important
		  aspects are adaptivity gained by using a learning system,
		  autonomous nature of the processing based on unsupervised
		  learning paradigm, and gradedness of the representation.
		  Examples in the application areas of information retrieval
		  and knowledge management are considered. For instance, the
		  visualization of self-organizing maps provides meaningful
		  context for documents.},
  dbinsdate	= {oldtimer}
}

@Article{	  horikawa97a,
  author	= {S. Horikawa},
  title		= {Fuzzy classification system using \mbox{self-organizing}
		  feature map},
  journal	= {Oki Technical Review},
  year		= {1997},
  volume	= {63},
  number	= {159},
  pages		= {23--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  horio00a,
  author	= {K. Horio and T. Yamakawa},
  title		= {Adaptive Self-Organizing Relationship Network and Its
		  App,lication to Adaptive {CO}ntrol},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {299--304},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@Article{	  horio01a,
  author	= {Horio, K. and Yamakawa, T.},
  title		= {Feedback self-organizing map and its application to
		  spatio-temporal pattern classification},
  journal	= {International-Journal-of-Computational-Intelligence-and-Applications}
		  ,
  year		= {2001},
  volume	= {1},
  pages		= {1--18},
  abstract	= {In this paper, a feedback self-organizing map (FSOM),
		  which is an extension of the self-organizing map (SOM) by
		  employing feedback loops, is proposed. The SOM consists of
		  an input layer and a competitive layer, and the input
		  vectors applied to the input layer are mapped to the
		  competitive layer keeping their spatial features. In order
		  to embed the temporal information to the SOM, feedback
		  loops from the competitive layer to the input layer are
		  employed. The winner unit in the competitive layer is not
		  assigned by only current input vector but also past winner
		  units, thus the temporal information can be embedded. The
		  effectiveness and validity of the proposed FSOM are
		  verified by applying it to a spatio-temporal pattern
		  classification.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  horio99a,
  author	= {Keiichi Horio and Takuma Haraguchi and Takeshi Yamakawa},
  title		= {An Intuitive Contrast Enchantmet of an Image Data
		  Employing the Self-Organizing Relationship ({SOR})},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  abstract	= {The user intuition of an image is very important factor to
		  evaluate an image. In this paper, the new image enhancement
		  method, which provides the enhanced image satisfying the
		  intuitive evaluation of the user, is proposed. The
		  self-organizing relationship (SOR) network proposed by the
		  authors is employed in order to obtain the relationship,
		  which reflects the user intuition, between intensity
		  histogram of an original image and in density mapping
		  curve. The SOR network can construct the relationship,
		  which corresponds to the arbitary evaluation, between input
		  vector and output vector by the learning. Employing the
		  user intuition as the evaluation, the relationship
		  corresponding to the user intuition is obtained. The
		  intensity histogram of original images and the intensity
		  mapping curve are employed as the input vector and the
		  output vector of the SOR network, the relationship between
		  them is constructed by SOR network. The intensity histogram
		  of an image is applied to the SOR network after the
		  learning, the intensity mapping curve which reflects the
		  user intution is generated.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  horio99b,
  author	= {Keiihci Horio and Ichiro Masui and Takeshi Yamakawa and
		  Takeshi Honda},
  title		= {Self-Organising Map Considering Correlation between
		  Elements in the Input Vector and Its Application to the
		  Clasiification of Facial Profile},
  booktitle	= {Proseedings of the 12th Annual Meeting of Biomedical Fuzzy
		  Systems Association},
  year		= {1999},
  pages		= {91--94},
  abstract	= {In this paper, we propose the novel self-organising map
		  (SOM), in which the correlation between the elements in the
		  input vector is considered. It is not considered that the
		  unneccessary or redundant elements might be included in the
		  input vector, and thus the distance between the input
		  vector and the weight vectors can be an appropriate measure
		  of distance. We propose the new self-organizing map
		  algorithm, in which the distance between the input vector
		  and the weight vectors are calculated considering
		  correlation between elements. The effectiveness and
		  varidity of the proposed method is verified by applying it
		  to classification of the facial profiles.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  horio99c,
  author	= {Keiichi Horio and Takeshi Yamakawa},
  title		= {A Recurrent Self-Organizing Map Including Feedback Loops
		  for Classification of Spatio-Temporal Patterns},
  booktitle	= {Emerging Knowledge Engineering and Connectionist-Based
		  Information Systems. Proceedings of the
		  ICONIP/ANZIIS/ANNES'99},
  editor	= {Nikola Kasabov and Kitty Ko},
  year		= {1999},
  pages		= {13--16},
  abstract	= {In this paper, the recurrent self-organizing map (RSOM)
		  employing feedback loops is proposed. It consists of the
		  input layer, the competitive layer and the feedback layer.
		  The outputs of the units in the competitive layer are fed
		  back to the input layer through the feedback layer. The
		  winner unit is not assigned by only current input vector
		  but also past winner units, thus the temporal information
		  can be embedded naturally. The effectiveness and validity
		  of the proposed RSOM is verified by applying it to a
		  spatio-temporal pattern classification.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  horio99d,
  author	= {Keiichi Horio and Takeshi Yamakawa},
  title		= {Self-Oraganizing Realtionship ({SOR}) network---Equivalent
		  to a Fuzzy Inference with Automatically Extracted Fuzzy
		  If-Then Rules},
  booktitle	= {1999 International Symposium on Nonlinear Theory and its
		  Applications (NOLTA'99) (Hawaii, USA, Nov. 28---Dec. 2,
		  1999)},
  year		= {1999},
  pages		= {53--6},
  abstract	= {In this paper, the novel mapping network named
		  self-organizing relationship (SOR) network, which can
		  approximate the desired I/O relationship by employing the
		  modified Kohonen's learning law, is proposed. In the
		  execution mode, SOR network acts like fuzzy inference. In
		  the disscussion, we compare the execution mode of the SOR
		  network to the fuzzy modeling.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  horio99e,
  author	= {Keiichi Horio and Takeshi Yamakawa},
  title		= {A recurrent Self-Organizing Map and Its Applications to a
		  Spatio-temporal Pattern Regognition},
  booktitle	= {Mathematical Modeling of Nonlinear Systems},
  editor	= {J. C. Misra and S. B. Sihna},
  volume	= {2},
  year		= {1999},
  pages		= {11--14},
  abstract	= {In this paper, the recurrent self-organizing map (RSOM),
		  which is an extension of Kohonen's self-organising map
		  (SOM) by employing the feedback loops, is proposed. The
		  RSOM consists of the input layer, the competitive layer and
		  the feedback layer. The output values of the units in the
		  competitive layer are fed back to the input layer through
		  the feedback layer. The winner unit is not assigned by only
		  current input vector but also past winner units. The
		  effectiveness and validity of the proposed RSOM is verified
		  by applying it to a spatio-temporal pattern
		  classification.},
  keywords	= {recurrent self-organising map, spatio-temporal
		  recognition, feedback, past winner units},
  dbinsdate	= {2002/1}
}

@InProceedings{	  horio99f,
  author	= {Keiichi Horio and Takuma Haragushi and Takeshi Yamakawa},
  title		= {Intensity Transformation Employing Self-Organizing
		  Relationship Network},
  booktitle	= {Proceedings of the 1st SOFT Kyushu Branch Annual
		  Conference},
  year		= {1999},
  pages		= {81--86},
  note		= {in Japanese},
  abstract	= {In this paper, a new image intensity transformation method
		  which employs the Self-Organizing Relationship (SOR)
		  network, is proposed. SOR network can construct the desired
		  input/output relationship of the target systems by the
		  learning. In the learning of SOR network, the arbitary
		  examples and its evaluation which is based on the
		  evaluation function or user intuition is used. After the
		  learning, SOR network operates as the input/output
		  relationship generator using the relationship which is
		  obtained by the learning. In the proposed method, SOR
		  network construct the relationship between intensity
		  histogram of an original image and intensity mapping curve
		  which is used for the intensity transformation of the
		  image. We apply this method to the contrast enhancement of
		  the image.},
  dbinsdate	= {2002/1}
}

@Article{	  horio99g,
  author	= {Keiichi Horio and Takuma Haraguchi and Takeshi Yamakawa},
  title		= {Image Enhancement Employing Subjectivity Based
		  Self-Organizing Relationship ({SOR}) Network},
  journal	= {Biomedical Fuffy Systems},
  year		= {1999},
  volume	= {1},
  number	= {1},
  pages		= {79--86},
  note		= {in Japanese},
  abstract	= {In almost of conventional image enhancement methods, the
		  intensity mapping curve based on the intensity histogram is
		  generated, and the intensity of each pixel in an original
		  image is transformed. In the process, the evaluation
		  function is defined, and the intensity mapping curve is
		  tuned in order to satisfy the function. However, user
		  impression for an image is based on his subjectivity, it
		  can not be represented by the objective evaluation
		  function. In this paper, the Self-Organizing Relationship
		  (SOR) network, which can costruct the input/output
		  relationship based on the subjective evaluation, is
		  proposed, and it is applied to obtain the relationship
		  between the intensity histogram and intensity mapping curve
		  based on the user subjectivity. The images transformed by
		  the conventional methods and the proposed method are showed
		  to seven users, almost users answered that the images
		  transformed by the proposed method are suitable for their
		  preference.},
  keywords	= {Self-Organizing, Input/Output Relationship, Evaluation
		  based on Subjectivity, Image, Intensity Histogram,
		  Intensity Mapping Curve},
  dbinsdate	= {2002/1}
}

@Article{	  hornik92a,
  author	= {Kurt Hornik and Chung-Ming Kuan},
  title		= {Convergence Analysis of Local Feature Extraction
		  Algorithms},
  journal	= {Neural Networks},
  volume	= 5,
  year		= 1992,
  pages		= {229--240},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  horowitz95a,
  author	= {Horowitz, R. and Alvarez, L. },
  title		= {Convergence properties of \mbox{self-organizing} neural
		  networks},
  booktitle	= {Proceedings of the 1995 American Control Conference},
  year		= {1995},
  volume	= {2},
  pages		= {1339--44},
  organization	= {Dept. of Mech. Eng. , California Univ. , Berkeley, CA,
		  USA},
  publisher	= {American Autom Control Council},
  address	= {Evanston, IL, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  horowitz96a,
  author	= {R. Horowitz and L. Alvarez},
  title		= {Self-organizing neural networks: convergence properties},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {7--12},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hortos01a,
  author	= {Hortos, W. S.},
  title		= {Hybrid evolutionary computing model for mobile agents of
		  wireless internet multimedia},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Priddy, K. L. and Keller, P. E. and Angeline, P. J.},
  volume	= {4390},
  pages		= {62--76},
  organization	= {Florida Institute of Technology, Orlando Graduate Center},
  publisher	= {},
  address	= {},
  abstract	= {The ecosystem is used as an evolutionary paradigm of
		  natural laws for the distributed information retrieval via
		  mobile agents to allow the computational load to be added
		  to server nodes of wireless networks, while reducing the
		  traffic on communication links. Based on the Food Web
		  model, a set of computational rules of natural balance form
		  the outer stage to control the evolution of mobile agents
		  providing multimedia services with a wireless Internet
		  protocol (WIP). The evolutionary model shows how mobile
		  agents should behave with the WIP, in particular, how
		  mobile agents can cooperate, compete and learn from each
		  other, based on an underlying competition for radio network
		  resources to establish the wireless connections to support
		  the quality of service (QoS) of user requests. Mobile
		  agents are also allowed to clone themselves, propagate and
		  communicate with other agents. A two-layer model is
		  proposed for agent evolution: the outer layer is based on
		  the law of natural balancing, the inner layer is based on a
		  discrete version of a Kohonen self-organizing feature map
		  (SOFM) to distribute network resources to meet QoS
		  requirements. The former is embedded in the higher OSI
		  layers of the WIP, while the latter is used in the resource
		  management procedures of Layers 2 and 3 of the protocol.
		  Algorithms for the distributed computation of mobile agent
		  evolutionary behavior are developed by adding a "learning"
		  state to the agent evolution state diagram. When an agent
		  is in an indeterminate state, it can communicate to other
		  agents. Computing methods can be replicated from other
		  agents. Then the agent transitions to the "mutating" state
		  to wait for a new information-retrieval goal. When a
		  wireless terminal or station lacks a network resource, an
		  agent in the "suspending" state can change its policy to
		  submit to the environment before it transitions to the
		  searching state. The agents "learn" the facts of agent
		  state information entered into an external database. In the
		  cloning process, two agents on a host station sharing a
		  common goal can be merged or "married" to compose a new
		  agent. Application of the two-layer set of algorithms for
		  mobile agent evolution, performed in a distributed
		  processing environment, is made to the QoS management
		  functions of the IP multimedia (IM) subnetwork of the
		  third-generation (3G) Wideband Code-division Multiple
		  Access (W-CDMA) wireless network.},
  dbinsdate	= {2002/1}
}

@Article{	  hortos95a,
  author	= {Hortos, W. S. },
  title		= {Application of neural networks to the dynamic spatial
		  distribution of nodes within an urban wireless network},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2492},
  number	= {pt. 1},
  pages		= {58--70},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  hortos98a,
  author	= {W. S. Hortos},
  title		= {\mbox{Self-organizing} feature maps for dynamic control of
		  radio resources in {CDMA} microcellular networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1998},
  volume	= {3390},
  pages		= {378--91},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hortos98b,
  author	= {W. S. Hortos},
  title		= {\mbox{Self-organizing} feature maps for dynamic control of
		  radio resources in {CDMA} {PCS} networks},
  booktitle	= {Proceedings of Virginia Tech's Eighth Symposium on
		  Wireless Personal Communications},
  publisher	= {Virginia Tech},
  year		= {1998},
  address	= {Blacksburg, VA, USA},
  pages		= {129--42},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hortos99a,
  author	= {Hortos, W. S.},
  title		= {Cascaded neural networks for sequenced propagation
		  estimation, multiuser detection, and adaptive radio
		  resource control of third-generation wireless networks for
		  multimedia services},
  booktitle	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3722},
  pages		= {261--75},
  abstract	= {A hybrid neural network approach is presented to estimate
		  radio propagation characteristics and multiuser
		  interference and to evaluate their combined impact on
		  throughput, latency and information loss in
		  third-generation (3G) wireless networks. A cascade model of
		  a MLP-NN for channel estimation, a recursive NN for
		  adaptive antenna control, a discrete-form Hopfield NN (HNN)
		  for joint multiuser detection, and a discrete-space Kohonen
		  self organising feature map (SOFM) is proposed for the
		  problem of allocating radio resources to meet the QoS
		  requirements of multimedia service demands in 3G wireless
		  networks. W-CDMA network parameters on the uplinks are
		  assumed to model the resources available to support the
		  diverse SIR and delay requirements for variable-rate audio,
		  high-rate packet data, and real-time video. Simulation
		  results for the performance of each of the first three NN
		  stages are presented for representative W-CDMA scenarios,
		  Finally, both the static and dynamic versions of the
		  complete NN cascade algorithm are simulated for the RRA of
		  multimedia extensions of published cellular network models.
		  The simulation results are informally compared to earlier
		  published results for single-stage HNN and SOFM techniques
		  applied to resource allocation in single-service voice
		  networks.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hortos99b,
  author	= {Hortos, W. S.},
  title		= {Adaptive radio resource control via cascaded neural
		  networks for sequenced propagation estimation and
		  multi-user detection in third-generation wireless
		  networks},
  booktitle	= {Virginia Tech's Ninth Symposium on Wireless Personal
		  Communications. Proceedings. Virginia Tech, Blacksburg, VA,
		  USA},
  year		= {1999},
  volume	= {},
  pages		= {113--33},
  abstract	= {A hybrid neural network approach is presented to predict
		  radio propagation characteristics and multi-user
		  interference and to evaluate their combined impact on
		  throughput, latency and information loss in
		  third-generation (3G) wireless networks. The three
		  performance parameters influence the quality of service
		  (QoS) for multimedia services for 3G networks. Candidate
		  radio interfaces for these networks employ a form of
		  wideband CDMA. Parameters of the radio propagation channel
		  are estimated, followed by control of an adaptive antenna
		  array at the base station to minimize interference, and
		  then joint multiuser detection is performed at the base
		  station receiver. These NN techniques provide their
		  estimates as inputs to a final-stage Kohonen
		  self-organizing feature map (SOFM). As the first stage of
		  the sequence, a modified feedforward multilayer perceptron
		  NN is trained on the pilot signals of the mobile
		  subscribers to estimate the parameters of shadowing,
		  multipath fading and delays on the uplinks. A recurrent NN
		  (RNN) forms the second stage to control base station
		  adaptive antenna arrays in order to minimize intra-cell
		  interference. The third stage is based on a Hopfield NN
		  (HNN), modified to detect multiple users on the uplink
		  radio channels to mitigate multiaccess interference,
		  control carrier-sense multiple-access (CSMA) protocols, and
		  refine handoff procedures. In the final stage, the SOFM,
		  operating in a hybrid continuous and discrete space,
		  adaptively allocates resources of antenna-based cell
		  sectorization, activity monitoring, variable-rate coding,
		  power control, handoff and caller admission to meet the QoS
		  for various multimedia services.},
  dbinsdate	= {2002/1}
}

@Article{	  hosaka02a,
  author	= {Hosaka, K. and Goya, T. and Umehara, D. and Kawai, M.},
  title		= {An efficient method for network topology identification
		  based on {SOM} algorithm},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-C}
		  ,
  year		= {2002},
  volume	= {122},
  pages		= {208--16},
  abstract	= {In this paper we consider a large number of wireless
		  terminals that are interconnected by a multihop wireless
		  network called an ad-hoc network. Design of routing
		  protocols is a crucial problem in ad-hoc networks. Location
		  information of wireless terminals is an effective measure
		  for ad-hoc network routing. This paper presents a method to
		  identify network topology implying terminal location and
		  connections among terminals. A modified self-organizing map
		  (SOM) algorithm is proposed to apply to the network
		  topology identification. This method exploits information
		  of the received power levels of the signals that are
		  transmitted by other terminals. This paper has evaluated
		  how network topology is identified by using an example
		  graph made by random numbers. The results show that only
		  one bit information about the received power level in each
		  terminal can identify network topology accurately with
		  average error of about 10% for more terminals than a
		  certain value.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hosokawa01a,
  author	= {Hosokawa, Masafumi and Hoshi, Takashi},
  title		= {Landform classification method using self-organizing map
		  and its application to earthquake damage evaluation},
  booktitle	= {International Geoscience and Remote Sensing Symposium
		  (IGARSS)},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {1684--1686},
  organization	= {Earthquake Disaster Section, Natl. Res. Inst. of
		  Fire/Disaster},
  publisher	= {Institute of Electrical and Electronics Engineers Inc.},
  address	= {},
  abstract	= {This paper presents a supervised classification method
		  using a self-organizing map(SOM) to classify typical
		  landforms based on a land cover map and a Digital Elevation
		  Model (DEM). The proposed method classified the landform of
		  Kobe city in Japan into hill, plateau, fan and reclaimed
		  land. These classified landforms were adopted for an
		  earthquake damage evaluation of the 1995 Hyogoken Nanbu
		  earthquake in Kobe. First, an amplification value for each
		  landform was assumed in order to calculate the peak ground
		  velocity(V<sub>max</sub>) of the ground motion in the study
		  area. Next, using empirical prediction methods based upon
		  the relationship between V<sub>max</sub> and the number of
		  wooden houses damaged by past earthquakes, we can calculate
		  predicted number of collapsed wooden houses. As a result,
		  we can obtain damage distribution map that corresponds with
		  the actual damage recorded following the 1995 earthquake.},
  dbinsdate	= {2002/1}
}

@Article{	  hosokawa01b,
  author	= {Hosokawa, M. and Ito, Y. and Hoshi, T.},
  title		= {Extraction of urban characteristics using polarimetric
		  {SAR} data and self-organizing map},
  journal	= {Transactions-of-the-Institute-of-Electronics,-Information-and-Communication-Engineers-B}
		  ,
  year		= {2001},
  volume	= {},
  pages		= {1043--51},
  abstract	= {For preventing urban fire disaster, it is important to
		  extract urban characteristics composed of structures,
		  vegetation and open space per unit area. In this paper, we
		  propose a supervised classifier which discriminates
		  polarimetric SAR data into the three categories using a
		  self-organizing map (SOM) and a counter propagation
		  learning approach after identifying one of the scattering
		  classes. The proposed classifier produces category maps
		  corresponding to the Kohonen layers using training data for
		  each scattering class. The SAR data is classified by
		  inputting both like and cross polarization power elements
		  into the learned SOM. In the experiment, PI-SAR data are
		  employed since the resolution of airborne SAR data is
		  higher than that of spaceborne SAR data. As a result of the
		  classification, the proposed classifier produces higher
		  accuracies than the conventional methods.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hosokawa99a,
  author	= {Hosokawa, Masafumi and Ito, Yosuke and Hoshi, Takashi},
  title		= {Remote sensing data classification method using
		  \mbox{self-organizing} map},
  booktitle	= {IEEE 1999 International Geoscience and Remote Sensing
		  Symposium. IGARSS'99.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1606--1608},
  abstract	= {A supervised classification method using a self-organizing
		  map (SOM) is proposed to classify remote sensing data. The
		  SOM structure is composed of two layers. One is an input
		  layer with nodes corresponding to spectral bands. The other
		  is an output layer with square array of nodes. First, a
		  feature map on the output layer is generated by inputting
		  training data into SOM. Each node in the feature map cannot
		  be corresponding to a category because the number of nodes
		  is generally greater than those of training data. Thus, a
		  cluster map is generated by comparing differentials among
		  weight vectors in nodes. Secondly, the training data is
		  re-inputted into the cluster map to find the relationship
		  between clusters and categories, that is, the cluster
		  including a fired node is labeled as the category to which
		  the training data belongs. In consequence of mapping, the
		  category map is obtained from the feature map. The proposed
		  classification method extracts liquefied area in Kobe
		  (Japan) damaged by the 1995 Hyogoken Nanbu earthquake using
		  the SPOT HRV data and the category map. As an experimental
		  result, it is shown that classification accuracies of the
		  proposed method are higher than those of the maximum
		  likelihood and the back-propagation methods.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hottinen94a,
  author	= {Ari Hottinen},
  title		= {Self-Organizing Multiuser Detection},
  booktitle	= {Proc. IEEE ISSTA'94, 3rd Int. Symposium on Spread Spectrum
		  Techniques {\&} Applications},
  year		= {1994},
  pages		= {152--156},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  hougen93a,
  author	= {Hougen, D. },
  title		= {Use of an eligibility trace to self-organize output},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1993},
  volume	= {1966},
  pages		= {436--47},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  howard98a,
  author	= {Howard, D. and Roberts, S. C. and Brankin, R.},
  title		= {Evolution of ship detectors for satellite {SAR} imagery},
  booktitle	= {Genetic Programming. Second European Workshop, EuroGP'99.
		  Proceedings},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1998},
  volume	= {},
  pages		= {135--48},
  abstract	= {A two-stage evolution scheme is proposed to obtain an
		  object-detector for an image analysis task, and is applied
		  to the problem of ship detection by inspection of the {SAR}
		  images taken by satellites. The scheme: (1) affords
		  practical evolution times, (2) is structured to discover
		  fast automatic detectors, (3) can produce small detectors
		  that shed light into the nature of the detection. Detectors
		  compare favorably in accuracy to those obtained using a SOM
		  neural network.},
  dbinsdate	= {oldtimer}
}

@Article{	  howell94a,
  author	= {E. S. Howell and E. Mere{\'n}yi and L. A. Lebofsky},
  title		= {Using Neural Networks to Classify Asteroid Spectra},
  journal	= {Journal Geogr. Res. },
  year		= 1994,
  volume	= 99,
  pages		= {10,847--10,865},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hrycej90a,
  author	= {T. Hrycej},
  title		= {Self-Organization by Delta Rule},
  booktitle	= {Proc. IJCNN'90, International Joint Conference on Neural
		  Networks, San Diego},
  year		= {1990},
  volume	= {2},
  pages		= {307--312},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  hrycej91a,
  author	= {D. Hrycej},
  title		= {Invariant features by self-organization},
  journal	= {Neurocomputing},
  year		= {1991},
  volume	= {3},
  number	= {5--6},
  pages		= {287--292},
  month		= {December},
  x		= {A new invariant filter based on Kohonen's idea of
		  self-organization is proposed. },
  dbinsdate	= {oldtimer}
}

@Article{	  hrycej92a,
  author	= {Thomas Hrycej},
  title		= {Supporting supervised learning by self-organization},
  journal	= {Neurocomputing},
  year		= {1992},
  volume	= {4},
  number	= {1--2},
  pages		= {17--30},
  dbinsdate	= {oldtimer}
}

@Article{	  hsieh93a,
  author	= {K. -R. Hsieh and W. -T. Chen},
  title		= {A neural network model which combines unsupervised and
		  supervised learning},
  journal	= {IEEE Trans. Neural Networks},
  year		= {1993},
  volume	= {4},
  number	= {2},
  pages		= {357--360},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  hsieh98a,
  author	= {Ching-Tang Hsieh and Chieh-Ching Chin and Kuang-Ming
		  Shen},
  title		= {Generalized fuzzy {K}ohonen clustering networks},
  journal	= {IEICE Transactions on Fundamentals of Electronics,
		  Communications and Computer Sciences},
  year		= {1998},
  volume	= {E81-A},
  number	= {10},
  pages		= {2144--50},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hsu00a,
  author	= {Arthur L. C. Hsu and Damminda Alahakoon and Saman
		  Halgamunge and Bala Srinivasan},
  title		= {Visualizing Cluster with Dynamic {SOM} Tree},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {257--263},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hsu01a,
  author	= {Hsu, A. L. and Halgarmuge, S. K.},
  title		= {Enhanced topology preservation of Dynamic Self-Organising
		  Maps for data visualisation},
  booktitle	= {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS
		  International Conference. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {3},
  pages		= {1786--91},
  abstract	= {Unsupervised knowledge discovery using Self Organising
		  Maps (SOM) has been successfully used in obtaining unbiased
		  and visualisable results. A Growing (or Dynamic) Self
		  Organising Maps (GSOM) is an extended version of the
		  original SOM with adaptive map size and controllable
		  spread. In experiments a GSOM usually has considerably
		  higher topographic error than SOM with similar quantisation
		  error. This can be undesirable in cases where, topology
		  preservation is important, therefore in this paper the
		  authors proposed an algorithm to assist the growing of the
		  dynamic self-organising map in achieving better topographic
		  quality whilst maintaining or even improving level of
		  quantisation error. Results have shown improvement of
		  topographic error when comparing to GSOM, and have better
		  topology preservation than non-topologically optimised SOM
		  with similar map size.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hsu91a,
  author	= {Chau-Yun Hsu and Meng-Hsiang Tsai and Wei-Mei Chen},
  title		= {A study of feature-mapped approach to the multiple
		  travelling salesmen problem},
  booktitle	= {Proc. Int. Symp. on Circuits and Systems},
  year		= {1991},
  volume	= {II},
  pages		= {1589--1592},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  hsu91b,
  author	= {Yuan-Yih Hsu and Chien-Chuen Yang},
  title		= {Design of artificial neural networks for short-term load
		  forecasting. {I}. {S}elf-organising feature maps for day
		  type identification},
  journal	= {IEE Proc. C [Generation, Transmission and Distribution]},
  year		= {1991},
  volume	= {138},
  number	= {5},
  pages		= {407--413},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hsu92a,
  author	= {Chau-Yun Hsu and Hwai-En Wu},
  title		= {An improved algorithm for {K}ohonen's
		  \mbox{self-organizing} feature maps},
  booktitle	= {1992 IEEE International Symposium on Circuits and
		  Systems},
  year		= {1992},
  volume	= {1},
  pages		= {328--31},
  organization	= {Dept. of Electr. Eng. , Tatung Inst. of Technol. , Taipei,
		  Taiwan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hu94a,
  author	= {Hu, Weidong and Yu, Wenxian and Wu, Jianhui and Fu,
		  Qiang},
  title		= {A fuzzy classification method of radar weak targets based
		  on \mbox{self-organizing} neural network},
  booktitle	= {PRICAI-94. Proceedings of the 3rd Pacific Rim
		  International Conference on Artificial Intelligence},
  year		= {1994},
  volume	= {1},
  pages		= {553--7},
  organization	= {Dept. of Electr. Eng. , Nat. Univ. of Defense Technol. ,
		  Changsha, China},
  publisher	= {Int. Acad. Publishers},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hu95a,
  author	= {Yu Hen Hu and Surekha Palreddy and Willis J. Tompkins},
  title		= {Customized {ECG} Beat Classifier Using Mixture of
		  Experts},
  booktitle	= {Proc. NNSP'95, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1995},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {459--464},
  annote	= {application, pattern recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hu95b,
  author	= {Dewen Hu and Zongtan Zhou and Zhengzhi Wang},
  title		= {A robot visuomotor system coordinated by
		  \mbox{self-organizing} neural network},
  booktitle	= {Proceedings of International Conference on Neural
		  Information Processing (ICONIP `95)},
  year		= {1995},
  editor	= {Zhong, Y. and Yang, Y. and Wang, M. },
  volume	= {2},
  pages		= {601--4},
  organization	= {Dept. Autom. Control, Changsha Inst. of Technol. , Hunan,
		  China},
  publisher	= {Publishing House of Electron. Ind},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hu95c,
  author	= {J. Q. Hu and E. Rose},
  title		= {On-line fuzzy modelling by data clustering using a neural
		  network},
  booktitle	= {Advances in Process Control 4},
  publisher	= {Instn. Chem. Eng},
  year		= {1995},
  address	= {Rugby, UK},
  pages		= {187--94},
  dbinsdate	= {oldtimer}
}

@Article{	  hu96a,
  author	= {Hu, Guangrui and Wu, Suo and Zhu, Jinbo},
  title		= {An adaptive local searching algorithm for speech
		  recognition using {SOM} neural network},
  journal	= {Journal of Shanghai Jiaotong University},
  year		= {1996},
  volume	= {30},
  number	= {7},
  pages		= {130--3},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hu97a,
  author	= {Yu Hen Hu and Thomas Knoblock and Jong-Ming Park},
  title		= {Nonlinear Committee Pattern Classification},
  booktitle	= {Neural Networks for Signal Processing VII. Proceedings of
		  the 1997 IEEE Workshop},
  publisher	= {IEEE Operations Center},
  year		= 1997,
  editor	= {Jose Principe and Lee Gile and Nelson Morgan and Elizabeth
		  Wilson},
  address	= {Piscataway, NJ},
  pages		= {568--577},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hu97b,
  author	= {J. Q. Hu and E. Rose},
  title		= {Fuzzy model using a modified {LVQ} network for sinter
		  strand process},
  booktitle	= {Proceedings of the 13th World Congress, International
		  Federation of Automatic Control. Vol.M. Chemical Process
		  Control, Mineral, Mining, Metals},
  publisher	= {Pergamon},
  year		= {1997},
  editor	= {J. J. Gertler and Jr. J. B. Cruz and M. Peshkin and M.
		  Kummel and T. McAvoy and A. Niemi},
  address	= {Oxford, UK},
  pages		= {397--402},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hu98a,
  author	= {Hu, Z. and Zhang, M.},
  title		= {Self-organizing neural network-based discrete optical flow
		  model for face recognition},
  booktitle	= {International Conference on Computational Intelligence and
		  Multimedia Applications 1998. ICCIMA 1998. World
		  Scientific, Singapore},
  year		= {1998},
  volume	= {},
  pages		= {372--7},
  abstract	= {A self-organizing neural network-based discrete optical
		  flow (SONDOF) model has been developed for face
		  recognition. 2D optical flow Vs and formulae of 2D
		  distinctly optical flow directions are presented. A special
		  self-organizing map neural network structure for achieving
		  the SONDOF model is also presented. The experimental
		  results show that the SONDOF model can find out the
		  orientations of face movement. This technique can be used
		  for moving pattern recognition.},
  dbinsdate	= {oldtimer}
}

@Article{	  hu98b,
  author	= {Hu, Weidong and Yu, Wenxian and Guo, Guirong},
  title		= {A new fuzzy {K}ohonen clustering network},
  journal	= {Acta Electronica Sinica},
  year		= {1998},
  volume	= {26},
  number	= {3},
  pages		= {117--19},
  dbinsdate	= {oldtimer}
}

@Article{	  huaichun98a,
  author	= {W. Huaichun and J. Dopazo and L. G. {de la Fraga} and Y.
		  P. Zhu and J. M. Carazo},
  title		= {Self-organizing tree-growing network for the
		  classification of protein sequences},
  journal	= {Protein Science},
  year		= {1998},
  volume	= {7},
  number	= {12},
  pages		= {2613--2622},
  dbinsdate	= {oldtimer}
}

@Article{	  huaichun98b,
  author	= {W. Huaichun and J. Dopazo and J. M. Carazo},
  title		= {Self-organizing tree growing network for classifying amino
		  acids},
  journal	= {Bioinformatics},
  year		= {1998},
  volume	= {14},
  number	= {4},
  pages		= {376--377},
  dbinsdate	= {oldtimer}
}

@Article{	  huang00b,
  author	= {Huang Jing and Chen Tian Lun},
  title		= {A kind of neural network model displaying self-organized
		  criticality},
  journal	= {Communications-in-Theoretical-Physics},
  year		= {2000},
  volume	= {33},
  pages		= {365--70},
  abstract	= {Based on the LISSOM model and the OFC earthquake model, we
		  introduce a self-organized neural network model, in which
		  the distribution of the avalanche sizes (unstable neurons)
		  shows power-law behavior. In addition, we analyze the
		  influence of various factors of the model on the power-law
		  behavior of the avalanche size distribution.},
  dbinsdate	= {2002/1}
}

@Article{	  huang01a,
  author	= {Huang, H. -Y. and Chen, Y. -S. and Hsu, W. -H.},
  title		= {Primary-view perception on a gray image: Region
		  segmentation and association},
  journal	= {Journal of the Chinese Institute of Engineers,
		  Transactions of the Chinese Institute of Engineers,Series
		  A/Chung-kuo Kung Ch'eng Hsuch K'an},
  year		= {2001},
  volume	= {24},
  number	= {2},
  month		= {March 2001},
  pages		= {221--235},
  organization	= {Department of Electrical Engineering, National Tsing Hua
		  University},
  publisher	= {},
  address	= {},
  abstract	= {The segmentation of scenes into perceptually meaningful
		  partitions possessing a useful relationship among them is
		  of great importance in image understanding. In this paper,
		  an effective approach performing image segmentation and
		  association among the segmented regions for a gray image is
		  presented. An unsupervised segmentation process using a
		  self-organization map (SOM) and spatial-distance
		  computation is presented for the segmentation of clusters
		  in our understanding system. An association process is
		  developed for the construction of spatial relationships
		  among the attended regions, which are obtained from the
		  segmented clusters using fuzzy relations. The algorithms
		  for each process are presented and exemplified with a
		  series of illustrations. This approach is applied to
		  simulating the primary-view perception on a natural gray
		  image. Experiments show that the proposed approach is
		  feasible. A possible linguistic presentation, based on the
		  attended regions and their relationships, is given for
		  every test image.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  huang01b,
  author	= {Yin Huang and Suganthan, P. N. and Krishnan, S. M. and
		  Xiang Cao},
  title		= {Evaluation of distance measures for partial image
		  retrieval using self-organizing map},
  booktitle	= {Artificial Neural Networks---ICANN 2001. International
		  Conference. Proceedings (Lecture Notes in Computer Science
		  Vol.2130). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {1042--7},
  abstract	= {Digital image libraries are becoming more common and
		  widely used as more visual information is produced at a
		  rapidly growing rate. With this immense growth, there is a
		  need to organize and index these databases so that we can
		  efficiently retrieve the desired images. In this paper, we
		  evaluate the performance of the self-organising maps (SOMs)
		  with different distance measures in retrieving similar
		  images when a full or a partial query image is presented to
		  the SOM. Our method makes use of RGB colour histograms. As
		  the RGB colour space is very large, another SOM is employed
		  to adaptively quantise the colour space prior to generating
		  the histograms. Some promising results are reported.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  huang01c,
  author	= {Huang, J. S. and Negnevitsky, M. and Nguyen, N. T.},
  title		= {An information system for monitoring of power quality
		  disturbances},
  booktitle	= {Managing Information Technology in a Global Environment.
		  2001 Information Resources Management Association
		  International Conference. Idea Group Publishing, Hershey,
		  PA, USA},
  year		= {2001},
  volume	= {},
  pages		= {1019--23},
  abstract	= {The paper presents a neural-fuzzy technique based
		  classifier for pattern recognition problems with uncertain
		  distributions. Neural networks in the architecture of
		  frequency sensitive competitive learning and learning
		  vector quantization are first employed to evaluate the
		  decision boundaries separating different patterns to be
		  classified. To deal with the uncertainties of the involved
		  recognition problems, however, the output of the neural
		  networks is used to activate a fuzzy-associative-memory
		  rule-base to accomplish the classification, instead of
		  being taken directly as the final identification. With the
		  Internet and the developed classifiers, an information
		  system can be built up for power quality monitoring over
		  whole power networks.},
  dbinsdate	= {2002/1}
}

@Article{	  huang02a,
  author	= {Huang, J. S. and Negnevitsky, M. and Nguyen, D. T.},
  title		= {A neural-fuzzy classifier for recognition of power quality
		  disturbances},
  journal	= {IEEE TRANSACTIONS ON POWER DELIVERY},
  year		= {2002},
  volume	= {17},
  number	= {2},
  month		= {APR},
  pages		= {609--616},
  abstract	= {This paper presents a neural-fuzzy technology-based
		  classifier for the recognition of power quality
		  disturbances. The classifier adopts neural networks in the
		  architecture of frequency sensitive competitive leaning and
		  learning vector quantization (LVQ). With given size of
		  codewords, the neural networks are trained to determine the
		  optimal decision boundaries separating different categories
		  of disturbances. To cope with the uncertainties in the
		  involved pattern recognition, the neural network outputs,
		  instead of being taken as the final classification, are
		  used to activate the fuzzy- associative-memory (FAM)
		  recalling for identifying the most possible type that the
		  input waveform may belong to. Furthermore, the input
		  waveforms are preprocessed by the wavelet transform for
		  feature extraction so as to improve the classifier with
		  respect to recognition accuracy and scheme simplicity. Each
		  subband of the transform coefficients is then utilized to
		  recognize the associated disturbances.},
  dbinsdate	= {2002/1}
}

@Article{	  huang02b,
  author	= {Huang, H. Y. and Chen, Y. S. and Hsu, W. H.},
  title		= {Color image segmentation using a self-organizing map
		  algorithm},
  journal	= {JOURNAL OF ELECTRONIC IMAGING},
  year		= {2002},
  volume	= {11},
  number	= {2},
  month		= {APR},
  pages		= {136--148},
  abstract	= {A color image segmentation methodology based on a self-
		  organizing map (SOM) is proposed. The method developed
		  takes into account the color similarity and spatial
		  relationship of objects within an image. According to the
		  features of color similarity, an image is first segmented
		  into coarse cluster regions. The resulting regions are then
		  treated by computing the spatial distance between any two
		  cluster regions, and the SOM with a labeling process is
		  applied. In this paper, the selection of the parameters for
		  the SOM algorithm was also investigated experimentally. The
		  experimental results show that the proposed system is
		  feasible, and that the segmented object regions are similar
		  to those perceived by human vision.},
  dbinsdate	= {2002/1}
}

@Article{	  huang92a,
  author	= {Z. Huang and A. Kuh},
  title		= {A combined \mbox{self-organizing} feature map and
		  multilayer perceptron for isolated word recognition},
  journal	= {IEEE Trans. Signal Processing},
  year		= {1992},
  volume	= {40},
  number	= {11},
  pages		= {2651--2657},
  month		= {November},
  abstract	= {A neural network system which combines a self-organizing
		  feature map and multilayer perception for the problem of
		  isolated word speech recognition is presented. A new method
		  combining self-organization learning and K-means clustering
		  is used for the training of the feature map, and an
		  efficient adaptive nearby-search coding method based on the
		  'locality' of the self-organization is designed. The coding
		  method is shown to save about 50% computation without
		  degradation in recognition rate compared to full-search
		  coding. Various experiments for different choices of
		  parameters in the system were conducted on the TI 20 word
		  database with best recognition rates as high as 99.5% for
		  both speaker-dependent and multispeaker-dependent tests.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  huang92b,
  author	= {Huang, K. Y. and Yang, H. Z. },
  title		= {A hybrid neural network for seismic pattern recognition},
  booktitle	= {IJCNN International Joint Conference on Neural Networks},
  year		= {1992},
  volume	= {3},
  pages		= {736--41},
  organization	= {Dept. of Comput. \& Inf. Sci. , Nat. Chiao Tung Univ. ,
		  Hsinchu, Taiwan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  huang95a,
  author	= {Shyh-Jier Huang and Chuan-Chang Hung},
  title		= {Genetic Algorithms Enhanced {K}ohonen's Neural Networks},
  volume	= {II},
  pages		= {708--712},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  huang95b,
  author	= {Shyh-Jier Huang and Chuan-Chang Hung},
  title		= {Genetic-based {K}ohonen's neural networks for power system
		  static security assessment},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks. Vol. 5. Fuzzy Logic and Evolutionary Programming.
		  Proceedings of the Artificial Neural Networks in
		  Engineering (ANNIE'95)},
  publisher	= {ASME Press},
  year		= {1995},
  editor	= {C. H. Dagli and M. Akay and C. L. P. Chen and B. R.
		  Fernandez and J. Ghosh},
  address	= {New York, NY, USA},
  pages		= {791--6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  huang96a,
  author	= {Y. S. Huang and K. Liu and C. Y. Suen and A. J. Shie and
		  L. I. Shyu and M. C. Liang and R. Y. Tsay and P. K. Huang},
  title		= {A simulated annealing approach to construct optimized
		  prototypes for nearest-neighbor classification},
  booktitle	= {Proceedings of the 13th International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  volume	= {4},
  address	= {Los Alamitos, CA, USA},
  pages		= {483--7},
  dbinsdate	= {oldtimer}
}

@Article{	  huang98a,
  author	= {Guang-Bin Huang and Haroon A. Babri and Hua-Tian Li},
  title		= {Ordering of Self-Organizing Maps in Multidimensional
		  Cases},
  journal	= {Neural Computation},
  year		= 1998,
  volume	= 10,
  pages		= {19--23},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hueter88a,
  author	= {G. Hueter},
  title		= {Solution of the {T}raveling {S}alesman {P}roblem with an
		  adaptive ring},
  booktitle	= {Proc. ICNN'88, International Conference on Neural
		  Networks},
  year		= {1988},
  volume	= {I},
  pages		= {85--92},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  huhse00a,
  author	= {Jutta Huhse and Andreas Zell},
  title		= {Evolution Strategy with Neighborhood Attraction},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Lehrstuhl Rechnerarchitektur, Wilhelm-Schickard-Institut
		  f\"{u}r Informatik, Ebehard-Karls-Universit\"{a}t
		  T\"{u}bingen},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  huhse01a,
  author	= {Huhse, J. and Zell, A.},
  title		= {Investigating the influence of the neighborhood attraction
		  factor to evolution strategies with neighborhood
		  attraction},
  booktitle	= {9th European Symposium on Artificial Neural Networks.
		  ESANN'2001. Proceedings. D-Facto, Evere, Belgium},
  year		= {2001},
  volume	= {},
  pages		= {179--84},
  abstract	= {The evolution strategy with neighborhood attraction (EN)
		  is a new combination of self-organizing maps (SOM) and
		  evolution strategies (ES). It adapts the neighborhood
		  relationship known from SOM to ES individuals to
		  concentrate them around the optimum of the problem.
		  Detailed investigations on the influence of one of the most
		  important EN-operators, the neighborhood attraction, were
		  performed on a variety of well-known optimization problems.
		  It could be shown that the parameter setting for the
		  neighborhood attraction has a very strong influence on the
		  convergence velocity and the robustness of the EN, and
		  suggestions for applicable parameter settings could be
		  made.},
  dbinsdate	= {2002/1}
}

@Article{	  hui01a,
  author	= {Hui Yu Huang and Yung Sheng Chen and Wen Hsing Hsu},
  title		= {Primary-view perception on a gray image: region
		  segmentation and association},
  journal	= {Journal-of-the-Chinese-Institute-of-Engineers},
  year		= {2001},
  volume	= {24},
  pages		= {221--35},
  abstract	= {The segmentation of scenes into perceptually meaningful
		  partitions possessing a useful relationship among them is
		  of great importance in image understanding. In this paper,
		  an effective approach performing image segmentation and
		  association among the segmented regions for a gray image is
		  presented. An unsupervised segmentation process using a
		  self-organization map (SOM) and spatial-distance
		  computation is presented for the segmentation of clusters
		  in our understanding system. An association process is
		  developed for the construction of spatial relationships
		  among the attended regions, which are obtained from the
		  segmented clusters using fuzzy relations. The algorithms
		  for each process are presented and exemplified with a
		  series of illustrations. This approach is applied to
		  simulating the primary-view perception on a natural gray
		  image. Experiments show that the proposed approach is
		  feasible. A possible linguistic presentation, based on the
		  attended regions and their relationships, is given for
		  every test image.},
  dbinsdate	= {2002/1}
}

@Article{	  hui02a,
  author	= {Hui Yu Huang and Yung Sheng Chen and Wen Hsing Hsu},
  title		= {Color image segmentation using a self-organizing map
		  algorithm},
  journal	= {Journal-of-Electronic-Imaging},
  year		= {2002},
  volume	= {11},
  pages		= {136--48},
  abstract	= {A color image segmentation methodology based on a
		  self-organizing map (SOM) is proposed. The method developed
		  takes into account the color similarity and spatial
		  relationship of objects within an image. According to the
		  features of color similarity, an image is first segmented
		  into coarse cluster regions. The resulting regions are then
		  treated by computing the spatial distance between any two
		  cluster regions, and the SOM with a labeling process is
		  applied. In this paper, the selection of the parameters for
		  the SOM algorithm was also investigated experimentally. The
		  experimental results show that the proposed system is
		  feasible, and that the segmented object regions are similar
		  to those perceived by human vision.},
  dbinsdate	= {2002/1}
}

@Article{	  hui96a,
  author	= {S. C. Hui and A. Goh},
  title		= {Incorporating fuzzy logic with neural networks for
		  document retrieval},
  journal	= {Engineering Applications of Artificial Intelligence},
  year		= {1996},
  volume	= {9},
  number	= {5},
  pages		= {551--60},
  dbinsdate	= {oldtimer}
}


@InProceedings{	  hung00a,
  author	= {Hung, Jeanson and Wang, Jung-Hua},
  title		= {Topology preserving using harmonic competitive neural
		  networks},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {2597--2600},
  organization	= {},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Topology preserving is mainly used to analyze the
		  structure of the input distribution. In some
		  implementations, it refers to a data visualization process
		  thereby high-dimensional input data can be mapped onto a
		  lower-dimension space where the spatial features of the
		  original input data can be visually revealed. In this
		  paper, we propose a powerful topology preserving method
		  based on a self-creating model called the Harmonic
		  Competitive Neural Network (HCNN). The HCNN network is
		  initialized as a triangular structure (i.e., three nodes
		  connected to each other) as in the Growing Cell Structure
		  (GCS). In order to approximate input distribution in a
		  self-organizing manner, training parameters are data-driven
		  and the network size needs not be pre-specified. Our goal
		  is to map the topological structure of input data with less
		  distortion error and computational cost in comparison with
		  other networks such as Self-organizing Feature Maps (SOFM)
		  or Topology Representing Networks (TRN).},
  dbinsdate	= {2002/1}
}

@Article{	  hung93a,
  author	= {Chuan-Chang Hung},
  title		= {Building a neuro-fuzzy learning control system},
  journal	= {AI Expert},
  year		= {1993},
  volume	= {8},
  number	= {11},
  pages		= {40--9},
  month		= {Nov},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hung94a,
  author	= {Hai-Lung Hung and Wei-Chung Lin},
  title		= {Dynamic Hierarchical Self-Organizing Neural Networks},
  pages		= {627--632},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {modification},
  dbinsdate	= {oldtimer}
}

@Article{	  huntsberger90a,
  author	= {T. L. Huntsberger and P. Ajjimarangsee},
  title		= {Parallel \mbox{self-organizing} feature maps for
		  unsupervised pattern recognition},
  journal	= {Int. J. General Systems},
  year		= {1990},
  volume	= {16},
  number	= {4},
  pages		= {357--372},
  dbinsdate	= {oldtimer}
}

@Article{	  huosheng00a,
  author	= {Huosheng Hu and Dongbing Gu},
  title		= {Landmark-based navigation of industrial mobile robots},
  journal	= {Industrial-Robot},
  year		= {2000},
  volume	= {27},
  pages		= {458--67},
  abstract	= {Landmark-based navigation strategy relies on
		  identification and subsequent recognition of distinctive
		  environment features or objects that are either known a
		  priori or extracted dynamically. This process has inherent
		  difficulties in practice due to sensor noise and
		  environment uncertainty. This paper proposes a navigation
		  algorithm that simultaneously locates the robots and
		  updates landmarks in a manufacturing environment. The key
		  issue addressed is how to improve the localization accuracy
		  for mobile robots in a continuous operation, in which the
		  Kalman filter algorithm is adopted to integrate odometry
		  data with scanner data to achieve the required robustness
		  and accuracy. The Kohonen neural networks are used to
		  recognize landmarks using scanner data in order to
		  initialize and re-calibrate the robot position by means of
		  triangulation when necessary.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hush89a,
  author	= {D. R. Hush and J. M. Salas},
  title		= {Classification with neural networks: a comparison},
  booktitle	= {Proc. ISE '89, Eleventh Annual Ideas in Science and
		  Electronics Exposition and Symposium},
  year		= {1989},
  editor	= {C. Christmann},
  pages		= {107--114},
  organization	= {Ideas in Sci. \& Electron. ; IEEE},
  publisher	= {Ideas in Sci. \& Electron},
  address	= {Albuquerque, NM},
  dbinsdate	= {oldtimer}
}

@Article{	  hush92a,
  author	= {D. R. Hush and B. Horne},
  title		= {An overview of neural networks. {I}. {S}tatic networks},
  journal	= {Informatica y Automatica},
  year		= {1992},
  volume	= {25},
  number	= {1},
  pages		= {19--36},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  husheng99a,
  author	= {Husheng, Yang and Lewis, I. R. and Griffiths, P. R.},
  title		= {Raman spectrometry and neural networks for the
		  classification of wood types. 2. {K}ohonen
		  \mbox{self-organizing} maps},
  journal	= {Spectrochimica Acta, Part A (Molecular and Biomolecular
		  Spectroscopy)},
  year		= {1999},
  volume	= {55},
  pages		= {2783--91},
  abstract	= {One- and two-dimensional Kohonen self-organizing maps
		  (SOMs) were successfully used for the unsupervised
		  differentiation of the Fourier transform Raman spectra of
		  hardwoods from softwoods. The SOMs were also applied to
		  differentiate temperate woods from tropical woods, and
		  results showed that the two types of woods could only be
		  partly differentiated. A semi-quantitative method that is
		  based on the Euclidean distances of the weight matrix has
		  been developed to assist the automatic clustering of the
		  neurons in a two-dimensional SOM.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hutchinson89a,
  author	= {R. A. Hutchinson and W. J. Welsh},
  title		= {Comparison of neural networks and conventional techniques
		  for feature location in facial images},
  booktitle	= {Proc. First IEE International Conference on Artificial
		  Neural Networks},
  year		= {1989},
  pages		= {201--205},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  hutsberger95a,
  author	= {Hutsberger, T. },
  title		= {Biologically motivated cross-modality sensory fusion
		  system for automatic target recognition},
  journal	= {Neural Networks},
  year		= {1995},
  volume	= {8},
  number	= {7--8},
  pages		= {1215--26},
  dbinsdate	= {oldtimer}
}

@Article{	  hutter92a,
  author	= {H. -P. Hutter},
  title		= {Speech recognition over the telephone line},
  journal	= {Mitteilungen AGEN},
  year		= {1992},
  volume	= {55},
  pages		= {9--22},
  month		= {June},
  note		= {(in German)},
  x		= {The codebooks are created by the classical LBG algorithm.
		  These codebooks are then optimized on word classes using
		  the so-called LVQ3 algorithm. ---LVQ-algoritmi tassa
		  sivuosassa},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hutter95a,
  author	= {H. -P. Hutter},
  title		= {Comparison of a new hybrid connectionist-{SCHMM} approach
		  with other hybrid approaches for speech recognition},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {3311--14},
  dbinsdate	= {oldtimer}
}

@Article{	  huwer96a,
  author	= {S. Huwer and J. Rahmel and A. v. Wangenheim},
  title		= {Data-driven registration for local deformations},
  journal	= {Pattern Recognition Letters},
  year		= {1996},
  volume	= {17},
  number	= {9},
  pages		= {951--7},
  dbinsdate	= {oldtimer}
}

@Article{	  hwang00a,
  author	= {Hwang, Wen-Jyi and Ye, Bo-Yuan and Lin, Chin-Tsai},
  title		= {Novel competitive learning algorithm for the parametric
		  classification with Gaussian distributions},
  journal	= {Pattern Recognition Letters},
  year		= {2000},
  volume	= {21},
  number	= {5},
  month		= {},
  pages		= {375--380},
  organization	= {Chung Yuan Christian Univ},
  publisher	= {Elsevier Science Publishers B.V.},
  address	= {Amsterdam},
  abstract	= {A competitive learning algorithm for the parametric
		  classification of Gaussian sources is presented in this
		  letter. The algorithm iteratively estimates the mean and
		  prior probability of each class during the training. Bayes
		  rule is then used for classification based on the estimated
		  information. Simulation results show that the proposed
		  algorithm outperforms k-means and LVQ algorithms for the
		  parametric classification.},
  dbinsdate	= {2002/1}
}

@Article{	  hwang01a,
  author	= {Wen Jyi Hwang and Faa Jeng Lin and Shi Chiang Liao and
		  Jeng Hsin Huang},
  title		= {A novel fuzzy entropy-constrained competitive learning
		  algorithm for image coding},
  journal	= {Neurocomputing},
  year		= {2001},
  volume	= {37},
  pages		= {197--208},
  abstract	= {A novel variable-rate vector quantizer (VQ) design
		  algorithm using both fuzzy and competitive learning
		  technique is presented. The algorithm enjoys better
		  rate-distortion performance than that of other existing
		  fuzzy clustering and competitive learning algorithms. In
		  addition, the learning algorithm is less sensitive to the
		  selection of initial reproduction vectors. Therefore, the
		  algorithm can be an effective alternative to the existing
		  variable-rate VQ algorithms for signal compression.},
  dbinsdate	= {2002/1},
  merjanote     = {last name checked from internet}
}

@InProceedings{	  hwang94a,
  author	= {Doo Sung Hwang and Mun Sung Han},
  title		= {Two Phase {SOFM}},
  pages		= {742--745},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, modification},
  dbinsdate	= {oldtimer}
}

@Article{	  hyotyniemi00a,
  author	= {Hyotyniemi, Heikki and Ylinen, Raimo},
  title		= {Modeling of visual flotation froth data},
  journal	= {Control Engineering Practice},
  year		= {2000},
  volume	= {8},
  number	= {3},
  month		= {},
  pages		= {313--318},
  organization	= {Helsinki Univ of Technology},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {In this paper, the principles of sensor fusion are
		  presented. A new sparse coding method based on a
		  generalization of the generalized Hebbian algorithm (GGHA)
		  is presented. The algorithm is realized using a
		  modification of the Kohonen network. The method is tested
		  on an image analysis of flotation froth, in order to find
		  features corresponding to the poisoning phenomenon in a
		  flotation cell. The features found are capable of
		  predicting the poisoning earlier than the ordinary process
		  instrumentation.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hyotyniemi93a,
  author	= {Heikki Hy{\"{o}}tyniemi},
  title		= {Optimal Control of Dynamic Systems Using Self-Organizing
		  Maps},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {850--853},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hyotyniemi95a,
  author	= {Heikki Hy{\"{o}}tyniemi},
  title		= {'{M}ode Maps' in Process Modeling},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {147--154},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hyotyniemi96a,
  author	= {H. Hy\"otyniemi},
  title		= {Text document classification with \mbox{self-organizing}
		  maps},
  booktitle	= {STeP '96---Genes, Nets and Symbols. Finnish Artificial
		  Intelligence Conference},
  publisher	= {Univ. Vaasa},
  year		= {1996},
  editor	= {J. Alander and T. Honkela and M. Jakobsson},
  address	= {Vaasa, Finland},
  pages		= {64--72},
  dbinsdate	= {oldtimer}
}

@Article{	  hyotyniemi96b,
  author	= {Hy\"otyniemi, Heikki},
  title		= {Constructing non-orthogonal feature bases},
  journal	= {IEEE International Conference on Neural Networks},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1996},
  number	= {},
  volume	= {3},
  pages		= {1759--1764},
  abstract	= {A feature extraction algorithm based on self-organizing
		  maps is presented. The converged feature map can be
		  interpreted as a non-orthogonal basis spanning the space of
		  the input vectors. The new algorithm can be shown to be a
		  generalization of the Generalized Hebbian Algorithm
		  (GHA).},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hyotyniemi97c,
  author	= {H. Hy\"otyniemi},
  title		= {State-space modeling using \mbox{self-organizing} maps},
  booktitle	= {5th European Symposium on Artificial Neural Networks ESANN
		  '97. Proceedings},
  publisher	= {D facto},
  year		= {1997},
  editor	= {M. Verleysen},
  address	= {Brussels, Belgium},
  pages		= {187--92},
  dbinsdate	= {oldtimer}
}

@InCollection{	  hyotyniemi97d,
  author	= {Heikki Hy{\"o}tyniemi},
  title		= {Minimum description length ({MDL}) principle and
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {124--129},
  dbinsdate	= {oldtimer}
}

@Article{	  hyun01a,
  author	= {Hyun Don Kim and Sung Bae Cho},
  title		= {Improvement of classification rate of handwritten digits
		  by combining multiple dynamic topology-preserving
		  self-organizing maps},
  journal	= {Journal-of-KISS:-Software-and-Applications},
  year		= {2001},
  volume	= {28},
  pages		= {875--84},
  abstract	= {The self-organizing map (SOM) is widely utilized in such
		  fields as data visualization and topology preserving
		  mapping. Since it should have the topology fixed before
		  training, it has some shortcomings in that it is difficult
		  to apply to practical problems, and its classification
		  capability is quite low despite better clustering
		  performance. To overcome these points, this paper proposes
		  the dynamic topology preserving self-organizing map (DTSOM)
		  that dynamically splits the output nodes on the map and
		  trains them, and attempts to improve the classification
		  capability by combining multiple DTSOMs. The K-Winner
		  method has been applied to combine DTSOMs, which produces K
		  outputs with the winner node selection method. This
		  produces even better performance than the conventional
		  combining methods such as majority voting, weighting, BKS,
		  Bayesian, Borda, Condorect and reliability sum. DTSOM
		  remedies the shortcomings of determining the topology in
		  advance, and the classification rate increases
		  significantly by combining multiple maps trained with
		  different features. Experimental results with handwritten
		  digit recognition indicate that the proposed method works
		  out the problems of conventional SOM effectively so to
		  improve the classification rate to 98.1%.},
  dbinsdate	= {2002/1}
}

@Article{	  hyun01b,
  author	= {Hyun Chul Cho and Keeseong Lee},
  title		= {The 3-D underwater object recognition using neural
		  networks and ultrasonic sensor fabricated with 1--3 type
		  piezoelectric composites},
  journal	= {Transactions-of-the-Korean-Institute-of-Electrical-Engineers,-C}
		  ,
  year		= {2001},
  volume	= {50},
  pages		= {324--8},
  abstract	= {In this study, the characteristics of an ultrasonic sensor
		  fabricated with PZT-polymer 1--3 type composites are
		  investigated. The 3-D underwater object recognition using
		  the self-made ultrasonic censor and SOFM neural network is
		  presented. The ultrasonic sensor is satisfied with the
		  required condition of a commercial ultrasonic sensor in
		  underwater conditions. The 3-D underwater object
		  recognition for the training data and the testing data is
		  100%. The experimental results have shown that the
		  ultrasonic sensor fabricated with PZT-polymer 1--3 type
		  composites can be applied for sonar systems.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  hyung00a,
  author	= {Hyung Su Kim and Kyeong June Mun and Hwa Seok Lee},
  title		= {Electric load forecasting and classification using neural
		  networks for the {EMS}},
  booktitle	= {Proceedings of the IASTED International Conference Power
		  and Energy Systems. IASTED/ACTA Press, Anaheim, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {171--5},
  abstract	= {This paper presents methods of short-term load forecasting
		  (STLF) using Kohonen neural networks and backpropagation
		  neural networks. Firstly, historical load data are divided
		  into 5 patterns for the each seasonal data using Kohonen
		  neural networks. Secondly, classified data are used as
		  inputs of backpropagation networks for next-day hourly load
		  forecasting. Next day hourly load for weekdays and weekends
		  except holidays is forecast. In load forecasting for summer
		  days, maximum and minimum temperature data as well as
		  historical hourly load data are used as inputs of neural
		  networks to reflect the relationship between temperature
		  and load. To evaluate the accuracy of the proposed method,
		  it was tested using hourly load data of Korea Electric
		  Power Corporation (KEPCO). Windows-based visual user
		  interface software is developed for the training and
		  testing of neural networks for STLF applications.},
  dbinsdate	= {2002/1}
}

@Article{	  hyvonen01a,
  author	= {Hyvonen, M. T. and Hiltunen, Y. and El-Deredy, W. and
		  Ojala, T. and Vaara, J. and Kovanen, P. T. and Ala-Korpela,
		  M.},
  title		= {Application of self-organizing maps in conformational
		  analysis of lipids},
  journal	= {JOURNAL OF THE AMERICAN CHEMICAL SOCIETY},
  year		= {2001},
  volume	= {123},
  number	= {5},
  month		= {FEB 7},
  pages		= {810--816},
  abstract	= {The characteristics of lipid assemblies are important for
		  the functions of biological membranes. This has led to an
		  increasing utilization of molecular dynamics simulations
		  for the elucidation of the structural features of
		  biomembranes. We have applied the self-organizing map (SOM)
		  to the analysis of the complex conformational data from a
		  1-ns molecular dynamics simulation of PLPC phospholipids in
		  a membrane assembly. Mapping of 1.44 million molecular
		  conformations to a two- dimensional array of neurons
		  revealed, without human intervention, the main
		  conformational features in hours. Both the whole molecule
		  and the characteristics of the unsaturated fatty acid
		  chains were analyzed. All major structural features were
		  easily distinguished, such as the orientational variability
		  of the headgroup, the mainly trans state dihedral angles of
		  the sn-l chain, and both straight and bent conformations of
		  the unsaturated sn-2 chain. Furthermore, presentation of
		  the trajectory of an individual lipid molecule on the map
		  provides information on conformational dynamics. The
		  present results suggest that the SOM method provides a
		  powerful tool for routinely gaining rapid insight to the
		  main molecular conformations as well as to the
		  conformational dynamics of any simulated molecular assembly
		  without the requirement of a priori knowledge.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ibbou95a,
  author	= {Smail Ibbou and Marie Cottrell},
  title		= {Multiple Correspondence analysis of a crosstabulations
		  matrix using the {K}ohonen algorithm},
  booktitle	= {Proc. ESANN'95, European Symp. on Artificial Neural
		  Networks},
  year		= {1995},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {27--32},
  dbinsdate	= {oldtimer}
}

@TechReport{	  ibbou98a,
  author	= {S. Ibbou},
  title		= {Treatment of qualitative variables by {K}ohonen algorithm.
		  {S}imultaneous classification of modalities and
		  individuals},
  institution	= {SAMOS, University of Paris},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  ibnkahla00a,
  author	= {Ibnkahla, Mohamed},
  title		= {Applications of neural networks to digital communications
		  ---a survey},
  journal	= {Signal Processing},
  year		= {2000},
  volume	= {80},
  number	= {7},
  month		= {Jul},
  pages		= {1185--1215},
  organization	= {Natl Polytechnic Inst of Toulouse},
  publisher	= {Elsevier Science Publishers B.V.},
  address	= {Amsterdam},
  abstract	= {Neural networks (NNs) are able to give solutions to
		  complex problems in digital communications due to their
		  nonlinear processing, parallel distributed architecture,
		  self-organization, capacity of learning and generalization,
		  and efficient hardware implementation. The paper gives an
		  overview of the applications of NNs to digital
		  communications such as channel identification and
		  equalization, coding and decoding, vector quantization,
		  image processing, nonlinear filtering, spread spectrum
		  applications, etc. The key issue in neural network
		  approaches is to find an appropriate architecture that
		  gives the best results. The paper shows, through several
		  examples, how to choose the neural network structures and
		  how to combine neural network algorithms with other
		  techniques such as adaptive signal processing, fuzzy
		  systems and genetic algorithms. Finally, the paper reviews
		  the mathematical approaches used to understand the learning
		  and convergence behavior of neural network algorithms.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ibnkahla95a,
  author	= {Ibnkahla, M. and Castanie, F. },
  title		= {Vector neural networks for digital satellite
		  communications},
  booktitle	= {ICC `95 Seattle. Communications---Gateway to
		  Globalization. 1995 IEEE International Conference on
		  Communications},
  year		= {1995},
  volume	= {3},
  pages		= {1865--9},
  organization	= {ENSEEIHT, Toulouse, France},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ichiki91a,
  author	= {H. Ichiki and M. Hagiwara and N. Nakagawa},
  title		= {Self-Organizing Multi-Layer Semantic Maps},
  booktitle	= {Proc. IJCNN'91, International Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {357--360},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  abstract	= {Self-organizing multi-layer semantic maps are proposed and
		  simulated. The semantic maps proposed by H. Ritter and T.
		  Kohonen have a feature that the semantic relationships in
		  the input data are reflected by their relative distances in
		  the maps. They consist of minimal two layers. Since the
		  proposed maps are multi-type, they can do higher level
		  information processing compared with the conventional
		  minimal two-layer semantic maps. The computer simulation
		  results indicate the effectiveness of the proposed maps;
		  the maps can do hierarchical self-classification both in
		  self-organizing symbol map and in role-based semantic
		  map.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ichiki93a,
  author	= {Hiroyuki Ichiki and Masafumi Hagiwara and Masao Nakagawa},
  title		= {{K}ohonen Feature Maps as a Supervised Learning Machine},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1944--1948},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  ichiki93b,
  author	= {Ichiki, H. and Hagiwara, M. and Nakagawa, M. },
  title		= {Multi-layer \mbox{self-organizing} semantic maps},
  journal	= {Transactions of the Institute of Electrical Engineers of
		  Japan, Part C},
  year		= {1993},
  volume	= {113-C},
  number	= {1},
  pages		= {36--42},
  month		= {Jan},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  idan91a,
  author	= {Y. Idan and R. C. Chevallier},
  title		= {Handwritten Digits Recognition by a supervised
		  {K}ohonen-Like Learning Algoritm},
  booktitle	= {Proc. IJCNN-91, International Joint Conference on Neural
		  Networks, Singapore},
  year		= {1991},
  volume	= {III},
  pages		= {2576--2581},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  idan92a,
  author	= {Idan, Y. and Auger, J. -M. and Darbel, N. and Sales, M.
		  and Chevallier, R. and Dorizzi, B. and Cazuguel, G. },
  title		= {Comparative study of neural networks and non parametric
		  statistical methods for off-line handwritten character
		  recognition},
  booktitle	= {Artificial Neural Networks, 2. Proceedings of the 1992
		  International Conference (ICANN-92)},
  year		= {1992},
  editor	= {Aleksander, I. },
  volume	= {2},
  pages		= {1607--10},
  organization	= {Telecom Paris, France},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ienne93a,
  author	= {Paolo Ienne and Marc A. Viredaz},
  title		= {{GENES IV}: A Bit-Serial Processing Element for a
		  Multi-Model Neural-Network Accelerator},
  booktitle	= {Proc. International Conference on Application-Specific
		  Array Processors (ASAP'93), Venice, Italy},
  year		= {1993},
  editor	= {Luigi Dadda and Benjamin Wah},
  pages		= {345--356},
  publisher	= {{IEEE} Computer Society Press, Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ienne94a,
  author	= {Ienne, P. and Viredaz, M. A. },
  title		= {Implementation of {K}ohonen's \mbox{self-organising} maps
		  on {MANTRA} {I}},
  booktitle	= {Proceedings of the Fourth International Conference on
		  Microelectronics for Neural Networks and Fuzzy Systems},
  year		= {1994},
  pages		= {273--9},
  organization	= {MANTRA Centre for Neuro-Mimetic Syst. , Swiss Federal
		  Inst. of Technol. , Lausanne, Switzerland},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  ienne95a,
  author	= {Ienne, P. and Viredaz, M. A. },
  title		= {{GENES} {IV}: a bit-serial processing element for a
		  multi-model neural-network accelerator},
  journal	= {Journal of VLSI Signal Processing},
  year		= {1995},
  volume	= {9},
  number	= {3},
  pages		= {257--73},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@Article{	  ienne97a,
  author	= {P. Ienne and P. Thiran and N. Vassilas},
  title		= {Modified \mbox{self-organizing} feature map algorithms for
		  efficient digital hardware implementation},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  volume	= {8},
  number	= {2},
  pages		= {315--30},
  abstract	= {This paper describes two variants of the Kohonen's
		  self-organizing feature map (SOFM) algorithm. Both variants
		  update the weights only after presentation of a group of
		  input vectors. In contrast, in the original algorithm the
		  weights are updated after presentation of every input
		  vector. The main advantage of these variants is to make
		  available a finer grain of parallelism, for implementation
		  on machines with a very large number of processors, without
		  compromising the desired properties of the algorithm. In
		  this work it is proved that, for one-dimensional (1-D) maps
		  and 1-D continuous input and weight spaces, the strictly
		  increasing or decreasing weight configuration forms an
		  absorbing class in both variants, exactly as in the
		  original algorithm. Ordering of the maps and convergence to
		  asymptotic values are also proved, again confirming the
		  theoretical results obtained for the original algorithm.
		  Simulations of a real-world application using
		  two-dimensional (2-D) maps on 12-D speech data are
		  presented to back up the theoretical results and show that
		  the performance of one of the variants is in all respects
		  almost as good as the original algorithm. Finally, the
		  practical utility of the finer parallelism made available
		  is confirmed by the description of a massively parallel
		  hardware system that makes effective use of the best
		  variant.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  iftekharuddin00a,
  author	= {Iftekharuddin, Khan M. and Razzaque, M. A.},
  title		= {Constraints in distortion-invariant target recognition
		  system simulation},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {4114},
  pages		= {20--31},
  organization	= {North Dakota State Univ},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {Automatic target recognition (ATR) is a mature but active
		  research area. In an earlier paper, we proposed a novel ATR
		  approach for recognition of targets varying in fine
		  details, rotation, and translation using a Learning Vector
		  Quantization (LVQ) Neural Network (NN). The proposed
		  approach performed segmentation of multiple objects and the
		  identification of the objects using LVQNN. In this current
		  paper, we extend the previous approach for recognition of
		  targets varying in rotation, translation, scale, and
		  combination of all three distortions. We obtain the
		  analytical results of the system level design to show that
		  the approach performs well with some constraints. The first
		  constraint determines the size of the input images and
		  input filters. The second constraint shows the limits on
		  amount of rotation, translation, and scale of input objects
		  for system implementation. The second constraint also
		  derives the combined constraints on translation and scale
		  of input objects. We present the simulation verification of
		  the constraints using DARPA's Moving and Stationary Target
		  Recognition (MSTAR) images with different depression and
		  pose angles. The simulation results using MSTAR images
		  verify the analytical constraints of the system level
		  design.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  iftekharuddin00b,
  author	= {Iftekharuddin, K. M. and Dani, A.},
  title		= {An efficient target recognition system for rotated
		  synthetic aperture radar images},
  booktitle	= {Smart Engineering System Design: Neural Networks, Fuzzy
		  Logic, Evolutionary Programming, Data Mining, and Complex
		  Systems. Vol.10. Proceedings of the Artificial Neural
		  Networks in Engineering Conference (ANNIE 2000). ASME, New
		  York, NY, USA},
  year		= {2000},
  volume	= {},
  pages		= {689--96},
  abstract	= {In our previous work (Iftekharuddin and Rentala, 1999), we
		  proposed an algorithm for the determination of exact
		  rotation angle for normal gray scale target images. In a
		  related work (Iftekharuddin et al., 1998), we also achieved
		  rotation invariant target recognition for similar and
		  dissimilar objects using gray scale images. One of the
		  major drawbacks of our previous work is that although it
		  could correctly compute the rotation angle, it failed to
		  differentiate between different but similar target images.
		  In our current work, we propose to develop an algorithm to
		  determine the exact angle of rotation for DARPA moving and
		  stationary target recognition (MSTAR) program's synthetic
		  aperture radar (SAR) images. Our proposed algorithm uses
		  edge information of the targets to differentiate among
		  different but similar MSTAR images. We also propose to
		  implement an integrated distortion-invariant ATR system
		  that would enable one to determine if an MSTAR image is
		  rotated or not, compute the angle of rotation between two
		  images, and obtain rotation-invariant target recognition
		  for similar and dissimilar objects using learning vector
		  quantization (LVQ) neural networks.},
  dbinsdate	= {2002/1}
}

@Article{	  igarashi94a,
  author	= {Igarashi, H. },
  title		= {Solutions for combinatorial optimisation problems using
		  neural computation},
  journal	= {Joho Shori},
  year		= {1994},
  volume	= {35},
  number	= {5},
  pages		= {468--70},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  iglesias99a,
  author	= {Iglesias, R. and Barro, S.},
  title		= {{SOAN}: \mbox{self-organizing} with adaptive neighborhood
		  neural network},
  booktitle	= {Foundations and Tools for Neural Modeling. International
		  Work-Conference on Artificial and Natural Neural Networks,
		  IWANN'99. Proceedings, (Lecture Notes in Computer Science
		  Vol.1606)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {1},
  pages		= {591--600},
  abstract	= {In this work we describe the design and functioning of a
		  new neural network based on vector quantification. This
		  network, which we call SOAN (self-organizing with adaptive
		  neigborhood) has a greater degree of learning flexibility
		  due to the use of an interaction radius between neurones
		  which varies spatially and temporally, and an adaptive
		  neighbourhood function. Secondly, we have introduced
		  mechanisms into the network with the aim of guaranteeing
		  that all of its neurones contribute as far as possible in
		  reducing the quantification error. Finally, we have carried
		  out several experiments obtaining highly favourable results
		  which after having been contrasted with those obtained with
		  the application of the SOM network, confirm the utility and
		  advantages of our approach.},
  dbinsdate	= {oldtimer}
}

@Article{	  iivarinen00a,
  author	= {Iivarinen, Jukka and Heikkinen, Katriina and Rauhamaa,
		  Juhani and Vuorimaa, Petri and Visa, Ari},
  title		= {Defect detection scheme for web surface inspection},
  journal	= {International Journal of Pattern Recognition and
		  Artificial Intelligence},
  year		= {2000},
  volume	= {14},
  number	= {6},
  month		= {Sep},
  pages		= {735--755},
  organization	= {Helsinki Univ of Technology},
  publisher	= {World Scientific Publ Co},
  address	= {Singapore},
  abstract	= {The goal of this work was to develop an improved defect
		  detection scheme for high-speed real-time web surface
		  inspection. This goal was realized by splitting the task
		  into two independent parts: feature extraction and
		  segmentation. Both parts were implemented using efficient
		  algorithms which were implemented in hardware that is
		  suitable and fast enough to be included in a working web
		  inspection system. The proposed scheme is based on some
		  derived texture features and a new self-organizing map
		  variant, the statistical self-organizing map. These
		  techniques offer several improvements over the gray-level
		  thresholding techniques that have been traditionally used
		  in commercial web inspection systems.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  iivarinen00b,
  author	= {Iivarinen, Jukka},
  title		= {Surface defect detection with histogram-based texture
		  features},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {4197},
  pages		= {140--145},
  organization	= {Helsinki Univ of Technology},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {In this paper the performance of two histogram-based
		  texture analysis techniques for surface defect detection is
		  evaluated. These techniques are the co-occurrence matrix
		  method and the local binary pattern method. Both methods
		  yield a set of texture features that are computed from a
		  small image window. The unsupervised segmentation procedure
		  is used in the experiments. It is based on the statistical
		  self-organizing map algorithm that is trained only with
		  fault-free surface samples. Results of experiments with
		  both feature sets are good and there is no clear difference
		  in their performances. The differences are found in their
		  computational requirements where the features of the local
		  binary pattern method are better in several aspects.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  iivarinen94a,
  author	= {Jukka Iivarinen and Kimmo Valkealahti and Ari Visa and
		  Olli Simula},
  title		= {Feature Selection with {S}elf-{O}rganizing {F}Eature
		  {M}aps},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {334--337},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, feature selection, pattern recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  iivarinen94b,
  author	= {Jukka Iivarinen and Teuvo Kohonen and Jari Kangas and Sami
		  Kaski},
  title		= {Visualizing the Clusters on the Self-Organizing Map},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conf. Proc. of Finnish Artificial Intelligence Society},
  pages		= {122--126},
  booktitle	= {Proc. Conf. on Artificial Intelligence Res. in Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {analysis, visualization},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  iivarinen95a,
  author	= {J. Iivarinen and M. Peura and A. Visa},
  title		= {Verification of a Multispectral Cloud Classifier},
  booktitle	= {Proc. 9th Scandinavian Conference on Image Analysis},
  year		= {1995},
  volume	= {1},
  pages		= {591--599},
  dbinsdate	= {oldtimer}
}

@TechReport{	  iivarinen95b,
  author	= {J. Iivarinen and K. Valkealahti and A. Visa and O.
		  Simula},
  title		= {Development of a Cloud Classifier},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1995},
  number	= {A25},
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InCollection{	  iivarinen96a,
  author	= {J. Iivarinen and J. Rauhamaa and A. Visa},
  title		= {Unsupervised segmentation of surface defects},
  booktitle	= {Proceedings of the 13th International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  volume	= {4},
  address	= {Los Alamitos, CA, USA},
  pages		= {356--60},
  dbinsdate	= {oldtimer}
}

@TechReport{	  iivarinen96b,
  author	= {J. Iivarinen and J. Rauhamaa and A. Visa},
  title		= {An Adaptive Approach to Segmentation of Surface Defects},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1996},
  number	= {A34},
  address	= {Espoo, Finland},
  abstract	= {A segmentation scheme to detect surface defects is
		  proposed. An unsupervised neural network, the
		  Self-Organizing Map, is used to estimate the distribution
		  of fault-free samples. An unknown sample is classified as a
		  defect if it differs enough from this estimated
		  distribution. A new scheme for determining this difference
		  is suggested. The scheme makes use of the Voronoi set of
		  each map unit and defines a new rule for finding the
		  best-matchinig map unit. The proposed scheme is general in
		  the sense that it can be applied to fault detection of
		  different types of surfaces. However, it may be necessary
		  to reselect features to take into account the specific
		  properties of the surface type. (Copyright (c) Helskinki;
		  University of Technology 1996.)},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  iivarinen98a,
  author	= {Jukka Iivarinen},
  title		= {Texture Segmentation and Shape Classification with
		  Histogram Techniques and Self-Organizing Maps},
  school	= {Helsinki University of Technology},
  year		= 1998,
  address	= {Espoo},
  dbinsdate	= {oldtimer}
}

@InCollection{	  iivarinen98b,
  author	= {J. Iivarinen and A. Visa},
  title		= {Unsupervised Image Segmentation with the Self-Organizing
		  Map and Statistical Methods},
  booktitle	= {Intelligent Robots and Computer Vision XVII: Algorithms,
		  Techniques, and Active Vision, Proc. SPIE 3522},
  year		= {1998},
  editor	= {D. P. Casasent},
  pages		= {516--526},
  abstract	= {A special type of image segmentation, a two-class
		  segmentation, is considered. Defect detection in quality
		  control applications is a typical two-class problem. The
		  main idea in the paper is to train the two-class classifier
		  with fault-free samples, this is an unexpected approach.
		  The reason is that defects are rare and expensive. The
		  proposed defect detection is based on the following idea:
		  an unknown sample is classified as a defect if it differs
		  enough from the estimated prototypes of fault-free samples.
		  The self-organizing map is used to estimate these
		  prototypes. Surface images are used to demonstrate the
		  proposed image segmentation procedure.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  iivarinen98c,
  author	= {J. Iivarinen and J. Rauhamaa},
  title		= {Surface Inspection of Web Materials Using the
		  Self-Organizing Map},
  booktitle	= {Intelligent Robots and Computer Vision XVII: Algorithms,
		  Techniques, and Active Vision, Proc. SPIE 3522},
  year		= {1998},
  editor	= {D. P. Casasent},
  pages		= {96--103},
  abstract	= {A surface inspection problem is divided into three parts,
		  into an image acquisition part, into a defect detection
		  part that is suitable for hardware implementation, and into
		  a defect classification part that is done in a user's
		  terminal. In the defect detection part extraction of
		  texture features is done and potential defect areas are
		  marked. The proposed scheme is taught only with examples of
		  fault-free surface. In the defect classification part
		  features describing the shape and internal structure of
		  defects are extracted and defects are classified into
		  different defect classes. Examples of defects are used to
		  train the classification system. Use of the self-organizing
		  map in defect detection and in defect classification makes
		  the proposed method adaptable to different types of
		  surfaces and to different types of defects. Only
		  reselection of features may be necessary to cope with
		  different surface and defect characteristics. The results
		  of experiments with base paper samples are encouraging.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  ikeda96a,
  author	= {N. Ikeda and M. Hagiwara},
  title		= {A Proposal of Novel Knowledge Representation (Area
		  Representation) and the Implementation by Neural Network},
  institution	= {IEICE},
  year		= 1996,
  volume	= {95},
  number	= {598},
  pages		= {227--34},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ikeda97a,
  author	= {N. Ikeda and M. Hagiwara},
  title		= {A Proposal of Novel Knowledge Representation (Area
		  Representation) and the Implementation by Neural Network},
  booktitle	= {International Conference on Computational Intelligence and
		  Neuroscience},
  year		= 1997,
  volume	= {III},
  pages		= {430--433},
  dbinsdate	= {oldtimer}
}

@Article{	  ikeda99a,
  author	= {Ikeda, N. and Hagiwara, M.},
  title		= {A novel knowledge representation (area representation) and
		  its implementation by neural network},
  journal	= {Systems and Computers in Japan},
  year		= {1999},
  volume	= {30},
  pages		= {34--42},
  abstract	= {A method of knowledge representation (area
		  representation), and a neural network based on it are
		  proposed. Knowledge representation is the fundamental and
		  important problem in the construction of an intelligent
		  system. Local representation and distributed representation
		  are typical examples but each has some merits and demerits.
		  Area representation is intermediate between local and
		  distributed representation and has the merits of both. The
		  proposed novel neural network based on area representation
		  uses the involution relation, where a lower-level concept
		  is included in a higher-level concept and hierarchical-type
		  representation of knowledge is possible. The network is
		  formed by a number of Kohonen feature map layers that are
		  coupled by a new learning algorithm known as neighborhood
		  Hebbian learning, and as a whole, a multidirectional
		  associative memory is constructed. The effectiveness of
		  area representation and its implementation by neural
		  networks are confirmed by computer simulation, where
		  inheritance of knowledge from higher-level concept or
		  recall from incomplete knowledge are investigated.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ikonen95a,
  author	= {E. Ikonen and U. Kortela},
  title		= {Intelligent online modelling of nonlinear processes},
  booktitle	= {Proceedings of the Third European Control Conference. ECC
		  95},
  publisher	= {Eur. Union Control Assoc},
  year		= {1995},
  volume	= {3},
  editor	= {A. Isidori and S. Bittanti and E. Mosca and A. {De Luca}
		  and M. D. {Di Benedetto} and G. Oriolo},
  address	= {Rome, Italy},
  pages		= {2414--19},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ikonen96a,
  author	= {E. Ikonen and U. Kortela},
  title		= {On-line modelling using adaptive training prototypes with
		  an application to the fluidized-bed combustion process},
  booktitle	= {Control of Power Plants and Power Systems (SIPOWER'95). A
		  Proceedings volume from the IFAC Symposium},
  publisher	= {Pergamon},
  year		= {1996},
  editor	= {R. Canales-Ruiz},
  address	= {Oxford, UK},
  pages		= {147--52},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ilvesmaeki99a,
  author	= {Ilvesmaeki, M. and Luoma, M.},
  title		= {Performance analysis of multi-class Internet traffic
		  classifier in a connection-oriented router environment},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.3842},
  year		= {1999},
  volume	= {3842},
  pages		= {70--81},
  abstract	= {In this work, we analyze the performance of a multi-class
		  Internet traffic classifier primarily in a
		  connection-oriented IP router environment. We define the
		  tasks and related concepts of traffic classification in the
		  Internet and then proceed to construct a multi-class
		  traffic classifier using the Learning Vector Quantization
		  algorithm classifier that has been previously used to
		  divide the traffic into two classes. We show how the
		  functionality of the 2-class LVQ classifier can easily be
		  extended to an arbitrary amount of classes, in this work to
		  three: the hard-interactive, the elastic and the best
		  effort service classes.},
  dbinsdate	= {2002/1}
}

@Article{	  ilvesmaki98a,
  author	= {Ilvesm\"aki, M. and Luoma, M. and Kantola, R.},
  title		= {Learning vector quantization in flow classification of
		  {IP} switched networks},
  journal	= {IEEE GLOBECOM 1998.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  volume	= {5},
  pages		= {3017--22},
  abstract	= {We discuss the flow classification in IP switched
		  networks. Previous work done with flow classification
		  methods has concentrated on optimizing the IP switch
		  performance. We examine the performance of several
		  previously introduced flow classification methods and then
		  we introduce the use of learning vector quantization (LVQ)
		  in flow classification. The LVQ classifier has the ability
		  to offer the user an intuitive traffic profile. The LVQ
		  classifier is found to successfully classify traffic flows
		  with feasible performance requirements while also providing
		  the user with an unambiguous traffic profile.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ilvesmaki98b,
  author	= {Ilvesm\"aki, M. and Kantola, R. and Luoma, M.},
  title		= {Adaptive flow classification in {IP} switching-the
		  measurement based approach},
  booktitle	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1998},
  volume	= {3529},
  pages		= {277--88},
  abstract	= {We first introduce the concept of IP flow classification
		  on a general conceptual level. The intention is to rise
		  above the technological details and create a conceptual
		  point of view on flow classification and closely related
		  issue. Then we move on to study and compare earlier flow
		  classification methods such as the all and selected flow
		  classifier and the packet count flow classifier. The
		  comparison of these methods is done with actual network
		  traffic and various performance metrics are presented. It
		  is found that while the traditional methods of flow
		  classification are found to reduce the resource usage of
		  the network elements, they provide the user with an
		  ambiguous traffic profile at the best. A measurement based
		  learning approach to flow classification is then presented.
		  We first introduce the list based flow classification
		  algorithm to act as the reference point to the novel
		  approach of using learning vector quantization in flow
		  classification. It is found that both the list classifier
		  and the learning vector quantization algorithm, when used
		  in flow classification, require only moderate performance
		  from the network elements while producing an intuitive and
		  user-comprehensible traffic profile being able to adapt to
		  traffic profile changes. The learning vector quantization
		  flow classifier is more sensitive to changing network
		  traffic profiles and functions somewhat more accurately
		  than the list classifier. While all measurement based
		  approaches suffer the delay of analyzing the measurement
		  data our results indicate that measurement based approach
		  to flow classification is able to provide users more
		  accurate service profiles in changing traffic environment
		  while stating reasonable performance demands to the network
		  equipment.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  indrayanto01a,
  author	= {A. Indrayanto and N. M. Allinson},
  title		= {An Investigation into catastrophic interference on a {SOM}
		  network},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {216--23},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  inggs95a,
  author	= {Inggs, M. R. and Robinson, A. R. },
  title		= {Neural approaches to ship target recognition},
  booktitle	= {Record of the IEEE 1995 International Radar Conference},
  year		= {1995},
  pages		= {386--91},
  organization	= {Cape Town Univ. , Rondebosch, South Africa},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  inggs99a,
  author	= {Inggs, M. R. and Robinson, A. D.},
  title		= {Ship target recognition using low resolution radar and
		  neural networks},
  journal	= {IEEE Transactions on Aerospace and Electronic Systems},
  year		= {1999},
  volume	= {35},
  pages		= {386--93},
  abstract	= {The classification of ship targets using low resolution
		  down-range radar profiles together with preprocessing and
		  neural networks is investigated. An implementation of the
		  Fourier-modified discrete Mellin transform is used as a
		  means for extracting features which are insensitive to the
		  aspect angle of the radar. Kohonen's self-organizing map
		  with learning vector quantization (LVQ) is used for the
		  classification of these feature vectors. The use of a
		  feedforward network trained with the backpropagation
		  algorithm is also investigated. The classification system
		  is applied to both simulated and real data sets.
		  Classification accuracies of up to 90% are reported for the
		  real data, provided target aspect angle information is
		  available to within an error not exceeding 30 deg.},
  dbinsdate	= {oldtimer}
}

@Article{	  inoue91a,
  author	= {Inoue, T. and Yamatani, K. and Itoh, K. and Ichioka, Y. },
  title		= {A \mbox{self-organizing} network for vector quantization
		  of spectral images},
  journal	= {International Journal of Optical Computing},
  year		= {1991},
  volume	= {2},
  number	= {4},
  pages		= {385--96},
  month		= {Dec},
  dbinsdate	= {oldtimer}
}

@Article{	  inoue95a,
  author	= {Inoue, T. and Abe, S. and Kayama, M. },
  title		= {{LSI} module placement method using {K}ohonen's feature
		  maps},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1995},
  volume	= {J78D-II},
  number	= {3},
  pages		= {520--31},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  inoue96a,
  author	= {T. Inoue and S. Abe and M. Kayama},
  title		= {LSI module placement using the {K}ohonen network},
  journal	= {Systems and Computers in Japan},
  year		= {1996},
  volume	= {27},
  number	= {6},
  pages		= {92--105},
  dbinsdate	= {oldtimer}
}

@InCollection{	  isasi-vinuela96a,
  author	= {P. Isasi-Vinuela and J. M. Molina-Lopez and A. Navia-
		  Vazquez},
  title		= {Hydroelectric power plant predictive maintenance relying
		  on neural network acoustic module},
  booktitle	= {Progress in Neural Information Processing. Proceedings of
		  the International Conference on Neural Information
		  Processing},
  publisher	= {Springer-Verlag},
  year		= {1996},
  volume	= {2},
  editor	= {S. -I. Amari and L. Xu and L. -W. Chan and I. King and K.
		  -S. Leung},
  address	= {Singapore},
  pages		= {1175--80},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ishida92a,
  author	= {Kazuo Ishida and Yutaka Matsumoto and Norio Okino},
  title		= {The Effect of Correlated Inputs on Discrete {K}ohonen
		  Networks},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {353--357},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ishida93a,
  author	= {Kazuo Ishida and Yutaka Matsumoto and Norio Okino},
  title		= {First Passage Time Analysis of Topologically Correct
		  Feature Maps in Discrete {K}ohonen Networks},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2460--2463},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ishii95a,
  author	= {N. Ishii and C. Kondo and A. Furukawa and K. Yamauchi},
  title		= {Acquisition of state transitions in neural network},
  booktitle	= {Proceedings. IEEE International Joint Symposia on
		  Intelligence and Systems},
  publisher	= {ASME Press},
  year		= {1995},
  editor	= {C. H. Dagli and M. Akay and C. L. P. Chen and B. R.
		  Fernandez and J. Ghosh},
  address	= {New York, NY, USA},
  pages		= {54--9},
  dbinsdate	= {oldtimer}
}

@Article{	  ishikawa00a,
  author	= {Masumi Ishikawa},
  title		= {Recognition of Hand-Gestures Based on Self-Organization
		  Using a DataGlove},
  journal	= {Australian Journal of Intelligent Information Processing
		  Systems},
  year		= {2000},
  key		= {},
  volume	= {6},
  number	= {2},
  pages		= {65--71},
  month		= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ishikawa00b,
  author	= {Ishikawa, H. and Ohta, M. and Kato, K.},
  title		= {A multimedia database support for document warehousing},
  booktitle	= {Proceedings of the IASTED International Conference.
		  Internet and Multimedia Systems and Applications. IASTED,
		  Anaheim, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {418--23},
  abstract	= {Structured data such as sales and customer data are stored
		  in data warehouses. Similarly, less-structured data such as
		  HTML texts, XML data, images, and videos are increasingly
		  accumulated in PC storage due to the spread of WWW. Such
		  less-structured data, collectively called multimedia
		  documents, are also precious as corporate assets. So we
		  need a document warehouse to analyze and manage multimedia
		  documents for corporate-wide information mining and reuse
		  like a data warehouse for structured data. We describe a
		  prototype document warehouse based on a multimedia
		  database, which supports management of simple and compound
		  documents, keyword-based and content-based retrieval,
		  rule-based classification, SOM-based clustering, and XML
		  active query facility.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ishikawa01a,
  author	= {Ishikawa, H. and Ohta, M. and Kato, K.},
  title		= {Document warehousing: A document-intensive application of
		  a multimedia database},
  booktitle	= {Proceedings of the International Workshop on Research
		  Issues in Data Engineering---Distributed Object Management
		  -RIDE-DOM},
  year		= {2001},
  editor	= {Aberer, K. and Liu, L.},
  volume	= {},
  pages		= {25--31},
  organization	= {Tokyo Metropolitan University},
  publisher	= {},
  address	= {},
  abstract	= {Nowadays, structured data such as sales are stored in data
		  warehouses for decision-making. Less-structured data such
		  as html texts, XML data, images, and videos are
		  increasingly accumulated in PC storage due to the spread of
		  the Internet technology such as WWW. Such less-Structured
		  data, collectively called multimedia documents, are also
		  precious as corporate assets. So we need to provide a
		  document warehouse to analyze and manage multimedia
		  documents for corporate-wide information mining and reuse
		  like a data warehouse. As a document-intensive application
		  of a multimedia database, we describe a prototype document
		  warehouse system, which supports management of documents,
		  keyword-Based and content-based retrieval, rule-based
		  classification, SOM-based clustering, and XML active query
		  facility based on ECA rules.},
  dbinsdate	= {2002/1}
}

@Article{	  ishikawa96a,
  author	= {S. Ishikawa and Y. Yokota and A. Iwata and Y. Yoshida},
  title		= {{ECG} coding using orthogonal wavelet transform followed
		  by learning vector quantization},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1996},
  volume	= {J79D-II},
  number	= {9},
  pages		= {1646--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ishikawa97a,
  author	= {H. Ishikawa and K. Kato and M. Ono and N. Yoshizawa and K.
		  Kubota and A. Kanaya},
  title		= {An extended object-oriented approach to a multimedia
		  database system for networked applications},
  booktitle	= {Proceedings. Eighth International Workshop on Database and
		  Expert Systems Applications},
  publisher	= {IEEE Computer Society},
  year		= {1997},
  editor	= {R. R. Wagner},
  address	= {Los Alamitos, CA, USA},
  pages		= {100--5},
  dbinsdate	= {oldtimer}
}

@Article{	  ishikawa98a,
  author	= {Ishikawa, Hiroshi and Kato, Koki and Ono, Miyuki and
		  Yoshizawa, Naomi and Kubota, Kazumi and Kanaya, Akiko},
  title		= {Extended object-oriented database approach to networked
		  multimedia applications},
  journal	= {Proc Int Conf Data Eng},
  year		= {1998},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  number	= {},
  volume	= {},
  pages		= {259--266},
  abstract	= {New multimedia applications, such as digital libraries and
		  document warehousing, require next-generation database
		  systems enabling users to efficiently and flexibly develop
		  and execute such networked applications. To this end, we
		  focus on development of a database system which enables
		  flexible and efficient acquisition, storage, access and
		  retrieval, and distribution and presentation of large
		  amounts of heterogeneous media data. We propose a
		  multimedia database system for networked multimedia
		  applications, based on an OODB model extended with agents.
		  We describe an early prototype multimedia database system
		  to verify the proposed approach, which supports multimedia
		  scripts, keyword-based and content-based view retrieval
		  with QOS control, Self-Organizing-Map-based clustering, and
		  WWW integration.},
  dbinsdate	= {oldtimer}
}

@Article{	  ishikawa99a,
  author	= {Ishikawa, Hiroshi and Kubota, Kazumi and Noguchi, Yasuo
		  and Kato, Koki and Ono, Miyuki and Yoshizawa, Naomi and
		  Kanemasa, Yasuhiko},
  title		= {Document warehousing based on a multimedia database
		  system},
  journal	= {Proc Int Conf Data Eng},
  year		= {1999},
  number	= {},
  volume	= {},
  pages		= {168--173},
  abstract	= {Nowadays, structured data such as sales and business forms
		  are stored in data warehouses for decision makers to use.
		  Further, unstructured data such as emails, html texts,
		  images, videos, and office documents are increasingly
		  accumulated in personal computer storage due to spread of
		  mailing, WWW, and word processing. Such unstructured data,
		  or what we call multimedia documents, are larger in volume
		  than structured data and precious as corporate assets as
		  well. So we need a document warehouse as a software
		  framework where multimedia documents are analyzed and
		  managed for corporate-wide information sharing and reuse
		  like a data warehouse for structured data. We describe a
		  prototype document warehouse system, which supports
		  management of simple and compound documents, keyword-based
		  and content-based retrieval, rule-based classification,
		  SOM-based clustering, and XML data query and view rules.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  isik93a,
  author	= {Can Isik and Farrukh Zia},
  title		= {Fuzzy Logic Control Using a {S}elf-{O}rganizing {M}ap},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {56--65},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  iske00a,
  author	= {Iske, B. and Ruckert, U. and Malmstrom, K. and Sitte, J.},
  title		= {A bootstrapping method for autonomous and in site learning
		  of generic navigation behaviour},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {4},
  pages		= {656--9},
  abstract	= {To understand the behaviour of natural autonomous systems,
		  research is carried out on artificial autonomous agents.
		  The paper focuses on how simple behaviours can be learnt
		  autonomously using a bootstrapping method. Firstly, a two
		  dimensional self-organising map is realised which provides
		  the agent's sense of orientation. Once this relative
		  positioning system has been established, the agent learns
		  to navigate towards a target using the reinforcement
		  learning technique of Q-learning. Since only neural network
		  processing is used, this technique emulates the distributed
		  and adaptive information processing found in natural
		  autonomous systems. Furthermore, due to its generality, the
		  neural implementation developed is transferable to other
		  artificial autonomous agents with different sensors and
		  effector suites.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  israel93a,
  author	= {Peggy Israel and Frank R. Parris},
  title		= {A Modified {LVQ2} Neural Network Classifier Whose
		  Performance Rivals Classical Methods for Pattern
		  Classification},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {III},
  pages		= {445--448},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ito00a,
  author	= {Masahiro Ito and Tsutomu Miyoshi and Hiroshi Masuyama},
  title		= {The Characteristics of the Torus Self-Organizing Map},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {239--44},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@Article{	  ito96a,
  author	= {R. Ito and T. Shida and T. Kindo},
  title		= {Competitive models for unsupervised clustering},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1996},
  volume	= {J79D-II},
  number	= {8},
  pages		= {1390--400},
  dbinsdate	= {oldtimer}
}

@Article{	  ito99a,
  author	= {Y. Ito and S. Omatu},
  title		= {Extended {LVQ} Neural Network Approach to Land Cover
		  Mapping},
  journal	= {IEEE Transactions on Geoscience and Remote Sensing},
  volume	= {37},
  pages		= {313--317},
  year		= {1999},
  abstract	= {A competitive neural network (NN) learned by the OLVQ1
		  algorithm has the potential to produce images for land
		  cover mapping. We propose an extended OLVQ1 that adds
		  learning ratios to the original algorithm. Applying it to
		  SPOT·XS data, we show that higher mapping accuracies can be
		  obtained compared to those of conventional methods.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  iwamida90a,
  author	= {H. Iwamida and S. Katagiri and E. McDermott and Y.
		  Tohkura},
  title		= {A Hybrid Speech Recognition System Using {HMM}s with an
		  {LVQ}-Trained Codebook},
  booktitle	= {Proc. ICASSP-90, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1990},
  volume	= {1},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {489--492},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  iwamida91a,
  author	= {H. Iwamida and others},
  title		= {Speaker-Independent Large Vocabulary Word Recognition
		  Using an {LVQ}/{HMM} Hybrid Algorithm},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {I},
  pages		= {553--556},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  iwamoto98a,
  author	= {K. Iwamoto and H. Tokutaka and K. Yoshihara and K.
		  Fujimura and T. Watanabe and S. Kishida},
  title		= {Application of {SOM} to Quantitative Chemical Data
		  Analysis},
  booktitle	= {Proceedings of the Fifth International Conference on
		  Neural Information Processing},
  year		= {1998},
  address	= {Kitakyushu, Japan},
  pages		= {1122--1125},
  dbinsdate	= {oldtimer}
}

@TechReport{	  iwamoto99a,
  author	= {K. Iwamoto and K. Obu-Cann and H. Tokutaka and K.
		  Fujimura},
  title		= {Clustering by {SOM} (Self-Organizing Maps) and {{MST}}
		  (Minimal Spanning Tree)},
  institution	= {IEICE},
  year		= {1999},
  note		= {(in Japanese)},
  key		= {NC98--157},
  dbinsdate	= {oldtimer}
}

@Article{	  iwata90a,
  author	= {A. Iwata and T. Tohma and H. Matsuo and N. Suzumura},
  title		= {A large scale neural network {'CombNET'}},
  journal	= {Trans. of the Inst. of Electronics, Information and
		  Communication Engineers},
  year		= {1990},
  volume	= {J73D-II},
  number	= {8},
  pages		= {1261--1267},
  month		= {August},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  iwata90b,
  author	= {A. Iwata and T. Tohma and H. Matsuo and N. Suzumura},
  title		= {A large scale neural network {'CombNET'} and its
		  application to {C}hinese character recognition},
  booktitle	= {INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  volume	= {I},
  pages		= {83--86},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  izquierdo91a,
  author	= {A. C. Izquierdo and J. C. Sueiro and J. A. Hernandez
		  Mendez},
  title		= {Self-organizing feature maps and their application to
		  digital coding of information},
  booktitle	= {Proc. IWANN'91, Int. Workshop on Artificial Neural
		  Networks. },
  year		= {1991},
  editor	= {A. Prieto},
  pages		= {401--408},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jacquet00a,
  author	= {Jacquet, W. and Corne, C. and Gresser, J. and Kihl, H.},
  title		= {Parallelized growing-pruning hyperplan-based
		  self-organising maps for function approximation},
  booktitle	= {Smart Engineering System Design: Neural Networks, Fuzzy
		  Logic, Evolutionary Programming, Data Mining, and Complex
		  Systems. Vol.10. Proceedings of the Artificial Neural
		  Networks in Engineering Conference (ANNIE 2000). ASME, New
		  York, NY, USA},
  year		= {2000},
  volume	= {},
  pages		= {69--75},
  abstract	= {Presents an optimized and parallelized variant of the
		  network of self-organized hyperplans HYPSOM
		  (HYperPlan-based Self-Organizing Map), that is meant for
		  the approximation of multivariable functions. This network,
		  initially equipped with a fixed structure, has been the
		  subject of several studies whose aim was to give a growing
		  structure. Our own study, presented in this paper, allowed
		  the validation of a learning algorithm based on the
		  addition and elimination of neurons, thus inducing the
		  adaptation of the network structure to the arbitrary
		  complexity of a function.},
  dbinsdate	= {2002/1}
}

@Article{	  jacquet01a,
  author	= {Jacquet, W. and Corne, C. and Kihl, H. and Gresser, J.},
  title		= {Parallelized growing-pruning hyperplane-based
		  self-organizing maps for function approximation},
  journal	= {International Journal of Smart Engineering System Design},
  year		= {2001},
  volume	= {3},
  number	= {4},
  month		= {},
  pages		= {257--264},
  organization	= {TROP Research Group, University of Mulhouse},
  publisher	= {Taylor and Francis Inc.},
  address	= {},
  abstract	= {This article presents an optimized and parallelized
		  variant of the HYPERplane-based Self-organizing-Map
		  (HYPSOM) algorithm [1], designed for the approximation of
		  multivariable functions. This network, initially equipped
		  with a fixed structure, has been the subject of several
		  studies whose aim was to endow it with a growing structure
		  [2]. This study validates a learning algorithm, based on
		  the addition and elimination of neurons, that permits the
		  adaptation of the network structure to the complexity of an
		  arbitrary function.},
  dbinsdate	= {2002/1}
}

@Article{	  jaime-rivas96a,
  author	= {R. Jaime-Rivas and J. Pineda-Castillo and J. M. Ibarra-
		  Zannatha},
  title		= {Texture discrimination through fractal geometry},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2755},
  pages		= {462--71},
  note		= {(Signal Processing, Sensor Fusion, and Target Recognition
		  V Conf. Date: 8--10 April 1996 Conf. Loc: Orlando, FL, USA
		  Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jakubowicz89a,
  author	= {O. G. Jakubowicz},
  title		= {Multi-layer multi-feature map architecture for situational
		  analysis},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  pages		= {23--30},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  jakubowicz90a,
  author	= {O. G. Jakubowicz},
  title		= {A biological plausible neural network model for processing
		  spatial knowledge},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1990},
  volume	= {1192},
  number	= {2},
  pages		= {528--535},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jalkanen99a,
  author	= {Jalkanen, J. P.},
  title		= {Monitoring a gas turbine with a self organising map},
  booktitle	= {Engineering Applications of Neural Networks. Proceedings
		  of the 5th International Conference on Engineering
		  Applications of Neural Networks (EANN'99)},
  publisher	= {Wydawnictwo Adam Marszalek},
  address	= {Torun, Poland},
  year		= {1999},
  volume	= {},
  pages		= {153--8},
  abstract	= {This paper introduces a process monitoring system made for
		  gas turbines. The monitoring system is based on a principal
		  component analysis as a data pre-processing tool and a
		  self-organising map as a monitoring tool. The paper
		  discusses also another preprocessing method, the
		  independent component analysis. The monitoring system uses
		  existing instrumentation and in the test case there were 19
		  measurements available. The original data space was
		  converted to a two-dimensional subspace using the principal
		  component analysis. The two created latent variables are
		  plotted in x,y-coordinates. The self-organising map is used
		  to monitor relationships between the latent variables. The
		  monitoring system still needs to be tested in a real
		  environment.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jameel94a,
  author	= {Jameel, A. and Koutsougeras, C. },
  title		= {Experiments with {K}ohonen's learning vector quantization
		  in handwritten character recognition systems},
  booktitle	= {Proceedings of the 37th Midwest Symposium on Circuits and
		  Systems},
  year		= {1994},
  editor	= {Bayoumi, M. A. and Jenkins, W. K. },
  volume	= {1},
  pages		= {595--8},
  organization	= {Dept. of Comput. Sci. , Tulane Univ. , New Orleans, LA,
		  USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  james00a,
  author	= {C. James and K. Kobayashi and J. Gotman},
  title		= {Seizure Detection with Self-Organising Feature Map},
  booktitle	= {Artificial Neural Networks in Medicine and Biology,
		  Prodeedings of the ANNIMAB-1 COnference, Göteborg, Sweden,
		  13--16 May 2000},
  pages		= {143--148},
  year		= {2000},
  editor	= {H. Malmgren and M. Boga and L. Niklasson},
  abstract	= {We have developed a system to detect the presence of
		  seizures in the multichannel scalp EEG. At the heart of the
		  system is the Self-Organising Feature Map (SOFM) that has
		  been trained on normal and epileptiform EEG segments of 12
		  patients (64 seizures). Following Preliminary spatial
		  analysis, autoregressinve (AR) parameters are extracted
		  from variable width segments which have been delineated
		  through the use of a non-linear energy operator. The AR
		  parameters are used as feature vectors for the SOFM
		  training process. Following initial training, probability
		  values are automatically assigned to the 'prototype'
		  seizure segments based on the consensus of 3 EEGers. The
		  use of a self-organising network retains objectivity in
		  calculating the prototype seizure segments. Preliminary
		  results (using the training set only) are given here. With
		  a detection threshold of $d_{th}=0.49$ the Sensitivity and
		  Selectivity were both measured at 75% with corresponding
		  false detection rate of 0.5 / hour. These preliminary
		  results indicate that the system shows promise for use as a
		  generic seizure detection system- i.e., a non-patient
		  specific seizure detection system.},
  dbinsdate	= {oldtimer}
}

@Article{	  james00b,
  author	= {James, C. and Fraser, D. and Lowe, D.},
  title		= {Clustering Epileptiform Discharges with an adaptive
		  subspace Self-Organizing Feature Map: A simulation study},
  journal	= {IEE Conference Publication},
  year		= {2000},
  volume	= {},
  number	= {476},
  month		= {},
  pages		= {238--243},
  organization	= {Aston Univ},
  publisher	= {IEE},
  address	= {Stevenage},
  abstract	= {We present the results of a study where synthetically
		  generated Epileptiform Discharges (EDs) superimposed on
		  normal background EEG are clustered by means of Kohonen's
		  Self-Organizing Feature Map (SOFM) using a set of basis
		  vectors representing adaptive subspaces in place of the
		  more usual weight vector at each node of the network. A
		  training set of synthetic EDs is generated using a
		  spherical head model assuming current dipole ED generators.
		  The synthetic EDs are superimposed onto normal background
		  EEG and a preliminary pre-processing stage is used to
		  extract Candidate EDs (CEDs) consisting of ED and non-ED
		  events. The data is clustered using an adaptive subspace
		  algorithm and the resulting map is calibrated using the
		  labeled synthetic data set. Preliminary results show that
		  the SOFM is well suited to clustering the pre-processed
		  CEDs, where strong clusters of `real' EDs are evident. The
		  next step of this research is to further our investigations
		  into the clustering of EDs using real data extracted from
		  the interictal EEG.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  james95a,
  author	= {James, D. L. and Miikkulainen, R. },
  title		= {SARDNET: a \mbox{self-organizing} feature map for
		  sequences},
  booktitle	= {Advances in Neural Information Processing Systems 7},
  year		= {1995},
  editor	= {Tesauro, G. and Touretzky, D. and Leen, T. },
  pages		= {577--84},
  organization	= {Dept. of Comput. Sci. , Texas Univ. , Austin, TX, USA},
  publisher	= {MIT Press},
  address	= {Cambridge, MA, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  james97a,
  author	= {C. J. James and R. D. Jones and P. J. Bones and G. J.
		  Carroll},
  title		= {The \mbox{self-organising} feature map in the detection of
		  epileptiform transients in the {EEG}},
  booktitle	= {Proceedings of the 18th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  `Bridging Disciplines for Biomedicine'},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {3},
  editor	= {H. Boom and C. Robinson and W. Rutten and M. Neuman and H.
		  Wijkstra},
  address	= {New York, NY, USA},
  pages		= {913--14},
  dbinsdate	= {oldtimer}
}

@Article{	  james99a,
  author	= {James, C. J. and Jones, R. D. and Bones, P. J. and
		  Carroll, G. J.},
  title		= {Detection of epileptiform discharges in the {EEG} by a
		  hybrid system comprising mimetic, self-organized artificial
		  neural network, and fuzzy logic stages},
  journal	= {Clinical Neurophysiology},
  year		= {1999},
  volume	= {110},
  pages		= {2049--63},
  abstract	= {A multi-stage system for automated detection of
		  epileptiform activity in the EEG has been developed and
		  tested on prerecorded data from 43 patients. The system is
		  centred on the use of an artificial neural network, known
		  as the self-organising feature map (SOFM), as a novel
		  pattern classifier. The role of the SOFM is to assign a
		  probability value to incoming candidate epileptiform
		  discharges (on a single channel basis). The multi-stage
		  detection system consists of three major stages: mimetic,
		  SOFM, and fuzzy logic. Fuzzy logic is introduced in order
		  to incorporate spatial contextual information in the
		  detection process. Through fuzzy logic it has been possible
		  to develop an approximate model of the spatial reasoning
		  performed by the electroencephalographer. The system was
		  trained on 35 epileptiform EEGs containing over 3,000
		  epileptiform events and tested on a different set of eight
		  EEGs containing 190 epileptiform events (including one
		  normal EEG). Results show that the system has a sensitivity
		  of 55.3% and a selectivity of 82% with a false detection
		  rate of just over seven per hour. In conclusion, based on
		  these initial results the overall performance is favourable
		  when compared with other leading systems in the literature.
		  This encourages the authors to further test the system on a
		  larger population base with the ultimate aim of introducing
		  it into routine clinical use.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jamsa-jounela01a,
  author	= {Jamsa-Jounela, S. -L. and Vermasvuori, M. and Haavisto, S.
		  and Kampe, J.},
  title		= {Industrial application of the intelligent fault diagnosis
		  system},
  booktitle	= {Proceedings of the American Control Conference},
  year		= {2001},
  editor	= {},
  volume	= {6},
  pages		= {4437--4442},
  organization	= {Helsinki University of Technology, Department of Chemical
		  Technology},
  publisher	= {},
  address	= {},
  abstract	= {Process monitoring and fault diagnosis have been widely
		  studied in recent years, and a large number of industrial
		  applications are reviewed. For further improvement of the
		  reliability and safety of the process and the process
		  equipment, the automatic early detection and localisation
		  of faults is of high interest. This paper presents the
		  intelligent process fault diagnosis system. The system is
		  capable of detecting faults of the process and the
		  equipment. The process monitoring is performed using
		  Kohonen Self-Organizing Maps (SOM) and the analysis of the
		  equipment failures are integrated to the system. The
		  structure of the integrated system is described and its
		  performance is illustrated by case studies.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  janet95a,
  author	= {Janet, J. A. and Gutierrez-Osuna, R. and Chase, T. A. and
		  White, M. and Luo, R. C. },
  title		= {Global self-localization for autonomous mobile robots
		  using \mbox{self-organizing} {K}ohonen neural networks},
  booktitle	= {Proceedings of the 1995 IEEE/RSJ International Conference
		  on Intelligent Robots and Systems. Human Robot Interaction
		  and Cooperative Robots},
  year		= {1995},
  volume	= {3},
  pages		= {504--9},
  organization	= {Dept. of Electr. \& Comput. Eng. , North Carolina State
		  Univ. , Raleigh, NC, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  janet95b,
  author	= {J. A. Janet and R. Gutierrez-Osuna and T. A. Chase and M.
		  White and R. C. Luo},
  title		= {Global self-localization for autonomous mobile robots
		  using region-and feature-based neural networks},
  booktitle	= {Proceedings of the 1995 IEEE IECON. 21st International
		  Conference on Industrial Electronics, Control, and
		  Instrumentation},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1142--7},
  dbinsdate	= {oldtimer}
}

@Article{	  janet97a,
  author	= {J. A. Janet and R. Gutierrez and T. A. Chase and M. W.
		  White and J. C. {Sutton III}},
  title		= {Autonomous mobile robot global self-localization using
		  {K}ohonen and region-feature neural networks},
  journal	= {Journal of Robotic Systems},
  year		= {1997},
  volume	= {14},
  number	= {4},
  pages		= {263--82},
  dbinsdate	= {oldtimer}
}

@InCollection{	  janet97b,
  author	= {J. A. Jan{\'e}t and S. M. Soggins and M. W. White and J.
		  C. {Sutton, III} and E. Grant and W. E. Snyder},
  title		= {Using a Hyper-Ellipsoid Clustering {K}ohonen for
		  Autonomouos Mobile Robot Map Building, Place Recognition
		  and Motion Planning},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {III},
  pages		= {1699--1704},
  dbinsdate	= {oldtimer}
}

@InCollection{	  janet98a,
  author	= {J. A. Janet and M. W. White and M. G. Kay and J. C.
		  {Sutton III} and J.J. Brickley},
  title		= {Fusing a hyper-ellipsoid clustering {K}ohonen network with
		  the {J}ulier-{U}hlmann-{K}alman filter for autonomous
		  mobile robot map building and tracking},
  booktitle	= {Proceedings of the 1998 IEEE International Conference on
		  Robotics and Automation},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1405--10},
  dbinsdate	= {oldtimer}
}

@Article{	  jang93a,
  author	= {Gyu-Sang Jang},
  title		= {A comparison of neural network performance for seismic
		  phase identification},
  journal	= {J. Franklin Inst. },
  year		= {1993},
  volume	= {330},
  number	= {3},
  pages		= {505--524},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  jang93b,
  author	= {Jang, Gyu Sang and Dowla, Farid and Vemuri, V.},
  title		= {Performance comparison of {SOM} neural network paradigms
		  for solving the seismic phase identification problem.},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering, Society of photo-optical instrumentation
		  engineers.},
  year		= {1993},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  number	= {},
  volume	= {1721},
  pages		= {265--276},
  abstract	= {This paper compares the performance of four different
		  types of neural networks while solving the seismic phase
		  identification problem, an important problem in the
		  Comprehensive (Nuclear) Test Ban Treaty (CTBT) verification
		  research. Central to the motivation to use neural networks
		  in CTBT research is the desire to improve the accuracy of
		  discriminating earthquake-generated seismograms from
		  explosion-generated seismograms. Traditional techniques of
		  analysis and interpretation of seismic events are typically
		  comprised of a series of complex steps involving
		  sophisticated signal processing as well as many manual
		  tasks. One of these steps is the problem of phase
		  identification, namely the discrimination of distinct
		  seismic waves within a seismogram and the focus of this
		  paper is on the use of neural networks in performing phase
		  identification. Using a database of 75 earthquakes and 75
		  underground nuclear explosions, the performance of several
		  types of neural networks was compared. The performance of
		  probabilistic neural network (PNN), networks using radial
		  basis function (RBF) as well as learning vector
		  quantization (LVQ) is compared with a specially designed
		  back propagation (BP) algorithm that combines the
		  conjugate-gradient method with a weight-elimination
		  strategy. The results indicate that the latter outperformed
		  all other tested methods.},
  dbinsdate	= {oldtimer}
}

@Article{	  jang97a,
  author	= {Inho Jang and Jongtae Rhee},
  title		= {Generalized machine cell formation considering material
		  flow and plant layout using modified \mbox{self-organizing}
		  feature maps},
  journal	= {Computers \& Industrial Engineering},
  year		= {1997},
  volume	= {33},
  number	= {3--4},
  pages		= {457--60},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jarvi94a,
  author	= {Antero J{\"{a}}rvi and Jaakko J{\"{a}}rvi},
  title		= {Shape Recognition with Modular Neural Networks},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conf. Proc. of Finnish Artificial Intelligence Society},
  pages		= {104--112},
  booktitle	= {Proc. Conf. on Artificial Intelligence Res. in Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {application, pattern recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jeney01a,
  author	= {Jeney, G. and Levendovszky, J. and Kovas, L.},
  title		= {Blind adaptive stochastic neural network for multiuser
		  detection},
  booktitle	= {IEEE Vehicular Technology Conference},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {1868--1872},
  organization	= {Budapest Univ. of Technol. and Econ., Department of
		  Telecommunications},
  publisher	= {},
  address	= {},
  abstract	= {In this paper some blind adaptive methods are introduced
		  for multiuser detection (MUD). The detector architecture
		  contains a channel identifier followed by a stochastic
		  Hopfield net. Blind channel identification is proposed to
		  be carried out by either the Kohonen algorithm or by a
		  novel adaptive decorrelation technique. Based on the
		  estimated channel parameters the stochastic Hopfield net
		  implements a near optimal decision. Besides describing the
		  related algorithms, the paper contains extensive
		  simulations to evaluate the performance of the proposed
		  detector structures.},
  dbinsdate	= {2002/1}
}

@Article{	  jeng00a,
  author	= {Jeng, Jeng Ywan and Mau, Tzuoh Fei and Leu, Shyeu Ming},
  title		= {Prediction of laser butt joint welding parameters using
		  back propagation and learning vector quantization
		  networks},
  journal	= {Journal of Materials Processing Technology},
  year		= {2000},
  number	= {1},
  volume	= {99},
  pages		= {207--218},
  abstract	= {Laser welding parameters include not only the laser power,
		  focused spot size, welding speed, focused position, etc.,
		  but also the welding gap and the alignment of the laser
		  beam with the center of the welding gap, these latter two
		  parameters being critical for a butt joint. These
		  parameters are controllable in the actual operation of
		  laser welding, but are interconnected and extremely
		  non-linear, such problems limit the industrial
		  applicability of the laser welding for butt joints. The
		  neural network technique is a useful tool for predicting
		  the operation parameters of a non-linear model. Back
		  propagation (BP) and learning vector quantization (LVQ)
		  networks are presented in this paper to predict the laser
		  welding parameters for butt joints. The input parameters of
		  the network include workpiece thickness and welding gap,
		  whilst the output parameters include optimal focused
		  position, acceptable welding parameters of laser power and
		  welding speed, and welding quality, including weld width,
		  undercut and distortion for the associated power and speed
		  used. The results of this research show a comprehensive and
		  usable prediction of the laser welding parameters for butt
		  joints using BP and LVQ networks. As a result, the
		  industrial applicability of laser welding for butt joints
		  can be expanded widely.},
  dbinsdate	= {oldtimer}
}

@Article{	  jennings93a,
  author	= {Jennings, A. M. and Graham, J. },
  title		= {A neural network approach to automatic chromosome
		  classification},
  journal	= {Physics in Medicine and Biology},
  year		= {1993},
  volume	= {38},
  number	= {7},
  pages		= {959--70},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@TechReport{	  jensen91a,
  author	= {Ole Bystrup Jensen and Martin Olsen and Thomas Rohde},
  title		= {Automatic Speech Recognition \& Neural Networks},
  institution	= {Computer Science Department, Aarhus University},
  year		= {1991},
  number	= {DAIMI IR-101},
  address	= {Aarhus, Denmark},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@Article{	  jeon00a,
  author	= {Young Jae Jeon and Jae Chul Kim},
  title		= {Application of {LVQ}3 for dissolved gas analysis for power
		  transformer},
  journal	= {Transactions-of-the-Korean-Institute-of-Electrical-Engineers,-A}
		  ,
  year		= {2000},
  volume	= {49},
  pages		= {31--6},
  abstract	= {To enhance the fault diagnosis ability for the dissolved
		  gas analysis (DGA) of a power transformer, this paper
		  proposes a learning vector quantization (LVQ) for the
		  incipient fault recognition. LVQ is suitable especially for
		  pattern recognition such as the fault diagnosis of power
		  transformers using DGA because it improves the performance
		  of the Kohonen neural network by placing emphasis on the
		  classification around the decision boundary. The
		  capabilities of the proposed diagnosis system for the
		  transformer DGA decision support have been extensively
		  verified through practical test data collected from the
		  Korea Electrical Power Corporation.},
  dbinsdate	= {2002/1},
  merjanote     = {last name assumed}
}

@Article{	  jeon97a,
  author	= {J. G. Jeon and Y. H. Kim and G. M. Park and K. T. Park},
  title		= {Multi-target tracking system using texture},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3024},
  number	= {pt. 1},
  pages		= {229--36},
  note		= {(Visual Communications and Image Processing '97 Conf.
		  Date: 12--14 Feb. 1997 Conf. Loc: San Jose, CA, USA Conf.
		  Sponsor: SPIE; Soc. Imaging Sci. \& Technol. ; IEEE
		  Circuits \& Syst. Soc)},
  dbinsdate	= {oldtimer}
}

@Article{	  jeong94a,
  author	= {Bong-Sik Jeong and Soo-Yound Lee},
  title		= {Automatic mesh generator based on \mbox{self-organizing}
		  finite-element tessellation for three-dimensional
		  electromagnetic field problems},
  journal	= {Microwave and Optical Technology Letters},
  year		= {1994},
  volume	= {7},
  number	= {15},
  pages		= {711--14},
  month		= {Oct},
  dbinsdate	= {oldtimer}
}

@Article{	  jeong95a,
  author	= {Bong-Sik Jeong and Soo-Young Lee and Chang-Hoi Ahn},
  title		= {Automatic mesh generator based on \mbox{self-organizing}
		  finite-element tessellation for electromagnetic field
		  problems},
  journal	= {IEEE Transactions on Magnetics},
  year		= {1995},
  volume	= {31},
  number	= {3},
  pages		= {1757--60},
  month		= {May},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jervis92a,
  author	= {B. W. Jervis and M. R. Saatchi and A. Lacey and G. M.
		  Papadourakis and M. Vourkas and T. Roberts and E. M. Allen
		  and N. R. Hudson and S. Oke},
  title		= {The application of unsupervised artificial neural networks
		  to the sub-classification of subjects at-risk of
		  {H}untington's {D}isease},
  booktitle	= {IEE Colloquium on 'Intelligent Decision Support Systems
		  and Medicine' (Digest No. 143)},
  year		= {1992},
  pages		= {5/1--9},
  organization	= {IEE},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  jervis94a,
  author	= {Jervis, B. W. and Saatchi, M. R. and Lacey, A. and
		  Roberts, T. and Allen, E. M. and Hudson, N. R. and Oke, S.
		  and Grimsley, M. },
  title		= {Artificial neural network and spectrum analysis methods
		  for detecting brain diseases from the {CNV} response in the
		  electroencephalogram},
  journal	= {IEE Proceedings-Science, Measurement and Technology},
  year		= {1994},
  volume	= {141},
  number	= {6},
  pages		= {432--40},
  month		= {Nov},
  dbinsdate	= {oldtimer}
}

@Article{	  ji00a,
  author	= {Ji, C. Y.},
  title		= {Land-use classification of remotely sensed data using
		  Kohonen Self-Organizing Feature Map neural networks},
  journal	= {PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING},
  year		= {2000},
  volume	= {66},
  number	= {12},
  month		= {DEC},
  pages		= {1451--1460},
  abstract	= {The use of Kohonen Self-Organizing Feature Map (KSOFM, or
		  feature map) neural networks for land-use/land-cover
		  classification from remotely sensed data is presented.
		  Different from the traditional multi-layer neural networks,
		  the KSOFM is a two-layer network that creates class
		  representation by self-organizing the connection weights
		  from the input patterns to the output layer. A test of the
		  algorithm is conducted by classifying a Landsat Thematic
		  Mapper (TM) scene for seven land-use/land-cover types,
		  benchmarked with the maximum-likelihood method and the Back
		  Propagation (BP) network. The network outperformes the
		  maximum-likelihood method for per-pixel classification when
		  four spectral bands are used. A further increase in
		  classification accuracy is achieved when neighborhood
		  pixels are incorporated. A similar accuracy is obtained
		  using the sp networks for classifications both with and
		  without neighborhood information. The feature map network
		  has the advantage of faster learning but has the drawback
		  of being a slow classification process. Learning by the
		  feature map is affected by a number of factors such as the
		  network size, the codebooks partitioning, the available
		  training samples, and the selection of the learning rate.
		  The feature map size controls the accuracy at which class
		  borders are formed, and a large mop may be used to obtain
		  accurate class representation. If is concluded that the
		  feature map method is a viable alternative for land-use
		  classification of remotely sensed data.},
  dbinsdate	= {2002/1}
}

@Article{	  ji01a,
  author	= {Pyeong Shik Ji and Jong Pil Lee and Jae Yoon Lim},
  title		= {Grouping method of loads to verify the aggregation of
		  component load models},
  journal	= {Transactions-of-the-Korean-Institute-of-Electrical-Engineers,-A}
		  ,
  year		= {2001},
  volume	= {50},
  pages		= {172--9},
  abstract	= {A component based method out of load modeling is to
		  aggregate the component load model according to the
		  composition rate of each component load at the load bus
		  based on circuit theory. But most component loads respond
		  to complex nonlinear characteristics with respect to
		  voltage and frequency variation due to the control
		  techniques and semiconductor elements applied to component
		  load. This approach needs to be verified through actual
		  experiment of the aggregation of the component load. To
		  identify this well known aggregation method, this paper
		  proposes a classifying method of component-load
		  characteristics for component loads to group by
		  quantitative analysis. The component load characteristics
		  were divided into several types by KSOM (Kohonen self
		  organizing map), which can classify a multi-dimension
		  vector, component load pattern, into a two-dimension
		  vector. Some ambiguous cases occurring from KSOM are
		  classified by the proposed closing degree.},
  dbinsdate	= {2002/1},
  merjanote     = {last name assumed, also Ji is Pehing-Shik Ji's last name in 
                   other papers too}
}

@Article{	  ji97a,
  author	= {Pyeong-Shik Ji and Sang-Cheon Nam and Jae-Yoon Lim and
		  Jung-Hoon Kim and Seung-Chan Chang},
  title		= {Load pattern classification using {K}ohonen
		  \mbox{self-organizing} map and fuzzy},
  journal	= {Transactions of the Korean Institute of Electrical
		  Engineers},
  year		= {1997},
  volume	= {46},
  number	= {9},
  pages		= {1314--18},
  dbinsdate	= {oldtimer}
}

@Article{	  jialong99a,
  author	= {Jialong, He and Li, Liu and Palm, G.},
  title		= {A discriminative training algorithm for VQ-based speaker
		  identification},
  journal	= {IEEE Transactions on Speech and Audio Processing},
  year		= {1999},
  volume	= {7},
  pages		= {353--6},
  abstract	= {A novel method, referred to as group vector quantization
		  (GVQ), is proposed to train VQ codebooks for closed-set
		  speaker identification. In GVQ training, speaker codebooks
		  are optimized for vector groups rather than for individual
		  vectors. An evaluation experiment has been conducted to
		  compare the codebooks trained by the Linde-Buzo-Grey (LBG),
		  the learning vector quantization (LVQ), and the GVQ
		  algorithms. It is shown that the frame scores from the GVQ
		  trained codebooks are less correlated, therefore, the
		  sentence level speaker identification rate increases more
		  quickly with the length of test sentences.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jiang91a,
  author	= {J. -X. Jiang and K. -C. Yi and Z. Hui},
  title		= {A new self-organization algorithm of forming a phoneme
		  map},
  booktitle	= {Proc. EUROSPEECH-91, 2nd European Conf. on Speech
		  Communication and Technology},
  year		= {1991},
  volume	= {I},
  pages		= {125--128},
  organization	= {Assoc. Belge Acoust. ; Assoc. Italiana di Acustica; CEC;
		  et al},
  publisher	= {Istituto Int. Comunicazioni},
  address	= {Genova, Italy},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jiang92a,
  author	= {J. W. Jiang and M. Jabri},
  title		= {A New Self-Organisation Strategy for Floorplan Design},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  volume	= {II},
  pages		= {510--515},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {Floorplanning of {VLSI} circuits. The presented approach
		  sounds healthy in this difficult combinatorial problem. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jiang92b,
  author	= {Jun Wei Jiang and M. Jabri},
  title		= {A new self-organisation strategy for floorplan design},
  booktitle	= {Proc. ACNN'92, Third Australian Conf. on Neural Networks},
  year		= {1992},
  editor	= {P. Leong and M. Jabri},
  pages		= {235--238},
  organization	= {Australian Neurosci. Soc. ; Australian Telecoms \&
		  Electron. Res. Board; et al},
  publisher	= {Sydney Univ},
  address	= {Sydney, NSW, Australia},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jiang94a,
  author	= {Xin Jiang and Zhengyu Gong and Fan Sun and huisheng Chi},
  title		= {A Speaker Recognition System Based on Auditory Model},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {IV},
  pages		= {595--600},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, speech recognition, hybrid},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jiang94b,
  author	= {Jiang, H. and Penman, J. },
  title		= {Using {K}ohonen feature maps to monitor the condition of
		  synchronous generators},
  booktitle	= {Proceedings of the Workshop on Neural Network Applications
		  and Tools},
  year		= {1994},
  editor	= {Lisboa, P. J. G. and Taylor, M. J. },
  pages		= {89--94},
  organization	= {Dept. of Eng. , Aberdeen Univ. , UK},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jiang95a,
  author	= {Jianmin Jiang},
  title		= {Performance assessment of five neural networks and
		  architecture design for image vector quantization},
  booktitle	= {IEE Colloquium 'Low Bit Image Coding' (Digest No.
		  1995/154)},
  year		= {1995},
  pages		= {2/1--6},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  jianrong97a,
  author	= {Tan Jianrong and Wei Xinting and Huang Chao},
  title		= {Assembly modeling of product information based on
		  self-organization},
  booktitle	= {Proceedings of the International Conference on
		  Manufacturing Automation, ICMA},
  publisher	= {Univ. Hong Kong},
  year		= {1997},
  volume	= {1},
  editor	= {S. T. Tan and T. N. Wong and I. Gibson},
  address	= {Hong Kong},
  pages		= {158--63},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jianxin91a,
  author	= {Jiang Jianxin and Yi Kechu and Hu Zheng},
  title		= {A new self-organization algorithm of forming a phoneme
		  map},
  booktitle	= {EUROSPEECH 91. 2nd European Conference on Speech
		  Communication and Technology Proceedings},
  year		= {1991},
  volume	= {1},
  pages		= {125--8},
  organization	= {Dept. of Inf. Eng. , Xidian Univ. , Xi'an, China},
  publisher	= {Istituto Int. Comunicazioni},
  address	= {Genova, Italy},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jianxin92a,
  author	= {Jiang Jianxin and Hu Zheng and Liu Feng},
  title		= {A hybrid neural-fuzzy-neural framework for speech
		  recognition},
  booktitle	= {IJCNN International Joint Conference on Neural Networks},
  year		= {1992},
  volume	= {4},
  pages		= {643--8},
  organization	= {Xidian Univ. , Xi'an, China},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jiegu95a,
  author	= {Li Jiegu and Liu Chaoyuan and Qi Zeyu},
  title		= {On the extraction of the face features},
  booktitle	= {Proceedings of Europe---China Workshop on Geometrical
		  Modelling and Invariants for Computer Vision},
  year		= {1995},
  editor	= {Mohr, R. and Chengke, W. },
  pages		= {321--5},
  organization	= {Shanghai Jiao Tong Univ. , Image Processing Inst. ,
		  China},
  publisher	= {Xidian Univ. Press},
  address	= {Xi'an, China},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jin00a,
  author	= {Jin, X. H. and Wang, C. C. and Cheng, T. C. and Li, F. Q.
		  and Dong, X.Z. and Jiang, Lei and Zhu, D.H.},
  title		= {Comparison of {PD} classification capabilities for
		  transformer failure and typical noise models with neural
		  network applications},
  booktitle	= {Conference on Electrical Insulation and Dielectric
		  Phenomena (CEIDP), Annual Report},
  year		= {2000},
  editor	= {},
  volume	= {1},
  pages		= {288--291},
  organization	= {Univ of Southern California},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper presents three kinds of Neural Networks (NNs)
		  for classifying Partial Discharges (PDs) of failure models
		  which are extracted from internal insulation configuration
		  of power transformer and typical noises in substations. The
		  test results show that three networks can fairly classify
		  the designed models. The performance of Back-Propagation
		  (BP), Learning Vector Quantization (LVQ) and Fuzzy ARTMAP
		  networks is evaluated. The classification accuracies
		  obtained from the three networks are compared with each
		  other. In addition to the classification accuracy, the
		  neural networks are analyzed for their generalization
		  capability and stability of the results. Best results
		  (accuracy and convergence time) are obtained with Fuzzy
		  ARTMAP network. Classification rate for the designed models
		  is 100% at voltage level 3. Simulation results server to
		  illustrate the properties of various networks we used as
		  well as the stability with respect to various critical
		  parameters.},
  dbinsdate	= {2002/1}
}

@Article{	  jing00a,
  author	= {Jing, Zhihong and Zhao, Junwei and Jing, Dong and Xia,
		  Junli and Li, Hong and Li, Ganghu},
  title		= {New method for underwater target classification},
  journal	= {Xibei Gongye Daxue Xuebao/Journal of Northwestern
		  Polytechnical University},
  year		= {2000},
  volume	= {18},
  number	= {3},
  month		= {Aug},
  pages		= {392--395},
  organization	= {Northwestern Polytechnical Univ},
  publisher	= {NPU},
  address	= {},
  abstract	= {A method based on fuzzy neural network (FNN) is presented
		  for classification of underwater targets to overcome the
		  deficiencies of the existing methods, which neglect some
		  fuzzy information in target patterns. The fuzzy c-mean
		  (FCM) algorithm is combined with Kohonen clustering network
		  (KCN) to form fuzzy Kohonen clustering network (FKCN). The
		  features of the targets are extracted by bi-spectrum to
		  reduce the effect of non-Gaussian noise. The learning
		  algorithm of FKCN is given. The algorithm modifies its
		  weights according to the cost function of FCM, so it
		  overcomes the shortcoming of KCN, which is dependent on
		  input sequence. Experimental results for three kinds of
		  real underwater target signals from passive sonar have
		  shown that FKCN is better than KCN.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  jinsang00a,
  author	= {Jinsang Kim and Chen, T.},
  title		= {Segmentation of image sequences using {SOFM} networks},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {3},
  pages		= {869--72},
  abstract	= {We present a segmentation technique for image sequences
		  using self organizing feature maps (SOFM). Our goal is to
		  develop a method which can identify homogeneous regions in
		  a frame to represent higher level objects for content based
		  manipulation of image sequences. The proposed scheme
		  extracts pixel based multiple features, such as motion and
		  textures, and then, different weights are applied to each
		  feature component based on motion confidence measures.
		  These multiple feature spaces are transformed to one
		  dimensional label space by using the SOFM. The
		  oversegmentation neural network outputs are merged in order
		  to generate desired segmentation resolution. Our
		  experimental results show the validity of the proposed
		  scheme.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  jinsang00b,
  author	= {Jinsang Kim and Chen, T.},
  title		= {An integrated approach to image sequence segmentation},
  booktitle	= {NORSIG2000. Nordic Signal Processing Symposium. Linkoping
		  Univ, Linkoping, Sweden},
  year		= {2000},
  volume	= {},
  pages		= {391--4},
  abstract	= {Semantic object segmentation is an important step for
		  object based coding, content based access and manipulation.
		  We propose a segmentation scheme for image sequences which
		  provides initial region information for the semantic object
		  representation of those applications. Our objective is to
		  develop a segmentation method which has a hardware-friendly
		  architecture, and incorporates static and dynamic features
		  simultaneously in one scheme. In the initial stage, a
		  multiple feature space consisting of luminance
		  (chrominance), motion, and texture features is transformed
		  to a one-dimensional label space by using self organizing
		  feature map (SOFM) neural networks. The next stage is an
		  edge fusion in which edge information is incorporated into
		  the neural network outputs to generate more precisely
		  located boundaries of the segmentation. The segmentation
		  results of both gray level image sequences and color image
		  sequences are evaluated using evaluation metrics. The
		  results show the validity of the proposed scheme.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  jirina00a,
  author	= {Marcel Jirina and Miroslav Snorek},
  title		= {Dynamically Constructed Topological Lattices for the
		  {SOFM}},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Department of Cybernetics and Department of Computers,
		  Faculty of Electrical Engineering, Czech Technical
		  University Prague},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  jirina96a,
  author	= {Jirina, Marcel Jr},
  title		= {Neuro-fuzzy network using extended {K}ohonen's map},
  journal	= {Neural Network World},
  year		= {1996},
  number	= {4},
  volume	= {6},
  pages		= {619--624},
  abstract	= {This paper presents a new approach to design a fuzzy
		  inference system based on an extended self-organizing
		  Kohonen's neural network. The neural-fuzzy network uses
		  standard Kohonen's network which is extended so that its
		  outputs are connected with inputs of the next single
		  perceptron layer. This new composed network has better
		  approximating abilities to represent a full fuzzy inference
		  system. The network is not unsupervised now and it allows
		  to train it on input-output data through supervised
		  learning. Features of the proposed method are shown on
		  sample data obtained from a biological purification
		  plant.},
  dbinsdate	= {oldtimer}
}

@Article{	  jiyong98a,
  author	= {Jiyong, Ma and Wen, Gao},
  title		= {Supervised Learning Gaussian Mixture Model},
  journal	= {Journal of Computer Science and Technology},
  year		= {1998},
  number	= {5},
  volume	= {13},
  pages		= {471--474},
  abstract	= {The traditional Gaussian Mixture Model (GMM) for pattern
		  recognition is an unsupervised learning method. The
		  parameters in the model are derived only by the training
		  samples in one class without taking into account the effect
		  of sample distributions of other classes, hence, its
		  recognition accuracy is not ideal sometimes. This paper,
		  introduces an approach for estimating the parameters in GMM
		  in a supervising way. The Supervised Learning Gaussian
		  Mixture Model (SLGMM) improves the recognition accuracy of
		  the GMM. An experimental example has shown its
		  effectiveness. The experimental results have shown that the
		  recognition accuracy derived by the approach is higher than
		  those obtained by the Vector Quantization (VQ) approach,
		  the Radial Basis Function (RBF) network model, the Learning
		  Vector Quantization (LVQ) approach and the GMM. In
		  addition, the training time of the approach is less than
		  that of Multilayer Perceptron (MLP).},
  dbinsdate	= {oldtimer}
}

@InCollection{	  jockusch90a,
  author	= {S. Jockusch},
  title		= {A neural network which adapts its structure to a given set
		  of patterns},
  booktitle	= {Parallel Processing in Neural Systems and Computers},
  editor	= {R. Eckmiller and G. Hartmann and G. Hauske},
  year		= {1990},
  pages		= {169--172},
  address	= {Amsterdam, Netherlands},
  publisher	= {Elsevier},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jockusch93a,
  author	= {Stefan Jockusch and Helge Ritter},
  title		= {Synthetic Face Expressions Generated by Self Organizing
		  Maps},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2077--2080},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jockusch94a,
  author	= {Stefan Jockusch and Helge Ritter},
  title		= {{S}elf {O}rganizing {M}aps and {LLM} networks for Image
		  Normalization, Generation, and Animation},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1105--1108},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, image analysis},
  dbinsdate	= {oldtimer}
}

@Article{	  jockusch94b,
  author	= {Stefan Jockusch and Helge Ritter},
  title		= {{Self-Organizing Maps}: Local Competition and Evolutionary
		  Optimization},
  journal	= {Neural Networks},
  year		= {1994},
  volume	= {7},
  number	= {8},
  pages		= {1229--1239},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jockusch94c,
  author	= {Jockusch, S. and Ritter, H. },
  title		= {Analysis-by-synthesis and example based animation with
		  topology conserving neural nets},
  booktitle	= {Proceedings ICIP-94},
  year		= {1994},
  volume	= {3},
  pages		= {953--7},
  organization	= {Dept. of Inf. Sci. , Bielefeld Univ. , Germany},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jockusch99a,
  author	= {Jockusch, J. and Ritter, H.},
  title		= {An instantaneous topological mapping model for correlated
		  stimuli},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {1},
  pages		= {529--34},
  abstract	= {Topology-representing networks, such as the SOM and the
		  growing neural gas (GNG) are powerful tools for the
		  adaptive formation of maps of feature and state spaces for
		  a broad range of applications. However, these algorithms
		  suffer severe difficulties when their training inputs are
		  strongly correlated. This makes them unsuitable for the
		  online formation of maps of state spaces whose exploration
		  occurs most naturally along trajectories, which is typical
		  in many applications in the fields of robotics and process
		  control. Based on investigations of the SOM and the GNG for
		  these cases, we devise a new network model, the
		  "instantaneous topological map" (ITM) that is able to
		  overcome these difficulties and form maps from strongly
		  correlated stimulus sequences in a fast and robust manner.
		  This makes the ITM highly suitable for mapping of state
		  spaces in control tasks in general and especially in
		  robotics, where workspace limitations are complex and
		  probably more easily explored than analyzed and coded by
		  hand.},
  dbinsdate	= {oldtimer}
}

@Article{	  johnson01a,
  author	= {Johnson, M. D. and Rokhsaz, K.},
  title		= {Using artificial neural networks and self-organizing maps
		  for detection of airframe icing},
  journal	= {Journal of Aircraft},
  year		= {2001},
  volume	= {38},
  number	= {2},
  month		= {March/April 2001},
  pages		= {224--230},
  organization	= {Wichita State University},
  publisher	= {},
  address	= {},
  abstract	= {A method of using artificial neural networks (ANNs) and
		  Kohonen self-organizing maps (SOMs) to detect airframe ice
		  is proposed and investigated. It is hypothesized that ANN
		  systems trained on the aircraft dynamics in real time would
		  converge to different connection weights for iced and clean
		  aircraft. Kohonen SOMs are proposed for detecting these
		  differences automatically and, therefore, recognizing
		  airframe ice accretion. This approach is shown to be
		  capable of acting in an advisory role for the flight crew.
		  The fidelity of the approach is shown to depend on the
		  level of atmospheric turbulence, as well as on the
		  magnitude of the elevator input.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  johnson89a,
  author	= {Martin Johnson and Nigel Allinson},
  title		= {Implementation of a variable cluster self organising
		  algorithm for high speed unsupervised pattern
		  classification (lost in $[0,1]^N$ space)},
  booktitle	= {Proc. SPIE---The Internatioanl Society for Optical
		  Engineering},
  volume	= {1197},
  year		= {1989},
  pages		= {109--116},
  organization	= {SPIE},
  publisher	= {Int Soc for Optical Engineering},
  address	= {Bellingham, WA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  johnson90a,
  author	= {M. J. Johnson and M. Brown and N. M. Allinson},
  title		= {Multidimensional self-organisation},
  booktitle	= {Proc. Int. Workshop on Cellular Neural Networks and their
		  Applications},
  year		= {1990},
  pages		= {254--263},
  publisher	= {University of Budapest},
  address	= {Budapest, Hungary},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  jones94a,
  author	= {Marggie Jones and David Vernon},
  title		= {Using Neural Networks to Learn Hand-Eye Co-ordination},
  journal	= {Neural Computing \& Applications},
  year		= {1994},
  volume	= {2},
  number	= {1},
  pages		= {2--12},
  annote	= {application, robot control},
  dbinsdate	= {oldtimer}
}

@Article{	  joo91a,
  author	= {Chang-Hee Joo and Jong-Soo Choi},
  title		= {Cardio-angiographic sequence coding using neural network
		  adaptive vector quantization},
  journal	= {Trans. Korean Inst. of Electrical Engineers},
  year		= {1991},
  volume	= {40},
  number	= {4},
  pages		= {374--381},
  month		= {April},
  note		= {(in Korean)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jose_del_coz99a,
  author	= {{Jose del Coz}, J. and Luaces, O. and Quevedo, J. R. and
		  Alonso, J. and Ranilla, J. and Bahamonde, A.},
  title		= {Self-organizing cases to find paradigms},
  booktitle	= {Foundations and Tools for Neural Modeling. International
		  Work-Conference on Artificial and Natural Neural Networks,
		  IWANN'99. Proceedings, (Lecture Notes in Computer Science
		  Vol.1606)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {1},
  pages		= {527--36},
  abstract	= {Case-based information systems can be seen as lazy machine
		  learning algorithms; they select a number of training
		  instances and then classify unseen cases as the most
		  similar stored instance. One of the main disadvantages of
		  these systems is the high number of patterns retained. In
		  this paper, a new method for extracting just a small set of
		  paradigms from a set of training examples is presented.
		  Additionally, we provide the set of attributes describing
		  the representative examples that are relevant for
		  classification purposes. Our algorithm computes the Kohonen
		  self-organizing maps attached to the training set to then
		  compute the coverage of each map node. Finally, a heuristic
		  procedure selects both the paradigms and the dimensions (or
		  attributes) to be considered when measuring similarity in
		  future classification tasks.},
  dbinsdate	= {oldtimer}
}

@Article{	  joshi96a,
  author	= {Anupam Joshi and Sanjiva Weerawarana and Narendran
		  Ramakrishnan and Elias N. Houstis and John R. Rice},
  title		= {Neuro-Fuzzy Support for Problem-Solving Environments: A
		  Step Toward Automated Solution of {PDE}s},
  journal	= {IEEE Computational Science \& Engineering},
  year		= 1996,
  volume	= 3,
  pages		= {44--56},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jossa01a,
  author	= {I. Jossa and U. Marschner and W. -J. Fischer},
  title		= {Signal-based feature extraction and {SOM} based dimension
		  reduction in a vibration monitoring microsystem},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {283--8},
  year		= {2001},
  editor	= {Nigel Allison and Hujun Yin and Lesley Allison and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  jossa98a,
  author	= {Jossa, I.},
  title		= {A short note about the application of
		  \mbox{self-organizing} neural networks to clustering},
  booktitle	= {6th European Congress on Intelligent Techniques and Soft
		  Computing. EUFIT '98},
  publisher	= {Verlag Mainz},
  address	= {Aachen, Germany},
  year		= {1998},
  volume	= {2},
  pages		= {1195--9},
  abstract	= {Kohonen (1982) introduced the self-organizing map, one of
		  the most powerful neural network paradigms. Many variations
		  and extensions of Kohonen's algorithm are developed in the
		  last years. These developments used in a wide range of very
		  different applications such as speech processing,
		  visuo-motor control of robots, time-series prediction, data
		  analysis and pattern recognition. This paper describes the
		  application of two extensions of Kohonen's algorithm, the
		  "neural-gas" network and the growing cell structures with
		  some advantages in the field of clustering.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  joutsensalo94a,
  author	= {Jyrki Joutsensalo},
  title		= {Nonlinear Data Compression and Representation by Combining
		  Self-Organizing Map and Subspace Rule},
  pages		= {637--640},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {hybrid, data compression},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  joutsensalo95a,
  author	= {Jyrki Joutsensalo and Antti Miettinen and Martin Zeindl},
  title		= {Nonlinear Dimension Reduction by Combining Competitive and
		  Distributed Learning},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {395--400},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  joutsensalo95b,
  author	= {Jyrki Joutsensalo and Antti Miettinen},
  title		= {Self-Organizing Operator Map for Nonlinear Dimension
		  Reduction},
  volume	= {I},
  pages		= {111--114},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  joutsiniemi01a,
  author	= {S. -L. Joutsiniemi and J. Nurminen and T. Kohonen},
  title		= {{SOM} in detecting states of sleep and wakefulness in
		  polysomnographic data},
  booktitle	= {Proceedings of the Fourth International Conference on
		  Neural Networks and Expert Systems in Medicine and
		  Healthcare, Milos Island, Greece, 20--22 september 2001},
  crossref	= {},
  key		= {},
  pages		= {97--101},
  year		= {2001},
  editor	= {George M. Papadourakis},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  joutsiniemi95a,
  author	= {Joutsiniemi, S. -L. and Kaski, S. and Larsen, T. A. },
  title		= {Self-organizing map in recognition of topographic patterns
		  of {EEG} spectra},
  journal	= {IEEE Transactions on Biomedical Engineering},
  year		= {1995},
  volume	= {42},
  number	= {11},
  pages		= {1062--8},
  month		= {Nov},
  dbinsdate	= {oldtimer}
}

@Article{	  juhola01a,
  author	= {Juhola, M. and Laurikkala, J. and Viikki, K. and Kentala,
		  E. and Pyykko, I},
  title		= {Classification of patients on the basis of otoneurological
		  data by using Kohonen networks},
  journal	= {ACTA OTO-LARYNGOLOGICA},
  year		= {2001},
  pages		= {50--52},
  abstract	= {Machine learning methods such as neural networks, decision
		  trees and genetic algorithms can be useful to aid in the
		  classification of patients. We tested Kohonen artificial
		  neural networks, which are known to be effective for
		  classification tasks. Our sample included patients with six
		  different diseases. The Kohonen network algorithm
		  recognized the four largest groups reliably, but the two
		  smallest groups were too small for the method. Neural
		  networks seem to be promising for the computer-aided
		  classification of otoneurological patients provided that
		  the number of patients used is sufficiently large.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  jukarainen94a,
  author	= {Tarmo Jukarainen and Esko K{\"{a}}rp{\"{a}}noja and Petri
		  Vuorimaa},
  title		= {Gas Recognition Using Learning Vector Quantization},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conf. Proc. of Finnish Artificial Intelligence Society},
  pages		= {155--160},
  booktitle	= {Proc. Conf. on Artificial Intelligence Res. in Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {application, pattern recognition, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jumpertz93a,
  author	= {Sylvie S. Jumpertz and Eduardo J. Garcia},
  title		= {Image Sequence Coding Using a Neural Vector Quantization},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {1020},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  jun93a,
  author	= {Young Pyo Jun and Hyunsoo Yoon and Jung Wan Cho},
  title		= {L* learning: a fast \mbox{self-organizing} feature map
		  learning algorithm based on incremental ordering},
  journal	= {IEICE Transactions on Information and Systems},
  year		= {1993},
  volume	= {E76-D},
  number	= {6},
  pages		= {698--706},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jun99a,
  author	= {Jun, Hui Her and Sung, Hae Jun and Jun, Heyog Choi and
		  Jung, Hyun Lee},
  title		= {A {B}ayesian neural network model for dynamic web document
		  clustering},
  booktitle	= {Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99.
		  `Multimedia Technology for Asia-Pacific Information
		  Infrastructure'.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1415--18},
  abstract	= {There has been lots of research to improve the precision
		  of IR system. These research have been studied on the
		  document ranking, user profiles, relevance feedback and the
		  information processing that includes document
		  classification, clustering, routing and filtering. This
		  paper proposes and incarnates method of neural approach
		  about the information processing which makes users can
		  search documents effectively and of the document
		  clustering. In this paper the system calculates entropy
		  between the query, the profile and the each of the web
		  documents each other; and clusters documents using the
		  calculated entropy as the value of the clustering variable
		  through SOM. As the Bayesian Neural Network model has high
		  classification accuracy with a rapid learning speed and
		  clustering, it is possible that dynamic document clustering
		  as it was combined with Bayesian probability model used in
		  real-time document classification. We used KTSET which is a
		  test collection to evaluate Korean IR system for the
		  experiment.},
  dbinsdate	= {oldtimer}
}

@Article{	  jung89a,
  author	= {Hae Mook Jung and Joo Hee Lee and Choong Woong Lee},
  title		= {An algorithm to update a codebook using a neural net},
  journal	= {J. {K}orean {I}nstitute of Telematics and Electronics},
  year		= {1989},
  volume	= {26},
  number	= {11},
  pages		= {228--237},
  dbinsdate	= {oldtimer}
}


@InProceedings{	  jung90a,
  author	= {T. -P. Jung and A. K. Krishnamurthy and S. C. Ahalt},
  title		= {The effects of distortion measures and feature sets on
		  neural network classifiers},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, San Diego},
  year		= {1990},
  volume	= {III},
  pages		= {251--256},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  junior00a,
  author	= {Junior, V. P. and Lopes, H. S.},
  title		= {Pattern recognition in electroencephalographic signals
		  using {LVQ} neural networks},
  booktitle	= {Smart Engineering System Design: Neural Networks, Fuzzy
		  Logic, Evolutionary Programming, Data Mining, and Complex
		  Systems. Vol.10. Proceedings of the Artificial Neural
		  Networks in Engineering Conference (ANNIE 2000). ASME, New
		  York, NY, USA},
  year		= {2000},
  volume	= {},
  pages		= {727--32},
  abstract	= {The objective of this work is the detection of the
		  non-averaged movement-related desynchronization (MRD) of
		  the mu rhythm of human EEG. The MRD was produced in the
		  contralateral sensorimotor cortex by means of two movement
		  tasks. After amplifying and filtering, the time response of
		  the mu band spectrum of a EEG lead was computed with the
		  FFT and used as predicates for training and testing an LVQ
		  neural network classifier. The performance was measured by
		  the geometric mean of the sensibility and specificity
		  indexes, calculated for every training epoch over test
		  data. The best detection performance was 88% for the single
		  movement task and 78% for repetition of movements, with an
		  average of 66% for both tasks. Results suggests further use
		  of this methodology for EEG pattern recognition.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  jurisica93a,
  author	= {Jurisica, L. and Sedlacek, M. },
  title		= {Self-organizing fuzzy controller with neural network},
  booktitle	= {Low Cost Automation 1992. Techniques, Components and
		  Instruments, Applications. Selected papers from the 3rd
		  IFAC Symposium},
  year		= {1993},
  editor	= {Kopacek, P. and Albertos, P. },
  pages		= {239--44},
  organization	= {Dept. of Autom. \& Control, Fac. of Electr. Eng. ,
		  Bratislava, Czechoslovakia},
  publisher	= {Pergamon},
  address	= {Oxford, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  jurkovic97a,
  author	= {F. Jurkovic},
  title		= {Direct and inverse modeling with max-min and max-product
		  neurons using in feedforward control},
  booktitle	= {4th International Workshop on Systems, Signals and Image
		  Processing. Proceedings},
  publisher	= {Poznan Univ. Technol},
  year		= {1997},
  editor	= {M. Domanski and R. Stasinski},
  address	= {Poznan, Poland},
  pages		= {45--7},
  dbinsdate	= {oldtimer}
}

@Article{	  jutamulia94a,
  author	= {Jutamulia, S. },
  title		= {Uses of joint transform correlators in supervised and
		  unsupervised hybrid computational-optical neural networks},
  journal	= {Optical Review},
  year		= {1994},
  volume	= {1},
  number	= {1},
  pages		= {39--40},
  month		= {Nov},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  jutten91a,
  author	= {C. Jutten and A. Guerin and H. L. Nguyen Thi},
  title		= {Adaptive optimization of neural algorithms},
  booktitle	= {Proc. IWANN'91, Int. Workshop on Artificial Neural
		  Networks},
  year		= {1991},
  editor	= {A. Prieto},
  pages		= {54--61},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kaarna01a,
  author	= {Kaarna, A. and Parkkinen, J.},
  title		= {Wavelet filter selection based on spectral features in
		  multispectral image compression},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Serpico, S. B.},
  volume	= {4170},
  pages		= {194--202},
  organization	= {Lappeenranta Univ. of Technology, Department of
		  Information Technology},
  publisher	= {},
  address	= {},
  abstract	= {The problem of selecting an appropriate wavelet filter is
		  always present in signal compression based on the wavelet
		  transform. In this report, we give a method to select a
		  wavelet filter for multispectral image compression. The
		  wavelet filter selection is based on the Learning Vector
		  Quantization (LVQ). In the training phase for the test
		  images, the best wavelet filter has been found by a careful
		  compression-decompression evaluation. Certain spectral
		  features are used in characterizing the pixel spectra. The
		  LVQ is used to form the best wavelet filter class for
		  different types of spectral images. When a new image is to
		  be compressed, a set of spectra from that image is
		  selected, the spectra are classified by the trained LVQ and
		  the filter associated to the largest class is selected for
		  the compression of the whole multispectral image. The
		  results show, that our method finds the most suitable
		  wavelet filter for compression of multispectral images.},
  dbinsdate	= {2002/1}
}

@Article{	  kaartinen98a,
  author	= {J. Kaartinen and Y. Hiltunen and P. T. Kovanen and M.
		  Ala-Korpela},
  title		= {Application of \mbox{self-organizing} maps for the
		  detection and classification of human blood plasma
		  lipoprotein lipid profiles on the basis of {1H} {NMR}
		  spectroscopy data},
  journal	= {NMR in Biomedicine},
  year		= {1998},
  volume	= {11},
  number	= {4--5},
  pages		= {168--76},
  dbinsdate	= {oldtimer}
}

@Article{	  kaban01a,
  author	= {Kaban, A. and Tino, P. and Girolami, M.},
  title		= {Local geometric properties of the latent trait projection
		  manifolds},
  journal	= {Computing-and-Information-Systems-Technical-Report. no.18;
		  Nov. 2001; p.1--11},
  year		= {2001},
  volume	= {},
  pages		= {1--11},
  abstract	= {Computing and visualizing local geometric properties of
		  the GTM projection manifold has been proposed and
		  demonstrated as a source of potentially valuable additional
		  visual information on the structure of the data being
		  visualized. In this work we extend these differential
		  geometric calculations to the more general latent trait
		  models, focusing primarily on the variants which are
		  suitable for the analysis of discrete data. Binary images
		  of handwritten digits and text based document data axe
		  utilized by way of demonstration.},
  dbinsdate	= {2002/1}
}

@InCollection{	  kacalak94a,
  author	= {W. Kacalak and K. Wawryn},
  title		= {Some aspects of the modified competitive self learning
		  neural network algorithm},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {103--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kacalak95a,
  author	= {W. Kacalak and K. Wawryn},
  title		= {A neural network approach to optimise trajectories of
		  mobile manipulator},
  booktitle	= {Proceedings of the Second International Symposium on
		  Methods and Models in Automation and Robotics},
  publisher	= {Wydawnictwo Uczelniane Politech. Szczecinskiej},
  year		= {1995},
  volume	= {2},
  editor	= {S. Banka and S. Domek and Z. Emirsajlow},
  address	= {Szczecin, Poland},
  pages		= {709--14},
  dbinsdate	= {oldtimer}
}

@Article{	  kadar97a,
  author	= {P. Kadar},
  title		= {Neural network based pattern matching application to power
		  system signal processing},
  journal	= {Nonlinear Analysis Theory, Methods \& Applications},
  year		= {1997},
  volume	= {30},
  number	= {3},
  pages		= {1655--61},
  note		= {(Second World Congress of Nonlinear Analysts Conf. Date:
		  10--17 July 1996 Conf. Loc: Athens, Greece Conf. Sponsor:
		  Educ. Minstr. Govern. Greece; Florida Inst. of Technol. ;
		  UNESCO; et al)},
  dbinsdate	= {oldtimer}
}

@Article{	  kaipainen00a,
  author	= {Kaipainen, Mauri and Karhu, Pasi},
  title		= {Bringing knowing-when and knowing-what together:
		  periodically tuned categorization and category-based timing
		  modeled with the Recurrent Oscillatory Self-Organizing Map
		  ({ROSOM})},
  journal	= {Minds and Machines},
  year		= {2000},
  volume	= {10},
  number	= {2},
  month		= {May},
  pages		= {203--229},
  organization	= {Univ of Helsinki},
  publisher	= {Kluwer Academic Publishers},
  address	= {Dordrecht},
  abstract	= {The study addresses the cyclically temporal aspect of
		  sequence recognition, storage and recall using the
		  Recurrent Oscillatory Self-Organizing Map (ROSOM), first
		  introduced by Kaipainen, Papadopoulos and Karhu (1997). The
		  unique solution of the network is that oscillatory States
		  are assigned to network units, corresponding to their
		  'readiness-to-fire'. The ROSOM is a categorizer, a temporal
		  sequence storage system and a periodicity detector designed
		  for use in an ambiguous cyclically repetitive environment.
		  As its external input, the model accepts a multidimensional
		  stream of environment-describing feature configurations
		  with implicit periodicities. The output of the model is one
		  or a few cycles abstracted from such a stream, mapped as
		  trajectories on a two-dimensional sheet with an
		  organization reminiscent of multi-dimensional scaling. The
		  model's capabilities are explored with a variety of
		  workbench data.},
  dbinsdate	= {2002/1}
}

@InCollection{	  kaipainen97a,
  author	= {Mauri Kaipainen and Pantelis Papadopoulos and Pasi Karhu},
  title		= {{MuSeq} recurrent oscillatory \mbox{self-organizing} map.
		  Classification and entrainment of temporal feature spaces},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {152--158},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kaipainen97b,
  author	= {M. Kaipainen and P. Papadopoulos and P. Karhu},
  title		= {Recurrent oscillatory \mbox{self-organizing} map: adapting
		  to complex environmental periodicities},
  booktitle	= {Proceedings of Advances in Computing Science---ASIAN '97,
		  Third Asian Computing Science Conference},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {R. K. Shyamasundar and K. Ueda},
  address	= {Berlin, Germany},
  pages		= {367},
  dbinsdate	= {oldtimer}
}

@Article{	  kalarickal99a,
  author	= {Kalarickal, G. J. and Marshall, J. A.},
  title		= {Models of receptive-field dynamics in visual cortex},
  journal	= {VISUAL NEUROSCIENCE},
  year		= {1999},
  volume	= {16},
  number	= {6},
  month		= {NOV-DEC},
  pages		= {1055--1081},
  abstract	= {The position, size, and shape of the receptive field (RF)
		  of some cortical neurons change dynamically, in response to
		  artificial scotoma conditioning (Pettet \& Gilbert, 1992)
		  and to retinal lesions (Chino et al., 1992; Darian-Smith \&
		  Gilbert, 1995) in adult animals. The RF dynamics are of
		  interest because they show how visual systems may
		  adaptively overcome damage (from lesions, scotomas, or
		  other failures), may enhance processing efficiency by
		  altering RF coverage in response to visual demand, and may
		  perform perceptual learning. This paper presents an
		  afferent excitatory synaptic plasticity rule and a lateral
		  inhibitory synaptic plasticity rule-the EXIN rules
		  (Marshall, 1995)-to model persistent RF changes after
		  artificial scotoma conditioning and retinal lesions. The
		  EXIN model is compared to the LISSOM model (Sirosh et al.,
		  1996) and to a neuronal adaptation model (Xing \& Gerstein,
		  1994). The rules within each model are isolated and are
		  analyzed independently, to elucidate their roles in adult
		  cortical RF dynamics. Based on computer simulations, the
		  EXIN lateral inhibitory synaptic plasticity rule and the
		  LISSOM lateral excitatory synaptic plasticity rule produced
		  the best fit with current neurophysiological data on visual
		  cortical plasticity in adult animals (Chino et al., 1992;
		  Pettet \& Gilbert, 1992; Darian-Smith \& Gilbert, 1995)
		  including (1) the retinal position and shape of the
		  expanding RFs; (2) the corticotopic direction in which
		  responsiveness returns to the silenced cortex; (3) the
		  direction of RF shifts; (4) the amount of change in
		  response to blank stimuli; and (5) the lack of dynamic RF
		  changes during conditioning with a retinal lesion in one
		  eye and the unlesioned eye kept open, in adult animals. The
		  effects of the LISSOM lateral inhibitory synaptic
		  plasticity rule during artificial scotoma conditioning are
		  in conflict with those of the other two LISSOM synaptic
		  plasticity rules. A novel "complementary scotoma"
		  conditioning experiment, in which stimulation of two
		  complementary regions of visual space alternates
		  repeatedly, is proposed to differentiate the predictions of
		  the EXIN and LISSOM rules.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kallio91a,
  author	= {K. Kallio and S. Haltsonen and E. Paajanen and T. Rosqvist
		  and T. Katila and P. Karp and P. Malmberg and P.
		  Piiril{\"{a}} and A. R. A. Sovij{\"{a}}rvi},
  title		= {Classification of Lung Sounds by Using Self-Organizing
		  Feature Maps},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {803--808},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kallioniemi01a,
  author	= {Kallioniemi, I. and Niinisto, A. and Saarinen, J. and
		  Friberg, A. T.},
  title		= {Characterization of random rough surfaces from scattered
		  intensities by neural networks},
  journal	= {JOURNAL OF MODERN OPTICS},
  year		= {2001},
  volume	= {48},
  number	= {9},
  month		= {JUL},
  pages		= {1447--1453},
  abstract	= {Optical scatterometry, a non-invasive characterization
		  method, is used to infer the statistical properties of
		  random rough surfaces. The Gaussian model with
		  rms-roughness sigma and correlation length Lambda is
		  considered in this paper but the employed technique is
		  applicable to any representation of random rough surfaces.
		  Surfaces with wide ranges of Lambda and sigma, up to 5
		  wavelengths (lambda), are characterized with neural
		  networks. Two models are used: self-organizing map (SOM)
		  for rough classification and multi-layer perceptron (MLP)
		  for quantitative estimation with nonlinear regression.
		  Models infer Lambda and sigma from scattering, thus
		  involving the inverse problem. The intensities are
		  calculated with the exact electromagnetic theory, which
		  enables a wide range of parameters. The most widely known
		  neural network model in practise is SOM, which we use to
		  organize samples into discrete classes with resolution
		  Delta Lambda = Delta sigma = 0.5 lambda. The more advanced
		  MLP model is trained for optimal behaviour by providing it
		  with known parts of input (scattering) and output (surface
		  parameters). We show that a small amount of data is
		  sufficient for an excellent accuracy on the order of 0.3
		  lambda and 0.15 lambda for estimating Lambda and sigma,
		  respectively.},
  dbinsdate	= {2002/1}
}

@Article{	  kalmar99a,
  author	= {Z. Kalmar and Z. Marczell and C. Szepesvari and A.
		  Lorincz},
  title		= {Parallel and Robust Skeletonization Built on Self
		  Organizing Elements},
  journal	= {Neural Networks},
  volume	= {12},
  pages		= {163--173},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kalnay91a,
  author	= {I. T. Kalnay and Y. Cheng},
  title		= {Measuring the effects of normalizing weight vectors on the
		  \mbox{self-organizing} map},
  booktitle	= {IJCNN-91, International Joint Conference on Neural
		  Networks, Seattle},
  year		= {1991},
  volume	= {II},
  pages		= {981},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kalviainen98a,
  author	= {Kalviainen, H. and Kukkonen, S. and Hyv\"arinen, T. and
		  Parkkinen, J.},
  title		= {Quality control in tile production},
  booktitle	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1998},
  volume	= {3522},
  pages		= {355--65},
  abstract	= {Studies visual quality control in the ceramics industry.
		  In tile manufacturing, it is important that in each set of
		  tiles, every single tile looks similar. For example, the
		  tiles should have similar color and texture. Our goal is to
		  design a machine vision system that can estimate the
		  sufficient similarity or same appearance to the human eye.
		  Currently, the estimation is usually done by human vision.
		  Differing from other approaches our aim is to use accurate
		  spectral representation of color, we compare spectral
		  features to the RGB color features. A laboratory system for
		  color measurement is built. Experimentations with five
		  classes of brown tiles are presented. We use chromaticity
		  RGB features and several spectral features for
		  classification with the k-NN classifier and with a neural
		  network, called the self-organizing map. We can classify
		  many of the tiles but there are several problems that need
		  further investigations: larger training and test sets are
		  needed, illumination effects must be studied further, and
		  more suitable spectral features are needed with more
		  sophisticated classifiers. It is also interesting to
		  develop further the neural approach.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kambhatla95a,
  author	= {N. Kambhatla and T. K. Leen},
  title		= {Classifying with {G}aussian mixtures and clusters},
  booktitle	= {Advances in Neural Information Processing Systems 7},
  publisher	= {MIT Press},
  year		= {1995},
  editor	= {G. Tesauro and D. Touretzky and T. Leen},
  address	= {Cambridge, MA, USA},
  pages		= {681--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kamel01a,
  author	= {Kamel, M. and Belkassim, S. and Mendonca, A. and Campilho,
		  A.},
  title		= {A neural network approach for the automatic detection of
		  microaneurysms in retinal angiograms},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2695--2699},
  organization	= {Dept. of Systems Design Engineering, University of
		  Waterloo},
  publisher	= {},
  address	= {},
  abstract	= {In this paper a neural network structure is used to
		  develop a system capable of detecting microaneurysms
		  locations in retinal angiograms. The LVQ (learning vector
		  quantization) neural network is used to classify the input
		  patterns into their desired classes using competitive
		  layers. The neurons in the competitive layers compete among
		  each other to produce subclasses. These subclasses are then
		  combined to produce the desired output classes. The input
		  vector of the neural network is derived from a grid of
		  smaller image windows. The presence of microanuerysms in
		  these windows is detected according to a novel multi-stage
		  training procedure that has proved to be very effective.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kamimura01a,
  author	= {Ryotaro Kamimura and Taeko Kamimura and Thomas Shultz},
  title		= {Cooperative Information Maximization to Realize
		  Self-Organizing Maps},
  booktitle	= {International Joint Conference on Neural Networks,
		  Washington, DC, USA, July 15--19},
  pages		= {},
  year		= {2001},
  month		= {July},
  note		= {CD-ROM},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kampfe01a,
  author	= {Kampfe, T. and Nattkemper, T. W. and Ritter, H.},
  title		= {Combining independent component analysis and
		  self-organizing maps for cell image classification},
  booktitle	= {Pattern Recognition. 23rd DAGM Symposium. Proceedings
		  (Lecture Notes in Computer Science Vol.2191).
		  Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {262--8},
  abstract	= {We consider the task of cell classification in fluorescent
		  micrographs. We combine the use of independent component
		  analysis (ICA) as a preprocessing step and a
		  self-organizing map for the resulting ICA feature space to
		  classify image patches into cell and non-cell images and to
		  investigate the features of image patches in the vicinity
		  of the classification border. We compare the classification
		  performance of ICA bases of different sizes, generated by
		  applying the infomax algorithm to image eigenspaces of
		  different dimensionalities. We find an optimal performance
		  for intermediate dimensionalities, characterized by the ICA
		  basis patterns, exhibiting salient features of an
		  "idealized" cell shape, and we achieve the classification
		  results comparable to a previous approach based on PCA
		  features.},
  dbinsdate	= {2002/1}
}

@Article{	  kanaya01a,
  author	= {Kanaya, S. and Kinouchi, M. and Abe, T. and Kudo, Y. and
		  Yamada, Y. and Nishi, T. and Mori, H. and Ikemura, T.},
  title		= {Analysis of codon usage diversity of bacterial genes with
		  a self-organizing map ({SOM}): characterization of
		  horizontally transferred genes with emphasis on the E. coli
		  O157 genome},
  journal	= {GENE},
  year		= {2001},
  volume	= {276},
  number	= {1--2},
  month		= {OCT 3},
  pages		= {89--99},
  abstract	= {With increases in the amounts of available DNA sequence
		  data, it has become increasingly important to develop tools
		  for comprehensive systematic analysis and comparison of
		  species- specific characteristics of protein-coding
		  sequences for a wide variety of genomes. In the present
		  study, we used a novel neural-network algorithm, a
		  self-organizing map (SOM), to efficiently and
		  comprehensively analyze codon usage in approximately 60,000
		  genes from 29 bacterial species simultaneously. This SOM
		  makes it possible to cluster and visualize genes of
		  individual species separately at a much higher resolution
		  than can be obtained with principal component analysis. The
		  organization of the SOM can be explained by the genome G +
		  C% and tRNA compositions of the individual species. We used
		  SOM to examine codon usage heterogeneity in the E. coli
		  O157 genome, which contains 'O157-unique segments'
		  (O-islands), and showed that SOM is a powerful tool for
		  characterization of horizontally transferred genes. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  kang01a,
  author	= {Kang, P. and Birtwhistle, D.},
  title		= {Condition monitoring of power transformer on-load
		  tap-changers. Part 1: Automatic condition diagnostics},
  booktitle	= {IEE Proceedings: Generation, Transmission and
		  Distribution},
  year		= {2001},
  editor	= {},
  volume	= {148},
  pages		= {301--306},
  organization	= {United Technologies Research Center},
  publisher	= {},
  address	= {},
  abstract	= {Automatic diagnostics for an on-load tap-changer (OLTC)
		  requires a reliable technique that can classify vibration
		  signals measured using an accelerometer mounted on the
		  tank. In the paper, the authors investigate the automatic
		  classification of OLTC vibration signatures using a
		  self-organising map (SOM) and develop a feature extraction
		  procedure that can extract essential features from the
		  original vibration signature. The proposed SOM signature
		  classifier is evaluated with a database established for one
		  type of distribution class OLTC. The application results
		  reveal the practical advantages of SOM for a number of
		  tasks in OLTC condition diagnostics.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kang01b,
  author	= {Kang, P. and Birtwhistle, D.},
  title		= {Condition monitoring of power transformer on-load
		  tap-changers. Part 2: Detection of ageing from vibration
		  signatures},
  booktitle	= {IEE Proceedings: Generation, Transmission and
		  Distribution},
  year		= {2001},
  editor	= {},
  volume	= {148},
  pages		= {307--311},
  organization	= {United Technologies Research Center},
  publisher	= {},
  address	= {},
  abstract	= {The paper describes a technique for on-line automatic
		  condition assessment of an on-load paper tap-changer (OLTC)
		  using a self-organising map (SOM). With a condition
		  indicator giving the correct indication of the current
		  condition status, an estimate can be made of the remaining
		  life of the equipment. The condition assessment technique
		  is demonstrated using the signatures collected by online
		  monitoring systems installed on selector type OLTCs in
		  distribution substations. Using the real-time fault
		  detection procedure, reliable identification of incipient
		  faults in the equipment can be achieved for the
		  pre-specified false alarming rate.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kang94a,
  author	= {Byoung-Ho Kang and Doo-Seoung Hwang and Jang-Hee Yoo},
  title		= {Square-Error Clustering Scheme and Clustering Networks},
  pages		= {333--334},
  booktitle	= {Proc. 3rd International Conference on Fuzzy Logic, Neural
		  Nets and Soft Computing},
  year		= {1994},
  publisher	= {Fuzzy Logic Systems Institute},
  address	= {Iizuka, Japan},
  annote	= {comparison, clustering},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kang94b,
  author	= {Myung-Kwang Kang and Seong-Kwon Lee and Soon-Hyob Kim},
  title		= {A study on the simulated annealing of self-organized map
		  algorithm for Korean phoneme recognition},
  booktitle	= {ICSLP 94. 1994 International Conference on Spoken Language
		  Processing},
  year		= {1994},
  volume	= {2},
  pages		= {471--4},
  organization	= {Dept. of Comput. Sci. , Kwangwoon Univ. , Seoul, South
		  Korea},
  publisher	= {Acoustical Soc. Japan},
  address	= {Tokyo, Japan},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kang95a,
  author	= {Byoung-Ho Kang and Jae-Woo Kim and Maeng-Sub Cho},
  title		= {Learning rate updating schemes of unsupervised learning},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {4},
  pages		= {3259--62},
  organization	= {Syst. Eng. Res. Inst. , Taejon, South Korea},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kang97a,
  author	= {Bong-Su Kang and Sung-Il Chien and Kil-Taek Lim and Jin-Ho
		  Kim},
  title		= {Large scale pattern recognition system using hierarchical
		  neural network and false-alarming nodes},
  booktitle	= {Proceedings. Ninth IEEE International Conference on Tools
		  with Artificial Intelligence},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {G. Sommer and J. J. Koenderink},
  address	= {Berlin, Germany},
  pages		= {196--203},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kang98a,
  author	= {Byoung Ho Kang and Jang Hee Yoo and Hong Gee Kim and Jin
		  Seo Kim and Maeng Sub Cho},
  title		= {Improved schemes for self organized feature map and
		  generalized learning vector quantization for developing
		  neural network building tool},
  booktitle	= {Proceedings of the 5th International Conference on Soft
		  Computing and Information/Intelligent Systems.
		  Methodologies for the Conception, Design and Application of
		  Soft Computing},
  publisher	= {World Scientific},
  address	= {Singapore},
  year		= {1998},
  volume	= {2},
  pages		= {638--9},
  abstract	= {A neural network building tool with a graphical user
		  interface was developed. In this tool, new methods for
		  self-organized feature maps and generalized learning vector
		  quantization for higher accuracy and fast training were
		  implemented. This tool can be used for image/voice
		  classification and recognition.},
  dbinsdate	= {oldtimer}
}

@Article{	  kang99a,
  author	= {Kang, Pengju and Birtwhistle, David},
  title		= {Self-organizing map for fault detection},
  journal	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  year		= {1999},
  number	= {},
  volume	= {9},
  pages		= {685--690},
  abstract	= {Contact movements in electromechanical power equipment
		  produce transient vibration signals that provide a rich
		  source of information to assess the condition of the
		  equipment. An adaptive fault detector based on
		  self-organizing map (SOM) is designed for the
		  identification of abnormalities existing in
		  electromechanical power equipment, whose vibration
		  signature is a series of well-defined transient bursts.
		  Non-decimated wavelet is utilized as a tool for smoothing
		  the envelopes of original vibration signals. Converting the
		  smoothed envelopes into the corresponding auto-correlation
		  functions achieves the purpose of automatic alignment of
		  the envelope signatures with different overall time shifts.
		  Fault detection is accomplished by comparing the Minimum
		  Quantization Error (MQE) of a newly acquired signature with
		  a predetermined threshold. The alarming threshold is
		  determined using Uniformly Most Powerful Test without the
		  prior statistical information on the faulty signatures.
		  Numerical results show that properly selected threshold for
		  MQEs ensures a high rate of reliable fault detection. In
		  English 10 Refs.},
  dbinsdate	= {oldtimer}
}

@Article{	  kang99b,
  author	= {Bong Su Kang and Kil Taek Lim and Sung Il Chien},
  title		= {{SOM}-{MLP} multi-layered neural network with
		  false-alarming nodes for large scale pattern recognition},
  journal	= {Journal of Electrical Engineering and Information
		  Science},
  year		= {1999},
  volume	= {4},
  pages		= {232--8},
  abstract	= {In this paper, an SOM-MLP multi-layered neural network was
		  studied for the large scale pattern recognition problem
		  such as the multilingual character recognition. The
		  multi-layered neural network is made of the
		  preclassification and the fine recognition modes. We
		  constructed clusters for the preclassification mode using
		  self-organizing map (SOM) learning and performed modifying
		  steps for reducing the number of clusters. The clusters
		  contain patterns that have the similar characteristics. We
		  adopted the multi-layer perceptron (MLP) networks to the
		  corresponding clusters for the fine recognition mode, and
		  we proposed the use of false-alarming nodes in the output
		  layer of the MLP network, which could be constructed on
		  error-prone negative examples quite similar to the patterns
		  of the selected cluster but actually belonging to different
		  nearby clusters through SOM's topology-preserving mapping.
		  The proposed system could be successfully adopted for
		  recognizing the large number of printed Korean/Chinese
		  characters database as well as IRIS database.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kangas85a,
  author	= {Jari Kangas and Olli Naukkarinen and Teuvo Kohonen and Kai
		  M{\"{a}}kisara and Olli Vent{\"{a}}},
  title		= {Phoneme Classification Experiments Using Phase
		  Information},
  institution	= {Helsinki University of Technology},
  year		= {1985},
  type		= {Report},
  number	= {TKK-F-A585},
  address	= {Espoo, Finland},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  kangas88a,
  author	= {Jari Kangas},
  title		= {Soinnittomien klusiilien erottelu {O}taniemen
		  Pu\-heen\-tun\-nis\-tus\-j{\"{a}}r\-jes\-tel\-m{\"{a}}s\-s{\"{a}}
		  ({C}lassification of Voiceless Stop Consonants in
		  {O}taniemi {S}peech {R}ecognition {S}ystem)},
  school	= {Helsinki University of Technology, Espoo, Finland},
  year		= {1988},
  note		= {(in Finnish)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas89a,
  author	= {Jari Kangas and Teuvo Kohonen and Jorma Laaksonen and Olli
		  Simula and Olli Vent{\"{a}}},
  title		= {Variants of \mbox{self-organizing} maps},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  pages		= {517--522},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas89b,
  author	= {Jari Kangas and Teuvo Kohonen},
  title		= {Using Transient Maps in Classification of Voiceless Stop
		  Consonants},
  booktitle	= {Proc. First Expert Systems Applications World Conference},
  year		= {1989},
  pages		= {321--326},
  publisher	= {IITT International},
  address	= {France},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas89c,
  author	= {Jari Kangas and Teuvo Kohonen},
  title		= {Transient Map Method in Stop Consonant Discrimination},
  booktitle	= {Proc. EUROSPEECH-89, European Conf. on Speech
		  Communication and Technology},
  year		= {1989},
  pages		= {345--348},
  publisher	= {ESCA},
  address	= {Berlin, Germany},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kangas90a,
  author	= {Jari A. Kangas and Teuvo K. Kohonen and Jorma T.
		  Laaksonen},
  title		= {Variants of {S}elf-{O}rganizing {M}aps},
  journal	= {IEEE Trans. Neural Networks},
  year		= {1990},
  volume	= {1},
  number	= {1},
  pages		= {93--99},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas90b,
  author	= {Jari Kangas},
  title		= {Time-Delayed Self-Organizing Maps},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, San Diego},
  year		= {1990},
  volume	= {II},
  pages		= {331--336},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  month		= {},
  annote	= {Description of the ideas of time delay self-organizing
		  maps. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas91a,
  author	= {Jari Kangas},
  title		= {Time-dependent \mbox{self-organizing} maps for speech
		  recognition},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1591--1594},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas91b,
  author	= {Jari Kangas},
  title		= {Phoneme Recognition Using Time-Dependent Versions of
		  Self-Organizing Maps},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  pages		= {101--104},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {},
  annote	= {Description of the time-delayed maps and the speech
		  recognition experiment with them. },
  dbinsdate	= {oldtimer}
}

@Article{	  kangas91c,
  author	= {Jari Kangas and Lea Leinonen and Anja Juvas},
  title		= {Recognition of Phonation Disorders by Phoneme Maps},
  journal	= {University of Oulu, Publications of the Department of
		  Logopedics and Phonetics},
  year		= {1991},
  address	= {University of Oulu, Oulu, Finland},
  volume	= {5},
  pages		= {23--32},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas92a,
  author	= {Jari Kangas},
  title		= {Temporal Knowledge in Locations of Activations in a
		  Self-Organizing Map},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {117--120},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas92b,
  author	= {Jari Kangas and Kari Torkkola and Mikko Kokkonen},
  title		= {Using {SOM}s as feature extractors for speech
		  recognition},
  booktitle	= {Proc. ICASSP-92, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kangas92c,
  author	= {J. Kangas and P. Utela},
  title		= {Itseorganisoituvan kartan k{\"{a}}ytt{\"{o}} puheen
		  kuvantamisessa ja mittaamisessa},
  journal	= {Tekniikka logopediassa ja foniatriassa},
  publisher	= {Suomen logopedis-foniatrinen yhdistys ry},
  address	= {Helsinki, Finland},
  year		= {1992},
  volume	= {26},
  pages		= {36--45},
  note		= {(in Finnish)},
  annote	= {editor P. Sonninen},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kangas93a,
  author	= {Jari Kangas},
  title		= {Self-Organizing Maps in Error Tolerant Transmission of
		  Vector Quantized Images},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1993},
  number	= {A21},
  address	= {SF-02150 Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  kangas94a,
  author	= {Jari Kangas},
  title		= {On the Analysis of Pattern Sequences by Self-Organizing
		  Maps},
  school	= {Helsinki University of Technology},
  year		= {1994},
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas94b,
  author	= {Jari Kangas and Teuvo Kohonen},
  title		= {Developmens and Applications of the Self-Organizing Map
		  and Related Algorithms},
  booktitle	= {Proc. IMACS Int. Symp. on Signal Processing, Robotics and
		  Neural Networks},
  year		= {1994},
  pages		= {19--22},
  publisher	= {IMACS},
  address	= {Lille, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas95a,
  author	= {Jari Kangas},
  title		= {Using {S}elf-{O}rganizing {M}ap in Error Tolerant
		  Transmission of Vector Quantized Images},
  volume	= {I},
  pages		= {517--522},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas95b,
  author	= {Jari Kangas},
  title		= {Sample Weighting When Training {S}elf-{O}rganizing {M}aps
		  for Image Compression},
  booktitle	= {Proc. NNSP'95, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1995},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {343--350},
  annote	= {application, image compression},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas95c,
  author	= {Jari Kangas},
  title		= {Increasing the Error tolerance in Transmission of Vector
		  Quantized Images by Self-Organizing Map},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {I},
  pages		= {287--291},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kangas95d,
  author	= {Jari Kangas},
  title		= {Utilizing the Similarity Preserving Properties of
		  Self-Organizig Maps in Vector Quantization of Images},
  volume	= {IV},
  pages		= {2081--2084},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  kangas96a,
  author	= {J. Kangas and T. Kohonen},
  title		= {Developments and applications of the
		  \mbox{self-organizing} map and related algorithms},
  journal	= {Mathematics and Computers in Simulation},
  year		= {1996},
  volume	= {41},
  number	= {1--2},
  pages		= {3--12},
  abstract	= {In this paper the basic principles and developments of an
		  unsupervised learning algorithm, the self-organizing map
		  (SOM) and a supervised learning algorithm, the learning
		  vector quantization (LVQ) are explained. Some practical
		  applications of the algorithms in data analysis, data
		  visualization and pattern recognition tasks are mentioned.
		  At the end of the paper new results are reported about
		  increased error tolerance in the transmission of vector
		  quantized images, provided by the topological ordering of
		  codewords by the SOM algorithm.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kangas96b,
  author	= {Jari Kangas and Samuel Kaski},
  title		= {Compression of vector quantization code sequences based on
		  code frequencies and spatial redundancies},
  booktitle	= {Proc. ICIP'96, IEEE International Conference on Image
		  Processing, Lausanne},
  publisher	= {IEEE Service Center},
  year		= 1996,
  volume	= {III},
  address	= {Piscataway, NJ},
  pages		= {463--466},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kangas98a,
  author	= {Jari Kangas and Samuel Kaski},
  title		= {3043 works that have been based on the Self-Organizing Map
		  ({SOM}) method developed by {K}ohonen},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= 1998,
  number	= {A49},
  address	= {Espoo, Finland},
  month		= {February},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kanstein94a,
  author	= {Andreas Kanstein and Karl Goser},
  title		= {Self-Organizing Maps Based on Differential Equations},
  booktitle	= {Proc. ESANN'94, European Symp. on Artificial Neural
		  Networks},
  year		= {1994},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {263--269},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kaplan98a,
  author	= {Kaplan, L. M. and Murenzi, R. and Namuduri, K. R. and
		  Cohen, M.},
  title		= {Improved template-based {SAR} {ATR} performance using
		  learning vector quantization},
  booktitle	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1998},
  volume	= {3462},
  pages		= {296--307},
  abstract	= {This paper investigates methods to improve template-based
		  synthetic aperture radar ({SAR}) automatic target
		  recognition (ATR). The approach utilizes clustering methods
		  motivated from the vector quantization (VQ) literature to
		  search for templates that best represent the signature
		  variability of target chips. The ATR performance using
		  these new templates is compared to the performance using
		  standard templates. For baseline {SAR} ATR, the templates
		  are generated over uniform angular bins in the pose space.
		  A merge method is able to generate templates that provide a
		  nonuniform sampling of the pose space, and the templates
		  produce modest gains in ATR performance over standard
		  templates.},
  dbinsdate	= {oldtimer}
}

@Article{	  kapogiannopoulos96a,
  author	= {G. S. Kapogiannopoulos and M. Papadakis},
  title		= {Character recognition using a biorthogonal discrete
		  wavelet transform},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2825},
  number	= {pt. 1},
  pages		= {384--93},
  annote	= {Wavelet Applications in Signal and Image Processing IV
		  Conf. Date: 6--9 Aug. 1996 Conf. Loc: Denver, CO, USA Conf.
		  Sponsor: SPIE},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kappen92a,
  author	= {Bert Kappen and Thomas Heskes},
  title		= {Learning Rules, Stochastic Processes and Local Minima},
  editor	= {I. Aleksander and J. Taylor},
  booktitle	= {Artificial Neural Networks, 2},
  volume	= {I},
  pages		= {71--78},
  address	= {Amsterdam, Netherlands},
  year		= 1992,
  publisher	= {North-Holland},
  dbinsdate	= {oldtimer}
}

@Article{	  kapusta00a,
  author	= {Kapusta, M. and Gajer, M. and Shomali, A.},
  title		= {Applying the neural network technique to transforming and
		  recognition of speech signals},
  journal	= {Pomiary-Automatyka-Kontrola. no.7; July 2000; p.16--18},
  year		= {2000},
  volume	= {},
  pages		= {16--18},
  abstract	= {The nature of a speech signal is very complicated, this
		  means that its visualisation and further analysis, without
		  some initial pre-processing, is very complicated and
		  doesn't always bring the desired effects. The speech signal
		  in most cases is represented by videograms. The analysis of
		  these forms of signal visualisation is not easy because of
		  difficulties in their interpretation. In this article the
		  use of a Kohonen neural network for visualising speech
		  signals uttered by children with a cleft palate is
		  proposed. The speech signal is converted to its spectrum
		  matrices representation, which constitutes the input for
		  the Kohonen neural network. Further a method for generating
		  a simplified form of speech signal (a poly-line figure)
		  based on the network's output is presented. In addition a
		  method for pathological speech signal recognition is
		  presented. Test results based on utterances obtained from
		  children with a cleft palate are presented.},
  dbinsdate	= {2002/1}
}

@Article{	  kapusta00b,
  author	= {Kapusta, M. and Gajer, M. and Shomali, A.},
  title		= {The usage of the Kohonen neural networks for the
		  pathological speech recognition},
  journal	= {Pomiary-Automatyka-Kontrola. no.7; July 2000; p.10--15},
  year		= {2000},
  volume	= {},
  pages		= {10--15},
  abstract	= {The nature of a speech signal is very complicated, this
		  means that its visualisation and further analysis, without
		  some initial pre-processing, is very complicated and
		  doesn't always bring the desired effects. A speech signal
		  in most cases is represented by videograms. The analysis of
		  these forms of signal visualisation is not easy because of
		  difficulties in their interpretation. In the article the
		  use of a Kohonen neural network for visualising speech
		  signals uttered by children with a cleft palate, is
		  proposed. The speech signal is converted to its spectrum
		  matrices representation, which in turn constitutes the
		  input for the Kohonen neural network. Further a method for
		  generating a simplified form of speech signal (a polyline
		  figure) based on the network's output, is discussed. In
		  addition, a method for pathological speech signal
		  recognition is proposed. Test results based on utterances
		  obtained from children with a cleft palate were also
		  presented.},
  dbinsdate	= {2002/1}
}

@Article{	  karakitsos02a,
  author	= {Karakitsos, P. and Kyroudes, A. and Pouliakis, A. and
		  Stergiou, E. B. and Voulgaris, Z. and Kittas, C.},
  title		= {Potential of the learning vector quantizer in the cell
		  classification of endometrial lesions in postmenopausal
		  women},
  journal	= {ANALYTICAL AND QUANTITATIVE CYTOLOGY AND HISTOLOGY},
  year		= {2002},
  volume	= {24},
  number	= {1},
  month		= {FEB},
  pages		= {30--38},
  abstract	= {OBJECTIVE: To investigate the potential of artificial
		  neural networks for cell identification in endometrial
		  lesions from postmenopausal women. STUDY DESIGN: The study
		  was performed on cytologic material obtained by the
		  Gynoscann endometrial cell sampler from 12 cases of
		  atrophic endometrium, 48 cases of hyperplasia without
		  cytologic atypia (18 cases of simple hyperplasia and 30
		  cases of complex hyperplasia), 12 cases of hyperplasia with
		  cytologic atypia (complex atypical hyperplasia) and 48
		  cases of adenocarcinoma (30 cases of well- differentiated,
		  12 cases of moderately differentiated and 6 cases of poorly
		  differentiated carcinoma). Front each case approximately
		  100 cells were examined using a custom image analysis
		  system. A learning vector quantizer (LVQ) identified the
		  collected data. RESULTS: Investigation of cells from
		  Endometrial Alterations with LVQ proved that according to
		  the nuclear characteristics, as expressed by morphometric
		  and textural measures, the endometrial cells front
		  postmenopausal women stay be identified as belonging to one
		  of the following three groups: atrophy, hyperplasia Without
		  cytologic atypia (simple and complex hyperplasia) arid
		  malignant neoplastic lesions (atypical complex and
		  adenocarcinoma). CONCLUSION: The role of nuclear
		  morphologic features in tire cytologic diagnosis of
		  endometrial alterations was confirmed. The overlap ill the
		  feature space observed indicates that cell characteristics
		  do riot form strictly separate clusters. That fact explains
		  the difficulty that morphologists have with the
		  reproducible identification of cells from endometrial
		  lesions ill postmenopausal women. Application of LVQ offers
		  a good classification at the cell level and promises to be
		  a powerful tool for classification on the individual
		  patient level and for the clarification of the natural
		  history of endometrial pathology.},
  dbinsdate	= {2002/1}
}

@Article{	  karayiannis00a,
  author	= {Karayiannis, Nicolaos B.},
  title		= {Soft learning vector quantization and clustering
		  algorithms based on ordered weighted aggregation
		  operators},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  volume	= {11},
  number	= {5},
  month		= {Sep},
  pages		= {1093--1105},
  organization	= {Univ of Houston},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper presents the development and investigates the
		  properties of ordered weighted learning vector quantization
		  (LVQ) and clustering algorithms. These algorithms are
		  developed by using gradient descent to minimize
		  reformulation functions based on aggregation operators. An
		  axiomatic approach provides conditions for selecting
		  aggregation operators that lead to admissible reformulation
		  functions. Minimization of admissible reformulation
		  functions based on ordered weighted aggregation operators
		  produces a family of soft LVQ and clustering algorithms,
		  which includes fuzzy LVQ and clustering algorithms as
		  special cases. The proposed LVQ and clustering algorithms
		  are used to perform segmentation of magnetic resonance (MR)
		  images of the brain. The diagnostic value of the segmented
		  MR images provides the basis for evaluating a variety of
		  ordered weighted LVQ and clustering algorithms.},
  dbinsdate	= {2002/1}
}

@Article{	  karayiannis00b,
  author	= {Karayiannis, N. B. and Zervos, N.},
  title		= {Entropy-constrained learning vector quantization
		  algorithms and their application in image compression},
  journal	= {JOURNAL OF ELECTRONIC IMAGING},
  year		= {2000},
  volume	= {9},
  number	= {4},
  month		= {OCT},
  pages		= {495--508},
  abstract	= {This paper presents entropy-constrained learning vector
		  quantization (ECLVQ) algorithms and their application in
		  image compression. The development of these algorithms
		  relies on reformulation, which is a powerful new
		  methodology that essentially establishes a link between
		  learning Vector quantization and clustering algorithms
		  developed using alternating optimization. ECLVQ algorithms
		  are developed in this paper by reformulating
		  entropy-constrained fuzzy clustering (ECFC) algorithms,
		  which were developed by minimizing an objective function
		  incorporating the partition entropy and the average
		  distortion between the feature vectors and their
		  prototypes. The proposed algorithms allow the gradual
		  transition from a maximally fuzzy partition to a nearly
		  crisp partition of the feature vectors during the learning
		  process. This paper presents two alternative
		  implementations of the proposed algorithms, which differ in
		  terms of the strategy employed for updating the prototypes
		  during learning. The proposed algorithms are tested and
		  evaluated on the design of codebooks used for image data
		  compression. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  karayiannis94a,
  author	= {Karayiannis, N. B. and Pin-I Pai},
  title		= {A fuzzy algorithm for learning vector quantization},
  booktitle	= {1994 IEEE International Conference on Systems, Man, and
		  Cybernetics. Humans, Information and Technology},
  year		= {1994},
  volume	= {1},
  pages		= {126--31},
  organization	= {Dept. of Electr. \& Comput. Eng. , Houston Univ. , TX,
		  USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  karayiannis94b,
  author	= {N. B. Karayiannis and P. -I. Pai},
  title		= {A family of fuzzy algorithms for learning vector
		  quantization},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {219--24},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis95a,
  author	= {Karayiannis, N. B. and Pin-I Pai},
  title		= {Fuzzy algorithms for learning vector quantization:
		  generalizations and extensions},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2492},
  number	= {pt. 1},
  pages		= {264--75},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  karayiannis95b,
  author	= {N. B. Karayiannis and M. Ravuri},
  title		= {An integrated approach to fuzzy learning vector
		  quantization and fuzzy c-means clustering},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks. Vol. 5. Fuzzy Logic and Evolutionary Programming.
		  Proceedings of the Artificial Neural Networks in
		  Engineering (ANNIE'95)},
  publisher	= {ASME Press},
  year		= {1995},
  editor	= {C. H. Dagli and M. Akay and C. L. P. Chen and B. R.
		  Fernandez and J. Ghosh},
  address	= {New York, NY, USA},
  pages		= {247--52},
  dbinsdate	= {oldtimer}
}

@InCollection{	  karayiannis95c,
  author	= {N. B. Karayiannis and Weigun Mi},
  title		= {A methodology for constructing fuzzy algorithms for
		  learning vector quantization},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks. Vol. 5. Fuzzy Logic and Evolutionary Programming.
		  Proceedings of the Artificial Neural Networks in
		  Engineering (ANNIE'95)},
  publisher	= {ASME Press},
  year		= {1995},
  editor	= {C. H. Dagli and M. Akay and C. L. P. Chen and B. R.
		  Fernandez and J. Ghosh},
  address	= {New York, NY, USA},
  pages		= {241--6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  karayiannis96a,
  author	= {N. B. Karayiannis},
  title		= {Weighted fuzzy learning vector quantization and weighted
		  generalized fuzzy c-means algorithms},
  booktitle	= {Proceedings of the Fifth IEEE International Conference on
		  Fuzzy Systems. FUZZ-IEEE '96},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {773--9},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis96b,
  author	= {Karayiannis, Nicolaos B. and Pai, Pin I.},
  title		= {Fuzzy algorithms for learning vector quantization},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1996},
  number	= {5},
  volume	= {7},
  pages		= {1196--1211},
  abstract	= {This paper presents the development of fuzzy algorithms
		  for learning vector quantization (FALVQ). These algorithms
		  are derived by minimizing the weighted sum of the squared
		  Euclidean distances between an input vector, which
		  represents a feature vector, and the weight vectors of a
		  competitive learning vector quantization (LVQ) network,
		  which represent the prototypes. This formulation leads to
		  competitive algorithms, which allow each input vector to
		  attract all prototypes. The strength of attraction between
		  each input and the prototypes is determined by a set of
		  membership functions, which can be selected on the basis of
		  specific criteria. A gradient-descent-based learning rule
		  is derived for a general class of admissible membership
		  functions which satisfy certain properties. The FALVQ 1,
		  FALVQ 2, and FALVQ 3 families of algorithms are developed
		  by selecting admissible membership functions with different
		  properties. The proposed algorithms are tested and
		  evaluated using the IRIS data set. The efficiency of the
		  proposed algorithms is also illustrated by their use in
		  codebook design required for image compression based on
		  vector quantization.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  karayiannis96c,
  author	= {N. B. Karayiannis},
  title		= {Weighted fuzzy learning vector quantization and weighted
		  fuzzy c-means algorithms},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1044--9},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis96d,
  author	= {Karayiannis, Nicolaos B. and Bezdek, James C. and Pal,
		  Nikhil R. and Hathaway, Richard J. and Pai, Pin I.},
  title		= {Repairs to G {LVQ} : a new family of competitive learning
		  schemes},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1996},
  number	= {5},
  volume	= {7},
  pages		= {1062--1071},
  abstract	= {First, we identify an algorithmic defect of the
		  generalized learning vector quantization (GLVQ) scheme that
		  causes it to behave erratically for a certain scaling of
		  the input data. We show that GLVQ can behave incorrectly
		  because its learning rates are reciprocally dependent on
		  the sum of squares of distances from an input vector to the
		  node weight vectors. Finally, we propose a new family of
		  models---the GLVQ-F family---that remedies the problem. We
		  derive competitive learning algorithms for each member of
		  the GLVQ-F model and prove that they are invariant to all
		  scalings of the data. We show that GLVQ-F offers a wide
		  range of learning models since it reduces to LVQ as its
		  weighting exponent (a parameter of the algorithm)
		  approaches one from above. As this parameter increases,
		  GLVQ-F then transitions to a model in which either all
		  nodes may be excited according to their (inverse) distances
		  from an input or in which the winner is excited while
		  losers are penalized. And as this parameter increases
		  without limit, GLVQ-F updates all nodes equally. We
		  illustrate the failure of GLVQ and success of GLVQ-F with
		  the IRIS data.},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis97a,
  author	= {Karayiannis, Nicolaos B.},
  title		= {Methodology for constructing fuzzy algorithms for learning
		  vector quantization},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  number	= {3},
  volume	= {8},
  pages		= {505--518},
  abstract	= {This paper presents a general methodology for the
		  development of fuzzy algorithms for learning vector
		  quantization (FALVQ). The design of specific FALVQ
		  algorithms according to existing approaches reduces to the
		  selection of the membership function assigned to the weight
		  vectors of an LVQ competitive neural network, which
		  represent the prototypes. According to the methodology
		  proposed in this paper, the development of a broad variety
		  of FALVQ algorithms can be accomplished by selecting the
		  form of the interference function that determines the
		  effect of the nonwinning prototypes on the attraction
		  between the winning prototype and the input of the network.
		  The proposed methodology provides the basis for extending
		  the existing FALVQ 1, FALVQ 2, and FALVQ 3 families of
		  algorithms. This paper also introduces two quantitative
		  measures which establish a relationship between the
		  formulation that led to FALVQ algorithms and the
		  competition between the prototypes during the learning
		  process. The proposed algorithms and competition measures
		  are tested and evaluated using the IRIS data set. The
		  significance of the proposed competition measures in
		  practical applications is illustrated by using various
		  FALVQ algorithms to perform segmentation of magnetic
		  resonance images of the brain.},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis97b,
  author	= {N. B. Karayiannis and J. C. Bezdek},
  title		= {An integrated approach to fuzzy learning vector
		  quantization and fuzzy c-means clustering},
  journal	= {IEEE Transactions on Fuzzy Systems},
  year		= {1997},
  volume	= {5},
  number	= {4},
  pages		= {622--8},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis97c,
  author	= {Karayiannis, Nicolaos B.},
  title		= {Learning vector quantization: A review},
  journal	= {International Journal of Smart Engineering System Design},
  year		= {1997},
  number	= {1},
  volume	= {1},
  pages		= {33--58},
  abstract	= {Vector Quantization (VQ) has been proven to be a very
		  powerful technique in a variety of applications. VQ can be
		  performed by learning vector quantization (LVQ) algorithms,
		  whose implementation is associated with a competitive
		  neural network. This paper presents a review of LVQ tools,
		  including Kohonen's (unlabeled data) LVQ, the generalized
		  LVQ (GLVQ) algorithm, GLVQ-F algorithms, and a family of
		  fuzzy LVQ (FLVQ) algorithms. A close relationship is also
		  established between FLVQ and fuzzy c-means algorithms.
		  Finally, a general methodology for the development of fuzzy
		  algorithms for learning vector quantization (FALVQ) is
		  presented. This formulation leads to the development of a
		  broad variety of competitive algorithms and adds an
		  important new dimension to neural network research and, in
		  particular, to unsupervised competitive learning.},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis97d,
  author	= {N. B. Karayiannis and G. W. Mi},
  title		= {Growing radial basis neural networks: merging supervised
		  and unsupervised learning with network growth techniques},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  volume	= {8},
  number	= {6},
  pages		= {1492--506},
  dbinsdate	= {oldtimer}
}

@InCollection{	  karayiannis98a,
  author	= {N. B. Karayiannis},
  title		= {Soft learning vector quantization and clustering
		  algorithms based on reformulation},
  booktitle	= {1998 IEEE International Conference on Fuzzy Systems
		  Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1441--6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  karayiannis98b,
  author	= {N. B. Karayiannis},
  title		= {Ordered weighted learning vector quantization and
		  clustering algorithms},
  booktitle	= {1998 IEEE International Conference on Fuzzy Systems
		  Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1388--93},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis99a,
  author	= {Karayiannis, Nicolaos B. and Pai, Pin I.},
  title		= {Family of fuzzy algorithms for learning vector
		  quantization},
  journal	= {International Journal of Engineering Intelligent Systems
		  for Electrical Engineering and Communications },
  year		= {1999},
  number	= {3},
  volume	= {7},
  pages		= {145--155},
  abstract	= {This paper proposes a family of fuzzy algorithms for
		  learning vector quantization (FALVQ). These sequential
		  learning vector quantization (LVQ) algorithms are developed
		  by using gradient descent to minimize a locally weighted
		  error for each input vector of a competitive LVQ network,
		  whose weight vectors represent the prototypes of the input
		  vectors. The uncertainty associated with the representation
		  of the input vectors by the prototypes is quantified by a
		  membership function, which is used in this approach to
		  regulate the competition between the prototypes for each
		  input vector. A family of membership functions defined in
		  terms of the generalized mean provide the basis for the
		  development of the generalized FALVQ 1 family of
		  algorithms. The algorithms resulting from this approach are
		  tested, evaluated and compared with existing learning
		  vector quantization techniques using the IRIS data set. The
		  efficiency of the proposed algorithms is also illustrated
		  by their use in codebook design required for image
		  compression based on vector quantization.},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis99b,
  author	= {Karayiannis, Nicolaos B.},
  title		= {An Axiomatic approach to soft learning vector quantization
		  and clustering},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1999},
  number	= {5},
  volume	= {10},
  pages		= {1153--1165},
  abstract	= {This paper presents an axiomatic approach to soft learning
		  vector quantization (LVQ) and clustering based on
		  reformulation. The reformulation of the fuzzy c-means (FCM)
		  algorithm provides the basis for reformulating
		  entropy-constrained fuzzy clustering (ECFC) algorithms.
		  This analysis indicates that minimization of admissible
		  reformulation functions using gradient descent leads to a
		  broad variety of soft learning vector quantization and
		  clustering algorithms. According to the proposed approach,
		  the development of specific algorithms reduces to the
		  selection of a generator function. Linear generator
		  functions lead to the FCM and fuzzy learning vector
		  quantization (FLVQ) algorithms while exponential generator
		  functions lead to ECFC and entropy-constrained learning
		  vector quantization (ECLVQ) algorithms. The reformulation
		  of LVQ and clustering algorithms also provides the basis
		  for developing uncertainty measures that can identify
		  feature vectors equidistant from all prototypes. These
		  measures are employed by a procedure developed to make soft
		  LVQ and clustering algorithms capable of identifying
		  outliers in the data set. This procedure is evaluated by
		  testing the algorithms generated by linear and exponential
		  generator functions on speech data.},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis99c,
  author	= {Karayiannis, N.~B. and Pai, P.~I.},
  title		= {Segmentation of Magnetic-Resonance Images Using Fuzzy
		  Algorithms for Learning Vector Quantization},
  journal	= {IEEE Transactions on Medical Imaging},
  year		= {1999},
  volume	= {18},
  number	= {2},
  pages		= {172--180},
  abstract	= {This paper evaluates a segmentation technique for magnetic
		  resonance (MR) images of the brain based on fuzzy
		  algorithms for learning vector quantization (FALVQ). These
		  algorithms perform vector quantization by updating all
		  prototypes of a competitive network through an unsupervised
		  learning process. Segmentation of {MR} images is formulated
		  as an unsupervised vector quantization process, where the
		  local values of different relaxation parameters form the
		  feature vectors which are represented by a relatively small
		  set of prototypes. The experiments evaluate a variety of
		  FALVQ algorithms in terms of their ability to identify
		  different tissues and discriminate between normal tissues
		  and abnormalities.},
  dbinsdate	= {oldtimer}
}

@Article{	  karayiannis99d,
  author	= {Karayiannis, Nicolaos B.},
  title		= {Reformulating learning vector quantization and radial
		  basis neural networks},
  journal	= {Fundamenta Informaticae, IOS Press},
  year		= {1999},
  number	= {1},
  volume	= {37},
  pages		= {137--175},
  abstract	= {This paper proposes a framework for developing a broad
		  variety of soft clustering and learning vector quantization
		  (LVQ) algorithms based on gradient descent minimization of
		  a reformulation function. According to the proposed
		  axiomatic approach to learning vector quantization, the
		  development of specific algorithms reduces to the selection
		  of a generator function. A linear generator function leads
		  to the fuzzy c-means (FCM) and fuzzy LVQ (FLVQ) algorithms
		  while an exponential generator function leads to entropy
		  constrained fuzzy clustering (ECFC) and entropy constrained
		  LVQ (ECLVQ) algorithms. The reformulation of clustering and
		  LVQ algorithms is also extended to supervised learning
		  models through an axiomatic approach proposed for
		  reformulating radial basis function (RBF) neural networks.
		  This approach results in a broad variety of admissible RBF
		  models, while the form of the radial basis functions is
		  determined by a generator function. This paper shows that
		  gradient descent learning makes reformulated RBF neural
		  networks an attractive alternative to conventional
		  feed-forward neural networks.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  karayiannis99e,
  author	= {N. B. Karayiannis},
  title		= {From aggregation operators to soft Learning Vector
		  Quatization and clustering algorithms},
  booktitle	= {Kohonen Maps},
  pages		= {47--56},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  karkimo01a,
  author	= {Katriina Karkimo},
  title		= {Sis{\"a}lt{\"o}kartta j{\"a}rjest{\"a}{\"a} tekstiarkiston},
  journal	= {Tietokone},
  year		= {2001},
  key		= {},
  volume	= {},
  number	= {},
  pages		= {},
  month		= {syyskuu},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  karpouzas95a,
  author	= {Karpouzas, I. and Jaulent, M. C. and Heudes, D. and
		  Bariety, J. L. and Degoulet, P. },
  title		= {An algorithm for the segmentation of grey-level medical
		  images},
  journal	= {Cybernetica},
  year		= {1995},
  volume	= {38},
  number	= {3},
  pages		= {195--9},
  dbinsdate	= {oldtimer}
}

@Article{	  karras98a,
  author	= {Karras, D. A. and Karkanis, S. A. and Mertzios, B. G.},
  title		= {Supervised and unsupervised neural network methods applied
		  to textile quality control based on improved wavelet
		  feature extraction techniques},
  journal	= {International Journal of Computer Mathematics},
  year		= {1998},
  number	= {1},
  volume	= {67},
  pages		= {169--181},
  abstract	= {This paper aims at investigating novel solutions to the
		  problem of textile defect detection from images, that can
		  find applications in building robust quality control vision
		  based systems in textile production. The proposed solutions
		  focus on detecting defects from the textural properties of
		  their corresponding wavelet transformed images. More
		  specifically a novel methodology is investigated for
		  discriminating defects in textile images by applying
		  supervised and unsupervised neural classification
		  techniques, employing multilayer perceptrons (MLP) -
		  trained with the on-line backpropagation algorithm---and
		  Kohonen's Self-Organizing Feature Maps (SOFM) respectively.
		  These parallel techniques are applied to innovative wavelet
		  based feature vectors. These vectors are extracted from the
		  wavelet transformed original images using the cooccurrence
		  matrices framework and SVD analysis. The results of the
		  proposed methodology are illustrated in defective textile
		  images where the defective areas are recognized with about
		  98.5% accuracy.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kartashov91a,
  author	= {A. P. Kartashov},
  title		= {Similarity-invariant recognition of visual images with
		  help of {K}ohonen's mapping formation algorithm},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1103--1106},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kartashov94a,
  author	= {Kartashov, A. and Erman, K. },
  title		= {A new class of neural networks: recognition invariant to
		  arbitrary transformation groups},
  booktitle	= {Cybernetics and Systems '94. Proceedings of the Twelfth
		  European Meeting on Cybernetics and Systems Research},
  year		= {1994},
  editor	= {Trappl, R. },
  volume	= {2},
  pages		= {1735--42},
  organization	= {Inst. fur Theor. Phys. , Linz Univ. , Austria},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kasabov00a,
  author	= {N. Kasabov and D. Deng and L. Erzegovezi and M. Fedrizzi
		  and A. Beber},
  title		= {On-line Decision Making and Prediction of Financial and
		  Macroeconomic Parameters on the Case Study of the European
		  Monetary Union},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Department of Information Science, University of Otago,
		  New Zealand; Department of Computer and Management
		  Sciences, University of Trento, Italy},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  kasabov01a,
  author	= {Kasabov, N.},
  title		= {Artificial neural networks for intelligent information
		  processing},
  journal	= {Chemical Engineer (London)},
  year		= {2001},
  volume	= {},
  number	= {720},
  month		= {June },
  pages		= {27--28},
  organization	= {Department of Information Science, University of Otago},
  publisher	= {},
  address	= {},
  abstract	= {The use of artificial neural networks (ANN) and
		  connectionist-based systems in solving real world problems
		  such as intelligent information processing is discussed.
		  The different types of ANNs discussed include the three
		  layer perceptron ANN; self organizing maps (SOM);
		  knowledge-based neural networks; and evolving connectionist
		  systems (ECS). The ECS models evolve their structure and
		  functionality from incoming data, rather than having a
		  predefined structure.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kasabov93a,
  author	= {Kasabov, N. and Nikovski, D. and Peev, E. },
  title		= {Speech recognition based on {K}ohonen
		  \mbox{self-organizing} feature maps and hybrid
		  connectionist systems},
  booktitle	= {Proceedings 1993 The First New Zealand International
		  Two-Stream Conference on Artificial Neural Networks and
		  Expert Systems},
  year		= {1993},
  editor	= {Kasabov, N. K. },
  pages		= {113--17},
  organization	= {Dept. of Inf. Sci. , Otago Univ. , Dunedin, New Zealand},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kasabov94a,
  author	= {N. Kasabov and E. Peev},
  title		= {Phoneme Recognition with Hierarchical {S}elf {O}rganised
		  Neural Networks and Fuzzy Systems---A Case Study},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {201--204},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, speech recognition, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kashiwagi93a,
  author	= {Norihito Kashiwagi and Toshikazu Tobi},
  title		= {Heating and Cooling Load Prediction Using a Neural Network
		  System},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {939--942},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {Many models have been proposed to identify and predict
		  system behavior, but the modeling is generally difficult,
		  especially in the case that the system is complex and has
		  the characteristics of nonlinearity. An artificial neural
		  network has the capability of learning the system behavior,
		  so we applied it to heating and cooling load prediction.
		  Kohonen's Feature Map was chosen as a network model, and
		  the extended Learning Vector Quantization (LVQ) which
		  realizes an associative memory was adopted as a learning
		  algorithm. The predictive results were good, and we were
		  able to confirm the feasibility of our model in the field
		  of load prediction.},
  dbinsdate	= {oldtimer}
}

@Article{	  kaski00a,
  author	= {Kaski, S. and Venna, J. and Kohonen, T.},
  title		= {Coloring that reveals cluster structures in multivariate
		  data},
  journal	= {Australian-Journal-of-Intelligent-Information-Processing-Systems}
		  ,
  year		= {2000},
  volume	= {6},
  pages		= {82--8},
  abstract	= {A method is introduced for assigning colors to displays of
		  cluster structures of high-dimensional data, such that the
		  perceptual differences of the colors reflect the distances
		  in the original data space as faithfully as possible. The
		  method consists of three parts: first the cluster
		  structures are discovered with the self-organizing map
		  (SOM) and then a new nonlinear projection method is applied
		  to map the cluster structures into the CIELab color space.
		  Finally the cluster structures are visualized using the
		  colors found by the projection. The projection method
		  preserves best the local data distances that are the most
		  important ones, while ensuring that the global order is
		  still discernible from the colors, too. This allows the
		  method to conform flexibly to the available color space.
		  The output space of the projection need not necessarily be
		  the color space projections onto, say, two dimensions can
		  be visualized as well.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kaski00b,
  author	= {S. Kaski and J. Nikkil{\"a} and T. Kohonen},
  title		= {Methods for Exploratory Cluster Analysis},
  booktitle	= {Proc. SSGRR'2000, Int. Conf. on Advances in Infrastructure
		  for Electronic Business, Science, and Education on the
		  Internet, L'Aquila, Italy July 31- August 6 },
  note		= {Proceedings on CD-ROM},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kaski01a,
  author	= {Kaski, S. and Sinkkonen, J. and Peltonen, J.},
  title		= {Learning metrics for self-organizing maps},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {2},
  pages		= {914--919},
  organization	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  publisher	= {},
  address	= {},
  abstract	= {We introduce methods that adapt the metric of the data
		  space to reflect relevance, as indicated by auxiliary data
		  associated with the primary data samples. The derived
		  metric is especially useful in descriptive data analysis by
		  unsupervised methods such as the Self-Organizing Maps. In
		  this work we use the new metric to refine SOM-based
		  analyses of the factors affecting the bankruptcy risk of
		  companies.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kaski01b,
  author	= {S. Kaski},
  title		= {{SOM}-based exploratory analysis of gene expression data},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {124--31},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kaski01c,
  author	= {S. Kaski and J. Sinkkonen},
  title		= {A topography-preserving latent variable model with
		  learning metrics},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {224--9},
  year		= {2001},
  editor	= {Nigel Allison and Hujun Yin and Lesley Allison and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  kaski01d,
  author	= {Kaski, S. and Sinkkonen, J. and Peltonen, J.},
  title		= {Bankruptcy analysis with self-organizing maps in learning
		  metrics},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2001},
  volume	= {12},
  number	= {4},
  month		= {July },
  pages		= {936--947},
  organization	= {Neural Networks Research Centre, Helsinki University of
		  Technology},
  publisher	= {},
  address	= {},
  abstract	= {We introduce a method for deriving a metric, locally based
		  on the Fisher information matrix, into the data space. A
		  self-organizing map (SOM) is computed in the new metric to
		  explore financial statements of enterprises. The metric
		  measures local distances in terms of changes in the
		  distribution of an auxiliary random variable that reflects
		  what is important in the data. In this paper the variable
		  indicates bankruptcy within the next few years. The
		  conditional density of the auxiliary variable is first
		  estimated, and the change in the estimate resulting from
		  local displacements in the primary data space is measured
		  using the Fisher information matrix. When a self-organizing
		  map is computed in the new metric it still visualizes the
		  data space in a topology-preserving fashion, but represents
		  the (local) directions in which the probability of
		  bankruptcy changes the most.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kaski01e,
  author	= {Samuel Kaski and Janne Nikkil{\"a} and Petri
		  T{\"o}r{\"o}nen and Eero Castr{\'e}n and Garry Wong},
  title		= {Analysis and visualization of gene expression data using
		  self-organizing maps},
  booktitle	= {In Proceedings of NSIP-01, IEEE-EURASIP Workshop on
		  Nonlinear Signal and Image Processing},
  crossref	= {},
  key		= {},
  pages		= {},
  year		= {2001},
  editor	= {},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {},
  note		= {Proceedings on CD-ROM},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kaski01g,
  author	= {Kaski, S. and Sinkkonen, J. and Peltonen, J.},
  title		= {Data visualization and analysis with self-organizing maps
		  in learning metrics},
  booktitle	= {Data Warehousing and Knowledge Discovery. Third
		  International Conference, DaWaK 2001. Proceedings (Lecture
		  Notes in Computer Science Vol.2114). Springer-Verlag,
		  Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {162--73},
  abstract	= {High-dimensional data can be visualized and analyzed with
		  the self-organizing map, a method for clustering data and
		  visualizing it on a lower-dimensional display. Results
		  depend on the (often Euclidean) distance measure of the
		  data space. We introduce an improved metric that emphasizes
		  important local directions by measuring changes in an
		  auxiliary, interesting property of the data points, for
		  example their class. A self-organizing map is computed in
		  the new metric and used for visualizing and clustering the
		  data. The trained map represents directions of highest
		  relevance for the property of interest. In data analysis it
		  is especially beneficial that the importance of the
		  original data variables throughout the data space can be
		  assessed and visualized. We apply the method to analyze the
		  bankruptcy risk of Finnish enterprises.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kaski01h,
  author	= {Kaski, S.},
  title		= {Learning metrics for exploratory data analysis},
  booktitle	= {Neural Networks for Signal Processing XI: Proceedings of
		  the 2001 IEEE Signal Processing Society Workshop. IEEE,
		  Piscataway, NJ, USA},
  year		= {2001},
  volume	= {},
  pages		= {53--62},
  abstract	= {Visualization and cluster analysis of multivariate data is
		  usually based on distances between samples in a data space.
		  The distance measure is often heuristically chosen, for
		  instance by choosing suitable features and then using a
		  global Euclidean metric. We have developed methods that
		  remove the arbitrariness by measuring distances only along
		  important (local) directions. The metric is learned from
		  auxiliary data that is paired with the primary data during
		  the learning process. It is assumed that changes in the
		  primary data are important or relevant if they cause
		  changes in the auxiliary data; for example, in analysis of
		  gene expression the auxiliary data can indicate the
		  functional classes of the genes. The new distance measures
		  can be used for instance in clustering and Self-Organizing
		  Map-based data visualization. The methods have so far been
		  applied in analysis of bankruptcy, text documents, and gene
		  expression.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kaski93a,
  author	= {Samuel Kaski and Sirkka-Liisa Joutsiniemi},
  title		= {Monitoring {EEG} Signal with the Self-Organizing Map},
  booktitle	= {Proc. ICANN'93, of International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {974--977},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kaski95a,
  author	= {Samuel Kaski and Teuvo Kohonen},
  title		= {Structures of Welfare and Poverty in the World Discovered
		  by the Self-Organizing Map},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= 1995,
  number	= {A24},
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kaski96a,
  author	= {S. Kaski and T. Honkela and K. Lagus and T. Kohonen},
  title		= {Creating an order in digital libraries with
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings of WCNN'96, World Congress on Neural Networks.
		  International Neural Network Society 1996 Annual Meeting},
  publisher	= {Lawrence Erlbaum Associates},
  year		= {1996},
  address	= {Mahwah, NJ, USA},
  pages		= {814--17},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kaski96b,
  author	= {S. Kaski},
  title		= {Computationally Efficient Approximation of a Probabilistic
		  Model for Document Representation in the {WEBSOM} Full-Text
		  Analysis Method},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1996},
  number	= {A38},
  address	= {Espoo, Finland},
  abstract	= {The WEBSOM is recently developed neural full-text document
		  exploration method that can also aid in information
		  retrieval and filtering. In WEBSOM the documents are
		  encoded as vectors in a feature space, created in an
		  unsupervised manner using the Self-Organizing Map. The
		  metric relations in the document vector space correspond to
		  semantic similarities in the contents of the documents. In
		  this article the feature extraction stage of the WEBSOM is
		  shown to form a computationally efficient approximation of
		  a probabilistic model for incorporating contextual
		  information into document representation. In addition, the
		  usefulness of some different kinds of automatically
		  extractable contextual information for the model is
		  compared. (Copyright (c) 1996 Helsinki University of
		  Technology.)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kaski96c,
  author	= {Samuel Kaski and Teuvo Kohonen},
  title		= {Exploratory data analysis by the \mbox{self-organizing}
		  map: {S}tructures of welfare and poverty in the world},
  booktitle	= {Neural Networks in Financial Engineering. Proceedings of
		  the Third International Conference on Neural Networks in
		  the Capital Markets, London, England, 11--13 October,
		  1995},
  publisher	= {World Scientific},
  year		= 1996,
  editor	= {Apostolos-Paul N. Refenes and Yaser Abu-Mostafa and John
		  Moody and Andreas Weigend},
  address	= {Singapore},
  pages		= {498--507},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kaski96d,
  author	= {Samuel Kaski and Krista Lagus},
  title		= {Comparing Self-Organizing Maps},
  booktitle	= {Proceedings of ICANN96, International Conference on
		  Artificial Neural Networks, Bochum, Germany, July 16--19},
  publisher	= {Springer},
  year		= 1996,
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbr{\"u}ggen and B. Sendhoff},
  series	= {Lecture Notes in Computer Science, vol. 1112},
  address	= {Berlin},
  pages		= {809--814},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kaski96e,
  author	= {S. Kaski and K. Lagus},
  title		= {Comparing \mbox{self-organizing} maps},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {809--14},
  dbinsdate	= {oldtimer}
}

@Book{		  kaski97a,
  author	= {Kaski, S.},
  title		= {Data Exploration Using Self-Organizing Maps. Acta
		  Polytechnica Scandinavica Mathematics, Computing and
		  Management in Engineering Series No. 82. Doctoral thesis.},
  year		= {1997},
  abstract	= {In this work, the methodology of using SOMS for
		  exploratory data analysis or data mining is reviewed and
		  developed further. The properties of the maps are compared
		  with the properties of related methods intended for
		  visualizing high-dimensional multivariate data sets. In a
		  set of case studies, the SOM algorithms is applied to
		  analyzing electroencephalograms, to illustrated structures
		  of the standard of living in the world, and to organizing
		  full-text document collections. Measures are proposed for
		  evaluating the quality of different types of maps in
		  representing a given data set, and for measuring the
		  robustness of the illustrations the maps produce. The same
		  measures may also be used for comparing the knowledge that
		  different maps represent.},
  dbinsdate	= {oldtimer}
}

@Article{	  kaski97b,
  author	= {Kaski, Samuel},
  title		= {Computationally efficient approximation of a probabilistic
		  model for document representation in the WEBSOM full-text
		  analysis method},
  journal	= {Neural Processing Letters},
  year		= {1997},
  number	= {2},
  volume	= {5},
  pages		= {139--151},
  abstract	= {WEBSOM is a recently developed neural method for exploring
		  full-text document collections, for information retrieval,
		  and for information filtering. In WEBSOM the full-text
		  documents are encoded as vectors in a document space
		  somewhat like in earlier information retrieval methods, but
		  in WEBSOM the document space is formed in an unsupervised
		  manner using the Self-Organizing Map algorithm. In this
		  article the document representations the WEBSOM creates are
		  shown to be computationally efficient approximations of the
		  results of a certain probabilistic model. The probabilistic
		  model incorporates information about the similarity of use
		  of different words to take into account their semantic
		  relations.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kaski98a,
  author	= {Samuel Kaski and Janne Nikkil{\"a} and Teuvo Kohonen},
  title		= {Methods for Interpreting a Self-Organized Map in Data
		  Analysis},
  booktitle	= {Proceedings of ESANN'98, 6th European Symposium on
		  Artificial Neural Networks, Bruges, April 22--24},
  year		= 1998,
  publisher	= {D-Facto},
  editor	= {Michel Verleysen},
  pages		= {185--190},
  address	= {Brussels, Belgium},
  abstract	= {The self-organizing map (SOM) can be used for forming
		  overviews of multivariate data sets and for visualizing
		  them on graphical map displays. Each map location
		  represents certain kinds of data items and the value of a
		  variable in the representations can be visualized in the
		  corresponding locations on the map display. Such component
		  plane displays contain all the information needed for
		  interpreting the map but information about the relations of
		  the variables remains implicit. We have developed methods
		  that visualize explicitly the contribution of each variable
		  in the organization of the map at different locations. It
		  is also possible to measure the contribution of each
		  variable in the cluster structure within an area of the map
		  to summarize, for instance, the characteristics of
		  clusters.},
  dbinsdate	= {oldtimer}
}

@Article{	  kaski98b,
  author	= {Kaski, Samuel and Honkela, Timo and Lagus, Krista and
		  Kohonen, Teuvo},
  title		= {WEBSOM---\mbox{self-organizing} maps of document
		  collections},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {101--117},
  abstract	= {With the WEBSOM method a textual document collection may
		  be organized onto a graphical map display that provides an
		  overview of the collection and facilitates interactive
		  browsing. Interesting documents can be located on the map
		  using a content-directed search. Each document is encoded
		  as a histogram of word categories which are formed by the
		  self-organizing map (SOM) algorithm based on the
		  similarities in the contexts of the words. The encoded
		  documents are organized on another self-organizing map, a
		  document map, on which nearby locations contain similar
		  documents. Special consideration is given to the
		  computation of very large document maps which is possible
		  with general-purpose computers if the dimensionality of the
		  word category histograms is first reduced with a random
		  mapping method and if computationally efficient algorithms
		  are used in computing the SOMs.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kaski98c,
  author	= {Samuel Kaski},
  title		= {Dimensionality Reduction by Random Mapping: Fast
		  Similarity Computation for Clustering},
  booktitle	= {Proceedings of IJCNN'98, International Joint Conference on
		  Neural Networks},
  publisher	= {IEEE Service Center},
  year		= 1998,
  pages		= {413--418},
  volume	= {1},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  kaski98d,
  author	= {S. Kaski and K. Lagus and T. Honkela and T. Kohonen},
  title		= {Statistical Aspects of the {WEBSOM} System in Organizing
		  Document Collections},
  journal	= {Computing Science and Statistics},
  year		= 1998,
  volume	= 29,
  pages		= {281--290},
  note		= {(Scott, D. W., ed.), Interface Foundation of North
		  America, Inc.: Fairfax Station, VA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kaski98e,
  author	= {S. Kaski and T. Kohonen},
  title		= {Tips for Processing and Color-Coding of Self-Organizing
		  Maps},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {195--202},
  dbinsdate	= {oldtimer}
}

@Article{	  kaski98f,
  author	= {S. Kaski and J. Kangas and T. Kohonen},
  title		= {Bibliography of Self-Organizing Map ({SOM}) Papers:
		  1981--1997},
  journal	= {Neural Computing Surveys},
  year		= {1998},
  volume	= {1},
  number	= {3 \& 4},
  pages		= {1--176},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kaski99a,
  author	= {Kaski, Samuel},
  title		= {Fast winner search for SOM-based monitoring and retrieval
		  of high-dimensional data},
  booktitle	= {IEE Conference Publication},
  year		= {1999},
  volume	= {2},
  pages		= {940--945},
  abstract	= {Self-Organizing Maps (SOMs) are widely used in engineering
		  and data-analysis tasks, but so far rarely in very
		  large-scale problems. The reason is the amount of
		  computation: while small SOMs can be computed starting from
		  the basic principles, rapid computation of large maps of
		  high-dimensional data requires special methods. Winner
		  search, finding the position of a data sample on the map,
		  is the computational bottleneck: comparison between the
		  data vector and all of the model vectors of the map is
		  required. In this paper a method is proposed for reducing
		  the amount of computation by restricting the search to
		  certain small-dimensional subspaces of the original space.
		  The method is suitable for applications in which the map
		  can be computed off-line, for instance in data monitoring,
		  classification, and information retrieval. In a case study
		  with the WEBSOM system that organizes text document
		  collections on a SOM, the amount of computation was reduced
		  to about 14% of the original, and even to 6.6% when
		  approximations were utilized.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kaski99b,
  author	= {Kaski, S. and Venna, J. and Kohonen, T.},
  title		= {Coloring that Reveals High-Dimensional Structures in
		  Data},
  booktitle	= {Proc. of 6th International Conference on Neural
		  Information Processing (ICONIP'99), Perth, Australia,
		  November 16--20},
  pages		= {729--734},
  year		= {1999},
  volume	= {2},
  address	= {Piscataway, NJ},
  publisher	= {IEEE Service Center},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kaski99c,
  author	= {Kaski, S. and Sinkkonen, J.},
  title		= {Metrics Induced by Maximizing Mutual Information},
  booktitle	= {Helsinki University of Technology, Publications in
		  Computer and Information Science, Report A55},
  year		= {1999},
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kasslin92a,
  author	= {Mika Kasslin and Jari Kangas and Olli Simula},
  title		= {Process State Monitoring Using Self-Organizing Maps},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1531--1534},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kastberger00a,
  author	= {Kastberger, G. and Kranner, G.},
  title		= {Visualization of multiple influences on ocellar flight
		  control in giant honeybees with the data-mining tool
		  Viscovery SOMine},
  journal	= {Behavior Research Methods, Instruments, \& Computers},
  year		= {2000},
  volume	= {32},
  pages		= {157--68},
  abstract	= {Viscovery SOMine is a software tool for the advanced
		  analysis and monitoring of numerical data sets. It was
		  developed for professional use in business, industry and
		  science, and to support dependency analysis, deviation
		  detection, unsupervised clustering, nonlinear regression,
		  data association, pattern recognition and animated
		  monitoring. Based on the concept of self-organizing maps
		  (SOMs), it employs a robust variant of unsupervised neural
		  networks-namely, T. Kohonen's (1997) Batch-SOM, which is
		  further enhanced with a new scaling technique for speeding
		  up the learning process. This tool provides a powerful
		  means by which to analyze complex data sets without prior
		  statistical knowledge. The data representation contained in
		  the trained SOM is systematically converted to be used in a
		  spectrum of visualization techniques, such as evaluating
		  dependencies between components, investigating geometric
		  properties of the data distribution, searching for clusters
		  or monitoring new data. We have used this software tool to
		  analyze and visualize multiple influences of the ocellar
		  system on free-flight behavior in giant honeybees.
		  Occlusion of ocelli affects orienting reactivities in
		  relation to the flight target, level of disturbance and
		  position of the bee in the flight chamber; it induces
		  phototaxis and makes orienting imprecise and dependent on
		  motivational settings. Ocelli permit the adjustment of
		  orienting strategies to environmental demands by enforcing
		  abilities such as centering or flight kinetics, and by
		  providing independent control of posture and flight
		  course.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  katagiri89a,
  author	= {S. Katagiri and E. McDermott and M. Yokota},
  title		= {A new algorithm for representing acoustic feature
		  dynamics},
  booktitle	= {Proc. ICASSP-89, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1989},
  volume	= {I},
  pages		= {322--325},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  katagiri90a,
  author	= {S. Katagiri and C. H. Lee},
  title		= {A new {HMM/ {LVQ} } hybrid algorithm for speech
		  recognition},
  booktitle	= {Proc. GLOBECOM'90, IEEE Global Telecommunications Conf.
		  and Exhibition. 'Communications: Connecting the Future'},
  year		= {1990},
  volume	= {II},
  pages		= {1032--1036},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kato96a,
  author	= {H. Kato and K. Furuta and S. Kondo},
  title		= {Hierarchical \mbox{self-organizing} neural network and its
		  application},
  booktitle	= {IIA'96/SOCO'96. International ICSC Symposia on Intelligent
		  Industrial Automation and Soft Computing},
  publisher	= {Int. Comput. Sci. Conventions},
  year		= {1996},
  editor	= {P. G. Anderson and K. Warwick},
  address	= {Millet, Alta. , Canada},
  pages		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kato97a,
  author	= {H. Kato and K. Furuta and S. Kondo},
  title		= {Characteristics of self-organized learning by topology
		  conserving neural networks},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1997},
  volume	= {J80D-II},
  number	= {1},
  pages		= {354--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  katoh98a,
  author	= {Katoh, A. and Fukui, Y.},
  title		= {Classification of facial expressions using
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings of the 20th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  Vol.20 Biomedical Engineering Towards the Year 2000 and
		  Beyond.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  volume	= {2},
  pages		= {986--9},
  abstract	= {Just as humans use body language or nonverbal language
		  such as gestures and facial expressions in communication,
		  computers will also be able to communicate with humans. In
		  medical engineering, it is possible that recognition of
		  facial expression can be applied to support communication
		  with persons who have trouble communicating verbally such
		  as infants and mental patients. The purpose of this study
		  is to enable recognition of human emotions by facial
		  expressions. Our observations of facial expressions found
		  that recognizing facial expressions by identifying changes
		  in important facial segments such as the eyebrow, the eyes
		  and the mouth by using sequences of images is important.
		  Self-organizing maps, which are neural networks, are used
		  to extract features of image sequences. The image sequences
		  of six types of facial expressions are recorded on VTR and
		  made into image sequences consisting of 30 images per
		  second. Gray levels of each segment are input into the
		  self-organizing map corresponding to each segment. The
		  neuron in the output layer, called the victory neuron,
		  reacts to the feature nearest the input segment. Our
		  analysis of the changes in victory neurons demonstrates
		  that they have characteristic features which correspond to
		  each of the six facial expressions.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kauko97a,
  author	= {Tom Kauko},
  title		= {Exploring the prices of residential apartments and
		  locality features within an artificial neural network
		  approach. {E}vidence from {F}inland},
  booktitle	= {AREUEA International Conference. UCLA: Berkeley, June
		  1997},
  year		= {1997},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kauniskangas94a,
  author	= {Hannu Kauniskangas and Olli Silv{\'{e}}n},
  title		= {Development Support for Visual Inspection Systems},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conf. Proc. of Finnish Artificial Intelligence Society},
  pages		= {149--154},
  booktitle	= {Proc. Conf. on Artificial Intelligence Res. in Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {application, visualization, hybrid},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kauppinen99a,
  author	= {Kauppinen, Hannu and Rautio, Hannu and Silven, Olli},
  title		= {Non-segmenting defect detection and SOM based
		  classification for surface inspection using color vision},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3826},
  pages		= {270--280},
  abstract	= {A non-segmenting defect detection technique combined with
		  a self-organizing map (SOM) based classifier and user
		  interface is proposed. This technique is illustrated with
		  examples from wood surface inspection.},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  kauppinen99b,
  author	= {Kauppinen, H.},
  title		= {Development of a Color Machine Vision Method for Wood
		  Surface Inspection},
  school	= {Department of Electrical Engineering and Infotech,
		  University of Oulu},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kauppinen99c,
  author	= {Kauppinen, H. and Silv\'en, O. and Piirainen, T.},
  title		= {Self-organizing Map Based User Interface for Visual
		  Surface Inspection},
  booktitle	= {Proc. 11th Scandinavian Conference on Image Analysis
		  (SCIA'99), June 7--11, Kangerlussuaq, Greenland},
  pages		= {801--808},
  year		= {1999},
  volume	= {2},
  dbinsdate	= {oldtimer}
}

@Article{	  kaustubha01a,
  author	= {Kaustubha, R. and Chakraborty, Bishwajit and Hegde, Amey
		  and Pereira, Ashley},
  title		= {Acoustic seafloor sediment classification using
		  self-organizing feature maps},
  journal	= {IEEE Transactions on Geoscience and Remote Sensing},
  year		= {2001},
  volume	= {39},
  number	= {12},
  month		= {December },
  pages		= {2722--2725},
  organization	= {Goa Engineering College},
  publisher	= {},
  address	= {},
  abstract	= {A self-organizing feature map (SOFM), a kind of artificial
		  neural network (ANN) architecture, is used in this work for
		  continental shelf seafloor sediment classification. Echo
		  data are acquired using an echosounding system from three
		  types of seafloor sediment areas. Excellent classification
		  ( [similar to] 100%) for an ideal output neuron grid size
		  of 15 \times 1 is obtained for a moving average of 35 input
		  snapshots.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kavuri92a,
  author	= {Surya N. Kavuri and Venkat Venkatasubramanian},
  title		= {Solving the Hidden Node Problem in Networks with
		  Ellipsoidal Units and Related Issues},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {I},
  pages		= {775--780},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kawahara97a,
  author	= {Shingo Kawahara and Toshimichi Saito},
  title		= {An Adaptive Self-Organizing Algorithm with Virtual
		  Connection},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {1},
  address	= {Singapore},
  pages		= {338--341},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kayama00a,
  author	= {Kayama, M. and Okamoto, T. and Cristea, A. I.},
  title		= {Exploratory activity support based on a semantic feature
		  map},
  booktitle	= {Adaptive Hypermedia and Adaptive Web-Based Systems.
		  International Conference, AH 2000. Proceedings (Lecture
		  Notes in Computer Science Vol.1892). Springer-Verlag,
		  Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {347--50},
  abstract	= {We propose a framework based on a subsymbolic approach for
		  the support of exploratory activities in a hyperspace. By
		  using it, it is possible to express the semantic features
		  of the whole hyperspace and the states of exploratory
		  activities in topological order. This approach is applied
		  to generate the navigation information for the exploratory
		  activity. The space explored is changed automatically by
		  using the semantic similarities of the nodes which
		  constitute that space. An extended self-organizing feature
		  map is used as the semantic feature map of the hyperspace.
		  This map is applied to express the user model and generate
		  the navigation strategy for the user. The exploratory
		  history of the user is mapped on it. Then, the semantic
		  relations between nodes are shown on the map. The result
		  reflects the exploratory state of the user, interpreted
		  with the help of a user model.},
  dbinsdate	= {2002/1}
}

@Article{	  kayama00b,
  author	= {Kayama, M. and Okamoto, T.},
  title		= {A navigation system based on self organizing feature map
		  for exploratory learning in hyperspace},
  journal	= {Transactions-of-the-Institute-of-Electronics,-Information-and-Communication-Engineers-D-I}
		  ,
  year		= {2000},
  volume	= {},
  pages		= {561--8},
  abstract	= {We propose a framework based on a sub-symbolic approach
		  for the support of exploratory activities in a hyperspace.
		  By using it, it is possible to express the semantic
		  features of the whole hyperspace and the states of
		  exploratory activities in topological order. This approach
		  is applied to generate the navigation information for the
		  exploratory activity. The space explored is changed
		  automatically by using the semantic similarities of the
		  nodes which constitute that space. An extended
		  self-organizing feature map is used as the semantic feature
		  map of the hyperspace, in short Hy-SOM. Hy-SOM is applied
		  to express the user model and generate the navigation
		  strategy for the user. The exploratory history of the user
		  is firstly mapped on the Hy-SOM. Next, the semantic
		  relations between nodes are shown on the map. The result
		  reflects the exploratory state of the user, interpreted
		  with the help of a user model.},
  dbinsdate	= {2002/1}
}

@Article{	  kayama01a,
  author	= {Kayama, M. and Okamoto, T.},
  title		= {A knowledge based navigation system with a semantic map
		  approach},
  journal	= {Educational-Technology-\&-Society},
  year		= {2001},
  volume	= {4},
  pages		= {},
  abstract	= {In this paper, we propose a framework based on a
		  subsymbolic approach for the support of exploratory
		  activities (e.g. learning, training and education) in a
		  hyperspace. By using our framework, it is possible to
		  extract the knowledge on the semantic features of the whole
		  hyperspace and the states of exploratory activities and
		  structure in topological order. This approach is applied to
		  generate the navigation information for the exploratory
		  activity. The space explored is changed automatically by
		  using the knowledge on semantic similarities of the nodes
		  which constitute that space. An extended self-organizing
		  feature map is used as the semantic feature map of the
		  hyperspace, in short Hy-SOM. Hy-SOM is applied to express
		  the user model and generate the navigation strategy for the
		  user. The exploratory history of the user is firstly mapped
		  on the Hy-SOM. Next, the semantic relations between nodes
		  are shown on the map. The result reflects the exploratory
		  state of the user, interpreted with the help of a user
		  model.},
  dbinsdate	= {2002/1}
}

@InCollection{	  kayama95a,
  author	= {M. Kayama and Y. Sugita and Y. Morooka and S. Fukuoka},
  title		= {Distributed diagnosis system combining the immune network
		  and learning vector quantization},
  booktitle	= {Proceedings of the 1995 IEEE IECON. 21st International
		  Conference on Industrial Electronics, Control, and
		  Instrumentation},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1531--6},
  abstract	= {A distributed diagnosis system combining the Immune
		  Network (IN) and Learning Vector Quantization (LVQ) is
		  proposed for accurately detecting faulty sensor outputs in
		  control plants. The system has two execution modes, namely,
		  its training mode, where the LVQ extracts a correlation
		  between each two sensors from their outputs when they work
		  properly, and its diagnosis mode, where the LVQ contributes
		  to testing each two sensors using the extracted
		  correlation, and the IN contributes to determining faulty
		  sensors by integrating the local testing results obtained
		  from the LVQ. With the proposed method, faulty sensors,
		  such as age deteriorated ones, which have been difficult to
		  be detected only by checking each sensor output
		  independently, can be specified.},
  dbinsdate	= {oldtimer}
}

@Article{	  kayama96a,
  author	= {M. Kayama and Y. Sugita and Y. Morooka},
  title		= {Sensor diagnosis system combining immune network and
		  learning vector quantization [industrial power system
		  reliability]},
  journal	= {Electrical Engineering in Japan},
  year		= {1996},
  volume	= {117},
  number	= {5},
  pages		= {44--56},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kayama98a,
  author	= {M. Kayama and Y. Sugita and Y. Morooka and J. Kumayama},
  title		= {Adaptively changed winning number {LVQ} for constructing
		  an accurate control model from enormous and low quality
		  plant data},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {701--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kayama99a,
  author	= {Kayama, M. and Okamoto, T.},
  title		= {A semantic map approach to a navigation system for
		  exploratory learning in hyperspace},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {839--44},
  abstract	= {We propose a framework based on a sub-symbolic approach
		  for the support of exploratory activities in a hyperspace.
		  By using it, it is possible to express the semantic
		  features of the whole hyperspace and the states of
		  exploratory activities in topological order. This approach
		  is applied to generate navigation information for
		  exploratory activity. The space explored changes
		  automatically using the semantic similarities of the nodes
		  which constitute that space. An extended self-organizing
		  feature map is used as the semantic feature map of
		  hyperspace, in short Hy-SOM. Hy-SOM is applied to express
		  the user model and generate the navigation strategy for the
		  user. The exploratory history of the user is first mapped
		  on to Hy-SOM. Next, the semantic relations between nodes
		  are shown on the map. The result reflects the exploratory
		  state of the user interpreted with the help of a user
		  model.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kaylani93a,
  author	= {Kaylani, T. and Mazzara, M. and DasGupta, S. and
		  Hohenberger, M. and Trejo, L. },
  title		= {Classification of ERP signals using neural networks},
  booktitle	= {Third Workshop on Neural Networks:
		  Academic/Industrial/NASA/ Defense. WNN92},
  year		= {1993},
  pages		= {304},
  organization	= {Dept. of Electr. Eng. , Temple Univ. , Philadelphia, PA,
		  USA},
  publisher	= {Soc. Comput. Simulation},
  address	= {San Diego, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  keith99a,
  author	= {Keith Magee, R. and Venkatesh, S. and Takatsuka, M.},
  title		= {An empirical study of neighbourhood decay in {K}ohonen's
		  self organising map},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1953--8},
  abstract	= {In this paper, empirical results are presented which
		  suggest that size and rate of decay of region size plays a
		  much more significant role in the learning, and especially
		  the development of topographic feature maps. Using these
		  results as a basis, a scheme for decaying region size
		  during SOM training is proposed. The proposed technique
		  provides near optimal training time. This scheme avoids the
		  need for sophisticated learning gain decay schemes, and
		  precludes the need for a priori knowledge of likely
		  training times. This scheme also has some potential uses
		  for continuous learning.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  keith99b,
  author	= {Keith Magee, R. and Venkatesh, S. and Takatsuka, M.},
  title		= {Beyond the topological map: developing alternate mappings
		  in self organisation},
  booktitle	= {Fifth International/National Biennial Conference on
		  Digital Image Computing, Techniques, and Applications.
		  DICTA99. Curtin Univ, Perth, WA, Australia},
  year		= {1999},
  volume	= {},
  pages		= {88--93},
  abstract	= {The self organising map is a well established unsupervised
		  learning technique which is able to form sophisticated
		  representations of an input data set. However conventional
		  self organising map (SOM) algorithms are limited to the
		  production of topological maps---that is, maps where
		  distance between points on the map has a direct
		  relationship to the Euclidean distance between the training
		  vectors corresponding to those points. It would be
		  desirable to be able to create maps which form clusters on
		  primitive attributes other than Euclidean distance; for
		  example, clusters based upon orientation or shape. Such
		  maps could provide a novel approach to pattern recognition
		  tasks by providing a new method to associate groups of
		  data. It is shown that the type of map produced by SOM
		  algorithms is a direct consequence of the lateral
		  connection strategy employed. Given this knowledge, a
		  technique is required to establish the feasibility of using
		  an alternative lateral connection strategy. Such a
		  technique is presented. Using this technique, it is
		  possible to rule out lateral connection strategies that
		  will not produce output states useful to the organisation
		  process. This technique is demonstrated using conventional
		  Laplacian interconnection as well as a number of novel
		  interconnection strategies.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kelly91a,
  author	= {Michael Kelly},
  title		= {Self-Organizing Map Training using Dynamic {K-D} Trees},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1041--1044},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kelly92a,
  author	= {Patrick M. Kelly and Don R. Hush and James M. White},
  title		= {An Adaptive Algorithm for Modifying Hyperellipsoidal
		  Decision Surfaces},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {196--201},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kemke93a,
  author	= {Christel Kemke and Andreas Wichert},
  title		= {Hierarchical {S}elf-{O}rganizing {F}eature {M}aps for
		  Speech Recognition},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {III},
  pages		= {45--47},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kenens96a,
  author	= {C. Kenens and W. Storm and D. M. Knotter and S. {De Gendt}
		  and W. Vandervorst and M. M. Heyns},
  title		= {Removal of organic contamination from silicon surfaces},
  booktitle	= {Proceedings of the Third International Symposium on Ultra
		  Clean Processing of Silicon Surfaces. UCPSS '96},
  publisher	= {Acco},
  year		= {1996},
  editor	= {M. Heyns and M. Meuris and P. Mertens},
  address	= {Leuven, Belgium},
  pages		= {107--10},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kennedy90a,
  author	= {J. Kennedy and F. Lavagetto and P. Morasso},
  title		= {Image coding using \mbox{self-organising} neural networks},
  booktitle	= {Proc. INNC'90 Int. Neural Network Conf. },
  year		= {1990},
  volume	= {I},
  pages		= {54},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  abstract	= {Summary form only given. Coding and compression for images
		  has been a popular topic of research for some time. The
		  paper describes a method of vector quantisation using a
		  Kohonen style self-organising network. The vector
		  quantisation has been performed on two types of image data,
		  spectral quantisation on 3-channel full colour digital
		  images, and spatial quantisation on monochromatic images.
		  The network is first trained on a single image, pixel by
		  pixel in the case of the colour images, and automatically
		  performs an optimal quantisation of the colours present in
		  the image for the network size. The network can be used to
		  code subsequent images of the same class. The weights in
		  the network can be used as a simple look-up table for the
		  image reconstruction phase.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kennedy90b,
  author	= {J. Kennedy and P. Morasso},
  title		= {Application of \mbox{self-organising} networks to signal
		  processing},
  booktitle	= {Proc. Neural Networks. EURASIP Workshop 1990},
  year		= {1990},
  editor	= {L. B. Almeida and C. J. Wellekens},
  pages		= {225--232},
  organization	= {Eur. Assoc. Signal Process},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  abstract	= {Kohenen (1988) has proposed a class of self-organising
		  networks that are single-layer collections of units with
		  two types of inputs: inputs from an external vector signal,
		  with adaptive weights, and inputs from recurrent
		  connections among the units, with fixed weights; the latter
		  are arranged in such a way to establish an hexagonal grid
		  and a Mexican-hat type pattern of local
		  excitation/inhibition. The self-organising construction of
		  maps of features provided by Kohonen nets is a simple and
		  very powerful concept that can be exploited in a variety of
		  ways. In the article the authors show two applications:
		  colour image compression and analysis of cursive
		  handwriting.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kennedy90c,
  author	= {Kennedy, J. and Morasso, P.},
  title		= {Self-organizing networks in handwriting analysis},
  booktitle	= {Parallel Architectures and Neural Networks},
  publisher	= {World Scientific Publishing},
  year		= {1990},
  editor	= {E. Caianiello},
  address	= {Singapore},
  pages		= {339--344},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kepuska88a,
  author	= {Veton Z. Kepuska and John N. Gowdy},
  title		= {{K}ohonen net for speaker dependent isolated word
		  recognition},
  booktitle	= {Proc. Annual Southeastern Symp. on System Theory 1988},
  year		= {1988},
  pages		= {388},
  organization	= {Univ of North Carolina at Charlotte, Electrical
		  Engineering Dep, Charlotte, NC, USA; IEEE, Charlotte
		  Section, Charlotte, NC, USA},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kepuska89a,
  author	= {Veton Z. Kepuska and John N. Gowdy},
  title		= {Phonemic speech recognition system based on a neural
		  network},
  booktitle	= {Proc. IEEE SOUTHEASTCON},
  year		= {1989},
  volume	= {II},
  pages		= {770--775},
  organization	= {Northern Telecom, USA; Southern Bell, USA; South Carolina
		  Electric and Gas},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kepuska89b,
  author	= {V. Z. Kepuska and J. N. Gowdy},
  title		= {Investigation of phonemic context in speech using
		  \mbox{self-organizing} feature maps},
  booktitle	= {Proc. ICASSP-89, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1989},
  volume	= {I},
  pages		= {504--507},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kepuskap90a,
  author	= {V. Z. Kepuska and J. N. Gowdy},
  title		= {On the effect of topological structure of the {K}ohonen
		  network on the performance of a hierarchical two layered
		  isolated word recognition system},
  booktitle	= {SOUTHEASTCON '90},
  year		= {1990},
  volume	= {I},
  pages		= {64--68},
  organization	= {IEEE; South Central Bell; Northern Telecom. ; AT\&T; et
		  al},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kessler93a,
  author	= {W. Kessler and D. Ende and R. W. Kessler and W.
		  Rosenstiel},
  title		= {Identification of Car Body Steel by an optical on line
		  System and {K}ohonen's \mbox{self-organizing} Map},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {860},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kessler94a,
  author	= {Kessler, W. and Kessler, R. W. and Kraus, M. and Kubler,
		  R. and Weinberger, K. },
  title		= {Improved prediction of the corrosion behaviour of car body
		  steel using a {K}ohonen self organising map},
  booktitle	= {IEE Colloquium on 'Advances in Neural Networks for Control
		  and Systems' (Digest No. 1994/136)},
  year		= {1994},
  pages		= {7/1--3},
  organization	= {Inst. fur Angewandte Forschung, Reutlingen, Germany},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  kestler99a,
  author	= {Kestler, H. A. and Schule, M. and Schwenker, F. and
		  Neumann, H. and Mattfeldt, T.},
  title		= {Neural classification of cytological smears from the
		  cervix},
  journal	= {Biomedizinische Technik},
  year		= {1999},
  volume	= {44},
  pages		= {17--24},
  abstract	= {Cytological smears obtained from the cervix are routinely
		  examined under the microscope as part of screening programs
		  for the early detection of cervical cancer. The aim of the
		  present study was to investigate whether a simple feature
		  extraction approach using only standard image processing
		  techniques combined with a neural classifier would lead to
		  acceptable results that might serve as a starting point for
		  the development of a fully automated screening system.
		  Gray-value images of 106 cervical smears (512*512 pixels)
		  divided into two groups-inconspicuous (57) and atypical
		  (49)-by an experienced pathologist on the basis of the
		  original smears were employed to evaluate the method. From
		  these images, 31 features quantifying properties of either
		  the cell nucleus or the cytoplasm were extracted. These
		  features were categorized with three different
		  architectures of a neural classifier: learning vector
		  quantization (LVQ), multilayer perceptron (MLP) and a
		  single perceptron. The results show a reclassification
		  accuracy of about 91% for all three algorithms Sensitivity
		  was uniform at approximately 78%, and specificity varied
		  between 75% and 91% in the leave-one-out evaluation. These
		  very good results provide strong encouragement for further
		  studies involving PAP scores and colour images.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  keuchel93a,
  author	= {Herman Keuchel and Ewald {von Puttkamer} and Uwe R.
		  Zimmer},
  title		= {{SPIN}---Learning and Forgetting Surface Classifications
		  with Dynamic Neural Networks},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {230--235},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  keymeulen92a,
  author	= {D. Keymeulen and J. Decuyper},
  title		= {On the \mbox{self-organizing} properties of topological
		  maps},
  booktitle	= {Toward a Practice of Autonomous Systems. Proc. First
		  European Conf. on Artificial Life},
  year		= {1992},
  editor	= {F. J. Varela and P. Bourgine},
  pages		= {64--69},
  organization	= {Cite des Sci. Ind. ; Banque de France; Fondation de
		  France; Electr. France; CEMAGREF; CNR; AFCET; CREA;
		  OFFILIB; Sun Microsyst},
  publisher	= {MIT Press},
  address	= {Cambridge, MA},
  dbinsdate	= {oldtimer}
}

@Article{	  khan98a,
  author	= {Imran Khan and Howard C. Card},
  title		= {Adaptive information agents using competitive learning},
  journal	= {Journal of Network and Computer Applications},
  year		= {1998},
  volume	= {21},
  number	= {2},
  month		= {April},
  abstract	= {This paper presents a design solution for a Personal
		  Adaptive Web (PAW) agent which reduces information overload
		  for Web users by autonomously retrieving documents that the
		  user is interested in. The PAW agent is a personal
		  assistant which learns different categories of Web
		  documents that the user is interested in, then finds and
		  suggests new similar documents to the user. It performs
		  seven subtasks to achieve its goal. It (i) monitors the
		  user while she is browsing the Web, (ii) determines the
		  relevant documents that the user visits using fuzzy logic
		  measures, (iii) textually analyses the relevant documents
		  to obtain document vectors using a modified form of the
		  inverse document frequency weight (IDFW) technique, (iv)
		  classifies the document vectors into categories using
		  unsupervised competitive learning, (v) scans the Web for
		  similar documents, (vi) classifies the new document vectors
		  using the trained neural network and (vii) decides whether
		  the new documents should be referred to the user using
		  fuzzy logic rules. In accomplishing the above seven
		  subtasks, a real time database, automatic text analysis
		  technique, competitive learning network and a fuzzy
		  inference system are incorporated into the PAW agent. 1998
		  Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  khaparde93a,
  author	= {S. A. Khaparde and Harish Gandhi},
  title		= {Use of {K}ohonen's Self-Organizing Network as a
		  Pre-Quantizer},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {II},
  pages		= {967--971},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  khedkar93a,
  author	= {Pratap S. Khedkar and Hamid R. Berenji},
  title		= {Generating Fuzzy Rules with Linear Consequents from Data},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {18--21},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  khobragade93a,
  author	= {Shyam W. Khobragade and Ajoy K. Ray},
  title		= {Connectionist Network for Feature Extraction and
		  Classification of {E}nglish Alphabetic Characters},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1606--1611},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  khunasaraphan94a,
  author	= {C. Khunasaraphan and T. Tanprasert and C. Lursinsap},
  title		= {Weight Shifting Technique For Recovering Faulty
		  {S}elf-{O}rganizing Neural Networks},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {IV},
  pages		= {234--239},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {modification, analysis},
  dbinsdate	= {oldtimer}
}

@InCollection{	  khunkay97a,
  author	= {S. Khunkay and K. Paithoonwattanakij},
  title		= {Image segmentation by fuzzy rule and {K}ohonen-constraint
		  satisfaction fuzzy C-mean},
  booktitle	= {Proceedings of ICICS, 1997 International Conference on
		  Information, Communications and Signal Processing. Theme:
		  Trends in Information Systems Engineering and Wireless
		  Multimedia Communications},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {713--17},
  dbinsdate	= {oldtimer}
}

@Article{	  khuwaja02a,
  author	= {Khuwaja, G. A.},
  title		= {An adaptive combined classifier system for invariant face
		  recognition},
  journal	= {Digital Signal Processing: A Review Journal},
  year		= {2002},
  volume	= {12},
  number	= {1},
  month		= {January },
  pages		= {21--46},
  organization	= {Department of Physics, Kuwait University},
  publisher	= {},
  address	= {},
  abstract	= {In classification tasks it may be wise to combine
		  observations from different sources. In this paper, to
		  obtain classification systems with both good generalization
		  performance and efficiency in space and time, a learning
		  vector quantization learning method based on combinations
		  of weak classifiers is proposed. The weak classifiers are
		  generated using automatic elimination of redundant hidden
		  layer neurons of the network on both the entire face images
		  and the extracted features: forehead, right eye, left eye,
		  nose, mouth, and chin. The neuron elimination is based on
		  the killing of blind neurons, which are redundant. The
		  classifiers are then combined through majority voting on
		  the decisions available from input classifiers. It is
		  demonstrated that the proposed system is capable of
		  achieving better classification results with both good
		  generalization performance and a fast training time on a
		  variety of test problems using a large and variable
		  database. The selection of stable and representative sets
		  of features that efficiently discriminate between faces in
		  a huge database is discussed. },
  dbinsdate	= {2002/1}
}

@Article{	  khuwaja02b,
  author	= {Khuwaja, G. A. and Laghari, M. S.},
  title		= {A parameter-based combined classifier for invariant facial
		  expression and gender recognition},
  journal	= {International Journal of Pattern Recognition and
		  Artificial Intelligence},
  year		= {2002},
  volume	= {16},
  number	= {1},
  month		= {February },
  pages		= {27--51},
  organization	= {Physics Department, Kuwait University},
  publisher	= {World Scientific Publishing Co. Pte. Ltd},
  address	= {},
  abstract	= {In this paper an learning vector quantization network
		  architecture based on varying parameters and eliminating
		  blind neurons<sup>28</sup> is developed that learns the
		  correlation of gender patterns and recognizes facial
		  expressions of human faces. The network is developed to
		  classify the 1---neutral, 2---smile or happiness, 3---anger
		  and 4---scream or fear expressions. A peak accuracy rate of
		  (a) 100% on the expression recognition task and (b) 100% on
		  the gender recognition task for random training and test
		  samples is achieved. The computer execution time for the
		  recognition is about 0.004 seconds per face image for the
		  latex expression classification task and 0.02 for the
		  gender recognition task on an IBM PC. It is demonstrated
		  that the proposed architecture is capable of achieving
		  better recognition results with both good generalization
		  performance and a fast training-time on a variety of test
		  problems. The developed system showed potential for real
		  life application domains.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kia94a,
  author	= {Seyed Jalal Kia and George Coghill},
  title		= {Soft Competitive Learning in the Extenden Differentiator
		  Network},
  pages		= {714--718},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {comparison},
  dbinsdate	= {oldtimer}
}

@Article{	  kiang01a,
  author	= {Kiang, M. Y.},
  title		= {Extending the Kohonen self-organizing map networks for
		  clustering analysis},
  journal	= {COMPUTATIONAL STATISTICS \& DATA ANALYSIS},
  year		= {2001},
  volume	= {38},
  number	= {2},
  month		= {DEC 28},
  pages		= {161--180},
  abstract	= {The self-organizing map (SOM) network was originally
		  designed for solving problems that involve tasks such as
		  clustering, visualization, and abstraction. While Kohonen's
		  SOM networks have been successfully applied as a
		  classification tool to various problem domains, their
		  potential as a robust substitute fur clustering and
		  visualization analysis remains relatively unresearched. We
		  believe the inadequacy of attention in the research and
		  application of using SOM networks as a clustering method is
		  due to its lack of procedures to generate groupings from
		  the SOM output. In this paper, we extend the original
		  Kohonen SOM network to include a contiguity-constrained
		  clustering method to perform clustering based on the output
		  map generated by the network. We compare the result with
		  that of the other clustering tools using a classic problem
		  from the domain of group technology. The result shows that
		  the combination of SOM and the contiguity-constrained
		  clustering method produce clustering results that are
		  comparable with that of the other clustering methods, We
		  further test the applicability of the method with two
		  widely referenced machine- learning cases and compare the
		  results with that of several popular statistical clustering
		  methods. },
  dbinsdate	= {2002/1}
}

@Article{	  kiang01b,
  author	= {Kiang, M. Y. and Kulkarni, U. R. and St Louis, R.},
  title		= {Circular/wrap-around self-organizing map networks: an
		  empirical study in clustering and classification},
  journal	= {JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY},
  year		= {2001},
  volume	= {52},
  number	= {1},
  month		= {JAN},
  pages		= {93--101},
  abstract	= {Kohonen's self-organizing map (SOM) network is one of the
		  most important network architectures developed during the
		  1980s. The main function of SOM networks is to map the
		  input data from an n-dimensional space to a
		  lower-dimensional (usually one or two dimensional) plot
		  while maintaining the original topological relations. A
		  well known limitation of the Kohonen network is the
		  'boundary effect' of nodes on or near the edge of the
		  network. The boundary effect is responsible for the undue
		  influence of the initial random weights assigned to the
		  nodes of the network, which can lead to incorrect
		  topological representations. To overcome this limitation,
		  we use a modified, 'circular', weight adjustment algorithm.
		  Our procedure is most effective with the class of problems
		  where the actual coordinates of the output map do not need
		  to correspond to the original input topology. This class of
		  problems includes applications requiring clustering or
		  classification of input data. We tested our method with a
		  well known example problem from the domain of Group
		  Technology, which is typical of this class of problems.
		  Test results show that the circular weight adjustment
		  procedure has better convergence properties, and that the
		  clusters formed using the circular approach are at least as
		  good as, and in many cases superior to, the basic SOM
		  method for these types of problems.},
  dbinsdate	= {2002/1}
}

@Article{	  kiang01c,
  author	= {Kiang, M. Y. and Kumar, A.},
  title		= {An evaluation of self-organizing map networks as a robust
		  alternative to factor analysis in data mining
		  applications},
  journal	= {Information-Systems-Research},
  year		= {2001},
  volume	= {12},
  pages		= {177--94},
  abstract	= {Kohonen's self-organizing map (SOM) network is one of the
		  most important network architectures developed during the
		  1980s. The main function of SOM networks is to map the
		  input data from an n-dimensional space to a lower
		  dimensional (usually one- or two-dimensional) plot while
		  maintaining the original topological relations. Therefore,
		  it can be viewed as an analog of factor analysis. We
		  evaluate the feasibility of using SOM networks as a robust
		  alternative to factor analysis and clustering for data
		  mining applications. Specifically, we compare SOM network
		  solutions to factor analytic and k-means clustering
		  solutions on simulated data sets with known underlying
		  factor and cluster structures. The comparisons indicate
		  that the SOM networks provide solutions superior to
		  unrotated factor solutions in general and provide more
		  accurate recovery of underlying cluster structures when the
		  input data are skewed. Our findings suggest that SOM
		  networks can provide robust alternatives to traditional
		  factor analysis and clustering techniques in data mining
		  applications.},
  dbinsdate	= {2002/1}
}

@Article{	  kiang95a,
  author	= {Kiang, M. Y. and Kulkarni, U. R. and Kar Yan Tam},
  title		= {Self-organizing map network as an interactive clustering
		  tool-an application to group technology},
  journal	= {Decision Support Systems},
  year		= {1995},
  volume	= {15},
  number	= {4},
  pages		= {351--74},
  publisher	= {Elsevier},
  annote	= {A conference paper in journal},
  abstract	= {The Self-Organizing Map (SOM) network, a variation of
		  neural computing networks, is a categorization network
		  developed by Kohonen. The theory of the SOM network is
		  motivated by the observation of the operation of the brain.
		  This paper presents the technique of SOM and shows how it
		  may be applied as a clustering tool to group technology. A
		  computer program for implementing the SOM neural networks
		  is developed and the results are compared with other
		  clustering approaches used in group technology. The study
		  demonstrates the potential of using the Self-Organizing Map
		  as the clustering tool for part family formation in group
		  technology.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kiang97a,
  author	= {M. Y. Kiang and U. R. Kulkarni and M. Goul and A.
		  Philippakis and R.T. Chi and E. Turban},
  title		= {Improving the effectiveness of \mbox{self-organizing} map
		  networks using a circular {K}ohonen layer},
  booktitle	= {Proceedings of the Thirtieth Hawaii International
		  Conference on System Sciences},
  publisher	= {IEEE Computer Society Press},
  year		= {1997},
  volume	= {5},
  editor	= {Jr. R. H. Sprague},
  address	= {Los Alamitos, CA, USA},
  pages		= {521--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kieffer91a,
  author	= {S. Kieffer and V. Morellas and M. Donath},
  title		= {Neural network learning of the inverse kinematic
		  relationships for a robot arm},
  booktitle	= {Proc. International Conference on Robotics and
		  Automation},
  year		= {1991},
  volume	= {III},
  pages		= {2418--2425},
  organization	= {IEEE},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kiernan95a,
  author	= {Kiernan, L. and Kambhampati, C. and Mitchell, R. J. },
  title		= {Using self organising feature maps for feature selection
		  in supervised neural networks},
  booktitle	= {Fourth International Conference on `Artificial Neural
		  Networks`},
  year		= {1995},
  pages		= {195--200},
  organization	= {Reading Univ. , UK},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kiernan96a,
  author	= {L. Kiernan and C. Kambhampati and R. J. Mitchell and K.
		  Warwick},
  title		= {Automatic integrated system load forecasting using mutual
		  information and neural networks},
  booktitle	= {Control of Power Plants and Power Systems (SIPOWER'95). A
		  Proceedings volume from the IFAC Symposium},
  publisher	= {Pergamon},
  year		= {1996},
  editor	= {R. Canales-Ruiz},
  address	= {Oxford, UK},
  pages		= {503--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kihl95a,
  author	= {Kihl, H. and Urban, J. P. and Gresser, J. and Hagmann, S.
		  },
  title		= {Neural network based hand-eye positioning with a
		  Transputer-based system},
  booktitle	= {High-Performance Computing and Networking. International
		  Conference and Exhibition. Proceedings},
  year		= {1995},
  editor	= {Hertzberger, B. and Serazzi, G. },
  pages		= {281--6},
  organization	= {Fac. des Sci. et Tech. , Univ. de Haute Alsace, Mulhouse,
		  France},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  kikuchi95a,
  author	= {Kikuchi, T. and Matsuoka, T. and Takeda, T. and Kishi, K.
		  },
  title		= {Automatic classification by a competitive learning neural
		  network},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1995},
  volume	= {J78D-II},
  number	= {10},
  pages		= {1543--7},
  month		= {Oct},
  dbinsdate	= {oldtimer}
}

@Article{	  kikuchi99a,
  author	= {Kikuchi, T. and Kishi, K. and Miyamichi, J.},
  title		= {An automatic data classification algorithm adjusted by
		  mutual information},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers D II},
  year		= {1999},
  volume	= {},
  pages		= {660--8},
  abstract	= {In the field of data mining there is a need for the
		  automatic data classification algorithms to handle an ever
		  increasing amount of real world data effectively. The data
		  classification algorithms utilizing neural networks,
		  competitive learning, leaky learning and the Kohonen
		  self-organizing map have been proposed so far. However, it
		  is pointed out that these algorithms have shortages such as
		  initial condition dependency and the necessity of
		  intractable adjustment of parameter values to obtain the
		  optimal classification results when applied to the real
		  world data with complex structures. To overcome these
		  problems, we propose a novel excellent semi-auto tuning
		  classification algorithm. An index to measure how well the
		  network will perform optimal classification is defined
		  using input-output mutual information and based on this
		  index the neural network parameters such as learning rates
		  and the number of iterations needed are suitably adjusted.
		  Using several sample data with gradually changing
		  distribution complexities, it is shown that the optimal
		  classification results are automatically obtained by using
		  the proposed new algorithm showing the effectiveness of the
		  proposed method.},
  dbinsdate	= {oldtimer}
}

@Article{	  kikuo99a,
  author	= {Kikuo, F. and Heizo, T. and Masumi, I.},
  title		= {Performance of improved {SOM}-{TSP} algorithm for
		  traveling salesman problem of many cities},
  journal	= {Transactions of the Institute of Electrical Engineers of
		  Japan, Part C},
  year		= {1999},
  volume	= {119},
  pages		= {875--82},
  abstract	= {Angeniol et al. (1988) applied Kohonen's Self-Organizing
		  Maps (SOM) to solve the traveling salesman problem (TSP)
		  and reached an adequate solution obtained in a short time
		  in comparison to a method depending on a conventional
		  neural network. We confirmed already that calculation time
		  is shortened further about TSP of 500 cities, by
		  introducing a momentum effect to the renewal coefficient of
		  original method (Angeniol's method; SOM-TSP). Here, we
		  report that we evaluated a performance of our improved
		  method about TSP for more cities, 1000, 2000, 10000.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kil95a,
  author	= {Rhee M. Kil and Young-in Oh},
  title		= {Vector Quantization Based on Genetic Algorithm},
  volume	= {I},
  pages		= {778--782},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@Article{	  killinger91a,
  author	= {M. Killinger and J. L. {De Bougrenet De La Tocnaye} and P.
		  Cambon},
  title		= {Controlling the grey level capacity of a bistable {FLC}
		  spatial light modulator},
  journal	= {Ferroelectrics},
  year		= {1991},
  volume	= {122},
  number	= {1--4},
  pages		= {89--99},
  annote	= {Conf. paper in journal},
  x		= {. . . Finally they discuss a possible optical
		  implementation of a self organizing memory based on the
		  model of the Kohonen map, using optically addressed SLMs.
		  },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kilpatrick95a,
  author	= {D. Kilpatrick and R. Williams},
  title		= {Unsupervised Classification of Antarctic Satellite Imagery
		  using {K}ohonen's Self-Organizing Feature Map},
  volume	= {I},
  pages		= {32--36},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim00a,
  author	= {Hyun-Don Kim and Sung-Bae Cho},
  title		= {Genetic Optimization of Structure-Adaptive Self-Organizing
		  Map for Efficient Classification},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {227--32},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kim00b,
  author	= {Kim, K. S. and Song, J. J. and Golshani, F. and
		  Panchanathan, S.},
  title		= {Automatic classification of cells using morphological
		  shape in peripheral blood images},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.4210},
  year		= {2000},
  volume	= {4210},
  pages		= {290--8},
  abstract	= {A novel technique for automatic analysis and
		  classification of cells in peripheral blood images is
		  presented. The purposes of this research are to analyze and
		  classify morphological shapes of mature red-blood cells and
		  white-blood cells in peripheral blood images. We first,
		  identify red-blood cells and white-blood cells in a blood
		  image captured from CCD camera attached to microscope.
		  Feature extraction is the second step. Finally blood cells
		  are classified using back propagation neural network.
		  Fifteen different classification clusters including normal
		  cells are in red blood cell. However, there are five
		  different normal categories in discrimination of white
		  blood cells. In other words, the system can tell whether a
		  given white cell belongs to one of the five normal classes
		  or not. A novel segmentation method is presented for
		  extraction of nucleus and cytoplasm which inherently
		  possess valuable clues in white blood cell classification.
		  Initially, seventy-six dimensions of a feature vector that
		  includes UNL Fourier descriptor shape, and color are
		  considered in red-blood cell classification. 38 dimensions
		  of a feature vector are considered in red blood cell
		  classification. Based on the proposed method, a prototype
		  system has implemented and evaluated with various
		  classification algorithms such as LVQ-3 (Learning Vector
		  Quantization) and K-NN (K-nearest neighbor). The experiment
		  results show that the proposed method outperforms on blood
		  cell classification compared with other alternatives.},
  dbinsdate	= {2002/1}
}

@Article{	  kim00c,
  author	= {Kim, Jinsang and Chen, Tom},
  title		= {Low-complexity fusion of intensity, motion, texture, and
		  edge for image sequence segmentation: A neural network
		  approach},
  journal	= {Neural Networks for Signal Processing---Proceedings of the
		  IEEE Workshop},
  year		= {2000},
  volume	= {2},
  number	= {},
  month		= {},
  pages		= {497--506},
  organization	= {Colorado State Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {We develop an image sequence segmentation scheme which
		  uses intensity, motion, edge, and texture features. The
		  proposed scheme is simple and inherently parallel in
		  nature. Motion confidence values are employed for a feature
		  weighting scheme in order to suppress unreliable feature
		  components. These feature vectors are quantized by training
		  self-organizing feature maps (SOFM). In order to generate
		  more meaningful boundaries of the segmentation, we also
		  develop an edge fusion algorithm in which an edge-linked
		  map extracted from a real-time edge linking algorithm is
		  incorporated for the segmentation. Experimental results
		  show the validity of our approach.},
  dbinsdate	= {2002/1}
}

@Article{	  kim00d,
  author	= {Kim, Daijin and Ahn, Sunha and Kang, Dae-Seong},
  title		= {Co-adaptation of self-organizing maps by evolution and
		  learning},
  journal	= {Neurocomputing},
  year		= {2000},
  volume	= {30},
  number	= {1},
  month		= {},
  pages		= {249--272},
  organization	= {DongA Univ},
  publisher	= {Elsevier Science B.V.},
  address	= {Amsterdam},
  abstract	= {This paper proposes two co-adaptation schemes of
		  self-organizing maps that incorporate the Kohonen's
		  learning into the GA evolution in an attempt to find an
		  optimal vector quantization codebook of images. The
		  Kohonen's learning rule used for vector quantization of
		  images is sensitive to the choice of its initial parameters
		  and the resultant codebook does not guarantee a minimum
		  distortion. To tackle these problems, we co-adapt the
		  codebooks by evolution and learning in a way that the
		  evolution performs the global search and makes
		  inter-codebook adjustments by altering the codebook
		  structures while the learning performs the local search and
		  makes intra-codebook adjustments by making each codebook's
		  distortion small. Two kinds of co-adaptation schemes such
		  as Lamarckian and Baldwin co-adaptation are considered in
		  our work. Simulation results show that the evolution guided
		  by a local learning provides the fast convergence, the
		  co-adapted codebook produces better reconstruction image
		  quality than the non-learned equivalent, and Lamarckian
		  co-adaptation turns out more appropriate for the VQ
		  problem.},
  dbinsdate	= {2002/1}
}

@Article{	  kim01a,
  author	= {Kim, K. -S. and Han, I.},
  title		= {The cluster-indexing method for case-based reasoning using
		  self-organizing maps and learning vector quantization for
		  bond rating cases},
  journal	= {Expert Systems with Applications},
  year		= {2001},
  volume	= {21},
  number	= {3},
  month		= {October },
  pages		= {147--156},
  organization	= {Graduate School of Management, Korea Adv. Inst. of
		  Sci./Technol.},
  publisher	= {},
  address	= {},
  abstract	= {This paper presents a hybrid data mining model for the
		  prediction of corporate bond rating. This model uses a new
		  case-indexing method of case-based reasoning (CBR), which
		  utilizes the cluster information of financial data in order
		  to improve classification accuracy. This method uses not
		  only case-specific knowledge of past problems like
		  conventional CBR, but also uses additional knowledge
		  derived from the clusters of cases. The cluster-indexing
		  method assumes that there are some distinct subgroups
		  (clusters) in each rated group. Competitive artificial
		  neural networks are used to generate the centroid values of
		  clusters because these techniques produce better adaptive
		  clusters than statistical clustering algorithms. The
		  experiments using corporate bond rating cases show that the
		  cluster-indexing CBR is superior to conventional CBR and
		  inductive learning-indexing CBR---a rival case indexing
		  method. },
  dbinsdate	= {2002/1}
}

@Article{	  kim01c,
  author	= {Kim, J. and Chen, T.},
  title		= {Multiple feature clustering for image sequence
		  segmentation},
  journal	= {Pattern Recognition Letters},
  year		= {2001},
  volume	= {22},
  number	= {11},
  month		= {September },
  pages		= {1207--1217},
  organization	= {Electrical Engineering Department, Colorado State
		  University},
  publisher	= {},
  address	= {},
  abstract	= {We present a segmentation scheme in order to develop a
		  method which can identify homogeneous regions to represent
		  higher level objects for content-based functionality. The
		  proposed scheme extracts multiple features, such as motion
		  and texture, on the pixel basis. Different weights are
		  applied to each feature components based on motion
		  confidence measures. The proposed scheme consists of two
		  phases. In the first phase, a multiple feature space is
		  transformed to one-dimensional label space using a
		  self-organizing feature maps (SOFM) neural network
		  clustering method. In the second phase, the neural network
		  outputs are merged in order to generate desired
		  segmentation resolution. Our experimental results and
		  performance analysis show the validity of the proposed
		  scheme. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  kim89a,
  author	= {Jung-Soo Kim and Chong-Min Kyung},
  title		= {Circuit placement in arbitrarily-shaped region using
		  self-organization},
  booktitle	= {International Symp. on Circuits and Systems},
  year		= {1989},
  volume	= {III},
  pages		= {1879--1882},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  kim89b,
  author	= {Sung Suk Kim and Tai Ho Lee},
  title		= {A neural net system \mbox{self-organizing} the distributed
		  concepts for speech recognition},
  journal	= {J. the Korean Inst. of Telematics and Electronics},
  year		= {1989},
  volume	= {26},
  number	= {5},
  pages		= {85--91},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim90a,
  author	= {Kiseok Kim and Kim Inbum Kim and Heeyeung Hwang},
  title		= {A study on the recognition of the {K}orean monothongs
		  using artificial neural net models},
  booktitle	= {Proc. 5th Jerusalem Conf. on Information Technology
		  (JCIT). Next Decade in Information Technology},
  year		= {1990},
  pages		= {364--371},
  organization	= {IEEE},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  kim91d,
  author	= {Dong-Kook Kim and Cha-Gyun Jeong and Hong Jeong},
  title		= {Korean phoneme recognition using neural networks},
  journal	= {Trans. Korean Inst. of Electrical Engineers},
  year		= {1991},
  volume	= {40},
  number	= {4},
  pages		= {360--373},
  month		= {April},
  note		= {(in Korean)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim91e,
  author	= {D. S. Kim and T. L. Huntsberger},
  title		= {Self-organizing neural networks for unsupervised pattern
		  recognition},
  booktitle	= {Tenth Annual Int. Phoenix Conf. on Computers and
		  Communications},
  year		= {1991},
  pages		= {39--45},
  organization	= {IEEE; Arizona State Univ. ; Univ. Arizona},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim91f,
  author	= {S. -S. Kim and C. -M. Kyung},
  title		= {Global placement of macro cells using self-organization
		  principle},
  booktitle	= {Proc. 1991 IEEE Int. Symp. on Circuits and Systems},
  year		= {1991},
  pages		= {V-3122--3125},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim91g,
  author	= {H. K. Kim and H. S. Lee},
  title		= {An extended {LVQ2} algorithm and its application to
		  phoneme classification},
  booktitle	= {Proc. EUROSPEECH-91, 2nd European Conf. on Speech
		  Communication and Technology},
  year		= {1991},
  volume	= {III},
  pages		= {1265--1268},
  organization	= {Assoc. Belge Acoust. ; Assoc. Italiana di Acustica; CEC;
		  et al},
  publisher	= {Istituto Int. Comunicazioni},
  address	= {Genova, Italy},
  dbinsdate	= {oldtimer}
}

@Article{	  kim92a,
  author	= {Woo Sung Kim and Sung Yang Bang},
  title		= {A study on Korean and {C}hinese character document reader
		  using neural network},
  journal	= {J. Korean Inst. of Telematics and Electronics},
  year		= {1992},
  volume	= {29B},
  number	= {2},
  pages		= {50--59},
  month		= {February},
  note		= {(in Korean)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim92b,
  author	= {Yoo Seok Kim and Jang Gyu Lee},
  title		= {Robust adaptive control of an autonomous mobile robot},
  booktitle	= {ICARCV '92. Second International Conference on Automation,
		  Robotics and Computer Vision},
  year		= {1992},
  volume	= {2},
  pages		= {INV-1. 7/1--5},
  organization	= {Dept. of Control \& Instrum. Eng. , Seoul Nat. Univ. ,
		  South Korea},
  publisher	= {Nanyang Technol. Univ},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim93a,
  author	= {K. Y. Kim and J. B. Ra},
  title		= {Edge Preserving Vector Quantization Using Self-Organizing
		  Map Based on Adaptive Learning},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1219--1222},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {The conventional Self-Organizing Map algorithm for vector
		  quantization is modified to reduce the edge degradation in
		  the reproduced image. The learning procedure is performed
		  by a proper selection of the learning rate, which is
		  adaptively determined according to the block activity. The
		  simulation results of 4x4 vector quantization for 512x512
		  image coding show the feasibility the proposed method.},
  dbinsdate	= {oldtimer}
}

@Article{	  kim93b,
  author	= {Kim, Seon Jong and Kim, Jin Ho and Choi, Heung Moon},
  title		= {Efficient algorithm for traveling salesman problems based
		  on \mbox{self-organizing} feature maps.},
  journal	= {Second IEEE International Conference on Fuzzy Systems},
  year		= {1993},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  number	= {},
  volume	= {},
  pages		= {1085--1090},
  abstract	= {Kohonen's self-organizing feature map (SOFM) has the
		  topological characteristics that can be effectively used in
		  solving traveling salesman problem (TSP). Angeniol et al.
		  actually applied SOFM in solving TSPs, but, due to the
		  duplication of the neuron as the winner for two different
		  cities, their algorithm requires at least kN output neurons
		  and 2kN connections for N-city TSP, where k is the number
		  of deletion process of neurons(k=3 in the Angeniol's work).
		  This paper presents, for large scale TSPs, an efficient
		  SOFM algorithm in which a winner neuron for each city is
		  not duplicated but excluded in the next competition.
		  Therefore, our algorithm requires just only the N output
		  neurons and 2N connections for N-city TSPs. And due to
		  direct use of the output potential, the proposed algorithm
		  can obtain better solutions. Simulation results show about
		  30% faster convergence and better solutions than
		  conventional algorithm for solving 30-city TSPs. Another
		  simulation results for large scale TSPs with 1000 cities
		  also show good performances of the proposed algorithm.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim93c,
  author	= {Nam-Chul Kim and Won-Hak Hong and Minsoo Suk and Jean
		  Koh},
  title		= {Segmentation Using a Competitive Learning Neural Network
		  for Image Coding},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2203--2206},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim93d,
  author	= {Baek-Sop Kim and Sang Hee Lee and Dae Keuk Kim},
  title		= {Determination of initial configuration for {LVQ} by using
		  {CNN}},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2456--2459},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {A method for determining the initial configuration for the
		  LVQ is proposed. It is based on the Condensed Nearest
		  Neighbor (CNN) rule followed by the K-means clustering
		  method. Experiments show that the proposed method is
		  generally better than the conventional ones which use the
		  k-NN or the K-means. And it is also shown that the
		  performance of the CNN is improved by applying the LVQ as a
		  post processing.},
  dbinsdate	= {oldtimer}
}

@Article{	  kim93e,
  author	= {Seon Jong Kim and Heung Moon Choi},
  title		= {An efficient algorithm based on \mbox{self-organizing}
		  feature maps for large scale traveling salesman problems},
  journal	= {Journal of the Korean Institute of Telematics and
		  Electronics},
  year		= {1993},
  volume	= {30B},
  number	= {8},
  pages		= {64--70},
  month		= {Aug},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim93f,
  author	= {Kim, S. -J. and Kim, J. -H. and Choi, H. -M. },
  title		= {An efficient algorithm for traveling salesman problems
		  based on \mbox{self-organizing} feature maps},
  booktitle	= {Second IEEE International Conference on Fuzzy Systems},
  year		= {1993},
  volume	= {2},
  pages		= {1085--90},
  organization	= {Dept. of Electron. , Kyungpook Nat. Univ. , Daegu, South
		  Korea},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim94a,
  author	= {Jongwan Kim and Jesung Ahn and Chong Sang Kim and Heeyeung
		  Hwang and Seongwon Cho},
  title		= {A New Competitive Learning Algorithm with Dynamic Output
		  Neuron Generation},
  pages		= {692--697},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim94b,
  author	= {Dou-Seok Kim and Soo-Young Lee and Mun-Sung Han and
		  Chong-Hyun Lee and Jeon-Gue Park and Sang-Weon Suh},
  title		= {Multi-dimensional {HMM} Parameter Estimation Using
		  Self-Organizing Feature Map for Speech Recognition},
  pages		= {541--542},
  booktitle	= {Proc. 3rd International Conference on Fuzzy Logic, Neural
		  Nets and Soft Computing},
  year		= {1994},
  publisher	= {Fuzzy Logic Systems Institute},
  address	= {Iizuka, Japan},
  annote	= {application, speech recognition},
  dbinsdate	= {oldtimer}
}

@Article{	  kim95a,
  author	= {Kim, Y. K. and Ra, J. B. },
  title		= {Adaptive learning method in \mbox{self-organizing} map for
		  edge preserving vector quantization},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1995},
  volume	= {6},
  number	= {1},
  pages		= {278--80},
  month		= {Jan},
  dbinsdate	= {oldtimer}
}

@Article{	  kim95b,
  author	= {Kim, J. H. and Cho, H. S.},
  title		= {Neural network-based inspection of solder joints using a
		  circular illumination},
  journal	= {Image and Vision Computing, Proc.},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  number	= {6},
  volume	= {13},
  pages		= {479--490},
  abstract	= {In this paper, we describe an approach to inspection of
		  solder joints on printed circuit boards by using a circular
		  illumination technique and a neural network classifier. The
		  illumination technique, consisting of three tiered circular
		  colour lamps and one colour camera, gives good visual cues
		  to infer 3D shape of the solder joint surface. A general
		  aspect of this inspection is that the shape of the solder
		  joint tends to greatly vary according to soldering
		  conditions. Due to this, a neural network classifier based
		  on a supervised version of Kohonen learning vector
		  quantization (LVQ) is proposed to automatically and
		  efficiently make classification criteria of the solder
		  joint shapes according to their quality. The practical
		  feasibility of the proposed approach is demonstrated by
		  building a prototype inspection machine and testing its
		  performance.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kim95c,
  author	= {Kyoung-Ok Kim and Young-Kyu Yang and Jong-Hoon Lee and
		  Kyung-Ho Choi and Tae-Kyun Kim},
  title		= {Classification of multispectral image using neural
		  network},
  booktitle	= {1995 International Geoscience and Remote Sensing
		  Symposium, IGARSS '95. Quantitative Remote Sensing for
		  Science and Applications},
  year		= {1995},
  editor	= {Stein, T. I. },
  volume	= {1},
  pages		= {446--8},
  organization	= {Korea Inst. of Sci. \& Technol. , Seoul, South Korea},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  kim95d,
  author	= {Young-Keun Kim and Jong-Beom Ra},
  title		= {Image coding using the \mbox{self-organizing} map of
		  multiple shell hypercube structure},
  journal	= {Journal of the Korean Institute of Telematics and
		  Electronics},
  year		= {1995},
  volume	= {32B},
  number	= {11},
  pages		= {153--62},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kim96a,
  author	= {Jung-Hoon Kim and Jae-Yoon Lim and Pyeong-Shik Ji and
		  Sung-Hyun Cho and Sang-Chun Nam},
  title		= {Load pattern classification using {K}ohonen network with
		  fuzzy},
  booktitle	= {ICEE '96. Proceedings of the International Conference on
		  Electrical Engineering},
  publisher	= {Int. Acad. Publishers},
  year		= {1996},
  volume	= {1},
  address	= {Beijing, China},
  pages		= {57--61},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kim96b,
  author	= {Bong-Hwan Kim and Tae-Yong Kim and Jeun-Woo Lee and
		  Heung-Moon Choi},
  title		= {DCT-based high speed vector quantization using classified
		  weighted tree-structured codebook},
  booktitle	= {1996 IEEE International Conference on Systems, Man and
		  Cybernetics. Information Intelligence and Systems},
  publisher	= {World Scientific},
  year		= {1996},
  volume	= {2},
  editor	= {A. -P. N. Refenes and Y. Abu-Mostafa and J. Moody and A.
		  Weigend},
  address	= {Singapore},
  pages		= {935--40},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kim97a,
  author	= {Jae-Chul Kim and Yong-Han Yoon and Do-Hyuk Choi and
		  Young-Jae Jeon},
  title		= {A {K}ohonen neural network approach for transformer fault
		  diagnosis using dissolved gas analysis},
  booktitle	= {ISAP '97 International Conference on Intelligent System
		  Application to Power Systems. Proceedings},
  publisher	= {Korean Inst. Electr. Eng},
  year		= {1997},
  editor	= {Y. -M. Park and J. -K. Park and K. Y. Lee},
  address	= {Seoul, South Korea},
  pages		= {336--40},
  dbinsdate	= {oldtimer}
}

@Article{	  kim97b,
  author	= {Eun-Soo Kim and Jin-Woo Cha and Chung-Sang Ryu},
  title		= {Three dimensional target recognition using mART neural
		  networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3069},
  pages		= {137--44},
  note		= {(Automatic Target Recognition VII Conf. Date: 22--24 April
		  1997 Conf. Loc: Orlando, FL, USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@Article{	  kim99a,
  author	= {Jin Soo Kim and Xiaohan Qin and Yarsun Hsu},
  title		= {Memory characterization of a parallel data mining
		  workload},
  journal	= {Workload Characterization: Methodology and Case Studies.
		  Based on the First Workshop on Workload Characterization.
		  IEEE Computer Society, Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {60--8},
  abstract	= {Studies a representative of an important class of emerging
		  applications: a parallel data mining workload. The
		  application, extracted from the IBM Intelligent Miner,
		  identifies groups of records that are mathematically
		  similar, based on a neural network model called a
		  self-organizing map. We examine and compare, in detail, two
		  implementations of the application: (1) temporal locality
		  or working set size; (2) spatial locality and memory block
		  utilization; (3) communication characteristics and
		  scalability; and (4) translation lookaside buffer (TLB)
		  performance. First, we find that the working set hierarchy
		  of the application is governed by two parameters, namely
		  the size of an input record and the size of prototype
		  array; it is independent of the number of input records.
		  Second, the application shows good spatial locality, with
		  the implementation optimized for sparse data sets having
		  slightly worse spatial locality. Third, due to the batch
		  update scheme, the application bears very low
		  communication. Finally, a two-way set-associative TLB may
		  result in severely skewed TLB performance in a
		  multiprocessor environment, caused by the large discrepancy
		  in the number of conflict misses. Increasing the set
		  associativity is more effective in mitigating the problem
		  than increasing the TLB size.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kimber90a,
  author	= {D. G. Kimber and M. A. Bush and G. N. Tajchman},
  title		= {Speaker-independent vowel classification using hidden
		  {M}arkov models and {LVQ2}},
  booktitle	= {Proc. ICASSP-90, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1990},
  volume	= {I},
  pages		= {497--500},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kindermann92a,
  author	= {J. Kindermann and C. Windheuser},
  title		= {Unsupervised Sequence Classification},
  booktitle	= {Proc. Workshop on Neural Networks for Signal Processing
		  2},
  year		= {1992},
  editor	= {Kung, S. Y. and Fallside, F. and S{\"o}renson, J. Aa. and
		  Kamm, C. A. },
  pages		= {184--193},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {August},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kinouchi01a,
  author	= {Yasuo Kinouchi and Tomikazu Sasho},
  title		= {Structure of Associative Memory System with Concept
		  Formation Based on Mutual Feature Reactions},
  booktitle	= {International Joint Conference on Neural Networks,
		  Washington, DC, USA, July 15--19},
  pages		= {},
  year		= {2001},
  month		= {July},
  note		= {CD-ROM},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kirk00a,
  author	= {Kirk, James S. and Zurada, Jacek M.},
  title		= {Two-stage algorithm for improved topography preservation
		  in self-organizing maps},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {2527--2532},
  organization	= {Univ of Louisville},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {It has been observed that the Kohonen self-organizing map
		  (SOM) has two goals, which are pursued simultaneously in
		  the standard training algorithm. The first goal is adequate
		  vector quantization, and the second is satisfactory
		  preservation of topography between input data and the
		  output map. Vector quantization by the SOM is performed
		  through the codebook vectors associated with vertices of
		  the map grid, each of which represents a number of input
		  data points. Topography preservation is achieved through
		  the edges of the grid, which impose an output-space
		  ordering on the (input space) codebook vectors. This paper
		  introduces a new batch training algorithm for
		  topography-preserving maps that approaches the two goals of
		  the SOM independently. The algorithm is the outgrowth of a
		  new topographical error metric that places greater emphasis
		  on the preservation of relationships between global input
		  data structures. Experimental results indicate that the
		  two-stage algorithm substantially improves the preservation
		  of global input data structures, although this improvement
		  comes at the cost of an increase in the number of
		  topological discontinuities on a local scale.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kirk01a,
  author	= {Kirk, J. S. and Chang, D. -J. and Zurada, J. M.},
  title		= {A self-organizing map with dynamic architecture for
		  efficient color quantization},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {2128--2132},
  organization	= {University of Louisville, Comp. Sci. and Engineering
		  Program},
  publisher	= {},
  address	= {},
  abstract	= {Color quantization is often used to convert 24-bit RGB
		  images to 8-bit palette-table images. However, in some
		  cases, the imposed 8 bits per pixel may be too stringent to
		  adequately represent the image. For other images, 8 bits
		  per pixel are unnecessarily generous. For image storage and
		  transmission, it is important to compress an image as much
		  as possible without exceeding an allowable level of
		  degradation. This paper describes the use of a
		  dynamically-growing self-organizing map (SOM) to determine
		  the palette-table required to adequately represent the
		  colors of an RGB image, given an allowable degree of
		  quantization error.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kirk01b,
  author	= {Kirk, J. S. and Zurada, J. M.},
  title		= {An evolutionary method of training topography-preserving
		  maps},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {2230--2234},
  organization	= {University of Louisville, Comp. Sci. and Engineering
		  Program},
  publisher	= {},
  address	= {},
  abstract	= {In this paper, we introduce an evolutionary training
		  method that can be used either to replace standard
		  self-organizing map (SOM) training, or to post-process a
		  trained SOM. The approach is motivated by a desire to
		  improve the way a map preserves relationships in the data
		  beyond the preservation of continuity. There are two stages
		  in the algorithm, prompted by the observation that there is
		  conflict in standard SOM training between the goal of
		  representing the probability distribution of the data and
		  the goal of preserving topology between input and output (a
		  conflict between competition and cooperation). By pursuing
		  these two goals in two separate stages of training, we are
		  able to focus on each goal individually and prevent each
		  from impeding the other. The use of a genetic algorithm in
		  the second stage determines the adjacencies of neurons in
		  the output map grid and allows greater control over the way
		  relationships between output neurons preserve the
		  relationships found in the input data. This is important
		  because it enhances the ability of the map to more
		  accurately represent the structure of the input data. It
		  may prove especially valuable when dealing with
		  high-dimensional data, when one cannot visually inspect the
		  map plotted in the data space to verify the quality of the
		  mapping.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kirk99a,
  author	= {Kirk, J. S. and Zurada, J. M.},
  title		= {Algorithms for improved topology preservation in
		  \mbox{self-organizing} maps},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {396--400},
  abstract	= {During the training of self-organizing maps (SOMs), there
		  is a conflict between the twin goals of topology
		  preservation between input and output and the minimization
		  of quantization error (QE). This is especially obvious when
		  the dimension of the input data (the dimension of the
		  codebook vectors) is higher than the dimension of the
		  output network (the dimension of the map grid). The
		  standard SOM training algorithm usually achieves a
		  reasonable balance between the two requirements but, in the
		  end, the need for a low QE overrides the desire for optimal
		  topology preservation. However, one can easily think of
		  applications for which topology preservation should be
		  given relatively greater weight than the standard algorithm
		  allows. The paper describes three modifications to the
		  incremental SOM learning algorithm that enhance its ability
		  to preserve topological relationships without increasing
		  the dimensionality of the network, but usually necessarily
		  at the expense of QE. Experiments are described which
		  demonstrate the new algorithms and compare their
		  performance to that of the standard SOM training.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kirkland94a,
  author	= {William R. Kirkland and D. P. Taylor},
  booktitle	= {Neural Networks in Telecommunications},
  title		= {Neural Network Channel Equalization},
  publisher	= {Kluwer Academic Publishers},
  year		= {1994},
  editor	= {Ben Yuhas and Nirwan Ansari},
  pages		= {141--171},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kirschfink94a,
  author	= {Kirschfink, H. and Rehborn, H. },
  title		= {Classification of traffic situations by using neural
		  networks},
  booktitle	= {ECAI 94. 11th European Conference on Artificial
		  Intelligence. Proceedings},
  year		= {1994},
  editor	= {Cohn, A. G. },
  pages		= {23--7},
  organization	= {Heusch/Boesefeldt GmbH, Aachen, Germany},
  publisher	= {Wiley},
  address	= {Chichester, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  kishida01a,
  author	= {Kishida, K. and Fukumoto, S. and Miyajima, H.},
  title		= {A construction method of fuzzy systems using vector
		  quantization},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-C}
		  ,
  year		= {2001},
  volume	= {121},
  pages		= {106--11},
  abstract	= {We propose a learning method of fuzzy inference rules
		  using a vector quantization, neural gas network. Some
		  models using self-organization or vector quantization by
		  neural networks have been proposed in previous studies.
		  These models show good results for the number of fuzzy
		  inference rules in high dimensional problems. However, most
		  of these models determine a distribution of initial fuzzy
		  inference rules by considering only input data. In this
		  paper, so as to make a more proper distribution of the
		  initial fuzzy inference rules in input space, we propose a
		  method considering not only input data but output data.
		  Further, the number of fuzzy inference rules is determined
		  to an objective value (threshold of inference error) in a
		  constructive way. In order to demonstrate the validity of
		  the proposed method, some numerical examples are
		  performed.},
  dbinsdate	= {2002/1}
}

@InCollection{	  kishida97a,
  author	= {K. Kishida and M. Maeda and H. Miyajima and S. Murashima},
  title		= {A self-tuning method of fuzzy modeling with learning
		  vector quantization},
  booktitle	= {Proceedings of the Sixth IEEE International Conference on
		  Fuzzy Systems},
  publisher	= {ESARDA Symposium Secretariat},
  year		= {1997},
  volume	= {1},
  editor	= {C. Foggi and F. Genoni and W. D. Lauppe and C. S. Sonnier
		  and G. Stein},
  address	= {Ispra, Italy},
  pages		= {397--402},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kita93a,
  author	= {Hajime Kita and Yoshikazu Nishikawa},
  title		= {Neural Network Model of Tonotopic Map Formation Based on
		  the Temporal Theory of Auditory Sensation},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {413--418},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  kitajima00a,
  author	= {Kitajima, Hiroshi and Hagiwara, Masafumi},
  title		= {Human posture estimation method from a single image using
		  genetic algorithm and fuzzy inference},
  journal	= {Systems and Computers in Japan},
  year		= {2000},
  volume	= {31},
  number	= {12},
  month		= {Nov},
  pages		= {52--61},
  organization	= {Keio Univ},
  publisher	= {Scripta Technica Inc},
  address	= {New York, NY},
  abstract	= {In this paper, we propose a method for estimating the
		  observation direction of a person by applying fuzzy
		  inference to the features obtained when a genetic algorithm
		  is used to recognizee human posture from a natural image.
		  Posture recognition uses the constraints obtained by
		  representing the human body as parts expressed as connected
		  structures to reduce the search space of the genetic
		  algorithm. First, the head, which is a relatively easy part
		  of the body to detect, is detected by pattern matching.
		  Then, based on the position of the head, the body, arms,
		  and legs are detected successively. A complex matching
		  model becomes unnecessary because each part is approximated
		  by a combination of straight lines and ellipses. The
		  generated image of each part constructed from straight
		  lines and ellipses is compared to the input image and
		  optimized to obtain some level of overlap by the genetic
		  algorithm. The obtained features are input to a fuzzy
		  inference neural network, and the observation direction of
		  the person can be estimated. The fuzzy rules can be
		  automatically extracted by using Kohonen's self-organizing
		  algorithm and the minimum least squares algorithm. The
		  method of this paper applies to many people, and its
		  effectiveness is verified.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kitajima95a,
  author	= {Nobukatsu Kitajima},
  title		= {A New Method for Initializing Reference Vectors in {LVQ}},
  volume	= {V},
  pages		= {2775--2779},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {A new method for setting initial locations of reference
		  vectors in Learning Vector Quantization (LVQ) is proposed
		  to obtain stably high-performance classification results.
		  The initial locations of reference vectors are important
		  for obtaining adequate results rapidly in the LVQ, because
		  the initial locations affect the convergence of LVQ. On the
		  basis of the convergence property of LVQ, this method
		  locates reference vectors in such a manner that they match
		  the probability distribution of training data with
		  Self-Organizing Map (SOM). Then, it determines the
		  categories of the reference vectors as representatives of
		  respective Voronoi regions. Numerical simulations confirm
		  better classification results with the present method than
		  with conventional methods.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kitamura96a,
  author	= {T. Kitamura and S. Takei},
  title		= {Speaker recognition model using two-dimensional mel-
		  cepstrum and predictive neural network},
  booktitle	= {Proceedings ICSLP 96. Fourth International Conference on
		  Spoken Language Processing},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  editor	= {H. T. Bunnell and W. Idsardi},
  address	= {New York, NY, USA},
  pages		= {1772--5},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kitaori95a,
  author	= {Kitaori, K. and Murakoshi, H. and Funakubo, N. },
  title		= {A new approach to solve the traveling salesman problem by
		  using the improved {K}ohonen`s \mbox{self-organizing}
		  feature map},
  booktitle	= {Proceedings of the 1995 IEEE IECON. 21st International
		  Conference on Industrial Electronics, Control, and
		  Instrumentation},
  year		= {1995},
  volume	= {2},
  pages		= {1384--8},
  organization	= {Dept. of Electron. Syst. Eng. , Tokyo Metropolitan Inst.
		  of Technol. , Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {This paper proposes methods that would develop the ability
		  of Kohonen's Self-Organizing Features Map (SOFM) to solve
		  optimization problem and also shows how useful SOFM is in
		  solving optimization problems. We focused on the Traveling
		  Salesman Problem (TSP) as a typical example of an
		  optimization problem. The conventional SOFM can solve the
		  TSP. But the solution is not the optimum solution because
		  the path intersects itself. Therefore, we propose new
		  methods to keep the path from intersecting itself at all
		  times. By adding these methods to the rule of changing
		  synaptic strengths, the path length is improved by
		  decreasing the iteration time and increasing the
		  convergence rate.},
  dbinsdate	= {oldtimer}
}

@Book{		  kiviluoto95a,
  author	= {Kiviluoto, K.},
  title		= {Topology Preservation in Self-Organizing Maps.},
  year		= {1995},
  abstract	= {This paper concentrates on the following aspects: (1)
		  Discussion on what kind of mapping is produced by the Self
		  Organizing Map (SOM) algorithm, (2) Introduction of a
		  quantitative measure of topology preservation, (3)
		  Introduction of a variant of SOM, called the AdSOM, with
		  locally adapting neighborhood radii. (Copyright (c) 1995 by
		  Helsinki, University of Technology.)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kiviluoto96a,
  author	= {K. Kiviluoto},
  title		= {Topology preservation in \mbox{self-organizing} maps},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {294--9},
  abstract	= {This paper concentrates on the following issues: 1.
		  Discussion on what kind of mapping is produced by the SOM
		  algorithm, 2. Introduction of a quantitative measure of
		  continuity for the mapping produced by SOM, 3. Introduction
		  of a variant of SOM, called the AdSOM, with locally
		  adapting neighborhood radii.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kiviluoto97a,
  author	= {Kimmo Kiviluoto and Pentti Bergius},
  title		= {Analyzing financial statements with the
		  \mbox{self-organizing} map},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {362--367},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kiviluoto98a,
  author	= {K. Kiviluoto},
  title		= {Comparing {2D} and {3D} Self-Organizing Maps in Financial
		  Data Visualization},
  booktitle	= {Proceedings of the International Conference on Soft
		  Computing and Information / Intelligent Systems
		  (IIZUKA'98)},
  year		= {1998},
  address	= {Iizuka, Japan},
  pages		= {68--71},
  abstract	= {The self-organizing map is used to visualize financial
		  statement data. The effect of increasing the map dimension
		  from two to three is first demonstrated in a
		  three-dimensional toy data example; then maps of both
		  dimensionalities are used to visually explore financial
		  data. It turns out that there are cases where a
		  two-dimensional map suggests that the data has separate
		  clusters sharing some common property, but a
		  three-dimensional map only finds a single cluster. This is
		  most likely a result of the two-dimensional map folding
		  itself into the input space that has an intrinsic dimension
		  higher than two, which produces artifacts appearing as
		  separate clusters.},
  dbinsdate	= {oldtimer}
}

@Article{	  kiviluoto98b,
  author	= {Kiviluoto, Kimmo},
  title		= {Predicting bankruptcies with the \mbox{self-organizing}
		  map},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {191--201},
  abstract	= {The self-organizing map is used for analysis of financial
		  statements, focusing on bankruptcy prediction. The
		  phenomenon of going bankrupt is analyzed qualitatively, and
		  companies are also classified into healthy and
		  bankrupt-prone ones. In the qualitative analysis, the
		  self-organizing map is used in a supervised manner: both
		  input and output vectors are represented in the weight
		  vector of each unit, and during training, the whole weight
		  vector is updated, but the best-matching unit search is
		  based on the input vector part only. In the quantitative
		  analysis, three classifiers that utilize the
		  self-organizing map are compared to linear discriminant
		  analysis and learning vector quantization. A modification
		  of the learning vector quantization algorithm to
		  accommodate the Neyman-Pearson classification criterion is
		  also presented.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kiviluoto98c,
  author	= {K. Kiviluoto and P. Bergius},
  title		= {Exploring Corporate Bankruptcy with Two-Level
		  Self-Organizing Maps. Decision Technologies for
		  Computational Management Science},
  booktitle	= {Proceedings of Fifth International Conference on
		  Computational Finance},
  publisher	= {Kluwer Academic Publishers},
  year		= {1998},
  address	= {Boston},
  pages		= {373--380},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kiviluoto98d,
  author	= {K. Kiviluoto and P. Bergius},
  title		= {Two-level \mbox{self-organizing} maps for analysis of
		  financial statements},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {189--92},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kiviluoto98e,
  author	= {K. Kiviluoto and P. Bergius},
  title		= {Maps for Analyzing Failures of Small and Medium-sized
		  Enterprises},
  booktitle	= {Visual Expolorations in Finance with Self-Organizing
		  Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {59--71},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kiziloglu93a,
  author	= {B. Kiziloglu and V. Tryba and W. Daehn},
  title		= {Digital Circuit Partition by Self-Organizing Maps: {A}
		  Comparison to Classical Methods},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2413--2416},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {The partitioning of integrated circuits can be executed by
		  using a modified algorithm of the self-organizing map.
		  After a short description of the modified algorithm, an
		  algorithm for the automatic partitioning is explained. The
		  performance of the algorithm is compared to Greedy and
		  random algorithms.},
  dbinsdate	= {oldtimer}
}

@Article{	  klein_gebbinck93a,
  author	= {{Klein Gebbinck}, M. S. and Verhoeven, J. T. M. and
		  Thijssen, J. M. and Schouten, T. E. },
  title		= {Application of neural networks for the classification of
		  diffuse liver disease by quantitative echography},
  journal	= {Ultrasonic Imaging},
  year		= {1993},
  volume	= {15},
  number	= {3},
  pages		= {205--17},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  klima93a,
  author	= {Klima, M. and Zahradnik, P. and Novak, M. and Dvorak, P.
		  },
  title		= {Simple motion detection methods in TV image for security
		  purposes},
  booktitle	= {Proceedings of The Institute of Electrical and Electronics
		  Engineers 1993 International Carnahan Conference on
		  Security Technology: Security Technology},
  year		= {1993},
  editor	= {Sanson, L. D. },
  pages		= {41--3},
  organization	= {Fac. of Electr. Eng. , Czech Tech. Univ. , Prague, Czech
		  Republic},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {TV security systems are extensively applied in many
		  different branches. This paper presents some simple
		  detection procedures and algorithms implemented on a
		  standard PC for detection and localisation of targets. The
		  approach is based upon motion detection and target
		  identification procedures suitable for fast and efficient
		  image processing on relatively simple hardware. Apart from
		  classical approach (spatiotemporal filtering), a neural
		  network oriented alternative (Kohonen map) is discussed.},
  dbinsdate	= {oldtimer}
}

@Article{	  klimek99a,
  author	= {Klimek, Lee and Wooley, Bruce and Bridges, Susan and
		  Hodges, Julia and Watkins, Andrew and Smolensky, Sarah},
  title		= {Comparison of the performances of a {B}ayesian algorithm
		  and a {K}ohonen map for clustering texture data},
  journal	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  year		= {1999},
  number	= {},
  volume	= {9},
  pages		= {777--784},
  abstract	= {With many clustering algorithms available, it may be
		  difficult to discern which is better for a given task. This
		  study compares the performance of two clustering
		  algorithms, the Bayesian classifier AutoClass and a Kohonen
		  map, for the task of identifying classes of different
		  textures in images based on statistics derived from
		  gray-level co-occurrence matrices. The performance of the
		  two algorithms is assessed in terms of quality of the
		  classification. Comparisons of quality are given in terms
		  of objective criteria such as cluster diameter,
		  intercluster distance, etc. as well as subjective
		  judgements by domain experts. Two different types of images
		  are used. The first type of image consists of standard
		  texture images in which textures classes are readily
		  identified by novices. The second type consists of
		  side-scanned sonar images in which the clusters are not
		  necessarily apparent to novices and are not always
		  classified consistently by domain experts (geologists).},
  dbinsdate	= {oldtimer}
}

@Article{	  knagenhjelm90a,
  author	= {Petter Knagenhjelm and Peter Brauer},
  title		= {Classification of vowels in continuous speech using
		  {M}{L}{P} and a hybrid net},
  journal	= {Speech Communication},
  year		= {1990},
  volume	= {9},
  number	= {1},
  pages		= {31--34},
  abstract	= {Two different Artificial Neural Network (ANN) classifiers
		  have been compared with a traditional
		  closest-mean-classifier, a VQ and a Kohonen-network with
		  respect to classification of vowels extracted from
		  continuous speech. The first ANN-classifier is a standard
		  three-layer perceptron network and the second
		  ANN-classifier uses the response from a 16 x 16 Kohonen map
		  as input to a two layer perceptron network. The result
		  shows that the best performance is achieved with the two
		  ANN-classifiers and indicates that a Kohonen map does not
		  deteriorate the information presented to the second layer
		  in the network and hence can be used instead of a first
		  hidden layer.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  knagenhjelm92a,
  author	= {Petter Knagenhjelm},
  title		= {A Recursive Design Method for Robust Vector Quantization},
  booktitle	= {Proc. ICSPAT-92, International Conference on Signal
		  Processing Applications and Technology},
  year		= {1992},
  pages		= {948--954},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  knagenhjelm93a,
  author	= {Petter Knagenhjelm},
  title		= {Competitive Learning in Robust Communication},
  school	= {Chalmers University of Technology},
  year		= {1993},
  address	= {G{\"{o}}teborg, Sweden},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  knobbe95a,
  author	= {Arno J. Knobbe and Joost N. Kok and Mark H. Overmars},
  title		= {Robot Motion Planning in Unknown Environments using Neural
		  Networks},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {375--380},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  knohl93a,
  author	= {Lars Knohl and Ansgar Rinscheid},
  title		= {Speaker Normalization and Adaptation Based on Feature-Map
		  Projection},
  booktitle	= {Proc. EUROSPEECH-93, 3rd European Conf. on Speech,
		  Communication and Technology},
  year		= {1993},
  volume	= {I},
  pages		= {367--370},
  publisher	= {ESCA},
  address	= {Berlin},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  knohl93b,
  author	= {Lars Knohl and Ansgar Rinscheid},
  title		= {Speaker Normalization with Self-Organizing Feature Maps},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {243--246},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {An efficient speaker-normalization method based on the
		  mapping of two self-organizing feature maps is developed.
		  The normalization system consists of a reference map that
		  is trained on the reference speaker's feature space and of
		  a test speaker's map, generated by a special, topology
		  maintaining retraining of the reference map. The retraining
		  procedure is called 'Forced Competitive Learning (FCL)'. It
		  allows for an 1:1-exchange of the feature vectors
		  represented by the neurons of the reference map for those
		  of the test map in the operation phase. Pilot tests on a 33
		  word database, including the 10 digits (3 male \& 2 female
		  speakers, 5 versions each) have been performed employing a
		  simple HMM-isolated-word recognizer. The evaluation was
		  based on speaker-dependent recognition and has shown an
		  average adaptation efficiency of rho identical with 0, 90.
		  By the use of topology-preserving feature maps and because
		  it is independent of an explicit search for codebook
		  correspondences, the method proposed can broadly be applied
		  as a front end to all kinds of VQ-based recognition
		  systems.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  knoll92a,
  author	= {Dean Knoll and James Ting-Ho Lo},
  title		= {Push-and-Pull for Piecewise Linear Machine Training},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {III},
  pages		= {573--578},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  knopf00a,
  author	= {Knopf, George K. and Sangole, Archana},
  title		= {Trinocular data registration using a three-dimensional
		  self-organizing feature map},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {2863--2868},
  organization	= {Univ of Western Ontario},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A three-dimensional self-organizing feature map (SOFM)
		  that associates redundant and complementary features
		  extracted from images acquired by a trinocular camera
		  system is described in this paper. The combined features
		  extracted from three views of the reference parts are used
		  to train the SOFM. The unsupervised learning algorithm
		  ensures that `similar' feature vectors will be assigned to
		  cluster units that lie in close spatial proximity in the 3D
		  feature map. The technique reduces the dimensionality of
		  the input by exploiting hidden redundancies in the training
		  data. During the identification phase, features in the
		  novel test part activate a number of cluster units that
		  have weights similar to the applied training input. If the
		  sum-of-square error (SSE) between the input and weights of
		  the cluster unit with the strongest response is greater
		  than a predefined tolerance, then the test object is
		  labeled as faulty part.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  knopf00b,
  author	= {Knopf, G. K. and Sangole, A.},
  title		= {Visualizing data association using self-organizing feature
		  maps},
  booktitle	= {Smart Engineering System Design: Neural Networks, Fuzzy
		  Logic, Evolutionary Programming, Data Mining, and Complex
		  Systems. Vol.10. Proceedings of the Artificial Neural
		  Networks in Engineering Conference (ANNIE 2000). ASME, New
		  York, NY, USA},
  year		= {2000},
  volume	= {},
  pages		= {471--6},
  abstract	= {Scientific data visualization involves creating simplified
		  representations of complex multi-dimensional data sets for
		  enhanced human interpretation. This paper describes how the
		  self-organizing feature map (SOFM) develops an internal
		  ordered representation that can associate seemingly
		  unrelated data sets and, in the process, provide a
		  mechanism to explore large numeric databases for patterns.
		  The non-statistical unsupervised clustering algorithm
		  ensures that "similar" feature vectors will be assigned to
		  spatially close cluster units in the feature map. The SOFM
		  reduces the dimensionality of the input vectors by
		  exploiting hidden redundancies in a large volume of data.
		  Data correlation occurs with little or no a priori
		  knowledge of how the inputs are related. Continuously
		  sampled random inputs and inputs generated using the Henon
		  chaotic attractor, are used to explore the visualization
		  capability of the feature maps. Temporally varying random
		  and chaotic inputs appear similar in the time domain but
		  exhibit distinct geometric patterns when mapped into a
		  SOFM. Spatial and color coded representations of the
		  feature map are used to interpret the "degree of
		  association" between past input data sets.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  knopf01a,
  author	= {Knopf, George K. and Sangole, Archana},
  title		= {Scientific data visualization using three-dimensional
		  self-organizing feature maps},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2001},
  editor	= {},
  volume	= {2},
  pages		= {759--764},
  organization	= {Dept. of Mech. and Mat. Eng., Faculty of Engineering
		  Science, University of Western Ontario},
  publisher	= {},
  address	= {},
  abstract	= {The goal of scientific data visualization is to transform
		  numeric or symbolic data into simple coherent patterns for
		  enhanced human interpretation. It involves a combination of
		  exploratory data analysis and data visualization techniques
		  that create a new level of information providing a deeper
		  look at the underlying structures present in high
		  dimensional data. This paper discusses how a spherical
		  self-organizing feature map (SOFM) enables multivariate
		  numeric data to take a geometric form by mapping high
		  dimensional data to a 3D space, thereby providing a
		  mechanism to explore large numeric databases for coherent
		  patterns. The patterns present in the numeric data are
		  given a shape based on similarity. The performance of the
		  proposed visualization algorithm is tested using coordinate
		  data from known geometry and multi-spectral satellite
		  data.},
  dbinsdate	= {2002/1}
}

@Article{	  kobayashi00a,
  author	= {Kobayashi, Y. and Ota, J. and Inoue, K. and Arai, T.},
  title		= {State and action space construction using vision
		  information},
  journal	= {Transactions-of-the-Society-of-Instrument-and-Control-Engineers}
		  ,
  year		= {2000},
  volume	= {36},
  pages		= {1029--36},
  abstract	= {To apply reinforcement learning in the real world, we need
		  to process sensor data adequately for action learning.
		  Since it is difficult to construct state space and to learn
		  the appropriate action simultaneously, we assume that an
		  evaluation is given to each step of action. Evaluations are
		  binary signals that mean actions are good or bad. Under
		  this condition, we propose a method of dividing and
		  clustering the state space. The TRN (topology representing
		  networks) algorithm is a vector quantization algorithm, and
		  it can preserve topology in the input space. We apply the
		  TRN algorithm to our problem with dynamically increasing
		  nodes and the radial basis function.},
  dbinsdate	= {2002/1}
}

@TechReport{	  kobayashi96a,
  author	= {Masaki Kobayashi and Katsunari Tanahashi and Kikuo
		  Fujimura and Heizo Tokutaka and Satoru Kishida},
  title		= {Study of improvement for the {K}ohonen's Self-Organizing
		  Feature Maps},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1996},
  number	= {NC95--163},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@Article{	  kocjancic00a,
  author	= {Kocjancic, R. and Zupan, J.},
  title		= {Modelling of the river flowrate: the influence of the
		  training set selection},
  journal	= {CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS},
  year		= {2000},
  volume	= {54},
  number	= {1},
  month		= {DEC 1},
  pages		= {21--34},
  abstract	= {A study of the influence of the training set selection,
		  the modelling technique, and the number of objects in the
		  training set was performed on a data set of 2 years' daily
		  measurements of atmospheric precipitation and river
		  flowrates. Twenty-five different data sets were prepared by
		  the following selection methods: random selection (RND),
		  Kohonen neural network selection, and Kennard-Stone
		  selection (K-S). On these data sets, 125 models
		  (regressions) were generated using the following five
		  methods: multiple linear regression (MLR), partial least
		  squares regression (PLSR) and two feed-forward neural
		  networks with the error back-propagation and Levenberg-
		  Marquardt learning algorithm (LM). The models were tested
		  using a single set separated from the rest of the data.
		  Additionally, a bottleneck neural network with
		  back-propagation learning was tested, since it combines
		  both modelling and mapping capabilities. The results are
		  discussed and two examples of model use are presented. The
		  best method for sample division into the
		  training/monitoring sets was the Kohonen map (KOH)
		  division. The best model obtained among those generated
		  yields the test set RMS error of 0.0845 and was achieved by
		  L-M neural net model on 400 randomly selected training
		  objects. },
  dbinsdate	= {2002/1}
}

@Article{	  kocjancic97a,
  author	= {R. Kocjancic and J. Zupan},
  title		= {Application of a feed-forward artificial neural network as
		  a mapping device},
  journal	= {Journal of Chemical Information and Computer Sciences},
  year		= {1997},
  volume	= {37},
  number	= {6},
  pages		= {985--9},
  dbinsdate	= {oldtimer}
}

@Article{	  koenig00a,
  author	= {Koenig, Andreas},
  title		= {Interactive visualization and analysis of hierarchical
		  neural projections for data mining},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  volume	= {11},
  number	= {3},
  month		= {May},
  pages		= {615--624},
  organization	= {Dresden Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Dimensionality reducing mappings, often also denoted as
		  multidimensional scaling, are the basis for multivariate
		  data projection and visual analysis in data mining.
		  Topology and distance preserving mapping techniques---e.g.,
		  Kohonen's self-organizing feature map (SOM) or Sammon's
		  nonlinear mapping (NLM)---are available to achieve
		  multivariate data projections for the following interactive
		  visual analysis process. For large data bases, however, NLM
		  computation becomes intractable. Also, if additional data
		  points or data sets are to be included in the projection, a
		  complete recomputation of the mapping is required. In
		  general, a neural network could learn the mapping and serve
		  for arbitrary additional data projection. However, the
		  computational costs would also be high, and convergence is
		  not easily achieved. In this work, a convenient
		  hierarchical neural projection approach is introduced,
		  where first an unsupervised neural network---e.g., an SOM -
		  quantizes the data base, followed by fast NLM mapping of
		  the quantized data. In the second stage of the hierarchy,
		  an enhancement of the NLM by a recall algorithm is applied.
		  The training and application of a second neural network,
		  which is learning the mapping by function approximation, is
		  quantitatively compared with this new approach. Efficient
		  interactive visualization and analysis techniques,
		  exploiting the achieved hierarchical neural projection for
		  data mining, are presented.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  koenig91a,
  author	= {A. Koenig and M. Glesner},
  title		= {An approach to the application of dedicated neural network
		  hardware for real time image compression},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1345--1348},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  x		= {Tarkista viela tama news. bib:sta. },
  dbinsdate	= {oldtimer}
}

@Article{	  kofidis96a,
  author	= {E. Kofidis and S. Theodoridis and C. Kotropoulos and I.
		  Pitas},
  title		= {Nonlinear adaptive filters for speckle suppression in
		  ultrasonic images},
  journal	= {Signal Processing},
  year		= {1996},
  volume	= {52},
  number	= {3},
  pages		= {357--72},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koh93a,
  author	= {Jean Koh and Minsoo Suk and Suchendra M. Bhandarkar},
  title		= {A Multi-Layer {K}ohonen'a Self-Organizing Feature Map for
		  Range Image Segmentation},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1270--1276},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {The goal of range image segmentation is to partition a
		  range image represented by a two-dimensional pixel array
		  into geometric primitives so that all the image pixels are
		  grouped into clusters with a common geometric
		  representation or property that could be used by
		  higher-level cognitive processes. This paper proposes and
		  describes a self-organizing neural network for range image
		  segmentation. the multi-layer Kohonen's Self-Organizing
		  Feature Map (MLKSFM) which is an extension of the
		  traditional single-layer Kohonen's Self-Organizing Feature
		  Map (KSFM) is seen to alleviate the shortcomings of the
		  latter in the context of range image segmentation. The
		  problem of range image segmentation is formulated as one of
		  vector quantization and is mapped onto the Multi-Layer
		  Kohonen's Self-Organizing Feature Map (MLKSFM). The
		  Multi-Layer Kohonen's Self-Organizing Feature Map (MLKSFM)
		  is currently implemented on the connection machine CM-2
		  which is a fine-grained SIMD computer. Experimental results
		  using both, synthetic and real range images are
		  presented.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koh93b,
  author	= {Koh, J. and Suk, M. and Bhandarkar, S. M. },
  title		= {A \mbox{self-organizing} neural network for hierarchical
		  range image segmentation},
  booktitle	= {Proceedings IEEE International Conference on Robotics and
		  Automation},
  year		= {1993},
  volume	= {2},
  pages		= {758--63},
  organization	= {Dept. of Electr. \& Comput. Eng. , Syracuse Univ. , NY,
		  USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  abstract	= {Range image segmentation is the process of partitioning a
		  range image represented by a two-dimensional pixel array
		  into geometric primitives so that all the image pixels are
		  grouped into clusters with a common geometric
		  representation or property that could be used by
		  higher-level cognitive processes. This paper proposes and
		  describes a self-organizing neural network for range image
		  segmentation. The Multi-Layer Kohonen's Self-Organizing
		  Feature Map (MLKSFM) which is an extension of the
		  traditional single-layer Kohonen's Self-Organizing Feature
		  Map (KSFM) is seen to alleviate the shortcomings of the
		  latter in the context of range image segmentation. The
		  problem of range image segmentation is formulated as one of
		  vector quantization and is mapped onto the MLKSFM. The
		  MLKSFM is currently implemented on the Connection Machine
		  CM-2 which is a fine-grained SIMD computer. Experimental
		  results using both, synthetic and real range images are
		  presented.},
  dbinsdate	= {oldtimer}
}

@Article{	  koh95a,
  author	= {Koh, J. and Suk, M. and Bhandarkar, M. },
  title		= {A multilayer \mbox{self-organizing} feature map for range
		  image segmentation},
  journal	= {Neural Networks},
  year		= {1995},
  volume	= {8},
  number	= {1},
  pages		= {67--86},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohle95a,
  author	= {Monika K{\"o}hle and Dieter Merkl},
  title		= {Semantic Classification of Documents without Domain
		  Knowledge},
  booktitle	= {Proceedings of the II Brasilian Symposium on Neural
		  Networks, Sao Carlos, Brazil, Oct 18--20},
  year		= 1995,
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohle96a,
  author	= {Monika K{\"o}hle and Dieter Merkl},
  title		= {Visualizing Similarities in High Dimensional Input Spaces
		  with a Growing and Splitting Neural Network},
  booktitle	= {Proceedings of ICANN96, International Conference on
		  Artificial Neural Networks, Bochum, Germany, July 16--19,
		  1996},
  publisher	= {Springer},
  year		= 1996,
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbr{\"u}ggen and B. Sendhoff},
  series	= {Lecture Notes in Computer Science, vol. 1112},
  address	= {Berlin},
  pages		= {581--586},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohle96b,
  author	= {Monika K{\"o}hle and Dieter Merkl},
  title		= {Things we observed when watching people walk:
		  Classification of gait patterns with \mbox{self-organizing}
		  maps},
  booktitle	= {Proc. ACNN'96, 7th Australian Conference on Neural
		  Networks, Canberra, April 10--12},
  year		= 1996,
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohle96c,
  author	= {M. Kohle and D. Merkl},
  title		= {Visualizing similarities in high dimensional input spaces
		  with a growing and splitting neural network},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {581--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohle96d,
  author	= {Monika K{\"o}hle and Dieter Merkl},
  title		= {Identification of Gait Pattern with Self-Organizing Maps
		  Based on Ground Reaction Force},
  booktitle	= {Proc. ESANN'96, European Symp. on Artificial Neural
		  Networks},
  year		= {1996},
  publisher	= {D facto conference services},
  address	= {Bruges, Belgium},
  editor	= {Michel Verleysen},
  pages		= {73--78},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohle97a,
  author	= {Monika K{\"o}hle and Dieter Merkl and Josef Kastner},
  title		= {Clinical Gait Analysis by Neural Networks: Issues and
		  Experiences},
  booktitle	= {Proc. CBMS'97, 10th IEEE Symposium on Computer-Based
		  Medical Systems},
  year		= 1997,
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohle97b,
  author	= {Kohle, M. and Merkl, D. and Kastner, J.},
  title		= {Assessment of ground reaction force patterns for human
		  gait malfunction identification},
  booktitle	= {Neural Networks in Engineering Systems. Proceedings of the
		  1997 International Conference on Engineering Applications
		  of Neural Networks},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1997},
  volume	= {1},
  pages		= {195--8},
  abstract	= {We perform a medical computing project based on gait
		  patterns collected form patients in an Austrian gait
		  analysis laboratory by using two ground reaction force
		  measurement platforms. The project aims at an assessment of
		  gait that is useful, on the one hand, as a support for
		  diagnosis and therapy considerations. On the other hand, it
		  is intended to give clues to a model of gait, to help
		  developing bio-feedback systems to train patients, and to
		  find effects of multiple diseases and still achieve
		  compensation. As a major benefit, the proposed approach is
		  exclusively based on observable data. Hence, we do not face
		  the tremendous effort of defining a biochemical model of
		  gait where parameters cannot be measured precisely. We
		  report on identification of gait malfunction with respect
		  to the location of ailment. The employed classification
		  approach is learning vector quantization which proves to be
		  highly robust in the results provided.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohle98a,
  author	= {Monika K{\"o}hle and Dieter Merkl},
  title		= {Experiments in Gait Pattern Classification with Neural
		  Networks of Adaptive Architecture},
  booktitle	= {Proceedings of ICANN98, the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer},
  year		= 1998,
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= 1,
  address	= {London},
  pages		= {293--298},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohlus93a,
  author	= {R. Kohlus and M. Bottlinger},
  title		= {Knowledge Extraction by Self Organizing Maps},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {1022},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen00a,
  author	= {T. Kohonen},
  title		= {An Unsupervised Learning Method that Produces Organized
		  Representation from Real Information},
  booktitle	= {Artificial Neural Networks in Medicine and Biology,
		  Prodeedings of the ANNIMAB-1 COnference, Göteborg, Sweden,
		  13--16 May 2000},
  pages		= {45--53},
  year		= {2000},
  editor	= {H. Malmgren and M. Boga and L. Niklasson},
  abstract	= {The neural-network theories aim at goals in medicine and
		  biology: modeling of the neural structures and functions,
		  and development of computational methods for the analysis
		  of experimental data. The Self-Organizing Map (SOM) was
		  originally intended for the explanation of certain brain
		  functions and organizations, but it has later been accepted
		  as a new statistical analysis method to many fields of
		  science and technology. At least 3700 scientific works on
		  the SOM have been published. In its basic form, the SOM
		  forms illustrative nonlinear projections of
		  high-dimensional data manifolds, and these projections,
		  usually produced on a two-dimensional grid, help in the
		  visualization and understanding of the relationships
		  between complex data sets.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen00b,
  author	= {Teuvo Kohonen},
  title		= {A Look into the Self-Organizing Maps},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen00d,
  author	= {T. Kohonen},
  title		= {Informaation k{\"a}sittely ja sen rajat},
  journal	= {Tietojenk{\"a}sittelytiede},
  year		= {2000},
  key		= {},
  volume	= {},
  number	= {},
  pages		= {15--9},
  month		= {joulukuu},
  note		= {Tietojenk{\"a}sittelytieteen Seura ry},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  kohonen00e,
  author	= {T. Kohonen and S. Kaski and K. Lagus and J. Saloj{\"a}rvi
		  and J. Honkela nad V. Paatero and A. Saarela},
  title		= {Self-Organization of a Massive Document Collection},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  key		= {},
  volume	= {11},
  number	= {},
  pages		= {574--85},
  month		= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InCollection{	  kohonen00f,
  author	= {Teuvo Kohonen},
  title		= {Data Mining by the Self-Organising Map Method},
  booktitle	= {Uncertainty in Intelligent and Information Systems},
  crossref	= {},
  key		= {},
  pages		= {3--22},
  publisher	= {World Scientific},
  year		= {2000},
  editor	= {B. Bouchon-Meunier and R. R. Yager and L. A. Zadeh},
  volume	= {20},
  number	= {},
  series	= {Advances in Fuzzy Systems---Applications and Theory},
  type		= {},
  chapter	= {},
  address	= {},
  edition	= {},
  month		= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kohonen00g,
  author	= {Teuvo Kohonen},
  title		= {New Lines in the Study of Self-Organizing Maps},
  booktitle	= {6 th International Conference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  crossref	= {},
  key		= {},
  pages		= {},
  year		= {2000},
  editor	= {},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {},
  note		= {Proceedings in CD-ROM},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kohonen00h,
  author	= {Kohonen, Teuvo},
  title		= {Self-Organizing Maps of massive document collections},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {2},
  pages		= {3--9},
  organization	= {Helsinki Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Huge document collections can be organized according to
		  textual similarities by the Self-Organizing Map (SOM)
		  algorithm, when statistical representations of the textual
		  contents are used as the feature vectors of the documents.
		  In a practical experiment we mapped 6,840,568 patent
		  abstracts onto a 1,002,240-node SOM. For the feature
		  vectors we selected 500-dimensional random projections of
		  the weighted word histograms.},
  dbinsdate	= {2002/1}
}

@Article{	  kohonen00i,
  author	= {Kohonen, Teuvo and Kaski, Samuel and Lagus, Krista and
		  Salojarvi, Jarkko and Honkela, Jukka and Paatero, Vesa and
		  Saarela, Antti},
  title		= {Self organization of a massive document collection},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  volume	= {11},
  number	= {3},
  month		= {May},
  pages		= {574--585},
  organization	= {Helsinki Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This article describes the implementation of a system that
		  is able to organize vast document collections according to
		  textual similarities. It is based on the self-organizing
		  map (SOM) algorithm. As the feature vectors for the
		  documents statistical representations of their vocabularies
		  are used. The main goal in our work has been to scale up
		  the SOM algorithm to be able to deal with large amounts of
		  high-dimensional data. In a practical experiment we mapped
		  6,840,568 patent abstracts onto a 1,002,240-node SOM. As
		  the feature vectors we used 500-dimensional vectors of
		  stochastic figures obtained as random projections of
		  weighted word histograms.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kohonen01a,
  author	= {Teuvo Kohonen},
  title		= {Evolution of Ideas for Self-Organizing Neural Networks},
  booktitle	= {Asian Pacific Symposium on Life Science and Systems
		  Engineering, 25--26 July, 2001},
  crossref	= {},
  key		= {},
  pages		= {36--8},
  year		= {2001},
  editor	= {},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kohonen01b,
  author	= {Teuvo Kohonen},
  title		= {Voivatko koneet oppia omaehtoisesti},
  booktitle	= {Studia Generalia 2000, Matematiikka, Kulttuurimme perusta
		  --- ja trauma},
  crossref	= {},
  key		= {},
  pages		= {81--92},
  year		= {2001},
  editor	= {},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {Helsingin vapaan sivistysty{\"o}n toimikunta},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  kohonen01c,
  author	= {Kohonen, T.},
  title		= {Self-organizing maps of massive databases},
  journal	= {International Journal of Engineering Intelligent Systems
		  for Electrical Engineering and Communications},
  year		= {2001},
  volume	= {9},
  number	= {4},
  month		= {December },
  pages		= {179--185},
  organization	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  publisher	= {},
  address	= {},
  abstract	= {The Self-Organizing Map (SOM) is a computational
		  projection method that usually maps a high-dimensional data
		  manifold onto a regular, low-dimensional (say, 2D) grid. A
		  model of some observation is associated with every node.
		  The SOM algorithm computes the collection of the models in
		  such a way that an arbitrary observation will be
		  represented by the closest model with an optimal average
		  overall accuracy. At the same time, the models will be
		  ordered over the grid according to their similarities,
		  which creates an abstract order and allows effective
		  browsing of the collection. Very different kinds of data
		  can be analyzed and visualized by the SOM: the first
		  example discussed in detail is a similarity graph of a vast
		  number of documents, viz. seven million patent abstracts,
		  which will be ordered according to their contents. Unlike
		  the other neural-network methods, however, the SOM can also
		  organize nonvectorial data. An example of this is the SOM
		  of 77 977 protein sequences. Methods by which such huge
		  mappings can be computed will be explained in this paper.},
  dbinsdate	= {2002/1}
}

@Article{	  kohonen01d,
  author	= {Teuvo Kohonen},
  title		= {Perspectives on the Research of Artificial Neural
		  Networks},
  journal	= {International Journal of Computer Research, Special Issue:
		  Past, Present and Future of Neural Networks},
  year		= {2001},
  key		= {},
  volume	= {10},
  number	= {2},
  pages		= {127--38},
  month		= {},
  note		= {Guest Editors: P.G. Anderson and G. Antoniou and V.
		  Mladenov and E. Oja and M. Paprzycki and N. C. Steele},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InBook{	  kohonen02a,
  author	= {Teuvo Kohonen},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Overture},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {1--12},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@TechReport{	  kohonen81a,
  author	= {Teuvo Kohonen},
  title		= {Self-Organized Formation of Generalized Topological Maps
		  of Observations in a Physical System},
  institution	= {Helsinki University of Technology},
  year		= {1981},
  type		= {Report},
  number	= {TKK-F-A450},
  address	= {Espoo, Finland},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen81b,
  author	= {Teuvo Kohonen},
  title		= {Hierarchical Ordering of Vectoral Data in a
		  Self-Organizing Process},
  institution	= {Helsinki University of Technology},
  year		= {1981},
  type		= {Report},
  number	= {TKK-F-A461},
  address	= {Espoo, Finland},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen81c,
  author	= {Teuvo Kohonen},
  title		= {Construction of Similarity Diagrams for Phonemes by a
		  Self-Organizing Algorithm},
  institution	= {Helsinki University of Technology},
  year		= {1981},
  type		= {Report},
  number	= {TKK-F-A463},
  address	= {Espoo, Finland},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen81d,
  author	= {Teuvo Kohonen},
  title		= {Automatic Formation of Topological Maps of Patterns in a
		  Self-Organizing System},
  booktitle	= {Proc. 2SCIA, Scand. Conf. on Image Analysis},
  year		= {1981},
  editor	= {Erkki Oja and Olli Simula},
  pages		= {214--220},
  organization	= {Suomen Hahmontunnistustutkimuksen Seura r. y. },
  address	= {Helsinki, Finland},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen82a,
  author	= {Teuvo Kohonen},
  title		= {Self-organizing formation of topologically correct feature
		  maps},
  journal	= {Biol. Cyb. },
  year		= {1982},
  volume	= {43},
  number	= {1},
  pages		= {59--69},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen82b,
  author	= {Teuvo Kohonen},
  title		= {Analysis of a simple \mbox{self-organizing} process},
  journal	= {Biol. Cyb. },
  year		= {1982},
  volume	= {44},
  number	= {2},
  pages		= {135--140},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen82c,
  author	= {Teuvo Kohonen},
  title		= {A Simple Paradigm for the Self-Organized Formation of
		  Structured Feature Maps},
  booktitle	= {Competition and Cooperation in Neural Nets, Lecture Notes
		  in Biomathematics},
  volume	= {45},
  publisher	= {Springer},
  year		= {1982},
  editor	= {{S. -i. } Amari and M. A. Arbib},
  chapter	= {},
  pages		= {248--266},
  address	= {Berlin, Heidelberg},
  note		= {U. S. ---Japan Joint Seminar on Competition and
		  Cooperation in Neural Nets, Kyoto, Japan, Feb. 15--19,
		  1982},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen82d,
  author	= {Teuvo Kohonen},
  title		= {Clustering, taxonomy, and topological maps of patterns},
  booktitle	= {Proc. 6ICPR, International Conference on Pattern
		  Recognition},
  year		= {1982},
  pages		= {114--128},
  publisher	= {IEEE Computer Soc. Press},
  address	= {Washington, DC},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen82e,
  author	= {Teuvo Kohonen},
  title		= {Primaarisen informaation organisoituminen ja koodaus},
  booktitle	= {Proc. Seminar on Frames, Pattern Recognition Processes,
		  and Natural Language},
  year		= {1982},
  pages		= {},
  publisher	= {The Linguistic Society of Finland},
  address	= {Helsinki, Finland},
  note		= {(in Finnish)},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen82f,
  author	= {Teuvo Kohonen and Erkki Oja},
  title		= {A Note on a Simple Self-Organizing Process},
  institution	= {Helsinki University of Technology},
  year		= {1982},
  type		= {Report},
  number	= {TKK-F-A474},
  address	= {Espoo, Finland},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen83a,
  author	= {Teuvo Kohonen},
  title		= {Self-Organizing Representations},
  booktitle	= {Topics in Technical Physics, Acta Polytechnica
		  Scandinavica, Applied Physics Series No. 138},
  publisher	= {Finnish Academy of Engineering Sciences},
  year		= {1983},
  editor	= {V{\"{a}}in{\"o} Kelh{\"{a}} and Mauri Luukkala and Turkka
		  Tuomi},
  chapter	= {},
  pages		= {80--85},
  address	= {Helsinki, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen83b,
  author	= {Teuvo Kohonen},
  title		= {Representation of Information in Spatial Maps Which Are
		  Produced by Self-Organization},
  booktitle	= {Synergetics of the Brain},
  year		= {1983},
  editor	= {E. Ba\c{s}ar and H. Flohr and H. Haken and A. J. Mandell},
  pages		= {264},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen83c,
  author	= {Teuvo Kohonen},
  title		= {Self-Organizing Mappings for Two-Dimensional (Visual)
		  Display of High-Dimensional Pattern Spaces},
  booktitle	= {Proc. 3SCIA, Scand. Conf. on Image Analysis},
  year		= {1983},
  editor	= {P. Johansen and P. W. Becker},
  pages		= {35--41},
  publisher	= {Studentlitteratur},
  address	= {Lund, Sweden},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen83d,
  author	= {Teuvo Kohonen and Pekka Lehti{\"o}},
  title		= {Tieto on kartalla},
  journal	= {Tiede 2000 (Finland)},
  year		= {1983},
  volume	= {2},
  pages		= {19--23},
  note		= {(in Finnish)},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@Book{		  kohonen84a,
  author	= {Teuvo Kohonen},
  title		= {{Self-Organization and Associative Memory}},
  publisher	= {Springer},
  year		= {1984},
  volume	= {8},
  series	= {Springer Series in Information Sciences},
  address	= {Berlin, Heidelberg},
  note		= {3rd ed. 1989. },
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen84b,
  author	= {Teuvo Kohonen},
  title		= {Self-Organized Formation of Feature Maps},
  booktitle	= {Cybernetic Systems: Recognition, Learning,
		  Self-Organization},
  publisher	= {Res. Studies Press},
  year		= {1984},
  editor	= {E. R. Caianiello and G. Musso},
  chapter	= {},
  pages		= {3--12},
  address	= {Letchworth, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen84c,
  author	= {Teuvo Kohonen and Kai M{\"{a}}kisara and Tapio
		  Saram{\"{a}}ki},
  title		= {Phonotopic Maps---Insightful Representation of
		  Phonological Features for Speech Recognition},
  booktitle	= {Proc. 7ICPR, International Conference on Pattern
		  Recognition},
  year		= {1984},
  pages		= {182--185},
  publisher	= {IEEE Computer Soc. Press},
  address	= {Los Alamitos, CA},
  monthf	= {hein{\"{a}}kuu},
  annote	= {Description of the phonotopic maps and the application to
		  speech recognition. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen84d,
  author	= {Teuvo Kohonen},
  title		= {Self-Organizing Feature Maps and Abstractions},
  booktitle	= {Artificial Intelligence and Information-Control Systems of
		  Robots, Proc. of the Third International Conference on
		  Artificial Intelligence and Information-Control Systems of
		  Robots},
  year		= {1984},
  editor	= {I. Plander},
  pages		= {39--45},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen84e,
  author	= {Teuvo Kohonen},
  title		= {Oppivien koneiden uusi tuleminen},
  journal	= {S{\"{a}}hk{\"o} (Finland)},
  year		= {1984},
  volume	= {57},
  number	= {8},
  pages		= {48--51},
  note		= {(in Finnish)},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen85a,
  author	= {Teuvo Kohonen},
  title		= {Representation of Sensory Information in
		  {S}elf-{O}rganizing {M}aps},
  booktitle	= {Proc. {COGNITIVA} 85},
  year		= {1985},
  pages		= {585--591},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen85b,
  author	= {Teuvo Kohonen},
  title		= {Representation of Sensory Information in
		  {S}elf-{O}rganizing {M}aps},
  booktitle	= {Proc. of the XIV International Conference on Medical
		  Physics, Espoo, Finland, August 11--16},
  year		= {1985},
  pages		= {1489},
  publisher	= {Finnish Soc. Med. Phys. and Med. Engineering},
  address	= {Helsinki, Finland},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen85c,
  author	= {Teuvo Kohonen},
  title		= {Pattern-Recognition Applications of Self-Organizing
		  Feature Maps},
  booktitle	= {Proc. 4SCIA, Scand. Conf. on Image Analysis},
  year		= {1985},
  pages		= {97--103},
  publisher	= {Tapir Publishers},
  address	= {Trondheim, Norway},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen86a,
  author	= {Teuvo Kohonen and Kai M{\"{a}}kisara},
  title		= {Representation of Sensory Information in Self-Organizing
		  Feature Maps},
  booktitle	= {AIP Conf. Proc. 151, Neural Networks for Computing},
  year		= {1986},
  editor	= {J. Denker},
  volume	= {},
  pages		= {271--276},
  publisher	= {Amer. Inst. of Phys. },
  address	= {New York, NY},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen86b,
  author	= {Teuvo Kohonen},
  title		= {Self-Organization, Memorization, and Associative Recall of
		  Sensory Information by Brain-like Adaptive Networks},
  journal	= {Int. J. Quantum Chemistry},
  year		= {1986},
  volume	= {13},
  pages		= {209--221},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen86c,
  author	= {Teuvo Kohonen},
  title		= {Learning Vector Quantization for Pattern Recognition},
  institution	= {Helsinki University of Technology},
  year		= {1986},
  type		= {Report},
  number	= {TKK-F-A601},
  address	= {Espoo, Finland},
  month		= { },
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen87a,
  author	= {Teuvo Kohonen},
  title		= {Adaptive, Associative, and Self-Organizing Functions in
		  Neural Computing},
  journal	= {Appl. Opt. },
  year		= {1987},
  volume	= {26},
  number	= {23},
  pages		= {4910--4918},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen87b,
  author	= {Teuvo Kohonen},
  title		= {Self-Organized Sensory Maps and Associative Memory},
  booktitle	= {Physics of Cognitive Processes},
  publisher	= {World Scientific},
  year		= {1987},
  editor	= {E. R. Caianiello},
  chapter	= {},
  pages		= {258--273},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen87c,
  author	= {Teuvo Kohonen},
  title		= {Sensory Maps and Their Self-Organized Formation},
  booktitle	= {Second World Congr. of Neuroscience, Book of Abstracts.
		  Neuroscience, Supplement to Volume 22},
  year		= {1987},
  volume	= {},
  pages		= {S100},
  organization	= {IBRO},
  publisher	= {Pergamon Press},
  address	= {Oxford},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen87d,
  author	= {Teuvo Kohonen},
  title		= {State of the Art in Neural Computing},
  booktitle	= {Proc. ICNN'87, International Conference on Neural
		  Networks},
  year		= {1987},
  volume	= {I},
  pages		= {79--90},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen87e,
  author	= {Teuvo Kohonen},
  title		= {Representation of Sensory Information in Self-Organizing
		  Feature Maps, and Relation of These Maps to Distributed
		  Memory Networks},
  booktitle	= {Optical and Hybrid Computing, SPIE Vol. 634},
  year		= {1987},
  editor	= {H. H. Szu},
  volume	= {},
  pages		= {248--259},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen87f,
  author	= {Teuvo Kohonen and Kari Torkkola and Makoto Shozakai and
		  Jari Kangas and Olli Vent{\"{a}}},
  title		= {Implementation of a Large Vocabulary Speech Recognizer and
		  Phonetic Typewriter for {F}innish and {J}apanese},
  booktitle	= {Proceedings of the European Conference on Speech
		  Technology},
  year		= {1987},
  pages		= {377--380},
  address	= {Edinburgh, U. K. },
  month		= {September 2--4},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen88b,
  author	= {Teuvo Kohonen and Kari Torkkola and Jari Kangas and Olli
		  Vent{\"{a}}},
  title		= {A Voice Activated Typewriter Based on Phonemes},
  booktitle	= {Papers from the 15th Meeting of Finnish Phoneticians,
		  Publication 31, Helsinki University of Technology,
		  Acoustics Laboratory},
  year		= {1988},
  volume	= {},
  pages		= {97--106},
  publisher	= {Helsinki University of Technology},
  address	= {Espoo, Finland},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen88c,
  author	= {Teuvo Kohonen},
  title		= {The 'neural' phonetic typewriter},
  journal	= {Computer},
  year		= {1988},
  volume	= {21},
  number	= {3},
  pages		= {11--22},
  annote	= {Description of Phonetic Typewriter. },
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen88d,
  author	= {Teuvo Kohonen},
  title		= {Representations of Sensory Information in Self-Organizing
		  Feature Maps, and the Relation of These Maps to Distributed
		  Memory Networks},
  booktitle	= {Computer Simulation in Brain Science},
  publisher	= {Cambridge University Press},
  year		= {1988},
  editor	= {R. M. J. Cotterill},
  chapter	= {},
  pages		= {12--25},
  address	= {Cambridge, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen88e,
  author	= {Teuvo Kohonen and Kari Torkkola and Makoto Shozakai and
		  Jari Kangas and Olli Vent{\"{a}}},
  title		= {Phonetic Typewriter for {F}innish and {J}apanese},
  booktitle	= {Proc. ICASSP-88, International Conference on Acoustics,
		  Speech, and Signal Processing},
  year		= {1988},
  volume	= {},
  pages		= {607--610},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen88f,
  author	= {Teuvo Kohonen and Gy{\"{o}}rgy Barna and Ronald Chrisley},
  title		= {Statistical Pattern Recognition with Neural Networks:
		  Benchmarking Studies},
  booktitle	= {Proc. ICNN'88, International Conference on Neural
		  Networks},
  year		= {1988},
  volume	= {I},
  pages		= {61--68},
  publisher	= {IEEE Computer Soc. Press},
  address	= {Los Alamitos, CA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen88g,
  author	= {Teuvo Kohonen},
  title		= {Keinotekoisen ja {l}uonnollisen {a}jattelun {e}roista},
  booktitle	= {Kognitiotiede},
  publisher	= {Gaudeamus},
  year		= {1988},
  editor	= {A. Hautam{\"{a}}ki},
  chapter	= {},
  pages		= {100--120},
  address	= {Helsinki, Finland},
  note		= {(in Finnish)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen88h,
  author	= {Teuvo Kohonen},
  title		= {Associative Memories and Representations of Knowledge as
		  Internal States in Distributed Systems},
  booktitle	= {European Seminar on Neural Computing},
  year		= {1988},
  volume	= {},
  pages		= {},
  publisher	= {British Neural Network Society},
  address	= {London, UK},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen88i,
  author	= {Teuvo Kohonen},
  title		= {An Introduction to Neural Computing},
  journal	= {Neural Networks},
  year		= {1988},
  volume	= {1},
  number	= {1},
  pages		= {3--16},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen88j,
  author	= {Teuvo Kohonen},
  title		= {Learning {V}ector {Q}uantization},
  journal	= {Neural Networks},
  year		= {1988},
  volume	= {1},
  number	= {Supplement 1},
  pages		= {303},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen88k,
  author	= {Teuvo Kohonen},
  title		= {Problems in Practical Pattern Recognition},
  journal	= {Neural Networks},
  year		= {1988},
  volume	= {1},
  number	= {Supplement 1},
  pages		= {29},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen89a,
  author	= {Teuvo Kohonen},
  title		= {On the significance of internal representations in neural
		  networks},
  booktitle	= {First IEE International Conference on Artificial Neural
		  Networks},
  year		= {1989},
  pages		= {1},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen89b,
  author	= {Teuvo Kohonen},
  title		= {Speech Recognition Based on Topology-Preserving Neural
		  Maps},
  booktitle	= {Neural Computing Architectures},
  publisher	= {North Oxford Academic Publishers/Kogan Page, Oxford, UK},
  year		= {1989},
  editor	= {Igor Aleksander},
  chapter	= {},
  pages		= {26--40},
  address	= {London},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen89c,
  author	= {Teuvo Kohonen},
  title		= {The 'Neural' Phonetic Typewriter},
  booktitle	= {The Second European Seminar on Neural Networks, London,
		  UK, February 16--17},
  year		= {1989},
  volume	= {},
  pages		= {},
  publisher	= {British Neural Networks Society},
  address	= {London, UK},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen89d,
  author	= {Teuvo Kohonen and Ronald Chrisley and Gy{\"{o}}rgy Barna},
  title		= {Statistical Pattern Recognition with Neural Networks},
  booktitle	= {Neural Networks from Models to Applications},
  publisher	= {I. D. S. E. T. },
  year		= {1989},
  editor	= {I. Personnaz and G. Dreyfus},
  chapter	= {},
  pages		= {160--167},
  address	= {Paris},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen89e,
  author	= {Teuvo Kohonen and Kai M{\"{a}}kisara},
  title		= {The Self-Organizing Feature Maps},
  journal	= {Physica Scripta},
  year		= {1989},
  volume	= {39},
  number	= {},
  pages		= {168--172},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen90a,
  author	= {Kohonen, T.},
  title		= {The Self-Organizing Map.},
  booktitle	= {Proceedings of the IEEE},
  year		= {1990},
  volume	= {78},
  pages		= {1464--1480},
  abstract	= {Among the architectures and algorithms suggested for
		  artificial neural networks, the Self-Organizing Map has the
		  special property of effectively creating spatially
		  organized "internal representations" or various features of
		  input signals and their abstractions. One novel result is
		  that the self-organization process can also discover
		  semantic relationships in sentences. The Self-Organizing
		  Map has been particularly successful in various pattern
		  recognition tasks involving very noisy signals. In
		  particular, these maps have been used in practical speech
		  recognition, and work is in progress on their application
		  to robotics, process control, telecommunications, etc. This
		  paper contains a survey of several basic facts and
		  results.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen90b,
  author	= {Teuvo Kohonen},
  title		= {Statistical Pattern Recognition Revisited},
  booktitle	= {Advanced Neural Networks},
  publisher	= {Elsevier},
  year		= {1990},
  editor	= {R. Eckmiller},
  chapter	= {},
  pages		= {137--144},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen90c,
  author	= {Teuvo Kohonen},
  title		= {Internal Representations and Associative Memory},
  booktitle	= {Parallel Processing in Neural Systems and Computers},
  publisher	= {Elsevier},
  year		= {1990},
  editor	= {R. Eckmiller and G. Hartman and G. Hauske},
  chapter	= {},
  pages		= {177--182},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen90d,
  author	= {Teuvo Kohonen},
  title		= {Unsupervised Learning Algorithms},
  booktitle	= {Neural Networks: Biological Computers or Electronic
		  Brains, Proc. International Conference Les Entr{\'{e}}tiens
		  de Lyon},
  year		= {1990},
  volume	= {},
  pages		= {29--36},
  publisher	= {Springer},
  address	= {Paris, France},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen90e,
  author	= {Teuvo Kohonen},
  title		= {Notes on Neural Computing and Associative Memory},
  booktitle	= {Brain Organization and Memory: Cells, Systems, and
		  Circuits},
  publisher	= {Oxford University Press},
  year		= {1990},
  editor	= {J. L. McGaugh and N. M. Weinberger and G. Lynch},
  chapter	= {},
  pages		= {323--337},
  address	= {New York, NY},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen90f,
  author	= {Teuvo Kohonen},
  title		= {The {S}elf-{O}rganizing {M}ap},
  booktitle	= {New Concepts in Computer Science: Proc. Symp. in Honour of
		  Jean-Claude Simon},
  year		= {1990},
  volume	= {},
  pages		= {181--190},
  publisher	= {AFCET},
  address	= {Paris, France},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen90g,
  author	= {Teuvo Kohonen},
  title		= {Improved Versions of {L}earning {V}ector {Q}uantization},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, San Diego},
  year		= {1990},
  volume	= {I},
  pages		= {545--550},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen90h,
  author	= {Teuvo Kohonen and Kimmo Raivio and Olli Simula and Olli
		  Vent{\"{a}} and Jukka Henriksson},
  title		= {Combining Linear Equalization and Self-Organizing
		  Adaptation in Dynamic Discrete-Signal Detection},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, San Diego},
  year		= {1990},
  volume	= {I},
  pages		= {223--228},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen90i,
  author	= {Teuvo Kohonen},
  title		= {Pattern Recognition by the {S}elf-{O}rganizing {M}ap},
  booktitle	= {Proc. Third Italian Workshop on Parallel Architectures and
		  Neural Networks},
  year		= {1990},
  volume	= {},
  pages		= {13--18},
  organization	= {SIREN},
  publisher	= {World Scientific},
  address	= {Singapore},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen90j,
  author	= {Teuvo Kohonen},
  title		= {Some Practical Aspects of the {S}elf-{O}rganizing {M}aps},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {II},
  pages		= {253--256},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen90k,
  author	= {Teuvo Kohonen and Kimmo Raivio and Olli Simula and Olli
		  Vent{\"{a}} and Jukka Henriksson},
  title		= {An Adaptive Discrete-Signal Detector Based on
		  {S}elf-{O}rganizing {M}aps},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {II},
  pages		= {249--252},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen91a,
  author	= {Teuvo Kohonen and Kimmo Raivio and Olli Simula and Jukka
		  Henriksson},
  title		= {Performance Evaluation of Self-Organizing Map Based Neural
		  Equalizer in Dynamic Discrete-Signal Detection},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1677--1680},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen91b,
  author	= {Teuvo Kohonen},
  title		= {The Hypermap Architecture},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1357--1360},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen91c,
  author	= {Teuvo Kohonen},
  title		= {Self-{O}rganizing {M}aps: {O}ptimization Approaches},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {981--990},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen91d,
  author	= {Teuvo Kohonen},
  title		= {Workstation-based phonetic typewriter},
  booktitle	= {Proc. IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1991},
  editor	= {B. H. Juang and S. Y. Kung and C. A. Kamm},
  volume	= {},
  pages		= {279--288},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen91e,
  author	= {Teuvo Kohonen},
  title		= {The Hypermap},
  booktitle	= {Proc. IJCNN-91, International Joint Conference on Neural
		  Networks, Singapore},
  year		= {1991},
  volume	= {},
  pages		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen92a,
  author	= {Teuvo Kohonen},
  title		= {Boosting the Computing Power in Pattern Recognition by
		  Unconventional Architectures},
  institution	= {Helsinki Univ. of Technology, Lab. of Computer and
		  Information Science},
  year		= {1992},
  type		= {Report},
  number	= {A15},
  address	= {Espoo, Finland},
  month		= {October},
  x		= {nt9093b---AN ACCESSION NUMBER: PB93140952XSP Miksikohan
		  tama ei ole jo mukana?},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen92b,
  author	= {Teuvo Kohonen and Samuel Kaski},
  title		= {The {S}elf-{O}rganizing {M}ap as a model for the formation
		  of memory representations},
  booktitle	= {Abstracts of the 15th Annual Meeting of the European
		  Neuroscience Association},
  year		= {1992},
  pages		= {280},
  publisher	= {Oxford University Press},
  address	= {Oxford, UK},
  note		= {Supplement No. 5 to the European J. Neuroscience},
  annote	= {SOM finds formant structures from vowels. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen92c,
  author	= {Teuvo Kohonen and Jari Kangas and Jorma Laaksonen and Kari
		  Torkkola},
  title		= {{LVQ\_PAK}: A program package for the correct application
		  of {L}earning {V}ector {Q}uantization algorithms},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  volume	= {I},
  pages		= {725--730},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen92d,
  author	= {Teuvo Kohonen},
  title		= {Learning-{V}ector {Q}uantization and the
		  {S}elf-{O}rganizing {M}ap},
  booktitle	= {Theory and Applications of Neural Networks},
  year		= {1992},
  editor	= {J. G. Taylor and C. L. T. Mannion},
  volume	= {},
  pages		= {235--242},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen92e,
  author	= {Teuvo Kohonen and Kimmo Raivio and Olli Simula and Jukka
		  Henriksson},
  title		= {Start-up behaviour of a neural network assisted decision
		  feedback equaliser in a two-path channel},
  booktitle	= {Proc. International Conference on Communications, Chicago,
		  Ill. },
  year		= {1992},
  pages		= {1523--1527},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen92f,
  author	= {Teuvo Kohonen},
  title		= {How to make a machine transcribe speech},
  booktitle	= {Applications of Neural Networks},
  year		= {1992},
  editor	= {H. G. Schuster},
  volume	= {},
  pages		= {25--34},
  publisher	= {VCH},
  address	= {Weinheim, Germany},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen92g,
  author	= {Teuvo Kohonen},
  title		= {An attempt to interpret the {S}elf-{O}rganizing {M}apping
		  Physiologically},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1992},
  type		= {Report},
  number	= {A16},
  address	= {},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen92h,
  author	= {Teuvo Kohonen},
  title		= {New developments of {L}earning Vector {Q}uantization and
		  the {S}elf-{O}rganizing Map},
  booktitle	= {Symp. on Neural Networks; Alliances and Perspectives in
		  Senri},
  year		= {1992},
  pages		= {},
  organization	= {Senri Int. Information Institute},
  address	= {Osaka, Japan},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen93a,
  author	= {Teuvo Kohonen},
  title		= {Things You Haven't Heard about the {S}elf-{O}rganizing
		  {M}ap},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  pages		= {1147--1156},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen93b,
  author	= {Teuvo Kohonen},
  title		= {Generalizations of the {S}elf-{O}rganizing {M}ap},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {457--462},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {It is pointed out in this paper that for the cells of a
		  Self-Organizing Map (SOM) one may select adaptive dynamic
		  operators in stead of simple static units. Their updating
		  may be made either by tuning their parameters, or in an
		  evolutionary process. This kind of generic principle is
		  supposed to define the category of SOM networks in a more
		  general sense.},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen93c,
  author	= {Teuvo Kohonen},
  title		= {Physiolocigal Interpretation of the Self-Organizing Map
		  Algorithm},
  journal	= {Neural Networks},
  year		= {1993},
  volume	= {6},
  number	= {7},
  pages		= {895--905},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen93d,
  author	= {Teuvo Kohonen},
  title		= {Boosting the Computing Power in Pattern Recognition by
		  Unconventional Architectures},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {IV},
  pages		= {1--4},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen93e,
  author	= {T. Kohonen},
  title		= {Aivot ja tietokoneet (The Brain and Intelligent
		  Machines)},
  journal	= {Acta Polytechnica Scandinavica, Applied Physics Series No.
		  188},
  year		= {1993},
  pages		= {37--41},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen94a,
  author	= {Teuvo Kohonen},
  title		= {What Generalizations of the {S}elf-{O}rganizing {M}ap Make
		  Sense},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {292--297},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {analysis, modifications},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen94b,
  author	= {Teuvo Kohonen},
  title		= {Physiological Model for the {S}elf-{O}rganizing {M}ap},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {III},
  pages		= {97--102},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {modification, analysis},
  dbinsdate	= {oldtimer}
}

@Book{		  kohonen95a,
  author	= {Teuvo Kohonen},
  title		= {{Self-Organizing Maps}},
  publisher	= {Springer},
  year		= {1995},
  volume	= {30},
  series	= {Springer Series in Information Sciences},
  address	= {Berlin, Heidelberg},
  note		= {(Second Extended Edition 1997)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen95b,
  author	= {Teuvo Kohonen},
  title		= {The {Adaptive-Subspace SOM (ASSOM)} and its Use for the
		  Implementation of Invariant Feature Detection},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {I},
  pages		= {3--10},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen95c,
  author	= {Teuvo Kohonen},
  booktitle	= {Computational Intelligence---A Dynamic System
		  Perspective},
  title		= {Chapter 2. Emergence of Invariant-Feature Detectors in
		  Self-Organization},
  publisher	= {IEEE Press},
  year		= {1995},
  editor	= {M. Palaniswami and Y. Attikiouzel and R. J. Marks II and
		  David Fogel and T. Fukuda},
  pages		= {17--31},
  address	= {New York},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen95d,
  author	= {T. Kohonen},
  title		= {Aivoalueiden ja muistin teoria},
  booktitle	= {Tutkimuksen etulinjassa},
  publisher	= {WSOY},
  year		= {1995},
  editor	= {J. Rydman},
  pages		= {251--262},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen95e,
  author	= {T. Kohonen},
  title		= {Learning Vector Quantization},
  booktitle	= {The Handbook of Brain Theory and Neural Networks},
  publisher	= {The MIT Press},
  address	= {Cambridge, Massachusetts},
  year		= {1995},
  pages		= {537--540},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen96a,
  author	= {Teuvo Kohonen and Jussi Hynninen and Jari Kangas and Jorma
		  Laaksonen and Kari Torkkola},
  title		= {{LVQ\_PAK}: {T}he {L}earning {V}ector {Q}uantization
		  Program Package},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1996},
  type		= {Report},
  number	= {A30},
  month		= {jan},
  abstract	= {Learning Vector Quantization (LVQ) is a group of
		  algorithms applicable to statistical pattern recognition,
		  in which the classes are described by a relatively small
		  number of codebook vectors, properly placed within each
		  zone such that the decision borders are approximated by the
		  nearest-neighbor rule. The LVQ-PAK program package contains
		  all programs necessary for the correct application of
		  certain Learning Vector Quantization algorithms in an
		  arbitrary statistical classification or pattern recognition
		  task, as well as a program for the monitoring of the
		  codebook vectors at any time during the learning process.
		  This report contains the last documentation was prepared
		  for bibliographical purposes.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen96b,
  author	= {Teuvo Kohonen and Jussi Hynninen and Jari Kangas and Jorma
		  Laaksonen},
  title		= {{SOM\_PAK}: {T}he {S}elf-{O}rganizing {M}ap Program
		  Package},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1996},
  type		= {Report},
  number	= {A31},
  month		= {jan},
  abstract	= {The Self-Ogranizing Map (SOM) represents the result of a
		  vector quantization algorithm that places a number of
		  reference or codebook vectors into a high-dimensional input
		  data space to approximate to its data sets in an ordered
		  fashion. The SOM-PAK program package contains all programs
		  necessary for the correct application of the
		  Self-Organizing Map algorithm in the visualization of
		  complex experimental data. This report contains the last
		  documentation prepared for bibliographical purposes.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen96c,
  author	= {T. Kohonen},
  title		= {New developments and applications of
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings International Workshop on Neural Networks for
		  Identification, Control, Robotics, and Signal/Image
		  Processing},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  address	= {Los Alamitos, CA, USA},
  pages		= {164--72},
  abstract	= {This paper reports on recent engineering applications of
		  the Self-Organizing Map.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen96d,
  author	= {Teuvo Kohonen and Samuel Kaski and Krista Lagus and Timo
		  Honkela},
  title		= {Very large two-level {SOM} for the browsing of
		  newsgroups},
  booktitle	= {Proceedings of ICANN96, International Conference on
		  Artificial Neural Networks, Bochum, Germany, July 16--19,
		  1996},
  publisher	= {Springer},
  year		= 1996,
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbr{\"u}ggen and B. Sendhoff},
  series	= {Lecture Notes in Computer Science, vol. 1112},
  address	= {Berlin},
  pages		= {269--274},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen96e,
  author	= {T. Kohonen},
  title		= {The Speedy {SOM}},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1996},
  number	= {A33},
  address	= {Espoo, Finland},
  abstract	= {The Self-Organizing Map (SOM) has already become a popular
		  algorithm. In principle, it would scale up to very high
		  dimensions unless computational reasons would set limits to
		  its implementation. The authors have the CNAPS
		  neurocomputer, a 512-processor parallel SIMD computer at
		  their disposal. The biggest SOMs the authors have
		  implemented with its aid had 315 inputs and 768 map units
		  (neurons). It seems, however, that there still exist
		  solutions to increase the map sizes, even when
		  general-purpose computers are used, and in general to speed
		  up the SOM computation. Some new solutions are introduced
		  in this report.},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen96f,
  author	= {T. Kohonen},
  title		= {The \mbox{self-organizing} map, a possible model of brain
		  maps},
  journal	= {Medical \& Biological Engineering \& Computing},
  year		= {1996},
  volume	= {34},
  number	= {suppl. 1, pt. 1},
  pages		= {5--8},
  annote	= {10th Nordic-Baltic Conference on Biomedical Engineering
		  Conf. Date: 9--13 June 1996 Conf. Loc: Tampere, Finland},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen96g,
  author	= {T. Kohonen and E. Oja and O. Simula and A. Visa and J.
		  Kangas},
  title		= {Engineering applications of the \mbox{self-organizing}
		  map},
  journal	= {Proceedings of the IEEE},
  year		= {1996},
  volume	= {84},
  number	= {10},
  pages		= {1358--84},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen96h,
  author	= {T. Kohonen},
  title		= {Emergence of invariant-feature detectors in the
		  adaptive-subspace \mbox{self-organizing} map},
  journal	= {Biological Cybernetics},
  year		= {1996},
  volume	= {75},
  number	= {4},
  pages		= {281--91},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen96i,
  author	= {T. Kohonen},
  title		= {Emergence of Invariant-Feature Detectors in the
		  Adaptive-Subspace Self-Organizing Maps},
  booktitle	= {Proc. 1996 IEEE Nordic Signal Processing Symposium
		  (NORSIG'96)},
  year		= {1996},
  pages		= {65--70},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen96j,
  author	= {T. Kohonen},
  title		= {Advances in the Development and Application of
		  Self-Organizing Maps},
  booktitle	= {Proc. 5th Turkish Symposium on Artificial Intelligence and
		  Neural Networks (TAINN'96)},
  editor	= {E. Alpaydin et al. },
  year		= {1996},
  pages		= {3--12},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen96k,
  author	= {T. Kohonen},
  title		= {Avaako neurolaskenta oven virtuaalimaailmaan?},
  journal	= {Futura},
  volume	= {1},
  year		= {1996},
  pages		= {7--11},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen96l,
  author	= {T. Kohonen},
  title		= {Self-Organizing Maps of Symbol Strings},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1996},
  number	= {A42},
  address	= {Espoo, Finland},
  abstract	= {It is shown in this report that unsupervised
		  Self-Organizing Maps (SOMs), as well as supervised learning
		  by Learning Vector Quantization (LVQ) can be defined for
		  string variables, too. Their computing becomes possible
		  when the SOM and the LVQ algorithms are expressed as batch
		  versions, and when the average over a list of symbol
		  strings is computed as the minimum sum of generalized
		  distance functions of a string from all the other strings.
		  Special considerations are necessary in order to initialize
		  the SOM properly. If a special distance measure called the
		  feature distance is used, the winner string can be located
		  almost immediately by the method called the Redundant Hash
		  Addressing, whereby the number of computations in winner
		  search is almost independent of the SOM size.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen97a,
  author	= {T. Kohonen},
  title		= {Exploration of large document collections by
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings of the Sixth Scandinavian Conference on
		  Artificial Intelligence},
  publisher	= {IOS Press},
  year		= {1997},
  editor	= {G. Grahne},
  address	= {Amsterdam, Netherlands},
  pages		= {5--7},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen97b,
  author	= {Teuvo Kohonen and Samuel Kaski and Harri Lappalainen},
  title		= {Self-Organized Formation of Various Invariant-Feature
		  Filters in the Adaptive-Subspace {SOM}},
  journal	= {Neural Computation},
  year		= 1997,
  volume	= 9,
  pages		= {1321--1344},
  dbinsdate	= {oldtimer}
}

@Book{		  kohonen97c,
  author	= {Kohonen, T. and Kaski, S. and Venna, J.},
  title		= {Automatic Coloring of Data According to Its Cluster
		  Structure.},
  year		= {1997},
  abstract	= {Statistical tables are often visualized with colors. For
		  instance, statistics related to different countries can be
		  visualized on a world map display. Such a display can be
		  easily interpreted and it provides an instant overview of
		  the distribution of the data. The interpretation of
		  displays of the clustering structure of the data requires
		  somewhat more knowledge about the underlying methodologies.
		  The authors have developed an automatic method which can be
		  used for coloring different clusters, i.e., data types,
		  with different colors.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen97d,
  author	= {Teuvo Kohonen and Samuel Kaski and Harri Lappalainen and
		  Jarkko Saloj{\"a}rvi},
  title		= {The adaptive-subspace \mbox{self-organizing} map
		  ({ASSOM})},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {191--196},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen97e,
  author	= {Teuvo Kohonen and Panu Somervuo},
  title		= {Self-organizing maps of symbol strings with application to
		  speech recognition},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {2--7},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen97f,
  author	= {Teuvo Kohonen},
  title		= {Exploration of Very Large Databases by Self-Organizing
		  Maps},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  pages		= {PL1-PL6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen97g,
  author	= {T. Kohonen},
  title		= {Emergence of Optimal Invariant-Feature Detectors in a New
		  Neural Network Architecture},
  booktitle	= {Proc. FNS'97, Fuzzy-Neuro-Systeme'97---Computational
		  Intelligence, Soest, Germany, March 12--14},
  year		= {1997},
  editor	= {A. Grauel and W. Becker and F. Belli},
  pages		= {44},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen98a,
  author	= {Kohonen, Teuvo},
  title		= {Self-organizing map},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {1--6},
  abstract	= {The self-organizing map (SOM) is an effective software
		  tool for the visualization of high-dimensional data. It
		  implements an orderly mapping of a high-dimensional
		  distribution onto a regular low-dimensional grid. It
		  converts complex, nonlinear statistical relationships
		  between high-dimensional data items into simple geometric
		  relationships on a low-dimensional display. The SOM usually
		  consists of a two-dimensional regular grid of nodes. A
		  model of some observation is associated with each node. The
		  SOM algorithm computes the models so that they optimally
		  describe the discrete or continuously distributed domains
		  of observations.},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen98b,
  author	= {Kohonen, Teuvo and Somervuo, Panu},
  title		= {Self-organizing maps of symbol strings},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {19--30},
  abstract	= {Unsupervised self-organizing maps (SOMs), as well as
		  supervised learning by Learning Vector Quantization (LVQ)
		  can be defined for string variables, too. Their computing
		  becomes possible when the SOM and the LVQ algorithms are
		  expressed as batch versions, and when the average over a
		  list of symbol strings is defined to be the string that has
		  the smallest sum of generalized distance functions from all
		  the other strings.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  kohonen98c,
  author	= {T. Kohonen},
  title		= {Computation of {VQ} and {SOM} Point Densities Using the
		  Calculus of Variations},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1998},
  number	= {A52},
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen98d,
  author	= {Teuvo Kohonen},
  title		= {Self-Organization of Very Large Document Collections:
		  State of the Art},
  booktitle	= {Proceedings of ICANN98, the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer},
  year		= 1998,
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= 1,
  address	= {London},
  pages		= {65--74},
  abstract	= {The self-organizing map (SOM) forms a nonlinear projection
		  from a high-dimensional data manifold onto a
		  low-dimensional grid. A representative model of some subset
		  of data is associated with each grid point. The SOM
		  algorithm computes an optimal collection of models that
		  approximates the data in the sense of some error criterion
		  and also takes into account the similarity relations of the
		  models. The models then become ordered on the grid
		  according to their similarity. When the SOM is used for the
		  exploration of statistical data, the data vectors can be
		  approximated by models of the same dimensionality. When
		  mapping documents, one can represent them statistically by
		  their word frequency histograms or some reduced
		  representations of the histograms that can be regarded as
		  data vectors. We have made SOMs of collections of over one
		  million documents. Each document is mapped onto some grid
		  point, with a link from this point to the document
		  database. The documents are ordered on the grid according
		  to their contents and neighboring documents can be browsed
		  readily. Keywords or key texts can be used to search for
		  the most relevant documents first. New effective coding and
		  computing schemes of the mapping are described.},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen98e,
  author	= {T. Kohonen and Oja, E.},
  title		= {Visual Feature Analysis by the Self-Organising Maps},
  journal	= {Neural Computing \& Applications},
  year		= {1998},
  volume	= {7},
  pages		= {273--286},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen98f,
  author	= {T. Kohonen},
  title		= {The Self-Organizing Map Algorithms and their Applications
		  in Science and Technology},
  booktitle	= {Proceedings of 6th European Congress on Intelligent
		  Techniques \& Soft Computing (EUFIT'98)},
  publisher	= {Elite Foundation},
  year		= {1998},
  address	= {Aachen, Germany},
  pages		= {193--194},
  abstract	= {The self-organizing map (SOM) is an orderly mapping of a
		  high-dimensional distribution of data onto a regular
		  low-dimensional grid. In many important applications the
		  input data can be defined in metric spaces, in which case
		  analytical algorithms for the optimization of the mapping
		  are derivable. Even when the primary data are not relatable
		  metrically, ordered SOM models can emerge in a process,
		  where the models are optimized by their probabilistic
		  variation.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen98g,
  author	= {T. Kohonen},
  title		= {The {SOM} Methodology},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer-Verlag},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {159--167},
  dbinsdate	= {oldtimer}
}

@InBook{	  kohonen99a,
  author	= {T. Kohonen},
  editor	= {O. Kuusi and I. Niiniluoto},
  title		= {Uudet informaation k{\"a}sittelyn menetelm{\"a}t ja niiden
		  tulevaisuusrelevanssi},
  chapter	= {},
  publisher	= {Tulevaisuuden tutkimuksen seura r.y.},
  year		= {1999},
  key		= {},
  volume	= {},
  number	= {},
  series	= {},
  type		= {},
  address	= {Helsinki},
  edition	= {},
  month		= {},
  pages		= {37--54},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kohonen99b,
  author	= {Kohonen, T.},
  title		= {Exploration of Statistical and Textual Information by
		  Means of Self-Organizing Maps},
  booktitle	= {Proc. of 52nd Session of the International Statistical
		  Institute (ISI'99), Helsinki, Finland, August 10--18},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen99c,
  author	= {Kohonen, T.},
  title		= {Comparison of SOM point densities based on different
		  criteria},
  journal	= {Neural Computation},
  year		= {1999},
  volume	= {11},
  pages		= {2081--95},
  abstract	= {Point densities of model (codebook) vectors in
		  self-organizing maps (SOMs) are evaluated in this article.
		  For a few one-dimensional SOMs with finite grid lengths and
		  a given probability density function of the input, the
		  numerically exact point densities have been computed. The
		  point density derived from the SOM algorithm turned out to
		  be different from that minimizing the SOM distortion
		  measure, showing that the model vectors produced by the
		  basic SOM algorithm in general do not exactly coincide with
		  the optimum of the distortion measure. A new computing
		  technique based on the calculus of variations has been
		  introduced. It was applied to the computation of point
		  densities derived from the distortion measure for both the
		  classical vector quantization and the SOM with general but
		  equal dimensionality of the input vectors and the grid,
		  respectively. The power laws in the continuum limit
		  obtained in these cases were found to be identical.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen99d,
  author	= {Kohonen, T.},
  title		= {Analysis of processes and large data sets by a
		  \mbox{self-organizing} method},
  booktitle	= {Proceedings of the Second International Conference on
		  Intelligent Processing and Manufacturing of Materials.
		  IPMM'99.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {1},
  pages		= {27--36},
  abstract	= {Frequently one must deal with natural processes and data
		  for which no known models can be derived from classical
		  systems theory. A solution is that relationships between
		  the elements are described by nonlinear functional
		  expansions called "neural networks". The most familiar
		  neural-network models make use of supervised learning,
		  which means that the data used for identification must be
		  verified, validated, and preclassified. Such data, however,
		  is very expensive and sometimes even impossible to acquire.
		  A different approach altogether is unsupervised learning
		  that uses raw data, usually available on mass. In the
		  article, the most widespread unsupervised-learning method,
		  the self-organizing map (SOM) algorithm is described. The
		  central idea in this algorithm and in self organization in
		  general, is to use a large number of relatively simple and
		  structurally similar, interacting, statistical submodels.
		  Each submodel describes only a limited domain of
		  observations, but since the submodels can communicate, they
		  can mutually decide what and how large a domain belongs to
		  each submodel. By virtue of such collective interactions it
		  becomes possible to span the whole data space nonlinearly,
		  thereby minimizing the average overall modeling error. As
		  the SOM implements a characteristic nonlinear projection
		  from the input space to a visual display, it can be used,
		  e.g., to reveal process states that otherwise would escape
		  notice. Applications to industry and "data mining" in
		  general are surveyed. The mapping of all electronically
		  available patent abstracts in the world onto a visual
		  display is also reported.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen99e,
  author	= {T. Kohonen and S. Kaski and K. Lagus and J. Salojärvi and
		  J. Honkela, V. Paatero and A. Saarela},
  title		= {Self organization of a massive text document collection},
  booktitle	= {Kohonen Maps},
  pages		= {171--182},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: large self-organising maps, text exploration,
		  knowledge discovery, patent abstracts, content-addressable
		  search},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen99f,
  author	= {Kohonen, T.},
  title		= {Fast evolutionary learning with batch-type
		  \mbox{self-organizing} maps},
  journal	= {Neural Processing Letters},
  year		= {1999},
  volume	= {9},
  pages		= {153--62},
  abstract	= {Although no distance function over the input data is
		  definable, it is still possible to implement the
		  self-organizing map (SOM) process using
		  evolutionary-learning operations. The process can be made
		  to converge more rapidly when the probabilistic trials of
		  conventional evolutionary learning are replaced by
		  averaging using the so-called batch map version of the
		  self-organizing map. Although no other condition or metric
		  than a fitness function between the input samples and the
		  models is assumed, an order in the map that complies with
		  the `functional similarity' of the models can be seen to
		  emerge. There exist two modes of use of this new principle:
		  representation of nonmetric input data distributions by
		  models that may have variable structures, and fast
		  generation of evolutionary cycles that resemble those
		  defined by the genetic algorithms. The spatial order in the
		  array of models can be utilized for finding more uniform
		  variations, such as crossings between functionally similar
		  models.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kohonen99g,
  author	= {Kohonen, T.},
  title		= {Data Mining by the Self-Organizing Map Method},
  booktitle	= {Uncertainty in Intelligent and Information Systems},
  publisher	= {World Scientific},
  year		= {1999},
  editor	= {Bouchon-Meunier, B. and Yager, R.R. and Zadeh, L.A.},
  dbinsdate	= {oldtimer}
}

@Article{	  kohonen99h,
  author	= {Kohonen, T. and Hari, R.},
  title		= {Where the Abstract Feature Maps of the Brain Might Come
		  from},
  journal	= {Trends in Neurosciences},
  year		= {1999},
  volume	= {22},
  pages		= {135--139},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kohonen99i,
  author	= {Kohonen, T.},
  title		= {Spotting relevant information in extremely large document
		  collections},
  booktitle	= {Computational Intelligence. Theory and Applications.
		  International Conference, 6th Fuzzy Days. Proceedings},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {59--61},
  abstract	= {The self-organizing map (SOM) converts statistical
		  relationships between high dimensional data into geometric
		  relationships on a low-dimensional grid. It can thus be
		  regarded as a projection and a similarity graph of the
		  primary data. The most important topological and metric
		  relationships of the primary data elements are shown on the
		  display. The SOM may also be thought to produce some kinds
		  of abstraction. These two aspects, visualization and
		  abstraction, can be utilized in many complex tasks: the
		  automatic organization of very large document collections
		  and searching relevant information from them are
		  discussed.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koikkalainen88a,
  author	= {P. Koikkalainen and E. Oja},
  title		= {Specification and implementation environment for neural
		  networks using communicating sequential processes},
  booktitle	= {Proc. ICNN'88, International Conference on Neural
		  Networks},
  year		= {1988},
  pages		= {533--540},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koikkalainen88b,
  author	= {P. Koikkalainen and E. Oja},
  title		= {Artificial neural networks---specification and
		  implementation through {O}ccam},
  booktitle	= {Proc. SteP-88, Finnish Artificial Intelligence Symp. },
  year		= {1988},
  pages		= {621--629},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koikkalainen90a,
  author	= {P. Koikkalainen and E. Oja},
  title		= {Neural system development via {C}arelia simulator},
  booktitle	= {Proc. COGNITIVA'90},
  year		= {1990},
  volume	= {II},
  pages		= {769--772},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koikkalainen90b,
  author	= {P. Koikkalainen and E. Oja},
  title		= {Self-organizing hierarchical feature maps},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {II},
  pages		= {279--285},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  koikkalainen91a,
  author	= {P. Koikkalainen and E. Oja},
  title		= {The {CARELIA} simulator-a development and specification
		  environment for neural networks},
  booktitle	= {Advances in Control Networks and Large Scale Parallel
		  Distributed Processing Models},
  publisher	= {Ablex},
  year		= {1991},
  editor	= {M. Frazer},
  chapter	= {},
  pages		= { 242--272},
  address	= {Norwood, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koikkalainen93a,
  author	= {Pasi Koikkalainen},
  title		= {Fast Organization of the {S}elf-{O}rganizing {M}ap},
  booktitle	= {Proc. Symp. on Neural Networks in Finland},
  year		= {1993},
  editor	= {Abhay Bulsari and Bj{\"{o}}rn Sax{\'{e}}n},
  pages		= {51--62},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koikkalainen94a,
  author	= {Pasi Koikkalainen},
  title		= {The {S}elf-{O}rganizing {T}emplate---natural way from
		  pixels to representations},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1137--1140},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, image processing},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koikkalainen94b,
  author	= {Pasi Koikkalainen},
  title		= {Progress with the Tree-Structured Self-Organizing Map},
  booktitle	= {Proc. ECAI'94, 11th European Conf. on Artificial
		  Intelligence},
  year		= {1994},
  editor	= {A. G. Cohn},
  pages		= {211--215},
  publisher	= {John Wiley {\&} Sons},
  address	= {New York},
  annote	= {modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koikkalainen95a,
  author	= {Pasi Koikkalainen},
  title		= {Fast Deterministic \mbox{self-organizing} maps},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {63--68},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Article{	  koikkalainen96a,
  author	= {P. Koikkalainen and J. Heikkonen and T. Honkanen and E.
		  Hakkinen and J. Mononen},
  title		= {Fault diagnostics of rotating machines via
		  self-organization},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2904},
  pages		= {460--8},
  annote	= {Intelligent Robots and Computer Vision XV: Algorithms,
		  Techniques, Active Vision, and Materials Handling Conf.
		  Date: 19--21 Nov. 1996 Conf. Loc: Boston, MA, USA Conf.
		  Sponsor: SPIE},
  dbinsdate	= {oldtimer}
}

@Article{	  koikkalainen96b,
  author	= {P. Koikkalainen and M. Varsta},
  title		= {Robot path generation for surface processing applications
		  via neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2904},
  pages		= {228--38},
  annote	= {Intelligent Robots and Computer Vision XV: Algorithms,
		  Techniques, Active Vision, and Materials Handling Conf.
		  Date: 19--21 Nov. 1996 Conf. Loc: Boston, MA, USA Conf.
		  Sponsor: SPIE},
  dbinsdate	= {oldtimer}
}

@InCollection{	  koikkalainen99a,
  author	= {P. Koikkalainen},
  title		= {Tree Structured Self-Organizing Maps},
  booktitle	= {Kohonen maps},
  pages		= {121--130},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: self-ogranisation, tree-structure, generative
		  model, Bayesian model},
  dbinsdate	= {oldtimer}
}

@TechReport{	  koistinen93a,
  author	= {P. Koistinen and L. Holmstr{\"{o}}m},
  title		= {A Framework for the Design of Feature Detectors by
		  Self-Organization},
  institution	= {{R}olf {N}evanlinna {I}nstitute},
  address	= {Helsinki, Finland},
  type		= {{R}esearch {R}eports {A}10},
  pages		= 34,
  year		= 1993,
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koistinen93b,
  author	= {Petri Koistinen},
  title		= {Unsupervised Formation of Feature Detectors Through
		  Residual Data Clustering},
  booktitle	= {Proc. Symp. on Neural Networks in Finland},
  year		= {1993},
  editor	= {Abhay Bulsari and Bj{\"{o}}rn Sax{\'{e}}n},
  pages		= {1--12},
  publisher	= {Finnish Artificial Intelligence Society, Helsinki,
		  Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koistinen93c,
  author	= {Petri Koistinen},
  title		= {Unsupervised Formation of Feature Detectors Using Residual
		  Inputs},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks, Amsterdam},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {219--223},
  publisher	= {Springer},
  address	= {London},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koizumi91a,
  author	= {Takuya Koizumi and Joji Urata and Shuji Taniguchi},
  title		= {A phoneme recognition using \mbox{self-organizing} feature
		  map and hidden {M}arkov models},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {777--782},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}


@InProceedings{	  kojima93a,
  author	= {Yoshihiro Kojima and Hiroshi Yamamoto and Toshiyuki Kohda
		  and Shigeo Sakaue and Susumu Maruno and Yasuharu Shimeki
		  and Kazutaka Kawakami and Mikio Mizutani},
  title		= {Recognition of Handwritten Numeric Characters Using Neural
		  Networks Designed on Approximate Reasoning Architecture},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2161--2164},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kokkonen89a,
  author	= {M. Kokkonen and K. Torkkola},
  title		= {Using \mbox{self-organizing} maps and multi-layered
		  feed-forward nets to obtain phonemic transcriptions of
		  spoken utterances},
  booktitle	= {Proc. EUROSPEECH-89, European Conf. on Speech
		  Communication and Technology},
  year		= {1989},
  volume	= {II},
  editor	= {J. P. Tubach and J. J. Mariani},
  pages		= {561--564},
  organization	= {Assoc. Belge des Acousticiens; Assoc. Recherche Cognitive;
		  Comm. Eur. Communities; et al},
  publisher	= {CEP Consultants},
  address	= {Edinburgh, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  kokkonen90a,
  author	= {M. Kokkonen and K. Torkkola},
  title		= {Using \mbox{self-organizing} maps and multi-layered
		  feed-forward nets to obtain phonemic transcriptions of
		  spoken utterances},
  journal	= {Speech Communication},
  year		= {1990},
  volume	= {9},
  number	= {5--6},
  pages		= {541--549},
  month		= {December},
  annote	= {Conf. paper in journal},
  x		= {Vastaava konf. paperi jo mukana. },
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  kokkonen91a,
  author	= {Mikko Kokkonen},
  title		= {Koartikulaatioilmi{\"o}iden mallittaminen
		  itseorganisoituvan piirrekartan topologian avulla},
  school	= {Helsinki University of Technology},
  address	= {Espoo, Finland},
  year		= {1991},
  dbinsdate	= {oldtimer}
}

@Article{	  kolehmainen00a,
  author	= {Kolehmainen, Mikko and Martikainen, Hannu and Hiltunen,
		  Teri and Ruuskanen, Juhani},
  title		= {Forecasting air quality parameters using hybrid neural
		  network modelling},
  journal	= {Environmental Monitoring and Assessment},
  year		= {2000},
  volume	= {65},
  number	= {1--2},
  month		= {Nov},
  pages		= {277--286},
  organization	= {Univ of Kuopio},
  publisher	= {Kluwer Academic Publishers},
  address	= {Dordrecht},
  abstract	= {Urban air pollution has emerged as an acute problem in
		  recent years because of its detrimental effects on health
		  and living conditions. The research presented here aims at
		  attaining a better understanding of phenomena associated
		  with atmospheric pollution, and in particular with aerosol
		  particles. The specific goal was to develop a form of air
		  quality modelling which can forecast urban air quality for
		  the next day using airborne pollutant, meteorological and
		  timing variables. Hourly airborne pollutant and
		  meteorological averages collected during the years
		  1995--1997 were analysed in order to identify air quality
		  episodes having typical and the most probable combinations
		  of air pollutant and meteorological variables. This
		  modelling was done using the Self-Organising Map (SOM)
		  algorithm, Sammon's mapping and fuzzy distance metrics. The
		  clusters of data that were found were characterised by
		  statistics. Several overlapping Multi-Layer Perceptron
		  (MLP) models were then applied to the clustered data, each
		  of which represented one pollution episode. The actual
		  levels for individual pollutants could then be calculated
		  using a combination of the MLP models which were
		  appropriate in that situation. The analysis phase of the
		  modelling gave clear and intuitive results regarding air
		  quality in the area where the data had been collected. The
		  resulting forecast showed that the modelling of gaseous
		  pollutants is more reliable than that of the particles.},
  dbinsdate	= {2002/1}
}

@Article{	  kolehmainen01a,
  author	= {Kolehmainen, M. and Ruuskanen, J. and Rissanen, E. and
		  Raatikainen, O.},
  title		= {Monitoring odorous sulfur emissions using self-organizing
		  maps for handling ion mobility spectrometry data},
  journal	= {Journal of the Air and Waste Management Association},
  year		= {2001},
  volume	= {51},
  number	= {7},
  month		= {},
  pages		= {966--971},
  organization	= {Department of Environmental Sciences, University of
		  Kuopio},
  publisher	= {},
  address	= {},
  abstract	= {Possibilities for monitoring emissions of reduced sulfur
		  compounds in pulp and paper mills were investigated using
		  ion mobility spectrometry (IMS) and a self-organizing map
		  (SOM) algorithm. The reduced sulfur compounds measured were
		  hydrogen sulfide (H<sub>2</sub>S), dimethyl sulfide (DMS),
		  and methyl mercaptan (MM). Attention was paid to momentary
		  concentrations because there is no monitoring device able
		  to measure peak concentrations of reduced sulfur compounds
		  under field conditions. These methods were evaluated by
		  measuring the reduced sulfur compounds first in the
		  laboratory and then at a process monitoring site at a pulp
		  factory. The aim was to find out whether it would be
		  possible to use the laboratory measurements to recognize
		  the same reduced sulfur compounds at the monitoring site.
		  Data collection was followed by analysis using the SOM
		  algorithm and Sammon's mapping. The results showed that the
		  IMS spectra of reduced sulfur compounds and their mixtures
		  can be distinguished from each other by computationally
		  intelligent methods and that the spectra from the process
		  monitoring site corresponded with the laboratory
		  measurements to a certain extent.},
  dbinsdate	= {2002/1}
}

@Article{	  kolehmainen01b,
  author	= {Kolehmainen, M. and Martikainen, H. and Ruuskanen, J.},
  title		= {Neural networks and periodic components used in air
		  quality forecasting},
  journal	= {Atmospheric Environment},
  year		= {2001},
  volume	= {35},
  number	= {5},
  month		= {},
  pages		= {815--825},
  organization	= {Univ of Kuopio},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {Forecasting of air quality parameters is one topic of air
		  quality research today due to the health effects caused by
		  airborne pollutants in urban areas. The work presented here
		  aims at comparing two principally different neural network
		  methods that have been considered as potential tools in
		  that area and assessing them in relation to regression with
		  periodic components. Self-organizing maps (SOM) represent a
		  form of competitive learning in which a neural network
		  learns the structure of the data. Multi-layer perceptrons
		  (MLPs) have been shown to be able to learn complex
		  relationships between input and output variables. In
		  addition, the effect of removing periodic components is
		  evaluated with respect to neural networks. The methods were
		  evaluated using hourly time series of NO<sub>2</sub> and
		  basic meteorological variables collected in the city of
		  Stockholm in 1994--1998. The estimated values for
		  forecasting were calculated in three ways: using the
		  periodic components alone, applying neural network methods
		  to the residual values after removing the periodic
		  components, and applying only neural networks to the
		  original data. The results showed that the best forecast
		  estimates can be achieved by directly applying a MLP
		  network to the original data, and thus, that a combination
		  of the periodic regression method and neural algorithms
		  does not give any advantage over a direct application of
		  neural algorithms.},
  dbinsdate	= {2002/1}
}

@MastersThesis{	  kolehmainen97a,
  author	= {Mikko Kolehmainen},
  title		= {Methods of computational intelligence in handling ion
		  mobility based {IMCELL}-measurement data from fermentation
		  process},
  school	= {University of Kuopio},
  year		= 1997,
  address	= {Kuopio, Finland},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  kolinummi00a,
  author	= {Kolinummi, P. and H\"am\"alainen, P. and H\"am\"alainen,
		  T. and Saarinen, J.},
  title		= {PARNEU: general-purpose partial tree computer},
  journal	= {Microprocessors and Microsystems},
  year		= {2000},
  volume	= {24},
  pages		= {23--42},
  abstract	= {PARNEU is a parallel co-processor system for a PC designed
		  for artificial neural networks, and other computationally
		  intensive applications. PARNEU topology includes a bus,
		  ring and reconfigurable partial tree, which are motivated
		  due to analysis of several algorithms. The architecture
		  provides very versatile mapping possibilities and allows
		  modular hardware implementation. An important feature is
		  practical expandability without signal and clock skew
		  problems. Analog Devices ADSP-21062 digital signal
		  processors and Xilinx field programmable gate arrays are
		  used for cost-effective and reliable implementation. PARNEU
		  programming is convenient due to C-primitives, which hide
		  the complex communication and allow high level language
		  software development. In addition, PARNEU can be remotely
		  used over Internet due to a TCP/IP server. The hardware
		  performance metrics as well as the application performance
		  for multilayer perceptron (MLP), self-organizing map (SOM)
		  and sparse distributed memory (SDM) neural networks are
		  given. Performance improvements of the order of 20--40
		  times are achieved compared to our previous neurocomputer
		  implementation called TUTNC.},
  dbinsdate	= {oldtimer}
}

@Article{	  kolinummi00b,
  author	= {Kolinummi, Pasi and Pulkkinen, Pasi and Hamalainen, Timo
		  and Saarinen, Jukka},
  title		= {Parallel implementation of self-organizing map on the
		  partial tree shape neurocomputer},
  journal	= {Neural Processing Letters},
  year		= {2000},
  volume	= {12},
  number	= {2},
  month		= {Oct},
  pages		= {171--182},
  organization	= {Tampere Univ of Technology},
  publisher	= {Kluwer Academic Publishers},
  address	= {Dordrecht},
  abstract	= {A parallel mapping of self-organizing map (SOM) algorithm
		  is presented for a partial tree shape neurocomputer
		  (PARNEU). PARNEU is a general purpose parallel
		  neurocomputer that is designed for soft computing
		  applications. Practical scalability and a reconfigurable
		  partial tree network are the main architectural features.
		  The presented neuron parallel mapping of SOM with on-line
		  learning illustrates a parallel winner neuron search and a
		  coordinate transfer that are performed in the partial tree
		  network. Phase times are measured to analyze speedup and
		  scalability of the mapping. The performance of the learning
		  phase in SOM with a four processor PARNEU configuration is
		  about 26 MCUPS and the recall phase performs 30 MCPS.
		  Compared to other mappings done for general purpose
		  neurocomputers, PARNEU's performance is very good.},
  dbinsdate	= {2002/1}
}

@InCollection{	  kolinummi97a,
  author	= {Pasi Kolinummi and Timo H{\"a}m{\"a}l{\"a}inen and Kimmo
		  Kaski},
  title		= {Mappings of {SOM} and {LVQ} on the Partial Tree Shape
		  Neurocomputer},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {II},
  pages		= {904--909},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kolinummi99a,
  author	= {Kolinummi, P. and Pulkkinen, P. and Hamalainen, T. and
		  Saarinen J.},
  title		= {Programming environment for reconfigurable multiple {DSP}
		  system},
  booktitle	= {Proceedings of the ISCA 12th International Conference.
		  Parallel and Distributed Systems. ISCA, Cary, NC, USA},
  year		= {1999},
  volume	= {},
  pages		= {546--51},
  abstract	= {The design of programming environment for a multiple
		  digital signal processor (DSP) system, called PARNEU is
		  presented. PARNEU hardware is recently designed and
		  implemented in our laboratory to support soft computing and
		  multimedia research in embedded environment.
		  Self-organizing map, multi-layer Perceptron and sparse
		  distributed memory algorithms have been accomplished while
		  video encoding and motion estimation algorithms are being
		  implemented. PARNEU system is a modular and scalable which
		  gives additional requirements for software development. The
		  support for reconfigurability is discussed in both hardware
		  and software levels. Description of library functions is
		  given together with the measured performance values.
		  Software overheads in user level operations are compared to
		  the hardware times. Different communication methods are
		  analysed. Novel features of our system are an intelligent
		  usage of direct memory access and software support for
		  reconfigurability, which are supported in both in software
		  and hardware level.},
  dbinsdate	= {2002/1}
}

@Article{	  komori92a,
  author	= {Takashi Komori and Shigeru Katagiri},
  title		= {{GPD} training of dynamic programming-based speech
		  recognizers},
  journal	= {J. Acoust. Soc. Japan},
  pages		= {341--349},
  volume	= {13},
  number	= {6},
  year		= {1992},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kondo94a,
  author	= {K. Kondo and H. Kamata and Y. Ishida},
  title		= {Speaker-Independent Spoken Digits Recognition Using
		  {LVQ}},
  pages		= {4448--4451},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, speech recognition},
  dbinsdate	= {oldtimer}
}

@Article{	  kondo96a,
  author	= {S. Kondo},
  title		= {A study of sequential learning on neural networks},
  journal	= {Record of Electrical and Communication Engineering
		  Conversazione Tohoku University},
  year		= {1996},
  volume	= {65},
  number	= {1},
  pages		= {133--4},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kong95a,
  author	= {J. H. L. Kong and G. P. M. D. Martin},
  title		= {A Review of a Hybrid Network: {K}ohonen Learning Vector
		  Quantization and Counterpropagation},
  volume	= {III},
  pages		= {1397--1402},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kong97a,
  author	= {Haosong Kong and Ling Guan},
  title		= {Eliminating impulse noise with random intensity
		  distributions by a \mbox{self-organizing} tree map},
  booktitle	= {Proceedings of the Eighth Australian Conference on Neural
		  Networks (ACNN'97)},
  publisher	= {Telstra Res. Lab},
  year		= {1997},
  editor	= {M. Dale and A. Kowalczyk and R. Slaviero and J.
		  Szymanski},
  address	= {Clayton, Vic. , Australia},
  pages		= {105--8},
  dbinsdate	= {oldtimer}
}

@Article{	  kong98a,
  author	= {Haosong Kong and Ling Guan},
  title		= {\mbox{Self-organizing} tree map for eliminating impulse
		  noise with random intensity distributions},
  journal	= {Journal of Electronic Imaging},
  year		= {1998},
  volume	= {7},
  number	= {1},
  pages		= {36--44},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kongkachandra99a,
  author	= {Kongkachandra, R. and Tamee, K. and Kimpan, C.},
  title		= {Improving Thai isolated word recognition by using
		  Karhunen-Loeve transformation and learning vector
		  quantization},
  booktitle	= {1999 IEEE International Symposium on Intelligent Signal
		  Processing and Communication Systems. Signal Processing and
		  Communications Beyond 2000. King Mongkuts Inst. Technol,
		  Bangkok, Thailand},
  year		= {1999},
  volume	= {},
  pages		= {777--80},
  abstract	= {This paper describes the methodology to recognize Thai
		  isolated words by integrating two approaches i.e.
		  Karhunen-Loeve transformation (KLT) and learning vector
		  quantization (LVQ) in feature extraction and recognition
		  processes, respectively. LVQ is a kind of supervised neural
		  network that employs the concept of the "winner-take-all",
		  property for classification tasks. From the input speech
		  waveform, we reduce the size of the acoustic vectors by
		  finding the independent features, called principal
		  components. We collect 80% size reduced of the speech
		  waveform by KLT along with its class name as input. The
		  codebook vectors in each class are equaled in initial
		  state. The LVQ training compares their similarities among
		  input vectors and weight vectors in the initial codebook
		  and then modifies the weight vectors that are nearly to the
		  input data, other weight vectors are stable in the next
		  time. By employing KL+LVQ, with the numeric patterns 0--9
		  from 20 male speakers pronouncing three times a word, the
		  average of recognition accuracy is 90%.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  konig93a,
  author	= {A. K{\"{o}}nig and X. Geng and M. Glesner},
  title		= {Hardware Implementation of {K}ohonen's Feature Map by
		  Scalar and {SIMD}-Array Processors},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {1046--1049},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  konishi90a,
  author	= {M. Konishi and Y. Otsuka and K. Matsuda and N. Tamura and
		  A. Fuki and K. Kadoguchi},
  title		= {Application of neural network to operation guidance in
		  blast furnace},
  booktitle	= {Third European Seminar on Neural Computing: The
		  Marketplace},
  year		= {1990},
  pages		= {13},
  publisher	= {IBC Tech. Services, London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  koo90a,
  author	= {M. W. Koo and C. K. Un},
  title		= {Speaker adaptation using learning vector quantisation},
  journal	= {Electronics Letters},
  year		= {1990},
  volume	= {26},
  number	= {20},
  pages		= {1731--1732},
  abstract	= {A codebook adaptation scheme using learning vector
		  quantisation (LVQ) which has highly, discriminant ability
		  for speaker adaptation is presented. The recognition
		  accuracy for new speakers can be improved over the VQ
		  adaptation method based on the minimum distortion criterion
		  using this scheme.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kopecz93a,
  author	= {J{\"{o}}rg Kopecz},
  title		= {A Cortical Structure for Real World Image Processing},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {I},
  pages		= {138--143},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kopecz95a,
  author	= {Klaus Kopecz},
  title		= {Unsupervised learning of sequences on maps with lateral
		  connectivity},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {I},
  pages		= {431--436},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kopecz97a,
  author	= {K. Kopecz and K. Mohraz},
  title		= {Relative time scales in the self-organization of pattern
		  classification: from 'one-shot' to statistical learning},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {249--54},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koppen94a,
  author	= {M. K{\"{o}}ppen},
  title		= {Practical Applications of Neural Networks in Texture
		  Analysis},
  pages		= {149--150},
  booktitle	= {Proc. 3rd International Conference on Fuzzy Logic, Neural
		  Nets and Soft Computing},
  year		= {1994},
  publisher	= {Fuzzy Logic Systems Institute},
  address	= {Iizuka, Japan},
  annote	= {application, texture analysis, image analysis},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koprinska00a,
  author	= {Koprinska, L. and Kasabov, N.},
  title		= {Evolving fuzzy neural network for camera operations
		  recognition},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {2},
  pages		= {523--6},
  abstract	= {Reports an application of an evolving fuzzy neural network
		  (EFuNN) for camera operations recognition. EFuNN features
		  one-pass learning, dynamical growing and shrinking
		  architecture and ability to accommodate new knowledge
		  without the need to retrain the network on both the
		  original and new data. The network learns from
		  pre-classified examples in the form of motion vector
		  patterns, extracted from MPEG compressed video, in order to
		  distinguish between six classes: static, panning, zooming,
		  object motion, tracking and dissolve. The performance of
		  EFuNN is compared with LVQ and the results are discussed.
		  In addition, the impact of the number of membership
		  functions and the contribution of the rule node aggregation
		  are analyzed.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  koprinska98a,
  author	= {Koprinska, I. and Carrato, S.},
  title		= {Segmentation of compressed video by learning vector
		  quantizer},
  booktitle	= {Engineering Benefits from Neural Networks. Proceedings of
		  the International Conference EANN '98},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1998},
  volume	= {},
  pages		= {9--16},
  abstract	= {An application of Kohonen's learning vector quantization
		  (LVQ) to the segmentation of MPEG-encoded video is
		  presented. Only information directly available in the
		  compressed stream is used, namely the motion vectors and
		  the macroblock coding mode in B and P frames. The LVQ
		  module is a part of a hybrid rule-based/neural system. It
		  is used to: (1) distinguish dissolves from object movement
		  and camera operations; (2) refine the rough location of the
		  "complex" boundaries of the gradual transitions; (3)
		  further divide shots into sub-shots. The results
		  demonstrate high classification accuracy and temporal
		  resolution without computationally expensive calculations
		  and need for threshold settings.},
  dbinsdate	= {oldtimer}
}

@Article{	  kordylewski01a,
  author	= {Kordylewski, H. and Graupe, D. and Liu, K.},
  title		= {A novel large-memory neural network as an aid in medical
		  diagnosis applications},
  journal	= {IEEE Transactions on Information Technology in
		  Biomedicine},
  year		= {2001},
  volume	= {5},
  number	= {3},
  month		= {September },
  pages		= {202--209},
  organization	= {Dept. of Elec. Eng. and Comp. Sci., University of
		  Illinois},
  publisher	= {},
  address	= {},
  abstract	= {This paper describes the application of a large memory
		  storage and retrieval (LAMSTAR) neural network to medical
		  diagnosis and medical information retrieval problems. The
		  network is based on Minsky's knowledge-lines (k-lines)
		  theory of memory storage and retrieval in the central
		  nervous system. It employs arrays of self-organized map
		  modules, such that the k-lines are implemented via link
		  weights (address correlation) that are being updated by
		  learning. The network also employs features of forgetting
		  and of interpolation and extrapolation, thus being able to
		  handle incomplete data sets. It can deal equally well with
		  exact and fuzzy information, thus being specifically
		  applicable to medical diagnosis where the diagnosis is
		  based on exact data, fuzzy patient interview information,
		  patient history, observed images, and test records.
		  Furthermore, the network can be operated in closed loop
		  with Internet search engines to intelligently use data from
		  the Internet in a higher hierarchy of learning. All of the
		  above features are shown to make the LAMSTAR network
		  suitable for medical diagnosis problems that concern large
		  data sets of many categories that are often incomplete and
		  fuzzy. Applications of the network to three specific
		  medical diagnosis problems are described: two from
		  nephrology and one related to an emergency-room drug
		  identification problem. It is shown that the LAMSTAR
		  network is hundreds and thousands times faster in its
		  training than back-propagation-based networks when used for
		  the same problem and with exactly the same information.},
  dbinsdate	= {2002/1}
}

@Article{	  korn90a,
  author	= {G. A. Korn},
  title		= {Interactive statistical experiments with template-matching
		  neural networks},
  journal	= {IEEE Trans. on Syst. , Man and Cybern. },
  year		= {1990},
  volume	= {20},
  number	= {5},
  pages		= {1146--1152},
  month		= {September-October},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kosaka00b,
  author	= {Kosaka, Toshihisa and Omatu, Sigeru},
  title		= {Classification of the Italian Liras using the {LVQ}
		  method},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {2769--2774},
  organization	= {Glory Ltd},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Bill money classification by transaction machines has
		  become important to make progress the office automation. In
		  this paper, a new method to classify the Italian Liras by
		  using the learning vector quantization (LVQ). The Italian
		  Liras of 8 kinds, 1,000, 2,000, 5,000, 10,000, 50,000
		  (new), 50,000 (old), 100,000 (new), 100,000 (old) Liras
		  with four directions A,B,C, and D are used where A and B
		  mean the normal direction and the upside down direction and
		  C and D mean the reverse version of A and B. The original
		  image with 128\times{}64 pixels are observed at the
		  transaction machine in which rotation and shift are
		  included. After correction of these effects, we select a
		  suitable aria which show the bill image and compressed as
		  the image with 64\times{}15 pixels to the neural networks.
		  Although the neural network of the LVQ type could process
		  any order of the dimension of the input data, the small
		  size is better to achieve the fast convergence result.
		  Thus, we have selected the above size of the image. The
		  thirty-two bills images are one set of the classification
		  pattern of the experiment. Total number of data sets is 30
		  and 10 data sets are used for training of the network and
		  the remaining 20 data sets are used to test the network.
		  After training the neural network, 20 data sets are tested
		  how well the LVQ network could work. From the simulation
		  results, the proposed method can offer the suitable
		  classification results for Italian Liras.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kosaka01a,
  author	= {Kosaka, Toshihisa and Omatu, Sigeru and Fujinaka, Toru},
  title		= {Bill classification by using the {LVQ} method},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {1430--1435},
  organization	= {Glory LTD Himeji},
  publisher	= {},
  address	= {},
  abstract	= {For the pattern classification problems the neuro-pattern
		  recognition which is the pattern recognition based on the
		  neural network approach has been paid an attention since it
		  can classify various patterns like human beings. In this
		  paper, we adopt the learning vector quantization(LVQ)
		  method to classify the various money. The reasons to use
		  the LVQ are that it can process the unsupervised
		  classification and treat many input data with small
		  computational burdens. We will construct the LVQ network to
		  classify the Italian Liras. Compared with a conventional
		  pattern matching technique, which has been adopted as a
		  classification method, the proposed method has shown
		  excellent classification results.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kosaka99a,
  author	= {Kosaka, T. and Omatu, S.},
  title		= {Classification of the Italian Liras using the {LVQ}
		  method},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {6},
  pages		= {845--50},
  abstract	= {For pattern classification problems, neuro-pattern
		  recognition which is pattern recognition based on a neural
		  network approach has been paid attention since it can
		  classify various patterns like human beings. We adopt the
		  learning vector quantization(LVQ) method to classify
		  various bill money. The reasons to use LVQ are that it can
		  process unsupervised classification and treat many input
		  data with small computational burden. We construct a LVQ
		  network to classify Italian Liras. Compared with a
		  conventional pattern matching technique, which has been
		  adopted as a classification method, the proposed method has
		  shown excellent classification results.},
  dbinsdate	= {oldtimer}
}

@Article{	  kosaka99b,
  author	= {Kosaka, T. and Taketani, N. and Omatu, S. and Ryo, K.},
  title		= {{US} dollar classifications by the {LVQ} method based on
		  reliability criterion},
  journal	= {Transactions of the Institute of Electrical Engineers of
		  Japan, Part C},
  year		= {1999},
  volume	= {119},
  pages		= {1359--64},
  abstract	= {Automatic classification of bill money has been well
		  developed and it is important that the classifier has high
		  accuracy. Generally, accuracy of classification is
		  represented as a recognition rate of sample data. To
		  evaluate the accuracy more strictly, we introduce a
		  reliability criterion. In the pattern recognition, neural
		  networks (NNs) have been adopted. Among them a competitive
		  NN has a simple structure and can explain the relation
		  between the inputs and the outputs more easily than a
		  layered NN based on the backpropagation method. Thus, we
		  use a competitive NN for the bill money classification and
		  use the learning vector quantization (LVQ) method for
		  training the NN. After introducing a reliability criterion
		  based on a probability distribution for the classification
		  by the LVQ method, we classify a US dollar by the LVQ
		  method and show the effectiveness of the proposed method.},
  dbinsdate	= {oldtimer}
}

@Article{	  kosaka99c,
  author	= {Kosaka, T. and Taketani, N. and Omatu, S.},
  title		= {Classification of Italian bills by a competitive neural
		  network},
  journal	= {Transactions of the Institute of Electrical Engineers of
		  Japan, Part C},
  year		= {1999},
  volume	= {119},
  pages		= {948--54},
  abstract	= {Automatic classification of bills has become important
		  according to the progress of office automation. This paper
		  is concerned with the new development of bill money
		  classification based on a competitive learning algorithm
		  where Italian Liras are adopted for classification. The
		  learning vector quantization (LVQ) method is used as
		  competitive learning. Original data of Lira bills may be
		  rotated and/or shifted. The authors show that the LVQ
		  method could be used effectively to classify the Lira bills
		  under such various conditions.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kosaka99d,
  author	= {Kosaka, T. and Taketani, N. and Omatu, S. and Ryo, K.},
  title		= {Discussion of reliability criterion for {US} dollar
		  classification by {LVQ} },
  booktitle	= {SMCia/99 Proceedings of the 1999 IEEE Midnight---Sun
		  Workshop on Soft Computing Methods in Industrial
		  Applications.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {},
  pages		= {28--33},
  abstract	= {A bill money classification has become automated and it is
		  important that the classifier has higher accuracy.
		  Generally, the accuracy of classification is represented as
		  the recognition rate of sample data. However, when
		  classifying bill money, we must evaluate the accuracy more
		  strictly. For pattern recognition a neural network (NN) is
		  studied and its ability is highly estimated. Among NNs a
		  competitive NN has a simple structure and can be analyzed
		  by the relation between the inputs and the outputs more
		  easily than a layered NN based on the backpropagation
		  method. Because of this, we use a competitive NN for bill
		  money classification and use the learning vector
		  quantization (LVQ) method for training the NN. We propose a
		  reliability criterion based on a probability distribution
		  for the classification by the LVQ method. Then we classify
		  US dollars by the LVQ and apply the reliability criterion
		  to the classification. We show that the proposed method of
		  bill money classification has higher accuracy.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kosaka99e,
  author	= {Kosaka, T. and Omatu, S.},
  title		= {Bill money classification by competitive learning},
  booktitle	= {SMCia/99 Proceedings of the 1999 IEEE Midnight---Sun
		  Workshop on Soft Computing Methods in Industrial
		  Applications.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {},
  pages		= {5--9},
  abstract	= {The progress of computer science enables us to process
		  complex and large scale computations and advanced pattern
		  recognition methods can be adopted for pattern
		  classification problems. Among them neuro-pattern
		  recognition, which means pattern recognition based on
		  neural networks, has been given attention since it has
		  classified various patterns like human beings. We adopt the
		  learning vector quantization (LVQ) method to classify
		  money. The reasons for using the LVQ are that it can
		  process unsupervised classification data and treat a large
		  amount of input data with a small computational burden. We
		  construct the LVQ network to classify Italian Lira.
		  Compared with a conventional pattern matching technique,
		  which has been adopted as a classification method, the
		  proposed method has shown excellent classification
		  results.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  koskela97a,
  author	= {T. Koskela and M. Varsta and J. Heikkonen and K. Kaski},
  title		= {Time series prediction using {RSOM} with local linear
		  modesl},
  institution	= {Helsinki University of Techology, Laboratory of
		  Computational Engineering},
  year		= 1997,
  number	= {B15},
  address	= {Espoo, Finland},
  abstract	= {A newly proposed Recurrent Self-Organizing Map (RSOM) is
		  studies in time series prediction. In this approach RSOM is
		  used to cluster the data to local data sets and local
		  linear models corresponding each of the map units are then
		  estimated based on the local data sets. A traditional way
		  of clustering the data is to use a windowing technique to
		  split it to input vectors of certain length.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  koskela98a,
  author	= {Koskela, T. and Varsta, M. and Heikkonen, J. and Kaski,
		  K.},
  title		= {Recurrent SOM with local linear models in time series
		  prediction},
  booktitle	= {6th European Symposium on Artificial Neural Networks.
		  ESANN'98. Proceedings},
  publisher	= {D-Facto},
  address	= {Brussels, Belgium},
  year		= {1998},
  volume	= {},
  pages		= {167--72},
  abstract	= {A recurrent self-organizing map (RSOM) is studied in three
		  different time series prediction cases. RSOM is used to
		  cluster the series into local data sets, for which
		  corresponding local linear models are estimated. RSOM
		  includes a recurrent difference vector in each unit which
		  allows storing context from the past input vectors. A
		  multilayer perceptron network and an autoregressive model
		  are used to compare the prediction results. In the studied
		  cases RSOM shows promising results.},
  dbinsdate	= {oldtimer}
}

@Article{	  koski96a,
  author	= {A. Koski},
  title		= {Primitive coding of structural {ECG} features},
  journal	= {Pattern Recognition Letters},
  year		= {1996},
  volume	= {17},
  number	= {11},
  pages		= {1215--22},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kosko90a,
  author	= {Bart Kosko},
  title		= {Stochastic competitive learning},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Kyoto},
  year		= {1990},
  volume	= {II},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {215--226},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  kosmatopoulos96a,
  author	= {Elias B. Kosmatopoulos and Manolas A. Christodoulou},
  title		= {Convergence Properties of A Class of Learning Vector
		  Quantization Algorithms},
  journal	= {IEEE Trans. on Image Processing},
  year		= {1996},
  volume	= {5},
  number	= {2},
  pages		= {361--368},
  month		= {February},
  abstract	= {In this paper, a mathematical analysis of a class of
		  learning vector quantization (LVQ) algorithms is presented.
		  Using an appropriate time-coordinate transformation, we
		  show that the LVQ algorithms under consideration can be
		  transformed into linear time-varying stochastic difference
		  equations. Using this fact, we apply stochastic Lyapunov
		  stability arguments, and we prove that the LVQ algorithms
		  under consideration do indeed converge, provided that some
		  appropriate conditions hold.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kostiainen01a,
  author	= {Kostiainen, T. and Lampinen, J.},
  title		= {Self-organizing map as a probability density model},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {394--399},
  organization	= {Laboratory of Computational Eng., Helsinki University of
		  Technology},
  publisher	= {},
  address	= {},
  abstract	= {The Self-Organizing Map, SOM, is a widely used tool in
		  exploratory data analysis. A major drawback of the SOM has
		  been the lack of a theoretically justified criterion for
		  model selection. Model complexity has a decisive effect on
		  the reliability of visual analysis, which is a main
		  application of the SOM. In particular, independence of
		  variables cannot be observed unless generalization of the
		  model is good. We describe the maximum likelihood
		  probability density model which follows from the SOM
		  training rule, and show how the density model can be
		  applied to choosing the correct model complexity, based on
		  the method of maximum likelihood.},
  dbinsdate	= {2002/1}
}

@Article{	  kothari97a,
  author	= {Kothari, Ravi and Bellando, John},
  title		= {Optical flow determination using topology preserving
		  mappings},
  journal	= {IEEE International Conference on Image Processing},
  year		= {1997},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  number	= {},
  volume	= {3},
  pages		= {344--347},
  abstract	= {Determining the optical flow is an ill-posed problem, and
		  requires the inclusion of a regularization term for
		  solution. In this paper we show that ordered maps produced
		  through self-organization reflect the topological
		  relationships of the input, and can thus inherently supply
		  the constraints required in obtaining the optical flow. Our
		  computational procedure is thus based on training a
		  self-organizing feature map with features from the first
		  frame of an image sequence, and observing the displacement
		  in the weights when the network is subsequently trained
		  with features drawn from the second frame. We show through
		  four simulations (three single object, and one multiple
		  object) that the weight displacements provide an accurate
		  representation of the optical flow.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kotilainen93a,
  author	= {Petri Kotilainen and Jukka Saarinen and Kimmo Kaski},
  title		= {Mapping of {SOM} Neural Network Algorithms to a General
		  Purpose Parallel Neurocomputer},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {1082},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kotilainen93b,
  author	= {Petri Kotilainen and Jukka Saarinen and Kimmo Kaski},
  title		= {Neural Network Computation in a Parallel Multiprocessor
		  Architecture},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1979--1982},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {A parallel multiprocessor architecture for general-purpose
		  neurocomputing applications is introduced. Methods to map
		  the multilayer perceptron, Kohonen's Self-Organising
		  Feature Map and Kanerva's Sparse Distributed Memory to the
		  suggested architecture are discussed. The mapping examples
		  include both the forward operation and training phase of
		  the networks. The computational performance of the
		  architecture is estimated for these three example cases.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kotropoulos92a,
  author	= {C. Kotropoulos and E. Aug{\'{e}} and I. Pitas},
  title		= {Two-Layer Learning Vector Quantizer for Color Image
		  Quantization},
  booktitle	= {Proc. EUSIPCO-92, Sixth European Signal Processing Conf.
		  },
  year		= {1992},
  editor	= {J. Vandewalle and R. Boite and M. Moonen and A.
		  Oosterlinck},
  volume	= {II},
  pages		= {1177--1180},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kotropoulos93a,
  author	= {Kotropoulos, C. and Pitas, I. and Magnisalis, X. and
		  Strintzis, M. G. },
  title		= {A variant of learning vector quantizer based on the L/sub
		  2/ mean for segmentation of ultrasonic images},
  booktitle	= {Proceedings of the 1993 IEEE International Symposium on
		  Circuits and Systems},
  year		= {1993},
  volume	= {1},
  pages		= {679--82},
  organization	= {Dept. of Electr. Eng. , Thessaloniki Univ. , Greece},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {In this paper, the segmentation of ultrasonic images using
		  self-organizing neural networks (NN) is investigated. A
		  modification of Learning Vector Quantizer (called L sub(2)
		  LVQ) is proposed so that the weight vectors of the output
		  neurons correspond to the L sub(2) mean instead of the
		  sample arithmetic mean of the input observations. The
		  convergence in the mean and in the mean square of the
		  proposed variant of LVQ are studied. Experimental results
		  show that L sub(2) LVQ outperforms other segmentation
		  techniques that employ thresholding a filtered ultrasonic
		  image with respect to the probability of detection for the
		  same probability of false alarm in all cases.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kotropoulos94a,
  author	= {C. Kotropoulos and I. Pitas and M. Gabbouj},
  title		= {Marginal median learning vector quantizer},
  booktitle	= {Signal Processing VII, Theories and Applications.
		  Proceedings of EUSIPCO-94. Seventh European Signal
		  Processing Conference},
  publisher	= {Eur. Assoc. Signal Process},
  year		= {1994},
  volume	= {3},
  editor	= {M. J. J. Holt and C. F. N. Cowan and P. M. Grant and W. A.
		  Sandham},
  address	= {Lausanne, Switzerland},
  pages		= {1496--9},
  dbinsdate	= {oldtimer}
}

@Article{	  kotropoulos98a,
  author	= {Kotropoulos, C. and Nikolaidis, N. and Bors, A. G. and
		  Pitas, I.},
  title		= {Robust and adaptive techniques in \mbox{self-organizing}
		  neural networks},
  journal	= {International Journal of Computer Mathematics},
  year		= {1998},
  number	= {1},
  volume	= {67},
  pages		= {183--200},
  abstract	= {Robust and adaptive training algorithms aiming at
		  enhancing the capabilities of self-organizing and Radial
		  Basis Function (RBF) neural networks are reviewed in this
		  paper. The following robust variants of Learning Vector
		  Quantizer (LVQ) are described: the order statistics LVQ,
		  the L sub(2) LVQ and the split-merge LVQ. Successful
		  application of the marginal median LVQ that belongs to the
		  class of order statistics LVQs in the self-organized
		  selection of the centers in RBF neural networks is
		  reported. Moreover, the use of the median absolute
		  deviation in the estimation of the covariance matrix of the
		  observations assigned to each hidden unit in RBF neural
		  networks is proposed. Applications that prove the
		  superiority of the proposed variants of LVQ and RBF neural
		  networks in noisy color image segmentation, color-based
		  image recognition, segmentation of ultrasonic images,
		  motion-field smoothing and moving object segmentation are
		  outlined.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kottow00a,
  author	= {D. Kottow and J. Ruiz-Del-Solar},
  title		= {Texture Segmentation by bio-inspired use of
		  Self-Organizing Networks},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Dept. of Electrical Eng., Universidad de Chile},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kottow99a,
  author	= {Kottow, D. and Ruiz-Del-Solar, J.},
  title		= {A new neural network model for automatic generation of
		  Gabor-like feature filters},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1947--52},
  abstract	= {The automatic selection of feature variables is a task of
		  increasing interest in the field of pattern recognition.
		  Neural models have recently been used for this purpose.
		  Among other models, the adaptive-subspace SOM (ASSOM)
		  stands out because of its simplicity and biological
		  plausibility. However, the main drawback of its application
		  in image processing systems is that a priori information is
		  necessary to choose a suitable network size and topology in
		  advance. This article introduces the adaptive-subspace
		  growing cell structures (ASGCS) network, which corresponds
		  to a further improvement of the ASSOM that overcomes its
		  main drawbacks. The ASGCS network is described and some
		  examples of automatic generation of Gabor-like feature
		  filter are given.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kou94a,
  author	= {Chenyuan Kou and Cheng-Tan Tung and Fu, H. C. },
  title		= {{FISOFM}: firearms identification based on {SOFM} model of
		  neural network},
  booktitle	= {Proceedings of The Institute of Electrical and Electronics
		  Engineers 28th Annual 1994 International Carnahan
		  Conference on Security Technology},
  year		= {1994},
  editor	= {Sanson, L. D. },
  pages		= {120--5},
  organization	= {Inst. of Comput. Sci. \& Inf. Eng. , Nat. Chiao Tung Univ.
		  , Hsinchu, Taiwan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kovacs00a,
  author	= {Kovacs, Laszlo and Terstyanszky, Gabor},
  title		= {Boundary region sensitive classification for the
		  counterpropagation neural network},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {1},
  pages		= {90--94},
  organization	= {Univ of Miskolc},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The basic problem of classification priori unknown faults
		  is related to rearrangement of existing classes and/or
		  introduction of new classes that requires management of
		  uncertain regions where input pattern vectors may belong to
		  several classes. The Counter Propagation neural network
		  (CPN) was selected to investigate the classification
		  problems because it integrates both supervised and
		  unsupervised learning to support diagnosis of both priori
		  known and unknown faults. The CPN network is taught to have
		  clusters that are described by codebook vectors in the
		  training phase. In the basic CPN network the distribution
		  of the codebook vectors is independent from the class
		  homogeneity as a result of the Kohonen SOM unsupervised
		  learning algorithm. Having unknown faults there are regions
		  where the distribution of codebook vectors is in-homogenous
		  like the class boundaries, i.e. the pattern vectors have a
		  larger attraction power than in the homogenous regions. To
		  diagnose unknown faults the codebook vector distribution
		  density should be increased in the inhomogeneous regions,
		  i.e. in class boundary regions and decreased in homogenous
		  regions. The basic CPN algorithm was modified incorporating
		  the class homogeneity to provide the rearrangement of
		  codebook vector to manage uncertain regions and to diagnose
		  priori unknown faults.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kowalski99a,
  author	= {Kowalski, J. and Strzelecki, M. and {De Vos}, A.},
  title		= {Kohonen neural network chip: preliminary results of
		  circuit test},
  booktitle	= {Proceedings of the 6th International Conference Mixed
		  Design of Integrated Circuits and Systems. MIXDES'99. Tech.
		  Univ. Poland, Lodz, Poland},
  year		= {1999},
  volume	= {},
  pages		= {503--6},
  abstract	= {Describes a VLSI circuit that implements a Kohonen neural
		  network. This chip is a part of a model parameter
		  estimation system. Circuit architecture and its operation
		  are discussed and preliminary results of circuit
		  measurements are shown. The chip was manufactured using
		  CMOS MIETEC 2.4 mu m n-well technology.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kraaijveld92a,
  author	= {M. A. Kraaijveld and J. Mao and A. K. Jain},
  title		= {A non-linear projection method based on {K}ohonen's
		  topology preserving maps},
  booktitle	= {Proc. 11ICPR, International Conference on Pattern
		  Recognition},
  year		= {1992},
  pages		= {41--45},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  kraaijveld95a,
  author	= {Martin A. Kraaijveld and Jianchang Mao and Anil K. Jain},
  title		= {A Nonlinear Projection Method Based on {K}ohonen's
		  Topology Preserving Maps},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1995},
  volume	= {6},
  number	= {3},
  pages		= {548--59},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kramer00a,
  author	= {A. A. Kramer and D. Lee and R. C. Axelrod},
  title		= {Use of a {K}ohonen Neural Network to Characterize
		  Respiratory Patients for Medical Intervention},
  booktitle	= {Artificial Neural Networks in Medicine and Biology,
		  Prodeedings of the ANNIMAB-1 COnference, Göteborg, Sweden,
		  13--16 May 2000},
  pages		= {192--196},
  year		= {2000},
  editor	= {H. Malmgren and M. Boga and L. Niklasson},
  abstract	= {Chronic Obstructive Pulmonary Disease (COPD) is one of the
		  leading causes of respitatory hospilisations in adults in
		  the USA. Prognosis correlates highly with early
		  diagnostics, however the disease may go unnoticed in its
		  early stages. A database of 25,000 individuals with
		  respitatory problems was received for further
		  investigation. The reported rate of COPD in this population
		  was 5.8%, which is fairly low. An unsupervised neural
		  network using the Kohonen architecture was applied to the
		  data in order to cluster patients into groups based on risk
		  factors for COPD. The network consisted of five output
		  neurons. After training characteristics of the groups were
		  examined. Three of the groups consisted of patients with
		  high percent of risk factors of COPD. Patients in two of
		  those gruoups were correctly diagnosed as having COPD, but
		  patients in the third group were underdiagnosed for COPD.
		  These patients should be re-examined by a pulmologist for
		  possible treatment of COPD. Thus Kohonen neural networks
		  may be a useful tool for clustering patients into groups
		  for differential medical intervention.},
  dbinsdate	= {oldtimer}
}

@Article{	  kraus95a,
  author	= {Kraus, G. and Gauglitz, G. },
  title		= {Optical reflectometric gas sensing: classification of
		  hydrocarbon vapours by pattern recognition applied to
		  {RIfS} sensor signals},
  journal	= {Chemometrics and Intelligent Laboratory Systems},
  year		= {1995},
  volume	= {30},
  number	= {2},
  pages		= {211--21},
  dbinsdate	= {oldtimer}
}

@Article{	  kraus95b,
  author	= {Kraus, Gerolf and Hierlemann, Andreas and Gauglitz,
		  Guenter and Goepel, Wolfgang},
  title		= {Analysis of complex gas mixtures by pattern recognition
		  with polymer based quartz microbalance sensor arrays},
  journal	= {International Conference on Solid-State Sensor and
		  Actuator, Eurosensors IX. Proceedings},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  number	= {},
  volume	= {1},
  pages		= {675--678},
  abstract	= {Polymer coated quartz microbalance gas sensor arrays were
		  used to analyse quaternary mixtures of volatile organic
		  compounds. The performance of the sensor arrays in
		  combination with qualitative data evaluation utilising
		  self-organising feature maps is described. The concept of
		  context preservation artificial neural networks is compared
		  to standard topology preservation with respect to its
		  classification properties for model partitioning. In the
		  studied case, context preserving maps yield more compact
		  clusters with smaller map dimensions.},
  dbinsdate	= {oldtimer}
}

@Article{	  kreis95a,
  author	= {Kreis, T. M. and Biedermann, R. and Juptner, W. P. O. },
  title		= {Evaluation of holographic interference patterns by
		  artificial neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2544},
  pages		= {11--24},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  krekelberg96a,
  author	= {B. Krekelberg and J. G. Taylor},
  title		= {Nitric Oxide: What Can It Compute?},
  journal	= {Network},
  year		= {1996},
  volume	= {8},
  pages		= {1--16},
  dbinsdate	= {oldtimer}
}

@Article{	  krishna00a,
  author	= {Krishna, K. Madhava and Kalra, Prem K.},
  title		= {Solving the local minima problem for a mobile robot by
		  classification of spatio-temporal sensory sequences},
  journal	= {Journal of Robotic Systems},
  year		= {2000},
  volume	= {17},
  number	= {10},
  month		= {Oct},
  pages		= {549--564},
  organization	= {Indian Inst of Technology},
  publisher	= {John Wiley \& Sons Inc},
  address	= {New York, NY},
  abstract	= {The local minima problem occurs when a robot navigating
		  past obstacles towards a desired target with no priori
		  knowledge of the environmental gets trapped in a loop. This
		  happens especially if the environment consists of concave
		  obstacles, mazes, and the like. To come out of the loop the
		  robot must comprehend its repeated ttraversal through the
		  same environment, which involves memorizing the environment
		  already seen. This paper proposes a new real-time collision
		  avoidance algorithm with the local minima problem solved by
		  classifying the environment based on the spatio-temporal
		  sensory sequences. A double layered classification scheme
		  is adopted. A fuzzy rule base does the spatial
		  classification at the first level and at the second level
		  Kohonen's self-organizing map and a fuzzy ART network is
		  used for temporal classification. The robot has no prior
		  knowledge of the environment and fuzzy rules govern its
		  obstacle repulsing and target attracting behaviors. As the
		  robot traverses the local environment is modeled and stored
		  in the form of neurons whose weights represent the
		  spatio-temporal sequence of sensor readings. A repetition
		  of a similar environment is mapped to the same neuron in
		  the network and this principle is exploited to identify a
		  local minima situation. Suitable steps are taken to pull
		  the robot out of the local minima. The method has been
		  tested on various complex environments with obstacle loops
		  and mazes, and its efficacy has been established.},
  dbinsdate	= {2002/1}
}

@Article{	  krishna00b,
  author	= {Krishna, K. M. and Kalra, P. K.},
  title		= {Spatial understanding and temporal correlation for a
		  mobile robot},
  journal	= {Spatial-Cognition-and-Computation},
  year		= {2000},
  volume	= {2},
  pages		= {219--59},
  abstract	= {Discusses a network model which simulates functionally
		  features intrinsic to human navigation, and their
		  incorporation into a behavior-based robot. Specifically, it
		  deals with implementing a memory based reasoning strategy
		  during real-time navigation. Memory is identified with
		  ability to cognize the local environment or scenario,
		  classify it in terms of previously learned primitives or
		  landmarks, remember and recollect such primitives at later
		  instants and correlate over time similar experiences. This
		  enhances the robot's navigation capabilities through
		  intelligent decisions due to spatial understanding, scene
		  recollection abilities and detecting local minimum traps
		  through place recognition. A double layered spatio-temporal
		  classification scheme consisting of a fuzzy rule-based
		  spatial classifier and a temporal classifier based on
		  self-organizing map and ART nets are adopted. The
		  classifier network reduces the robot's experience of its
		  environment consisting of a stream of sensor patterns into
		  weight vectors that signify a particular landmark. An
		  extension of the network architecture is also introduced to
		  cognize the presence of dynamic obstacles amidst stationary
		  ones.},
  dbinsdate	= {2002/1}
}

@Article{	  krishna01a,
  author	= {Krishna, K. M. and Kalra, P. K.},
  title		= {Perception and remembrance of the environment during
		  real-time navigation of a mobile robot},
  journal	= {ROBOTICS AND AUTONOMOUS SYSTEMS},
  year		= {2001},
  volume	= {37},
  number	= {1},
  month		= {OCT 31},
  pages		= {25--51},
  abstract	= {This paper deals with the advantages of incorporating
		  cognition and remembrance capabilities in a sensor-based
		  real-time navigation algorithm. The specific features of
		  the algorithm apart from real-time collision avoidance
		  include spatial comprehension of the local scenario of the
		  robot, remembrance and recollection of such comprehended
		  scenarios and temporal correlation of similar scenarios
		  witnessed during different instants of navigation. These
		  features enhance the robot's performance by providing for a
		  memory-based reasoning whereby the robot's forthcoming
		  decisions are also affected by its previous experiences
		  during the navigation apart from the current range inputs.
		  The environment of the robot is modeled by classifying
		  temporal sequences of spatial sensory patterns. A fuzzy
		  classification scheme coupled to Kohonen's self- organizing
		  map and fuzzy ART network determines this classification. A
		  detailed comparison of the present method with other recent
		  approaches in the specific case of local minimum detection
		  and avoidance is also presented. As for escaping the local
		  minimum barrier is concerned this paper divulges a new
		  system of rules that lead to shorter paths than the other
		  methods. The method has been tested in concave, maze- like,
		  unstructured and altered environments and its efficacy
		  established. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  krivda93a,
  author	= {Krivda, A. and Gulski, E. },
  title		= {Neural networks as a tool for recognition of partial
		  discharges},
  booktitle	= {International Conference on Partial Discharge},
  year		= {1993},
  pages		= {84--5},
  organization	= {High Voltage Lab. , Delft Univ. of Technol. ,
		  Netherlands},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kroes94a,
  author	= {B. A. Kroes and E. J. H. Kerckhoffs and L. Rothkrantz and
		  F. W. Wedman},
  title		= {Simulation of various connectionist systems on a 2nd
		  generation hypercube computer: performance and efficiency
		  results},
  booktitle	= {CISS. First Joint Conference of International Simulation
		  Societies Proceedings},
  publisher	= {SCS},
  year		= {1994},
  editor	= {J. Halin and W. Karplus and R. Rimane},
  address	= {San Diego, CA, USA},
  pages		= {392--7},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  krolikowski00a,
  author	= {Rafal Krolikowski},
  title		= {Exploitation of Self-Organising Maps for the Reduction of
		  Non-Stationary Noise in Speech Signals},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Sound Engineering Department, Technical University of
		  Gdansk},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  krose94a,
  author	= {B. J. A. Kr{\"{o}}se and M. Eecen},
  title		= {Self-learning maps for path planning in sensor space},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1303--1306},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, robots, path planning},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  krose94b,
  author	= {Krose, B. J. A. and Eecen, M. },
  title		= {A \mbox{self-organizing} representation of sensor space
		  for mobile robot navigation},
  booktitle	= {IROS '94. Proceedings of the IEEE/RSJ/GI International
		  Conference on Intelligent Robots and Systems. Advanced
		  Robotic Systems and the Real World},
  year		= {1994},
  volume	= {1},
  pages		= {9--14},
  organization	= {Dept. of Math. \& Comput. Sci. , Amsterdam Univ. ,
		  Netherlands},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  krovi91a,
  author	= {R. Krovi and W. E. Pracht},
  title		= {Feasibility of self organization in image compression},
  booktitle	= {Proc. IEEE/ACM Int. Conference on Developing and Managing
		  Expert System Programs},
  year		= {1991},
  editor	= {J. Feinstein and E. Awad and L. Medsker and E. Turban},
  pages		= {210--214},
  organization	= {IEEE; ACM},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  krumhansl01a,
  author	= {Krumhansl, C. L. and Toiviainen, P.},
  title		= {Tonal cognition},
  booktitle	= {BIOLOGICAL FOUNDATIONS OF MUSIC},
  year		= {2001},
  pages		= {77--91},
  abstract	= {This article presents a self-organizing map (SOM) neural
		  network model of tonality based on experimentally
		  quantified tonal hierarchies. A toroidal representation of
		  key distances is recovered in which keys are located near
		  their neighbors on the circle of fifths, and both parallel
		  and relative major/minor key pairs are proximal. The map is
		  used to represent dynamic changes in the sense of key as
		  cues to key become more or less clear and modulations
		  occur. Two models, one using tone distributions and the
		  other using tone transitions, are proposed for key-finding.
		  The tone transition model takes both pitch and temporal
		  distance between tones into account. Both models produce
		  results highly comparable to those of musically trained
		  listeners, who performed a probe tone task for ten
		  nine-chord sequences. A distributed mapping of tonality is
		  used to visualize activation patterns that change over
		  time. The location and spread of this activation pattern is
		  similar for experimental results and the key-finding
		  model.},
  dbinsdate	= {2002/1}
}

@Article{	  kuan91a,
  author	= {Chung-Ming Kuan and Kurt Hornik},
  title		= {Convergence of Learning Algorithms with Constant Learning
		  Rates},
  journal	= {IEEE Trans. on Neural Networks},
  volume	= 2,
  number	= 5,
  page		= 484,
  month		= sep,
  year		= 1991,
  dbinsdate	= {oldtimer}
}

@Article{	  kuang92a,
  author	= {Kuang, Z. and Kuh, A. },
  title		= {A combined \mbox{self-organizing} feature map and
		  multilayer perceptron for isolated word recognition},
  journal	= {IEEE Transactions on Signal Processing},
  year		= {1992},
  volume	= {40},
  number	= {11},
  pages		= {2651--7},
  month		= {Nov},
  dbinsdate	= {oldtimer}
}

@Article{	  kuban_altinel00a,
  author	= {{Kuban Altinel}, I. and Aras, N. and Oommen, B. J.},
  title		= {Fast, efficient and accurate solutions to the Hamiltonian
		  path problem using neural approaches},
  journal	= {Computers \& Operations Research},
  year		= {2000},
  volume	= {27},
  pages		= {461--94},
  abstract	= {Unlike its cousin, the Euclidean traveling salesman
		  problem (TSP), there has been no documented all-neural
		  solution to the Euclidean Hamiltonian Path Problem (HPP).
		  The reason for this is the fact that the heuristics which
		  map the cities onto the neurons "lose their credibility"
		  because the underlying cyclic property of the order of the
		  neurons used in the TSP is lost in the HPP. We present
		  three neural solutions to the HPP. The first of these,
		  GSOM-HPP, is a generalization of Kohonen's self-organizing
		  map (SOM) as modified by B. Angeniol et al. (1988). The
		  second and third methods use the recently introduced
		  self-organizing neural network, the Kohonen Network
		  Incorporating Explicit Statistics (KNIES) (B.J. Oommen et
		  al., 1998). The primary difference between KNIES and
		  Kohonen's SOM is that unlike SOM, every iteration in the
		  training phase includes two distinct modules: attracting
		  module and dispersing module. As a result of SOM and the
		  dispersing module introduced in KNIES the neurons
		  individually find their places both statistically and
		  topologically, and also collectively maintain their mean as
		  the mean of the data points which they represent. The new
		  philosophy, which has previously been used to effectively
		  solve the Euclidean Traveling Salesman Problem (TSP), is
		  now extended to solve the Euclidean Hamiltonian Path (HPP).
		  These algorithms have also been rigorously tested.
		  Experimental results for problems obtained by modifying
		  selected instances from the traveling salesman problem
		  library (TSPLIB) (G. Reinett, 1991) for the HPP, indicate
		  that they are both accurate and efficient. The paper also
		  contains a systematic strategy by which the quality of any
		  HPP algorithm can be quantified.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kuh90a,
  author	= {A. Kuh and G. Iseri and A. Mathur and Z. Huang},
  title		= {Hybrid neural network and pattern classification learning
		  algorithms},
  booktitle	= {1990 IEEE Int. Symp. on Circuits and Systems},
  year		= {1990},
  volume	= {IV},
  pages		= {2512--2515},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kuhn95a,
  author	= {Kuhn, D. and Buessler, J. L. and Urban, J. P. and Gresser,
		  J. },
  title		= {Cooperation of neural networks applied to a robotic
		  hand-eye coordination task},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {4},
  pages		= {3694--9},
  organization	= {Fac. des Sci. et Tech. , Univ. de Haute Alsace, Mulhouse,
		  France},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  kukkonen01a,
  author	= {Kukkonen, S. and Kaelviaeinen, H. and Parkkinen, J.},
  title		= {Color features for quality control in ceramic tile
		  industry},
  journal	= {Optical Engineering},
  year		= {2001},
  volume	= {40},
  number	= {2},
  month		= {February 2001},
  pages		= {170--177},
  organization	= {Lappeenranta Univ. of Technology, Department of
		  Information Technology},
  publisher	= {},
  address	= {},
  abstract	= {We study visual quality control in the ceramics industry.
		  In tile manufacturing, it is important that in each set of
		  tiles, every single tile looks similar. Currently, the
		  estimation is usually done by human vision. Our goal is to
		  design a machine vision system that can estimate the
		  sufficient similarity, or same appearance, to the human
		  eye. Our main approach is to use accurate spectral
		  representation of color, and compare spectral features to
		  the RGB color features. A laboratory system for color
		  measurements is built. Experimentations with five classes
		  of brown tiles are presented and discussed. In addition to
		  the k-nearest neighbor (k-NN) classifier, a neural network
		  called the self-organizing map (SOM) is used to provide
		  understanding of the spectral features. Every single
		  spectrum in each tile of a training set is used as input to
		  a 2-D SOM. The SOM is analyzed to understand how spectra
		  are clustered. As a result, tiles are classified using a
		  trained 2-D SOM. It is also of interest to know whether the
		  order of spectral colors can be determined. In our
		  approach, all spectra are clustered in a 1-D SOM, and each
		  pixel (spectrum) is presented by pseudocolors according to
		  the trained nodes. Finally, the results are compared to
		  experiments with human vision. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  kukkonen99a,
  author	= {Kukkonen, S. and Kalviainen, H. and Parkkinen, J.},
  title		= {Visual inspection by spectral features in the ceramics
		  industry},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.3826},
  year		= {1999},
  volume	= {3826},
  pages		= {64--75},
  abstract	= {Visual quality control is an important application area of
		  machine vision. In the ceramics industry, it is essential
		  that in each set of ceramic tiles every single tile looks
		  similar, while considering e.g. color and texture. Our goal
		  is to design a machine vision system that can estimate the
		  sufficient similarity or same appearance to the human eye.
		  Currently, the estimation is usually done by human vision.
		  Our main approach is to use accurate spectral
		  representation of color, and compare spectral features to
		  the RGB color features. The authors have recently proposed
		  preliminary methods and results for the classification of
		  color features. In the paper the approach is developed
		  further to cope with illumination effects and to take more
		  advantage of spectral features more. Experiments with five
		  classes of brown tiles are discussed. Besides the k-NN
		  classifier, a neural network, called the Self-Organizing
		  Map (SOM) is used for understanding spectral features.
		  Every single spectrum in each tile is used as input to a
		  2-D SOM with 30*30 nodes or neurons. The SOM is analyzed in
		  order to understand how spectra are clustered. As a result,
		  the nodes are labeled according to the classes. Another
		  interest is to know whether we can find the order of
		  spectral colors. In our approach, all spectra are clustered
		  by 32 nodes in a 1-D SOM, and each pixel (spectrum) is
		  presented by pseudocolors according to the trained nodes.
		  Thus, each node corresponds to one pseudocolor and every
		  spectrum is mapped into one of these nodes. Finally, the
		  results are compared to experiments with human vision.},
  dbinsdate	= {2002/1}
}

@Article{	  kukolj96a,
  author	= {D. Kukolj and D. Popovic and F. Kulic and M. Borota},
  title		= {Power system stability assessment with combined trained
		  artificial neural networks},
  journal	= {Elektroprivreda},
  year		= {1996},
  volume	= {49},
  number	= {3},
  pages		= {7--13},
  dbinsdate	= {oldtimer}
}

@Article{	  kulkarni95a,
  author	= {Kulkarni, U. R. and Kiang, M. Y. },
  title		= {Dynamic grouping of parts in flexible manufacturing
		  systems---A \mbox{self-organizing} neural networks
		  approach},
  journal	= {European Journal of Operational Research},
  year		= {1995},
  volume	= {84},
  number	= {1},
  pages		= {192--212},
  month		= {July},
  abstract	= {Artificial Intelligence (AI) has recently been recognized
		  as a worthwhile tool for supporting manufacturing
		  operations. This paper reviews AI-related approaches to
		  Group Technology (GT) and presents the Self-Organizing Map
		  (SOM) network, a special type of neural networks, as an
		  intelligent tool for grouping parts and machines. SOM can
		  learn from complex, multi-dimensional data and transform
		  them into visually decipherable clusters. What sets this
		  technique apart from others in GT is that SOM offers the
		  flexibility of choosing from multiple grouping
		  alternatives. SOM can be used in a dynamic situation where
		  quick response to changes in part designs, process plans,
		  or manufacturing conditions is essential, and thus it can
		  be more easily integrated into a Flexible Manufacturing
		  System. The paper proposes a framework of an intelligent
		  system that integrates the neural networks approach and a
		  knowledge-based system to provide decision supporting
		  functions.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kultanen90a,
  author	= {P. Kultanen and E. Oja and L. Xu},
  title		= {{R}andomized {H}ough {T}ransform {(RHT)} in engineering
		  drawing vectorization system},
  booktitle	= {Proc. IAPR Workshop on Machine Vision Applications},
  year		= {1990},
  pages		= {173--176},
  publisher	= {International Association for Pattern Recognition},
  address	= {New York, NY},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kultanen90b,
  author	= {P. Kultanen and L. Xu and E. Oja},
  title		= {Randomized {H}ough Transform {(RHT)}},
  booktitle	= {Proc. 10ICPR, International Conference on Pattern
		  Recognition},
  year		= {1990},
  pages		= {631---635},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kumar94a,
  author	= {Alok Kumar and Victor E. McGee},
  title		= {Forecasting and Decision-Making using Feature Vector
		  Analysis ({FEVA})},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {II},
  pages		= {278--283},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {data analysis},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kume99a,
  author	= {Kume, H. and Osana, Y. and Hagiwara, M.},
  title		= {Solving the binding problem with feature integration
		  theory},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {1},
  pages		= {200--5},
  abstract	= {We propose a neural network model of visual system based
		  on the feature integration theory. The proposed model has a
		  structure based on the hierarchical structure of visual
		  system and selectiveness of information by visual
		  attention. The proposed model consists of two stages: the
		  feature recognition stage and the feature integration
		  stage. In the feature recognition stage, there are two
		  modules: the form recognition module and the color
		  recognition module. In these modules, information of form
		  and color is separately processed in parallel. The form
		  recognition module is constructed using the neocognitron,
		  and the color recognition module is based on the LVQ neural
		  network. The feature integration stage is based on the
		  feature integration theory, which is a representative
		  theory for explaining all phenomena occurring in visual
		  system as a consistent process. We carried out computer
		  simulations and confirmed that the proposed model can
		  recognize plural objects and solve the binding problem.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kunstmann93a,
  author	= {Niels Kunstmann and Claus Hillermeier and Paul Tavan},
  title		= {Associative Memories that can form Hypotheses: Phase coded
		  Network Architectures},
  booktitle	= {Proc. ICANN'93. International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {504--507},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  kuo01a,
  author	= {Kuo, R. J. and Chi, S. C. and Teng, P. W.},
  title		= {Generalized part family formation through fuzzy
		  self-organizing feature map neural network},
  journal	= {Computers and Industrial Engineering},
  year		= {2001},
  volume	= {40},
  number	= {1--2},
  month		= {June 2001},
  pages		= {79--100},
  organization	= {Department of Industrial Engineering, Natl. Taipei Univ.
		  of Technology, 1 Section 3},
  publisher	= {},
  address	= {},
  abstract	= {Group technology (GT) addresses the problem of the part
		  family formation. Similar parts, based on a certain
		  similarity of characteristics, are grouped into a family. A
		  design engineer facing the task of developing a new part
		  can use a GT code or an image of the part to determine
		  whether similar parts exist in a computer aided design
		  (CAD) database. The manufacturing engineer can design the
		  cellular manufacturing system based on different families.
		  These can dramatically shorten both the design and the
		  manufacturing life cycle. However, owing to some
		  unavoidable factors, like brightness of light and shift of
		  the part, the crisp network cannot recognize the parts
		  correctly under the above-mentioned conditions. Thus, the
		  present study is dedicated to developing a novel fuzzy
		  neural network (FNN) for clustering the parts into several
		  families based on the image captured from the vision
		  sensor. The proposed network, which possesses the fuzzy
		  inputs as well the fuzzy weights, integrates the
		  self-organizing feature map (SOM) neural network and the
		  fuzzy set theory. The model evaluation results showed that
		  the proposed FNN can provide a more accurate decision
		  compared to the fuzzy c-means algorithm. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  kuo94a,
  author	= {Kuo, R. J. and Cohen, P. H. and Kumara, S. R. T. },
  title		= {Neural network driven fuzzy inference system},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {3},
  pages		= {1532--6},
  organization	= {Machining Res. Lab. , Pennsylvania State Univ. ,
		  University Park, PA, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kuo99a,
  author	= {Kuo, R. J. and Chi, S. C. and Den, B. W.},
  title		= {A fuzzy {K}ohonen's feature map neural network with
		  application to group technology},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3098--101},
  abstract	= {This paper proposes a novel fuzzy neural network for
		  clustering the parts into several families. The proposed
		  network, which has fuzzy inputs as well as fuzzy weights,
		  integrates the Kohonen's feature map neural network and the
		  fuzzy set theory. The model evaluation results show that
		  the proposed fuzzy neural network can provide more accurate
		  decision compared to the fuzzy c-means algorithm and
		  k-means algorithm.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kuramoti93a,
  author	= {Yasunori Kuramoti and Akio Takimoto and Hisahito Ogawa},
  title		= {Optical Neural Network having a Function of Relative
		  Feature Extraction without Inhibitory Connections},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2023--3026},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurata00a,
  author	= {Koji Kurata and Jun Shirakura and Koji Wada and Kaoru
		  Kida},
  title		= {Self-Organization Model of Information Separation},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {286--92},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@Article{	  kurdila96a,
  author	= {A. J. Kurdila and J. L. Petersen},
  title		= {Adaptation of centers of approximation for nonlinear
		  tracking control},
  journal	= {Journal of Guidance, Control, and Dynamics},
  year		= {1996},
  volume	= {19},
  number	= {2},
  pages		= {363--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurfess96a,
  author	= {Kurfess, F. J. and Welch, L. R. },
  title		= {Categorization of programs using neural networks},
  booktitle	= {Proceedings IEEE Symposium and Workshop on Engineering of
		  Computer-Based Systems},
  year		= {1996},
  pages		= {420--6},
  organization	= {Dept. of Comput. \& Inf. Sci. , New Jersey Inst. of
		  Technol. , Newark, NJ, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurimo00a,
  author	= {Kurimo, Mikko},
  title		= {Fast latent semantic indexing of spoken documents by using
		  self-organizing maps},
  booktitle	= {ICASSP, IEEE International Conference on Acoustics, Speech
		  and Signal Processing---Proceedings},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {2425--2428},
  organization	= {IDIAP},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper describes a new latent semantic indexing (LSI)
		  method for spoken audio documents. The framework is
		  indexing broadcast news from radio and TV as a combination
		  of large vocabulary continuous speech recognition (LVCSR),
		  natural language processing (NLP) and information retrieval
		  (IR). For indexing, the documents are presented as vectors
		  of word counts, whose dimensionality is rapidly reduced by
		  random mapping (RM). The obtained vectors are projected
		  into the latent semantic subspace determined by SVD, where
		  the vectors are then smoothed by a self-organizing map
		  (SOM). The smoothing by the closest document clusters is
		  important here, because the documents are often short and
		  have a high word error rate (WER). As the clusters in the
		  semantic subspace reflect the news topics, the SOMs provide
		  an easy way to visualize the index and query results and to
		  explore the database. Test results are reported for TREC's
		  spoken document retrieval databases.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kurimo00b,
  author	= {Kurimo, M.},
  title		= {Indexing spoken audio by {LSA} and {SOM}s},
  booktitle	= {Signal Processing X Theories and Applications. Proceedings
		  of EUSIPCO 2000. Tenth European Signal Processing
		  Conference. Tampere Univ. Technology, Tampere, Finland},
  year		= {2000},
  volume	= {4},
  pages		= {2177--80},
  abstract	= {This paper presents an indexing system for spoken audio
		  documents. The framework is indexing and retrieval of
		  broadcast news. The proposed indexing system applies latent
		  semantic analysis (LSA) and self-organizing maps (SOM) to
		  map the documents into a semantic vector space and to
		  display the semantic structures of the document collection.
		  The SOM is also used to enhance the indexing of the
		  documents that are difficult to decode. Relevant index
		  terms and suitable index weights are computed by smoothing
		  the document vectors with other documents which are close
		  to it in the semantic space. Experimental results are
		  provided using the test data of the TREC's spoken document
		  retrieval track.},
  dbinsdate	= {2002/1}
}

@MastersThesis{	  kurimo92a,
  author	= {Mikko Kurimo},
  title		= {Adaptiivisten
		  vektori\-kvan\-ti\-soin\-ti\-me\-ne\-tel\-mien ja
		  k{\"{a}}t\-ket\-ty\-jen {M}arkov~-mal\-lien kombinaatioita
		  puheentunnistuksessa ({C}ombinations of adaptive vector
		  quantization methods and hidden {M}arkov models in speech recognition)},
  note		= {(in Finnish)},
  school	= {Helsinki University of Technology, Espoo, Finland},
  year		= {1992},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurimo92b,
  author	= {Mikko Kurimo and Kari Torkkola},
  title		= {Application of {SOM}s and {LVQ} in training continuous
		  density hidden {M}arkov models},
  booktitle	= {Proc. International Conference on Spoken Language
		  Processing},
  year		= {1992},
  month		= {},
  monthf	= {Lokakuu},
  publisher	= {University of Alberta},
  address	= {Edmonton, Alberta, Canada},
  pages		= {543--546},
  volume	= {1},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurimo92c,
  author	= {Mikko Kurimo and Kari Torkkola},
  title		= {Training Continuous Density Hidden {M}arkov Models in
		  Association with Self-Organizing Maps and {LVQ}},
  booktitle	= {Proc. Workshop on Neural Networks for Signal Processing},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1992},
  month		= {},
  monthf	= {Elokuu},
  pages		= {174--183},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurimo92d,
  author	= {Mikko Kurimo and Kari Torkkola},
  title		= {Combining {LVQ} with continuous density hidden {M}arkov
		  models in speech recognition},
  booktitle	= {Proc. SPIE's Conf. on Neural and Stochastic Methods in
		  Image and Signal Processing},
  year		= {1992},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  pages		= {726--734},
  month		= {},
  monthf	= {Hein{\"{a}}kuu},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurimo93a,
  author	= {Mikko Kurimo},
  title		= {Using {LVQ} to Enhance Semi-Continuous Hidden {M}arkov
		  Models for Phonemes},
  booktitle	= {Proc. EUROSPEECH-93, 3rd European Conf. on Speech,
		  Communication and Technology},
  year		= {1993},
  volume	= {III},
  pages		= {1731--1734},
  organization	= {ESCA},
  address	= {Berlin},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurimo94a,
  author	= {Mikko Kurimo},
  title		= {Corrective Tuning by Applying {LVQ} for Continuous Density
		  and Semi-Continuous {M}arkov Models},
  booktitle	= {Proc. Int. Symp. on Speech, Image Processing and Neural
		  Networks},
  year		= {1994},
  volume	= {II},
  pages		= {718--721},
  organization	= {{IEEE} Hong Kong Chapter of Signal Processing},
  address	= {Hong Kong},
  annote	= {application, speech recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurimo94b,
  author	= {Mikko Kurimo},
  title		= {Hybrid training method for tied mixture density hidden
		  {{M}arkov} models using {Learning Vector Quantization} and
		  {Viterbi} estimation},
  booktitle	= {Proc. NNSP'94, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1994},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {362--371},
  annote	= {application, speech recognition},
  dbinsdate	= {oldtimer}
}

@Misc{		  kurimo94c,
  author	= {Mikko Kurimo},
  title		= {Application of {L}earning {V}ector {Q}uantization and
		  {S}elf-{O}rganizing {M}aps for training continuous density
		  and semi-continuous {M}arkov models},
  school	= {Helsinki University of Technology},
  year		= {1994},
  address	= {Espoo, Finland},
  note		= {Thesis for the degree of Licentiate of Technology,
		  Helsinki University of Technology, Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kurimo96a,
  author	= {M. Kurimo},
  title		= {Segmental {LVQ3} training for phoneme-wise tied mixture
		  density {HMM}s},
  booktitle	= {Signal Processing VIII, Theories and Applications.
		  Proceedings of EUSIPCO-96, Eighth European Signal
		  Processing Conference},
  publisher	= {Edizioni LINT Trieste},
  year		= {1996},
  volume	= {3},
  editor	= {G. Ramponi and G. L. Sicuranza and S. Carrato and S.
		  Marsi},
  address	= {Trieste, Italy},
  pages		= {1599--602},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurimo96b,
  author	= {M. Kurimo and P. Somervuo},
  title		= {Using the Self-Organizing Map to Speed up the Probability
		  Density Estimation for Speech Recognition with Mixture
		  Density {HMM}s},
  booktitle	= {Proc. of 4th International Conference on Spoken Language
		  Processing},
  year		= {1996},
  pages		= {358--361},
  dbinsdate	= {oldtimer}
}

@Article{	  kurimo97a,
  author	= {M. Kurimo},
  title		= {Using \mbox{self-organizing} maps and learning vector
		  quantization for mixture density hidden Markov models},
  journal	= {Acta Polytechnica Scandinavica, Mathematics Computing and
		  Management in Engineering Series},
  year		= {1997},
  volume	= {87},
  pages		= {1--55},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kurimo97b,
  author	= {Mikko Kurimo},
  title		= {{SOM} based density function approximation for mixture
		  density {HMM}s},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {8--13},
  dbinsdate	= {oldtimer}
}

@Article{	  kurimo97c,
  author	= {Kurimo, M.},
  title		= {Training Mixture Density HMMs with SOM and {LVQ} .},
  journal	= {Computer Speech \& Language},
  year		= {1997},
  volume	= {11},
  number	= {4},
  month		= {October},
  pages		= {321--343},
  abstract	= {The objective of this paper is to present experiments and
		  discussions of how some neural network algorithms can help
		  the phoneme recognition with mixture density hidden Markov
		  models (MDHMMs). In MDHMMs the modeling of the stochastic
		  observation processes associated with the states is based
		  on the estimation of the probability density function of
		  the short-time observation in each state as a mixture of
		  Gaussian densities. The Learning Vector Quantization (LVQ)
		  is used to increase the discrimination between different
		  phoneme models both during the initialization of the
		  Gaussian codebooks and during the actual MDHMM training.
		  The Self-Organizing Map (SOM) is applied to provide a
		  suitably smoothed mapping of the training vectors to
		  accelerate the convergence of the actual training. The
		  obtained codebook topology can also be exploited in the
		  recognition phase to speed up the calculations to
		  approximate the observation probabilities. The experiments
		  with LVQ and SOMs show reductions both in the average
		  phoneme recognition error rate and in the computational
		  load compared to the maximum likelihood training and the
		  Generalized Probabilistic Descent (GPD). The lowest final
		  error rate, however, is obtained by using several training
		  algorithms successively. Additional reductions from the
		  onlike system of about 40% in the error rate are obtained
		  by uing the same training methods, but with advanced and
		  higher dimensional feature vectors. (Copyright (c) 1997
		  Helsinki University of Technology.)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kurimo98a,
  author	= {M. Kurimo},
  title		= {Self-Organization in Mixture Densities of {HMM} Based
		  Speech Recognition},
  booktitle	= {Proceedings of the European Symposium on Artificial Neural
		  Networks (ESANN'98)},
  publisher	= {Springer-Verlag},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {Bruges, Belgium},
  pages		= {237--242},
  abstract	= {In this paper experiments are presented to apply
		  self-organizing map (SOM) and learning vector quantization
		  (LVQ) for training mixture density hidden {M}arkov models
		  (HMMs) in automatic speech recognition. The decoding of
		  spoken words into text is made using speaker dependent, but
		  vocabulary and context independent phoneme HMMs. Each HMM
		  has a set of states and the output density of each state is
		  a unique mixture of the Gaussian densities. The mixture
		  densities are trained by segmental versions of SOM and
		  LVQ3. SOM is applied to initialize and smooth the mixture
		  densities and LVQ3 to simply and robustly decrease
		  recognition errors.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kurimo99a,
  author	= {M. Kurimo},
  title		= {Indexing Audio Documents by using Latent Semantic Analysis
		  and SOM},
  booktitle	= {Kohonen Maps},
  pages		= {363--374},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: audio indexing, latent semantic analysis,
		  self-organising map, speech recognition, information
		  retrieval},
  dbinsdate	= {oldtimer}
}

@InCollection{	  kuroda97a,
  author	= {K. Kuroda and K. Harada and M. Hagiwara},
  title		= {Large scale on-line handwritten Chinese character
		  recognition using improved syntactic pattern recognition},
  booktitle	= {1997 IEEE International Conference on Systems, Man, and
		  Cybernetics. Computational Cybernetics and Simulation},
  publisher	= {Springer-Verlag},
  year		= {1997},
  volume	= {5},
  editor	= {M. Leman},
  address	= {Berlin, Germany},
  pages		= {4530--5},
  abstract	= {In this paper, we propose an original method for the
		  recognition of on-line handwritten Chinese characters using
		  an improved syntactic pattern recognition. Syntactic
		  pattern recognition is a method that converts a pattern
		  into a string of symbols using a finite set of features and
		  then analyzes them structurally using grammar. So it is
		  effective for such patterns as structurally constructed
		  Chinese characters. We use Kohonen's self-organizing
		  feature map for feature extraction, to get optimal sets of
		  prototypical waveforms of peaks from sample data
		  automatically. The strings of symbols are converted into
		  matrices which express features of the successors, and are
		  analyzed by simple calculations between matrices. Moreover
		  in order to symbolize and analyze efficiently and
		  accurately in a large scale, we employ hierarchical
		  approach for the proposed method. Using free writing
		  characters, we obtained 99.49% recognition rate for
		  training patterns and 94.34% for test patterns.},
  dbinsdate	= {oldtimer}
}

@Article{	  kuroda99a,
  author	= {Kuroda, Kazuhiro and Harada, Ken and Hagiwara, Masafumi},
  title		= {Large scale on-line handwritten Chinese character
		  recognition using successor method based on stochastic
		  regular grammar},
  journal	= {Pattern Recognition},
  year		= {1999},
  number	= {8},
  volume	= {32},
  pages		= {1307--1315},
  abstract	= {In this paper, we propose an original method for the
		  recognition of on-line handwritten Chinese characters using
		  the successor method based on the stochastic regular
		  grammar. We use Kohonen's self-organizing feature map for
		  feature extraction to get optimal sets of prototypical
		  waveforms of peaks from sample data automatically. The
		  strings of symbols are converted into matrices using the
		  stochastic successor method, and are analyzed by simple
		  calculation between matrices. In order to symbolize and
		  analyze input patterns efficiently and accurately in a
		  large scale, we employ a hierarchical approach. Using
		  unrestricted handwritten characters, we obtained 94.34%
		  recognition rate for the test patterns.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kurogi96a,
  author	= {Kurogi, S. and Hachimaru, M. and Shougang, R.},
  title		= {Two-layered {LVQ} net evaluating memorized vectors for
		  tracking control},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {175--8},
  abstract	= {Most conventional artificial neural nets memorize the
		  linear combination of input and weight vectors without
		  evaluating the value of vectors, which contributes to the
		  generalization of smooth functions but sometimes does not
		  work well for discrete functions. Here, we present a
		  modified LVQ learning algorithm which evaluates weight
		  vectors and memorizes input vectors properly. We also show
		  a two-layered LVQ net which interpolates certain continuous
		  functions and reduces the necessary memory capacity. The
		  net is applied to tracking control of a plant, and the
		  results show better performance than the conventional BP
		  nets.},
  dbinsdate	= {oldtimer}
}

@Article{	  kuroiwa00a,
  author	= {Kuroiwa, J. and Inawashiro, S. and Miyake, S. and Aso,
		  H.},
  title		= {Self-organization of orientation maps in a formal neuron
		  model using a cluster learning rule},
  journal	= {Neural Networks},
  year		= {2000},
  number	= {1},
  volume	= {13},
  pages		= {31--40},
  abstract	= {Self-organization of orientation maps due to external
		  stimuli in the primary visual area of the cerebral cortex
		  is studied in a two-layered neural network which consists
		  of formal neuron models with a sigmoidal output function. A
		  cluster learning rule is proposed as an extended Hebbian
		  learning rule, where a modification of synaptic connections
		  is influenced by an activation of neighboring output
		  neurons. By making use of self-consistent Monte Carlo
		  method, we evaluate output responses of neurons against
		  explicit inputs after the learning. An orientation map
		  calculated from the output responses reproduces
		  characteristic features of biological ones. Moreover
		  quantitative analysis of our results are consistent with
		  those of experimental results. It is shown that the cluster
		  learning rule plays an important role in forming smooth
		  changes of preferred orientations.},
  dbinsdate	= {oldtimer}
}

@Article{	  kurth00a,
  author	= {Kurth, C. and Gillam, F. and Steinhoff, B. J.},
  title		= {{EEG} spike detection with a Kohonen feature map},
  journal	= {ANNALS OF BIOMEDICAL ENGINEERING},
  year		= {2000},
  volume	= {28},
  number	= {11},
  month		= {NOV-DEC},
  pages		= {1362--1369},
  abstract	= {Artificial neural networks are widely used for pattern
		  recognition tasks. For spike detection in
		  electroencephalography (EEC;), feedforward networks trained
		  by the back-propagation algorithm are preferred by most
		  authors. Opposed to this. we examined the off-line spike
		  detection abilities of a Kohonen feature map (KFM), which
		  is different from feedforward networks in certain aspects.
		  The EEG data for the training set were obtained from
		  patients with intractable partial epilepsies of
		  mesiotemporal (n=2) or extratemporal (n=2) origin. For each
		  patient the training set for the KFM included the same
		  patterns of background activity and artifacts as well as
		  the typical individual spike patterns. Three
		  different-sized networks were examined ( 15X15 cells, 25X25
		  cells, and 60X60 cells in the Kohonen layer). To
		  investigate the quality of spike detection the results
		  obtained with the KFM were compared with the findings of
		  two board-certified electroencephalographers. Application
		  of a threshold based on the partial invariance of spike
		  recognition against translation of the EEG provided an
		  average sensitivity and selectivity of 80.2% at crossover
		  threshold (71%-86%) depending on the networksize and noise.
		  Multichannel EEG processing in real time will be available
		  soon. In conclusion, pattern-based automated spike
		  detection with a KFM is a promising approach in clinical
		  epileptology and stems to be at least as accurate as other
		  more-established methods of spike detection. },
  dbinsdate	= {2002/1}
}

@Article{	  kurth01a,
  author	= {Kurth, C. and Wegerer, V. and Degner, D. and Sperling, W.
		  and Kornhuber, J and Paulus, W and Bleich, S},
  title		= {Risk assessment of alcohol withdrawal seizures with a
		  Kohonen feature map},
  journal	= {NEUROREPORT},
  year		= {2001},
  volume	= {12},
  number	= {6},
  month		= {MAY 8},
  pages		= {1235--1238},
  abstract	= {Recently, it has been suggested that alcohol-induced
		  hyperhomocysteinaemia in patients suffering from chronic
		  alcoholism might be a risk factor for alcohol withdrawal
		  seizures. In the present follow-up study 12 patients with
		  chronic alcoholism who suffered from withdrawal seizures
		  had significantly higher revers of homocysteine (Hcy) on
		  admission (71.43 +/- 25.84 mol/l) than patients (n=37) who
		  did not develop seizures (32.60+/-24.87 mol/l; U=37.50,
		  p=0.0003). Using a logistic regression analysis, withdrawal
		  seizures were best predicted by a high Hcy level on
		  admission (p<0.01; odds ratio 2.07). Based on these
		  findings we developed an artificial neural network system
		  (Kohonen feature map, KFM) for an improved prediction of
		  the risk of alcohol withdrawal seizures. Forty-nine
		  patients with chronic alcoholism (12 with alcohol
		  withdrawal seizures and 37 without seizures) were
		  randomized into a training set and a test set. Best results
		  for sensitivity of the KFM was 83.3% (five of six seizure
		  patients were predicted correctly) with a specificity of
		  94.4% (one false positive prediction of 19 patients). We
		  conclude that in patients with alcohol-induced
		  hyperhomocysteinaemia the KFM is a useful tool to predict
		  alcohol withdrawal seizures. NeuroReport 12:1235--1238 },
  dbinsdate	= {2002/1}
}

@InProceedings{	  kurz92a,
  author	= {Andreas Kurz},
  title		= {Building Maps for Path-Planning and Navigation Using
		  Learning Classification of External Sensor Data},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {587--590},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kusumoputro00a,
  author	= {Kusumoputro, Benyamin and Budiarto, Hary and Jatmiko,
		  Wisnu},
  title		= {Fuzzy learning vector quantization neural network and its
		  application for artificial odor recognition system},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {4055},
  pages		= {374--381},
  organization	= {Univ of Indonesia},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {},
  abstract	= {In this paper, a kind of fuzzy algorithm for learning
		  vector quantization is developed and used as pattern
		  classifiers with a supervised learning paradigm in
		  artificial odor discrimination system. In this type of
		  FLVQ, the neuron activation is derived through fuzziness of
		  the input data, so that the neural system could deal with
		  the statistical of the measurement error directly. During
		  learning, the similarity between the training vector and
		  the reference vectors are calculated, and the winning
		  reference vector is updated in two ways. Firstly, by
		  shifting the central position of the fuzzy reference vector
		  toward or away from the input vector, and secondly, by
		  modifying its fuzziness. Two types of fuzziness
		  modifications are used, i.e., a constant modification
		  factor and a variable modification factor. This type of
		  FLVQ is different in nature with FALVQ, and in this paper,
		  the performance of FNLVQ network is compared with that of
		  FALVQ in artificial odor recognition system. Experimental
		  results show that both FALVQ and FNLVQ provided high
		  recognition probability in determining various
		  learn-category of odors, however, the FNLVQ neural system
		  has the ability to recognize the unlearn-category of odor
		  that could not recognized by FALVQ neural system.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kusumoputro00b,
  author	= {Kusumoputro, Benyamin and Fanany, Ivan and Indrawati,
		  Dian},
  title		= {Bispectrum analysis for speaker identification in noisy
		  environment with Karhunen-Loeve transformation technique},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {4044},
  pages		= {143--149},
  organization	= {Univ of Indonesia},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {The work described in this paper addresses the problem for
		  extracting bispectrum feature of speech data. Very often
		  the bispectrum feature extraction and data reduction are
		  complicated due to some limiting constraints, i.e., no
		  prior knowledge of feature's distribution and higher
		  dimensionality of bispectrum data. In this article we
		  developed an adaptive feature extraction mechanism based on
		  cascade neural network in conjunction with feature's
		  dimensionality reduction based on Karhunen-Loeve
		  transformation technique. An adaptive codebook generation
		  algorithm which is a cascade configuration of SOFM (Self
		  Organizing Feature Map) and LVQ (Learning Vector
		  Quantization) was used before the K-L transformation. The
		  transformation was experimentally shown as an effective
		  procedure for orthogonalization and dimensionality
		  reduction of bispectrum feature. Performance of our speaker
		  identification system was perceived to be significantly
		  increased eventhough using limited number of channels in
		  noisy environment. We also tried to improve the capability
		  of adaptive codebook generation algorithm by applying
		  simplified differential competitive learning (SDCL)
		  network.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kusumoputro01a,
  author	= {Kusumoputro, B. and Triyanto, A. and Fanany, M. I. and
		  Jatmiko, W.},
  title		= {Speaker identification in noisy environment using
		  bispectrum analysis and probabilistic neural network},
  booktitle	= {Proceedings Fourth International Conference on
		  Computational Intelligence and Multimedia Applications.
		  ICCIMA 2001. IEEE, Los Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {282--7},
  abstract	= {The paper describes the application of a neural processing
		  for extracting bispectrum feature of speech data, and the
		  use of probabilistic neural network as a classifier in an
		  automatic speech recognition system. The usually used
		  feature extraction paradigm in the early development of the
		  speech recognition system is power spectrum analysis,
		  however, the recognition rate of this system is not high
		  enough, especially when a Gaussian noise is added to the
		  utterance speech data. In this paper, we developed a
		  speaker identification system using bispectrum feature
		  analysis. To analyse the distribution of the bispectrum
		  data along its two dimensional representation, we developed
		  an adaptive feature extraction mechanism of the bispectrum
		  speech data based on cascade neural network. A cascade
		  configuration of SOFM (Self-Organizing Feature Map) and LVQ
		  (Learning Vector Quantization) is used as an adaptive
		  codebook generation algorithm for determining the feature
		  distribution of the bispectrum speech data. The K-L
		  transformation (K-LT) technique is then used as a
		  preprocessing element before the neural classifier is
		  utilized. This K-LT has shown as an effective procedure for
		  orthogonalization and dimensionality reduction of the
		  codebook vectors generated from bispectrum data.
		  Experimental results show that our system could perform
		  with high recognition rate on the undirected utterance
		  speech, especially when a higher number of codebook vectors
		  are utilized. It is also shown that the use of PNN could
		  increase the recognition rate significantly, even using
		  speech data with additional Gaussian noise.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kusumoputro01b,
  author	= {Kusumoputro, B. and Saptawijaya, A. and Murni, A.},
  title		= {Comparison of hybrid neural systems of {KSOM}-{BP}
		  learning in artificial odor recognition system},
  booktitle	= {Proceedings Fourth International Conference on
		  Computational Intelligence and Multimedia Applications.
		  ICCIMA 2001. IEEE, Los Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {276--81},
  abstract	= {This report proposes an adaptive recognition system, which
		  is based on Kohonen self-organization network (KSOM). As
		  the goals in the research on artificial neural network are
		  to improve the recognition capability of the network and at
		  the same time minimize the time needed for learning the
		  patterns, these goals could be achieved by combining two
		  types of learning, i.e. supervised learning and
		  unsupervised learning. We have developed a new kind of
		  hybrid neural learning system, combining unsupervised KSOM
		  and supervised back-propagation learning rules. This hybrid
		  neural system will henceforth be referred to as hybrid
		  adaptive SOM with winning probability function and
		  supervised BP or KSOM(WPF)-BP. This hybrid neural system
		  could estimate the cluster distribution of given data, and
		  directed it into predefined number of cluster neurons
		  through creation and deletion mechanism. Comparison with
		  other developed hybrid neural system is done for
		  determination of various odors from Martha Tilaar Cosmetics
		  product in an artificial odor recognition system. The
		  performance of our developed learning system in term of its
		  recognition ability and its learning time is explored in
		  this report.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kusumoputro99a,
  author	= {Kusumoputro, B.},
  title		= {Comparison of {FA} {LVQ} and modified backpropagation in
		  artificial odor discrimination system},
  booktitle	= {Proceedings of the Eighteenth IASTED International
		  Conference Modelling, Identification and Control},
  publisher	= {ACTA Press},
  address	= {Anaheim, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {434--7},
  abstract	= {An artificial odor recognition system is developed to
		  mimic the human sensory test in cosmetics, perfume and
		  beverage industries. The author had developed an artificial
		  odor discrimination system, which is composed of an arrayed
		  quartz-resonator sensor and a pattern recognition system.
		  To improve the system's capability, a hybrid neural system
		  with a supervised learning paradigm is developed and used
		  as a pattern classifier. In the paper, the performance of
		  the hybrid system is investigated, together with that of a
		  FALVQ (fuzzy algorithm learning vector quantization) neural
		  system.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kusumoputro99b,
  author	= {Kusumoputro, B. and Widyanto, M. R. and Fanany, M. I. and
		  Budiarto, H.},
  title		= {Improvement of artificial odor discrimination system using
		  fuzzy- {LVQ} neural network},
  booktitle	= {Proceedings Third International Conference on
		  Computational Intelligence and Multimedia Applications.
		  ICCIMA'99},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {474--8},
  abstract	= {An artificial odor recognition system is developed in
		  order to mimic the human sensory test in cosmetics, perfume
		  and beverage industries. A backpropagation neural network
		  is used as the pattern recognition system and shows high
		  recognition capability. However, the system only works
		  efficiently when it is used to discriminate a limited
		  number of odors. The unlearned odor will be classified as
		  one of the already learned category. To improve the
		  system's capability, a fuzzy learning vector quantization
		  neural network is developed and utilized in experiments on
		  four different ethanol concentrations, and three different
		  kinds of fragrance odor from Martha Tilaar Cosmetics. The
		  results shows that the FLVQ has a comparable ability for
		  recognizing the already known category of odors. However,
		  the FLVQ algorithm can cluster the unknown odor in a
		  different new class of odor.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kwan00a,
  author	= {Kwan, H. K. and Li, T. X.},
  title		= {{ARMA} lattice modeling for isolated word speech
		  recognition},
  booktitle	= {Proceedings of the 43rd IEEE Midwest Symposium on Circuits
		  and Systems. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {3},
  pages		= {1186--90},
  abstract	= {In this paper, we introduce an auto-regressive moving
		  average (ARMA) lattice model for speech modeling. The
		  speech characteristics are modeled and expressed in the
		  form of lattice reflection coefficients for classification.
		  Self Organization Map (SOM) is used to build codebooks for
		  classification and recognition of the lattice reflection
		  coefficients. Experimental results based on an isolated
		  word speech database of 10 words/names indicate that the
		  ARMA lattice model achieves superior recognition
		  performance as compared to those of the conventional
		  auto-regressive (AR) model.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kwan01a,
  author	= {Kwan, C. and Xu, R. and Haynes, L.},
  title		= {A new data clustering technique and its applications},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Dasarathy, B. V.},
  volume	= {4384},
  pages		= {1--5},
  organization	= {Intelligent Automation, Inc.},
  publisher	= {},
  address	= {},
  abstract	= {A new approach to data clustering is presented in this
		  paper. The approach consists of three steps. First,
		  preprocessing of raw sensor data was performed. Intelligent
		  Automation, Incorporated (IAI) used Fast Fourier Transform
		  (FFT) in the preprocessing stage to extract the significant
		  frequency components of the sensor signals. Second,
		  Principal Component Analysis (PCA) was used to further
		  reduce the dimension of the outputs of the preprocessing
		  stage. PCA is a powerful technique for extracting the
		  features inside the input signals. The dimensionality
		  reduction can reduce the size of the neural network
		  classifier in the next stage. Consequently the training and
		  recognition time will be significantly reduced. Finally,
		  neural network classifier using Learning Vector
		  Quantization (LVQ) is used for data classification. The
		  algorithm was successfully applied to two commercial
		  systems at Boeing: Auxiliary Power Units and solenoid valve
		  system.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  kwiatkowska95a,
  author	= {Ewa Kwiatkowska and Imad S. Torsun},
  title		= {Hybrid neural network system for cloud classification from
		  satellite images},
  volume	= {IV},
  pages		= {1907--1912},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kwok00a,
  author	= {Kwok, T. and Smith, K. A.},
  title		= {A self-organizing neural network with attractor nodes for
		  combinatorial optimization},
  booktitle	= {Smart Engineering System Design: Neural Networks, Fuzzy
		  Logic, Evolutionary Programming, Data Mining, and Complex
		  Systems. Vol.10. Proceedings of the Artificial Neural
		  Networks in Engineering Conference (ANNIE 2000). ASME, New
		  York, NY, USA},
  year		= {2000},
  volume	= {},
  pages		= {209--16},
  abstract	= {A self-organizing neural network (SONN) with attractor
		  nodes for solving combinatorial optimization problems
		  (COPs) is proposed in this paper as an attempt to alleviate
		  the problems associated with existing SONN-based methods
		  for COPs, such as problem-dependent ad-hoc adjustments of
		  parameters, oscillations during the convergence process,
		  and convergence to local minima. Output nodes with the
		  attractor properties of the logistic map are introduced as
		  the functional basis of the network. The model is
		  implemented to solve the N-queens problem as an example.
		  Improved optimization performance, in terms of feasibility,
		  robustness and efficiency, is measured and described in the
		  chosen parameter spaces.},
  dbinsdate	= {2002/1}
}

@Article{	  kyan01a,
  author	= {Kyan, M. J. and Guan, L. and Arnison, M. R. and Cogswell,
		  C. J.},
  title		= {Feature extraction of chromosomes from 3-D confocal
		  microscope images},
  journal	= {IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING},
  year		= {2001},
  volume	= {48},
  number	= {11},
  month		= {NOV},
  pages		= {1306--1318},
  abstract	= {An investigation of local energy surface detection
		  integrated with neural network techniques for image
		  segmentation is presented, as applied in the feature
		  extraction of chromosomes from image datasets obtained
		  using an experimental confocal microscope. Use of the
		  confocal microscope enables biologists to observe dividing
		  cells (living or preserved) within a three- dimensional
		  (3-D) volume, that can be visualised from multiple aspects,
		  allowing for increased structural insight. The Nomarski
		  differential interference contrast mode used for imaging
		  translucent specimens, such as chromosomes, produces images
		  not suitable for volume rendering. Segmentation of the
		  chromosomes from this data is,. thus, necessary. A neural
		  network based on competitive learning, known as Kohonen's
		  self- organizing feature map (SOFM) was used to perform
		  segmentation, using a collection of statistics or features
		  defining the image. Our past investigation showed that
		  standard features such as the localized mean and variance
		  of pixel intensities provided reasonable extraction of
		  objects such as mitotic chromosomes, but surface detail was
		  only moderately resolved. In this current work, a
		  biologically inspired feature known as local energy is
		  investigated as an alternative image statistic based on
		  phase congruency in the image. This, along with different
		  combinations of other image statistics, is applied in a
		  SOFM, producing 3-D images exhibiting vast improvement in
		  the level of detail and clearly isolating the chromosomes
		  from the background.},
  dbinsdate	= {2002/1}
}

@Article{	  kyan99a,
  author	= {Kyan, Matthew J. and Guan, Ling and Arnison, Matthew R.
		  and Cogswell, Carol J.},
  title		= {Feature extraction of chromosomes from {3D} confocal
		  microscope images},
  journal	= {IEEE International Conference on Image Processing},
  year		= {1999},
  number	= {},
  volume	= {2},
  pages		= {843--847},
  abstract	= {Use of a confocal light microscope enables biologists to
		  observe dividing cells (living or preserved) within a 3D
		  volume that can be visualized from multiple aspects. The
		  Nomarski differential interference contrast (DIC) mode used
		  for imaging translucent specimens, such as chromosomes,
		  produces images not suitable for volume rendering.
		  Segmentation of the chromosomes from this data is thus
		  necessary. Kohonen's self-organizing feature map (SOFM) was
		  used to perform segmentation, based on a collection of
		  various statistics or features defining the image. In the
		  past, classical features such as the mean and variance of
		  pixel intensities have been used, providing reasonable
		  extraction of chromosome bodies, while only mildly
		  resolving surface detail. In this investigation, a local
		  energy feature detector was implemented, producing an
		  alternative image statistic based on phase congruency in
		  the image. This, along with combinations of other image
		  statistics, was applied to the SOFM, producing a series of
		  resultant 3D images exhibiting vast improvements in the
		  level of detail defining the internal structure of the
		  specimen chromosomes.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  kyriakou99a,
  author	= {Kyriakou, K. and Michaelidas, S. C. and Pattichis, C. and
		  Christodoulou, C.},
  title		= {Cloud classification from satellite imagery with
		  artificial neural networks},
  booktitle	= {Engineering Applications of Neural Networks. Proceedings
		  of the 5th International Conference on Engineering
		  Applications of Neural Networks (EANN'99)},
  publisher	= {Wydawnictwo Adam Marszalek},
  address	= {Torun, Poland},
  year		= {1999},
  volume	= {},
  pages		= {253--8},
  abstract	= {The purpose of this pilot project is to develop a computer
		  aided system based on artificial neural networks and
		  texture analysis that will facilitate the automated
		  interpretation of cloud images. This would speed up the
		  interpretation process and provide continuity in the
		  application of satellite imagery in the process of weather
		  forecasting. A series of 98 (370 cloud cases) infrared
		  satellite images from the geostationary satellite METEOSAT7
		  covering the period from 6 to 31 January 1999 were
		  processed in this study. Seven different texture features
		  were extracted from the cloud images. Subsequently, these
		  six classes were further grouped into three major cloud
		  classes. For each feature set an SOFM classifier was
		  trained with 210 cases and evaluated with 160 cases. The
		  percentage of correct classifications for the evaluation
		  set for the best feature sets was in the region of 76% to
		  74%.},
  dbinsdate	= {oldtimer}
}

@Article{	  kyu01a,
  author	= {Kyu, S. M. and Murata, J. and Hirasawa, K.},
  title		= {Behavior learning of autonomous robots by modified
		  learning vector quantization},
  journal	= {Transactions-of-the-Society-of-Instrument-and-Control-Engineers}
		  ,
  year		= {2001},
  volume	= {37},
  pages		= {1162--8},
  abstract	= {This paper presents a method for searching for the optimal
		  paths for autonomously moving agents in mazes by modified
		  Learning Vector Quantization (LVQ) in a reinforcement
		  learning framework. LVQ algorithm is faster than Q-learning
		  algorithms because LVQ concentrates on the best behavior in
		  available behaviors while Q-learning algorithms calculate
		  values of all available behaviors and choose the best
		  behavior among them. However, ordinary LVQ sometimes
		  mis-learns in the reinforcement learning environment due to
		  erroneous teacher signals. In this paper a new LVQ
		  algorithm is proposed to overcome this problem, which finds
		  the optimal path more efficiently.},
  dbinsdate	= {2002/1}
}

@Article{	  kyung01a,
  author	= {Kyung Sup Kim and Ingoo Han},
  title		= {The cluster-indexing method for case-based reasoning using
		  self-organizing maps and learning vector quantization for
		  bond rating cases},
  journal	= {Expert-Systems-with-Applications},
  year		= {2001},
  volume	= {21},
  pages		= {147--56},
  abstract	= {This paper presents a hybrid data mining model for the
		  prediction of corporate bond rating. This model uses a new
		  case-indexing method of case-based reasoning (CBR), which
		  utilizes the cluster information of financial data in order
		  to improve classification accuracy. This method uses not
		  only case-specific knowledge of past problems like
		  conventional CBR, but also uses additional knowledge
		  derived from the clusters of cases. The cluster-indexing
		  method assumes that there are some distinct subgroups
		  (clusters) in each rated group. Competitive artificial
		  neural networks are used to generate the centroid values of
		  clusters because these techniques produce better adaptive
		  clusters than statistical clustering algorithms. The
		  experiments using corporate bond rating cases show that the
		  cluster-indexing CBR is superior to conventional CBR and
		  inductive learning indexing CBR, a rival case indexing
		  method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  laakso01a,
  author	= {S. Laakso and J. Laaksonen and M. Koskela and E. Oja},
  title		= {Self-organising maps of web link information},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {146--151},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  laaksonen00a,
  author	= {Laaksonen, Jorma and Koskela, Markus and Laakso, Sami and
		  Oja, Erkki},
  title		= {Pic{SOM}---content-based image retrieval with
		  self-organizing maps},
  journal	= {Pattern Recognition Letters},
  year		= {2000},
  volume	= {21},
  number	= {13--14},
  month		= {Dec},
  pages		= {1199--1207},
  organization	= {Helsinki Univ of Technology},
  publisher	= {Elsevier Science Publishers B.V.},
  address	= {Amsterdam},
  abstract	= {We have developed a novel system for content-based image
		  retrieval in large, unannotated databases. The system is
		  called PicSOM, and it is based on tree structured
		  self-organizing maps (TS-SOMs). Given a set of reference
		  images, PicSOM is able to retrieve another set of images
		  which are similar to the given ones. Each TS-SOM is formed
		  with a different image feature representation like color,
		  texture, or shape. A new technique introduced in PicSOM
		  facilitates automatic combination of responses from
		  multiple TS-SOMs and their hierarchical levels. This
		  mechanism adapts to the user's preferences in selecting
		  which images resemble each other. Thus, the mechanism
		  implements a relevance feedback technique on content-based
		  image retrieval. The image queries are performed through
		  the World Wide Web and the queries are iteratively refined
		  as the system exposes more images to the user.},
  dbinsdate	= {2002/1}
}

@Article{	  laaksonen01a,
  author	= {Laaksonen, J. and Koskela, M. and Laakso, S. and Oja, E.},
  title		= {Self-organising maps as a relevance feedback technique in
		  Content-Based Image Retrieval},
  journal	= {PATTERN ANALYSIS AND APPLICATIONS},
  year		= {2001},
  volume	= {4},
  number	= {2--3},
  pages		= {140--152},
  abstract	= {Self-Organising Maps (SOMs) can be used in implementing a
		  powerful relevance feedback mechanism for Content-Based
		  Image Retrieval (CBIR). This payer introduces the PicSOM
		  CBIR system, and describes the use of SOMs as a relevance
		  feedback technique in it. The technique is based on the
		  SOM's inherent property of topology-preserving mapping from
		  a high-dimensional feature space to a two-dimensional grid
		  of artificial neurons. On this grid similar images are
		  mapped in nearby locations. As image similarity must, in
		  unannotated databases, he based on low- level visual
		  features, the similarity of images is dependent on the
		  feature extraction scheme used. Therefore in PicSOM there
		  exists a separate tree-structured SOM for each different
		  feature type. The incorporation of the relevance feedback
		  and the combination of the outputs from the SOMs are
		  performed as two successive processing steps. The proposed
		  relevance feedback technique is described, analysed
		  qualitatively. and visualised in the paper. Also, its
		  performance is compared with a reference method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  laaksonen91a,
  author	= {Jorma T. Laaksonen},
  title		= {A New Reliability-Based Phoneme Segmentation Method for
		  the 'Neural' Phonetic Typewriter},
  volume	= {I},
  pages		= {97--100},
  booktitle	= {Proc. EUROSPEECH-91, 2nd European Conf. on Speech
		  Communication and Technology},
  organization	= {Assoc. Belge Acoust. ; Assoc. Italiana di Acustica; CEC;
		  et al},
  publisher	= {Istituto Int. Comunicazioni},
  address	= {Genova, Italy},
  year		= {1991},
  dbinsdate	= {oldtimer}
}

@InCollection{	  laaksonen96a,
  author	= {J. Laaksonen and E. Oja},
  title		= {Classification with learning k-nearest neighbors},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1480--3},
  abstract	= {The nearest neighbor (NN) classifiers, especially the k-NN
		  algorithm, are among the simplest and yet most efficient
		  classification rules and are widely used in practice. We
		  introduce three adaptation rules that can be used in
		  iterative training of a k-NN classifier. This is a novel
		  approach both from the statistical pattern recognition and
		  the supervised neural network learning points of view. The
		  suggested learning rules resemble those of the well-known
		  Learning Vector Quantization (LVQ) method, but at the same
		  time the classifier utilizes the fact that increasing the
		  number of samples that the classification is based on leads
		  to improved classification accuracy. The performances of
		  the suggested learning rules are compared with the usual
		  k-NN rules and the LVQ1 algorithm.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  laaksonen97a,
  author	= {Jorma Laaksonen},
  title		= {Local subspace classifier and local subspace {SOM}},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {32--37},
  dbinsdate	= {oldtimer}
}

@Article{	  laaksonen97b,
  author	= {Jorma Laaksonen},
  title		= {Subspace Classifiers in Recognition of Handwritten
		  Digits},
  journal	= {Acta Polytechnica Scandinavica, Mathematics, Computing and
		  Management in Engineering Series, No. 84},
  year		= 1997,
  note		= {Dr. Tech. Thesis, Helsinki University of Technology},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  laaksonen98a,
  author	= {Laaksonen, J. and Hurri, J. and Oja, E. and Kangas, J.},
  title		= {Comparison of adaptive strategies for online character
		  recognition},
  booktitle	= {ICANN 98. Proceedings of the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer-Verlag London},
  address	= {London, UK},
  year		= {1998},
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= {1},
  pages		= {245--50},
  abstract	= {Results on a comparison of adaptive recognition techniques
		  for online recognition of handwritten Latin alphabets are
		  presented. The classification strategies compared are based
		  on first compressing or distilling a large database of
		  handwritten characters to a small set of character
		  prototypes. Each adaptive classifier then either modifies
		  the original prototypes or conditionally adds new
		  prototypes when they become available from the user of the
		  system. In each case, the classification decision uses the
		  1-nearest neighbor (1-NN) rule for the distances between
		  the input character and the stored prototypes. The
		  distances are calculated using dynamic time warping (DTW).
		  One of the adaptive learning strategies features an
		  extension of the neural learning vector quantization (LVQ)
		  algorithm to the DTW distance metric. All the methods
		  concerned exhibit automatic unsupervised learning from user
		  input simultaneously with the normal mode of operation. The
		  presented experiments show that the assessed methods
		  produce different tradeoffs between the accuracy and
		  complexity of classification. Every version is, however,
		  able to adapt to the user's writing style with only a very
		  few handwritten characters.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  laaksonen99a,
  author	= {Laaksonen, J. and Koskela, M. and Oja, E.},
  title		= {{PicSOM}: \mbox{self-organizing} maps for content-based
		  image retrieval},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {2470--3},
  abstract	= {Content-based image retrieval is an important approach to
		  the problem of processing the increasing amount of visual
		  data. It is based on automatically extracted features from
		  the content of the images, such as color, texture, shape
		  and structure. We have started a project to study methods
		  for content-based image retrieval using the self-organizing
		  map (SOM) as the image similarity scoring method. Our image
		  retrieval system, named PicSOM, can be seen as a SOM-based
		  approach to relevance feedback which is a form of
		  supervised learning to adjust the subsequent queries based
		  on the user's responses during the information retrieval
		  session. In PicSOM, a separate tree structured SOM (TS-SOM)
		  is trained for each feature vector type in use. The system
		  then adapts to the user's preferences by returning her more
		  images from those SOMs where her responses have been most
		  densely mapped.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  laaksonen99c,
  author	= {Laaksonen, J. and Koskela, M. and Oja, E.},
  title		= {Application of tree structured \mbox{self-organizing} maps
		  in content-based image retrieval},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  year		= {1999},
  publisher	= {IEE},
  address	= {London, UK},
  volume	= {1},
  pages		= {174--9},
  abstract	= {We have developed an image retrieval system, named PicSOM,
		  which uses tree structured self-organizing maps as the
		  method for retrieving images similar to a given set of
		  reference images. A novel technique introduced in the
		  PicSOM system facilitates automatic combination of the
		  responses from multiple TS-SOMs and their hierarchical
		  levels. This mechanism aims at adapting to user's
		  preferences in selecting which images resemble to each
		  other in the particular sense interested by the user. The
		  image queries are performed through the World Wide Web and
		  the queries are iteratively refined as the system exposes
		  more images to the user.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  laaksonen99d,
  author	= {Laaksonen, J. and Koskela, M. and Oja, E.},
  title		= {{PicSOM}---A Framework for Content-Based Image Database
		  Retrieval using Self-Organizing Maps},
  booktitle	= {Proc. of 11th Scandinavian Conference on Image Analysis
		  (SCIA'99), Kangerlussuaq, Greenland, June 7--11},
  pages		= {151--156},
  year		= {1999},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  laaksonen99e,
  author	= {Laaksonen, J. and Koskela, M. and Oja, E.},
  title		= {Content-based image retrieval using \mbox{self-organizing}
		  maps},
  booktitle	= {Visual Information and Information Systems. Third
		  International Conference, VISUAL'99. Proceedings (Lecture
		  Notes in Computer Science Vol.1614)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {541--8},
  abstract	= {We have developed an image retrieval system named Pic-SOM
		  which uses tree structured self-organizing maps (TS-SOMs)
		  as the method for retrieving images similar to a given set
		  of reference images. A novel technique introduced in the
		  PicSOM system facilitates automatic combination of the
		  responses from multiple TS-SOMs and their hierarchical
		  levels. This mechanism aims at adapting to the user's
		  preferences in selecting which images resemble each other
		  in the particular sense the user is interested in. The
		  image queries are performed through the World Wide Web and
		  the queries are iteratively refined as the system exposes
		  more images to the user.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  laaksonen99f,
  author	= {Laaksonen, J. and Koskela, M. and Oja, E.},
  title		= {{PicSOM}: Self-Organizing Maps for Content-Based Image
		  Retrieval},
  booktitle	= {Proc. of International Joint Conference on Neural Networks
		  (IJCNN'99), Washington, D.C., USA, July 10--16},
  year		= {1999},
  month		= {July},
  note		= {CD-ROM},
  dbinsdate	= {oldtimer}
}

@Article{	  labonte00a,
  author	= {Labonte, G.},
  title		= {On a neural network that performs an enhanced
		  nearest-neighbour matching},
  journal	= {PATTERN ANALYSIS AND APPLICATIONS},
  year		= {2000},
  volume	= {3},
  number	= {3},
  pages		= {267--278},
  abstract	= {We review some of the main methods of solving the image
		  matching problem in Particle-Tracking Velocimetry (PTV).
		  This is a technique of Experimental Fluid Dynamics for
		  determining the velocity fields of moving fluids. This
		  problem is a two- dimensional random-points matching
		  problem chat constitutes a prototypal problem, analogous to
		  the one-dimensional matching problem for Julesz [1]
		  random-dot stereograms. Our study deals with a particular
		  method of solution, namely the neural network algorithm
		  proposed by Labonte [2,3]. Our interest in this neural
		  network comes from the fact that it has been shown to
		  outperform the best matching methods in PTV, and the belief
		  that it is actually a method applicable to many other
		  instances of the correspondence problem. We obtain many new
		  results concerning the nature of this algorithm, the main
		  one of which consists in showing how this neural network
		  functions as an enhancer for nearest-neighbour particle
		  image matching. We calculate its complexity, and produce
		  two different types of learning curves for it. We exhibit
		  the fact that thr RMS error of the neural network decreases
		  at lease exponentially with the number of cycles of thr
		  neural network. The neural network constructs a
		  Self-Organised Map (SOM), which corresponds to distorting
		  back the two photos until they merge into a single photo.
		  We explain how this distortion is driven, under the network
		  dynamics, by the few good nearest-neighbours (sometimes as
		  few as 20%) that exist initially. These are able to pull
		  with them the neighbouring images, toward their matching
		  partners. We report the results of measurements that
		  corroborate our analysis of this process.},
  dbinsdate	= {2002/1}
}

@InCollection{	  labonte98a,
  author	= {G. Labonte},
  title		= {A {SOM} neural network that reveals continuous
		  displacement fields},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {880--4},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ladage92a,
  author	= {Ladage, R. N. and Carbone, K. },
  title		= {Scatterer identification using neural networks},
  booktitle	= {Proceedings of the IEEE 1992 National Aerospace and
		  Electronics Conference, NAECON 1992},
  year		= {1992},
  volume	= {3},
  pages		= {900--4},
  organization	= {McDonnell Douglas Corp. , Richland, WA, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  lades93a,
  author	= {M. Lades and J. C. Vorbruggen and J. Buhmann and J. Lange
		  and C. {von} {der} Malsburg and R. P. Hurtz and W. Konen},
  title		= {Distortion invariant object recognition in the dynamic
		  link architectures},
  journal	= {IEEE Trans. on Computers},
  year		= {1993},
  volume	= {42},
  number	= {3},
  pages		= {300--311},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  lagus00a,
  author	= {Lagus, K.},
  title		= {Text mining with the {WEBSOM}},
  journal	= {Acta-Polytechnica-Scandinavica,-Mathematics-and-Computing-Series.
		  no.Ma110; 2000; p.1--54},
  year		= {2000},
  volume	= {},
  pages		= {1--54},
  abstract	= {Text mining applies methods from data mining and
		  exploratory data analysis to analyse text collections and
		  to convey information to the user in an intuitive manner.
		  In the WEBSOM method, the self-organizing map (SOM)
		  algorithm is used to automatically organize very large and
		  high-dimensional collections of text documents on to 2D map
		  displays. The map forms a document landscape where similar
		  documents appear close to each other at points of the
		  regular map grid. The landscape can be labelled with
		  automatically identified descriptive words that convey
		  properties of each area and act as landmarks during
		  exploration. With an HTML-based interactive tool, the
		  ordered landscape can be used in browsing the document
		  collection and in performing searches on the map. An
		  organized map offers an overview of an unknown document
		  collection, helping the user to familiarize himself with
		  the domain. Map displays that are already familiar can be
		  used as visual frames of reference for conveying properties
		  of unknown text items. Static, thematically arranged
		  document landscapes provide meaningful backgrounds for
		  dynamic visualizations of data properties. Search results
		  can be visualized in the context of related documents.
		  Experiments on document collections of various sizes, text
		  types and languages show that the WEBSOM method is scalable
		  and generally applicable. Preliminary results in a text
		  retrieval experiment indicate that, even when the
		  additional value provided by the visualization is
		  disregarded, the document maps perform at least comparably
		  with more conventional retrieval methods.},
  dbinsdate	= {2002/1}
}

@Article{	  lagus02a,
  author	= {Lagus, Krista},
  title		= {Text retrieval using self-organized document maps},
  journal	= {Neural Processing Letters},
  year		= {2002},
  volume	= {15},
  number	= {1},
  month		= {February },
  pages		= {21--29},
  organization	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  publisher	= {},
  address	= {},
  abstract	= {A map of text documents arranged using the Self-Organizing
		  Map (SOM) algorithm (1) is organized in a meaningful manner
		  so that items with similar content appear at nearby
		  locations of the 2-dimensional map display, and (2)
		  clusters the data, resulting in an approximate model of the
		  data distribution in the high-dimensional document space.
		  This article describes how a document map that is
		  automatically organized for browsing and visualization can
		  be success fully utilized also in speeding up document
		  retrieval. Furthermore, experiments on the well-known CISI
		  collection show significantly improved performance compared
		  to Salton's vector space model, measured by average
		  precision (AP) when retrieving a small, fixed number of
		  best documents. Regarding comparison with Latent Semantic
		  Indexing the results are inconclusive.},
  dbinsdate	= {2002/1}
}

@InCollection{	  lagus96b,
  author	= {Krista Lagus and Samuel Kaski and Timo Honkela and Teuvo
		  Kohonen},
  title		= {Browsing digital libraries with the aid of
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings of the Fifth International World Wide Web
		  Conference WWW5, May 6--10, Paris, France},
  publisher	= {EPGL},
  year		= 1996,
  volume	= {Poster Proceedings},
  pages		= {71--79},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lagus96c,
  author	= {Krista Lagus and Timo Honkela and Samuel Kaski and Teuvo
		  Kohonen},
  title		= {{WEBSOM}---A Status Report},
  booktitle	= {Proceedings of STeP'96, Finnish Artificial Intelligence
		  Conference},
  publisher	= {Finnish Artificial Intelligence Society},
  year		= 1996,
  editor	= {Jarmo Alander and Timo Honkela and Matti Jakobsson},
  address	= {Vaasa, Finland},
  pages		= {73--78},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lagus96d,
  author	= {Krista Lagus and Timo Honkela and Samuel Kaski and Teuvo
		  Kohonen},
  title		= {Self-organizing maps of document collections: {A} new
		  approach to interactive exploration},
  booktitle	= {Proceedings of the Second International Conference on
		  Knowledge Discovery and Data Mining},
  publisher	= {AAAI Press},
  year		= 1996,
  editor	= {Evangelios Simoudis and Jiawei Han and Usama Fayyad},
  address	= {Menlo Park, California},
  pages		= {238--243},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lagus97a,
  author	= {Krista Lagus},
  title		= {Map of WSOM'97 Abstracts---Alternative Index},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {368--372},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lagus98a,
  author	= {Lagus, K.},
  title		= {Generalizability of the {WEBSOM} method to document
		  collections of various types},
  booktitle	= {6th European Congress on Intelligent Techniques and Soft
		  Computing. EUFIT '98},
  publisher	= {Verlag Mainz},
  address	= {Aachen, Germany},
  year		= {1998},
  volume	= {1},
  pages		= {210--14},
  abstract	= {WEBSOM is a method in which the self-organizing map
		  algorithm is used to automatically organize collections of
		  documents on a map to enable easy exploration of the
		  collection. The article illustrates with case studies how
		  collections of various types of text can be successfully
		  organized using the WEBSOM. The emphasis is on describing
		  the particular challenges that each type of material poses,
		  as well as on identifying properties of a text collection
		  that affect the choices made at each progressing stage.
		  Properties such as the size of the document collection, the
		  size of the vocabulary, the domain, the style of writing,
		  and the language are considered.},
  dbinsdate	= {oldtimer}
}

@Article{	  lagus99a,
  author	= {Lagus, K. and Honkela, T. and Kaski, S. and Kohonen, T.},
  title		= {{WEBSOM} for textual data mining},
  journal	= {Artificial-Intelligence-Review},
  year		= {1999},
  volume	= {13},
  pages		= {345--64},
  abstract	= {New methods that are user-friendly and efficient are
		  needed for guidance among the masses of textual information
		  available in the Internet and the World Wide Web. We have
		  developed a method and a tool called the WEBSOM which
		  utilizes the self-organizing map algorithm (SOM) for
		  organizing large collections of text documents onto visual
		  document maps. The approach to processing text is
		  statistically oriented, computationally feasible, and
		  scalable; over a million text documents have been ordered
		  on a single map. The authors consider different kinds of
		  information needs and tasks regarding organizing,
		  visualizing, searching, categorizing and filtering textual
		  data. Furthermore, we discuss and illustrate with examples
		  how document maps can aid in these situations. An example
		  is presented where a document map is utilized as a tool for
		  visualizing and filtering a stream of incoming electronic
		  mail messages.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lagus99b,
  author	= {Lagus, K. and Kaski, S.},
  title		= {Keyword selection method for characterizing text document
		  maps},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  year		= {1999},
  publisher	= {IEE},
  address	= {London, UK},
  volume	= {1},
  pages		= {371--6},
  abstract	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  laha01a,
  author	= {Laha, A. and Pal, N. R.},
  title		= {Some novel classifiers designed using prototypes extracted
		  by a new scheme based on self-organizing feature map},
  journal	= {IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-
		  CYBERNETICS},
  year		= {2001},
  volume	= {31},
  number	= {6},
  month		= {DEC},
  pages		= {881--890},
  abstract	= {We propose two new comprehensive schemes for designing
		  prototype-based classifiers. The scheme addresses all major
		  issues (number of prototypes, generation of prototypes, and
		  utilization of the prototypes) involved in the design of a
		  prototype-based classifier. First we use Kohonen's self-
		  organizing feature map (SOFM) algorithm to produce a
		  minimum number (equal to the number of classes) of initial
		  prototypes. Then we use a dynamic prototype generation and
		  tuning algorithm (DYNAGEN) involving merging, splitting,
		  deleting, and retraining of the prototypes to generate an
		  adequate number of useful prototypes. These prototypes are
		  used to design a "1 nearest multiple prototype (1-NMP)"
		  classifier. Though the classifier performs quite well, it
		  cannot reasonably deal with large variation of variance
		  among the data from different classes. To overcome this
		  deficiency we design a "1 most similar prototype (1-MSP)"
		  classifier. We use the prototypes generated by the
		  SOFM-based DYNAGEN algorithm and associate with each of
		  them a zone of influence. A norm (Euclidean)- induced
		  similarity measure is used for this. The prototypes and
		  their zones of influence are fine-tuned by minimizing an
		  error function. Both classifiers are trained and tested
		  using several data sets, and a consistent improvement in
		  performance of the latter over the former has been
		  observed. We also compared our classifiers with some
		  benchmark results available in the literature.},
  dbinsdate	= {2002/1}
}

@Article{	  laha01b,
  author	= {Laha, A. and Pal, N. R.},
  title		= {Dynamic generation of prototypes with self-organizing
		  feature maps for classifier design},
  journal	= {PATTERN RECOGNITION},
  year		= {2001},
  volume	= {34},
  number	= {2},
  month		= {FEB},
  pages		= {315--321},
  abstract	= {We propose a new scheme for designing a nearest-prototype
		  classifier using Kohonen's self-organizing feature map
		  (SOFM). The net starts with the minimum number of
		  prototypes which is equal to the number of classes. Then on
		  the basis of the classification performance, new prototypes
		  are generated dynamically. The algorithm merges similar
		  prototypes and deletes less significant prototypes. If
		  prototypes are deleted or new prototypes appear then they
		  are fine tuned using Kohonen's SOFM algorithm with the
		  winner-only update strategy. This adaptation continues
		  until the system satisfies a termination condition. The
		  classifier has been tested with several well-known data
		  sets and the results obtained are quite satisfactory. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  lai93a,
  author	= {Yuan-Cheng Lai and Shiaw-Shian Yu and Sheng-Lin Chou},
  title		= {Hybrid Learning Vector Quantization},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2587--2590},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  laine01a,
  author	= {Laine, S. J.},
  title		= {Combining off-line and on-line information in process
		  study using the self-organizing map ({SOM})},
  booktitle	= {SMCia/01. Proceedings of the 2001 IEEE Mountain Workshop
		  on Soft Computing in Industrial Applications. IEEE,
		  Piscataway, NJ, USA},
  year		= {2001},
  volume	= {},
  pages		= {71--6},
  abstract	= {This paper presents how process control problems can be
		  studied and solved by combining offline and online
		  information. Offline information is accurate and versatile,
		  it is used to define the problem. Online information
		  describes the state of the process in real time. The online
		  variables containing information of the offline defined
		  problem are selected using a variable selection algorithm.
		  These variables are used to create an online observer of
		  the problem. This observer can be used to solve process
		  control problems. The main algorithms used in this paper
		  are the variable selection technique and the
		  self-organizing map (SOM). The methodology is illustrated
		  using the case of the concentrator of the Outokumpu Hitura
		  mine.},
  dbinsdate	= {2002/1}
}

@Article{	  laitinen02a,
  author	= {Laitinen, N. and Rantanen, J. and Laine, S. and
		  Antikainen, O. and Rasanen, E. and Airaksinen, S. and
		  Yliruusi, J.},
  title		= {Visualization of particle size and shape distributions
		  using self-organizing maps},
  journal	= {CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS},
  year		= {2002},
  volume	= {62},
  number	= {1},
  month		= {APR 28},
  pages		= {47--60},
  abstract	= {In pharmaceutical process technology, characterization of
		  the sizes and shapes of different particles is essential.
		  However, comparisons and analysis of different size and
		  shape characteristics of particles are very difficult. In
		  this investigation, we used the self-organizing map (SOM)
		  to visualize the size and shape distributions obtained with
		  image analysis (IA) of a series of model particles and
		  particles created by fluidized bed granulation. Thereafter,
		  the SOM visualization was compared to principal component
		  analysis (PCA) results of the same data. This study shows
		  that the self- organizing map is a usefal and interpretive
		  method for analysis of large data sets of particle size and
		  shape distributions. The results indicate that the
		  self-organizing map was capable of creating an intuitive
		  presentation of the differences in the studied particle
		  populations. The choice of data analysis tools should
		  always be made with great consideration.},
  dbinsdate	= {2002/1}
}

@Article{	  lakany00a,
  author	= {Lakany, H. M.},
  title		= {Generic kinematic pattern for human walking},
  journal	= {Neurocomputing},
  year		= {2000},
  volume	= {35},
  number	= {},
  month		= {Nov},
  pages		= {27--54},
  organization	= {Univ of Edinburgh},
  publisher	= {Elsevier Science B.V.},
  address	= {Amsterdam},
  abstract	= {The aim of this work is to investigate the existence of a
		  generic feature vector based on kinematic data for normal
		  walking. The paper describes a method to quantify generic
		  features of the sagittal angles of the lower extremities of
		  human subjects. The idea is to extract salient features
		  from hip, knee and ankle sagittal angles to characterize
		  normal and pathological walking. The algorithm is based on
		  transforming the trajectories of the flexion/extension of
		  joints of subjects using the continuous wavelet transform
		  to represent a feature vector which is then fed to a
		  self-organizing map for clustering. The algorithm proved to
		  be successful in distinguishing between normal subjects
		  according to their age group, gender and also
		  distinguishing between normal and pathological subjects.
		  Rules are extracted from self-organizing map to determine
		  the salient features characterizing each cluster as well as
		  differentiating it from others.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lakany01a,
  author	= {H. M. Lakany},
  title		= {Human gait analysis using {SOM}},
  booktitle	= {Advances in Self-Organising Maps},
  crossref	= {},
  key		= {},
  pages		= {29--38},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {Springer},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InCollection{	  lakany97a,
  author	= {H. M. Lakany and G. M. Hayes},
  title		= {Object localisation in 2d images using a temporal
		  {K}ohonen network},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {148--151},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lalonde94a,
  author	= {Marc Lalonde and Jean-Jules Brault},
  title		= {Comparison of Sequences Generated by a {S}elf-{O}rganizing
		  {F}eature {M}ap using {D}ynamic {P}rogramming},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {III},
  pages		= {110--116},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, sequence analysis, trajectory analysis},
  dbinsdate	= {oldtimer}
}

@Article{	  lamar00a,
  author	= {Lamar, Marcus Vinicius and Bhuiyan, Shoaib and Iwata,
		  Akira},
  title		= {Hand gesture recognition using T-Comb{NET}: a net neural
		  network model},
  journal	= {IEICE Transactions on Information and Systems},
  year		= {2000},
  volume	= {383-D},
  number	= {11},
  month		= {Nov},
  pages		= {1986--1995},
  organization	= {Nagoya Inst of Technology},
  publisher	= {IEICE of Japan},
  address	= {Tokyo},
  abstract	= {This paper presents a new neural network structure, called
		  Temporal-ComNET (T-CombNET), dedicated to the time series
		  analysis and classification. It has been developed from a
		  large scale Neural Network structure, CombNET-II, which is
		  designed to deal with a very large vocabulary, such as
		  Japanese character recognition. Our specific modifications
		  of the original ComNET-II model allow it to do temporal
		  analysis, and to be used in large set of human movements
		  recognition system. In T-CombNET structure one of most
		  important parameter to be set is the space division
		  criterion. In this paper we analyze some practical
		  approaches and present an Interclass Distance Measurement
		  based criterion. The T-CombNET performance is analyzed
		  applying to in a practical problem, Japanese Kana finger
		  spelling recognition. The obtained results show a superior
		  recognition rate when compared to different neural network
		  structures, such as Multi-Layer Perceptron, Learning Vector
		  Quantization, Elman and Jordan Partially Recurrent Neural
		  Networks, CombNET-II, k-NN, and the proposed T-CombNET
		  structure.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lamar99a,
  author	= {Lamar, M. V. and Bhuiyan, Md. S. and Iwata, A.},
  title		= {Hand gesture recognition using morphological principal
		  component analysis and an improved {CombNET-II}},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {57--62},
  abstract	= {A new neural network structure dedicated to time series
		  recognition, T-CombNET, is presented. The model is
		  developed from a large scale neural network CombNet-II,
		  designed to deal with a very large vocabulary for character
		  recognition. Our specific modifications of the original
		  CombNet-II model allows it to do temporal analysis, and to
		  be used in a large set of human movement recognition
		  systems. This paper also presents a feature extraction
		  method based on morphological principal component analysis
		  that completely describes a hand gesture in 2-dimensional
		  time varying vector. The proposed feature extraction method
		  along with the T-CombNET structure were then used to
		  develop a complete Japanese Kana hand alphabet recognition
		  system consisting of 42 static postures and 34 hand
		  motions. We obtained a superior recognition rate of 99.4%
		  in the gesture recognition experiments when compared to
		  different neural network structures like multi-layer
		  perceptron, learning vector quantization (LVQ), Elman and
		  Jordan partially recurrent neural networks, CombNET-II and
		  the proposed T-CombNET structure.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lamberton96a,
  author	= {Damine Lamberton and Gilles Pag{\`{e}}s},
  title		= {On the critical points of the 1-dimensional Competitive
		  Learning Vector Quantization Algorithm},
  booktitle	= {Proc. ESANN'96, European Symp. on Artificial Neural
		  Networks},
  year		= {1996},
  publisher	= {D facto conference services},
  address	= {Bruges, Belgium},
  editor	= {Michel Verleysen},
  pages		= {97--102},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lambrinos95a,
  author	= {Dimitrios Lambrinos and Christian Scheier and Rolf
		  Pfeifer},
  title		= {Unsupervised Classification of Sensory-Motor states in a
		  Real World Artifact using a Temporal {K}ohonen Map},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {467--472},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Article{	  lamedica96a,
  author	= {R. Lamedica and A. Prudenzi and M. Sforna and M. Caciotta
		  and V. O. Cencellli},
  title		= {A neural network based technique for short-term
		  forecasting of anomalous load periods},
  journal	= {IEEE Transactions on Power Systems},
  year		= {1996},
  volume	= {11},
  number	= {4},
  pages		= {1749--56},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lamirel01a,
  author	= {Lamirel, J. -C.},
  title		= {Using images for enhancing discovering task in a {DL}
		  context},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Yeung, M.M and Li, C. and Lienhart, R. W.},
  volume	= {4315},
  pages		= {373--383},
  organization	= {LORIA},
  publisher	= {},
  address	= {},
  abstract	= {A lot of experiments have shown that images, graphics and
		  iconographic resources, thanks to their explanatory power,
		  can be considered as a very fundamental component of a
		  man-machine interface. The goal of our approach is to make
		  use of this oustanding property of the images in order to
		  provide a Digital Library with Information Discovering
		  capabilities. This paper presents the new MicroNOMAD tool
		  for information discovery in multimedia databases. The core
		  model of the tool is based on a extension of the Kohonen
		  SOM model. Its main characteristic is both to provide an
		  user with emergent analyses of a database content and with
		  querying and browsing guidelines through the use of an
		  advanced topographic interface model.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lampinen00a,
  author	= {Lampinen, J. and Kostiainen, T.},
  title		= {Self-organizing map in data analysis. Notes on overfitting
		  and overinterpretation},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {239--44},
  abstract	= {The self-organizing map, SOM, is a widely used tool in
		  exploratory data analysis. Visual inspection of the SOM can
		  be used to list potential dependencies between variables,
		  that are then validated with more principled statistical
		  methods. We discuss the use of the SOM in searching for
		  dependencies in the data. We point out that simple use of
		  the SOM may lead to excessive number of false hypotheses.
		  We formulate the exact probability density model for which
		  the SOM training gives the maximum likelihood estimate and
		  show how the model parameters (neighborhood and kernel
		  width) can be chosen to avoid overfitting. The conditional
		  distributions from the true density model offer a
		  consistent way to quantify and test the dependencies
		  between variables.},
  dbinsdate	= {2002/1}
}

@InBook{	  lampinen02a,
  author	= {Jouko Lampinen and Timo Kostiainen},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Generative Probability Density Model in the
		  Self-Organizing Map},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {75--94},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lampinen89a,
  author	= {J. Lampinen and E. Oja},
  title		= {Fast self-organization by the {P}robing {A}lgorithm},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  pages		= {503--507},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lampinen89b,
  author	= {J. Lampinen and E. Oja},
  title		= {Self-organizing maps for spatial and temporal {AR}
		  models},
  booktitle	= {Proc. 6 SCIA, Scand. Conf. on Image Analysis},
  year		= {1989},
  editor	= {Matti Pietik{\"{a}}inen and Juha R{\"{o}}ning},
  pages		= {120--127},
  publisher	= {Suomen Hahmontunnistustutkimuksen seura r. y. },
  address	= {Helsinki, Finland},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lampinen90a,
  author	= {J. Lampinen and E. Oja},
  title		= {Fast computation of {K}ohonen self-organization},
  booktitle	= {Neurocomputing: Algorithms, Architectures, and
		  Applications, {NATO ASI} Series F: Computer and Systems
		  Sciences, vol. 68},
  publisher	= {Springer},
  year		= {1990},
  editor	= {F. Fogelman-Souli{\'{e}} and J. Herault},
  chapter	= {},
  pages		= {65---74},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lampinen90b,
  author	= {J. Lampinen and E. Oja},
  title		= {Distortion tolerant feature extraction with {G}abor
		  functions and topological coding},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  volume	= {I},
  pages		= {301--304},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  lampinen91a,
  author	= {J. Lampinen},
  title		= {Feature extractor giving distortion invariant hierarchical
		  feature space},
  journal	= {Proc. SPIE---The Internatioanl Society for Optical
		  Engineering},
  year		= {1991},
  volume	= {1469},
  number	= {pt. 1},
  pages		= {832--842},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {Conf. paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lampinen91b,
  author	= {J. Lampinen},
  title		= {Distortion tolerant pattern recognition using invariant
		  transformations and hierarchical {SOFM} clustering},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {99--104},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  lampinen92a,
  author	= {J. Lampinen and E. Oja},
  title		= {Clustering properties of hierarchical
		  \mbox{self-organizing} maps},
  journal	= {J. Mathematical Imaging and Vision},
  year		= {1992},
  volume	= {2},
  number	= {2--3},
  pages		= {261--272},
  month		= {November},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lampinen92b,
  author	= {Jouko Lampinen},
  title		= {On Clustering Properties of Hierarchical Self-Organizing
		  Maps},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1219--1222},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  lampinen92c,
  author	= {Jouko Lampinen},
  title		= {Neural Pattern Recognition: Distortion Tolerance by
		  Self-Organizing Maps},
  school	= {Lappenranta University of Technology},
  schoolf	= {Lappeenrannan Teknillinen korkeakoulu},
  year		= {1992},
  address	= {Lappeenranta, Finland},
  addressf	= {Lappeenranta},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lampinen94a,
  author	= {Jouko Lampinen and Ossi Taipale},
  title		= {Optimization and Simulation of Quality Properties in Paper
		  Machine with Neural Networks},
  pages		= {3812--3815},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, process modeling, hybrid},
  dbinsdate	= {oldtimer}
}

@Article{	  lampinen95a,
  author	= {Jouko Lampinen and Erkki Oja},
  title		= {Distortion Tolerant Pattern Recognition Based on
		  Self-Organizing Feature Extraction},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1995},
  volume	= {6},
  number	= {3},
  pages		= {539--547},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lampinen95b,
  author	= {Jouko Lampinen and Seppo Smolander},
  title		= {Fast associative mapping with look-up tables},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {315--320},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Article{	  lampinen96a,
  author	= {J. Lampinen and S. Smolander},
  title		= {Self-organizing feature extraction in recognition of wood
		  surface defects and color images},
  journal	= {International Journal of Pattern Recognition and
		  Artificial Intelligence},
  year		= 1996,
  volume	= 10,
  pages		= {97--113},
  dbinsdate	= {oldtimer}
}

@Book{		  lampinen97a,
  author	= {Lampinen, J. and Laaksonen, J. and Oja, E.},
  title		= {Neural Network Systems, Techniques and Applications in
		  Pattern Recognition. Research rept.},
  year		= {1997},
  abstract	= {The purpose of the present review study is to discuss the
		  ways in which neural networks can enter the pattern
		  recognition (PR) problem and how they might be useful
		  compared to other approaches. Comparisons are made both
		  from an analytical and practical point of view. In Section
		  2, we introduce the PR problem and show the general
		  solution as a sequence of consequent, mutually optimized
		  stages. The two stages in which neural networks seem to be
		  the most useful are feature extraction and classification
		  covered in Sections 3 and 4. Then in Section 5,
		  applications are explained, and Section 6 presents some
		  conclusions. An extensive publication list is given at the
		  end of this report.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lampinen99a,
  author	= {Lampinen, J. and Kostiainen, T.},
  title		= {Overtraining and model selection with the
		  \mbox{self-organizing} map},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1911--15},
  abstract	= {We discuss the importance of finding the correct model
		  complexity and regularization level, in the self-organizing
		  map (SOM) algorithm. The complexity of the SOM is
		  determined mainly by the width of the final neighborhood,
		  which is usually chosen ad hoc or set to zero for optimal
		  quantization error. However, if the SOM is used for
		  visualizing the joint probability distribution of the data,
		  then care must be taken not to overfit the model to the
		  data sample, similarly as with any statistical model. We
		  propose a heuristic criterion for model selection in SOM,
		  and demonstrate by simulations that the criterion can be
		  used for selecting the neighborhood that suppresses
		  overfitting.},
  dbinsdate	= {oldtimer}
}

@Article{	  lan94a,
  author	= {Tao Lan and Jiang Jiguang and Xiao Dachuan},
  title		= {Artificial neural networks for power system transient
		  security assessment},
  journal	= {Journal of Tsinghua University},
  year		= {1994},
  volume	= {34},
  number	= {4},
  pages		= {62--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lancini91a,
  author	= {R. Lancini and F. Perego and S. Tubaro},
  title		= {Some experiments on vector quantization using neural
		  nets},
  booktitle	= {Proc. GLOBECOM'91, Global Telecommunications Conf.
		  Countdown to the New Millennium. Featuring a Mini-Theme on:
		  Personal Communications Services (PCS). },
  year		= {1991},
  volume	= {I},
  pages		= {135--139},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lancini94a,
  author	= {Rosa Lancini},
  booktitle	= {Neural Networks in Telecommunications},
  title		= {Image Vector Quantization by Neural Networks},
  publisher	= {Kluwer Academic Publishers},
  year		= {1994},
  editor	= {Ben Yuhas and Nirwan Ansari},
  pages		= {287--303},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  lancini95a,
  author	= {Lancini, R. and Tubaro, S. },
  title		= {Adaptive vector quantization for picture coding using
		  neural networks},
  journal	= {IEEE Transactions on Communications},
  year		= {1995},
  volume	= {43},
  number	= {2--4,},
  pages		= {pt. 1},
  month		= {Feb-April},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lane97a,
  author	= {D. Lane and P. Nolan},
  title		= {Application of pattern matching techniques to example
		  based diagnosis},
  booktitle	= {Applications of Artificial Intelligence in Engineering
		  XII. [Full papers on CD ROM]},
  publisher	= {Comput. Mech. Publications},
  year		= {1997},
  editor	= {R. A. Adey and G. Rzevski and R. Teti},
  address	= {Southampton, UK},
  pages		= {113--14},
  dbinsdate	= {oldtimer}
}

@Article{	  lang98a,
  author	= {M. J. Lang},
  title		= {Application of a {K}ohonen Network Classifier in {TeV}
		  Gamma Ray Astronomy},
  journal	= {Journal of Physics G: Nuclear and Particle Physics},
  volume	= {24},
  pages		= {2279--2287},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lange96a,
  author	= {J. S. Lange and H. Freiesleben},
  title		= {A parameter-free non-growing \mbox{self-organizing} map
		  based upon gravitational principles: algorithm and
		  applications},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {827--32},
  dbinsdate	= {oldtimer}
}

@Article{	  lange97a,
  author	= {J. S. Lange and P. Hermanoski and H. Freiesleben},
  title		= {A parameter free \mbox{self-organizing} map for the
		  analysis of pp-reactions at {COSY}},
  journal	= {Nuclear Instruments and Methods in Physics Research A},
  year		= 1997,
  volume	= 389,
  pages		= {214--218},
  dbinsdate	= {oldtimer}
}

@Article{	  lange97b,
  author	= {J. S. Lange and P. Schonmeier and H. Freiesleben},
  title		= {Parallelization of analyses using \mbox{self-organizing}
		  maps with {PVM}},
  journal	= {Nuclear Instruments and Methods in Physics Research A},
  year		= 1997,
  volume	= 389,
  pages		= {274--76},
  dbinsdate	= {oldtimer}
}

@Article{	  lange99a,
  author	= {Lange, J.~S. and Fukunaga, C. and Tanaka, M. and Bozek,
		  A.},
  title		= {Transputer Self-Organizing Map Algorithm for Beam
		  Background Rejection at the Belle Silicon Vertex Detector},
  journal	= {NUCLEAR INSTRUMENTS \& METHODS IN PHYSICS RESEARCH SECTION
		  A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED
		  EQUIPMENT},
  year		= {1999},
  volume	= {420},
  number	= {1--2},
  pages		= {288--309},
  abstract	= {A growing self-organizing map using a gravitational
		  algorithm was implemented on a transputer to study possible
		  separation of GEANT simulated beam background events and
		  physics events (e+e-->B°B over-bar °, qq over-bar , ggg,
		  2&gamma;,&tau;+&tau;-). A fraction of 75.0% of beam
		  background events can be rejected, 96.9% of physics events
		  pass the classification. The decision time is &tau; less
		  than or equal 2 ms, thus the system could be used online as
		  level 3 trigger as well as for offline data filtering
		  purposes.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  langi94a,
  author	= {Langi, A. and Ferens, K. and Kinsner, W. and Kect, T. and
		  Sawatzky, G. },
  title		= {Intelligent storm identification system using a
		  hierarchical neural network},
  booktitle	= {1994 Canadian Conference on Electrical and Computer
		  Engineering. Conference Proceedings},
  year		= {1994},
  editor	= {Baird, C. R. and El-Hawary, M. E. },
  volume	= {2},
  pages		= {501--4},
  organization	= {Dept. of Electr. \& Comput. Eng. , Manitoba Univ. ,
		  Winnipeg, Man. , Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  langinmaa90a,
  author	= {Anu Langinmaa and Ari Visa},
  title		= {Yhten{\"{a}}inen menetelm{\"{a}} paperin laadunmittaukseen},
  journal	= {Tekniikan n{\"{a}}k{\"o}alat TEKES, Helsinki, Finland},
  year		= {1990},
  volume	= {5},
  pages		= {10--11},
  note		= {(in Finnish)},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lapidot01a,
  author	= {I. Lapidot (Voitovetsky) and H. Guterman},
  title		= {{VQ}-based clustering algorithm of
		  piecewise-dependant-data},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {95--101},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  larkin94a,
  author	= {A. B. Larkin and E. L. Hines and S. M. Thomas},
  title		= {The {E}uclidean Memory Array---A Vector Quantization
		  Technique for the Processing of Data from Interview Forms},
  journal	= {Neural Computing \& Applications},
  year		= {1994},
  volume	= {2},
  number	= {1},
  pages		= {53--57},
  annote	= {application, modification, data analysis},
  dbinsdate	= {oldtimer}
}

@Article{	  lau98a,
  author	= {K. T. Lau and S. T. Lee},
  title		= {A {CMOS} Winner Takes All Circuit for Self Organizing
		  Neural Networks},
  journal	= {International Journal of Electronics},
  volume	= {84},
  pages		= {131--136},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  lau99a,
  author	= {Lau, K. T. and McAlernon, P. and Slater, J. M.},
  title		= {Discrimination of chemically similar organic vapours and
		  vapour mixtures using the Kohonen network},
  journal	= {ANALYST},
  year		= {1999},
  volume	= {125},
  number	= {1},
  pages		= {65--70},
  abstract	= {A Kohonen network was employed to discriminate between a
		  series of chemically similar alcohols and mixtures of
		  organic solvents. The input data for the Kohonen analysis
		  was generated using an optimized eight-sensor array
		  designed to sample the headspace of the solvents. Different
		  sizes of output grid were investigated to devise a network
		  that gave optimum discrimination and maintained
		  relationships within the data set. When the output grid was
		  large compared to the number of classes in the sample set,
		  discrimination was shown to be enhanced compared to a small
		  output grid. An advantage of the small output grid is that
		  it was shown to maintain information within the original
		  data set. The Kohonen network generated easily
		  distinguishable output patterns, which could be used as an
		  alternative to pattern recognition or in conjunction with
		  output grid maps.},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  lavigna89a,
  author	= {Anthony LaVigna},
  title		= {Nonparametric Classification using Learning Vector
		  Quantization},
  school	= {University of Maryland},
  address	= {College Park, MD},
  year		= {1989},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lawrence96a,
  author	= {S. Lawrence and C. L. Giles and Ah Chung Tsoi},
  title		= {Convolutional neural networks for face recognition},
  booktitle	= {Proceedings 1996 IEEE Computer Society Conference on
		  Computer Vision and Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  address	= {Los Alamitos, CA, USA},
  pages		= {217--22},
  abstract	= {Faces represent complex, multidimensional, meaningful
		  visual stimuli and developing a computational model for
		  face recognition is difficult. We present a hybrid neural
		  network solution which compares favorably with other
		  methods. The system combines local image sampling, a
		  self-organizing map neural network, and a convolutional
		  neural network. The self-organizing map provides a
		  quantization of the image samples into a topological space
		  where inputs that are nearby in the original space are also
		  nearby in the output space, thereby providing
		  dimensionality reduction and invariance to minor changes in
		  the image sample, and the convolutional neural network
		  provides for partial invariance to translation, rotation,
		  scale, and deformation. The method is capable of rapid
		  classification, requires only fast, approximate
		  normalization and preprocessing, and consistently exhibits
		  better classification performance than the eigenfaces
		  approach on the database considered as the number of images
		  per person in the training database is varied from 1 to 5.
		  With 5 images per person the proposed method and eigenfaces
		  result in 3.8% and 10.5% error respectively. The recognizer
		  provides a measure of confidence in its output and
		  classification error approaches zero when rejecting as few
		  as 10% of the examples. We use a database of 400 images of
		  40 individuals which contains quite a high degree of
		  variability in expression, pose, and facial details.},
  dbinsdate	= {oldtimer}
}

@Article{	  lawrence97a,
  author	= {S. Lawrence and C. L. Giles and Ah Chung Tsoi and A. D.
		  Back},
  title		= {Face recognition: a convolutional neural-network
		  approach},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  volume	= {8},
  number	= {1},
  pages		= {98--113},
  dbinsdate	= {oldtimer}
}

@Article{	  lawrence99a,
  author	= {Lawrence, R. D. and Almasi, G. S. and Rushmeier, H. E.},
  title		= {A scalable parallel algorithm for \mbox{self-organizing}
		  maps with applications to sparse data mining problems},
  journal	= {Data Mining and Knowledge Discovery},
  year		= {1999},
  volume	= {3},
  pages		= {171--95},
  abstract	= {We describe a scalable parallel implementation of the self
		  organizing map (SOM) suitable for data-mining applications
		  involving clustering or segmentation against large data
		  sets such as those encountered in the analysis of customer
		  spending patterns. The parallel algorithm is based on the
		  batch SOM formulation in which the neural weights are
		  updated at the end of each pass over the training data. The
		  underlying serial algorithm is enhanced to take advantage
		  of the sparseness often encountered in these data sets.
		  Analysis of a realistic test problem shows that the batch
		  SOM algorithm captures key features observed using the
		  conventional on-line algorithm, with comparable convergence
		  rates. Performance measurements on an SP2 parallel computer
		  are given for two retail data sets and a publicly available
		  set of census data. These results demonstrate essentially
		  linear speedup for the parallel batch SOM algorithm, using
		  both a memory-contained sparse formulation as well as a
		  separate implementation in which the mining data is
		  accessed directly from a parallel file system. We also
		  present visualizations of the census data to illustrate the
		  value of the clustering information obtained via the
		  parallel SOM method.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lazaro92a,
  author	= {A. S. Lazaro and L. Alonso and V. Cardenoso},
  title		= {A double neural network for word recognition},
  booktitle	= {Proc. Tenth IASTED International Conference Applied
		  Informatics},
  year		= {1992},
  editor	= {M. H. Hamza},
  pages		= {5--8},
  organization	= {IASTED},
  publisher	= {Acta Press},
  address	= {Zurich, Switzerland},
  dbinsdate	= {oldtimer}
}

@Article{	  lazaro94a,
  author	= {Lazaro, S. and Alonso, L. and Alonso, C. and {de la
		  Fuente}, P. and Llamas, C. },
  title		= {Isolated word recognition with a hybrid neural network},
  journal	= {International Journal of Mini and Microcomputers},
  year		= {1994},
  volume	= {16},
  number	= {3},
  pages		= {134--40},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  le94a,
  author	= {Le, D. X. and Thoma, G. R. and Wechsler, H. },
  title		= {Document classification using connectionist models},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {5},
  pages		= {3009--14},
  organization	= {Lister Hill Center for Biomed. Commun. , Nat. Libr. of
		  Med. , Bethesda, MD, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  le95a,
  author	= {Le, D. X. and Thoma, G. R. and Wechsler, H. },
  title		= {Classification of binary document images into textual or
		  nontextual data blocks using neural network models},
  journal	= {Machine Vision and Applications},
  year		= {1995},
  volume	= {8},
  number	= {5},
  pages		= {289--304},
  dbinsdate	= {oldtimer}
}

@Article{	  le_bail89a,
  author	= {E. {Le Bail} and A. Mitiche},
  title		= {Vector quantization of images using {K}ohonen neural
		  network},
  journal	= {Traitement du Signal},
  year		= {1989},
  volume	= {6},
  number	= {6},
  pages		= {529--539},
  note		= {(in French)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  le_beux96a,
  author	= {S. {Le Beux} and G. Cazuguel and B. Solaiman and C. Roux},
  title		= {Automatic feature determination using unsupervised neural
		  networks. Application to image registration},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1406--9},
  abstract	= {In this paper, a new solution is proposed to automatically
		  determine characteristic features in images, a task that is
		  often needed in image analysis. Two unsupervised neural
		  networks are used: the Kohonen's Self-Organizing Map and a
		  Fukushima's Neocognitron like model. Both networks are used
		  to cluster subsets extracted from the image in an
		  unsupervised learning procedure. This procedure uses no a
		  priori characteristic feature definition. Then a simple
		  strategy is used to define the characteristic subsets.
		  Experimental results are given and an application to image
		  registration is presented.},
  dbinsdate	= {oldtimer}
}

@Article{	  le_blanc01a,
  author	= {Le Blanc, L. A. and Hashemi, R. R. and Rucks, C. T.},
  title		= {Pattern development for vessel accidents: A comparison of
		  statistical and neural computing techniques},
  journal	= {Expert Systems with Applications},
  year		= {2001},
  volume	= {20},
  number	= {2},
  month		= {Feb},
  pages		= {163--171},
  organization	= {Berry Coll},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {This paper describes a sample of over 900 vessel accidents
		  that occurred on the lower Mississippi River. Two different
		  techniques, one statistical and the other based on a neural
		  network model, were used to build logical groups of
		  accidents. The objective in building the groups was to
		  maximize between-group variation and minimize within-group
		  variation. The result was groups whose records were as
		  homogenous as possible. A clustering algorithm (i.e., a
		  non-inferential statistical technique) generated sets of
		  three, four and five groups. A Kohonen neural network model
		  (i.e., a self-organizing map) also generated sets of three,
		  four and five groups. The two sets of parallel groups were
		  radically different as to the relative number of records in
		  each group. In other words, when the two sets of groups
		  were constructed by the respective techniques, the
		  membership of each comparable group within the two
		  different sets was substantially different. Not only was
		  the respective record count in each group substantially
		  different, so were the descriptive statistics describing
		  each comparable set of groups. These results have
		  significant implications for marine policy makers.
		  Important policy variables include safety factors such as
		  weather, speed of current, time of operation, and location
		  of accidents, but mandatory utilization of a voluntary
		  vessel tracking service may be subject to debate.},
  dbinsdate	= {2002/1}
}

@PhDThesis{	  leber93a,
  author	= {Jean-Fran{\c{c}}ois Leber},
  title		= {The Recognition of Acoustical Signals Using Neural
		  Networks and an Open Simulator},
  school	= {Eidgen{\"{o}}ss. Techn. Hochsch. },
  year		= {1993},
  address	= {Z{\"{u}}rich, Switzerland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lebert93a,
  author	= {Ed Lebert and R. Hans Phaf},
  title		= {Improving Categorization with Calm Maps},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {59--62},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lech91a,
  author	= {M. Lech and Y. Hua},
  title		= {Vector quantization of images using neural networks and
		  simulated annealing},
  booktitle	= {Neural Networks for Signal Processing. Proc. of the 1991
		  IEEE Workshop},
  year		= {1991},
  editor	= {B. H. Juang and S. Y. Kung and C. A. Kamm},
  pages		= {552--561},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lech92a,
  author	= {M. Lech and Y. Hua},
  title		= {Image vector quantization using neural networks and
		  simulated annealing},
  booktitle	= {International Conference on Image Processing and its
		  Applications},
  year		= {1992},
  organization	= {IEE},
  publisher	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  leder01a,
  author	= {Leder, C. and Rehtanz, C.},
  title		= {Electric power system's stability assessment and
		  online-provision of control actions using self-organizing
		  maps},
  booktitle	= {Bio-Inspired Applications of Connectionism. 6th
		  International Work-Conference on Artificial and Natural
		  Neural Networks, IWANN 2001. Proceedings, Part II. (Lecture
		  Notes in Computer Science Vol.2085). Springer-Verlag,
		  Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {704--10},
  abstract	= {Power utilities are interested in operating their grid
		  closer to technical limits. Moreover competition leads to
		  system states which the operators in control centers are
		  not familiar with. In order to operate the higher stressed
		  power system secure, even in critical situations, an
		  efficient security assessment must provide high-quality
		  state information instead of thousands of single values.
		  Furthermore, the energy management system (EMS) must give
		  proposals for control actions. The self-organizing map
		  (SOM) supports both tasks efficiently. The paper presents a
		  SOM-based solution for fast security assessment and the
		  provision of control actions. The application to a real
		  power system also shows the capability of the tool for
		  expressive visualization.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lee00a,
  author	= {Lee, H. S. and Younan, N. H.},
  title		= {Investigation into unsupervised clustering techniques},
  booktitle	= {Conference Proceedings---IEEE SOUTHEASTCON},
  year		= {2000},
  editor	= {},
  volume	= {},
  pages		= {124--130},
  organization	= {Mississippi State Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {In this paper, the performance of several unsupervised
		  clustering techniques is compared using two clearly
		  separated 3-D data sets that are not separable by any
		  hyperplane. The result shows that the self-organizing
		  feature map can cluster data sets successfully without any
		  prior information of given data while the k-means and the
		  fuzzy k-means algorithm fail to cluster correctly.},
  dbinsdate	= {2002/1}
}

@Article{	  lee00b,
  author	= {Kwang Ho Lee and Young Moon Park and Gwang Won Kim and
		  June Ho Park},
  title		= {An application of Kohonen neural networks to dynamic
		  security assessment},
  journal	= {Transactions-of-the-Korean-Institute-of-Electrical-Engineers,-A},
  year		= {2000},
  volume	= {49},
  pages		= {253--8},
  abstract	= {This paper presents an application of Kohonen neural
		  networks to assess the dynamic security of power systems.
		  The dynamic security assessment (DSA) is an important
		  factor in power system operation, but conventional
		  techniques have not achieved the desired speed and
		  accuracy. The critical clearing time (CCT) is an attribute
		  which provides significant information about the quality of
		  the post-fault system behaviour. The function of Kohonen
		  networks is a mapping of the pre-fault system conditions
		  into the neurons based on the CCTs. The power flow on each
		  line is used as the input data, and an activated output
		  neuron has information of the CCT of each contingency. The
		  trajectory of the activated neurons during load changes can
		  be used in on-line DSA efficiently, The applicability of
		  the proposed method is demonstrated using a 9-bus
		  example.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name pased on similarity to other references}
}

@Article{	  lee00c,
  author	= {Kwan Yong Lee and Shin Young Lim and Seong Won Cho},
  title		= {Human iris recognition system using wavelet transform and
		  {LVQ}},
  journal	= {Transactions-of-the-Korean-Institute-of-Electrical-Engineers,-D}
		  ,
  year		= {2000},
  volume	= {49},
  pages		= {389--98},
  abstract	= {The popular methods to check the identity of individuals
		  include passwords and ID cards. These conventional methods
		  for user identification and authentication are not
		  altogether reliable because they can be stolen and
		  forgotten. As an alternative of the existing methods,
		  biometric technology has been paid much attention for the
		  last few decades. In this paper, we propose an efficient
		  system for recognizing the identity of a living person by
		  analyzing iris patterns, which have a higher level of
		  stability and distinctiveness than other biometric
		  measurements. The proposed system is based on wavelet
		  transform and a competitive neural network with the
		  improved mechanisms. After preprocessing the iris data
		  acquired through a CCD camera, feature vectors are
		  extracted by using Haar wavelet transform. LVQ (learning
		  vector quantization) is exploited to classify these feature
		  vectors. We improve the overall performance of the proposed
		  system by optimizing the size of feature vectors and by
		  introducing an efficient initialization of the weight
		  vectors and a new method for determining the winner in
		  order to increase the recognition accuracy of LVQ. From the
		  experiments, we confirmed that the proposed system has a
		  great potential of being applied to real applications in an
		  efficient and effective way.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name assumed from internet. Form KwanYong Lee would 
                   suggest that Lee is last name}
}

@InProceedings{	  lee01a,
  author	= {J. A. Lee and N. Donckers and M. Verleysen},
  title		= {Recursive learning rules for {SOM}s},
  booktitle	= {Advances in Self-Organising Maps},
  crossref	= {},
  key		= {},
  pages		= {67--72},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {Springer},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  lee01b,
  author	= {Lee, M. L. and Schneider, G.},
  title		= {Scaffold architecture and pharmacophoric properties of
		  natural products and trade drugs: Application in the design
		  of natural product-based combinatorial libraries},
  journal	= {JOURNAL OF COMBINATORIAL CHEMISTRY},
  year		= {2001},
  volume	= {3},
  number	= {3},
  month		= {MAY-JUN},
  pages		= {284--289},
  abstract	= {Natural products were analyzed to determine whether they
		  contain appealing novel scaffold architectures for
		  potential use in combinatorial chemistry. Ring systems were
		  extracted and clustered on the basis of structural
		  similarity. Several such potential scaffolds for
		  combinatorial chemistry were identified that are not
		  present in current trade drugs. For one of these scaffolds
		  a virtual combinatorial library was generated.
		  Pharmacophoric properties of natural products, trade drugs,
		  and the virtual combinatorial library were assessed using a
		  self- organizing map. Obviously, current trade drugs and
		  natural products have several topological pharmacophore
		  patterns in common. These features can be systematically
		  explored with selected combinatorial libraries based on a
		  combination of natural product-derived and synthetic
		  molecular building blocks.},
  dbinsdate	= {2002/1}
}

@Article{	  lee01c,
  author	= {Lee, J. H. and Yu, S. J. and Park, S. C.},
  title		= {Design of intelligent data sampling methodology based on
		  data mining},
  journal	= {IEEE Transactions on Robotics and Automation},
  year		= {2001},
  volume	= {17},
  number	= {5},
  month		= {October },
  pages		= {637--649},
  organization	= {Department of Industrial Engineering, KAIST},
  publisher	= {},
  address	= {},
  abstract	= {Data mining is the automated discovery of nontrivial,
		  previously unknown, and potentially useful knowledge
		  embedded in a database. With the increase of automated data
		  generation and gathering in semiconductor manufacturing,
		  mining interesting information from huge databases becomes
		  of utmost concern. In this paper, we present a new and
		  better application of data mining techniques by designing
		  an intelligent in-line measurement sampling method for
		  process parameter monitoring in a wafer fab. The sampling
		  method specifies the chip locations within the wafer to be
		  measured, and the number of measured chip locations per
		  wafer in order to represent a good sensitivity of 100%
		  wafer coverage and defect detection. To more effectively
		  detect all the abnormalities of process parameters, we
		  extract the spatial defect features in the historical wafer
		  bin map data and then cluster the chip locations having
		  similar defect features through SOM neural network. To more
		  efficiently design the sampling method, we merge the
		  homogeneous clusters through a statistical homogeneity test
		  and then select chip location having the best detection
		  power of each of the existing bins through interactive
		  explorative data analysis of SOM weight vectors. We
		  illustrate the effectiveness of the proposed sampling
		  method using actual fab data, and the results indicate that
		  if the sampled chip locations are chosen rationally by
		  optimal data mining techniques, that sampling can provide
		  accurate detection of all defects.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lee01d,
  author	= {Lee, Chung-Hong and Yang, Hsin-Chang},
  title		= {Text mining of bilingual parallel corpora with a measure
		  of semantic similarity},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {470--475},
  organization	= {Dept. of Information Management, Chang Jung University},
  publisher	= {},
  address	= {},
  abstract	= {This paper describes a new application of a text-mining
		  algorithm to the text sources of bilingual parallel
		  corpora. The ultimate task, being undertaken in the context
		  of a Chinese-English machine translation project, will be
		  to develop a language-neutral method to discovery similar
		  documents from multilingual text collections. Using a
		  variation of automatic clustering techniques, which apply a
		  neural net approach namely the Self-Organizing Maps (SOM),
		  we have conducted several experiments to uncover associated
		  documents based on Chinese-English bilingual parallel
		  corpora, and a hybrid Chinese-English corpus. The
		  experiments show some interesting results and a couple of
		  potential ways for future work towards the field of
		  multilingual information discovery. Besides, for exploring
		  the impacts on linguistics issues with the machine learning
		  approach to mining sensible linguistics elements from
		  multilingual texts, we have examined the resulting term
		  associations and text associations from the view of
		  cross-lingual text similarity. To evaluate semantic
		  relatedness of the mined bilingual texts, we applied a
		  measure technique of semantic similarity in the resulting
		  bilingual document clusters and word clusters. This paper
		  presents algorithms that enable multilingual text mining
		  based on the self-organizing map (SOM) for automatically
		  grouping similar multilingual texts (i.e. Chinese and
		  English texts), along with a means in measuring their
		  semantic similarity to resolve the difficulties of
		  syntactic and semantic ambiguity in multilingual
		  information access.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lee01e,
  author	= {Chung-Hong Lee and Hsin-Chang Yang},
  title		= {Developing an adaptive search engine for e-commerce using
		  a Web mining approach},
  booktitle	= {Proceedings International Conference on Information
		  Technology: Coding and Computing. IEEE Comput. Soc, Los
		  Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {604--8},
  abstract	= {Discusses current work using an adaptive learning
		  algorithm to dynamically create the content of an
		  e-commerce search engine so that the implicit knowledge
		  extracted by the Web text mining module can be provided in
		  the B2B (business-to-business) portal. In this paper, we
		  develop an algorithmic approach for automatically
		  discovering implicit customer knowledge from the Internet
		  by means of a Web mining method. Using a variation of the
		  automatic thesaurus generation techniques, namely the
		  self-organizing map (SOM) neural net, we have conducted
		  several experiments in a specific domain in which we
		  created a functional thesaurus of numerous supplier- and
		  product-specific terms. Further, we applied such a
		  thesaurus in a topic hierarchy-based text database, as an
		  organized text source of a search engine for a novel B2B
		  e-commerce portal.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name chekced from internet}
}

@Article{	  lee01f,
  author	= {Lee, Jang Hee and You, Sung Jin and Park, Sang Chan},
  title		= {New intelligent {SOFM}-based sampling plan for advanced
		  process control},
  journal	= {Expert Systems with Applications},
  year		= {2001},
  volume	= {20},
  number	= {2},
  month		= {Feb},
  pages		= {133--151},
  organization	= {Korea Advanced Inst of Science and Technology},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {Sample measurement inspecting for a process parameter is a
		  necessity in semiconductor manufacturing because of the
		  prohibitive amount of time involved in 100% inspection
		  while maintaining sensitivity to all types of defects and
		  abnormality. In current industrial practice, sample
		  measurement locations are chosen approximately evenly
		  across the wafer, in order to have all regions of the wafer
		  equally well represented, but they are not adequate if
		  process-related defective chips are distributed with
		  spatial pattern within the wafer. In this paper, we propose
		  the methodology for generating effective measurement
		  sampling plan for process parameter by applying the
		  Self-Organizing Feature Map (SOFM) network, unsupervised
		  learning neural network, to wafer bin map data within a
		  certain time period. The sampling plan specifies which
		  chips within the wafer need to be inspected, and how many
		  chips within the wafer need to be inspected for a good
		  sensitivity of 100% wafer coverage and defect detection. We
		  finally illustrate the effectiveness of our proposed
		  sampling plan using actual semiconductor fab data.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lee89a,
  author	= {T. Lee and A. M. Peterson},
  title		= {Implementing a self-development neural network using
		  doubly linked lists},
  booktitle	= {Proc. 13th Annual Int. Computer Software and Applications
		  Conf. },
  year		= {1989},
  pages		= {672--679},
  organization	= {IEEE},
  publisher	= {IEEE Computer Society Press},
  address	= {Washington, DC},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lee90a,
  author	= {T. -C. Lee and I. D. Scherson},
  title		= {{K}ohonen's \mbox{self-organizing} feature map in a
		  partitioned parallel associative processor},
  booktitle	= {Proc. Fourth Annual Parallel Processing Symp. },
  year		= {1990},
  volume	= {I},
  pages		= {365--374},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  organization	= {IEEE; California State Univ. Fullerton},
  dbinsdate	= {oldtimer}
}

@Article{	  lee92a,
  author	= {Il-Byung Lee and Kwan-Yong Lee},
  title		= {Neural network character recognition research},
  journal	= {Korea Information Science Soc. Review},
  year		= {1992},
  volume	= {10},
  number	= {2},
  pages		= {27--38},
  note		= {(in Korean)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lee93a,
  author	= {Shi-Chen Lee and Jiann-Ming Wu and Cheng-Yuan Liou},
  title		= {Sequential Self-Organization for the Traveling Salesman
		  Problem},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {842--845},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lee93b,
  author	= {Sukhan Lee and Schunichi Shimoji},
  title		= {{BAYESNET}: {B}ayesian Classification Network Based on
		  Biased Random Competition Using {G}aussian Kernels},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1354--1359},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lee93c,
  author	= {Lee, V. C. S. and Hung, S. L. },
  title		= {Automatic cloud identification based on
		  \mbox{self-organizing} map},
  booktitle	= {Proceedings of the 1993 Summer Computer Simulation
		  Conference. Twenty-Fifth Annual Summer Computer Simulation
		  Conference},
  year		= {1993},
  editor	= {Schoen, J. },
  pages		= {301--6},
  organization	= {Dept. of Comput. Sci. , City Polytech. of Hong Kong,
		  Kowloon, Hong Kong},
  publisher	= {SCS},
  address	= {San Diego, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  lee93d,
  author	= {Choong Hwan Lee and Dong Su Seong and Kyn Ho Park},
  title		= {Face recognition using \mbox{self-organizing} map},
  journal	= {Journal of the Korea Information Science Society},
  year		= {1993},
  volume	= {20},
  number	= {11},
  pages		= {1730--8},
  month		= {Nov},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lee94a,
  author	= {YoungJun Lee and Vladimir Cherkassky and James R. Slagle},
  title		= {Adaptive Fuzzy-Rule-Based Classifier},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {699--704},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {comparison, fuzzy},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lee94b,
  author	= {Kun Chang Lee and Jinsung Kim},
  title		= {Hybrid neural network-driven reasoning approach to
		  bankruptcy prediction: comparison with {MDA}, {ACLS}, and
		  neural network},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {3},
  pages		= {1787--92},
  organization	= {Center for Artificial Intelligence Res. , Kyonggi Univ. ,
		  Suwon, South Korea},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  lee95a,
  author	= {Keeseong Lee},
  title		= {{3-D} object recognition and restoration using an
		  ultrasound sensor array},
  journal	= {Transactions of the Korean Institute of Electrical
		  Engineers},
  year		= {1995},
  volume	= {44},
  number	= {5},
  pages		= {671--7},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@Article{	  lee95b,
  author	= {Dong-Hahk Lee and Young Hwan Kim},
  title		= {Image {VQ} using two-stage \mbox{self-organizing} feature
		  map in the transform domain},
  journal	= {Journal of the Korean Institute of Telematics and
		  Electronics},
  year		= {1995},
  volume	= {32B},
  number	= {3},
  pages		= {57--65},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  lee96a,
  author	= {Geunbae Lee and Sangeok Kim and Jong-Hyeok Lee},
  title		= {Implementation of voice commandable multimodal Korean text
		  editor based on {LVQ-HMM-FSN}},
  journal	= {Journal of KISS[C] [Computing Practices]},
  year		= {1996},
  volume	= {2},
  number	= {2},
  pages		= {206--17},
  dbinsdate	= {oldtimer}
}

@Article{	  lee96b,
  author	= {Dong-Hahk Lee and Young Hwan Kim},
  title		= {Image vector quantization using a two-stage
		  \mbox{self-organizing} feature map},
  journal	= {International Journal of Electronics},
  year		= {1996},
  volume	= {80},
  number	= {6},
  pages		= {703--16},
  dbinsdate	= {oldtimer}
}

@Article{	  lee96c,
  author	= {Seong-Whan Lee and Hee-Seon Park},
  title		= {Multi-lingual large-set Oriental character recognition
		  using a hierarchical neural network classifier},
  journal	= {Computer Processing of Oriental Languages},
  year		= {1996},
  volume	= {10},
  number	= {2},
  pages		= {129--45},
  annote	= {Twenty Year Anniversary Conference of Computer Processing
		  of Oriental Languages Conf. Date: 23--25 Nov. 1995 Conf.
		  Loc: Honolulu, HI, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  lee96d,
  author	= {Sukhan Lee and Jack Chien-Jan Pan},
  title		= {Unconstrained Handwritten Numeral Recognition Based on
		  Radial Basis Competitive and Cooperative Networks with
		  Spatio-Temporal Feature Representation},
  journal	= {IEEE Transactions on Neural Networks},
  year		= 1996,
  volume	= 7,
  pages		= {455--474},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lee96e,
  author	= {Ching-Feng Lee and Wen-Pin Tai},
  title		= {Portfolio selection with \mbox{self-organizing} maps},
  booktitle	= {Progress in Neural Information Processing. Proceedings of
		  the International Conference on Neural Information
		  Processing},
  publisher	= {Springer-Verlag},
  year		= {1996},
  volume	= {2},
  editor	= {S. -I. Amari and L. Xu and L. -W. Chan and I. King and K.
		  -S. Leung},
  address	= {Singapore},
  pages		= {716--21},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lee96f,
  author	= {Seong-Whan Lee and Jong-Soo Kim},
  title		= {Multi-lingual, multi-font and multi-size large-set
		  character recognition using \mbox{self-organizing} neural
		  network},
  booktitle	= {Proceedings of the Third International Conference on
		  Document Analysis and Recognition},
  publisher	= {World Scientific},
  year		= {1996},
  volume	= {1},
  editor	= {J. A. Reggia and E. Ruppin and R. Sloan Berndt},
  address	= {Singapore},
  pages		= {28--33},
  dbinsdate	= {oldtimer}
}

@Article{	  lee96g,
  author	= {Lee, Kun Chang and Han, Ingoo and Kwon, Youngsig},
  title		= {Hybrid neural network models for bankruptcy predictions},
  journal	= {Decision Support Systems},
  year		= {1996},
  number	= {1},
  volume	= {18},
  pages		= {63--72},
  abstract	= {The objective of this paper is to develop the hybrid
		  neural network models for bankruptcy prediction. The
		  proposed hybrid neural network models are (1) a
		  MDA-assisted neural network, (2) an ID3-assisted neural
		  network, and (3) a SOFM(self organizing feature
		  map)-assisted neural network. Both the MDA-assisted neural
		  network and the ID3-assisted neural network are the neural
		  network models operating with the input variables selected
		  by the MDA method and ID3 respectively. The SOFM-assisted
		  neural network combines a backpropagation model (supervised
		  learning) with a SOFM model (unsupervised learning). The
		  performance of the hybrid neural network model is evaluated
		  using MDA and ID3 as a benchmark. Empirical results using
		  Korean bankruptcy data show that hybrid neural network
		  models are very promising neural network models for
		  bankruptcy prediction in terms of predictive accuracy and
		  adaptability.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lee99a,
  author	= {Lee, R. and Ozdamar, O.},
  title		= {Analysis of wavelet preprocessed auditory brainstem
		  responses with \mbox{self-organizing} feature maps},
  booktitle	= {Proceedings of the First Joint BMES/EMBS Conference. 1999
		  IEEE Engineering in Medicine and Biology 21st Annual
		  Conference and the 1999 Annual Fall Meeting of the
		  Biomedical Engineering Society.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {1},
  pages		= {448},
  abstract	= {Auditory brainstem responses (ABR), recorded from several
		  subjects with normal hearing, were used to train several
		  self-organizing networks (SON). The resultant
		  self-organizing feature maps (SOFM), using intra-subject
		  data, showed promising results with respect to
		  classification of ABR into low, mid, high and no-response
		  regions. Although initial training with averaged ABR was
		  lengthy, wavelet preprocessing helped to reduce
		  computational time while retaining the same promising
		  results.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lee99b,
  author	= {Minho Lee and Sang Woo Ban and Jun Ki Cho and Chang Jin
		  Seo and Soon Ki Jung},
  title		= {Modeling of saccadic movements using neural networks},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {2386--9},
  abstract	= {We propose a new computational model for mimicking the
		  behavior of a human eye movement during saccades. The
		  different characteristics of two types of saccades, such as
		  a reflexive saccade and an intentional saccade, are
		  reflected on the proposed model. We divided the visual
		  pathway for generating a saccadic eye movement into three
		  parts, of which each part was modeled using different
		  neural networks. The visual pathway from the visual
		  receptors to the visual cortex including the frontal eye
		  field was modeled by the self-organizing feature map, and
		  the visual pathway from the visual cortex to the superior
		  colliculus was modeled by a modified learning vector
		  quantization network. The visual pathway front the superior
		  colliculus to the motoneuron is modeled by a multilayer
		  neural network with backpropagation learning algorithm.
		  Experimental results from computer simulation show that the
		  proposed computational model is able to mimic well the
		  behavior of the human eye movement for two different
		  saccades.},
  dbinsdate	= {oldtimer}
}

@Article{	  leem95a,
  author	= {Choon Seong Leem and Dornfeld, D. A. and Dreyfus, S. E. },
  title		= {A customized neural network for sensor fusion in on-line
		  monitoring of cutting tool wear},
  journal	= {Transactions of the ASME. Journal of Engineering for
		  Industry},
  year		= {1995},
  volume	= {117},
  number	= {2},
  pages		= {152--9},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  leem96a,
  author	= {Choon Seong Leem and D. A. Dornfeld},
  title		= {Design and implementation of sensor-based tool-wear
		  monitoring systems},
  journal	= {Mechanical Systems and Signal Processing},
  year		= {1996},
  volume	= {10},
  number	= {4},
  pages		= {439--58},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lehmann91a,
  author	= {C. Lehmann and F. Blayo},
  title		= {A {VLSI} implementation of a generic systolic synaptic
		  building block for neural networks},
  booktitle	= {Proc. VLSI for Artificial Intelligence and Neural
		  Networks},
  year		= {1991},
  editor	= {J. G. Delgado-Frias and W. R. Moore},
  pages		= {325--334},
  publisher	= {Plenum},
  address	= {New York, NY},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lehmann93a,
  author	= {Christian Lehmann},
  title		= {Self-Organisation of Large Feature Maps using Local
		  Computations: Analysis and {VLSI} Integration},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {1082},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lehrasab96a,
  author	= {N. Lehrasab and S. Fararooy},
  title		= {Intelligent multiple sensor early failure warning system
		  for train rotary door operator},
  booktitle	= {IEE Colloquium on Target Tracking and Data Fusion (Digest
		  No. 1996/253)},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {S. -I. Amari and L. Xu and L. -W. Chan and I. King and K.
		  -S. Leung},
  address	= {Singapore},
  pages		= {14/1--9},
  dbinsdate	= {oldtimer}
}

@Article{	  lehtinen97a,
  author	= {Lehtinen, Jan Christian and Forsstrom, Jari and Koskinen,
		  Pertti and Penttila, Tuula Anneli and Jarvi, Timo and
		  Anttila, Leena},
  title		= {Visualization of clinical data with neural networks: Case
		  study: Polycystic ovary syndrome},
  journal	= {International Journal of Medical Informatics},
  year		= {1997},
  number	= {2},
  volume	= {44},
  pages		= {145--155},
  abstract	= {In medicine, the use of neural networks has concentrated
		  mainly on classification problems. Clinicians are often
		  interested in knowing what a patient's status is compared
		  with other similar cases. Compared with biostatistics
		  neural networks have one major drawback: the reliability of
		  the classification is difficult to express. Therefore,
		  clear visualization of the measurements can be more helpful
		  than the calculated probability of a disease. The
		  self-organizing map is the most widely used neural network
		  for data visualization. Although, visualization can be
		  attached to almost any feed-forward network as well. In
		  this paper, we describe a topology-preserving feed-forward
		  network and compare it with the self-organizing map. The
		  two neural network models are used in a case study on the
		  diagnosis of polycystic ovary syndrome, which is a common
		  female endocrine disorder characterized by menstrual
		  abnormalities, hirsutism and infertility.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  leinonen91a,
  author	= {Lea Leinonen and Jari Kangas and Kari Torkkola and Anja
		  Juvas},
  title		= {Pattern Recognition of Hoarse and Healthy Voices by the
		  Self-Organizing Map},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1385--1388},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  leinonen91b,
  author	= {Lea Leinonen and Jari Kangas and Kari Torkkola and Anja
		  Juvas and Heikki Rihkanen and Riitta Mujunen},
  title		= {Itseorganisoituva kartta {\"{a}\"{a}nen} ja
		  {\"{a}\"{a}nt\"{a}misen} kuvantamisessa},
  journal	= {Suomen Logopedis-Foniatrinen Aikakauslehti},
  year		= {1991},
  volume	= {10},
  number	= {2},
  pages		= {4--9},
  note		= {(in Finnish)},
  dbinsdate	= {oldtimer}
}

@Article{	  leinonen92a,
  author	= {Lea Leinonen and Jari Kangas and Kari Torkkola and Anja
		  Juvas},
  title		= {Dysphonia Detected by Pattern Recognition of Spectral
		  Composition},
  journal	= {J. Speech and Hearing Res. },
  year		= {1992},
  volume	= {35},
  pages		= {287--295},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@Article{	  leinonen92b,
  author	= {L. Leinonen and J. Kangas and K. Torkkola},
  title		= {{\"{A}}{\"{a}}nih{\"{a}}iri{\"{o}}iden tunnistus
		  itseorganisoivalla kartalla},
  journal	= {Tekniikka logopediassa ja foniatriassa},
  publisher	= {Suomen logopedis-foniatrinen yhdistys ry},
  address	= {Helsinki, Finland},
  year		= {1992},
  volume	= {26},
  pages		= {41--45},
  note		= {(in Finnish)},
  annote	= {editor P. Sonninen},
  dbinsdate	= {oldtimer}
}

@Article{	  leinonen93a,
  author	= {L. Leinonen and T. Hiltunen and K. Torkkola and J.
		  Kangas},
  title		= {Self-organized acoustic feature map in detection of
		  fricative-vowel coarticulation},
  journal	= {J. Acoust. Soc. of America},
  year		= {1993},
  volume	= {93},
  number	= {6},
  pages		= {3468--3474},
  month		= {June},
  x		= {Korvaa leinonen93jasa:n},
  dbinsdate	= {oldtimer}
}

@Article{	  leinonen93b,
  author	= {Lea Leinonen and Riitta Mujunen and Jari Kangas and Kari
		  Torkkola},
  title		= {Acoustic pattern recognition of fricative-vowel
		  coarticulation by the Self-Organizing Map},
  journal	= {Folia Phoniatrica},
  year		= {1993},
  volume	= {45},
  pages		= {173--181},
  dbinsdate	= {oldtimer}
}

@Article{	  leinonen93c,
  author	= {Lea Leinonen and Tapio Hiltunen and Jari Kangas and Anja
		  Juvas and Heikki Rihkanen},
  title		= {Detection of Dysphonia by Pattern Recognition of Speech
		  Spectra},
  journal	= {Scand. J. Log. Phon. },
  year		= {1993},
  volume	= {18},
  pages		= {159--167},
  annote	= {application, speech analysis},
  dbinsdate	= {oldtimer}
}

@Article{	  leinonen96a,
  author	= {L. Leinonen and K. Valkealahti and H. Rihkanen},
  title		= {Visual Imaging of Voice Quality with the Self-Organizing
		  Map},
  journal	= {Suomen logopedis-foniatrinen aikakauslehti},
  year		= {1996},
  volume	= {16},
  pages		= {89--96},
  dbinsdate	= {oldtimer}
}

@Article{	  leinonen97a,
  author	= {L. Leinonen and T. Hiltunen and I. Linnankoski and M. -L.
		  Laakso},
  title		= {Expression of emotional-motivational connotations with a
		  one-word utterance},
  journal	= {Journal of the Acoustical Society of America},
  year		= {1997},
  volume	= {102},
  number	= {3},
  pages		= {1853--63},
  dbinsdate	= {oldtimer}
}

@Article{	  leinonen97b,
  author	= {Lea Leinonen and Tapio Hiltunen and Maija-Liisa Laakso and
		  Heikki Rihkanen and H{\aa}kan Poppius},
  title		= {Categorization of Voice Disorders with Six Perceptual
		  Dimensions},
  journal	= {Folia Phoniatrica et Logopaedica},
  year		= 1997,
  volume	= 49,
  pages		= {9--20},
  dbinsdate	= {oldtimer}
}

@InCollection{	  leinonen99a,
  author	= {L. Leinonen},
  title		= {Self-organizing map in categorization of voice qualities},
  booktitle	= {Kohonen Maps},
  pages		= {329--334},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: self-organising map, voice quality, phonation,
		  dysphonia},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  leisenberg94a,
  author	= {Manfred Leisenberg},
  title		= {The Intelligent Bionic Ear---a new concept of an adaptive,
		  artificial neural net based cochlear implant system using
		  speaker independent signal representation},
  booktitle	= {Proc. IMACS Int. Symp. on Signal Processing, Robotics and
		  Neural Networks},
  year		= {1994},
  pages		= {594--597},
  publisher	= {IMACS},
  address	= {Lille, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  leisenberg95a,
  author	= {Leisenberg, M. },
  title		= {Hearing aids for the profoundly deaf based on neural net
		  speech processing},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  year		= {1995},
  volume	= {5},
  pages		= {3535--8},
  organization	= {Inst. of Sound \& Vibration Res. , Southampton Univ. , UK},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  leisenberg95b,
  author	= {M. Leisenberg},
  title		= {Unsupervised neural networks for speech perception with
		  cochlear implant systems for the profoundly deaf},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {462--70},
  dbinsdate	= {oldtimer}
}

@InCollection{	  leivian97a,
  author	= {Robert Leivian and William Peterson and Mike Gardner},
  title		= {CorDex: a knowledge discovery tool},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {63--68},
  dbinsdate	= {oldtimer}
}

@TechReport{	  leman89a,
  author	= {Marc Leman and Patrick van Renterghem},
  title		= {Transputer implementation of the {K}ohonen feature map for
		  a music recognition task},
  institution	= {University of Ghent, Inst. for Psychoacoustics and
		  Electronic Music},
  year		= {1989},
  number	= {SM-IPEM-\#17},
  address	= {Ghent, Belgium},
  month		= {October},
  annote	= {Implementation paper. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lemos93a,
  author	= {R. A. Lemos and M. Nakamura and H. Kuwano},
  title		= {Applying a Self-Organizing Map to Sensor-Array
		  Characterization},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {2009--2012},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {As a basic application of neural networks, the authors
		  implemented a self-organizing map (SOM) as an algorithm to
		  classify the response vectors from a sensor array exposed
		  to various chemical vapors. Our chemical sensing system
		  consists of an array of piezoelectric quartz-crystal
		  microbalance (QCM) sensors, each coated with a different
		  polymer membrane. Typically, statistical analysis are
		  employed to characterize the sensor response to various
		  gases and to classify each individual gas. However, because
		  the sorption-desorption cycle can require a long time to
		  come to equilibrium, the initial vectors do not contain
		  much unique information. We replaced principal-component
		  analysis with the self-organizing map as a visual method of
		  finding the time at which the sensor-array signals become
		  unique and of estimating the quality of the extracted
		  features. In addition, we found that the SOM can accurately
		  classify response vectors faster than principal-component
		  analysis.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lendasse00a,
  author	= {Lendasse, A. and Lee, J. and Wertz, V. and Verleysen, M.},
  title		= {Time series forecasting using {CCA} and Kohonen maps.
		  Application to electricity consumption},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {329--34},
  abstract	= {Using large regressors in non-linear time series
		  forecasting makes the fitting of the model difficult. This
		  paper shows how to reduce the size of regressors in order
		  to improve the forecasting performances, using the
		  Curvilinear Component Analysis as projection tool. The
		  method is applied to the Polish electrical load
		  forecasting.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lendasse98a,
  author	= {A. Lendasse and M. Verleysen and E. {de Bodt} and P.
		  Gregoire and M. Cottrell},
  title		= {Forecasting Time-series by {K}ohonen Classification},
  booktitle	= {Proceedings of ESANN'98},
  year		= {1998},
  editor	= {M. Verleysen},
  publisher	= {Editions D Facto},
  address	= {Bruxelles},
  pages		= {221--226},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lennon92a,
  author	= {Lennon, S. and Ambikairajah, E. },
  title		= {A two-layer {K}ohonen neural network using a cochlear
		  model as a front-end processor for a speech recognition
		  system},
  booktitle	= {Neural Networks for Signal Processing II. Proceedings of
		  the IEEE-P Workshop},
  year		= {1992},
  pages		= {139--48},
  organization	= {Dept. of Electron. Eng. , Regional Tech. Coll. , Athlone,
		  Ireland},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lensu98a,
  author	= {Lensu, A. and Koikkalainen, P.},
  title		= {Analysis of multi-choice questionnaires through
		  \mbox{self-organizing} maps},
  booktitle	= {ICANN 98. Proceedings of the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer-Verlag},
  year		= {1998},
  volume	= {1},
  pages		= {305--10},
  address	= {London},
  abstract	= {This paper describes how self-organizing maps (SOM) can be
		  used to analyse multichoice gallups. In this method, the
		  use of a single SOM for all available data is replaced with
		  the use of multiple SOMs trained with subsets of gallup
		  questions. The subgroupings located from these maps are
		  then used to train a new concluding SOM that is more
		  readable than any single SOM analysis would be.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lensu99a,
  author	= {Lensu, A. and Koikkalainen, P.},
  title		= {Similar document detection using \mbox{self-organizing}
		  maps},
  booktitle	= {1999 Third International Conference on Knowledge-Based
		  Intelligent Information Engineering Systems. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {},
  pages		= {174--7},
  abstract	= {This paper describes how similar free-form textual
		  documents can be matched using the self-organizing maps
		  (SOMs). The analysis chain is made of three parts: first,
		  similar words are located using an alphabet occurrence
		  coding and SOM; second, three-word contexts are clustered
		  using codes obtained from the word SOM to build a context
		  map; and third, whole documents are clustered using codes
		  from the context SOM. Although this work is inspired by the
		  WEBSOM method, it is quite different since our goal was to
		  build a fast system, which is tolerant to the special
		  features of different languages.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lescure99a,
  author	= {Lescure, P. and Meas Yedid, V. and Dupoisot, H. and
		  Stamon, G.},
  title		= {Color segmentation of biological microscopic images},
  booktitle	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3647},
  pages		= {182--93},
  abstract	= {The project consists in extracting biological objects from
		  the background of an image in order to determine their
		  three dimensions, namely their thickness. The small size of
		  the photographed objects induces the formation of light
		  interferences. The observed interference colors are related
		  to the properties of the thin objects. Segmentation
		  techniques used for this application are divided into three
		  major types: edge extraction, region growing and splitting,
		  and clustering. Generally, edge segmentation works on each
		  separated RGB channel but it leads to a data fusion
		  problem. Region growing and splitting methods commonly deal
		  with features extraction. Color is a possible feature. The
		  color image segmentation can be either monodimensional or
		  multidimensional, using classification methods. For the
		  monodimensional segmentation, the gray level is used alone.
		  For the multidimensional case, one can take into account
		  the vectorial character of colors, using color clustering.
		  In this general context the aim of the project is to
		  evaluate how a specific color space can improve the
		  segmentation. Standard color segmentation algorithms are
		  used: (1) C-means; (2) backpropagation neural network; and
		  (3) learning vector quantization. The results are compared
		  with gray level algorithms such as the Otsu (1979)
		  thresholding and ISODATA, applied to each color channel.
		  They show first that there is not only one good color
		  representation space, and secondly, that data clusters are
		  relatively close to each other, which explains why
		  segmentation is so difficult in this class of pictures.},
  dbinsdate	= {oldtimer}
}

@Article{	  lesteven96a,
  author	= {S. Lesteven and P. Poincot and F. Murtagh},
  title		= {Neural networks and information extraction in astronomical
		  information retrieval},
  journal	= {Vistas in Astronomy},
  year		= {1996},
  volume	= {40},
  number	= {pt. 3},
  pages		= {395--400},
  note		= {(Workshop on Strategies and Techniques of Information for
		  Astronomy Conf. Date: 21--22 June 1996 Conf. Loc:
		  Strasbourg, France)},
  dbinsdate	= {oldtimer}
}

@Article{	  leung97a,
  author	= {Leung, Chi Sing and Chan, Lai Wan},
  title		= {Transmission of vector quantized data over a noisy
		  channel},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  number	= {3},
  volume	= {8},
  pages		= {582--589},
  abstract	= {In the transmission of vector quantized data, the vector
		  quantizer and the communication system are usually designed
		  separately. With such an approach, the channel noise
		  results in significant degradations in the performance of
		  the vector quantizer. To solve this problem, we should
		  properly create the mapping from the codebook of the
		  quantizer to the channel signal set of the communication
		  system. This paper proposes a new approach to construct
		  such a mapping based on the ordering property of the
		  self-organizing feature map (SOFM). We use the neighborhood
		  structure of the SOFM and the neighborhood structure of the
		  channel signal set to construct the mapping. Simulation
		  results confirm that the proposed approach is robust with
		  respect to channel noise.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  leung99a,
  author	= {Leung, T. S. and White, P. R. and Collis, W.B. and Brown,
		  E. and Salmon, A.P.},
  title		= {Characterization of paediatric heart murmurs using
		  \mbox{self-organizing} map},
  booktitle	= {Annual International Conference of the IEEE Engineering in
		  Medicine and Biology---Proceedings},
  year		= {1999},
  volume	= {2},
  pages		= {926},
  abstract	= {This paper discusses the characterization of paediatric
		  heart murmurs using self-organizing map (SOM). Features are
		  first extracted from the time-frequency representations of
		  the murmurs and then used as inputs to the SOM. In the
		  resulting two dimensional SOM, samples from four groups of
		  patients fall into four clusters showing the four groups
		  have inherently different distributions.},
  dbinsdate	= {oldtimer}
}

@Article{	  leung99b,
  author	= {Leung, C.~S. and Chan, L.~W.},
  title		= {Design of Trellis-Coded Vector Quantizers Using {K}ohonen
		  Maps},
  journal	= {Neural Networks},
  year		= {1999},
  volume	= {12},
  number	= {6},
  pages		= {907--914},
  dbinsdate	= {oldtimer}
}

@Article{	  lewis01a,
  author	= {Lewis, O. M. and Ware, J. A. and Jenkins, D. H.},
  title		= {Identification of residential property sub-markets using
		  evolutionary and neural computing techniques},
  journal	= {NEURAL COMPUTING \& APPLICATIONS},
  year		= {2001},
  volume	= {10},
  number	= {2},
  pages		= {108--119},
  abstract	= {This paper expands on previous work considering methods of
		  stratifying property clam in order to enhance its
		  susceptibility to modelling for mortgage value estimation.
		  Previous work [1] considered a clustering approach using a
		  Kuhonen Self-Organising Map (SOM) to stratify the training
		  data prior to training a suite of MLPs. Although the
		  results were encouraging, the approach suffers from its
		  estimation of trainability post-clustering, The following
		  method ameliorates the approach by replacing the static
		  clustering step with a dynamic genetic algorithm
		  implementation. The results show a healthy improvement in
		  accuracy over the non-stratified approah, and a more
		  consistent level of accuracy compared rt with the Kohonen
		  SOM approach. The paper concludes by analysing the
		  underlying content of the derived stratas. thus providing a
		  'human readable' element to the approach that enhances its
		  potential for acceptance by valuation institutions for as a
		  complementary technique to traditional valuation methods.},
  dbinsdate	= {2002/1}
}

@Article{	  lewis97a,
  author	= {O. M. Lewis and J. A. Ware and D. Jenkins},
  title		= {A novel neural network technique for the valuation of
		  residential property},
  journal	= {Neural Computing \& Applications},
  year		= {1997},
  volume	= {5},
  number	= {4},
  pages		= {224--9},
  dbinsdate	= {oldtimer}
}

@Article{	  li00a,
  author	= {Li, Jia and Najmi, Amir and Gray, Robert M.},
  title		= {Image classification by a two-dimensional hidden {M}arkov
		  model},
  journal	= {IEEE Transactions on Signal Processing},
  year		= {2000},
  number	= {2},
  volume	= {48},
  pages		= {517--533},
  abstract	= {For block-based classification, an image is divided into
		  blocks, and a feature vector is formed for each block by
		  grouping statistics extracted from the block. Conventional
		  block-based classification algorithms decide the class of a
		  block by examining only the feature vector of this block
		  and ignoring context information. In order to improve
		  classification by context, an algorithm is proposed that
		  models images by two dimensional (2-D) hidden {M}arkov
		  models (HMM's). The HMM considers feature vectors
		  statistically dependent through an underlying state process
		  assumed to be a {M}arkov mesh, which has transition
		  probabilities conditioned on the states of neighboring
		  blocks from both horizontal and vertical directions. Thus,
		  the dependency in two dimensions is reflected
		  simultaneously. The HMM parameters are estimated by the EM
		  algorithm. To classify an image, the classes with maximum a
		  posteriori probability are searched jointly for all the
		  blocks. Applications of the HMM algorithm to document and
		  aerial image segmentation show that the algorithm
		  outperforms CARTTM, LVQ, and Bayes VQ.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  li00b,
  author	= {Li, Sheng Tun and Chou, Shih Wei and Pan, Jeng Jong},
  title		= {Multi-resolution spatio-temporal data mining for the study
		  of air pollutant regionalization},
  booktitle	= {Proceedings of the Hawaii International Conference on
		  System Sciences},
  year		= {2000},
  volume	= {},
  pages		= {33},
  abstract	= {Spatio-temporal data mining involves extracting and
		  analyzing useful information embedded in a large
		  spatio-temporal database. Cluster analysis, one of the data
		  mining techniques, provides the capability to investigate
		  the spatio-temporal variation of data. Previous studies in
		  cluster analysis indicate that the optimal number of
		  clusters could be varied with the temporal scale of input
		  data. This study employs multi-scale wavelet transforms and
		  self-organizing map neural networks to mine air pollutant
		  data. Experimental results show that regions determined
		  from wavelet transform approach can reduce the local small
		  regions using a small scale input data and improve the
		  over-smoothed regions using one large scale input data. The
		  results of cluster analysis using data generated from
		  discrete wavelet transform and continuous wavelet transform
		  also discussed in this paper. Data generated from
		  continuous wavelet transform provide detailed
		  time-variation features that can be used to detect the air
		  pollutant spatial variation in a selected time period. In
		  English},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  li00c,
  author	= {Li, R. and Kim, J.},
  title		= {Image compression using fast transformed vector
		  quantization},
  booktitle	= {Proceedings 29th Applied Imagery Pattern Recognition
		  Workshop. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {},
  abstract	= {Digital image compression is an important technique in
		  digital image processing. To improve its performance, we
		  attempt to speed up the design process and achieve the
		  highest compression ratio where possible. For speed
		  improvement, we used a fast Kohonen self-organizing neural
		  network algorithm to achieve big saving in codebook
		  construction time. For compression purpose, we propose a
		  new approach, called fast transformed vector quantization
		  (FTVQ), by combining together the features of speed
		  improvement, transform coding and vector quantization. We
		  use several experiments to demonstrate the feasibility of
		  this FTVQ approach.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  li00d,
  author	= {Li, F. and Chen, C.},
  title		= {Sizing a flexible spinning reserve level with artificial
		  neural networks},
  booktitle	= {2000 IEEE Power Engineering Society Winter Meeting.
		  Conference Proceedings. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {2},
  pages		= {1005--10},
  abstract	= {The paper presents a neural network based method to
		  formulate an economic flexible spinning reserve strategy,
		  so that more reserve is withheld when contingencies are
		  likely to occur, and less reserve is kept otherwise. The
		  method firstly employs a Kohonen neuron network (KNN) to
		  analyse historical data, such as the hourly spinning
		  reserve, hourly utilised spinning reserve, and the hourly
		  demand data. This data is clustered into different groups,
		  where they could be reserve over-held, under-held or
		  satisfactory to different degrees. This is followed by
		  suggestions of appropriate reserve level for each of the
		  groups. Based on the analysis results and the suggested
		  reserve level, the paper then trains a fuzzy neuron network
		  to determine future spinning reserve so as to both minimise
		  operating costs and enhance system reliability.},
  dbinsdate	= {2002/1}
}

@Article{	  li01a,
  author	= {Li, B. and Chellappa, R. and Zheng, Q. and Der, S. and
		  Nasrabadi, N. and Chan, L. and Wang, L.},
  title		= {Experimental evaluation of {FLIR} {ATR} approaches---A
		  comparative study},
  journal	= {Computer Vision and Image Understanding},
  year		= {2001},
  volume	= {84},
  number	= {1},
  month		= {October },
  pages		= {5--24},
  organization	= {Sharp Laboratories of America},
  publisher	= {Academic Press Inc.},
  address	= {},
  abstract	= {This paper presents an empirical evaluation of a number of
		  recently developed Automatic Target Recognition algorithms
		  for Forward-Looking Infrared (FLIR) imagery using a large
		  database of real FLIR images. The algorithms evaluated are
		  based on convolutional neural networks (CNN), principal
		  component analysis (PCA), linear discriminant analysis
		  (LDA), learning vector quantization (LVQ), modular neural
		  networks (MNN), and two model-based algorithms, using
		  Hausdorff metric-based matching and geometric hashing. The
		  evaluation results show that among the neural approaches,
		  the LVQ- and MNN-based algorithms perform the best; the
		  classical LDA and the PCA methods and our implementation of
		  the geometric hashing method ended up in the bottom three,
		  with the CNN- and Hausdorff metric-based methods in the
		  middle. Analyses show that the less-than-desirable
		  performance of the approaches is mainly due to the lack of
		  a good training set. },
  dbinsdate	= {2002/1}
}

@Article{	  li01b,
  author	= {Li Ning and Mao Sixin and Li Youfu},
  title		= {Effective feature analysis for color image segmentation},
  journal	= {Transactions-of-Nanjing-University-of-Aeronautics-\&-Astronautics}
		  ,
  year		= {2001},
  volume	= {18},
  pages		= {206--12},
  abstract	= {An approach for color image segmentation is proposed based
		  on the contributions of color features to segmentation
		  rather than the choice of a particular color space. The
		  determination of effective color features depends on the
		  analysis of various color features from each tested color
		  image via the designed feature encoding. It is different
		  from the previous methods where self-organized feature map
		  is used for constructing the feature encoding so that the
		  feature-encoding can self-organize the effective features
		  for different color images. Fuzzy clustering is applied for
		  the final segmentation when the well-suited color features
		  and the initial parameter are available. The proposed
		  method has been applied in segmenting different types of
		  color images and the experimental results show that it
		  outperforms the classical clustering method. The study
		  shows that the feature encoding approach offers great
		  promise in automating and optimizing the segmentation of
		  color images.},
  dbinsdate	= {2002/1}
}

@Article{	  li01d,
  author	= {Li, Shutao and Wang, Yaonan},
  title		= {The segmentation of kiln flame image based on neural
		  networks},
  journal	= {Chinese-Journal-of-Scientific-Instrument},
  year		= {2001},
  volume	= {22},
  pages		= {10--12},
  abstract	= {Accurate segmentation of flame images in rotary kiln is
		  very important for the extraction of working parameters. In
		  this paper, four neural networks, i.e. multilayer
		  perception, radial basis function, learning vector
		  quantization and self-organizing feature mapping, are used
		  to segment the flame image. Normalized color intensity
		  values are selected as training sample of neural networks.
		  The neural networks are trained by supervised and
		  unsupervised algorithms, respectively. The results of
		  segmenting actual images show that the proposed approach is
		  feasible.},
  dbinsdate	= {2002/1},
  merjanote     = {last name guessed, again more lastnamesounding selected}
}

@Article{	  li01c,
  author	= {Li, D. and Song, Y. and Ye, F.},
  title		= {On line monitoring of burning through for short circuit
		  {CO}<sub>2</sub> arc welding based on the self-organize
		  feature map neural networks},
  journal	= {Chinese Journal of Mechanical Engineering (English
		  Edition)},
  year		= {2001},
  volume	= {14},
  number	= {2},
  month		= {June 2001},
  pages		= {106--110},
  organization	= {Mechatronics Engineering Department, South China
		  University of Technology},
  publisher	= {},
  address	= {},
  abstract	= {A method for automatic detection of burning through of
		  short circuit CO<sub>2</sub> arc welding is presented. It
		  is based on the extraction of arc signal features as well
		  as classification of the obtained features using
		  sell-organize feature map (SOM) neural networks in order to
		  get the weld quality information, for example, to determine
		  if there is defect in the product. This is important for
		  the on-line monitoring of weld quality especially in
		  robotic welding and lay the foundation for the further
		  real-time control of weld quality.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  li01e,
  author	= {Li, S. and Li, T.},
  title		= {Interoperable Web-based data mining system by Java
		  distributed object computing},
  booktitle	= {Proceedings of the Hawaii International Conference on
		  System Sciences},
  year		= {2001},
  editor	= {},
  volume	= {},
  pages		= {68},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {The development of Web-based data mining systems has
		  received a lot of attention in recent years. It plays the
		  key-enabling role for competitive businesses in
		  E-commerceera. A cost-effective and prompt approach for
		  this task is to integrate and coordinate existing data
		  mining applications in a seamless manner. In this paper, we
		  propose a new methodology for developing a Web-based data
		  mining system. This system relies on the Java distributed
		  object computing to tackle the issues of interoperability
		  in heterogeneous environments, namely, language, platform,
		  visual object model, and data access. The effectiveness of
		  the proposed system is demonstrated by integrating two
		  powerful data mining tools, SOM_PAK and Nenet, and the
		  experiment on the iris data. The methodology can facilitate
		  the collaboration of intelligent components seamlessly in a
		  "plug-N-work" manner but without re-engineering.},
  dbinsdate	= {2002/1}
}

@Article{	  li02a,
  author	= {Li, Weidong and Parkin, Robert M. and Coy, Joanne and Gu,
		  Fengshou},
  title		= {Acoustic based condition monitoring of a diesel engine
		  using self-organising map networks},
  journal	= {Applied Acoustics},
  year		= {2002},
  volume	= {63},
  number	= {7},
  month		= {July },
  pages		= {699--711},
  organization	= {Department of Mechanical Engineering, Queen's University},
  publisher	= {Elsevier Science Ltd},
  address	= {},
  abstract	= {In this paper, the acoustic based condition monitoring of
		  a diesel engine is investigated. Firstly, an experimental
		  test rig is set up to simulate engine faults. Acoustic
		  signals are measured from the test diesel engine under
		  different conditions. To analyse the measured acoustic
		  signals, a two-dimensional topological seif-organising map
		  (SOM) network is employed in this paper to perform feature
		  extraction. The extracted features are processed by both
		  statistical and spectral methods. The results show that the
		  extracted features are able to show the differences between
		  the engine's normal and faulty conditions.},
  dbinsdate	= {2002/1}
}

@Article{	  li02b,
  author	= {Li, Sheng-Tun},
  title		= {A web-aware interoperable data mining system},
  journal	= {Expert Systems with Applications},
  year		= {2002},
  volume	= {22},
  number	= {2},
  month		= {February },
  pages		= {135--146},
  organization	= {Department of Information Management, NKFUST},
  publisher	= {},
  address	= {},
  abstract	= {The development of web-aware data mining systems has
		  received a great deal of attention in recent years. It
		  plays a key enabling role for competitive businesses in the
		  E-commerce era. One of the challenges in developing
		  web-aware data mining systems is to integrate and
		  coordinate existing data mining applications in a seamless
		  manner so that cost-effective systems can be developed
		  without the need of costly proprietary products. In this
		  paper we present an approach for developing an
		  interoperable web-aware data mining system to achieve this
		  purpose. This approach applies Remote Method Invocation and
		  high level code wrapper of Java distributed object
		  computing to address the issues of interoperability in
		  heterogeneous environments, which includes programming
		  language, platform, and visual object model. The
		  effectiveness of the proposed system is demonstrated
		  through the integration and enhancement of the two
		  well-known standalone data mining tools, SOM_PAK and Nenet,
		  and runs with the iris data and air pollution data. },
  dbinsdate	= {2002/1}
}

@Article{	  li02c,
  author	= {Li, R. Y. and Kim, J. and Al-Shamakhi, N.},
  title		= {Image compression using transformed vector quantization},
  journal	= {Image and Vision Computing},
  year		= {2002},
  volume	= {20},
  number	= {1},
  month		= {Jan 1 },
  pages		= {37--45},
  organization	= {Department of Electrical Engineering, North Carolina A and
		  T State Univ.},
  publisher	= {},
  address	= {},
  abstract	= {Vector quantization (VQ) is an important technique in
		  digital image compression. To improve its performance, we
		  would like to speed up the design process and achieve the
		  highest compression ratio possible. To speed up the
		  process, we used a fast Kohonen self-organizing neural
		  network algorithm to achieve big saving in codebook
		  construction time. To obtain better reconstructed images,
		  we propose a new approach called the transformed vector
		  quantization (TVQ), combining the features of transform
		  coding and VQ. We use several data sets to demonstrate the
		  feasibility of this TVQ approach. A comparison of
		  reconstructed image quality is made between the TVQ and VQ.
		  Also, a comparison is made between a TVQ and a standard
		  JPEG approach. },
  dbinsdate	= {2002/1}
}

@Article{	  li89a,
  author	= {Li, J. and Manikopoulos, C. N.},
  title		= {Multi-stage vector quantization based on the
		  self-organization feature maps.},
  journal	= {Visual Communications and Image Processing IV},
  year		= {1989},
  number	= {},
  volume	= {1199},
  pages		= {1046--1055},
  abstract	= {A neural network clustering algorithm, termed
		  Self-Organization Feature Maps (SOFM) proposed by Kohonen,
		  is used to design a vector quantizer. The SOFM algorithm
		  differs from the LBG algorithm in that the former forms a
		  codebook adaptively but not iteratively. For every input
		  vector, the weight between the input node and the
		  corresponding output node is updated by encouraging a shift
		  toward the center of gravity in the due influence region.
		  Some important properties are discussed, demonstrated by
		  examples, and compared with the LBG algorithm. Based on
		  this clustering algorithm, a very practical image sequence
		  coding scheme is proposed, which consists of two cascade
		  neural networks.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  li91a,
  author	= {Kung-Pu Li},
  title		= {A Learning Algorithm with Multiple Criteria for
		  Self-Organizing Feature Maps},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1353--1356},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  li91b,
  author	= {Tao Li and Luyuan Fang and Andrew Jennings},
  title		= {Self-Organizing Neural Trees for Hierarchical
		  Classification and Vector Quantization},
  institution	= {Concordia University, Department of Computer Science},
  year		= {1991},
  number	= {CS-NN-91--5},
  address	= {Montreal, Quebec, Canada},
  month		= {September},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  li92a,
  author	= {Ken Q-Q Li and Pose, R. },
  title		= {Ordered search---a new method of image compression with
		  {K}ohonen networks},
  booktitle	= {ICARCV '92. Second International Conference on Automation,
		  Robotics and Computer Vision},
  year		= {1992},
  volume	= {1},
  pages		= {NW-1. 7/1--5},
  organization	= {Dept. of Comput. Sci. , Monash Univ. , Clayton, Vic,
		  Australia},
  publisher	= {Nanyang Technol. Univ},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  li92b,
  author	= {Ying-Ming Li and Jabri, M. A. },
  title		= {Global routing using a neural network strategy},
  booktitle	= {ICARCV '92. Second International Conference on Automation,
		  Robotics and Computer Vision},
  year		= {1992},
  volume	= {1},
  pages		= {INV-9. 3/1--5},
  organization	= {Syst. Eng. \& Design Autom. Lab. , Sydney Univ. , NSW,
		  Australia},
  publisher	= {Nanyang Technol. Univ},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@Article{	  li93a,
  author	= {Li, X. and Gasteiger, J. and Zupan, J.},
  title		= {On the topology distortion in \mbox{self-organizing}
		  feature maps},
  journal	= {Biological Cybernetics},
  year		= {1993},
  number	= {2},
  volume	= {70},
  pages		= {189--198},
  abstract	= {One of the main properties of the feature maps generated
		  by Kohonen's self-organizing net is their preservation of
		  the topology of the target object. However, since the net
		  and the target object in general have different topological
		  structures, there are usually also certain distortions of
		  topology in the feature maps. For a better understanding of
		  such distortions we present a continuous model for the
		  Kohonen net based on topology theory. This model stresses
		  the description of the topological behavior of the feature
		  maps while suppressing the statistical aspects. From the
		  point of view of this model a well-trained Kohonen net
		  describes a matching between the net and the object,
		  whereby both of them are usually partitioned into
		  universally connected parts, and the topology distortion
		  occurs on the boundary of the parts. Simulations
		  demonstrate that the topology distortions within the
		  feature maps normally take a regular structure and provide
		  useful information about the target object.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  li93b,
  author	= {S. Z. Li},
  title		= {Self-Organization of Surface Shapes},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1173--1176},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  li94a,
  author	= {Tao Li and S. Klasa and Y. Y. Tang},
  title		= {Data mapping for parallel programs with changing size
		  windows},
  booktitle	= {Seventh International Conference on Parallel and
		  Distributed Computing Systems},
  publisher	= {Int. Soc. Comput. \& Their Appl. -ISCA},
  year		= {1994},
  address	= {Raleigh, NC, USA},
  pages		= {640--3},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  li95a,
  author	= {Robert Li and Earnest Sherrod and Huaxiao Si},
  title		= {Image Vector Quantization Using An Improved
		  {S}elf-{O}rganizing Neural Network Approach},
  volume	= {I},
  pages		= {548--551},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  li95b,
  author	= {Rui-Ping Li and Mukaidono, M. },
  title		= {Proportional learning law and local minimum escape in
		  clustering networks},
  booktitle	= {Proceedings of International Conference on Neural
		  Information Processing (ICONIP `95)},
  year		= {1995},
  editor	= {Zhong, Y. and Yang, Y. and Wang, M. },
  volume	= {1},
  pages		= {192--5},
  organization	= {Dept. of Comput. Sci. , Meiji Univ. , Kawasaki, Japan},
  publisher	= {Publishing House of Electron. Ind},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@Article{	  li95c,
  author	= {Tao Li and Lixin Tao},
  title		= {Topological feature maps on parallel computers},
  journal	= {International Journal of High Speed Computing},
  year		= {1995},
  volume	= {7},
  number	= {4},
  pages		= {531--46},
  dbinsdate	= {oldtimer}
}

@Article{	  li97a,
  author	= {Robert Y. Li and Gary L. Lebby},
  title		= {A Modified Approach for Constructing the Self-Organized
		  Layer in a Multilayer Feedforward Neural Network},
  journal	= {Information Sciences},
  year		= 1997,
  volume	= 98,
  pages		= {69--81},
  dbinsdate	= {oldtimer}
}

@Article{	  li99a,
  author	= {Li, Weigang},
  title		= {Parallel \mbox{self-organizing} map},
  journal	= {Transactions of Nonferrous Metals Society of China
		  (English Edition)},
  year		= {1999},
  number	= {1},
  volume	= {9},
  pages		= {172--180},
  abstract	= {Parallel self-organizing map was proposed for information
		  parallel processing. There are two layers of neutrons
		  connected together, and the number of neutrons in the
		  layers and connections is equal to that of total elements
		  of input signals, while the weight updating is managed
		  through a sequence of operations among some unitary
		  transformation and operation matrixes, so the conventional
		  repeated learning procedure is modified to learn just once
		  and an algorithm is developed to realize this new learning
		  method. The comparison of this method and Kohonen's method
		  was given. Its parallel mode may be used for quantum
		  computation.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  li99b,
  author	= {Li, Weigang and {da Silva}, N. C.},
  title		= {Implementation of parallel \mbox{self-organizing} map for
		  the classification of images},
  booktitle	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3722},
  pages		= {284--92},
  abstract	= {A study of a parallel self-organizing map (parallel-SOM)
		  is proposed to modify a self-organizing map for parallel
		  computing environments. In this model, the conventional
		  repeated learning procedure is modified to learn just once.
		  The once learning manner is more similar to human learning
		  and memorizing activities. During training, every
		  connection between neurons of input/output layers is
		  considered as an independent processor. In this way, all
		  elements of every matrix are calculated simultaneously.
		  This synchronization feature improves the weight updating
		  sequence significantly. In the paper, the detail sequence
		  of parallel-SOM is demonstrated through the classification
		  of a coin for understanding the properties of the proposed
		  model. In a conventional computing environment (one
		  processor), the parallel-SOM can be implemented without the
		  once learning and parallel weight updating features. As an
		  application, its implementation for the classification of
		  meteorological radar images is also shown.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  li99c,
  author	= {Li, Weigang and {da Silva}, N. C.},
  title		= {A study of parallel neural networks},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1113--16},
  abstract	= {A parallel self-organizing map (parallel-SOM) is proposed
		  to modify a self-organizing map for parallel computing
		  environments. In this model, the conventional repeated
		  learning procedure is modified to learn just once. The once
		  learning manner is more similar to human learning and
		  memorizing activities. During training, every connection
		  between neurons of input and output layers is considered as
		  an independent processor. In this way, all elements of
		  every matrix are calculated simultaneously. This
		  synchronization feature improves the weight updating
		  sequence significantly. In the paper, parallel-SOM is
		  implemented in a conventional computing environment (one
		  processor), without the once learning and parallel weight
		  updating features to show the correction of the algorithm.
		  As an application parallel-SOM is used for the
		  classification of meteorological radar images.},
  dbinsdate	= {oldtimer}
}

@Article{	  liang94a,
  author	= {Ruey-Hsun Liang and Yuan-Yih Hsu},
  title		= {Hydroelectric generation scheduling using
		  \mbox{self-organizing} feature maps},
  journal	= {Electric Power Systems Research},
  year		= {1994},
  volume	= {30},
  number	= {1},
  pages		= {1--8},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@Article{	  liang95a,
  author	= {Ruey-Hsun Liang and Yuan-Yih Hsu},
  title		= {A hybrid artificial neural network-differential dynamic
		  programming approach for short-term hydro scheduling},
  journal	= {Electric Power Systems Research},
  year		= {1995},
  volume	= {33},
  number	= {2},
  pages		= {77--86},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liangjiang00a,
  author	= {Liangjiang Wang and Bridges, S. M. and Boggess, L. C. and
		  Varco J. J.},
  title		= {Neural network classification of leaf reflectance spectra
		  for predicting nutrient deficiency of cotton},
  booktitle	= {Smart Engineering System Design: Neural Networks, Fuzzy
		  Logic, Evolutionary Programming, Data Mining, and Complex
		  Systems. Vol.10. Proceedings of the Artificial Neural
		  Networks in Engineering Conference (ANNIE 2000). ASME, New
		  York, NY, USA},
  year		= {2000},
  volume	= {},
  pages		= {1037--43},
  abstract	= {Describes the application of neural networks to the
		  classification of leaf reflectance spectra for diagnosing
		  the nitrogen (N) and potassium (K) nutrient status in
		  cotton plants. Three different types of classifiers are
		  constructed, using the backpropagation algorithm, radial
		  basis functions and learning vector quantization,
		  respectively. We discuss the use of majority voting to
		  combine the classifier predictions, and the use of AdaBoost
		  (Y. Freund and R.E. Schapire, 1997) to improve the
		  performance of the classifiers. By using these methods, we
		  are able to achieve a relatively high recognition accuracy
		  for both N-deficiency prediction (~81%) and K-deficiency
		  prediction (~91%), despite the fact that the field data are
		  very noisy and the number of available training instances
		  is limited.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  liassidou99a,
  author	= {Liassidou, F. and Michaelides, S. C. and Neocleous, S. C.
		  and Schizas, C. N.},
  title		= {Identification of synoptic patterns on weather charts by
		  artificial neural networks},
  booktitle	= {Engineering Applications of Neural Networks. Proceedings
		  of the 5th International Conference on Engineering
		  Applications of Neural Networks (EANN'99)},
  publisher	= {Wydawnictwo Adam Marszalek},
  address	= {Torun, Poland},
  year		= {1999},
  volume	= {},
  pages		= {247--52},
  abstract	= {The purpose of the present research is to investigate
		  whether identification of synoptic patterns on weather
		  charts can be made objectively by training artificial
		  neural networks. In order to achieve this, the daily
		  weather analyses at 0000 UTC for 1996 were employed. The
		  respective data consist of the grid point values of the
		  geopotential height of the 500 hPa isobaric surface. A
		  uniform grid-point spacing of 2.5 degrees is used and the
		  geographical area covered by the investigation lies between
		  25 degrees N and 65 degrees N and between 20 degrees W and
		  50 degrees E, covering Europe, the Middle East and the
		  North African coast. An unsupervised learning
		  self-organizing feature map algorithm (Kohonen) was used.
		  The results referred to in this study employ a generation
		  of 15 synoptic classes. The main conclusion from this
		  endeavor is that the present technique produced a
		  satisfactory classification of the synoptic patterns over
		  the geographical region mentioned above. This
		  classification exhibits a strong seasonal relationship.},
  dbinsdate	= {oldtimer}
}

@Article{	  lieberman97a,
  author	= {M. A. Lieberman and R. B. Patil},
  title		= {Evaluation of learning vector quantization to classify
		  cotton trash},
  journal	= {Optical Engineering},
  year		= {1997},
  volume	= {36},
  number	= {3},
  pages		= {914--21},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lightowler97a,
  author	= {N. Lightowler and C. T. Spracklen and A. R. Allen},
  title		= {A modular approach to implementation of the
		  \mbox{self-organising} map},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {130--135},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lighttowler99a,
  author	= {Lighttowler, N. and Allen, A. R. and Grant, H. and Hendry,
		  D. C. and Spracklen, C. T.},
  title		= {The modular map},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {851--6},
  abstract	= {The modular map is a fully digital implementation of an
		  artificial neural network (ANN) inspired by the
		  self-organising map (SOM). By utilising a combination of
		  full custom and semi-custom design techniques a device has
		  been developed that implements 256 neurons. While an
		  individual device is a self-contained neural network
		  suitable for real-time applications, these devices have
		  been designed as building blocks for modular neural network
		  systems to provide a scaleable implementation. These
		  devices can be combined to form massively parallel
		  implementations of the SOM in a way that minimises
		  increases in training time for the network.},
  dbinsdate	= {oldtimer}
}

@Article{	  likas99a,
  author	= {Likas, A.},
  title		= {A reinforcement learning approach to online clustering},
  journal	= {Neural Computation},
  year		= {1999},
  volume	= {11},
  pages		= {1915--32},
  abstract	= {A general technique is proposed for embedding online
		  clustering algorithms based on competitive learning in a
		  reinforcement learning framework. The basic idea is that
		  the clustering system can be viewed as a reinforcement
		  learning system that learns through reinforcements to
		  follow the clustering strategy we wish to implement. In
		  this sense, the reinforcement guided competitive learning
		  (RGCL) algorithm is proposed that constitutes a
		  reinforcement based adaptation of learning vector
		  quantization (LVQ) with enhanced clustering capabilities.
		  In addition, we suggest extensions of RGCL and LVQ that are
		  characterized by the property of sustained exploration and
		  significantly improve the performance of those algorithms,
		  as indicated by experimental tests on well-known data
		  sets.},
  dbinsdate	= {oldtimer}
}

@Article{	  likhovidov97a,
  author	= {V. Likhovidov},
  title		= {Variational approach to unsupervised learning algorithms
		  of neural networks},
  journal	= {Neural Networks},
  year		= {1997},
  volume	= {10},
  number	= {2},
  pages		= {273--89},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lim00a,
  author	= {Kil-Taek Lim and Yun-Seok Nam and Hye-Kyu Kim and Sung-Il
		  Chien},
  title		= {Classification of handwritten numerals using modular
		  neural networks},
  booktitle	= {Proceedings of the International Conference on Artificial
		  Intelligence. IC-AI'2000. CSREA Press, Athens, GA, USA},
  year		= {2000},
  volume	= {2},
  pages		= {875--81},
  abstract	= {We propose a method to classify handwritten numerals using
		  modular neural networks with image dithering. Basically the
		  'divide and conquer' strategy is employed. The initial
		  clusters produced by SOM learning are extended to further
		  include the clusters which overlap each other. Each MLP is
		  assigned to each extended cluster. The gating network to
		  combine the decisions of the expert MLP networks is
		  designed and trained on such clusters. To further enhance
		  recognizing capability, the recognition methods using
		  dithering patterns are also advanced. The experimental
		  results demonstrated that the proposed method produces very
		  good recognition performance.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from internet}
}

@InProceedings{	  limboonruang00a,
  author	= {Limboonruang, P. and Thipakorn, B. and Demeechai, T.},
  title		= {Zero redundancy error protection of images using
		  self-organizing-maps},
  booktitle	= {IEEE APCCAS 2000. 2000 IEEE Asia-Pacific Conference on
		  Circuits and Systems. Electronic Communication Systems.
		  IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {},
  pages		= {89--92},
  abstract	= {This paper proposes the approach of zero redundancy error
		  protection in transmitting images using Self-Organizing
		  Maps (SOM). SOM is a kind of neural network arranging the
		  groups of cluster input having similar characteristics
		  adjacent to each other. SOM is used in this research to
		  design a mapping codebook of gray levels to bit patterns
		  such that similar levels are mapped to similar bit
		  patterns. The codebook designed by SOM for image
		  transmission over noisy channels (Binary Symmetric Channel)
		  are presented and compared with that of using Natural
		  codebook and Graycode codebook. The results indicated that
		  SOM improves the PSNR over both the Natural codebook and
		  Graycode codebook.},
  dbinsdate	= {2002/1}
}

@Article{	  lin00a,
  author	= {Lin, T. Y. and Tseng, C. H.},
  title		= {Optimum design for Artificial Neural Networks: An example
		  in a bicycle derailleur system},
  journal	= {Engineering Applications of Artificial Intelligence},
  year		= {2000},
  number	= {1},
  volume	= {13},
  pages		= {3--14},
  abstract	= {The integration of neural networks and optimization
		  provides a tool for designing network parameters and
		  improving network performance. In this paper, the Taguchi
		  method and the Design of Experiment (DOE) methodology are
		  used to optimize network parameters. The users have to
		  recognize the application problems and choose a suitable
		  Artificial Neural Network model. Optimization problems can
		  then be defined according to the model. The Taguchi method
		  is first applied to a problem to find out the more
		  important factors, then the DOE methodology is used for
		  further analysis and forecasting. A Learning Vector
		  Quantization example is shown for an application to bicycle
		  derailleur systems.},
  dbinsdate	= {oldtimer}
}

@Article{	  lin00b,
  author	= {Chin Teng Lin and Yin Cheung Lee and Her Chang Pu},
  title		= {Satellite sensor image classification using cascaded
		  architecture of neural fuzzy network},
  journal	= {IEEE Transactions on Geoscience and Remote Sensing},
  year		= {2000},
  volume	= {38},
  pages		= {1033--43},
  abstract	= {Satellite sensor images usually contain many complex
		  factors and mixed pixels, so a high classification accuracy
		  is not easy to attain. Especially, for a nonhomogeneous
		  region, gray values of satellite sensor images vary greatly
		  and thus, direct statistic gray values fail to do the
		  categorization task correctly. The goal of this paper is to
		  develop a cascaded architecture of neural fuzzy networks
		  with feature mapping (CNFM) to help the clustering of
		  satellite sensor images. In the CNFM, a Kohonen's
		  self-organizing feature map (SOFM) is used as a
		  preprocessing layer for the reduction of feature domain,
		  which combines original multi-spectral gray values,
		  structural measurements from co-occurrence matrices, and
		  spectrum features from wavelet decomposition. In addition
		  to the benefit of dimensional reduction of feature space,
		  Kohonen's SOFM can remove some noisy areas and prevent the
		  following training process from being overoriented to the
		  training patterns. The condensed measurements are then
		  forwarded into a neural fuzzy network, which performs
		  supervised learning for pattern classification. The
		  proposed cascaded approach is an appropriate technique for
		  handling the classification problem in areas that exhibit
		  large spatial variation and interclass heterogeneity (e.g.,
		  urban-rural infringing areas). The CNFM is a general and
		  useful structure that can give us favorable results in
		  terms of classification accuracy and learning speed.
		  Experimental results indicate that our structure can retain
		  high accuracy of classification (90% in average), while the
		  training time is substantially reduced if our system is
		  compared to the commonly used backpropagation network.},
  dbinsdate	= {oldtimer}
}

@Article{	  lin01a,
  author	= {Lin, H. and Wang, X. and Lu, J. and Yahagi, T.},
  title		= {Analysis of a neural detector based on self-organizing map
		  in a 16 {QAM} system},
  journal	= {IEICE Transactions on Communications},
  year		= {2001},
  volume	= {E84-B},
  number	= {9},
  month		= {September },
  pages		= {2628--2634},
  organization	= {Graduate Sch. of Sci. and Technology, Chiba University},
  publisher	= {},
  address	= {},
  abstract	= {A signal suffers from nonlinear, linear, and additive
		  distortion when transmitted through a channel. Linear
		  equalizers are commonly used in receivers to compensate for
		  linear channel distortion. As an alternative, novel
		  equalizer structures utilizing neural computation have been
		  developed for compensating for nonlinear channel
		  distortion. In this paper, we propose a neural detector
		  based on self-organizing map (SOM) in a 16 QAM system. The
		  proposed scheme uses the SOM algorithm and symbol-by-symbol
		  detector to form a neural detector, and it adapts well to
		  the changing channel conditions, including nonlinear
		  distortions because of the topology-preserving property of
		  the SOM algorithm. According to the theoretical analysis
		  and computer simulation results, the proposed scheme is
		  shown to have better performance than traditional linear
		  equalizer when facing with nonlinear distortion.},
  dbinsdate	= {2002/1}
}

@Article{	  lin01b,
  author	= {Lin, W. -S. and Tsai, C. -H.},
  title		= {Self-organizing fuzzy control of multi-variable systems
		  using learning vector quantization network},
  journal	= {Fuzzy Sets and Systems},
  year		= {2001},
  volume	= {124},
  number	= {2},
  month		= {Dec 1 },
  pages		= {197--212},
  organization	= {Department of Electrical Engineering, National Taiwan
		  University},
  publisher	= {},
  address	= {},
  abstract	= {Using learning vector quantization (LVQ) network to
		  construct a self-organizing fuzzy controller (SOFC) for
		  multi-variable nonlinear composite systems is developed in
		  this paper. The LVQ network is used to provide information
		  about the better locations of the IF-part membership
		  functions through un-supervised learning. The generated
		  fuzzy rule base is applied to the SOFC and updated by a
		  self-learning procedure. Using Lyapunov stability methods,
		  the proposed adaptive scheme is proven to provide the SOFC
		  some degree of robust properties and guarantee uniform
		  ultimate boundedness in the presence of disturbances,
		  measurement noise and perturbed initialization error. The
		  effectiveness of the proposed controller has been
		  demonstrated numerically by applying to control a two-link
		  manipulator. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  lin01c,
  author	= {Lin, P. and Jules, K.},
  title		= {An intelligent system for monitoring the microgravity
		  environment quality on-board the international space
		  station},
  booktitle	= {Conference Record---IEEE Instrumentation and Measurement
		  Technology Conference},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {2117--2122},
  organization	= {Cleveland State University, Mechanical Engineering Dept.},
  publisher	= {},
  address	= {},
  abstract	= {An intelligent system for monitoring the microgravity
		  environment quality on-board the International Space
		  Station is presented. The monitoring system uses a new
		  approach by combining Kohonen's self-organizing feature
		  map, learning vector quantization and back propagation
		  neural network to classify and recognize the known and
		  unknown patterns. Finally, fuzzy logic is employed to
		  assess the level of confidence associated with each
		  vibrating source activation detected by the system.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lin91a,
  author	= {X. Lin and D. Soergel and G. Marchionini},
  title		= {A \mbox{Self-organizing} Semantic Map for Information
		  Retrieval},
  booktitle	= {Proc. 14th. Ann. Int. ACM/SIGIR Conf. on R \& D In
		  Information Retrieval},
  year		= {1991},
  pages		= {262--269},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  lin92a,
  author	= {Lin, Wei Chung and Tsao, Eric Chen Kuo and Chen, Chin Tu},
  title		= {Constraint satisfaction neural networks for image
		  segmentation.},
  journal	= {Pattern Recognition},
  year		= {1992},
  number	= {7},
  volume	= {25},
  pages		= {679--693},
  month		= {July},
  abstract	= {Image segmentation is a process to divide an image into
		  segments with uniform and homogeneous attributes such as
		  graytone or texture. An image segmentation problem can be
		  casted as a Constraint Satisfaction Problem (CSP) by
		  interpreting the process as one of assigning labels to
		  pixels subject to certain spatial constraints. A class of
		  Constraint Satisfaction Neural Networks (CSNNs), different
		  from the conventional algorithms, is proposed for image
		  segmentation. In the network, each neuron represents one
		  possible label of an object in a CSP and the
		  interconnections between the neurons constitutes the
		  constraints. In the context of image segmentation, each
		  pixel in an n x n image can be considered as an object,
		  i.e. there are n super(2) objects in the CSP. Suppose that
		  each object is to be assigned one of m labels. Then, the
		  CSNN consists of n x n x m neurons which can be conceived
		  as a three-dimensional (3D) array. The connections and the
		  topology of the CSNN are used to represent the constraints
		  in a CSP. The initial condition for this network is set up
		  by Kohonen's self-organizing feature map. The mechanism of
		  the CSNN is to find a solution that satisfies all the
		  constraints in order to achieve a global consistency. The
		  final solution outlines segmented areas an simultaneously
		  satisfies the given constraints. From our extensive
		  experiments, the results show that this CSNN method is a
		  very promising approach for image segmentation. Due to its
		  network structure, it lends itself admirably to parallel
		  implementation and is potentially faster than conventional
		  image segmentation algorithms.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lin92b,
  author	= {Lin, X. },
  title		= {Visualization for the document space},
  booktitle	= {Proceedings of Visualization '92},
  year		= {1992},
  pages		= {274--81},
  organization	= {Center for Comput. Legal Res. , Pace Univ. , White Plains,
		  NY, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lin95a,
  author	= {Siming Lin and Jennie Si and A. B. Schwartz},
  title		= {Self-Organization of Motor Cortical Discharge Patterns},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {I},
  pages		= {133--138},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Article{	  lin95b,
  author	= {Lin, K. H. C. and Tung-Bo Chen and Von-Wun Soo},
  title		= {Neural network learning and encoding of thematic role
		  assignments in parsing of simple {C}hinese sentences},
  journal	= {Journal of Information Science and Engineering},
  year		= {1995},
  volume	= {11},
  number	= {1},
  pages		= {109--26},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lin96a,
  author	= {Jiann-Horng Lin and Can Isik},
  title		= {A maximum entropy radial basis function network based
		  neuro-fuzzy controller},
  booktitle	= {Proceedings of the Fifth IEEE International Conference on
		  Fuzzy Systems. FUZZ-IEEE '96},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {156--61},
  dbinsdate	= {oldtimer}
}

@Article{	  lin96b,
  author	= {Siming Lin and J. Si and A. B. Schwartz},
  title		= {Self-organizing model of motor cortical activities during
		  drawing},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2718},
  pages		= {540--51},
  annote	= {Smart Structures and Materials 1996. Smart Sensing,
		  Processing, and Instrumentation Conf. Date: 26--28 Feb.
		  1996 Conf. Loc: San Diego, CA, USA Conf. Sponsor: SPIE;
		  ASME; Soc. Experimental Mech. ; U. S. Army Res. Office},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lin96c,
  author	= {S. Lin and J. Si},
  title		= {Convergence properties of {SOFM} algorithm for vector
		  quantization},
  booktitle	= {Proceedings of 1997 IEEE International Symposium on
		  Circuits and Systems. Circuits and Systems in the
		  Information Age. ISCAS '97},
  publisher	= {MIT Press},
  year		= {1996},
  volume	= {1},
  editor	= {D. S. Touretzky and M. C. Mozer and M. E. Hasselmo},
  address	= {Cambridge, MA, USA},
  pages		= {509--12},
  dbinsdate	= {oldtimer}
}

@Article{	  lin97a,
  author	= {Xia Lin},
  title		= {Map Displays for Information Retrieval},
  journal	= {Journal of the American Society for Information Science},
  year		= 1997,
  volume	= 48,
  pages		= {40--54},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lin97b,
  author	= {Chung-Chih Lin and Jeng-Ren Duann and Hui-Cheng Cheng and
		  Jyh-Horng Chen},
  title		= {A cascade algorithm combined {K}ohonen feature map with
		  fuzzy C-means applied in {MR} brain image segmentation},
  booktitle	= {Proceedings of the 18th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  `Bridging Disciplines for Biomedicine'},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {3},
  editor	= {H. Boom and C. Robinson and W. Rutten and M. Neuman and H.
		  Wijkstra},
  address	= {New York, NY, USA},
  pages		= {1079--80},
  dbinsdate	= {oldtimer}
}

@Article{	  lin97c,
  author	= {Juan K. Lin and David G. Grier and Jack D. Cowan},
  title		= {Faithful Representation of Separable Distributions},
  journal	= {Neural Computation},
  year		= 1997,
  volume	= 9,
  pages		= {1305--1320},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lin97d,
  author	= {Siming Lin and J. Si},
  title		= {Weight convergence and weight density of the multi-
		  dimensional {SOFM} algorithm},
  booktitle	= {Proceedings of the 1997 American Control Conference},
  publisher	= {American Autom. Control Council},
  year		= {1997},
  volume	= {4},
  address	= {Evanston, IL, USA},
  pages		= {2404--8},
  dbinsdate	= {oldtimer}
}

@Article{	  lin97e,
  author	= {Siming Lin and J. Si and A. B. Schwartz},
  title		= {Self-organization of firing activities in monkey's motor
		  cortex: trajectory computation from spike signals},
  journal	= {Neural Computation},
  year		= {1997},
  volume	= {9},
  number	= {3},
  pages		= {607--21},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lin97f,
  author	= {Jiann-Horng Lin and C. Isik},
  title		= {Fuzzy modeling and control based on maximum entropy
		  \mbox{self-organizing} nets and cell state mapping},
  booktitle	= {1997 Annual Meeting of the North American Fuzzy
		  Information Processing Society---NAFIPS},
  publisher	= {IEEE},
  year		= {1997},
  editor	= {C. Isik and V. Cross},
  address	= {New York, NY, USA},
  pages		= {45--50},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lin97g,
  author	= {Juan K. Lin and David G. Grier and Jack D. Cowan},
  title		= {Source Separation and Density Estimation by Faithful
		  Equivariant {SOM}},
  booktitle	= {Advances in Neural Information Processing Systems 9},
  publisher	= {The MIT Press},
  year		= 1997,
  editor	= {Michael C. Mozer and Michael I. Jordan and Thomas
		  Petsche},
  pages		= {536--542},
  address	= {Cambridge, MA},
  dbinsdate	= {oldtimer}
}

@Article{	  lin98a,
  author	= {Siming Lin and Jennie Si},
  title		= {Weight-value convergence of the {SOM} algorithm for
		  discrete input},
  journal	= {Neural Computation},
  year		= {1998},
  volume	= {10},
  number	= {4},
  pages		= {807--14},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lin98b,
  author	= {Lin, Cheng Wang and Der, S. and Nasrabadi, N. M. and
		  Rizvi, S. A.},
  title		= {Automatic target recognition using neural networks},
  booktitle	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1998},
  volume	= {3466},
  pages		= {278--89},
  abstract	= {Composite classifiers that are constructed by combining a
		  number of component classifiers have been designed and
		  evaluated on the problem of automatic target recognition
		  (ATR) using forward-looking infrared (FLIR) imagery. Two
		  existing classifiers, one based on learning vector
		  quantization and the other on modular neural networks, are
		  used as the building blocks for our composite classifiers.
		  A number of classifier fusion algorithms are analyzed.
		  These algorithms combine the outputs of all the component
		  classifiers and classifier selection algorithms, which use
		  a cascade architecture that relies on a subset of the
		  component classifiers. Each composite classifier is
		  implemented and tested on a large data set of real FLIR
		  images. The performances of the proposed composite
		  classifiers are compared based on their classification
		  ability and computational complexity. It is demonstrated
		  that the composite classifier based on a cascade
		  architecture greatly reduces computational complexity with
		  a statistically insignificant decrease in performance in
		  comparison to standard classifier fusion algorithms.},
  dbinsdate	= {oldtimer}
}

@Article{	  lin98c,
  author	= {Lin, Jenn Huei Jerry and Chang, Jyh Shan and Chiueh, Tzi
		  Dar},
  title		= {Heterogeneous recurrent neural networks},
  journal	= {IEICE Transactions on Fundamentals of Electronics,
		  Communications and Computer Sciences},
  year		= {1998},
  number	= {3},
  volume	= {},
  pages		= {489--499},
  abstract	= {Noise cancellation and system identification have been
		  studied for many years, and adaptive filters have proved to
		  be a good means for solving such problems. Some neural
		  networks can be treated as nonlinear adaptive filters, and
		  are thus expected to be more powerful than traditional
		  adaptive filters when dealing with nonlinear system
		  problems. In this paper, two new heterogeneous recurrent
		  neural network (HRNN) architectures will be proposed to
		  identify some nonlinear systems and to extract a fetal
		  electrocardiogram (ECG), which is corrupted by a much
		  larger noise signal, Mother's ECG. The main difference
		  between a heterogeneous recurrent neural network (HRNN) and
		  a recurrent neural network (RNN) is that a complete neural
		  network is used for the feedback path along with an error
		  back-propagation (BP) neural network as the feedforward
		  one. Different feedback neural networks can be used to
		  provide different feedback capabilities. In this paper, a
		  BP neural network is used as the feedback network in the
		  architecture we proposed. And a self-organizing feature
		  mapping (SOFM) network is used next as an alternative
		  feedback network to form another heterogeneous recurrent
		  neural network (HRNN). The heterogeneous recurrent neural
		  networks (HRNN) successfully solve these two problems and
		  prove their superiority to traditional adaptive filters and
		  BP neural networks.},
  dbinsdate	= {oldtimer}
}

@Article{	  lin99a,
  author	= {Lin, C. T. and Chen, H. C. and Nunamaker, J. F.},
  title		= {Verifying the proximity and size hypothesis for
		  self-organizing maps},
  journal	= {JOURNAL OF MANAGEMENT INFORMATION SYSTEMS},
  year		= {1999},
  volume	= {16},
  number	= {3},
  month		= {WIN},
  pages		= {57--70},
  abstract	= {The Kohonen Self-Organizing Mag (SOM) is an unsupervised
		  learning technique for summarizing high-dimensional data so
		  that similar inputs are, in general, mapped close to one
		  another. When applied to textual data, SOM has been shown
		  to be able to group together related concepts in a data
		  collection and to present major topics within the
		  collection with larger regions. This article presents
		  research in which we sought to validate these properties of
		  SOM, called the Proximity and Size Hypotheses, through a
		  user evaluation study. Building upon our previous research
		  in automatic concept generation and classification, we
		  demonstrated that the Kohonen SOM was able to perform
		  concept clustering effectively, based on its concept
		  precision and recall7 scores as judged by human experts. We
		  also demonstrated a positive relationship between the size
		  of an SOM region and the number of documents contained in
		  the region. We believe this research has established the
		  Kohonen SOM algorithm as an intuitively appearing and
		  promising neural- network-based textual classification
		  technique for addressing part of the longstanding
		  "information overload" problem.},
  dbinsdate	= {2002/1}
}

@TechReport{	  lindroos92a,
  author	= {Martti Lindroos},
  title		= {Itseorganisoituvan neuraaliverkon laitteistototeutus},
  institution	= {Tampere University of Technology, Electronics Laboratory},
  number	= {10--92},
  address	= {Tampere, Finland},
  year		= {1992},
  note		= {(in Finnish)},
  dbinsdate	= {oldtimer}
}

@Article{	  lindsey00a,
  author	= {Lindsey, Clark S. and Stromberg, Michael},
  title		= {Image classification using the frequencies of simple
		  features},
  journal	= {Pattern Recognition Letters},
  year		= {2000},
  number	= {3},
  volume	= {21},
  pages		= {265--268},
  abstract	= {We investigate using the frequency of simple features to
		  provide image signatures for input to a classifier. In an
		  approach inspired by the n-gram technique for text
		  classification, a binary image is scanned with a small
		  window, e.g. 3×3 matrix and the occurrences of all possible
		  features patterns within that window are counted. A vector
		  with an element for each possible feature is then created
		  with the element coefficients proportional to the frequency
		  of the corresponding features or p-grams, e.g. the vector
		  would have 512 elements for a 3×3 window. We tested the
		  method by calculating the p-grams of artificially created
		  images of four different objects and presenting them to a
		  self-organizing map (SOM). We found this classification
		  scheme successful for this limited image domain. The p-gram
		  encoding scheme provides invariance to translation of the
		  objects within the image and tolerance to scale variations
		  as well. In English 8 Refs.},
  dbinsdate	= {oldtimer}
}

@Article{	  ling92a,
  author	= {Ding Ling and Li Junyi and Xi Yugeng},
  title		= {Generalized self-organized learning in neural network
		  modelling for nonlinear plants},
  journal	= {Acta Electronica Sinica},
  year		= {1992},
  volume	= {20},
  number	= {10},
  pages		= {56--60},
  month		= {Oct},
  note		= {(in Chinese)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  linsker87a,
  author	= {R. Linsker},
  title		= {Towards an Organizing Principle for a Layered Perceptual
		  Network},
  booktitle	= {Neural Information Processing Systems},
  publisher	= {Amer. Inst. Phys. },
  address	= {New York, NY},
  year		= {1987},
  editor	= {D. Z. Anderson},
  chapter	= {},
  pages		= {485--494},
  dbinsdate	= {oldtimer}
}

@Article{	  liong00a,
  author	= {Liong, Shie-Yui and Lim, Wee-Han and Kojiri, Toshiharu and
		  Hori, Tomoharu},
  title		= {Advance flood forecasting for flood stricken Bangladesh
		  with a fuzzy reasoning method},
  journal	= {Hydrological Processes},
  year		= {2000},
  volume	= {14},
  number	= {3},
  month		= {Feb 28},
  pages		= {431--448},
  organization	= {Kyoto Univ},
  publisher	= {John Wiley \& Sons Ltd},
  address	= {Chichester},
  abstract	= {An artificial Neural Network (NN) was successfully
		  applied, in an earlier study, as a prediction tool to
		  forecast water level at Dhaka (Bangladesh), for up to seven
		  lead days in advance, with a high accuracy level. In
		  addition, this high accuracy degree was accompanied with a
		  very short computational time. Both make NN a desirable
		  advance warming forecasting tool. In a later study, a
		  sensitivity analysis was also performed to retain only the
		  most sensitive gauging stations for the Dhaka station. The
		  resulting reduction of gauging stations insignificantly
		  affects the prediction accuracy level. The work concerning
		  the possibility of measurement failure in any of the
		  gauging stations during the critical flow level at Dhaka
		  requires prediction tools which can interpret linguistic
		  assessment of flow levels. A fuzzy logic approach is
		  introduced with two or three membership functions,
		  depending on necessity, for the input stations with five
		  membership functions for the output station. Membership
		  functions for each station are derived from their
		  respective water level frequency distributions, after the
		  Kohonen neural network is used to group the data into
		  clusters. The proposed approach in deriving membership
		  function shows a number of advances over the approach
		  commonly used. When prediction results are compared with
		  measured data, the prediction accuracy level is comparable
		  with that of the data driven neural network approach.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  liou93a,
  author	= {Cheng-Yuan Liou and Hsin-Chang Yang},
  title		= {Spatial Topology Distance for Handprinted Character
		  Recognition},
  booktitle	= {Proc. ICANN'93 International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {918--921},
  publisher	= {Springer},
  address	= {London},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liou93b,
  author	= {Cheng-Yuan Liou and Chwan-Yi Shiah},
  title		= {Perception of Speech Signals Using Self-Organization on
		  Linear Neuron Array},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {251--254},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {A continuous speech recognition system with finite set of
		  Chinese words is devised for selected applications. With
		  proper design of the self-organizing map for the speech
		  signals, the precedence relations among the spectral
		  patterns within a token period can be preserved by the
		  topology preservations and the serious nonlinear time
		  warping can thus be overcome. The one dimensional
		  hierarchical relations among the sequential spectral
		  patterns are able to be represented by the topology map
		  developed on the linear array of neurons. We then devise
		  two kinds of perception energies based on the trained map.
		  One of the energies is derived from properly fitting a
		  precedence curve on the sequential excitation patterns of
		  the map during a whole word period. The other energy is
		  obtained from the accumulation of total excitations on the
		  map during a word period. Thresholds for the perception
		  energies are then designed experimentally. A set of 1309
		  linear array maps are used for representing the total 1309
		  standard Chinese word pronunciations. Each linear array
		  contains 100 equally spaced and linearly ordered neurons. A
		  verification of the system on a personal computer with a
		  modern DSP board has been performed and the result was
		  quite satisfactory.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liou93c,
  author	= {Cheng-Yuan Liou and Wen-Pin Tai},
  title		= {Exploring Orderliness by Self-Organization},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1618--1621},
  organization	= {JNNS},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liou94a,
  author	= {Ren-Jean Liou and Mahmood R. Azimi-Sadjadi and Donald L.
		  Reinke},
  title		= {Detection and Classification of Cloud Data From
		  Geostationary Satellite Using Artificial Neural Networks},
  pages		= {4327--4332},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, pattern recognition},
  dbinsdate	= {oldtimer}
}

@Article{	  liou96a,
  author	= {Liou, Cheng Yuan and Wu, Jiann Ming},
  title		= {Self-organization using Potts models},
  journal	= {Neural Networks},
  year		= {1996},
  number	= {4},
  volume	= {9},
  pages		= {671--684},
  abstract	= {In this work, we use Potts neurons for the competitive
		  mechanism in a self-organization model. We obtain new
		  algorithms on the basis of a Potts neural network for
		  coherent mapping, and we remodel the Durbin algorithm and
		  the Kohonen algorithm with mean field annealing. The
		  resulting dimension-reducing mappings possess a highly
		  reliable topology preservation such that the nearby
		  elements in the parameter space are ordered as similarly as
		  possible on the cortex-like map, and the objective function
		  costs between neighboring cortical points are as smooth as
		  possible. The proposed Potts neural network contains two
		  sets of interactive dynamics for two kinds of mappings, one
		  from the parameter space to the cortical space and the
		  other in the reverse way. We present a theoretical approach
		  to developing self-organizing algorithms with a novel
		  decision principle for competitive learning. We find that
		  one Potts neuron is able to implement the Kohonen
		  algorithm. Both implementation and simulation results are
		  encouraging.},
  dbinsdate	= {oldtimer}
}

@Article{	  liou96b,
  author	= {Cheng-Yuan Liou and Hsin-Chang Yang},
  title		= {Handprinted Character Recognition Based on Spatial
		  Topology Distance Measurement},
  journal	= {IEEE Transactions on Pattern Analysis and Machine
		  Intelligence},
  year		= 1996,
  volume	= 18,
  pages		= {941--945},
  dbinsdate	= {oldtimer}
}

@Article{	  liou99a,
  author	= {Liou, C. Y. and Tai, W. P.},
  title		= {Conformal self-organization for continuity on a feature
		  map},
  journal	= {Neural Networks},
  year		= {1999},
  number	= {6},
  volume	= {12},
  pages		= {893--905},
  abstract	= {The self-organization model with a conformal-mapping
		  adaptation is studied in this work. This model is designed
		  to provide conformal transformation to meet the
		  conformality requirement in biological morphology and
		  geometrical surface mapping. This model spans the network
		  field in the input space where topological conformality is
		  preserved. The converged network provides not only the
		  organized clustering features of the input but also a
		  specific mapping representation. This facilitates the
		  Kohonen's self-organization model in exploring the input in
		  a continuous conformality sense. Simulations for morphing
		  applications are described.},
  dbinsdate	= {oldtimer}
}

@Article{	  lipmann87a,
  author	= {Richard P. Lippmann},
  title		= {An introduction to computing with neural nets},
  journal	= {IEEE Acoustics, Speech and Signal Processing Magazine},
  year		= {1987},
  pages		= {4--22},
  month		= {April},
  annote	= {Very often citated paper. Basic description about many
		  nn-models, Contains some well-known errors. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lipmann88a,
  author	= {Richard P. Lippmann},
  title		= {NEURAL NETS FOR COMPUTING},
  booktitle	= {Proc. ICASSP-88, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1988},
  pages		= {1--6},
  organization	= {IEEE, Acoustics, Speech and Signal Processing Soc, New
		  York, NY, USA},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {DIALOG No: 02687002 EI Monthly No: EIM8812--060144
		  Introduktio. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lipmann88b,
  author	= {R. P. Lippmann},
  title		= {A survey of neural network models},
  booktitle	= {Proc. ICS'88, Third International Conference on
		  Supercomputing},
  year		= {1988},
  editor	= {L. P. Kartashev and S. I. Kartashev},
  pages		= {35--40},
  volume	= {I},
  publisher	= {Int. Supercomputing Inst. },
  address	= {St. Petersburg, FL},
  dbinsdate	= {oldtimer}
}

@Article{	  lipmann89a,
  author	= {R. P. Lippmann},
  title		= {Pattern classification using neural networks},
  journal	= {IEEE Communications Magazine},
  year		= {1989},
  volume	= {27},
  number	= {11},
  pages		= {47--50},
  month		= {November},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lipponen98a,
  author	= {Lipponen, S. and M\"akikallio, T. and Tulppo, M. and
		  R\"oning, J.},
  title		= {Finding structure in fitness data},
  booktitle	= {Proc. 2nd International Conference on The Practical
		  Application of Knowledge Discovery and Data Mining, March
		  25--27, London, UK},
  year		= {1998},
  pages		= {101--109},
  dbinsdate	= {oldtimer}
}

@Article{	  liqin95a,
  author	= {Shen Liqin and Qi Feihu},
  title		= {Color spatial quantization and compression method based on
		  palette technique},
  journal	= {Acta Electronica Sinica},
  year		= {1995},
  volume	= {23},
  number	= {9},
  pages		= {103--5},
  dbinsdate	= {oldtimer}
}

@Article{	  liqin96a,
  author	= {Shen Liqin and Qi Feihu},
  title		= {Color spatial quantization and compression technique based
		  on palette},
  journal	= {High Technology Letters [English Language Edition]},
  year		= {1996},
  volume	= {2},
  number	= {1},
  pages		= {51--4},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lirov91a,
  author	= {Y. Lirov},
  title		= {Optimal dimensioning of counterpropagation neural
		  networks},
  booktitle	= {IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {II},
  pages		= {455--459},
  x		= {. . . It includes a mechanized Kohonen layer configurator,
		  which combines A* . . . Mechanized Kohonen layer
		  configurator;},
  dbinsdate	= {oldtimer}
}

@Article{	  lirov92a,
  author	= {Y. Lirov},
  title		= {Computer aided neural network engineering},
  journal	= {Neural Networks},
  year		= {1992},
  volume	= {5},
  number	= {4},
  pages		= {711--719},
  month		= {July-August},
  dbinsdate	= {oldtimer}
}

@Article{	  lisboa92a,
  author	= {P. J. G. Lisboa},
  title		= {Single layer perceptron for the recognition of
		  hand-written digits},
  journal	= {Int. J. Neural Networks---Res. \& Applications},
  year		= {1992},
  volume	= {3},
  number	= {1},
  pages		= {17--22},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lisogurski98a,
  author	= {Lisogurski, D. and Birch, G. E.},
  title		= {Identification of finger flexions from continuous {EEG} as
		  a brain computer interface},
  booktitle	= {Proceedings of the 20th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  Vol.20 Biomedical Engineering Towards the Year 2000 and
		  Beyond.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  volume	= {4},
  pages		= {2004--7},
  abstract	= {Much of the research in the development of a Brain
		  Computer Interface (BCI) has focused on differentiating
		  between several possible commands rather than the
		  identification of control signals from continuous EEG. This
		  generally results in the detection of many unintended
		  commands while the operator is trying to rest. This work
		  implements an Asynchronous Signal Detector (ASD) capable of
		  identifying index finger flexions from a continuous
		  sampling of surface electrodes. Spatiotemporal features are
		  classified using Learning Vector Quantization (LVQ). The
		  ASD can function as a stand alone BCI capable of
		  recognizing a single control signal or operate in
		  conjunction with an existing BCI method to recognize
		  multiple commands.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liszka-hackzell95a,
  author	= {Liszka-Hackzell, J. },
  title		= {Categorization of fetal heart rate patterns using neural
		  networks},
  booktitle	= {Computers in Cardiology 1994},
  year		= {1995},
  pages		= {97--100},
  organization	= {Dept. of Med. Inf. , Linkoping Inst. of Technol. ,
		  Sweden},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  liszka01a,
  author	= {Liszka Hackzell, J. J.},
  title		= {Categorization of fetal heart rate patterns using neural
		  networks},
  journal	= {Journal-of-Medical-Systems},
  year		= {2001},
  volume	= {25},
  pages		= {269--76},
  abstract	= {Digitized data from cardiotocography (CTG) measurements
		  (fetal heart rate and uterine contractions) have been used
		  for categorization of typical heart rate patterns before
		  and during delivery. Short time series of CTG data, of
		  about 7 min duration, have been used in the categorization
		  process. In the first part of the study, selected CTG data,
		  corresponding to 10 typical cases, was used for purely
		  auto-associative unsupervised training of a self-organizing
		  map (SOM) neural network. The network may then be used for
		  objective categorization of CTG patterns through the map
		  coordinates produced by the network. The SOM coordinates
		  were then compared. In the second part of the study, a
		  hybrid neural network consisting of a SOM network and a
		  backpropagation network was trained with data corresponding
		  to a number of basic heart rate patterns as described by
		  eight manually selected indices. Test data (which was
		  different from the training data) was then used to check
		  the performance of the network. The study showed that a
		  categorization process in which neural networks are used
		  can be reliable and agrees well with manual categorization.
		  Since the categorization by neural networks is very fast
		  and does not involve any human effort, it may be useful in
		  patient monitoring.},
  dbinsdate	= {2002/1}
}

@Article{	  litke90a,
  author	= {H. -D. Litke},
  title		= {Neurocomputers. 2. Learning from the human brain},
  journal	= {NET},
  year		= {1990},
  volume	= {44},
  number	= {7--8},
  pages		= {330--337},
  month		= {July-August},
  x		= { Hopfield-Kohonen model; . . . },
  dbinsdate	= {oldtimer}
}

@InCollection{	  littman92a,
  author	= {E. Littman and A. Meyering and J. Walter and Th. Wengerek
		  and H. Ritter},
  title		= {Neural Networks for Robotics},
  booktitle	= {Applications of Neural Networks},
  publisher	= {VCH},
  year		= {1992},
  editor	= {K. Schuster},
  pages		= {79--103},
  address	= {Weinheim, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  liu00a,
  author	= {Liu, Xingyuan and Zheng, Yingren},
  title		= {Analysis on the parameters to affect behavior of
		  rock-socketed segment of piles},
  journal	= {Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock
		  Mechanics and Engineering},
  year		= {2000},
  number	= {3},
  volume	= {19},
  pages		= {383--386},
  abstract	= {Based on the neural network model of limit load of
		  rock-socketed segment of pile, the analysis method of
		  characteristic parameters is proposed by means of
		  self-organizing character map network. The analysis results
		  are consistent with practical results. Meanwhile, the
		  relation curves between parameter and limit load are given
		  and some useful results are obtained.},
  dbinsdate	= {oldtimer}
}

@Article{	  liu00b,
  author	= {Liu, Y. and Zhao, B. and Xia, S. W.},
  title		= {Self-organizing network with fuzzy hyperellipsoidal
		  classifying and its application in unconstrained
		  handwritten numeral recognition},
  journal	= {Qinghua Daxue Xuebao/Journal of Tsinghua University},
  year		= {2000},
  volume	= {40},
  number	= {9},
  month		= {September },
  pages		= {120--124},
  organization	= {Dep. of Automation, Tsinghua Univ.},
  publisher	= {},
  address	= {},
  abstract	= {A self-organizing network with the fuzzy
		  hyperellipsoidal-classifying (FHECF) algorithm was proposed
		  to recognize handwritten numerals. The SOM clustering and
		  the adaptive principle extraction (APEX) algorithm were
		  used to reproduce the original learning result, with some
		  small nodes including their coordinates and covariance
		  matrices, to represent the main distributions of the
		  training set. Then, the nodes that gave worse performances
		  were split by fuzzy hyperellipsoidal clustering (FHEC)
		  algorithm and the new nodes were modified to gain a better
		  learning result. The results shows that the algorithm
		  identifies the suitable number of network nodes and the
		  hyperellipsoidal classifying result to provide a more
		  precise training requirement. With the help of supervised
		  learning algorithms such as learning vector quantization
		  (LVQ), the network gains a better performance. In
		  experiments recognizing unconstrained handwritten numerals,
		  the algorithm has also shown satisfying performance.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  liu00c,
  author	= {Chii Tung Liu and Pol Lin Tai and Chen, A. Y. -J. and
		  Peng, C.-H. and Jia Shung Wang},
  title		= {A content-based scheme for {CT} lung image retrieval},
  booktitle	= {IEEE International Conference on Multi-Media and Expo},
  year		= {2000},
  editor	= {},
  volume	= {},
  pages		= {1203--1206},
  organization	= {Department of Computer Science, National Tsing Hua
		  University},
  publisher	= {},
  address	= {},
  abstract	= {In this paper, a content-based scheme to retrieve lung
		  Computed Tomographic images (CT) is presented. It consists
		  of a visual-based user interface to allow the query be made
		  by line-drawing the interested (abnormal) regions; and a
		  training scheme to classify the relationship between the
		  images stored in database. The system will output a set of
		  candidate images that are textural-similar to the query
		  image. We marked the abnormal portions of each training
		  image by polygonal or rectangular shape manually because it
		  needs the knowledge of expertise. Then, the texture
		  features of each marked region are extracted on the DCT or
		  SADCT transform domain. In the training stage, the
		  extracted DCT/SADCT coefficients are fed into a Kohonen
		  self-organizing network to find the relationship for
		  classification. In the query stage, the system first checks
		  which texture category is the query image in then uses some
		  geometrical characteristics to identify the candidate image
		  of most likely. The experimental results show that 96% of
		  queries can be correctly retrieved, where the original
		  image is in the candidate set.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from internet}
}

@InProceedings{	  liu00d,
  author	= {Chii Tung Liu and Pol Lin Tai and Chen, A. Y. J. and Chen
		  Hsiang Peng and Jia Shung Wang},
  title		= {A content-based medical teaching file assistant for {CT}
		  lung image retrieval},
  booktitle	= {ICECS 2000. 7th IEEE International Conference on
		  Electronics, Circuits and Systems. IEEE, Piscataway, NJ,
		  USA},
  year		= {2000},
  volume	= {1},
  pages		= {361--5},
  abstract	= {In this paper, a content-based scheme for assisting the
		  construction of a teaching file system to retrieve lung
		  Computed Tomographic (CT) images is presented. The system
		  uses visual-based user interface to allow the user to enter
		  or query an image by selecting the region of interest
		  (ROI); and uses neural network to classify the relationship
		  between the images stored in database. The system will
		  output a set of candidate images that are textural-similar
		  to the query image. We marked the abnormal portions of each
		  training image by rectangular shape manually because it
		  needs the knowledge of expertise. Then, the texture
		  features of each marked region are extracted by selecting
		  the most important coefficients of 2D FFT. In the training
		  stage, the system uses a Kohonen self-organizing network to
		  classify those extracted FFT coefficients. In the query
		  stage, the system first checks which texture category the
		  query image in, then uses some geometrical characteristics
		  to identify the most likely candidate image. The
		  experimental results show that on average 92% of original
		  images can be correctly retrieved with the displacement up
		  to 22% of the block size.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from internet}
}

@Article{	  liu01a,
  author	= {Liu, Cheng-Lin and Nakagawa, Masaki},
  title		= {Evaluation of prototype learning algorithms for
		  nearest-neighbor classifier in application to handwritten
		  character recognition},
  journal	= {Pattern Recognition},
  year		= {2001},
  volume	= {34},
  number	= {3},
  month		= {Mar},
  pages		= {601--615},
  organization	= {Hitachi Ltd},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {Prototype learning is effective in improving the
		  classification performance of nearest-neighbor (NN)
		  classifier and in reducing the storage and computation
		  requirements. This paper reviews some prototype learning
		  algorithms for NN classifier design and evaluates their
		  performance in application to handwritten character
		  recognition. The algorithms include the well-known LVQ and
		  some parameter optimization approaches that aim to minimize
		  an objective function by gradient search. We also propose
		  some new algorithms based on parameter optimization and
		  evaluate their performance together with the existing ones.
		  Eleven prototype learning algorithms are tested in
		  handwritten numeral recognition on the CENPARMI database
		  and in handwritten Chinese character recognition on the
		  ETL8B2 database. The experimental results show that the
		  algorithms based on parameter optimization generally
		  outperform the LVQ. Particularly, the minimum
		  classification error (MCE) approach of Juang and Katagiri
		  (IEEE Trans. Signal Process. 40 (12) (1992) 3043), the
		  generalized LVQ (GLVQ) of Sato and Yamada (Proceedings of
		  the 14th ICPR, Vol. I, Brisbane, 1998, p. 322) and a new
		  algorithm MAXP1 yield best results.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  liu01b,
  author	= {Liu, Yi-Hung and Huang, Han-Pang},
  title		= {Off-line recognition of a handwritten Chinese zither
		  score},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2632--2637},
  organization	= {Robotics Laboratory, Department of Mechanical Engineering,
		  National Taiwan University},
  publisher	= {},
  address	= {},
  abstract	= {A Chinese zither score is different form a western staff.
		  The Chinese zither score is handwritten, and is a
		  combination of fingerings, scales, and several different
		  types of notes. In this paper, we first construct pattern
		  classes for fingerings and scales we frequently play. A
		  specific segmentation method is derived in accordance with
		  the zither score. After segmentation, all meaningful
		  individuals can be found out and the weighted cross
		  counting feature is used to extract features. A cascaded
		  architecture of neural network with feature map (CANF) is
		  proposed to obtain high recognition rates. The CANF
		  cascades a supervised neural network trained by back
		  propagation (BPNN) with an unsupervised neural network,
		  Kohonen's self-organized feature map (SOFM). The SOFM can
		  reduce the dimension of feature space and remove the
		  redundancy of features in transformation such that the
		  learning time of BPNN can be speeded up and the recognition
		  rate can be improved. In our experiment, a real Chinese
		  zither score is segmented, and the CANF shows a 100%
		  perfect recognition rate.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  liu01c,
  author	= {Liu, Q. and Levinson, S. and Wu, Y. and Huang, T.},
  title		= {Robot speech learning via entropy guided {LVQ} and memory
		  association},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {2176--2181},
  organization	= {FX Palo Alto Laboratory},
  publisher	= {},
  address	= {},
  abstract	= {The goal of this project is to teach a computer-robot
		  system to understand human speech through natural
		  human-computer interaction. To achieve this goal, we
		  develop an interactive and incremental learning algorithm
		  based on entropy-guided LVQ and memory association.
		  Supported by this algorithm, the robot has the potential to
		  learn unlimited sounds progressively. Experimental results
		  of a multilingual short-speech learning task are given
		  after the presentation of the learning system. Further
		  investigation of this learning system will include
		  human-computer interactions that involve more modalities,
		  and applications that use the proposed idea to train home
		  appliances.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  liu01d,
  author	= {Chunping Liu and Ling Konq and Peihua Shen and Deshen
		  Xia},
  title		= {Multi-source remote sensing data fusion using fuzzy
		  self-organization mapping network and modified
		  Dempster-Shafer evidential reasoning method to
		  classification},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.4556},
  year		= {2001},
  volume	= {4556},
  pages		= {71--9},
  abstract	= {By integrating the Fuzzy Kohonen Clustering Network (FKCN)
		  with Fuzzy Dempster-Shafer Evidential Reasoning Theory
		  (FDSERT), a new multi-source data fusion of remote sensing
		  information is proposed in this paper. The new algorithm
		  can be applied in the classification of remote sensing
		  images through FKCN learning and FDSERT fusion.
		  Experimental results comparing the FKCN algorithm indicates
		  that the classification algorithm of multi-source data
		  fusion of remote sensing is superior to that of the FKCN
		  algorithm. The algorithm can obviously improve
		  classification accuracy. At the same time, the algorithm
		  can make the best of expert knowledge. Therefore the
		  algorithm is an effective classification algorithm of
		  remote sensing images.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name guessed, based on internet (not necessarily
                   same person)}
}

@Article{	  liu02a,
  author	= {Cheng-Lin Liu and Sako, H. and Fujisawa, H.},
  title		= {Performance evaluation of pattern classifiers for
		  handwritten character recognition},
  journal	= {International-Journal-on-Document-Analysis-and-Recognition}
		  ,
  year		= {2002},
  volume	= {4},
  pages		= {191--204},
  abstract	= {This paper describes a performance evaluation study in
		  which some efficient classifiers are tested in handwritten
		  digit recognition. The evaluated classifiers include a
		  statistical classifier (modified quadratic discriminant
		  function, MQDF), three neural classifiers, and an LVQ
		  (learning vector quantization) classifier. They are
		  efficient in that high accuracies can be achieved at
		  moderate memory space and computation cost. The performance
		  is measured in terms of classification accuracy,
		  sensitivity to training sample size, ambiguity rejection,
		  and outlier resistance. The outlier resistance of neural
		  classifiers is enhanced by training with synthesized
		  outlier data. The classifiers are tested on a large data
		  set extracted from NIST SD19. As results, the test
		  accuracies of the evaluated classifiers are comparable to
		  or higher than those of the nearest neighbor (1-NN) rule
		  and regularized discriminant analysis (RDA). It is shown
		  that neural classifiers are more susceptible to small
		  sample size than MQDF, although they yield higher
		  accuracies on large sample size. As a neural classifier,
		  the polynomial classifier (PC) gives the highest accuracy
		  and performs best in ambiguity rejection. On the other
		  hand, MQDF is superior in outlier rejection even though it
		  is not trained with outlier data. The results indicate that
		  pattern classifiers have complementary advantages and they
		  should be appropriately combined to achieve higher
		  performance.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked form internet.}
}

@InProceedings{	  liu90a,
  author	= {Jian-Qin Liu and Nan-Ning Zheng},
  title		= {A new neural network model based approach to unsupervised
		  image segmentation},
  booktitle	= {Communications on the Move. Singapore. ICCS/ISITA '92},
  year		= {1990},
  editor	= {Ng, C. S. and Yeo, T. S. and Yeo, S. P. },
  volume	= {3},
  pages		= {1404--8},
  organization	= {Inst. of AI \& Robotics, Xi'an Jiaotong Univ. , Xi'an,
		  China},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liu92a,
  author	= {Liu, J. and Wang, D. },
  title		= {Data compression for image recognition using neural
		  network},
  booktitle	= {IJCNN International Joint Conference on Neural Networks},
  year		= {1992},
  volume	= {4},
  pages		= {333--8},
  organization	= {Sch. of Electr. \& Electron. Eng. , Nanyang Technol. Inst.
		  , Singapore},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  liu92b,
  author	= {Liu, H. and Yun, D. Y. Y. },
  title		= {Adaptive image segmentation by quantization},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1766},
  pages		= {322--32},
  annote	= {A conference paper in journal},
  abstract	= {Segmentation of images into textural homogeneous regions
		  is a fundamental problem in an image understanding system.
		  Most region-oriented segmentation approaches suffer from
		  the problem of different thresholds selecting for different
		  images. In this paper an adaptive image segmentation based
		  on vector quantization is presented. It automatically
		  segments images without preset thresholds. The approach
		  contains a feature extraction module and a two-layer
		  hierarchical clustering module, a vector quantizer (VQ)
		  implemented by a competitive learning neural network in the
		  first layer. A near-optimal competitive learning algorithm
		  (NOLA) is employed to train the vector quantizer. NOLA
		  combines the advantages of both Kohonen self- organizing
		  feature map (KSFM) and K-means clustering algorithm. After
		  the VQ is trained, the weights of the network and the
		  number of input vectors clustered by each neuron form a 3-
		  D topological feature map with separable hills aggregated
		  by similar vectors. This overcomes the inability to
		  visualize the geometric properties of data in a
		  high-dimensional space for most other clustering
		  algorithms. The second clustering algorithm operates in the
		  feature map instead of the input set itself. Since the
		  number of units in the feature map is much less than the
		  number of feature vectors in the feature set, it is easy to
		  check all peaks and find the 'correct' number of clusters,
		  also a key problem in current clustering techniques. In the
		  experiments, we compare our algorithm with K-means
		  clustering method on a variety of images. The results show
		  that our algorithm achieves better performance.},
  dbinsdate	= {oldtimer}
}

@Article{	  liu92c,
  author	= {Hui Liu and Yun, D. Y. Y. },
  title		= {Competitive learning algorithms for image coding},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 1},
  pages		= {408--17},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liu93a,
  author	= {Hui Liu and David Y. Y. Yun},
  title		= {{S}elf-{O}rganizing Finite State Vector Quantization for
		  Image Coding},
  booktitle	= {Proc. Int. Workshop on Application of Neural Networks to
		  Telecommunications},
  year		= {1993},
  editor	= {Joshua Alspector and Rodney Goodman and Timothy X. Brown},
  pages		= {176--182},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, image coding},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liu94a,
  author	= {Xiaohui Liu and Gongxian Cheng and John Wu},
  title		= {Managing the Noisy Glaucomatous Test Data by
		  Self-Organizing Maps},
  pages		= {649--652},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, pattern recognition},
  dbinsdate	= {oldtimer}
}

@Article{	  liu94b,
  author	= {Liu, X. and Cheng, G. and Wu, J. X. },
  title		= {Identifying the measurement noise in glaucomatous testing:
		  an artificial neural network approach},
  journal	= {Artificial Intelligence in Medicine},
  year		= {1994},
  volume	= {6},
  number	= {5},
  pages		= {401--15},
  month		= {Oct},
  dbinsdate	= {oldtimer}
}

@Article{	  liu95a,
  author	= {Liu, H. },
  title		= {Ordered {K}ohonen vector quantization for very low bit
		  rate interframe video coding},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2419},
  pages		= {71--80},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  liu95b,
  author	= {Liu, C. y. and Li, J. g. },
  title		= {Auto-clustering of mugshots using multi-layer {K}ohonen
		  network},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2424},
  pages		= {611--19},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  liu95c,
  author	= {Chao-Yuan Liu and Jie-Gu Li},
  title		= {Multilayer {K}ohonen network and its separability
		  analysis},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2492},
  number	= {pt. 2},
  pages		= {788--95},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  liu96a,
  author	= {Li Liu and Jialong He and G. Palm},
  title		= {Signal modeling for speaker identification},
  booktitle	= {1996 IEEE International Conference on Acoustics, Speech,
		  and Signal Processing Conference Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {665--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  liu96b,
  author	= {Xiaohui Liu and Gongxian Cheng and J. Wu},
  title		= {Analysing visual field data by \mbox{self-organising}
		  maps},
  booktitle	= {Fourth European Congress on Intelligent Techniques and
		  Soft Computing Proceedings, EUFIT '96},
  publisher	= {Verlag Mainz},
  year		= {1996},
  volume	= {2},
  address	= {Aachen, Germany},
  pages		= {1435--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  liu98a,
  author	= {Hao Liu and Yun Shao},
  title		= {An improved learning vector quantization neural network
		  for land cover classification with multi-temporal Radarsat
		  images},
  booktitle	= {IGARSS '98. Sensing and Managing the Environment. 1998
		  IEEE International Geoscience and Remote Sensing. Symposium
		  Proceedings},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {4},
  editor	= {T. I. Stein},
  address	= {New York, NY, USA},
  pages		= {1787--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liu99a,
  author	= {Liu, Qiong and Rui, Yong and Huang, Thomas and Levinson,
		  Stephen},
  title		= {Video sequence learning and recognition via dynamic SOM},
  booktitle	= {IEEE International Conference on Image Processing (4 Oct
		  24-Oct 28 1999)},
  year		= {1999},
  volume	= {},
  pages		= {93--97},
  abstract	= {Information contained in the video sequences is crucial
		  for an autonomous robot or a computer to learn and respond
		  to its surrounding environment. In the past, robot vision
		  is mainly concentrated on still image processing and small
		  `image cube' processing. Continuous video sequence learning
		  and recognition is rarely addressed in the literature due
		  to its high requirement on dynamic processing. In this
		  paper, we propose a novel neural network structure called
		  Dynamic Self-Organizing Map (DSOM) for video sequence
		  processing. The proposed technique has been tested on
		  simulation data sets, and the results validate its
		  learning/recognition ability.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liu99b,
  author	= {Liu, J. N. K. and Lee, R. S. T.},
  title		= {Rainfall forecasting from multiple point sources using
		  neural networks},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {429--34},
  abstract	= {Weather forecasting has been one of the most challenging
		  problems around the world for more than half a century. Not
		  only because of its practical value in meteorology, but it
		  is also a typically "unbiased" time-series forecasting
		  problem in scientific researches. The paper describes the
		  methodology to short-term rainfall forecasting using neural
		  networks. It extends a previous study relying on
		  observational data from a single point station to multiple
		  point sources with time-series weather records in the Hong
		  Kong region. Preprocessing procedures were important for
		  this neural network modeling which was based on a
		  backpropagation architecture. This involved variable
		  transformation, classification and the use of genetic
		  algorithms for input selection. Compared with previous
		  studies on a single point source using a similar network
		  and others like radial basis function networks, learning
		  vector quantization and naive Bayesian network, the results
		  are very promising. This neural-based rainfall forecasting
		  system is useful and parallel to traditional forecasts from
		  the Hong Kong Observatory.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  liu99c,
  author	= {Jyh Charn Liu and Gouchol Pok},
  title		= {Texture edge detection by feature encoding and predictive
		  model},
  booktitle	= {1999 IEEE International Conference on Acoustics, Speech,
		  and Signal Processing. Proceedings. ICASSP99.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1105--8},
  abstract	= {Texture boundaries or edges are useful information for
		  segmenting heterogeneous textures into several classes.
		  Texture edge detection is different from the conventional
		  edge detection that is based on the pixel-wise changes of
		  gray level intensities, because textures are formed by
		  patterned placement of texture elements over some regions.
		  We propose a prediction-based texture edge detection method
		  that includes encoding and prediction modules as its major
		  components. The encoding module projects n-dimensional
		  texture features onto a 1-dimensional feature map through
		  the SOFM algorithm to obtain scalar features, and the
		  prediction module computes the predictive relationship of
		  the scalar features with respect to their neighbors sampled
		  from 8 directions. The variance of prediction errors is
		  used as the measure for detection of edges. In the
		  experiments with the micro-textures, our method has shown
		  its effectiveness in detecting the texture edges.},
  dbinsdate	= {oldtimer}
}

@Article{	  livens96a,
  author	= {S. Livens and P. Scheunders and G. {Van de Wouwer} and D.
		  {van Dyck} and H. Smets and J. Winkelmans and W. Bogaerts},
  title		= {A texture analysis approach to corrosion image
		  classification},
  journal	= {Microscopy, Microanalysis, Microstructures},
  year		= {1996},
  volume	= {7},
  number	= {2},
  pages		= {143--52},
  dbinsdate	= {oldtimer}
}

@Article{	  liya95a,
  author	= {Chen Liya and Qi Feihu},
  title		= {Object extraction using {K}ohonen neural network},
  journal	= {Journal of Shanghai Jiaotong University},
  year		= {1995},
  volume	= {29},
  number	= {6},
  pages		= {24--8},
  dbinsdate	= {oldtimer}
}

@Article{	  llobet99a,
  author	= {Llobet, E. and Hines, E. L. and Gardner, J. W. and Franco,
		  S.},
  title		= {Non-destructive banana ripeness determination using a
		  neural network-based electronic nose},
  journal	= {Measurement Science \& Technology},
  year		= {1999},
  volume	= {10},
  pages		= {538--48},
  abstract	= {An electronic nose based system, which employs an array of
		  inexpensive commercial tin-oxide odour sensors, has been
		  used to analyse the state of ripeness of bananas. Readings
		  were taken from the headspace of three sets of bananas
		  during ripening over a period of 8--14 days. A
		  principal-components analysis and investigatory techniques
		  were used to define seven distinct regions in multisensor
		  space according to the state of ripeness of the bananas,
		  predicted from a classification of banana-skin colours.
		  Then three supervised classifiers, namely Fuzzy ARTMAP, LVQ
		  and MLP, were used to classify the samples into the
		  observed seven states of ripeness. It was found that the
		  Fuzzy ARTMAP and LVQ classifiers outperformed the MLP
		  classifier, with accuracies of 90.3% and 92%, respectively,
		  compared with 83.4%. Furthermore, these methods were able
		  to predict accurately the state of ripeness of unknown sets
		  of bananas with almost the same accuracy, i.e. 90%.
		  Finally, it is shown that the Fuzzy ARTMAP classifier,
		  unlike LVQ and MLP, is able to perform efficient on-line
		  learning in this application without forgetting previously
		  learnt knowledge. All of these characteristics make the
		  Fuzzy-ARTMAP-based electronic nose a very attractive
		  instrument with which to determine non-destructively the
		  state of ripeness of fruit.},
  dbinsdate	= {oldtimer}
}

@Article{	  lo91a,
  author	= {Z. -P. Lo and B. Bavarian},
  title		= {Comparison of a neural network and a piecewise linear
		  classifier},
  journal	= {Pattern Recognition Letters},
  year		= {1991},
  volume	= {12},
  number	= {11},
  pages		= {549--655},
  month		= {November},
  dbinsdate	= {oldtimer}
}

@Article{	  lo91b,
  author	= {Z. -P. Lo and B. Bavarian},
  title		= {On the rate of convergence in topology preserving neural
		  networks},
  journal	= {Biol. Cyb. },
  year		= {1991},
  volume	= {65},
  number	= {1},
  pages		= {55--63},
  annote	= {Analysis of the neighborhood interaction function in SOM,
		  Anatomical evidence against the uniform neighborhood
		  currently used},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lo91c,
  author	= {Z. -P. Lo and M. Fujita and B. Bavarian},
  title		= {Analysis and application of \mbox{self-organizing} sensory
		  mapping},
  booktitle	= {Proc. Conf. IEEE International Conference on Syst. , Man,
		  and Cybern. 'Decision Aiding for Complex Systems'},
  year		= {1991},
  volume	= {III},
  pages		= {1599--1604},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lo91d,
  author	= {Z. -P. Lo and B. Bavarian},
  title		= {Improved rate of convergence in {K}ohonen neural network},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {II},
  pages		= {201--206},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {The neighborhood interaction function selection in the
		  Kohonen self-organizing feature map neural network is
		  analyzed for improving the rate of convergence. The
		  definition of the neighborhood interaction function is
		  motivated by anatomical evidence as opposed to what is
		  currently used, which is a uniform neighborhood interaction
		  set. By selecting a neighborhood interaction function with
		  a neighborhood amplitude of interaction which is decreasing
		  in spatial domain the topological order is always enforced
		  and the rate of self-organization to final equilibrium
		  state is improved. One simulation is carried out to show
		  the improvement in rate between using a neighborhood
		  interaction function vs. using a neighborhood interaction
		  set. An error measure functional is further defined to
		  compare the two approaches quantitatively.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lo91e,
  author	= {Z. -P. Lo and B. Bavarian},
  title		= {A neural piecewise linear classifier for pattern
		  classification},
  booktitle	= {IJCNN-91: International Joint Conference on Neural
		  Networks, Seattle},
  year		= {1991},
  volume	= {I},
  pages		= {263--268},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {A neural piecewise linear classifier, based on the Kohonen
		  learning vector quantization (LVQ2) and Kohonen
		  self-organizing feature map is proposed. The classifier has
		  two stages and a feedback loop. In the first stage, the
		  kohonen self-organizing feature map network is used to find
		  approximate position of the prototype vectors for each
		  class. In the second stage, the Kohonen LVQ2, supervised
		  learning algorithm, is used to fine tune the position of
		  the approximate prototype vectors in each of those modules.
		  Depending on the intrinsic complexity of the class
		  distribution and overall partitioning of the space, the
		  neural classifier automatically increases the number of
		  neurons, improving the error performance. The classifier is
		  tested on a set of high dimensional real data obtained from
		  ship images. The performance is compared with a piecewise
		  linear tree classifier and a neural classifier.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lo91f,
  author	= {Zhen-Ping Lo and M. Fujita and B. Bavarian},
  title		= {Analysis of neighborhood interaction in {K}ohonen neural
		  networks},
  booktitle	= {Proc. Fifth Int. Parallel Processing Symp. },
  year		= {1991},
  pages		= {246--249},
  organization	= {IEEE},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lo91g,
  author	= {Zhen-Ping Lo and B. Bavarian},
  title		= {A neural algorithm for variable thresholding of images},
  booktitle	= {Proc. Fifth Int. Parallel Processing Symp. },
  year		= {1991},
  pages		= {228--233},
  organization	= {IEEE},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  x		= {A two-stage thresholding for gray scale images is
		  presented in this paper. . . . The state of the neurons is
		  updated using the Kohonen self-organizing learning
		  algorithm. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lo92a,
  author	= {Zhen-Ping Lo and Yaoqi Qu and Behnam Bavarian},
  title		= {Analysis of a Learning Algorithm for Neural Network
		  Classifiers},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {I},
  pages		= {589--594},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lo92b,
  author	= {Zhen-Ping Lo and Yaoqi Yu and Behnman Bavarian},
  title		= {Derivation of Learning Vector Quantization Algorithms},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {III},
  pages		= {561--566},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lo92c,
  author	= {Zhen-Ping Lo and Yaoqi Yu and Behnam Bavarian},
  title		= {Two Theorems for the {K}ohonen Mapping Neural Network},
  booktitle	= {Proc. IJCNN'92, Int. Joint Conference on Neural Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {755--760},
  dbinsdate	= {oldtimer}
}

@Article{	  lo93a,
  author	= {Zhen-Ping Lo and Yaoqi Yu and Benham Bavarian},
  title		= {Analysis of the Convergence Properties of Topology
		  Preserving Neural Networks},
  journal	= {IEEE Trans. on Neural Networks},
  volume	= 4,
  number	= 2,
  month		= mar,
  pages		= {207--220},
  year		= 1993,
  dbinsdate	= {oldtimer}
}

@Article{	  lo94a,
  author	= {Zhen-Ping Lo and Bavarian, B. },
  title		= {Development of a two-stage neural network classifier},
  journal	= {Journal of Artificial Neural Networks},
  year		= {1994},
  volume	= {1},
  number	= {3},
  pages		= {307--27},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lo95a,
  author	= {Lo, K. L. and Peng, L. J. and Maqueen, J. F. and Ekwue, A.
		  O. and Cheng, D. T. Y. },
  title		= {Application of {K}ohonen \mbox{self-organising} neural
		  network to static security assessment},
  booktitle	= {Fourth International Conference on `Artificial Neural
		  Networks`},
  year		= {1995},
  pages		= {387--92},
  organization	= {Strathclyde Univ. , Glasgow, UK},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lo95b,
  author	= {K. L. Lo and R. J. Y. Tsai},
  title		= {Power system transient stability analysis by using
		  modified {K}ohonen network},
  booktitle	= {1995 IEEE International Conference on Neural Networks
		  Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {893--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lo97a,
  author	= {Joseph Y. Lo and Carey E. {Floyd, Jr. }},
  title		= {Self-Organizing Maps for Analyzing Mammographic Findings},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {2472--2474},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lo99a,
  author	= {Lo, You Shen and Pei, Soo Chang},
  title		= {Color image segmentation using local histogram and
		  Self-Organization of {K}ohonen Feature Map},
  booktitle	= {IEEE International Conference on Image Processing},
  year		= {1999},
  volume	= {3},
  pages		= {232--235},
  abstract	= {Segmentation is an important step for image analysis, but
		  a good segment algorithm which can handle color image with
		  texture area and has less computation time is rare. We
		  propose to use the local window image histogram, which is
		  easy to compute and could quickly collect the information
		  of neighbors, together with the Self-Organization of
		  Kohonen Feature Map (SOFM) neural network, which can
		  efficiently cluster data and has parallel hardware
		  structure, as a segmentation kernel. Under the Euclidean
		  distance function with input data normalization and the
		  simplified Mahalanobis distance function, this algorithm
		  will have very good segmentation results for natural images
		  either full with texture or mixed with smooth scene.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lobo95a,
  author	= {Victor Lobo and Fernando Moura-Pires},
  title		= {Ship noise classification using {K}ohonen networks},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {601--604},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lobo98a,
  author	= {V. J. Lobo and R. Swiniarski and F. Moura-Pires},
  title		= {Pruning a classifier based on a \mbox{self-organizing} map
		  using Boolean function formalization},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1910--15},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lobo98b,
  author	= {Lobo, V. J. and Bandeira, N. and Moura Pires, F.},
  title		= {Ship recognition using distributed self organizing maps},
  booktitle	= {Engineering Benefits from Neural Networks. Proceedings of
		  the International Conference EANN '98},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1998},
  volume	= {},
  pages		= {326--9},
  abstract	= {A ship recognition system by the classification of
		  acoustic signatures is presented. Recognition is achieved
		  by matching received signatures with clusters in a map
		  obtained by distributed training of a Kohonen
		  self-organizing map. The system is presented as implemented
		  using a parallel virtual machine (PVM) layer running over a
		  network of Intel-based PC running Microsoft Windows 95.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lobo98c,
  author	= {Lobo, V. J. and Bandeira, N. and Moura Pires, F.},
  title		= {Distributed {K}ohonen networks for passive sonar based
		  classification},
  booktitle	= {Proceedings of the International Conference on
		  Multisource-Multisensor Information Fusion. FUSION '98},
  publisher	= {CSREA Press},
  address	= {Athens, GA, USA},
  year		= {1998},
  volume	= {1},
  pages		= {403--9},
  abstract	= {A brief overview of the problem is given. We then proceed
		  to describe the program that we developed, and its two
		  innovative features: the distributed training of the neural
		  network on a network of PC and the use of a binary variant
		  of SOM. Finally we present the results obtained.},
  dbinsdate	= {oldtimer}
}

@Article{	  loccufier97a,
  author	= {M. Loccufier},
  title		= {Neural network techniques: a tutorial on interconnection,
		  learning and stability},
  journal	= {Journal A},
  year		= {1997},
  volume	= {38},
  number	= {4},
  pages		= {3--15},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  loncelle92a,
  author	= {J{\'{e}}r{\^{o}}me Loncelle and Nicolas Derycke and
		  Fran{\c{c}}oise Fogelman-Souli{\'{e}}},
  title		= {Cooperation of {GBP} and {LVQ} Networks for Optical
		  Character Recognition},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  volume	= {III},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {694--699},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  loncelle92b,
  author	= {J{\'{e}}r{\^{o}}me Loncelle and Nicolas Derycke and
		  Fran{\c{c}}oise Fogelman Souli{\'{e}}},
  title		= {Optical Character Recognition and Cooperating Neural
		  Networks techniques},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1591--1594},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  lonnblad91a,
  author	= {L. L{\"{o}}nnblad and C. Peterson and H. Pi and T.
		  R{\"{o}}gnvaldsson},
  title		= {Self-organizing Networks for Extracting Jet Features},
  journal	= {Computer Physics Communications},
  year		= {1991},
  volume	= {67},
  number	= {},
  pages		= {193--209},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lopez-gonzalo93a,
  author	= {Eduardo L{\'{o}}pez-Gonzalo and Luis A.
		  Hern{\'{a}}ndez-G{\'{o}}mez},
  title		= {Fast Vector Quantization Using Neural Maps for {CELP} at
		  2400 {BPS}},
  booktitle	= {Proc. EUROSPEECH-93, 3rd European Conf. on Speech,
		  Communication and Technology},
  year		= {1993},
  volume	= {I},
  pages		= {55--58},
  publisher	= {ESCA},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  lopez-rubio01a,
  author	= {Lopez-Rubio, E. and Munoz-Perez, J. and Gomez-Ruiz, J.
		  A.},
  title		= {Invariant pattern identification by self-organising
		  networks},
  journal	= {Pattern Recognition Letters},
  year		= {2001},
  volume	= {22},
  number	= {9},
  month		= {July },
  pages		= {983--990},
  organization	= {ETSI Informatica, Departmento Lenguajes y Computacion,
		  Universidad de Malaga},
  publisher	= {},
  address	= {},
  abstract	= {A new method for shape identification is proposed. It is
		  not affected by similarity transformations (scalings,
		  translations and rotations). The procedure is based on a
		  robust index that gives the deformation of a
		  self-organising feature map (SOFM) when it is trained with
		  the object to identify. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  lossmann98a,
  author	= {Lossmann, E. and Meister, A.},
  title		= {Investigation of phase coupling of harmonic signal
		  components using third order statistics and classification
		  based on learning vector quantization},
  booktitle	= {BEC '98. Proceedings. 6th Biennial Conference on
		  Electronics and Microsystems Technology. Tallin Tech. Univ,
		  Tallinn, Estonia},
  year		= {1998},
  volume	= {},
  pages		= {143--6},
  abstract	= {A quadratic phase coupling (QPC) detection algorithm is
		  proposed, based on the third-order cumulant-based
		  autoregressive (AR) model coefficients as input features to
		  the learning vector quantization-based nonlinear
		  classifier. The performance of the algorithm was tested
		  using AR model orders 2 and 6 of signals consisting of
		  three sinusoids of equal amplitude and additive Gaussian
		  noise (SNR ranged from -5 dB to noiseless signal). AR
		  models were computed on the basis of second, third, and
		  mixed 2nd and 3rd-order cumulants. Correct detection
		  probability of the QPC is approximately 80%, regardless of
		  the AR model order when using the third-order
		  cumulant-based models. Second-order cumulant-based models
		  fail to feature the QPC.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  lowe97a,
  author	= {D. Lowe and M. E. Tipping},
  title		= {NeuroScale: novel topographic feature extraction using
		  {RBF} networks},
  booktitle	= {Advances in Neural Information Processing Systems 9.
		  Proceedings of the 1996 Conference},
  publisher	= {MIT Press},
  year		= {1997},
  editor	= {M. C. Mozer and M. I. Jordan and T. Petsche},
  address	= {London, UK},
  pages		= {543--9},
  dbinsdate	= {oldtimer}
}

@Article{	  lowther98a,
  author	= {D. A. Lowther and W. Mai},
  title		= {On automatic mesh generation using {K}ohonen maps},
  journal	= {IEEE Transactions on Magnetics},
  year		= {1998},
  volume	= {34},
  number	= {5, pt.1},
  pages		= {3391--4},
  dbinsdate	= {oldtimer}
}

@Article{	  lozano95a,
  author	= {Lozano, J. and Novic, M. and Rius, F. X. and Zupan, J. },
  title		= {Modelling metabolic energy by neural networks},
  journal	= {Chemometrics and Intelligent Laboratory Systems},
  year		= {1995},
  volume	= {28},
  number	= {1},
  pages		= {61--72},
  month		= {April},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  lozano98a,
  author	= {S. Lozano and F. Guerrero and L. Onieva and J. Larraneta},
  title		= {{K}ohonen maps for solving a class of location-allocation
		  problems},
  journal	= {European Journal of Operational Research},
  year		= {1998},
  volume	= {108},
  number	= {1},
  pages		= {106--17},
  dbinsdate	= {oldtimer}
}

@Article{	  lu00a,
  author	= {Lu, J. and Srikanchana, R. and McClain, M. and Wang, Y.
		  and Xuan, J. and Sesterhenn, I.A. and Freedman, M.T. and
		  Mun, S.K.},
  title		= {Statistical volumetric model for characterization and
		  visualization of prostate cancer},
  journal	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  volume	= {3976},
  number	= {},
  month		= {},
  pages		= {142--153},
  organization	= {Catholic Univ of America},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {To reveal the spatial pattern of localized prostate cancer
		  distribution, a three-dimensional (3-D) statistical
		  volumetric model, showing the probability map of prostate
		  cancer distribution, together with the anatomical structure
		  of the prostate, has been developed from 90
		  digitally-imaged surgical specimens. Through an enhanced
		  virtual environment with various visualization modes, this
		  master model permits for the first time an accurate
		  characterization and understanding of prostate cancer
		  distribution patterns. The construction of the statistical
		  volumetric model is characterized by mapping all of the
		  individual models onto a generic prostate site model, in
		  which a self-organizing scheme is used to decompose a group
		  of contours representing multifold tumors into localized
		  tumor elements. Next crucial step of creating the master
		  model is the development of an accurate multi-object and
		  non-rigid registration/warping scheme incorporating various
		  variations among these individual models in true 3-D. This
		  is achieved with a multi-object based principle-axis
		  alignment followed by an affine transform, and further
		  fine-tuned by a thin-plate spline interpolation driven by
		  the surface based deformable warping dynamics. Based on the
		  accurately mapped tumor distribution, a standard finite
		  normal mixture is used to model the cancer volumetric
		  distribution statistics, whose parameters are estimated
		  using both the K-means and expectation-maximization
		  algorithms under the information theoretic criteria. Given
		  the desired number of tissue samplings, the prostate needle
		  biopsy site selection is optimized through a probabilistic
		  self-organizing map thus achieving a maximum likelihood of
		  cancer detection. We describe the details of our theory and
		  methodology, and report our pilot results and evaluation of
		  the effectiveness of the algorithm in characterizing
		  prostate cancer distributions and optimizing needle biopsy
		  techniques.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  lu90a,
  author	= {Shin-Yee Lu},
  title		= {Pattern Classification Using Self Organizing Feature
		  Maps},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, San Diego},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1990},
  volume	= {III},
  pages		= {471--476 },
  dbinsdate	= {oldtimer}
}

@Article{	  lu90b,
  author	= {Taiwei Lu and F. T. S. Yu and D. A. Gregory},
  title		= {Self-organizing optical neural network for unsupervised
		  learning},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1990},
  volume	= {1296},
  pages		= {378--391},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {Conf. paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  lu90c,
  author	= {Taiwei Lu and F. T. S. Yu and D. A. Gregory},
  title		= {Self-organizing optical neural network for unsupervised
		  learning},
  journal	= {Optical Engineering},
  year		= {1990},
  volume	= {29},
  number	= {9},
  pages		= {1107--1113},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lu91a,
  author	= {S. -Y. Lu and J. E. Hernandez and G. A. Clark},
  title		= {Texture segmentation by clustering of {G}abor feature
		  vectors},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {683--688},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  lu92a,
  author	= {C. -C. Lu and Y. H. Shin},
  title		= {A neural network based image compression system},
  journal	= {IEEE Transactions on Consumer Electronics},
  year		= {1992},
  volume	= {38},
  number	= {1},
  pages		= {25--29},
  month		= {February},
  dbinsdate	= {oldtimer}
}

@Article{	  lu95a,
  author	= {Lu, Y. -C. and Chang, K. C. },
  title		= {A neural network approach for high resolution target
		  classification},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2484},
  pages		= {558--66},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  lubkin02a,
  author	= {Lubkin, Jeremy and Cauwenberghs, Gert},
  title		= {{VLSI} implementation of fuzzy adaptive resonance and
		  learning vector quantization},
  journal	= {Analog Integrated Circuits and Signal Processing},
  year		= {2002},
  volume	= {30},
  number	= {2},
  month		= {February },
  pages		= {149--157},
  organization	= {Electrical and Computer Engineering, Johns Hopkins
		  University},
  publisher	= {},
  address	= {},
  abstract	= {We present a mixed-mode VLSI chip performing unsupervised
		  clustering and classification, implementing models of Fuzzy
		  Adaptive Resonance Theory (ART) and Learning Vector
		  Quantization (LVQ), and extending to variants such as
		  Kohonen Self-Organizing Maps (SOM). The parallel processor
		  classifies analog vectorial data into a digital code in a
		  single clock, and implements on-line learning of the analog
		  templates, stored locally and dynamically using the same
		  adaptive circuits for on-chip quantization and refresh. The
		  unit cell performing fuzzy choice and vigilance functions,
		  adaptive resonance learning and long-term analog storage,
		  measures 43 \mu{}m \times 43 \mu{}m in 1.2 \mu{}m CMOS
		  technology. Experimental learning results from a fabricated
		  8-input, 16-category prototype are included.},
  dbinsdate	= {2002/1}
}

@Article{	  lubkin98a,
  author	= {Lubkin, J. and Cauwenberghs, G.},
  title		= {A learning parallel analog-to-digital vector quantizer},
  journal	= {Journal of Circuits, Systems and Computers},
  year		= {1998},
  volume	= {8},
  pages		= {605--14},
  abstract	= {A mixed-mode VLSI architecture for learning vector
		  quantization (LVQ), with on-chip adaptation and dynamic
		  storage of the analog templates, converts analog vectorial
		  data in parallel to digital format. The architecture is
		  digitally configurable and extends to commonly used
		  algorithms for codebook adaptation besides k-means
		  clustering such as fuzzy adaptive resonance theory (ART)
		  and Kohonen self-organizing maps. The analog memory and
		  adaptive element of the LVQ cell comprise 6 MOS transistors
		  and one capacitor, and provide for robust self-refresh of
		  the dynamic analog storage. Total cell size including
		  distance and adaptive computations is 80*70 lambda in
		  scalable MOSIS technology. Experimental results from a
		  fabricated 16*16 cell prototype in 2 mu m CMOS are
		  included.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lubkin99a,
  author	= {Lubkin, J. and Cauwenberghs, G.},
  title		= {{VLSI} implementation of fuzzy adaptive resonance and
		  learning vector quantization},
  booktitle	= {Proceedings of the Seventh International Conference on
		  Microelectronics for Neural, Fuzzy and Bio-Inspired
		  Systems},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {147--54},
  abstract	= {We present a mixed-mode VLSI chip performing unsupervised
		  clustering and classification, implementing models of fuzzy
		  adaptive resonance theory (ART) and learning vector
		  quantization (LVQ), and extending to variants such as
		  Kohonen self-organizing maps (SOM). The parallel processor
		  classifies analog vectorial data into a digital code in a
		  single clock, and implements on-line learning of the analog
		  templates, stored locally and dynamically using the same
		  adaptive circuits for on-chip quantization and refresh. The
		  unit cell performing fuzzy choice and vigilance functions,
		  adaptive resonance learning and long-term analog storage,
		  measures 71 mu m*71 mu m in 2 mu m CMOS. Experimental
		  learning results are included from a 16-input, 16-category
		  prototype on a 2.2 mm*2.2 mm chip, operating at 10
		  ksample/s parallel data rate and 2 mW power dissipation.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  lucas89a,
  author	= {A. E. Lucas and J. Kittler},
  title		= {A comparative study of the {K}ohonen and multiedit neural
		  net learning algorithms},
  booktitle	= {Proc. First IEE International Conference on Artificial
		  Neural Networks},
  year		= {1989},
  pages		= {7--11},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  luckman90a,
  author	= {A. J. Luckman and M. Allinson},
  title		= {Modelling peripheral pre-attention and foveal fixation for
		  search directed machine vision systems},
  journal	= {Proc. Society of Photo-optical Instrumentation Engineers},
  year		= {1990},
  volume	= {1197},
  number	= {},
  pages		= {98--108},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  luckman92a,
  author	= {A. J. Luckman and N. M. Allinson},
  title		= {A multiple resolution facial feature location network with
		  perceptual feedback},
  booktitle	= {Visual Search},
  publisher	= {Taylor \& Francis},
  year		= {1992},
  editor	= {D. Brogner},
  chapter	= {},
  pages		= {169--178},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ludwig95a,
  author	= {L. Ludwig and W. Kessler and J. G{\"{o}}bbert and W.
		  Rosenstiel},
  title		= {{SOM} with Topological Interpolation for the Prediction of
		  Interference Spectra},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {379--387},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  luo00a,
  author	= {Luo, X. and Singh, C. and Patton, A. D.},
  title		= {Power system reliability evaluation using self organizing
		  map},
  booktitle	= {2000 IEEE Power Engineering Society Winter Meeting.
		  Conference Proceedings. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {2},
  pages		= {1103--8},
  abstract	= {Artificial neural networks (ANN) based on the self
		  organizing map (SOM) algorithm has received considerable
		  attention. This paper proposes a new method for power
		  system reliability evaluation by combining Monte Carlo
		  simulation and self organizing map which greatly reduces
		  the computing burden of the loss of load probability
		  calculation compared to Monte Carlo simulation only. A case
		  study of the IEEE RTS system is presented demonstrating the
		  efficiency of this approach.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  luo01a,
  author	= {Luo, Jen-Hon and Tseng, Din-Chang},
  title		= {Land cover classification of {SPOT} image by local
		  majority voting},
  booktitle	= {International Geoscience and Remote Sensing Symposium
		  (IGARSS)},
  year		= {2001},
  editor	= {},
  volume	= {6},
  pages		= {2931--2933},
  organization	= {Department of Electronic Engineering, Ming-Hsin Institute
		  of Technology},
  publisher	= {Institute of Electrical and Electronics Engineers Inc.},
  address	= {},
  abstract	= {We proposed a hierarchy scheme for the SPOT image land
		  cover classification. In the first level, we combine the
		  statistical classifier, maximum likelihood classification
		  (MLC); the neural network classifier, Learning Vector
		  Quantization (LVQ); and use a 3 \times 3 window to extract
		  second-order statistical features to classify the image. If
		  the pixel can't reach the same label in this stage, it is
		  processed in the second level. In the second stage, the
		  first-order statistical features of each point in a window
		  region are extracted. Then, the majority voting is used to
		  label the pixel, the central point of the window, which is
		  unclassified in the first level.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  luo93a,
  author	= {Ren C. Luo and Harsh Potlapalli and David Hislop},
  title		= {Traffic Sign Recognition in Outdoor Environments Using
		  Reconfigurable Neural Networks},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1306--1309},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  luo94a,
  author	= {Ren C. Luo and Harsh Potlapalli},
  title		= {Landmark Recognition using Projection Learning for Mobile
		  Robot Navigation},
  pages		= {2703--2708},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {pattern recognition, comparison},
  dbinsdate	= {oldtimer}
}

@InCollection{	  luo94b,
  author	= {Zhi-Wei Luo and K. Asada and M. Yamakita and K. Ito},
  title		= {Self-organization of an uniformly distributed visuo-motor
		  map through controlling the spatial variation},
  booktitle	= {Distributed Autonomous Robotic Systems},
  publisher	= {Springer-Verlag},
  year		= {1994},
  editor	= {H. Asama and T. Fukuda and T. Arai and I. Endo},
  address	= {Tokyo, Japan},
  pages		= {279--88},
  dbinsdate	= {oldtimer}
}

@Article{	  luo99a,
  author	= {Luo, X. and Singh, C. and Patton, A. D.},
  title		= {Using \mbox{self-organizing} map in identification of
		  load-loss state},
  journal	= {PowerTech Budapest 99. Abstract Records.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {},
  pages		= {132},
  abstract	= {This paper presents a self-organizing map (SOM) based
		  method for power system load-loss state classification.
		  This classifier maps vectors of an N-dimensional space to a
		  2-dimensional net in a nonlinear way while preserving the
		  topological order of the input vectors. Input features to
		  SOM are real and reactive power at each load bus and
		  available real power generation at each generation bus.
		  After the training of the SOM, the generalization
		  capability of the SOM can cope with various operating
		  conditions which have not been encountered during the
		  training phase and hence give a correct classification
		  result. The effectiveness of the proposed method has been
		  demonstrated on a 9-bus test system. This proposed method
		  is useful for power system operation, power system
		  reliability assessment and state screening.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  luo99b,
  author	= {Luo, X. and Singh, C. and Patton, A. D.},
  title		= {Loss-of-load state identification using
		  \mbox{self-organizing} map},
  booktitle	= {1999 IEEE Power Engineering Society Summer Meeting.
		  Conference Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {670--5},
  abstract	= {This paper presents a method for classifying power system
		  states as loss-of-load or not using Kohonen's
		  self-organizing map (SOM). The main feature of SOM is the
		  ability to map input data from an n-dimensional space to a
		  lower dimensional (usually two dimensional) space while
		  maintaining the original topological relationships. Real
		  and reactive power at each load bus and available real
		  power generation at each generation bus are taken as input
		  features. OPF calculations are performed on the weights of
		  each neuron in the map to determine whether the neuron is
		  representative of loss-of-load or not. The loss-of-load
		  status of a new system state can be quickly identified by
		  the loss-of-load status of the nearest neuron. An example
		  illustrating the approach shows that the SOM can accurately
		  classify the loss-of-load status of power system states.
		  This proposed method is useful for power system operation,
		  power system reliability assessment and state screening.},
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  lutey88a,
  author	= {M. K. Lutey},
  title		= {Problem Specific Applications for Neural Networks},
  school	= {Air Force Inst. of Tech. },
  year		= {1988},
  address	= {Wright-Patterson AFB, OH},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  luttrell01a,
  author	= {S. P. Luttrell},
  title		= {Adaptive subspave encoders using stochastic vector
		  quantisers},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {102--9},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  luttrell88a,
  author	= {S. P. Luttrell},
  title		= {Self-organizing multilayer topographic mappings},
  booktitle	= {Proc. ICNN'88, International Conference on Neural
		  Networks},
  year		= {1988},
  pages		= {93--100},
  volume	= {I},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  luttrell89a,
  author	= {S. P. Luttrell},
  title		= {Self-organisation: {A} derivation from first principles of
		  a class of learning algorithms},
  booktitle	= {Proc. IJCNN'89. Int Joint Conf. on Neural Networks},
  year		= {1989},
  pages		= {495--498},
  volume	= {{II}},
  organization	= {IEEE Technical Activities Board, Neural Network Committee,
		  USA; Int Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  luttrell89b,
  author	= {Stephen P. Luttrell},
  title		= {Hierarchical \mbox{self-organizing} networks},
  booktitle	= {Proc. 1st IEE Conf. of Artificial Neural Networks},
  year		= {1989},
  pages		= {2--6},
  publisher	= {British Neural Network Society},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  luttrell89c,
  author	= {S. P. Luttrell},
  title		= {Image compression using a multilayer neural network},
  journal	= {Pattern Recognition Letters},
  year		= {1989},
  volume	= {10},
  number	= {},
  pages		= {1--7},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  luttrell89d,
  author	= {S. P. Luttrell},
  title		= {Hierarchical vector quantisation},
  journal	= {Proc. IEE Part I},
  year		= {1989},
  volume	= {136},
  number	= {},
  pages		= {405--413},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  luttrell90a,
  author	= {Stephen P. Luttrell},
  title		= {Derivation of a class of training algoritms},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1990},
  volume	= {1},
  number	= {2},
  pages		= {229--232},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@TechReport{	  luttrell90b,
  author	= {S. P. Luttrell},
  title		= {A trainable texture anomaly detector using the {A}daptive
		  {C}luster {E}xpansion ({ACE}) method},
  institution	= {RSRE},
  year		= {1990},
  number	= {4437},
  address	= {Malvern, UK},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  luttrell90c,
  author	= {S. P. Luttrell},
  title		= {Asymptotic code vector density in topographic vector
		  quantisers},
  institution	= {RSRE},
  year		= {1990},
  number	= {4392},
  address	= {Malvern, UK},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  luttrell91a,
  author	= {Stephen P. Luttrell},
  title		= {Code vector density in topographic mappings: scalar case},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1991},
  volume	= {2},
  number	= {4},
  pages		= {427--436},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  luttrell91b,
  author	= {S. P. Luttrell},
  title		= {Self-supervised training of hierarchical vector
		  quantisers},
  booktitle	= {Proc. 2nd IEE Conf. on Artificial Neural Networks},
  year		= {1991},
  pages		= {5--9},
  publisher	= {British Neural Network Society},
  address	= {London, UK},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  luttrell91c,
  author	= {S. P. Luttrell},
  title		= {Self-supervision in multilayer adaptive networks},
  institution	= {RSRE},
  year		= {1991},
  number	= {4467},
  address	= {Malvern, UK},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Misc{		  luttrell92a,
  author	= {S. P. Luttrell},
  title		= {Image anomaly detector},
  howpublished	= {British Patent Application 9202752. 3},
  year		= {1992},
  dbinsdate	= {oldtimer}
}

@Article{	  luttrell92b,
  author	= {S. P. Luttrell},
  title		= {Self-supervised adaptive networks},
  journal	= {IEE Proc. F [Radar and Signal Processing]},
  year		= {1992},
  volume	= {139},
  number	= {6},
  pages		= {371--377},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@TechReport{	  luttrell92c,
  author	= {S. P. Luttrell},
  title		= {Code vector density in topographic mappings},
  institution	= {DRA, Malvern, UK},
  year		= {1992},
  number	= {4669},
  address	= {},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  luttrell93a,
  author	= {S. P. Luttrell},
  title		= {The {M}arkov chain theory of vector quantisers},
  institution	= {DRA, Malvern, UK},
  year		= {1993},
  number	= {4742},
  address	= {},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  luttrell94a,
  author	= {S. P. Luttrell},
  title		= {A {B}ayesian analysis of Self-Organising Maps},
  journal	= {Neural Computation},
  year		= {1994},
  volume	= {6},
  number	= {5},
  pages		= {767--794},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  luttrell95a,
  author	= {Luttrell, S. P. },
  title		= {Using \mbox{self-organising} maps to classify radar range
		  profiles},
  booktitle	= {Fourth International Conference on `Artificial Neural
		  Networks`},
  year		= {1995},
  pages		= {335--40},
  organization	= {Defence Res. Agency, UK},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ma00a,
  author	= {Qing Ma and Kyoko Kanzaki and Masaki Murata and Kiyotaka
		  Uchimoto and Jitoshi Isahara},
  title		= {Self-Organizing Japanese Semantic Maps},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {188--94},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ma00b,
  author	= {Ma, Qing and Kanzaki, Kyoko and Murata, Masaki and
		  Utiyama, Masao and Uchimoto, Kiyotaka and Isahara,
		  Hitoshi},
  title		= {Self-organizing semantic maps of Japanese nouns in terms
		  of adnominal constituents},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {91--96},
  organization	= {Ministry of Posts and Telecommunications},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {As a beginning study on self-organizing a general Japanese
		  semantic map, which will be very useful in natural language
		  processing, particularly in document organization and
		  information retrieval, this paper describes the
		  construction of a semantic map of Japanese nouns mapped
		  according to their adnominal constituents. These maps are
		  not only an important part of a general Japanese semantic
		  map that we aim to construct, but can also be a powerful
		  tool for supporting the analysis of the relation between
		  head nouns and their adnominal constituents, an important
		  issue in studies of Japanese pragmatics.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ma01a,
  author	= {Ma, Xiaoyan and Li, Chunxia and Zhang, Xianda},
  title		= {The modification of intelligent target detection in
		  nonstationary clutter},
  booktitle	= {CIE International Conference of Radar Proceedings},
  year		= {2001},
  editor	= {},
  volume	= {},
  pages		= {324--328},
  organization	= {Tsinghua University},
  publisher	= {},
  address	= {},
  abstract	= {In non-stationary background, like sea clutter, the
		  intelligent detection method independent of the statistical
		  model, has the obvious advantages. Based on the strategy by
		  S. Haykin, a new intelligent detection scheme is proposed
		  to improve the detection performances, in which the Kohonen
		  NN and the modified fuzzy NN are used. A variety of
		  comparison experiments have been done with both the
		  simulated data and the real sea clutter data between our
		  proposed scheme and the S.Haykin's scheme, which show
		  clearly our method has a higher detection ability and a
		  lower false-alarm rate.},
  dbinsdate	= {2002/1}
}

@Article{	  ma01b,
  author	= {Ma, Ye and Li, Nang},
  title		= {Simulation studies of analog circuits fault approach based
		  on self-organizing feature map neural networks},
  journal	= {Journal-of-System-Simulation},
  year		= {2001},
  volume	= {13},
  pages		= {582--4},
  abstract	= {In this paper, the construction of AC fault dictionary and
		  its search based on self-organizing feature map are
		  discussed. The method is very efficient in identification
		  and location of singlel/many soft/hard faults. The effects
		  of components tolerance on correct fault location are
		  reduced because of extensiveness of neural networks.},
  dbinsdate	= {2002/1},
  merjanote     = {last name checked from internet}
}

@Article{	  ma94a,
  author	= {Ma, H. and Kumeda, K. and Kamei, K. and Inoue, K. },
  title		= {A proposal of improved fuzzy learning vector quantization
		  method},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1994},
  volume	= {J77D-II},
  number	= {4},
  pages		= {887--9},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@Article{	  macabrey92a,
  author	= {N. Macabrey and T. Baumann and A. J. Germond},
  title		= {Load forecasting on an electrical system with the aid of
		  the {K}ohonen neural network},
  journal	= {Bulletin des Schweizerischen Elektrotechnischen Vereins \&
		  des Verbandes Schweizerischer Elektrizit{\"a}tswerke},
  year		= {1992},
  volume	= {83},
  number	= {5},
  pages		= {13--19},
  note		= {(in French)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  macda01a,
  author	= {Macda, M. and Miyajima, H.},
  title		= {Properties of deletion methods in competitive learning},
  booktitle	= {Proceedings---IEEE International Symposium on Circuits and
		  Systems},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {707--710},
  organization	= {Kurume Natl. College of Technology},
  publisher	= {},
  address	= {},
  abstract	= {In this paper, we describe properties of deletion methods
		  in competitive learning. From the viewpoint of deleting
		  mechanisms of reference vectors, we introduce approaches
		  termed the adaptivity and sensitivity deletions
		  participated in the criteria of partition error and
		  distortion error, respectively. Experimental results show
		  the effectiveness of the present approaches in the average
		  distortion.},
  dbinsdate	= {2002/1}
}

@Article{	  macdonald00a,
  author	= {MacDonald, Donald and Fyfe, Colin},
  title		= {Kernel self organizing map},
  journal	= {International Conference on Knowledge-Based Intelligent
		  Electronic Systems, Proceedings, KES},
  year		= {2000},
  volume	= {1},
  number	= {},
  month		= {},
  pages		= {317--320},
  organization	= {Univ of Paisley},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {We review a recently developed method of performing
		  k-means clustering in a high dimensional feature space and
		  extend it to give the resultant mapping topology preserving
		  properties. We show the results of the new algorithm on the
		  standard data set, random numbers drawn uniformly from
		  [0,1)<sup>2</sup> and on the Olivetti database of faces.
		  The new algorithm converges extremely quickly.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  macdonald99a,
  author	= {MacDonald, D. and McGlinchey, S. and Kawala, J. and Fyfe,
		  C.},
  title		= {Comparison of {K}ohonen, scale-invariant and {GTM}
		  \mbox{self-organising} maps for interpretation of spectral
		  data},
  booktitle	= {7th European Symposium on Artificial Neural Networks.
		  ESANN'99. Proceedings},
  publisher	= {D-Facto},
  address	= {Brussels, Belgium},
  year		= {1999},
  volume	= {},
  pages		= {117--22},
  abstract	= {We investigate the use of artificial neural networks in
		  classifying hyperspectral data. Such data when collected
		  from remote sensors provides extremely detailed coverage
		  of, for example, the mineralogical composition of planetary
		  surfaces. However, the volume of data supplied often
		  overwhelms traditional classifiers. When we wish to
		  investigate such data sets in an open-ended manner, the use
		  of unsupervised learning is a pre-requisite. A set of
		  remotely sensed spectral images are use to train several
		  different topology preserving neural networks. In each
		  method, the data is projected onto a two dimensional grid
		  designed to visualise the data set in a low dimensional
		  space. Such mappings allow graceful degradation of the
		  classifications given by the mappings since nearby data
		  points are mapped to the same or similar classifications.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  macek99a,
  author	= {Macek, T. and Snorek, M.},
  title		= {Web-based simulation of artificial neural nets},
  booktitle	= {Modelling and Simulation: A Tool for the Next Millennium.
		  13th European Simulation Multiconference 1999. ESM'99},
  publisher	= {SCS},
  address	= {San Diego, CA, USA},
  year		= {1999},
  volume	= {1},
  pages		= {320--3},
  abstract	= {This paper presents the plans and first results of a
		  project concerning a simulation environment for neural
		  nets. The authors created a set of modules that can be used
		  for the testing of small size neural nets. The modules are
		  suitable to be used for learning ANN theory. The
		  environment is implemented in Java and available on the
		  web. Programs are accompanied by html documentation and
		  with a description of the models and examples. The neural
		  network models addressed in the project are:
		  backpropagation, Kohonen, Hopfield and ADAM neural nets.},
  dbinsdate	= {oldtimer}
}

@Article{	  macq92a,
  author	= {Macq, D. and Legat, J. D. and Jespers, P. G. A. },
  title		= {Analog storage of adjustable synaptic weights},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 2},
  pages		= {712--18},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  macq93a,
  author	= {Macq, Damien and Verlcysen, Michel and Jespers, Paul and
		  Legat, Jean Didier},
  title		= {Analog implementation of a {K}ohonen map with on-chip
		  learning.},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1993},
  number	= {3},
  volume	= {4},
  pages		= {456--461},
  abstract	= {Kohonen maps are self-organizing neural networks that
		  classify and quantify n-dimensional data into a one- or
		  two-dimensional array of neurons (1)-(2). Most applications
		  of Kohonen maps use simulations on conventional computers,
		  eventually coupled to hardware accelerators or dedicated
		  neural computers. The small number of different operations
		  involved in the combined learning and classification
		  process makes however the Kohonen model particularly suited
		  to a dedicated VLSI implementation, taking full advantage
		  of the parallelism and speed that can be obtained on the
		  chip. We propose here a fully analog implementation of a
		  one-dimensional Kohonen map, with on-chip learning and
		  refreshment of on-chip analog synaptic weights. The small
		  number of transistors in each cell allows a high degree of
		  parallelism in the operations, what greatly improves the
		  computation speed compared to other implementations. This
		  paper will emphasize on the storage of analog synaptic
		  weights, based on the principle of current copiers; it will
		  be shown that this technique can be used successfully for
		  the realization of VLSI Kohonen maps.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  macwhinney96a,
  author	= {Brian MacWhinney},
  title		= {Lexical connectionism},
  booktitle	= {Cognitive approaches to language learning},
  publisher	= {The MIT Press},
  year		= 1997,
  editor	= {P. Broeder and J. Murre},
  address	= {Cambridge, MA},
  dbinsdate	= {oldtimer}
}

@Article{	  madani97a,
  author	= {K. Madani and A. Bengharbi and V. Amarger},
  title		= {Neural fault diagnosis techniques for non-linear analogue
		  circuits},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3077},
  pages		= {491--502},
  note		= {(Applications and Science of Artificial Neural Networks
		  III Conf. Date: 21--24 April 1997 Conf. Loc: Orlando, FL,
		  USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  madekivi88a,
  author	= {Seppo Madekivi},
  title		= {Experiments on automatic classification of shallow water
		  acoustic signal sources using two pattern recognition
		  methods},
  booktitle	= {Proc. ICASSP-88, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1988},
  pages		= {2693--2696},
  organization	= {IEEE, Acoustics, Speech and Signal Processing Soc, New
		  York, NY, USA},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  maeda96a,
  author	= {M. Maeda and H. Miyajima and S. Murashima},
  title		= {An adaptive learning and self-deleting neural network for
		  vector quantization},
  journal	= {IEICE Transactions on Fundamentals of Electronics,
		  Communications and Computer Sciences},
  year		= {1996},
  volume	= {E79-A},
  number	= {11},
  pages		= {1886--93},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  maekawa94a,
  author	= {Satoshi Maekawa and Hajime Kita and Yoshikazu Nishikawa},
  title		= {A Competitive System with Adaptive Gain Tuning},
  pages		= {2813--2818},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {control application, modification},
  dbinsdate	= {oldtimer}
}

@InCollection{	  maenou97a,
  author	= {Takatoshi Maenou and Kikuo Fujimura and Satoru Kishida},
  title		= {Optimizations of {TSP} by {SOM} method},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {1013--1016},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  maggioni91a,
  author	= {Christoph Maggioni and Brigitte Wirtz},
  title		= {A Neural Net Approach to 3-{D} Pose Estimation},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {75--80},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  magnisalis92a,
  author	= {X. Magnisalis and E. Auge and M. G. Strintzis},
  title		= {Parallel implementation of the learning vector quantizer
		  with application in ultrasound image lesion recognition},
  booktitle	= {Parallel and Distributed Computing in Engineering Systems.
		  Proc. IMACS/IFAC Int. Symp. },
  year		= {1992},
  editor	= {S. Tzafestas and P. Borne and L. Grandinetti},
  pages		= {383--386},
  organization	= {IMACS; IFAC},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mahonen00a,
  author	= {Petri M\"{a}h\"{o}nen and Filippo Cortiglioni and Pasi
		  Hakala},
  title		= {Automated Galaxy Survey Classications using
		  Self-Organizing Maps},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {VTT, Networking Research, P.O. Box 1100, FIN-90570 Oulu,
		  Finland; University of Oulu, Department of Physical
		  Sciences, Oulu, Finland; Tuorola Observatory, University of
		  Turku, Finland},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mahonen00b,
  author	= {Mahonen, P. and Cortiglioni, F. and Frantti, T.},
  title		= {Automated galaxy survey classification using {SOM} and
		  hybrid algorithms},
  booktitle	= {Proceedings of the IASTED International Conference. Signal
		  and Image Processing. IASTED/ACTA Press, Anaheim, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {245--50},
  abstract	= {We report progress done in the development of an automated
		  star/galaxy classifier for processing images that are
		  generated with large galaxy surveys. We explore different
		  approaches to automated classification, involving
		  parameterized and non-parameterized classification using
		  supervised and non-supervised neural networks. We also
		  introduce a brief study on a hybrid system consisting of
		  two-step classification using fuzzy algorithms.},
  dbinsdate	= {2002/1}
}

@Article{	  mahonen95a,
  author	= {P. H. M{\"{a}}h{\"{o}}nen and P. J. Hakala},
  title		= {Automated Source Classification using a {K}ohonen
		  Network},
  journal	= {The Astrophysical Journal},
  year		= {1995},
  volume	= {452},
  number	= {1},
  pages		= {L77-L80},
  month		= {October},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mailachalam00a,
  author	= {Mailachalam, B. and Srikanthan, T.},
  title		= {A robust parallel architecture for adaptive color
		  quantization},
  booktitle	= {Proceedings International Conference on Information
		  Technology: Coding and Computing. IEEE Comput. Soc, Los
		  Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {164--9},
  abstract	= {Adaptive color quantization (ACQ) as described here is one
		  method of color image compression employed for image
		  storage and display in low cost monitors. Due to the nature
		  of the palette based mapping employed in ACQ, it is prone
		  to large errors due to noises introduced in transmission or
		  additional lossy compression. This paper proposes a robust
		  method of ACQ using a three dimensional self organizing map
		  (3D SOM) which is tolerant to noises. The robustness of the
		  3D SOM is due to the property of such maps to cluster the
		  color vectors in a topological manner. An efficient
		  parallel architecture based on the SIMD structure and
		  parallel winner determination scheme, is presented for the
		  implementation of the 3D SOM. The VLSI area-time efficiency
		  of this parallel architecture over the current
		  implementations is also established.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  maillard92a,
  author	= {Eric Maillard and Benoit Zerr and Jean Merckle},
  title		= {Classification of Texture by an Association Between a
		  Perceptron and a Self-Organizing Feature Map},
  booktitle	= {Proc. EUSIPCO-92, Sixth European Signal Processing
		  Conference},
  year		= {1992},
  editor	= {J. Vandewalle and R. Boite and M. Moonen and A.
		  Oosterlinck},
  volume	= {II},
  pages		= {1173--1176},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  maillard94a,
  author	= {E. Maillard and J. Gresser},
  title		= {Reduced Risk of {K}ohonen's Feature Map Non-Convergence by
		  an Individual Size of the Neighborhood},
  pages		= {704--707},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  maillard94b,
  author	= {E. Maillard and B. Solaiman},
  title		= {A Neural Network Based on {LVQ2} with Dynamic Building of
		  the Map},
  pages		= {766--770},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {modification, hybrid},
  dbinsdate	= {oldtimer}
}

@Article{	  makino91a,
  author	= {S. Makino and A. Ito and M. Endo and K. Kido},
  title		= {A {J}apanese text dictation system based on phoneme
		  recognition and a dependency grammar},
  journal	= {IEICE Trans. },
  year		= {1991},
  volume	= {E74},
  number	= {7},
  pages		= {1773--1782},
  month		= {July},
  x		= {Vast. konf. julkaisu jo mukana},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  makino91b,
  author	= {S. Makino and A. Ito and M. Endo and K. Kido},
  title		= {A {J}apanese text dictation system based on phoneme
		  recognition and a dependency grammar},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {I},
  pages		= {273--276},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  makino92a,
  author	= {S. Makino and M. Endo and T. Sone and K. Kido},
  title		= {Recognition of phonemes in continuous speech using a
		  modified {LVQ2} method},
  journal	= {J. Acoustical Society of Japan [E]},
  year		= {1992},
  volume	= {13},
  number	= {6},
  pages		= {351--360},
  month		= {November},
  note		= {(in English)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  makipaa97a,
  author	= {Mikko M{\"a}kip{\"a}{\"a} and Pekka Heinonen and Erkki Oja},
  title		= {Using the {SOM} in supporting diabetes therapy},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {51--56},
  dbinsdate	= {oldtimer}
}

@Article{	  maksimovic99a,
  author	= {Maksimovic, R. and Popovic, M.},
  title		= {Classification of tetraplegics through automatic movement
		  evaluation},
  journal	= {Medical Engineering \& Physics},
  year		= {1999},
  volume	= {21},
  pages		= {313--27},
  abstract	= {The general problem of classification of functional
		  movements in humans with spinal cord injuries requires the
		  following questions to be answered: what are the essential
		  kinematic parameters that one has to observe during the
		  movement? Is it possible to estimate preserved motor skills
		  based on kinematics? Which computational method for
		  identification is suited to geometric feature analysis? To
		  answer these questions the authors have developed the
		  methodology which has two phases: (1) recordings of a
		  series of specified arm movements; and (2) custom made
		  software for graphical presentation of arm movements and
		  the design of wavelet and neural networks for movement
		  classification. The proposed protocol is automated and both
		  graphical presentation and neural networks allow easy
		  interpretation of the instrumented assessment to accomplish
		  automatic classification of arm movements in tetraplegics.
		  The protocol was evaluated on 16 spinal cord injury (SCI)
		  patients and seven healthy control subjects for three
		  different arm movements. The classification rate yielded
		  results in the range 46--100% for movement trials that were
		  tested. The application of neural networks for
		  classification of arm movements is completed with results
		  using different neural networks: backpropagation, radial
		  basis, recurrent (Elman), self-organizing and Learning
		  Vector Quantization (LVQ).},
  dbinsdate	= {oldtimer}
}

@Article{	  maksoud01a,
  author	= {Maksoud, T. M. A. and Ahmed, M. R. and Koura, M.},
  title		= {Improving wheel-workpiece contact detection using a hybrid
		  neural network},
  journal	= {PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS
		  PART B- JOURNAL OF ENGINEERING MANUFACTURE},
  year		= {2001},
  volume	= {215},
  number	= {11},
  pages		= {1595--1602},
  abstract	= {Eliminating the gap between the grinding wheel and
		  workpiece is a major time-wasting step during grinding
		  operations. Reducing the time of this step has attracted
		  many investigations. Several researchers have investigated
		  the variation in some process parameters during the
		  wheel-workpiece contact. These parameters include grinding
		  force, grinding power and acoustic emission. During the
		  approach of the grinding wheel to the workpiece, there are
		  three primary stages which have an effect on these
		  parameters: hydrodynamic stage, grit contact stage and
		  wheel contact stage. A few researchers introduced a method
		  to identify the start of the wheel contact stage, which is
		  the practical contact stage. Most of these methods depend
		  on predefined threshold values for some measured
		  parameters. This paper introduces a new methodology to
		  identify the wheel- workpiece contact event in surface
		  grinding operations. A hybrid neural network with
		  supervised learning is used to extract the contact event
		  from the measured parameters. It consists of two neural
		  nets. The first is a self-organizing map neural network
		  with unsupervised learning and the second is a feedforward
		  neural network with supervised learning. Using this hybrid
		  network produces first self-organized clusters for the
		  input data at the first network and then the second network
		  recognizes these clusters. This results in the detection
		  and classification of the contact events automatically from
		  the measured data. This presents a very important step
		  towards the full control of grinding operations in a
		  computer numerically controlled (CNC) environment.},
  dbinsdate	= {2002/1}
}

@Article{	  malhotra00a,
  author	= {Malhotra, R. and Malhotra, D. K.},
  title		= {Identifying potential loan defaulters in the credit union
		  environment: a comparative analysis of statistical and
		  neural network models},
  journal	= {Journal-of-Information-Technology-Cases-and-Applications-(JITCA)}
		  ,
  year		= {2000},
  volume	= {2},
  pages		= {20--48},
  abstract	= {A number of credit-scoring models, that attempt to
		  accurately classify consumer loan applications as
		  potentially "good" or "bad", have been developed to aid
		  traditional judgmental (intuitive) methods. This study
		  explores the performance of different neural network
		  models: backpropagation with adaptive learning,
		  backpropagation with Levenberg-Marquardt approach, and
		  Learning Vector Quantization to identify problem loans from
		  among the applications the loan officer has initially
		  screened. Field data from 12 case organizations (credit
		  unions) was analyzed to evaluate the performance of these
		  three neural network techniques against the logistics
		  regression model. The neural network models' performance is
		  found to be significantly better in identifying potential
		  "bad" loans.},
  dbinsdate	= {2002/1}
}

@InCollection{	  malko94a,
  author	= {J. Malko and H. Mikolajczak},
  title		= {An artificial neural network based model for short term
		  electric load forecasting},
  booktitle	= {Proceedings of the Twelfth IASTED International Conference
		  Applied Informatics},
  publisher	= {IASTED},
  year		= {1994},
  editor	= {M. H. Hamza},
  address	= {Anaheim, CA, USA},
  pages		= {135--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  malko95a,
  author	= {J. Malko and H. Mikolajczak and W. Skorupski},
  title		= {Artificial neural network based models for short-and
		  long-term load forecasting in the power system},
  booktitle	= {Stockholm Power Tech International Symposium on Electric
		  Power Engineering},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {595--600},
  dbinsdate	= {oldtimer}
}

@InCollection{	  malko96a,
  author	= {J. Malko},
  title		= {Short term electric load forecasting case study: power
		  system of Poland},
  booktitle	= {31st Universities Power Engineering Conference. Conference
		  Proceedings},
  publisher	= {Technol. Educ. Inst. Iraklio},
  year		= {1996},
  volume	= {3},
  address	= {Iraklio, Greece},
  pages		= {1058--60},
  dbinsdate	= {oldtimer}
}

@Article{	  malmgren99a,
  author	= {Malmgren, B. A. and Winter, A.},
  title		= {Climate zonation in {P}uerto {R}ico based on principal
		  components analysis and an artificial neural network},
  journal	= {Journal of Climate},
  year		= {1999},
  volume	= {12},
  pages		= {977--85},
  abstract	= {The authors analyzed climate data, seasonal averages of
		  precipitation, and maximum, mean, and minimum temperatures
		  over the years 1960--90, from 18 stations spread around the
		  island of Puerto Rico in the Caribbean, to determine
		  whether these distinguish the existence of climate zones in
		  Puerto Rico. An R-mode principal components analysis (PCA),
		  with varimax rotation to the seasonal data in order to
		  reduce their dimensionality, was applied. The first five
		  principal components, found by cross validation to be
		  statistically significant, account for 99% of the
		  variability in the 16 variables included in the analysis.
		  These five components are related to annual variation in
		  mean and minimum temperature (first PC), annual maximum
		  temperature (second PC), and spring, summer, and fall
		  precipitation (third through fifth PCs). A self-organizing
		  map, an artificial neural network algorithm, was then
		  employed to classify the first five PC scores in an optimal
		  fashion. The scores were classified by the neural network
		  into four climatic zones, each with a distinct geographic
		  coverage in Puerto Rico. One zone, marked by the highest
		  mean and minimum annual temperatures, is located along the
		  northern, eastern and southern coasts of Puerto Rico. The
		  stations referred to the second zone are also from
		  relatively low altitudes in the northern and eastern parts
		  of the island, but they are not located along the immediate
		  coastline. Intermediately high mean and minimum
		  temperatures mark this zone. The third zone consists of
		  stations from high altitudes in the central mountain range
		  and is characterized by the lowest annual mean and minimum
		  temperatures. To the south of the third zone, a fourth zone
		  is identified, which is marked by the highest annual
		  maximum temperatures.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  malmstrom01a,
  author	= {Malmstrom, K. and Sitte, J. and Iske, B.},
  title		= {Perception stimulated generation of simple robot
		  navigation behaviour},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Choset, H. M. and Gage, D. W. and Stein, M. R.},
  volume	= {4195},
  pages		= {228--239},
  organization	= {School of Computing Science, Queensland University of
		  Technology},
  publisher	= {},
  address	= {},
  abstract	= {Newborn animals go through a period of adaptation and
		  learning before they reach their full sensory-motor
		  capabilities. A similar development strategy could be
		  advantageous for newly built autonomous robots. In this
		  paper we describe how a robot equipped with a generic,
		  adaptive sensory and motor system configures itself and
		  acquires target approach behaviour purely from the exposure
		  to sensory stimulation. Selforganising feature maps (SOM)
		  and reinforcement learning provide the necessary adaptive
		  mechanisms. These mechanisms are generic in the sense that
		  they contain minimal dependencies on the actual devices
		  used for sensor and actuator implementation. Our results
		  demonstrate the behaviour acquisition can be achieved in
		  autonomous fashion, in real time on a real minirobot.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  malmstrom94a,
  author	= {Malmstrom, K. and Munday, L. and Sitte, J. },
  title		= {A simple robust robotic vision system using {K}ohonen
		  feature mapping},
  booktitle	= {Proceedings of the 1994 Second Australian and New Zealand
		  Conference on Intelligent Information Systems},
  year		= {1994},
  pages		= {135--9},
  organization	= {Fac. of Build Environ. \& Eng. , Queensland Univ. of
		  Technol. , Brisbane, Qld. , Australia},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  mamlook95a,
  author	= {Mamlook, R. and Thompson, W. E. },
  title		= {Multiple-class identification algorithm using genetic
		  neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2484},
  pages		= {681--8},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  mamlook95b,
  author	= {R. Mamlook and W. E. Thompson},
  title		= {Multiple-class identification algorithm using genetic
		  neural networks},
  booktitle	= {ICECS '95. International Conference on Electronics,
		  Circuits and Systems. Proceedings},
  publisher	= {Higher Council for Sci. \& Technol},
  year		= {1995},
  address	= {Amman, Jordan},
  pages		= {399--404},
  dbinsdate	= {oldtimer}
}

@Article{	  mancuso01a,
  author	= {Mancuso, S.},
  title		= {Clustering of grapevine (Vitis vinifera L.) genotypes with
		  Kohonen neural networks},
  journal	= {VITIS},
  year		= {2001},
  volume	= {40},
  number	= {2},
  month		= {JUN},
  pages		= {59--63},
  abstract	= {Self-organizing maps generated by Kohonen neural networks
		  provide a method to transform multidimensional problems
		  common in ampelography into lower dimensional problems. In
		  this study the clustering efficiency of Kohonen neural
		  networks was evaluated to characterize and identify 10
		  Sangiovese-related and 10 "coloured" (fruit gives intense
		  red colour to the wine) grapevine accessions, on the basis
		  of the elliptic Fourier coefficients of the leaves. The
		  non-supervised learning algorithm used allowed apriori
		  classification of the accessions. The results enabled us to
		  distinguish between 16 accessions and to denote two pairs
		  of synonyms, To obtain quantitative information regarding
		  relationships among these accessions, Kohonen neural
		  networks were trained with different numbers of neurons in
		  the Kohonen output layer permitting the graphical
		  representation of the similarity by construction of a
		  dendrogram. In agreement with previous studies based on
		  molecular markers and neural network technology, a high
		  similarity was found for the ecotypes (1) Prugnolo acerbo,
		  Prugnolo dolce and Prugnolo medio and (2) Brunelletto and
		  Prugnolo gentile. Among the Sangiovese-related accessions
		  the so-called Casentino ecotype diverged from all the
		  others, probably indicating a different origin. Producing
		  easily comprehensible low-dimensional maps, the Kohonen
		  neural networks approach proposed here allows to study
		  complex ampelographic data elucidating relationships that
		  can not be detected by traditional data analysis tools.},
  dbinsdate	= {2002/1}
}

@InCollection{	  manduca94a,
  author	= {A. Manduca},
  title		= {Multi-parameter image visualization with
		  \mbox{self-organizing} maps},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {593--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  manduca94b,
  author	= {Armando Manduca},
  title		= {Multi-Parameter Medical Image Visualization With
		  Self-Organizing Maps},
  pages		= {3990--3995},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, clustering, visualization},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  manduca94c,
  author	= {Manduca, A. },
  title		= {Multi-spectral medical image visualization with
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings ICIP-94},
  year		= {1994},
  volume	= {1},
  pages		= {633--7},
  organization	= {Dept. of Physiol. \& Biophys. , Mayo Clinic, Rochester,
		  MN, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  manduca96a,
  author	= {A. Manduca},
  title		= {Multispectral image visualization with nonlinear
		  projections},
  journal	= {IEEE Transactions on Image Processing},
  year		= {1996},
  volume	= {5},
  number	= {10},
  pages		= {1486--90},
  dbinsdate	= {oldtimer}
}

@Article{	  manevitz97a,
  author	= {L. Manevitz and M. Yousef and D. Givoli},
  title		= {Finite-element mesh generation using
		  \mbox{self-organizing} neural networks},
  journal	= {Microcomputers in Civil Engineering},
  year		= {1997},
  volume	= {12},
  number	= {4},
  pages		= {233--50},
  dbinsdate	= {oldtimer}
}

@Article{	  manevitz97b,
  author	= {L. Manevitz},
  title		= {Interweaving {K}ohonen maps of different dimensions to
		  handle measure zero constraints on topological mappings},
  journal	= {Neural Processing Letters},
  year		= {1997},
  volume	= {5},
  number	= {2},
  pages		= {153--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mangeas95a,
  author	= {Morgan Mangeas and Andreas S. Weigend and Corinne Muller},
  title		= {Forecasting electricity demand using nonlinear mixture of
		  experts},
  volume	= {II},
  pages		= {48--53},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@Article{	  mangiameli96a,
  author	= {P. Mangiameli and S. K. Chen and D. West},
  title		= {A comparison of {SOM} neural network and hierarchical
		  clustering methods},
  journal	= {European Journal of Operational Research},
  year		= {1996},
  volume	= {93},
  number	= {2},
  pages		= {402--17},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  manhaeghe92a,
  author	= {Manhaeghe, C. and Lemahieu, I. and Vogelaers, D. },
  title		= {{3D} modelling of left ventricle tomograms using {K}ohonen
		  feature maps},
  booktitle	= {Signal Processing VI---Theories and Applications.
		  Proceedings of EUSIPCO-92, Sixth European Signal Processing
		  Conference},
  year		= {1992},
  editor	= {Vandewalle, J. and Boite, R. and Moonen, M. and
		  Oosterlinck, A. },
  volume	= {3},
  pages		= {1725--8},
  organization	= {Lab. for Electron. \& Metrol. , Ghent Univ. , Belgium},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  manhaeghe94a,
  author	= {Manhaeghe, C. and Lemahieu, I. and Vogelaers, D. and
		  Colardyn, F. },
  title		= {Automatic initial estimation of the left ventricular
		  myocardial midwall in emission tomograms using {K}ohonen
		  maps},
  journal	= {IEEE Transactions on Pattern Analysis and Machine
		  Intelligence},
  year		= {1994},
  volume	= {16},
  number	= {3},
  pages		= {259--66},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  manian00a,
  author	= {Manian, Vidya and Hernandez, Roger and Vasquez, Ramon},
  title		= {Classifier performance for {SAR} image classification},
  booktitle	= {International Geoscience and Remote Sensing Symposium
		  (IGARSS)},
  year		= {2000},
  editor	= {},
  volume	= {1},
  pages		= {156--158},
  organization	= {Univ of Puerto Rico at Mayaguez},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Classifier robustness is important for classification of
		  remote sensing images. This paper investigates the use of
		  the supervised Maximum Likelihood (ML) classifier and the
		  unsupervised K-means algorithm. Classifier adaptability to
		  other data sets is considered. Also, a method is presented
		  to fuse classifiers for better performance with application
		  to SAR images. The performance of neural network classifier
		  such as the Learning Vector Quantization (LVQ) technique is
		  also studied and is used in the classifier integration
		  algorithm. The paper presents results with SAR images.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  manickam00a,
  author	= {Manickam, S. and Abidi, S. S. R.},
  title		= {Unsupervised case classification using Kohonen
		  "self-organizing feature map" in a case-based reasoning
		  system},
  booktitle	= {2000 TENCON Proceedings. Intelligent Systems and
		  Technologies for the New Millennium. IEEE, Piscataway, NJ,
		  USA},
  year		= {2000},
  volume	= {2},
  pages		= {524--7},
  abstract	= {Case based reasoning (CBR) is a relatively recent problem
		  solving technique that is attracting increasing attention.
		  Major areas where CBR is used are: diagnosis, help desk,
		  assessment, design and decision support. CBR solves new
		  problems by adapting previously successful solutions to
		  similar problems. The paper presents a technique that uses
		  Kohonen "self-organizing feature map" (SOM) in improving
		  and enhancing the indexing and retrieving method of cases
		  in a CBR system by clustering cases with similar properties
		  together. The SOM approach has proven to be an efficient
		  method for clustering large data collections, and
		  simultaneously presenting the user with a particular planar
		  representation of the clusters. By using SOM, the system
		  could learn about the emergence of any indices that had not
		  previously been thought significant and thus, increasing
		  the flexibility of the system when it comes to a different
		  indexing scheme. It could also help cases to be retrieved
		  quickly. The nodes in the network converge to form clusters
		  to represent groups of entities with similar properties.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  manikopoulos89a,
  author	= {C. N. Manikopoulos and J. Li},
  title		= {Adaptive image sequence coding with neural network vector
		  quantization},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  pages		= {573},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  manikopoulos90a,
  author	= {C. Manikopoulos and G. Antoniou and S. Metzelopoulou},
  title		= {{LVQ} of image sequence source and {ANS} classification of
		  finite state machine for high compression coding},
  booktitle	= {Proc. IJCNN'90, International Joint Conference on Neural
		  Networks},
  year		= {1990},
  volume	= {I},
  pages		= {481--486},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  manikopoulos91a,
  author	= {C. N. Manikopoulos and J. Li and G. Antoniou},
  title		= {Neural net adaptive encoding of image sequence data},
  journal	= {J. New Generation Computer Systems},
  year		= {1991},
  volume	= {4},
  number	= {2},
  pages		= {99--115},
  dbinsdate	= {oldtimer}
}

@Article{	  manikopoulos92a,
  author	= {C. N. Manikopoulos and G. E. Antoniou},
  title		= {Adaptive encoding of a videoconference image sequence via
		  neural networks},
  journal	= {J. Electrical and Electronics Engineering,Australia},
  year		= {1992},
  volume	= {12},
  number	= {3},
  pages		= {233--241},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mann88a,
  author	= {Jim Mann and Richard Lippmann and Bob Berger and Jack
		  Raffel},
  title		= {Self-organizing neural net chip},
  booktitle	= {Proc. Custom Integrated Circuits Conference},
  year		= {1988},
  pages		= {10. 3/1--5},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mann89a,
  author	= {James R. Mann and Sheldon Gilbert},
  title		= {An analog \mbox{self-organizing} neural network chip},
  booktitle	= {Advances in Neural Information Processing Systems I},
  year		= {1989},
  editor	= {David S. Touretzky},
  pages		= {739--747},
  publisher	= {Morgan Kaufmann},
  address	= {San Mateo, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mann90a,
  author	= {R. Mann and S. Haykin},
  title		= {A Parallel Implementation of {K}ohonen's Feature Maps on
		  the Warp Systolic Computer},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  volume	= {II},
  pages		= {84--87 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mann91a,
  author	= {Richard Mann and Simon Haykin},
  title		= {Application of the Self-Organizing Feature Map and
		  Learning Vector Quantization to Radar Clutter
		  Classification},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1699--1702},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  manohar92a,
  author	= {M. Manohar and J. C. Tilton},
  title		= {Progressive vector quantization of multispectral image
		  data using a massively parallel {SIMD} machine},
  booktitle	= {DCC '92. Data Compression Conf. },
  year		= {1992},
  editor	= {J. A. Storer and M. Cohn},
  pages		= {181--190},
  organization	= {IEEE; NASA/CESDIS},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  manohar96a,
  author	= {Mareboyana Manohar and James C. Tilton},
  title		= {Progressive Vector Quantization on a Massively Parallel
		  {SIMD} Machine with Application to Multispectral Image
		  Data},
  journal	= {IEEE Trans. on Image Processing},
  year		= {1996},
  volume	= {5},
  number	= {1},
  pages		= {142--147},
  month		= {January},
  dbinsdate	= {oldtimer}
}

@InCollection{	  manping98a,
  author	= {Li Manping and Wang Yuying and Zhang Xiurong},
  title		= {An approach to generate membership function by using
		  {K}ohonen's {SOFM} nets},
  booktitle	= {Methodology and Tools in Knowledge-Based Systems. 11th
		  International Conference on Industrial and Engineering
		  Applications of Artificial Intelligence and Expert Systems.
		  IEA-98-AIE. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1998},
  volume	= {1},
  editor	= {J. Mira and A. Pasqual {del Pobil} and M. Ali},
  address	= {Berlin, Germany},
  pages		= {220--4},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mantyla00a,
  author	= {Mantyla, V. -M. and Mantyjarvi, J. and Seppanen, T. and
		  Tuulari, E.},
  title		= {Hand gesture recognition of a mobile device user},
  booktitle	= {IEEE International Conference on Multi-Media and Expo},
  year		= {2000},
  editor	= {},
  volume	= {},
  pages		= {281--284},
  organization	= {Technical Research Centre of Finland},
  publisher	= {},
  address	= {},
  abstract	= {Experiments with acceleration sensors is described for
		  static and dynamic gesture recognition of a mobile device
		  user. Static gestures are recognized with the
		  self-organizing mapping scheme of Kohonen while a hidden
		  Markov model is used for recognizing dynamic gestures. An
		  experimental sensor box for the research of
		  context-awareness is also explained. Experimental results
		  show great promise of the chosen technologies for
		  recognizing gestures of a user of a mobile device.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  mantysalo92a,
  author	= {Jyri M{\"{a}}ntysalo and Kari Torkkola and Teuvo Kohonen},
  title		= {{LVQ}-based speech recognition with high-dimensional
		  context vectors},
  booktitle	= {Proc. International Conference on Spoken Language
		  Processing},
  year		= {1992},
  pages		= {539--542},
  publisher	= {University of Alberta},
  address	= {Edmonton, Alberta, Canada},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mantysalo92b,
  author	= {Jyri M{\"{a}}ntysalo and Kari Torkkola and Teuvo Kohonen},
  title		= {Experiments on the use of {LVQ} in phoneme-level
		  segmentation of speech},
  booktitle	= {Proc. 2nd Workshop on Neural Networks for Speech
		  Processing},
  year		= {1993},
  editor	= {Marco Gori},
  pages		= {39--52},
  publisher	= {Edizioni Lint Trieste},
  address	= {Trieste, Italy},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mantysalo93a,
  author	= {Jyri M{\"{a}}ntysalo and Kari Torkkola and Teuvo Kohonen},
  title		= {Handling Context-Dependecies in Speech by {LVQ}},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {389--394},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  mantysalo94a,
  author	= {Jyri M{\"{a}}ntysalo and Kari Torkkola and Teuvo Kohonen},
  title		= {Mapping context dependent acoustic information into
		  context independent form by {LVQ}},
  journal	= {Speech Communication},
  year		= {1994},
  volume	= {14},
  number	= {2},
  pages		= {119--130},
  annote	= {context modeling, lvq, hmm},
  dbinsdate	= {oldtimer}
}

@Article{	  mao95a,
  author	= {Mao, Jianchang and Jain, Anil K},
  title		= {Artificial neural networks for feature extraction and
		  multivariate data projection},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1995},
  number	= {2},
  volume	= {6},
  month		= {March},
  pages		= {296--317},
  abstract	= {A number of networks and learning algorithms which provide
		  new or alternative tools for feature extraction and data
		  projection is proposed. The networks include a network
		  (SAMANN) for Sammon's nonlinear projection, a linear
		  discriminant analysis (LDA) network, a nonlinear
		  discriminant analysis (NDA) network, and a network for
		  nonlinear projection (NP-SOM) based on Kohonen's
		  self-organizing map. Five representative neural networks
		  for feature extraction and data projection based on a
		  visual judgement of two-dimensional projection maps and
		  quantitative criteria on data sets with various properties
		  are evaluated.},
  dbinsdate	= {oldtimer}
}

@Article{	  marabini94a,
  author	= {R. Marabini and J. M. Carazo},
  title		= {Pattern recognition and classification of images of
		  biological macromolecules using artificial neural
		  networks},
  journal	= {Biophysical Journal},
  year		= 1994,
  volume	= 66,
  pages		= {1804--1814},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  maragoudakis01a,
  author	= {Maragoudakis, M. and Kermanidis, K. and Fakotakis, N. and
		  Kokkinakis G},
  title		= {Learning automatic acquisition of subcategorization frames
		  using Bayesian inference and support vector machines},
  booktitle	= {Proceedings 2001 IEEE International Conference on Data
		  Mining. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {623--5},
  abstract	= {Learning Bayesian belief networks (BBN) from corpora and
		  support vector machines (SVM) have been applied to the
		  automatic acquisition of verb subcategorization frames for
		  Modern Greek. We are incorporating minimal linguistic
		  resources, i.e. basic morphological tagging and phrase
		  chunking, to demonstrate that verb subcategorization, which
		  is of great significance for developing robust natural
		  language human computer interaction systems, could be
		  achieved using large corpora, without having any
		  general-purpose, syntactic parser at all. In addition,
		  apart from BBN and SVM, which have not previously used for
		  this task, we have experimented with three well-known
		  machine learning methods (feedforward backpropagation
		  neural networks, learning vector quantization and decision
		  tables), which are also being applied to the task of verb
		  subcategorization frame defection for the first time. We
		  argue that both BBN and SVM are well suited for learning to
		  identify verb subcategorization frames. Empirical results
		  will support this claim. Performance has been methodically
		  evaluated using two different corpora types, one balanced
		  and one domain-specific in order to determine the unbiased
		  behaviour of the trained models. Limited training data are
		  proved to endow with satisfactory results. We have been
		  able to achieve precision exceeding 80% on the
		  identification of subcategorization frames which were not
		  known beforehand.},
  dbinsdate	= {2002/1}
}

@InCollection{	  marana97a,
  author	= {A. N. Marana and L. da F. Costa and S. A. Velastin and R.
		  A. Lotufo},
  title		= {Oriented Texture Classification Based on Self-Organizing
		  Neural Network and {H}ough Transform},
  booktitle	= {Proceedings of ICASSP'97, 1997 International Conference on
		  Acoustics, Speech, and Signal Processing},
  publisher	= {IEEE Computer Society Press},
  year		= 1997,
  address	= {Los Alamitos, CA},
  pages		= {2773--2775},
  dbinsdate	= {oldtimer}
}

@Article{	  marco98a,
  author	= {S. Marco and A. Ortega and A. Pardo and J. Samitier},
  title		= {Gas identification with tin oxide sensor array and
		  \mbox{self-organizing} maps: adaptive correction of sensor
		  drifts},
  journal	= {IEEE Transactions on Instrumentation and Measurement},
  year		= {1998},
  volume	= {47},
  number	= {1},
  pages		= {316--21},
  dbinsdate	= {oldtimer}
}

@Article{	  maren91a,
  author	= {A. J. Maren},
  title		= {Neural networks for enhanced human-computer interactions},
  journal	= {IEEE Control Systems Magazine},
  year		= {1991},
  volume	= {11},
  number	= {5},
  pages		= {34--36},
  month		= {August},
  annote	= {Discusses the use of neural networks to create adaptive
		  models of users and communications channels for use in
		  designing system response characteristics},
  dbinsdate	= {oldtimer}
}

@Article{	  marengo02a,
  author	= {Marengo, E. and Aceto, M. and Maurino, V.},
  title		= {Classification of Nebbiolo-based wines from Piedmont
		  (Italy) by means of solid-phase microextraction-gas
		  chromatography-mass spectrometry of volatile compounds},
  journal	= {JOURNAL OF CHROMATOGRAPHY A},
  year		= {2002},
  volume	= {943},
  number	= {1},
  month		= {JAN 11},
  pages		= {123--137},
  abstract	= {Sixty-eight samples of wines from Piedmont (Italy) were
		  analysed to determine their content of volatile compounds,
		  using the solid-phase microextraction (SPME) technique
		  coupled with gas chromatography-mass spectrometry (GC-MS).
		  Samples were from five groups of wines: Barolo, Barbaresco,
		  Nebbiolo d'Alba, Roero and Langhe Nebbiolo, all produced
		  from the Nebbiolo grape in the Langhe and Roero areas
		  (province of Cuneo, Piedmont) but differing in vintage
		  (respectively, 3 years, 2 years, I year, 8 months and few
		  months) and production zone. Thirty-five analytes were
		  identified; peak area data, corrected for internal
		  standard, were used for pattern recognition treatments.
		  Principal components analysis, hierarchical cluster
		  analysis, Kohonen self organising map, stepwise linear
		  discriminant analysis and soft independent modelling of
		  class analogy were applied to the data, revealing a good
		  separation between the five groups. A main factor, strictly
		  connected to wine vintage, was identified and found to be
		  related to some analytes. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  marguerat94a,
  author	= {Marguerat, C. },
  title		= {Artificial neural network algorithms on a parallel DSP
		  system},
  booktitle	= {Transputers '94. Proceedings of the International
		  Conference},
  year		= {1994},
  editor	= {Becker, M. and Litzler, L. and Tehel, M. },
  pages		= {278--87},
  organization	= {Microcomput. Lab. , Swiss Federal Inst. of Technol. ,
		  Lausanne, Switzerland},
  publisher	= {IOS Press},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InCollection{	  mariage97a,
  author	= {Jean-Jacques Mariage},
  title		= {Dynamic neighbourhoods in self organizing maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {175--180},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  marin01a,
  author	= {Marin, J. and Ragsdale, D. and Sirdu, J.},
  title		= {A hybrid approach to the profile creation and intrusion
		  detection},
  booktitle	= {Proceedings DARPA Information Survivability Conference and
		  Exposition II. DISCEX'01. IEEE Comput. Soc, Los Alamitos,
		  CA, USA},
  year		= {2001},
  volume	= {1},
  pages		= {69--76},
  abstract	= {Anomaly detection involves characterizing the behaviors of
		  individuals or systems and recognizing behavior that is
		  outside the norm. This paper describes some preliminary
		  results concerning the robustness and generalization
		  capabilities of machine learning methods in creating user
		  profiles based on the selection and subsequent
		  classification of command line arguments. We base our
		  method on the belief that legitimate users can be
		  classified into categories based on the percentage of
		  commands they use in a specified period. The hybrid
		  approach we employ begins with the application of expert
		  rules to reduce the dimensionality of the data, followed by
		  an initial clustering of the data and subsequent refinement
		  of the cluster locations using a competitive network called
		  Learning Vector Quantization. Since Learning Vector
		  Quantization is a nearest neighbor classifier, and new
		  record presented to the network that lies outside a
		  specified distance is classified as a masquerader. Thus,
		  this system does not require anomalous records to be
		  included in the training set.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  marinelli99a,
  author	= {Marinelli, Anne Marie P. and Kaplan, Lance M. and
		  Nasrabadi, Nasser M.},
  title		= {{SAR} {ATR} using a modified learning vector quantization
		  algorithm},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3721},
  pages		= {343--354},
  abstract	= {We addressed the problem of classifying 10 target types in
		  imagery formed from synthetic aperture radar ({SAR}). By
		  executing a group training process, we show how to increase
		  the performance of 10 initial sets of target templates
		  formed by simple averaging. This training process is a
		  modified learning vector quantization (LVQ) algorithm that
		  was previously shown effective with forward-looking
		  infrared (FLIR) imagery. For comparison, we ran the LVQ
		  experiments using coarse, medium, and fine template sets
		  that captured the target pose signature variations over
		  60°, 40°, and 20°, respectively. Using sequestered test
		  imagery, we evaluated how well the original and post-LVQ
		  template sets classify the 10 target types. We show that
		  after the LVQ training process, the coarse template set
		  outperforms the coarse and medium original sets. And, for a
		  test set that included untrained version variants, we show
		  that classification using coarse template sets nearly
		  matches that of the fine template sets. In a related
		  experiment, we stored 9 initial template sets to classify 9
		  of the target types and used a threshold to separate the
		  10th type, previously found to be a `confusing' type. We
		  used imagery of all 10 targets in the LVQ training process
		  to modify the 9 template sets. Overall classification
		  performance increased slightly and an equalization of the
		  individual target classification rates occurred, as
		  compared to the 10-template experiment. The {SAR} imagery
		  that we used is publicly available from the Moving and
		  Stationary Target Acquisition and Recognition (MSTAR)
		  program, sponsored by the Defense Advanced Research
		  Projects Agency (DARPA).},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  marinelli99b,
  author	= {Marinelli, A. M. P. and Kaplan, L. M. and Nasrabadi, N.
		  M.},
  title		= {{SAR} {ATR} using learning vector quantization},
  booktitle	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3647},
  pages		= {14--25},
  abstract	= {We address the problems of recognizing 10 types of
		  vehicles in imagery formed from synthetic aperture radar
		  ({SAR}). {SAR} provides all-weather, day, or night imagery
		  of the battlefield. To aid in the analysis of the copious
		  amounts of imagery available today, automatic target
		  recognition (ATR) algorithms, which are either
		  template-based or model-based, are needed. We enhanced
		  template-based algorithms by using an artificial neural
		  network (ANN) to increase the discriminating
		  characteristics of 10 initial sets of templates. The ANN is
		  a modified learning vector quantization (LVQ) algorithm,
		  previously shown effective with forward-looking infrared
		  (FLIR) imagery. For comparison, we ran the experiments with
		  LVQ using three different sized template sets. These
		  template sets captured the target signature variations over
		  60 degrees , 40 degrees , and 20 degrees . We allowed LVQ
		  to modify the templates, as necessary, using the training
		  imager from all 10 targets. The resulting templates
		  represent the 10 target types with greater separability in
		  feature space. Using sequestered test imagery, we compared
		  the pre-and post-LVQ template sets in terms of their
		  ability to discriminate the 10 target types. All training
		  and test imagery is publicly available from the Moving and
		  Stationary Target Acquisition and Recognition program
		  sponsored by the Defense Advanced Research Projects
		  Agency.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  markon95a,
  author	= {S. Markon and H. Kita and Y. Nishikawa},
  title		= {A voice-controlled elevator using neural networks},
  booktitle	= {Proceedings of International Conference on Neural
		  Information Processing (ICONIP `95)},
  publisher	= {Publishing House of Electron. Ind},
  year		= {1995},
  volume	= {2},
  editor	= {Y. Zhong and Y. Yang and M. Wang},
  address	= {Beijing, China},
  pages		= {929--34},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  marks87a,
  author	= {K. M. Marks},
  title		= {Multi Users auf einer Prolog-Datenbasis},
  booktitle	= {Proc. 1st Interface Prolog User Day},
  year		= {1987},
  publisher	= {Interface Computer GmbH},
  address	= {Munich, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  marks88a,
  author	= {Karl M. Marks and Karl F. Goser},
  title		= {Analysis of {VLSI} process data based on
		  \mbox{self-organizing} feature maps},
  booktitle	= {Proc. of Neuro-N\^{i}mes, Int. Workshop on Neural Networks
		  and their Applications},
  year		= {1988},
  pages		= {337--348},
  publisher	= {EC2},
  address	= {Nanterre, France},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  marpaka94a,
  author	= {Marpaka, D. R. and Hwang, W. R. },
  title		= {Neurocontroller for power systems using
		  \mbox{self-organizing} neural networks},
  booktitle	= {Proceedings of the American Power Conference},
  year		= {1994},
  volume	= {1},
  pages		= {778--83},
  organization	= {Dept. of Electr. \& Comput. Eng. , Tennessee State Univ. ,
		  Nashville, TN, USA},
  publisher	= {Illinois Inst. Technol},
  address	= {Chicago, IL, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  marques96a,
  author	= {J. S. Marques and A. J. Abrantes},
  title		= {A class of probabilistic shape models},
  booktitle	= {Proceedings. 1997 IEEE Computer Society Conference on
		  Computer Vision and Pattern Recognition},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {B. Yuan and X. Tang},
  address	= {New York, NY, USA},
  pages		= {1054--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  marshall89a,
  author	= {J. A. Marshall},
  title		= {Self-Organizing Architectures for Computing Visual Depth
		  from Motion Parallax},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {227--234 },
  dbinsdate	= {oldtimer}
}

@Article{	  marshall90a,
  author	= {J. Marshall},
  title		= {Self-Organizing Neural Networks for Perception of Visual
		  Motion},
  journal	= {Neural Networks},
  volume	= {3},
  year		= {1990},
  number	= {1},
  pages		= {45--74 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  marsland00a,
  author	= {Stephen Marsland and Ulrich Nehmzow and Jonathan Shapiro},
  title		= {Novelty Detection for Robot Neotaxis},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Department of Computer Science, University of Manchester},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  marsland00b,
  author	= {Marsland, S. and Nehmzow, U. and Shapiro, J.},
  title		= {A real-time novelty detector for a mobile robot},
  journal	= {3rd Eurel Workshop and Masterclass. European Advanced
		  Robotics Systems Development. Univ. Salford, Salford, UK;
		  2000; 2 vol. 312+344 pp.p.8 pp},
  year		= {2000},
  volume	= {2},
  pages		= {8},
  abstract	= {Recognising new or unusual features of an environment is
		  an ability which is potentially very useful to a robot.
		  This paper demonstrates an algorithm which achieves this
		  task by learning an internal representation of "normality"
		  from sonar scans taken as a robot explores the environment.
		  This model of the environment is used to evaluate the
		  novelty of each sonar scan presented to it with relation to
		  the model. Stimuli which have not been seen before, and
		  therefore have more novelty, are highlighted by the filter.
		  The filter has the ability to forget about features which
		  have been learned, so that stimuli which are seen only
		  rarely recover their response over time. A number of robot
		  experiments are presented which demonstrate the operation
		  of the filter. A Kohonen self-organising map is used for
		  the processing.},
  dbinsdate	= {2002/1}
}

@Article{	  martin-del-brio93a,
  author	= {Bonifacio {Mart{\'{\i}}n-del-Br{\'{\i}}o} and Carlos
		  Serrano-Cinca},
  title		= {Self-organizing Neural Networks for the Analysis and
		  Representation of Data: {SOM} Financial Cases},
  journal	= {Neural Computing \& Application},
  year		= {1993},
  volume	= {1},
  number	= {3},
  pages		= {193--206},
  dbinsdate	= {oldtimer}
}

@InCollection{	  martin-del-brio95a,
  author	= {B. Martin-Del-Brio and N. Medrano-Marques and J.
		  Blasco-Alberto},
  title		= {Feature map architectures for pattern recognition:
		  techniques for automatic region selection},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {D. W. Pearson and N. C. Steele and R. F. Albrecht},
  address	= {Vienna, Austria},
  pages		= {124--7},
  dbinsdate	= {oldtimer}
}

@InCollection{	  martin-del-brio95b,
  author	= {B. Martin-Del-Brio and J. Blasco-Alberto},
  title		= {Hardware-oriented models for {VLSI} implementation of
		  \mbox{self-organizing} maps},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {712--19},
  dbinsdate	= {oldtimer}
}

@Article{	  martin-del-brio96a,
  author	= {Bonifacio {Mart{\'{\i}}n-del-Br{\'{\i}}o}},
  title		= {A Dot Product Neuron for Hardware Implementation of
		  Competitive Networks},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1996},
  volume	= {3},
  number	= {2},
  pages		= {529--532},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martin-del-brio98a,
  author	= {Martin-Del-Brio, B. and Medrano-Marques, N. and {Hernandez
		  Sanchez}, S.},
  title		= {A low-cost neuroprocessor board for emulating the {SOFM}
		  neural model},
  booktitle	= {1998 IEEE International Conference on Electronics,
		  Circuits and Systems. Surfing the Waves of Science and
		  Technology.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  volume	= {3},
  pages		= {297--300},
  abstract	= {In this paper, the design and implementation of a low-cost
		  PC-board for emulating the self-organising map neural model
		  is shown. This neuroprocessor, based on Xilinx's FPGAs, is
		  built and tested, and its performance is analysed and
		  compared to computer simulations. The resulting neuroboard
		  is just a first step towards the development of a general
		  purpose neuroemulator based on FPGAs, in which the
		  reconfiguration properties of these programmable logic
		  devices will be broadly exploited.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martin-merino01a,
  author	= {Martin-Merino, M. and Munoz, A. and Dimitriadis, Y.},
  title		= {Incorporating asymmetry into {SOM} and Sammon algorithms
		  for visual map generation},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {1908--1913},
  organization	= {University Pontificia of Salamanca},
  publisher	= {},
  address	= {},
  abstract	= {The interest in developing visual word maps that help
		  users to find information in the web has grown up in the
		  last years. Neural based techniques have been proposed to
		  generate word maps but most of them rely on the use of
		  symmetric measures. In this work we propose extended
		  versions of Self Organizing Maps (SOM) and Sammon mapping
		  algorithms that can handle efficiently asymmetric
		  dissimilarities. The modified algorithms are compared to
		  their symmetric counterparts in a real document database
		  using 2 objective error measures. Our proposed asymmetric
		  algorithms improve the maps generated by their symmetric
		  counterparts.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  martin-merino01b,
  author	= {Martin-Merino, M. and Munoz, A.},
  title		= {Self Organizing Map and Sammon Mapping for asymmetric
		  proximities},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {429--435},
  abstract	= {Self Organizing Maps (SOM) and Sammon Mapping (SM) are two
		  information visualization techniques widely used in the
		  data mining community. These techniques assume that the
		  similarity matrix for the data set under consideration is
		  symmetric. However there are many interesting problems
		  where asymmetric proximities arise, like text mining
		  problems are. In this work we propose modified versions of
		  SOM and SM to deal with data where the proximity matrix is
		  asymmetric. The algorithms are tested using a real document
		  database, and performance is reported using appropriate
		  measures. As a result, the asymmetric algorithms proposed
		  outperform their symetric counterparts.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  martin-smith93a,
  author	= {P. Mart{\'{i}}n-Smith and F. J. Pelayo and A. Diaz and J.
		  Ortega and A. Prieto},
  title		= {A Learning Algorithm to Obtain {S}elf-{O}rganizing {M}aps
		  using Fixed Neighborhood {K}ohonen networks},
  booktitle	= {New Trends in Neural Computation, Lecture Notes in
		  Computer Science No. 686},
  publisher	= {Springer},
  year		= {1993},
  editor	= {J. Mira and J. Cabestany and A. Prieto},
  pages		= {297--304},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martin-smith99a,
  author	= {Martin-Smith, P. and Pelayo, F. J. and Ros, E. and Prieto,
		  A.},
  title		= {Supervised {VQ} learning based on temporal inhibition},
  booktitle	= {Foundations and Tools for Neural Modeling. International
		  Work-Conference on Artificial and Natural Neural Networks,
		  IWANN'99. Proceedings, (Lecture Notes in Computer Science
		  Vol.1606)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {1},
  pages		= {610--20},
  abstract	= {In the context of supervised vectorial quantization (VQ)
		  learning algorithms, we present an algorithm for supervised
		  learning with temporal inhibition (SLTI) that exploits the
		  self-organizing properties arising from a particular
		  process of temporal inhibition of the winning units in
		  competitive learning. This exploitation consists of
		  establishing independence capabilities in the
		  initialization of the prototypes (weight vectors), together
		  with generalization capabilities, which to a certain extent
		  solve some of the critical problems involved in the use of
		  conventional algorithms such as LVQs and DSM. Another
		  original aspect of this paper is the inclusion in SLTI of a
		  simple rule for prototype adaptation, which incorporates
		  certain useful features that make possible to plan the
		  configuration of the SLTI parameters with specific goals in
		  order to approach classification tasks of varied complexity
		  and natures (versatility). This versatility is
		  experimentally demonstrated with synthetic data comprising
		  nonlinearly-separable classes, overlapping classes and
		  interleaved classes with a certain degree of overlap.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martin94a,
  author	= {Martin, P. and {del Pobil}, A. P. },
  title		= {Application of artificial neural networks to the robot
		  path planning problem},
  booktitle	= {Applications of Artificial Intelligence in Engineering IX.
		  Proceedings of the Ninth International Conference},
  year		= {1994},
  editor	= {Rzevski, G. and Adey, R. A. and Russell, D. W. },
  pages		= {73--80},
  organization	= {Dept. of Comput. Sci. , Jaume I Univ. , Castellon, Spain},
  publisher	= {Comput. Mech. Publications},
  address	= {Southampton, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinelli94a,
  author	= {Martinelli, G. and Mascioli, F. M. F. },
  title		= {Enhancement of \mbox{self-organising} feature maps by
		  linear pre-processing},
  booktitle	= {Neural Nets Wirn Vietri 93---Proceedings of the 5th
		  Italian Workshop on Neural Nets},
  year		= {1994},
  editor	= {Caianiello, E. R. },
  organization	= {INFOCOM Dept. , Rome Univ. , Italy},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InCollection{	  martinetz89a,
  author	= {Martinetz, Thomas and Ritter, Helge and Schulten, Klaus},
  booktitle	= {Connectionism in Perspective},
  title		= {{{K}ohonen's} \mbox{Self-organizing} Map for Modeling the
		  Formation of the Auditory Cortex of a Bat},
  publisher	= {North-Holland},
  year		= {1989},
  editor	= {Pfeifer, R. and Schreter, Z. and Fogelman-Souli{\'{e}}, F.
		  and Steels, L. },
  pages		= {403--412},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinetz89b,
  author	= {T. Martinetz and H. Ritter and K. Shulten},
  title		= {3{D}-neural net for learning visuomotor-coordination of a
		  robot arm},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  pages		= {351--356},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinetz90a,
  author	= {Thomas Martinetz and Helge Ritter and Klaus Schulten},
  title		= {Learning of Visuo-Motor Coordination of a Robot Arm with
		  Redundant Degrees of Freedom},
  booktitle	= {Proc. International Conference on Parallel Processing in
		  Neural Systems and Computers {(ICNC)}, D\"usseldorf},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  year		= 1990,
  pages		= {431--434},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinetz90b,
  author	= {Thomas Martinetz and Helge Ritter and Klaus Schulten},
  title		= {Learning of Visuomotor-Coordination of a Robot Arm with
		  Redundant Degrees of Freedom},
  booktitle	= {Proc. ISRAM-90, Third Int. Symp. on Robotics and
		  Manufacturing},
  year		= {1990},
  pages		= {521--526},
  address	= {Vancouver, Canada},
  annote	= {A five-joint robot arm + an improved learning algorithm.
		  },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinetz90c,
  author	= {T. M. Martinetz and K. J. Schulten},
  title		= {Hierarchical neural net for learning control of a robot's
		  arm and gripper},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {II},
  pages		= {747--752},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {To each node in a 3-dimensional SOM corresponds a second,
		  2-dim SOM. This controls the orientation of the gripper
		  while the 3-D SOM controls it's place. },
  dbinsdate	= {oldtimer}
}

@Article{	  martinetz90d,
  author	= {Thomas M. Martinetz and Helge J. Ritter and Klaus J.
		  Schulten},
  title		= {Three-dimensional neural net for learning visuomotor
		  coordination of a robot arm},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1990},
  volume	= {1},
  number	= {1},
  pages		= {131--136},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinetz91a,
  author	= {Thomas Martinetz and Klaus Schulten},
  title		= {A "{N}eural-{G}as" Network Learns Topologies},
  booktitle	= {Proc. International Conference on Artificial Neural
		  Networks {\rm (Espoo, Finland)}},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {397--402},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  martinetz92a,
  author	= {Thomas Martinetz},
  title		= {Selbstorganisierende neuronale Netzwerkmodelle zur
		  Bewegungssteuerung},
  school	= {Technische Universit{\"{a}}t M{\"{u}}nchen},
  year		= {1992},
  address	= {M{\"{u}}nchen, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  martinetz93a,
  author	= {T. Martinetz and K. Schulten},
  title		= {A neural network for robot control: cooperation between
		  neural units as a requirement for learning},
  journal	= {Computers \& Electrical Engineering},
  year		= {1993},
  volume	= {19},
  number	= {4},
  pages		= {315--312},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinetz93b,
  author	= {Thomas Martinetz},
  title		= {Competitive {H}ebbian Learning Rule Forms Perfectly
		  Topology Preserving Maps},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {427--434},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinetz93c,
  author	= {Thomas Martinetz and Klaus Schulten},
  title		= {A Neural Network with {H}ebbian-like Adaptation Rules
		  Learning Visuomotor Coordination of a {PUMA} Robot},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {II},
  pages		= {820--822C},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  martinetz93d,
  author	= {Thomas M. Martinetz and Stanislav G. Berkovich and Klaus
		  J. Schulten},
  title		= {'{N}eural-Gas' Network for Vector Quantization and its
		  Application to Time-Series Prediction},
  journal	= {{IEEE} Trans. on Neural Networks},
  year		= {1993},
  volume	= {4},
  number	= {4},
  pages		= {558--569},
  dbinsdate	= {oldtimer}
}

@Article{	  martinetz94a,
  author	= {Thomas Martinetz and Klaus Schulten},
  title		= {Topology Representing Networks},
  journal	= {Neural Networks},
  year		= {1994},
  volume	= {7},
  number	= {2},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinez01a,
  author	= {Martinez, P. and Aguilar, P. L. and Perez, R. M. and
		  Linaje, M. and Preciado J. C. and Plaza A.},
  title		= {Self-organizing map for hyperspectral image analysis},
  booktitle	= {Bio-Inspired Applications of Connectionism. 6th
		  International Work-Conference on Artificial and Natural
		  Neural Networks, IWANN 2001. Proceedings, Part II. (Lecture
		  Notes in Computer Science Vol.2085). Springer-Verlag,
		  Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {208--18},
  abstract	= {We present a neural network methodology used for
		  classifying a hyperspectral image referenced as Indian
		  Pines. The network parameters (learning and neighborhood
		  function) are adjusted using a test battery generated from
		  the image, selecting the values that give the best
		  robustness and discrimination capacity. The availability of
		  ground truth allows us to introduce a new statistical
		  measure to quantify the resulting classification accuracy.
		  The results of this methodology show an accuracy of 80% in
		  the classification.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  martinez95a,
  author	= {Martinez, W. M. },
  title		= {A natural language processor with neural networks},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {4},
  pages		= {3156--61},
  organization	= {Dept. of Electr. \& Comput. Eng. , Puerto Rico Univ. ,
		  Mayaguez, Puerto Rico},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinez_cabeza_de_vaca_alajarin99a,
  author	= {{Martinez Cabeza de Vaca Alajarin}, J. and {Tomas
		  Balibrea}, L. M.},
  title		= {Automatic classification system of marble slabs in
		  production line according to texture and color using
		  artificial neural networks},
  booktitle	= {Computer Analysis of Images and Patterns. 8th
		  International Conference, CAIP'99. Proceedings (Lecture
		  Notes in Computer Science Vol.1689)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {167--74},
  abstract	= {This article describes the algorithms and the mechatronic
		  system developed for the clustering and classification of
		  marble slabs on production line according to their texture.
		  The method used for the recognition of textures is based on
		  the Sum and Difference Histograms, a faster version of the
		  Co-occurrence Matrices, and the classifier has been
		  implemented by using an LVQ neural network. For each
		  pattern (a marble slab color image), a set of statistical,
		  texture-dependant, parameters is extracted. The input of
		  the classifier (the LVQ network) is the set of parameters
		  calculated before, normalized in the range [0,1], which
		  forms a vector that characterize the pattern shown to the
		  net; the desired output of the network is the class where
		  the pattern belongs to (supervised learning). In our tests,
		  seven different color spaces were used, each one with three
		  different neighbourhoods of pixels. The selected samples
		  chosen for testing the algorithms have been marble slabs of
		  "Crema Marfil Sierra de la Puerta" type. The neural network
		  has been implemented by using MATLAB.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martinez_cabeza_de_vaca_alajarin99b,
  author	= {{Martinez Cabeza de Vaca Alajarin}, J. and {Tomas
		  Balibrea}, L. M.},
  title		= {Marble slabs quality classification system using texture
		  recognition and neural networks methodology},
  booktitle	= {7th European Symposium on Artificial Neural Networks.
		  ESANN'99. Proceedings},
  publisher	= {D-Facto},
  address	= {Brussels, Belgium},
  year		= {1999},
  volume	= {},
  pages		= {75--80},
  abstract	= {This article describes the use of an LVQ neural network
		  for the clustering and classification of marble slabs
		  according to their texture. The method used for the
		  recognition of textures is based on the sum-and-difference
		  histograms, a faster version of the cooccurrence matrices.
		  The input of the network is a vector of statistical
		  parameters which characterize the pattern shown to the net,
		  and the desired output is the class to which the pattern
		  belongs (supervised learning). The samples chosen for
		  testing the algorithms have been marble slabs of type
		  "Crema Marfil Sierra de la Puerta". The neural network has
		  been implemented using MATLAB.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  martins01a,
  author	= {Martins, W. and Meira e Silva, J. C.},
  title		= {Multidimensional data ranking using self-organising maps
		  and genetic algorithms},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2382--2387},
  organization	= {Federal University of Goias, School of Electrical
		  Engineering, PIRENEUS Research Group},
  publisher	= {},
  address	= {},
  abstract	= {There are applications that require ordered instances
		  modeled by high dimensional vectors. Despite the reasonable
		  quantity of papers on the areas of classification and
		  clustering and its crescent importance, papers on ranking
		  are rare. Usual solutions are not generic and demand expert
		  knowledge on the specification of the weight of each
		  component and, therefore, the definition of a ranking
		  function. This paper proposes a generic procedure for
		  ranking, based on one-dimensional self-organizing maps
		  (SOMs). Additionally, the similarity metric used by SOM is
		  modified and automatically adjusted to the context by a
		  genetic search. This process seeks for the best ranking
		  that matches the desired probability distribution provided
		  by the specialist expectation. Promising results were
		  achieved on the ranking of data from blood banks
		  inspections.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  martins94a,
  author	= {Martins, W. and Allinson, N. M. },
  title		= {Improving adaptive logic networks: initialization and
		  confidence},
  booktitle	= {World Congress on Neural Networks-San Diego. 1994
		  International Neural Network Society Annual Meeting},
  year		= {1994},
  volume	= {4},
  pages		= {IV/39--44},
  organization	= {Dept. of Electron. , York Univ. , UK},
  publisher	= {Lawrence Erlbaum Associates},
  address	= {Hillsdale, NJ, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  marttinen93a,
  author	= {Kari Marttinen},
  title		= {{SOM} in Statistical Analysis: Supermarket Customer
		  Profiling},
  booktitle	= {Proc. of the Symp. on Neural Networks in Finland, {\AA}bo
		  Akademi, Turku, January 21. },
  year		= {1993},
  editor	= {Abhay Bulsari and Bj{\"{o}}rn Sax{\'{e}}n},
  pages		= {75--80},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  dbinsdate	= {oldtimer}
}

@InCollection{	  mas_ribes98a,
  author	= {J. -M. {Mas Ribes} and B. Macq},
  title		= {Speeding up fractal image coding by combined {DCT} and
		  {K}ohonen neural net method},
  booktitle	= {Proceedings of the 1998 IEEE International Conference on
		  Acoustics, Speech and Signal Processing, ICASSP '98},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1085--8},
  dbinsdate	= {oldtimer}
}

@Article{	  masaru00a,
  author	= {Masaru, T. and Sigeru, O. and Toshihisa, K.},
  title		= {Classification of three fatigue levels for bills using
		  acoustic frequency band energy patterns},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-C}
		  ,
  year		= {2000},
  volume	= {120},
  pages		= {1602--8},
  abstract	= {This paper proposes a method to classify three fatigue
		  levels of bills using acoustic frequency band energy
		  patterns by a neural network. The proposed method deals
		  with an acoustic frequency band energy pattern from the
		  acoustic signal which has been generated by the bill
		  passing through a banking machine. The competitive neural
		  network is used to classify a bill into one of three
		  different fatigue levels. We use two types of the LVQ
		  algorithm for training of the competitive neural network.
		  The experimental results show the effectiveness of the
		  proposed method.},
  dbinsdate	= {2002/1}
}

@Article{	  mascarilla94a,
  author	= {Mascarilla, L. },
  title		= {Rule extraction based on neural networks for satellite
		  image interpretation},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1994},
  volume	= {2315},
  pages		= {657--68},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  masson90a,
  author	= {E. Masson and Yih-Jeou Wang},
  title		= {Introduction to computation and learning in artificial},
  journal	= {European J. Operational Res. },
  year		= {1990},
  volume	= {47},
  number	= {1},
  pages		= {1--28},
  x		= {A short historical overview . . . },
  dbinsdate	= {oldtimer}
}

@Article{	  matera98a,
  author	= {Matera,F. },
  title		= {Learning Vector Quantization Networks},
  journal	= {Subst. Use Misuse},
  year		= {1998},
  pages		= {271--282},
  volume	= {33},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mathis96a,
  author	= {Mathis, G. and Mousset, E.},
  title		= {Combining a {NN}-based feature extractor with a
		  classifier-a case study},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {483--6},
  abstract	= {Four different possibilities of combination of a shared
		  weights feedforward neural network (SWFNN)-based feature
		  extractor with a classifier are compared, using an example
		  of an application to geophysics. The aim is to speed up the
		  real-time process of regional seismic event localization,
		  which is dependent on an efficient and accurate performance
		  in the phase detection and labelling subtasks. We are
		  interested in designing a single system capable of
		  labelling four cases related to different event propagation
		  modes (Pn/Pg/Sn/Sg) and detecting eventual false alarm
		  cases caused by an upstream automatic process. In the
		  current comparison, the same first-stage SWFNN is
		  successively combined with a wavelet network, a Bayesian
		  classifier, a LVQ-based classifier and a decision tree.
		  Particular attention is given to the entire system ability
		  to separate the "noise class" from the four others. From
		  this point of view, the combination involving the wavelet
		  network achieves the best results, whereas the two latter
		  yield a poorer performance than the first-stage neural
		  network used alone.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  matsuoka93a,
  author	= {Kiyotoshi Matsuoka and Mitsuru Kawamoto},
  title		= {A Self-Organizing Neural Network for Principal Component
		  Analysis, Orthogonal Projection and Novelty Filtering},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {501--504},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  matsuoka95a,
  author	= {Toshinobu Matsuoka and Yoshihisa Ishida},
  title		= {{DB} Matching-Based Spoken Digit Recognition Using {LVQ}},
  volume	= {V},
  pages		= {2900--2903},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  matsuyama93a,
  author	= {Yasuo Matsuyama and Masayoshi Tan},
  title		= {Multiply Descent Cost Competitive Learning as an Aid for
		  Multimedia Image Processing},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2061--2064},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {An integration of neural and ordinary computations toward
		  multimedia processing is presented. The handled media is a
		  combination of still images and animations. The
		  neurocomputation here is the multiply descent cost
		  competitive learning. This algorithm generates two types of
		  feature maps. One of them, an optimized grouping pattern of
		  pixels by self-organization, is used. A data-compressed
		  still image can be recovered from this feature map by
		  virtue of the multiply descent cost competitive learning.
		  Next, this map is contorted according to a user's request.
		  At the final step, a movie is virtually generated from the
		  compressed still image via a set of animation tools. Thus,
		  neurocomputation can be a useful item in the toolbox for
		  creating the virtual reality besides the real-world
		  computing.},
  dbinsdate	= {oldtimer}
}

@Article{	  matsuyama98a,
  author	= {Y. Matsuyama},
  title		= {Multiple descent cost competition: restorable
		  self-organization and multimedia information processing},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1998},
  volume	= {9},
  number	= {1},
  pages		= {106--22},
  dbinsdate	= {oldtimer}
}

@Article{	  mattfeldt00a,
  author	= {Mattfeldt, T. and Gottfried, H. W. and Schmidt, V. and
		  Kestler, H. A.},
  title		= {Classification of spatial textures in benign and cancerous
		  glandular tissues by stereology and stochastic geometry
		  using artificial neural networks},
  journal	= {JOURNAL OF MICROSCOPY-OXFORD},
  year		= {2000},
  volume	= {198},
  month		= {MAY},
  pages		= {143--158},
  abstract	= {Stereology and stochastic geometry can be used as
		  auxiliary tools for diagnostic purposes in tumour
		  pathology. Whether first-order parameters or
		  stochastic-geometric functions are more important for the
		  classification of the texture of biological tissues is not
		  known. In the present study, volume and surface area per
		  unit reference volume, the pair correlation function and
		  the centred quadratic contact density function of
		  epithelium were estimated in three case series of benign
		  and malignant lesions of glandular tissues. The information
		  provided by the latter functions was summarized by the
		  total absolute areas between the estimated curves and their
		  horizontal reference lines. These areas are considered as
		  indicators of deviation of the tissue texture from a
		  completely uncorrelated volume process and from the Boolean
		  model with convex grains, respectively. We used both areas
		  and the first- order parameters for the classification of
		  cases using artificial neural networks (ANNs). Learning
		  vector quantization and multilayer feedforward networks
		  with backpropagation were applied as neural paradigms.
		  Applications included distinction between mastopathy and
		  mammary cancer (40 cases), between benign prostatic
		  hyperplasia and prostatic cancer (70 cases) and between
		  chronic pancreatitis and pancreatic cancer (60 cases). The
		  same data sets were also classified with linear
		  discriminant analysis. The stereological estimates in
		  combination with ANNs or discriminant analysis provided
		  high accuracy in the classification of individual cases.
		  The question of which category of estimator is the most
		  informative cannot be answered globally, but must be
		  explored empirically for each specific data set. Using
		  learning vector quantization, better results could often be
		  obtained than by multilayer feedforward networks with
		  backpropagation.},
  dbinsdate	= {2002/1}
}

@Article{	  mattfeldt01a,
  author	= {Mattfeldt, T. and Wolter, H. and Kemmerling, R. and
		  Gottfried, H. W. and Kestler, H. A.},
  title		= {Cluster analysis of comparative genomic hybridization
		  ({CGH}) data using self-organizing maps: Application to
		  prostate carcinomas},
  journal	= {ANALYTICAL CELLULAR PATHOLOGY},
  year		= {2001},
  volume	= {23},
  number	= {1},
  pages		= {29--37},
  abstract	= {Comparative genomic hybridization (CGH) is a modern
		  genetic method which enables a genome-wide survey of
		  chromosomal imbalances. For each chromosome region, one
		  obtains the information whether there is a loss or gain of
		  genetic material, or whether there is no change at that
		  region. Usually it is not possible to evaluate all 46
		  chromosomes of a metaphase, therefore several (up to 20 or
		  more) metaphases are analyzed per individual, and expressed
		  as average. Mostly one does not study one individual alone
		  but groups of 20--30 individuals. Therefore, large amounts
		  of data quickly accumulate which must be put into a logical
		  order. In this paper we present the application of a
		  self-organizing map (Genecluster) as a tool for cluster
		  analysis of data from pT2N0 prostate cancer cases studied
		  by CGH. Self-organizing maps are artificial neural networks
		  with the capability to form clusters on the basis of an
		  unsupervised learning rule, i.e., in our examples it gets
		  the CGH data as only information (no clinical data). We
		  studied a group of 40 recent cases without follow-up, an
		  older group of 20 cases with follow-up, and the data set
		  obtained by pooling both groups. In all groups good
		  clusterings were found in the sense that clinically similar
		  cases were placed into the same clusters on the basis of
		  the genetic information only. The data indicate that losses
		  on chromosome arms 6q, 8p and 13q are all frequent in pT2N0
		  prostatic cancer, but the loss on 8p has probably the
		  largest prognostic importance.},
  dbinsdate	= {2002/1}
}

@Article{	  mattfeldt01b,
  author	= {Mattfeldt, T. and Kestler, H. A. and Hautmann, R. and
		  Gottfried, H. W.},
  title		= {Prediction of postoperative prostatic cancer stage on the
		  basis of systematic biopsies using two types of artificial
		  neural networks},
  journal	= {EUROPEAN UROLOGY},
  year		= {2001},
  volume	= {39},
  number	= {5},
  month		= {MAY},
  pages		= {530--536},
  abstract	= {Objective: The choice of therapy for prostatic cancer
		  should depend on a rational preoperative estimate of tumor
		  stage. Artificial neural networks were used to predict
		  postoperative staging of prostatic cancer from sextant
		  biopsies and routinely available preoperative data.
		  Methods: In group I (97 cases), nonorgan confinement (tumor
		  stage greater than or equal to pT3a) was predicted on the
		  basis of age and six histopathological variables from
		  sextant biopsies. In group II (77 cases), nonorgan
		  confinement and extraprostatic organ infiltration (tumor
		  classification greater than or equal to pT3b) were
		  predicted from age, four histopathological variables, the
		  preoperative PSA level, and the total prostate volume
		  estimated by preoperative ultrasonography. Learning vector
		  quantization (LVQ) networks were applied for this purpose
		  and compared to multilayer perceptrons (MLP) and linear
		  discriminant analysis (LDA). Results: Nonorgan confinement
		  could be predicted correctly in 90% of newly presented
		  cases from sextant biopsy histopathology alone. A similar
		  accuracy of predicting nonorgan confinement (83%) was
		  obtained by combining preoperative biopsy histology with
		  clinical data. Extraprostatic organ infiltration could be
		  predicted correctly in 82%. The best results were obtained
		  by LVQ networks, followed by MLP networks and LDA.
		  Conclusion: The postoperative tumor stage of prostatic
		  cancer can be estimated with high accuracy, sensitivity and
		  specificity from preoperative routine parameters using
		  artificial neural networks, especially LVQ networks. The
		  results suggest that this methodology should be evaluated
		  in a larger prospective study. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  matthews95a,
  author	= {C. P. Matthews and K. Warwick},
  title		= {Practical Application of {S}elf {O}rganizing {F}eature
		  {M}aps to Process Modelling},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {449--452},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@InCollection{	  matz97a,
  author	= {G. Matz and T. Albrecht and T. Hunte},
  title		= {Gas-sensor-array for chemical accidents and fires},
  booktitle	= {Sensor 95},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {J. Mira and R. Moreno-Diaz and J. Cabestany},
  address	= {Berlin, Germany},
  pages		= {369--74},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mauduit91a,
  author	= {N. Mauduit and M. Duranton and J. Gobert and J. -A.
		  Sirat},
  title		= {Building up neuromimetic machines with {LNeuro} 1. 0},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {602--607},
  organization	= {IEEE; Int. Neural Networks Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  mauduit92a,
  author	= {N. Mauduit and M. Duranton and J. Gobert and J. -A.
		  Sirat},
  title		= {{Lneuro 1. 0}: a piece of hardware {LEGO} for building
		  neural network systems},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1992},
  volume	= {3},
  number	= {3},
  pages		= {414--422},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  maurer91a,
  author	= {W. J. Maurer and F. U. Dowla and S. P. Jarpe},
  title		= {Seismic event classification using Self-Organizing Neural
		  Networks},
  booktitle	= {Australian Conf. on Neural Networks},
  year		= {1991},
  organization	= {Department of Energy, Washington, DC},
  x		= {nt9093---AN ACCESSION NUMBER: DE92005243XSP},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  maurer92a,
  author	= {W. J. Maurer and F. U. Dowla and S. P. Jarpe},
  title		= {Seismic event classification using \mbox{self-organizing}
		  neural networks},
  booktitle	= {Proc. Third Australian Conf. on Neural Networks (ACNN
		  '92)},
  year		= {1992},
  editor	= {P. Leong and M. Jabri},
  pages		= {162--165},
  publisher	= {Sydney Univ},
  address	= {Sydney, Australia},
  dbinsdate	= {oldtimer}
}

@Article{	  maurer92b,
  author	= {Maurer, W. J. and Dowla, F. U. and Jarpe, S. P. },
  title		= {Seismic event interpretation using \mbox{self-organizing}
		  neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 2},
  pages		= {950--8},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mayberry00a,
  author	= {Mayberry, M. R. and Miikkulainen, R.},
  title		= {Combining maps and distributed representations for
		  shift-reduce parsing},
  booktitle	= {HYBRID NEURAL SYSTEMS},
  year		= {2000},
  pages		= {144--157},
  abstract	= {Simple Recurrent Networks (SRNS) have been widely used in
		  natural language processing tasks. However, their ability
		  to handle long-term dependencies between sentence
		  constituents is rather limited. NARX networks have recently
		  been shown to outperform SRNs by preserving past
		  information in explicit delays from the network's prior
		  output. Determining the number of delays, however, is
		  problematic in itself. In this study on a shift-reduce
		  parsing task, we demonstrate a hybrid localist- distributed
		  approach that yields comparable performance in a more
		  concise manner. A SARDNET self-organizing map is used to
		  represent the details of the input sequence in addition to
		  the recurrent distributed representations of the SRN and
		  NARX networks. The resulting architectures can represent
		  arbitrarily long sequences and are cognitively more
		  plausible.},
  dbinsdate	= {2002/1}
}

@InCollection{	  mayberry99a,
  author	= {M. R. {Mayberry III} and R. Miikkulainen},
  title		= {Combining Maps and Distributed Representations for
		  Shift-Reduce Parsing},
  booktitle	= {Hybrid Neural Symbolic Integration},
  publisher	= {Springer},
  year		= {1999},
  editor	= {Stefan Wermter and Ron Sun},
  address	= {New York},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mayberry99b,
  author	= {M. R. {Mayberry III} and R. Miikkulainen},
  title		= {Using a Sequential {SOM} to Parse Long-term Dependencies},
  booktitle	= {Proceedings of the 21st Annual Meeting of the Cognitive
		  Science Society (COGSCI-98)},
  year		= {1999},
  publisher	= {Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mayberry99c,
  author	= {M. R. {Mayberry III} and R. Miikkulainen},
  title		= {{SARDSRN}: A Neural Network Shift-Reduce Parser},
  booktitle	= {Proceedings of the Sixteenth International Joint
		  Conference on Artificial Intelligence (IJCAI-99)},
  year		= {1999},
  address	= {Stockholm, Sweden},
  abstract	= {Simple recurrent networks (SRNs) have been widely used in
		  natural language tasks. SARDSRN extends the SRN by
		  explicitly representing the input sequence in a SARDNET
		  self-organizing map. The distributed SRN component leads to
		  good generalization and robust cognitive properties,
		  whereas the SARDNET map provides exact representations of
		  the sentence constituents. This combination allows SARDSRN
		  to learn to parse sentences with more complicated structure
		  than can the SRN alone, and suggests that the approach
		  could scale up to realistic natural language.},
  dbinsdate	= {oldtimer}
}

@Article{	  mayer00a,
  author	= {Mayer, N. and Herrmann, J. M. and Geisel, T.},
  title		= {Retinotopy and spatial phase in topographic maps},
  journal	= {Neurocomputing},
  year		= {2000},
  volume	= {32},
  number	= {},
  month		= {},
  pages		= {447--452},
  organization	= {Max-Planck-Inst fuer Stroemungsforschung},
  publisher	= {Elsevier Science B.V.},
  address	= {Amsterdam},
  abstract	= {Self-organizing maps have been successfully applied as
		  models of map formation in the mammalian primary visual
		  cortex. We study a map of high-dimensional stimuli such as
		  patches of filtered natural images onto a cortical layer of
		  neurons. Since most map algorithms reproduce essentially
		  distance relations the representation of stimuli of
		  opposite spatial phase causes a distortion of the
		  retinotopy of the map. We suggest that a related algorithm,
		  the so-called adaptive subspace map, can produce
		  topographic maps by converting the spatial phase to an
		  internal property of local networks, such that phase is not
		  topographically mapped at the experimental level. Finally,
		  we compare other properties of this model with its
		  biological counterpart.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  mayer98a,
  author	= {Mayer, N. and Herrmann, M. and Bauer, H. U and Geisel,
		  T.},
  title		= {A cortical interpretation of ASSOMs},
  booktitle	= {ICANN 98. Proceedings of the 8th International Conference
		  on Artificial Neural Networks.},
  publisher	= {Springer-Verlag},
  year		= {1998},
  volume	= {2},
  pages		= {961--6},
  address	= {London},
  abstract	= {Self-organizing maps have been successfully used to model
		  map formation in the visual cortex of mammals. When
		  applying natural images as stimuli, properties of the maps
		  obtained for low-dimensional input manifolds, such as
		  retinotopy, are not equally well reproducible. The present
		  study points to the virtues of the adaptive subspace
		  self-organizing map (ASSOM) in modeling neural maps. Since
		  the representation of position and orientation and that of
		  stimulus phase are automatically mapped to different
		  hierarchical levels of the ASSOM, topography is established
		  for orientation and position, but not for phases. This
		  agrees to evidence for the absence of smooth phase maps.
		  Further, we show that some biologically implausible
		  conditions of the ASSOM rule can be relaxed.},
  dbinsdate	= {oldtimer}
}

@Article{	  mayol_cuevas98a,
  author	= {{Mayol Cuevas}, W. W. and {Juarez Guerrero}, J. and {Munoz
		  Gutierrez}, S.},
  title		= {First approach to tactile texture recognition},
  journal	= {Proceedings of IEEE International Conference on Systems,
		  Man, and Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  number	= {},
  volume	= {5},
  pages		= {4246--4250},
  abstract	= {Tactile texture recognition seems to be a very important
		  research area with various interesting applications which
		  include medical, geological and more autonomous mobile
		  robotics; despite the great amount of tasks that biological
		  beings solve using the sense of touch, no much work is done
		  in this area (except for pressure-related sensing), then,
		  there are only a few papers in literature that uses dynamic
		  tactile sensing strategies, i.e. the kind of exploration
		  that we and the biological beings use to recognize a
		  texture. In this paper we present a system for tactile
		  texture recognition, using sound-understanding techniques.
		  We develop a sensing `pen' with an electret piezoelectric
		  microphone, covered by a rugged material, such pen is
		  rubbed over the material that we want to identify, the
		  sound produced is segmented, the FFT is obtained and the
		  result is introduced to a learning vector quantization
		  technique (LVQ). We explore 18 common materials which
		  includes surfaces from glass to a real human beard. We
		  achieve more than 93% of recognition over the 18 textures,
		  and when the system makes an error it gives a similar
		  texture as result.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mazaeva01a,
  author	= {Mazaeva, N. and Ntuen, C. and Lebby, G.},
  title		= {Self-Organizing Map ({SOM}) model for mental workload
		  classification},
  booktitle	= {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS
		  International Conference. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {3},
  pages		= {1822--5},
  abstract	= {Development of reliable mental workload measurement and
		  classification techniques have been an area of concern in
		  human factors engineering. Artificial neural networks (ANN)
		  have been used to model workload by performing EEG data
		  classification In the present study, a self-organizing map
		  (SOM) neural network was used to simulate workload metrics.
		  SOM is an unsupervised algorithm that clusters similar
		  input vectors to allow its output neurons to compete among
		  themselves to become activated SOM functional features are
		  considered to be similar to those of the human brain since
		  the latter is capable of organizing heterogeneous. sensory
		  inputs. For purposes of this study, EEG data was
		  preprocessed via Fast Fourier analysis, temporally
		  segmented and reduced by principal component analysis (PCA)
		  prior to inputting it to the network. The network was
		  trained using 2/3 of available data and tested with
		  remaining 1/3 of the data to classify workload into six
		  categories ranging from very low to overload. The SOM was
		  able to cluster the training data into 6 output categories
		  and differentiate between workload classes when presented
		  with the test data set. The results indicated that
		  implementation of self-organizing map networks offers a
		  robust method for analyzing electrophysiological data
		  signals related to work performance and could potentially
		  be used as a tool for extraction of workload correlates
		  from EEG data. Knowledge about workload metrics and
		  reliable classification methods can be utilized in the
		  design of adaptive human-machine systems that control
		  information flow to prevent operator overload.},
  dbinsdate	= {2002/1}
}

@Article{	  mcalernon99a,
  author	= {Mcalernon, P. and Slater, J.~M. and Lau, K.~T.},
  title		= {Mapping of Chemical Functionality Using an Array of
		  Quartz-Crystal Microbalances in Conjunction with {K}ohonen
		  Self-Organizing Maps},
  journal	= {Analyst},
  year		= {1999},
  volume	= {124},
  number	= {6},
  pages		= {851--857},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mcauliffe90a,
  author	= {J. D. McAuliffe and L. E. Atlas and C. Rivera},
  title		= {A comparison of the {LBG} algorithm and {K}ohonen neural
		  network paradigm for image vector quantization},
  booktitle	= {Proc. ICASSP-90, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1990},
  volume	= {IV},
  pages		= {2293--2296},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mcdermott89a,
  author	= {McDermott, Erik and Katagiri, Shigeru},
  title		= {Shift-Invariant, Multi-Category Phoneme Recognition using
		  {{K}ohonen's} {LVQ2}},
  booktitle	= {Proc. ICASSP-89, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1989},
  volume	= {I},
  pages		= {81--84},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mcdermott90a,
  author	= {Erik McDermott},
  title		= {{LVQ3} for Phoneme Recognition},
  booktitle	= {Proc. Acoust. Soc. of Japan},
  year		= {1990},
  pages		= {151--152},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  mcdermott91a,
  author	= {E. McDermott and S. Katagiri},
  title		= {{LVQ}-based shift-tolerant phoneme recognition},
  journal	= {IEEE Trans. on Signal Processing},
  year		= {1991},
  volume	= {39},
  number	= {6},
  pages		= {1398--1411},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@Article{	  mcdermott94a,
  author	= {McDermott, Erik and Katagiri, Shigeru},
  title		= {Prototype-based minimum classification error/generalized
		  probabilistic descent training for various speech units},
  journal	= {Computer Speech \& Language},
  year		= {1994},
  number	= {4},
  volume	= {8},
  pages		= {351--368},
  abstract	= {In previous work we reported high classification rates for
		  learning vector quantization (LVQ) networks trained to
		  classify phoneme tokens shifted in time. It has since been
		  shown that the framework of minimum classification error
		  (MCE) and generalized probabilistic descent (GPD) can treat
		  LVQ as a special case of a general method for gradient
		  descent on a rigorously defined classification loss measure
		  that closely reflects the misclassification rate. This
		  framework allows us to extend LVQ into a prototype-based
		  minimum error classifier (PBMEC) appropriate for the
		  classification of various speech units which the original
		  LVQ was unable to treat. Speech categories are represented
		  using a prototype-based multi-state architecture
		  incorporating a dynamic time warping procedure. We present
		  results for the difficult E-set task, as well as for
		  isolated word recognition for a vocabulary of 5240 words,
		  that reveal clear gains in performance as a result of using
		  PBMEC. In addition, we discuss the issue of smoothing the
		  loss function from the perspective of increasing classifier
		  robustness.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  mcglinchey97a,
  author	= {Stephen McGlinchey and Colin Fyfe},
  title		= {An angular quantising self organising map for scale
		  invariant classification},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {91--95},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mcglinchey98a,
  author	= {McGlinchey, S. and Fyfe, C.},
  title		= {Invariant feature maps for analysis of orientations in
		  image data},
  booktitle	= {6th European Symposium on Artificial Neural Networks.
		  ESANN'98. Proceedings},
  publisher	= {D-Facto},
  address	= {Brussels, Belgium},
  year		= {1998},
  volume	= {},
  pages		= {215--20},
  abstract	= {We present a method that uses competitive learning and a
		  neighbourhood function in a similar way to the
		  self-organising map (SOM). The network consists of a number
		  of modules that are positioned in an array (normally in one
		  or two dimensions) where each module performs a subspace
		  projection and the rotation of these subspaces is weighted
		  by the neighbourhood function. Nonlinear activation
		  functions are introduced so that each node performs
		  nonlinear PCA on the training data captured in its Voronoi
		  region. We show that this network may be used for position
		  invariant detection of bars at varying orientations.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mcinerney94a,
  author	= {M. McInerney and A. Dhawan},
  title		= {Training the Self-Organizing Feature Map using Hybrids of
		  Genetic and {K}ohonen Methods},
  pages		= {641--644},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {modification, topology learning},
  dbinsdate	= {oldtimer}
}

@InCollection{	  mckinstry96a,
  author	= {J. McKinstry and C. Guest},
  title		= {Self-organizing map develops V1 organization given
		  biologically realistic input},
  booktitle	= {1997 IEEE International Conference on Neural Networks.
		  Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  editor	= {A. {Del Guerra}},
  address	= {New York, NY, USA},
  pages		= {338--43},
  dbinsdate	= {oldtimer}
}

@InCollection{	  mckinstry97a,
  author	= {Jeff McKinstry and Clark Guest},
  title		= {Self-Organizing Map Develops {V1} Organization Given
		  Biologically Realistic Input},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {I},
  pages		= {338--343},
  dbinsdate	= {oldtimer}
}

@Article{	  mcmullen01a,
  author	= {McMullen, P. R.},
  title		= {A Kohonen self-organizing map approach to addressing a
		  multiple objective, mixed-model {JIT} sequencing problem},
  journal	= {International Journal of Production Economics},
  year		= {2001},
  volume	= {72},
  number	= {1},
  month		= {Jun 30 2001},
  pages		= {59--71},
  organization	= {Department of Management, College of Business, Auburn
		  University},
  publisher	= {},
  address	= {},
  abstract	= {A technique is presented which addresses a JIT
		  production-scheduling problem where two objectives are
		  present---minimization of setups between differing products
		  and optimization of schedule flexibility. These two
		  objectives are inversely related to each other, and, as a
		  result, simultaneously obtaining desirable results for both
		  is problematic. An efficient frontier approach is employed
		  to address this situation, where the most desirable
		  sequences in terms of both objectives are found. Finding
		  the efficient frontier requires addressing the
		  combinatorial complexity of sequencing problems. The
		  artificial neural network approach of a Kohonen
		  self-organizing map (SOM) is used to find sequences which
		  are desirable in terms of both the number of setups and
		  flexibility. The Kohonen SOM was used to find sequences for
		  several problems from the literature. Experimental results
		  suggest that the SOM approach provides near-optimal
		  solutions in terms of the two objectives, in addition to
		  comparing formidably with other search heuristics. Results
		  also show, however, that the SOM approach performs poorly
		  with regard to CPU time.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  means94a,
  author	= {Means, R. W. },
  title		= {High speed parallel hardware performance issues for neural
		  network applications},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {1},
  pages		= {10--16},
  organization	= {HNC Inc. , San Diego, CA, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  medl95a,
  author	= {A. Medl and F. Perschl and G. Schmidt},
  title		= {Detection of multiple faults by means of nonlinear
		  observer and learning vector quantization techniques},
  booktitle	= {Proceedings of the Third European Control Conference. ECC
		  95},
  publisher	= {Eur. Union Control Assoc},
  year		= {1995},
  volume	= {3},
  editor	= {A. Isidori and S. Bittanti and E. Mosca and A. {De Luca}
		  and M. D. {Di Benedetto} and G. Oriolo},
  address	= {Rome, Italy},
  pages		= {2005--10},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  medrano-marques99a,
  author	= {Medrano-Marques, N. J. and Martin-del-Brio, B.},
  title		= {Topology preservation in {SOFM}: an Euclidean versus
		  Manhattan distance comparison},
  booktitle	= {Foundations and Tools for Neural Modeling. International
		  Work-Conference on Artificial and Natural Neural Networks,
		  IWANN'99. Proceedings, (Lecture Notes in Computer Science
		  Vol.1606)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {1},
  pages		= {601--9},
  abstract	= {The self-organising feature map (SOFM) is one of the
		  unsupervised neural models of most widespread use. Several
		  studies have been carried out in order to determine the
		  degree of topology-preservation for this data projection
		  method, and the influence of the distance measure used,
		  usually Euclidean or Manhattan distance. In this paper, by
		  using a new topology-preserving representation of the SOFM
		  and the well-known Sammon's stress (1969), graphical and
		  numerical comparisons are shown between both possibilities
		  for the distance measure. Our projection method, based on
		  the relative distances between neighbouring neurons, gives
		  similar information to those of the Sammon projection, but
		  in a graphical way.},
  dbinsdate	= {oldtimer}
}

@Article{	  meena95a,
  author	= {Meena, K. and Ganapathy, V. and Balasubramaniam, A.},
  title		= {Efficient \mbox{self-organizing} map for pattern
		  clustering},
  journal	= {Advances in Modelling \& Analysis B},
  year		= {1995},
  number	= {1},
  volume	= {33},
  pages		= {20--32},
  abstract	= {Computational requirements of any clustering algorithm are
		  identified as the bottleneck in the effective exploratory
		  data analysis task. ISODATA algorithm is discussed as an
		  adaptive clustering algorithm in which the parameters are
		  dynamically changed. This algorithm is further enhanced in
		  terms of speed called 'FAST ISODATA'. Some computational
		  logic has been proposed to reduce the time complexity and
		  these algorithms identify true clusters only when they are
		  'linearly separable'. For linearly inseparable clusters,
		  the features of neural networks are exploited. A clustering
		  algorithm using neural networks is implemented. The
		  performance of these algorithms is studied using real and
		  synthetic data sets and the results are quite encouraging.
		  Experimental work is carried out with some synthetic, real
		  and remotely sensed data sets. Methods described for the
		  Euclidean distance based K-MEANS clustering really show
		  better speedups, when compared to the general Euclidean
		  distance based clustering.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  meinicke00a,
  author	= {Peter Meinicke and Helge Ritter},
  title		= {Topographic Local {PCA} Maps},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Neuroinformatics Group, University of Bielefeld},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  meister93a,
  author	= {J. Meister},
  title		= {A neural network harmonic family classifier},
  journal	= {J. Acoust. Soc. of America},
  year		= {1993},
  volume	= {93},
  number	= {3},
  pages		= {1488--1495},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mel88a,
  author	= {Bartlett W. Mel},
  title		= {{MURPHY}: A Robot that Learns by Doing},
  booktitle	= {Proc. First IEEE Conf. on Neural Information Processing
		  Systems},
  editor	= {Dana Z. Anderson},
  year		= {1988},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {544--553 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  melo00a,
  author	= {Melo, S. L. and Caloba, L. P. and Nadal, J.},
  title		= {Arrhythmia analysis using artificial neural network and
		  decimated electrocardiographic data},
  booktitle	= {Computers in Cardiology},
  year		= {2000},
  editor	= {},
  volume	= {},
  pages		= {73--76},
  organization	= {Federal Univ of Rio de Janeiro},
  publisher	= {IEEE},
  address	= {Los Alamitos, CA},
  abstract	= {This work shows an Artificial Neural Network using the
		  Kohonen Layer architecture in a modified approach to
		  support supervised learning, and the evaluation of its
		  performance in the classification of QRS complexes of the
		  Electrocardiogram (ECG) from patients with cardiac
		  arrhythmias. A second aim of this study was to investigate
		  the ability of ANN to classify QRS complexes when the
		  original data samples are used as input variables. The
		  classifier was developed and tested with the MIT-BIH
		  Arrhythmia Database. The obtained results become equivalent
		  to the most sophisticated methods in the literature when
		  input data are properly pre-processed and the final
		  classifier is allowed to adapt to the normal pattern of
		  each analyzed patient.},
  dbinsdate	= {2002/1}
}

@Article{	  melssen93a,
  author	= {W. J. Melssen and J. R. M. Smits and G. H. Rolf and G.
		  Kateman},
  title		= {Two-dimensional mapping of {IR} spectra using a parallel
		  implemented \mbox{self-organizing} feature map},
  journal	= {Chemometrics and Intelligent Laboratory Systems},
  year		= {1993},
  volume	= {18},
  number	= {2},
  pages		= {195--204},
  month		= {February},
  dbinsdate	= {oldtimer}
}

@Article{	  melssen94a,
  author	= {Melssen, W. J. and Smits, J. R. M. and Buydens, L. M. C.
		  and Kateman, G. },
  title		= {Using artificial neural networks for solving chemical
		  problems. {II}. {K}ohonen \mbox{self-organising} feature
		  maps and {H}opfield networks},
  journal	= {Chemometrics and Intelligent Laboratory Systems},
  year		= {1994},
  volume	= {23},
  number	= {2},
  pages		= {267--91},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  melton92a,
  author	= {Matthew S. Melton and Tan Phan and Douglas S. Reeves and
		  David E. {Van den Bout}},
  title		= {The {TInMANN} {VLSI} chip},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1992},
  volume	= {3},
  number	= {3},
  pages		= {375--384},
  dbinsdate	= {oldtimer}
}

@Article{	  melvin95a,
  author	= {Melvin, D. G. and Penman, J. },
  title		= {Fusing human knowledge with neural networks in machine
		  condition monitoring systems},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2492},
  number	= {pt. 1},
  pages		= {276--83},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  memmi00a,
  author	= {Daniel Memmi and Jean-Guy Meunier},
  title		= {Using Competitive Networks for Text Mining},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {LEIBNIZ-IMAG-CNRS, LANCI-UQAM},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  menard90a,
  author	= {F. Menard and F. Fogelman-Souli{\'{e}}},
  title		= {Application of the Topological Maps Algorithm to the
		  Recognition of Bi-Dimensional Electrophoresis Images},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  year		= {1990},
  pages		= {99--102 },
  dbinsdate	= {oldtimer}
}

@Article{	  mendelson01a,
  author	= {Mendelson, S. and Nelken, I.},
  title		= {Recurrence methods in the analysis of learning processes},
  journal	= {Neural-Computation},
  year		= {2001},
  volume	= {13},
  pages		= {1839--61},
  abstract	= {The goal of most learning processes is to bring a machine
		  into a set of "correct" states. In practice, however, it
		  may be difficult to show that the process enters this
		  target set. We present a condition that ensures that the
		  process visits the target set infinitely often almost
		  surely. This condition is easy to verify and is true for
		  many well-known learning rules. To demonstrate the utility
		  of this method, we apply it to four types of learning
		  processes: the perceptron, learning rules governed by
		  continuous energy functions, the Kohonen rule, and the
		  committee machine.},
  dbinsdate	= {2002/1}
}

@Article{	  menendez96a,
  author	= {C. Menendez and J. B. Ordieres and F. Ortega},
  title		= {Importance of information pre-processing in the
		  improvement of neural network results},
  journal	= {Expert Systems},
  year		= {1996},
  volume	= {13},
  number	= {2},
  pages		= {95--103},
  dbinsdate	= {oldtimer}
}

@Article{	  meng00a,
  author	= {Meng, Zhuo and Pao, Yoh-Han},
  title		= {Visualization and self-organization of multidimensional
		  data through equalized orthogonal mapping},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  volume	= {11},
  number	= {4},
  month		= {Jul},
  pages		= {1031--1038},
  organization	= {Computer Associates Int, Inc},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A new approach to dimension-reduction mapping of
		  multidimensional pattern data is presented. The motivation
		  for this work is to provide a computationally efficient
		  method for visualizing large bodies of complex
		  multidimensional data as a relatively 'topologically
		  correct' lower dimensional approximation. Examples of the
		  use of this approach in obtaining meaningful
		  two-dimensional (2-D) maps and comparisons with those
		  obtained by the self-organizing map (SOM) and the
		  neural-net implementation of Sammon's approach are also
		  presented and discussed. In this method, the mapping
		  equalizes and orthogonalizes the lower dimensional outputs
		  by reducing the covariance matrix of the outputs to the
		  form of a constant times the identity matrix. This new
		  method is computationally efficient and 'topologically
		  correct' in interesting and useful ways.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  menhaj99a,
  author	= {Menhaj, M. B. and Jahanian, H. R.},
  title		= {An analytical alternative for SOM},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1939--42},
  abstract	= {This paper introduces a new self-organizing system, called
		  a continuous self organising map (CSOM). The behavior of
		  this model, which is represented by a set of nonlinear
		  differential equations, is identical to Kohonen's
		  self-organizing neural network. This paper shows that CSOM
		  can be viewed as an appropriate non-algorithmic model for
		  the class of one-dimensional Kohonen algorithms with an
		  arbitrary number of inputs.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mercier99a,
  author	= {Mercier, G. and Mouchot, M. C and Cazuguel, G.},
  title		= {Joint classification and compression of hyperspectral
		  images},
  booktitle	= {IEEE 1999 International Geoscience and Remote Sensing
		  Symposium. IGARSS'99.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {2035--7},
  abstract	= {The problem of compressing hyperspectral images using a
		  classification point of view is examined. The goal is to
		  compress with loss an image in order to obtain interesting
		  compression ratio with the constraint to be near lossless
		  for a classification algorithm. The authors' proposed
		  method is based on a spectral vector quantization performed
		  by the Kohonen's self organizing map and an entropy coding.
		  This method gives high compression ratio (up to 100:1) and
		  appears to have the same strategy of a spectral angle
		  mapper algorithm. Thus, it is possible, on the first hand,
		  to make classifications into the compressed domain, or on
		  the other hand to classify the dictionary of vector
		  quantization to have its semantic meaning. This algorithm
		  was applied to CASI images with 48 spectral bands acquired
		  over Saint-Michel in France for green seaweed proliferation
		  monitoring. It proved to be very efficient for compressing
		  images while still remaining of excellent quality for
		  monitoring usage.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merelo91a,
  author	= {J. J. Merelo and M. A. Andrade and C. Urena and A. Prieto
		  and F. Mor{\'{a}}n},
  title		= {Application of vector quantization algorithms to protein
		  classification and secondary structure computation},
  booktitle	= {Proc. IWANN'91, Int. Workshop on Artificial Neural
		  Networks},
  year		= {1991},
  editor	= {A. Prieto},
  pages		= {415--421},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merelo91b,
  author	= {J. J. Merelo and M. A. Andrare and A. Prieto and F.
		  Mor{\'{a}}n},
  title		= {Protein Classification through a feature map},
  booktitle	= {Neuro-N\^{i}mes '91. Fourth Int. Workshop on Neural
		  Networks and Their Applications},
  year		= {1991},
  pages		= {765--768},
  publisher	= {EC2},
  dbinsdate	= {oldtimer}
}

@Article{	  merelo94a,
  author	= {Merelo, Juan J and Andrade, Miguel A and Prieto, Alberto
		  and Moran, Federico},
  title		= {Proteinotopic feature maps},
  journal	= {Neurocomputing},
  year		= {1994},
  number	= {4},
  volume	= {6},
  pages		= {443--454},
  abstract	= {In this paper a system based on Kohonen's SOM
		  (Self-Organizing Map) for protein classification according
		  to Circular Dichroism (CD) spectra is described. As a
		  result, proteins with different secondary structures are
		  clearly separated through a completely unsupervised
		  training process. The algorithm is able to extract features
		  from a high-dimensional vector (CD spectra) and map it to a
		  2-dimensional network. A new measure, called distortion,
		  has been introduced to test SOM performance. Distortion can
		  be used to fine tune and optimize some of the parameters of
		  the SOM algorithm.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  merelo95a,
  author	= {J. J. Merelo and A. Prieto},
  title		= {{G- {LVQ} }, a combination of genetic algorithms and
		  {LVQ}},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {D. W. Pearson and N. C. Steele and R. F. Albrecht},
  address	= {Vienna, Austria},
  pages		= {92--5},
  dbinsdate	= {oldtimer}
}

@InCollection{	  merelo97a,
  author	= {J. J. Merelo and A. Prieto and F. Moran and R. Marabini
		  and J. M. Carazo},
  title		= {A GA-optimized neural network for classification of
		  biological particles from electron-microscopy images},
  booktitle	= {Biological and Artificial Computation: From Neuroscience
		  to Technology. International Work Conference on Artificial
		  and Natural Neural Networks, IWANN'97. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {J. Mira and R. Moreno-Diaz and J. Cabestany},
  address	= {Berlin, Germany},
  pages		= {1174--82},
  dbinsdate	= {oldtimer}
}

@Article{	  merelo98a,
  author	= {Merelo, J. J. and Prieto, A. and Moran, F. and Marabini,
		  R. and Carazo, J. M.},
  title		= {Automatic classification of biological particles from
		  electron-microscopy images using conventional and
		  genetic-algorithm optimized learning vector quantization},
  journal	= {Neural Processing Letters},
  year		= {1998},
  number	= {1},
  volume	= {8},
  pages		= {55--65},
  abstract	= {Automatic classification of transmission
		  electron-microscopy images is an important step in the
		  complex task of determining the structure of biological
		  macromolecules. The process of 3D reconstruction from a set
		  of such images implies their previous classification into
		  homogeneous image classes. In general, different classes
		  may represent either distinct biochemical specimens or
		  specimens from different directions of an otherwise
		  homogenous specimen. In this paper, a neural network
		  classification algorithm has been applied to a real-data
		  case in which it was known a priori the existence of two
		  differentiated views of the same specimen. Using two
		  labeled sets as a reference, the parameters and
		  architecture of the classifier were optimized using a
		  genetic algorithm. The global automatic process of training
		  and optimization is implemented using the previously
		  described G-LVQ (genetic learning vector quantization)
		  algorithm, and compared to a non-optimized version of the
		  algorithm, Kohonen's LVQ (learning vector quantization).
		  Using a part of the sample as training set, the results
		  presented here show an efficient (approximately 90%)
		  average classification rate of unknown samples in two
		  classes. Finally, the implication of this kind of automatic
		  classification of algorithms in the determination of three
		  dimensional structure of biological particles is discussed.
		  This paper extends the results already presented in [11],
		  and also improves them.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merelo99a,
  author	= {Merelo, J. J. and Rivas, V. and Romero, G. and Castillo,
		  P. and Pascual, A. and Carazo, J. M.},
  title		= {Improved automatic classification of biological particles
		  from electron-microscopy images using genetic neural nets},
  booktitle	= {Engineering Applications of Bio-Inspired Artificial Neural
		  Networks. International Work-Conference on Artificial and
		  Natural Neural Networks, IWANN'99. Proceedings, (Lecture
		  Notes in Computer Science Vol.1607)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {2},
  pages		= {373--82},
  abstract	= {In this paper several neural network classification
		  algorithms have been applied to a real-world data case of
		  electron microscopy data classification. Using several
		  labeled sets as a reference, the parameters and
		  architecture of the classifiers, LVQ (learning vector
		  quantization) trained codebooks and BP (backpropagation)
		  trained feedforward neural nets were optimized using a
		  genetic algorithm. The automatic process of training and
		  optimization is implemented using a new version of the
		  G-LVQ (genetic learning vector quantization) and G-PROP
		  (genetic backpropagation) algorithms, and compared to a
		  nonoptimized version of the algorithms, Kohonen's LVQ and
		  MLP trained with QuickProp. Dividing the all available
		  samples in three sets, for training, testing and
		  validation, the results presented here show a low average
		  error for unknown samples. In this problem, G-PROP
		  outperforms G-LVQ, but G-LVQ obtains codebooks with less
		  parameters than the perceptrons obtained by G-PROP. The
		  implication of this kind of automatic classification
		  algorithms in the determination of three dimensional
		  structure of biological particles is finally discussed.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merenyi95a,
  author	= {E. Merenyi and R. Singer and W. H. Farrand},
  title		= {Classification of the {{LCVF}} {{AVIRIS}} test site with a
		  {K}ohonen artificial neural network},
  booktitle	= {Summaries of the 4th Annual JPL Airborne Geoscience
		  Workshop},
  volume	= {1},
  year		= {1995},
  pages		= {117--120},
  dbinsdate	= {oldtimer}
}

@InCollection{	  merenyi96a,
  author	= {E. Mere{\'n}yi and J. V. Taranik and T. B. Minor and W. H.
		  Farrand},
  title		= {Quantitative Comparison of Neural Network and Conventional
		  Classifiers For Hyperspectral Imagery},
  booktitle	= {Summaries of the Sixth Annual JPL Airborne Earth Science
		  Workshop, Pasadena, CA, March 4--8},
  year		= 1996,
  editor	= {R. O. Green},
  volume	= {1: AVIRIS Workshop},
  dbinsdate	= {oldtimer}
}

@Article{	  merenyi96b,
  author	= {E. Mere{\'n}yi and R. B. Singer and J. S. Miller},
  title		= {Mapping of Spectral Variations On the Surface of Mars From
		  High Spectral Resolution Telescopic Images},
  journal	= {ICARUS},
  year		= 1996,
  volume	= 124,
  pages		= {280--295},
  dbinsdate	= {oldtimer}
}

@Article{	  merenyi96c,
  author	= {E. Mere{\'n}yi and K. S. Edgett and R. B. Singer},
  title		= {Deucalionis Regio, Mars: Evidence For a New Type of
		  Immobile Weathered Soil Unit},
  journal	= {ICARUS},
  year		= 1996,
  volume	= 124,
  pages		= {296--307},
  dbinsdate	= {oldtimer}
}

@Article{	  merenyi97a,
  author	= {E. Mere{\'n}yi and E. S. Howell and L. A. Lebofsky and A.
		  S. Rivkin},
  title		= {Prediction of Water In Asteroids from Spectral Data
		  Shortward of 3 Microns},
  journal	= {ICARUS},
  year		= 1997,
  volume	= 129,
  pages		= {421--439},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl00a,
  author	= {Merkl, D. and Rauber, A.},
  title		= {Uncovering the hierarchical structure of text archives by
		  using an unsupervised neural network with adaptive
		  architecture},
  booktitle	= {KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS},
  year		= {2000},
  pages		= {384--395},
  abstract	= {Discovering the inherent structure in data has become one
		  of the major challenges in data mining applications. It
		  requires the development of stable and adaptive models that
		  are capable of handling the typically very high-dimensional
		  feature spaces. In this paper we present the Growing
		  Hierarchical Self- Organizing Map (GH-SOM), a neural
		  network model based on the self-organizing map. The main
		  feature of this extended model is its capability of growing
		  both in terms of map size as well as in a three-dimensional
		  tree-structure in order to represent the hierarchical
		  structure present in a data collection. This capability,
		  combined with the stability of the self-organizing map for
		  high-dimensional feature space representation, makes it an
		  ideal tool for data analysis and exploration. We
		  demonstrate the potential of this method with an
		  application from the information retrieval domain, which is
		  prototypical of the high-dimensional feature spaces
		  frequently encountered in today's applications.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  merkl00b,
  author	= {Merkl, D. and Rauber, A.},
  title		= {Digital libraries-classification and visualization
		  techniques},
  booktitle	= {Proceedings 2000 Kyoto International Conference on Digital
		  Libraries: Research and Practice. IEEE Comput. Soc, Los
		  Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {434--8},
  abstract	= {The constantly increasing flood of available textual
		  information demands the development of powerful tools to
		  organize, search, and explore these document libraries.
		  Within the framework of the SOMLib digital library project
		  we have proposed the utilization of unsupervised artificial
		  neural networks for document classification and an
		  intuitive user interface relying on metaphor graphics for
		  visualization of the contents of the digital library.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  merkl93a,
  author	= {Dieter Merkl and A Min Tjoa and Gerti Kappel},
  title		= {Structuring a Library of Reusable Software Components
		  Using an Artificial Neural Network},
  booktitle	= {Proc. AQuIS'93, 2nd International Conference of Achieving
		  Quality in Software, Venice, Italy},
  year		= {1993},
  pages		= {169--180},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl93b,
  author	= {Dieter Merkl},
  title		= {Structuring Software for Reuse---The case of
		  Self-Organizing Maps},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2468--2471},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {This paper is concerned with the application of Kohonen's
		  self-organizing map in the area of software reuse. Although
		  software reuse has a long historical tradition in research,
		  what is still missing is an appropriate way to structure
		  software repositories according to the semantic
		  similarities of the stored software components. With this
		  paper we describe an approach to overcome this
		  inconvenience by applying Kohonen's self-organizing map to
		  the description of software components. As a result we
		  obtain a semantically structured software library.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  merkl93c,
  author	= {Dieter Merkl and A Min Tjoa and Gerti Kappel},
  title		= {Retrieval of Reusable Software Based on Sematic
		  Similarity: An Artificial Neural Network Approach},
  institution	= {Institut f{\"{u}}r Angewandte Informatik und
		  Informationssysteme, Universit{\"{a}}t Wien},
  year		= {1993},
  address	= {Vienna, Austria},
  month		= {November},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl94a,
  author	= {Dieter Merkl and A Min Tjoa and Gerti Kappel},
  title		= {A {S}elf-{O}rganizing {M}ap that Learns the Semantic
		  Similarity of Reusable Software Components},
  editor	= {A. C. Tsoi and T. Downs},
  pages		= {13--16},
  booktitle	= {Proc. ACNN'94, 5th Australian Conf. on Neural Networks},
  publisher	= {Univ. Queensland},
  address	= {St. Lucia, Australia},
  year		= {1994},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl94b,
  author	= {Dieter Merkl and A Min Tjoa and Gerti Kappel},
  title		= {Application of Self-Organizing Feature Maps With Lateral
		  Inhibition to Structure a Library of Reusable Sotware
		  Components},
  pages		= {3905--3908},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, clustering},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl94c,
  author	= {Dieter Merkl and A Min Tjoa and Gerti Kappel},
  title		= {Learning the Semantic Similarity of Reusable Sotware
		  Components},
  booktitle	= {Proc. ICSR'94, 3rd International Conference on Software
		  Reuse},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, clustering, semantic features},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  merkl94d,
  author	= {Dieter Merkl},
  title		= {Self-Organization of Software Libraries: An Artificial
		  Neural Network Approach},
  school	= {Institut f{\"{u}}r Angewandte Informatik und
		  Informationssysteme, Universit{\"{a}}t Wien},
  year		= {1994},
  annote	= {application, clustering, semantic features},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl94e,
  author	= {Dieter Merkl and A Min Tjoa},
  title		= {The Representation of Semantic Similarity Between
		  Documents by Using Maps: Application of an Artificial
		  Neural Network to Organize Software Libraries},
  booktitle	= {Proc. FID'94, General Assembly Conf. and Congress of the
		  Int. Federation for Information and Documentation},
  year		= {1994},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl95a,
  author	= {Dieter Merkl},
  title		= {A Connectionist View on Document Classification},
  booktitle	= {Proc. ADC'95, 6th Australian Database Conf. },
  year		= {1995},
  annote	= {application, clustering, semantic features},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl95b,
  author	= {Dieter Merkl},
  title		= {Content-Based Document Classification with Highly
		  Compressed Input Data},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {239--244},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl95c,
  author	= {Dieter Merkl},
  title		= {Content-Based Software Classification by
		  Self-Organization},
  volume	= {II},
  pages		= {1086--1091},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl95d,
  author	= {Merkl, D. and Schweighofer, E. and Winiwater, W. },
  title		= {Analysis of legal thesauri based on \mbox{self-organising}
		  feature maps},
  booktitle	= {Fourth International Conference on `Artificial Neural
		  Networks`},
  year		= {1995},
  pages		= {29--34},
  organization	= {Vienna Univ. of Technol. , Austria},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl95e,
  author	= {Merkl, D. },
  title		= {The effects of lateral inhibition on learning speed and
		  precision of a \mbox{self-organizing} feature map},
  booktitle	= {Proceedings of the Sixth Australian Conference on Neural
		  Networks (ACNN`95)},
  year		= {1995},
  editor	= {Charles, M. and Latimer, C. },
  pages		= {168--71},
  organization	= {Dept. of Inf. Eng. , Wien Univ. , Austria},
  publisher	= {Univ. Sydney},
  address	= {Sydney, NSW, Australia},
  dbinsdate	= {oldtimer}
}

@InCollection{	  merkl97a,
  author	= {Dieter Merkl},
  title		= {Lessons learned in text document classification},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {316--321},
  dbinsdate	= {oldtimer}
}

@InCollection{	  merkl97b,
  author	= {Dieter Merkl and Andreas Rauber},
  title		= {Alternative ways for cluster visualization in
		  \mbox{self-organizing} maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {106--111},
  dbinsdate	= {oldtimer}
}

@Article{	  merkl97c,
  author	= {D. Merkl},
  title		= {Exploration of text collections with hierarchical feature
		  maps},
  journal	= {SIGIR Forum},
  year		= {1997},
  volume	= {7},
  pages		= {186--95},
  note		= {Special Issue (20th Annual International ACM SIGIR
		  Conferencce on Research and Development in Information
		  Retrieval Conf. Date: 27--31 July 1997 Conf. Loc:
		  Philadelphia, PA, USA)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  merkl98a,
  author	= {D. Merkl and A. Rauber},
  title		= {Cluster connections: a visualization technique to reveal
		  cluster boundaries in \mbox{self-organizing} maps},
  booktitle	= {Neural Nets WIRN-VIETRI-97. Proceedings of the 9th Italian
		  Workshop on Neural Nets},
  publisher	= {Springer-Verlag London},
  year		= {1998},
  editor	= {M. Marinaro and R. Tagliaferri},
  address	= {London, UK},
  pages		= {324--9},
  dbinsdate	= {oldtimer}
}

@Article{	  merkl98b,
  author	= {Merkl, Dieter},
  title		= {Text classification with \mbox{self-organizing} maps:
		  {SOM} lessons learned},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {61--77},
  abstract	= {The self-organizing map has already found appreciation for
		  document classification in the information retrieval
		  community. The map display is a highly effective and
		  intuitive metaphor for orientation in the information space
		  established by a document collection. In this paper we
		  discuss ways for using self-organizing maps for document
		  classification. Furthermore, we argue in favor of paying
		  more attention to the fact that document collections lend
		  themselves naturally to a hierarchical structure defined by
		  the subject matter of the documents. We take advantage of
		  this fact by using a hierarchically organized neural
		  network, built up from a number of independent
		  self-organizing maps in order to enable the true
		  establishment of a document taxonomy. As a highly
		  convenient side effect of using such an architecture, the
		  time needed for training is reduced substantially and the
		  user is provided with an even more intuitive metaphor for
		  visualization. Since the single layers of self-organizing
		  maps represent different aspects of the document collection
		  at different levels of detail, the neural network shows the
		  document collection in a form comparable to an atlas where
		  the user may easily select the most appropriate degree of
		  granularity depending on the actual focus of interest
		  during the exploration of the document collection.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  merkl98c,
  author	= {Merkl, D. and Hasenauer, H.},
  title		= {Using neural networks to predict individual tree
		  mortality},
  booktitle	= {Engineering Benefits from Neural Networks. Proceedings of
		  the International Conference EANN '98},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1998},
  volume	= {},
  pages		= {197--204},
  abstract	= {Within forest growth modeling it is customary to employ
		  LOGIT models to predict individual tree mortality. We use
		  learning vector quantization and the self-organizing map as
		  different formalisms to predict individual tree mortality.
		  The data set for this study came from permanent sample
		  plots in uneven-aged Norway spruce (Picea abies L. Karst)
		  stands in Austria. After parameterizing the LOGIT model and
		  training the two different network types we evaluate the
		  differences in the resulting mortality predictions using an
		  independent test data set. The results indicate that the
		  LVQ performs slightly better than the conventional LOGIT
		  approach as well as the self organizing map.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  merkl99a,
  author	= {D. Merkl},
  title		= {Document Classification with Self-Organizing Maps},
  booktitle	= {Kohonen Maps},
  pages		= {183--197},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: self-organising map, hierarchical feature map,
		  information retrieval, classification, vector space model},
  dbinsdate	= {oldtimer}
}

@InCollection{	  meroth95a,
  author	= {A. M. Meroth and H. H. Klahr and A. J. Schwab},
  title		= {Neural-network aided finite-element mesh generation},
  booktitle	= {Ninth International Symposium on High Voltage
		  Engineering},
  publisher	= {Inst. High Voltage Eng},
  year		= {1995},
  volume	= {8},
  address	= {Graz, Austria},
  pages		= {8859/1--4},
  dbinsdate	= {oldtimer}
}

@InCollection{	  meyer-base96a,
  author	= {A. Meyer-Base},
  title		= {Quadratic-type Lyapunov functions for competitive neural
		  networks with different time-scales},
  booktitle	= {Advances in Neural Information Processing Systems 8.
		  Proceedings of the 1995 Conference},
  publisher	= {MIT Press},
  year		= {1996},
  editor	= {D. S. Touretzky and M. C. Mozer and M. E. Hasselmo},
  address	= {Cambridge, MA, USA},
  pages		= {337--43},
  dbinsdate	= {oldtimer}
}

@Article{	  meyer-base98a,
  author	= {A. Meyer-Base},
  title		= {Dynamic analysis of continuous \mbox{self-organizing}
		  cortical maps},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1998},
  volume	= {3390},
  pages		= {586--92},
  dbinsdate	= {oldtimer}
}

@InCollection{	  meyer-base98b,
  author	= {A. Meyer-Base},
  title		= {On the existence and stability of solutions in
		  \mbox{self-organizing} cortical maps},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1516--19},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  meyer93a,
  author	= {Meyer, J. W. },
  title		= {A new metric for \mbox{self-organizing} feature maps
		  allows mapping of arbitrary parallel programs},
  booktitle	= {Proceedings of the Fifth International Conference on Tools
		  with Artificial Intelligence TAI '93},
  year		= {1993},
  pages		= {452--3},
  organization	= {Tech. Inf. 2, Tech. Univ. Hamburg-Harburg, Germany},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  meyer94a,
  author	= {Meyer, J. W. },
  title		= {Self-organizing processes},
  booktitle	= {Parallel Processing: CONPAR 94---VAPP VI. Third Joint
		  International Conference on Vector and Parallel Processing
		  Proceedings},
  year		= {1994},
  editor	= {Buchberger, B. and Volkert, J. },
  pages		= {842--53},
  organization	= {Tech. Univ. Hamburg-Harburg, Germany},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  meyer98a,
  author	= {Meyer, B.},
  title		= {Self-organizing graphs---a neural network perspective of
		  graph layout},
  booktitle	= {Graph Drawing. 6th International Symposium, GD'98.
		  Proceedings},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1998},
  volume	= {},
  pages		= {246--62},
  abstract	= {The paper presents self-organizing graphs, a novel
		  approach to graph layout based on a competitive learning
		  algorithm. This method is an extension of self-organization
		  strategies known from unsupervised neural networks, namely
		  from Kohonen's self-organizing map. Its main advantage is
		  that it is very flexibly adaptable to arbitrary types of
		  visualization spaces, for it is explicitly parameterized by
		  a metric model of the layout space. Yet the method consumes
		  comparatively little computational resources and does not
		  need any heavy-duty preprocessing. Unlike with other
		  stochastic layout algorithms, not even the costly repeated
		  evaluation of an objective function is required. To our
		  knowledge this is the first connectionist approach to graph
		  layout. The paper presents applications to 2D-layout as
		  well as to 3D-layout and to layout in arbitrary metric
		  spaces, such as networks on spherical surfaces.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  meyering92a,
  author	= {A. Meyering and H. Ritter},
  title		= {Visuelles Lernen mit Neuronalen {N}etzen},
  booktitle	= {Maschinelles Lernen---Modellierung von Lernen mit
		  Maschinen},
  publisher	= {Springer, Berlin, Heidelberg},
  year		= {1992},
  editor	= {K. Reiss and M. Reiss and H. Spandl},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  meyering92b,
  author	= {Andrea Meyering and Helge Ritter},
  title		= {Learning to recognize 3{D}-Hand Postures from Perspective
		  Pixel Images},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {821--824},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  meyering92c,
  author	= {A. Meyering and H. Ritter},
  title		= {Learning 3{D}-Shape-Perception with Local Linear Maps},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  volume	= {IV},
  pages		= {432--436},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  mi01a,
  author	= {Mi, Y. and Ishii, M. and Tsoukalas, L. H.},
  title		= {Flow regime identification methodology with neural
		  networks and two-phase flow models},
  journal	= {Nuclear Engineering and Design},
  year		= {2001},
  volume	= {204},
  number	= {1--3},
  month		= {February 2001},
  pages		= {87--100},
  organization	= {1290 Nuclear Engineering, Purdue University},
  publisher	= {},
  address	= {},
  abstract	= {Vertical two-phase flows often need to be categorized into
		  flow regimes. In each flow regime, flow conditions share
		  similar geometric and hydrodynamic characteristics.
		  Previously, flow regime identification was carried out by
		  flow visualization or instrumental indicators. In this
		  research, to avoid any instrumentation errors and any
		  subjective judgments involved, vertical flow regime
		  identification was performed based on theoretical two-phase
		  flow simulation with supervised and self-organizing neural
		  network systems. Statistics of the two-phase flow impedance
		  were used as input to these systems. They were trained with
		  results from an idealized simulation that was mainly based
		  on Mishima and Ishii's flow regime map, the drift flux
		  model, and the newly developed model of slug flow. These
		  trained systems were verified with impedance signals
		  measured by an impedance void-meter. The results
		  conclusively demonstrate that the neural network systems
		  are appropriate classifiers of vertical flow regimes. The
		  theoretical models and experimental databases used in the
		  simulation are shown to be reliable.},
  dbinsdate	= {2002/1}
}

@Article{	  michaelides01a,
  author	= {Michaelides, S. C. and Pattichis, C. S. and Kleovoulou,
		  G.},
  title		= {Classification of rainfall variability by using artificial
		  neural networks},
  journal	= {International-Journal-of-Climatology},
  year		= {2001},
  volume	= {21},
  pages		= {1401--14},
  abstract	= {In this paper. the usefulness of artificial neural
		  networks (ANNs) as a suitable tool for the study of the
		  medium and long-term climatic variability is examined. A
		  method for classifying the inherent variability of climatic
		  data. as represented by the rainfall regime. is
		  investigated. The rainfall recorded at a climatological
		  station in Cyprus over a long time period has been used in
		  this paper as the input for various ANN and cluster
		  analysis models. The analysed rainfall data cover the time
		  span 1917--1995. Using these values, two different
		  procedures were followed for structuring the input vectors
		  for training the ANN models: (a) each 1-year subset
		  consisting of the 12 monthly elements. and (b) each 2-year
		  subset consisting of the 24 monthly elements. Several ANN
		  models with a varying number of output nodes have been
		  trained using an unsupervised learning paradigm, namely,
		  the Kohonen self-organizing feature maps algorithm. For
		  both the 1- and 2-year subsets, 16 classes were empirically
		  considered as the optimum for computing the prototype
		  classes of weather variability for this meteorological
		  parameter. The classification established by using the ANN
		  methodology is subsequently compared with the
		  classification generated by using cluster analysis, based
		  on the agglomerative hierarchical clustering algorithm. To
		  validate the classification results, the rainfall
		  distributions for the more recent years 1996, 1997 and 1998
		  were utilized. The respective 1- and 2-year distributions
		  for these years were assigned to particular classes for
		  both the ANN and cluster analysis procedures. Compared with
		  cluster analysis, the ANN models were more capable of
		  detecting even minor characteristics in the rainfall
		  waveshapes investigated, and they also performed a more
		  realistic categorization of the available data. It is
		  suggested that the proposed ANN methodology can be applied
		  to more climatological parameters, and with longer
		  cycles.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  michaelis95a,
  author	= {Bernd Michaelis and Olaf Schnelting and Udo Seiffert and
		  R{\"{u}}diger Mecke},
  title		= {Motion Estimation Using a Compounded {S}elf {O}rganizing
		  {M}ap---Multi Layer Perceptron Network},
  volume	= {III},
  pages		= {103--106},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InCollection{	  michaelis96a,
  author	= {Bend Michaelis and Olaf Schnelting and Udo Seiffert and
		  R{\"u}diger Mecke},
  title		= {Application of Artificial Neural Networks for Improved
		  Motion Analysis},
  booktitle	= {Proc. SIPA'96, International Conference on Signal and
		  Image Processing},
  publisher	= {IASTED/Acta Press},
  year		= 1996,
  address	= {Anaheim},
  pages		= {248--251},
  dbinsdate	= {oldtimer}
}

@InCollection{	  michaelis96b,
  author	= {Bend Michaelis and Olaf Schnelting and Udo Seiffert and
		  R{\"u}diger Mecke},
  title		= {Adaptive Filtering of Distorted Displacement Vector Fields
		  Using Artificial Neural Networks},
  booktitle	= {Proc. ICPR'96, International Conference on Pattern
		  Recognition},
  publisher	= {IEEE Press},
  address	= {Piscataway, NJ},
  year		= 1996,
  volume	= {IV},
  pages		= {335--339},
  dbinsdate	= {oldtimer}
}

@InCollection{	  michaelis96c,
  author	= {B. Michaelis and O. Schnelting and U. Seiffert and R.
		  Mecke},
  title		= {Motion estimation using a compounded
		  \mbox{self-organizing} map-multi layer perceptron network},
  booktitle	= {WCNN '95. World Congress on Neural Networks. 1995
		  International Neural Network Society Annual Meeting},
  publisher	= {World Scientific},
  year		= {1996},
  volume	= {3},
  editor	= {E. Binaghi and P. A. Brivio and A. Rampini},
  address	= {Singapore},
  pages		= {103--6},
  dbinsdate	= {oldtimer}
}

@Article{	  michalopoulou95a,
  author	= {Michalopoulou, Z. -H. and Alexandrou, D. and {de
		  Moustier}, C. },
  title		= {Application of neural and statistical classifiers to the
		  problem of seafloor characterization},
  journal	= {IEEE Journal of Oceanic Engineering},
  year		= {1995},
  volume	= {20},
  number	= {3},
  pages		= {190--7},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@Book{		  michie94,
  title		= {Machine Learning, Neural and Statistical Classification},
  publisher	= {Ellis Horwood},
  year		= 1994,
  editor	= {D. Michie and D. J. Spiegelhalter and C. C. Taylor},
  address	= {New York},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  midenet90a,
  author	= {S. Midenet and A. Grumbach},
  title		= {Supervised learning based on {K}ohonen's
		  \mbox{self-organising} feature maps},
  booktitle	= {Proc. INNC'90 Int. Neural Network Conf. },
  year		= {1990},
  volume	= {II},
  pages		= {773--776},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  mihalik01a,
  author	= {Mihalik, J. and Labovsky, R.},
  title		= {Neural network approaches for predictive vector
		  quantization of an image},
  journal	= {Neural Network World},
  year		= {2001},
  volume	= {11},
  number	= {1},
  month		= {},
  pages		= {33--48},
  organization	= {Department Electronics, Faculty of Electrical Engineering,
		  Technical University Kosice},
  publisher	= {},
  address	= {},
  abstract	= {The paper deals with a predictive vector quantization of
		  an image based on a neural network architectures, where a
		  vector predictor is implemented by three-layer neural
		  network with various hidden nodes and bias units, sigmoid
		  function as nonlinearity and where vector quantizer is
		  implemented by Kohonen self-organizing feature maps, it
		  means the codebook is obtained by neural network clustering
		  algorithm. We have tested an influence of a number of
		  hidden nodes, various convergention rates of a learning
		  algorithm and a presence of the sigmoid function to a mean
		  square prediction error. Next we have studied an influence
		  of codebook size to a mean square quantization error, that
		  means a performance of predictive vector quantization
		  system for various bit rates. The image of Lena of size 512
		  \times 512 pels was coded for various bit rates, where we
		  have used one-dimensional and two-dimensional vector
		  prediction of the blocks of pels.},
  dbinsdate	= {2002/1}
}

@Article{	  mihelic92a,
  author	= {F. Mihelic and I. Ipsic and S. Dobrisek and N. Pavesic},
  title		= {Feature representations and classification procedures for
		  {S}lovene phoneme recognition},
  journal	= {Pattern Recognition Letters},
  year		= {1992},
  volume	= {13},
  number	= {12},
  pages		= {879--891},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@TechReport{	  miikkulainen87a,
  author	= {Risto Miikkulainen},
  title		= {Self-Organizing Process Based on Lateral Inhibition and
		  Weight Redistribution},
  institution	= {Computer Science Department, University of California, Los
		  Angeles, CA},
  year		= {1987},
  number	= {UCLA-AI-87--16},
  annote	= {SOM on the level of local computations and biological
		  plausibility. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  miikkulainen89a,
  author	= {Risto Miikkulainen and Michael G. Dyer},
  title		= {Encoding Input/Output Representations in Connectionist
		  Cognitive Systems},
  booktitle	= {Proc. of the 1988 Connectionist Models Summer School},
  pages		= {347--356},
  year		= {1989},
  editor	= {David S. Touretzky and Geoffrey E. Hinton and Terrence J.
		  Sejnowski},
  publisher	= {Morgan Kaufmann},
  address	= {San Mateo, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  miikkulainen90a,
  author	= {Miikkulainen, R.},
  title		= {Script recognition with hierarchical feature maps.},
  journal	= {Connection Science},
  year		= {1990},
  number	= {1},
  volume	= {2},
  pages		= {83--101},
  abstract	= {The hierarchical feature map system recognizes an input
		  story as an instance of a particular script by classifying
		  it at three levels: scripts, tracks and role bindings. The
		  recognition taxonomy, i.e. the breakdown of each script
		  into the tracks and roles, is extracted automatically and
		  independently for each script from examples of script
		  instantiations in an unsupervised self-organizing process.
		  The process resembles human learning in that the
		  differentiation of the most frequently encountered scripts
		  become gradually the most detailed. The resulting structure
		  is a hierarchical pyramid of feature maps. The hierarchy
		  visualizes the taxonomy and the maps lay out the topology
		  of each level. The number of input lines and the
		  self-organization time are considerably reduced compared to
		  the ordinary single-level feature mapping. The system can
		  recognize incomplete stories and recover the missing
		  events.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  miikkulainen90b,
  author	= {Risto Miikkulainen},
  title		= {A Distributed Feature Map Model of the Lexicon},
  booktitle	= {Proc. 12th Annual Conf. of the Cognitive Science Society},
  year		= {1990},
  pages		= {447--454},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  miikkulainen90c,
  author	= {Risto Miikkulainen},
  title		= {{DISCERN}: {A} Distributed Artificial Neural Network Model
		  of Script Processing and Memory},
  school	= {Computer Science Department, University of California, Los
		  Angeles},
  year		= {1990},
  note		= {(Tech. Rep UCLA-AI-90--05)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  miikkulainen91a,
  author	= {Risto Miikkulainen},
  title		= {A Neural Network Model of Script Processing and Memory},
  booktitle	= {Proc. Int. Workshop on Fundamental Res. for the Next
		  Generation of Natural Language Processing},
  year		= {1991},
  publisher	= {ATR International},
  address	= {Kyoto, Japan},
  dbinsdate	= {oldtimer}
}

@Article{	  miikkulainen91b,
  author	= {Risto Miikkulainen and Michael G. Dyer},
  title		= {Natural Language Processing with Modular Neural Networks
		  and Distributed Lexicon},
  journal	= {Cognitive Science},
  volume	= {15},
  pages		= {343--399},
  year		= {1991},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  miikkulainen91c,
  author	= {Risto Miikkulainen},
  title		= {Self-Organizing Process Based on Lateral Inhibition and
		  Synaptic Resource Redistribution},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {415--420},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  miikkulainen92a,
  author	= {Risto Miikkulainen},
  title		= {Trace feature map: a model of episodic associative
		  memory},
  journal	= {Biol. Cyb. },
  year		= {1992},
  volume	= {66},
  number	= {3},
  pages		= {273--282},
  annotate	= {Associative memory by modifications in SOM-algorithm},
  dbinsdate	= {oldtimer}
}

@Book{		  miikkulainen93a,
  author	= {Risto Miikkulainen},
  title		= {Subsymbolic Natural Language Processing: {A}n Integrated
		  Model of Scripts, Lexicon, and Memory},
  publisher	= {MIT Press},
  year		= {1993},
  address	= {Cambridge, MA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  miikkulainen93b,
  author	= {Risto Miikkulainen},
  title		= {{DISCERN:} {A} Distributed Neural Network Model of Script
		  Processing and Memory},
  booktitle	= {Proc. Third Twente Workshop on Language Technology},
  year		= {1993},
  publisher	= {Computer Science Department, University of Twente},
  address	= {Twente, Netherlands},
  note		= {(in press)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  miikkulainen94a,
  author	= {Risto Miikkulainen},
  title		= {Integrated Connectionist Models: Building AI Systems on
		  Subsymbolic Foundations},
  booktitle	= {Artificial Intelligence and Neural Networks: Steps toward
		  Principled Integration},
  publisher	= {Academic Press},
  year		= 1994,
  editor	= {V. Honavar and L. Uhr},
  pages		= {483--508},
  address	= {New York},
  dbinsdate	= {oldtimer}
}

@Article{	  miikkulainen95a,
  author	= {R. Miikkulainen},
  title		= {Script-Based Inference and Memory Retrieval in Subsymbolic
		  Story Processing},
  journal	= {Applied Intelligence},
  year		= {1995},
  volume	= {5},
  pages		= {137--163},
  dbinsdate	= {oldtimer}
}

@Article{	  miikkulainen97a,
  author	= {R. Miikkulainen},
  title		= {Dyslexic and Category-Specific Impairments in a
		  Self-Organizing Feature Map Model of the Lexicon},
  journal	= {Brain and Language},
  year		= {1997},
  volume	= {59},
  pages		= {334--366},
  dbinsdate	= {oldtimer}
}

@InCollection{	  miikkulainen99a,
  author	= {R. Miikkulainen},
  title		= {Text and Discourse Understanding: The {DISCERN} System},
  booktitle	= {A Handbook of Natural Language Processing: Techniques and
		  Applications for the Processing of Language as Text},
  publisher	= {Marcel Dekker},
  year		= {1999},
  editor	= {R. Dale and H. Moisl and H. Somers},
  address	= {New York},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  miikkulainen99b,
  author	= {R. Miikkulainen and J. A. Bednar and Y. Choe and J.
		  Sirosh},
  title		= {Modeling Self-Organization in the Visual Cortex},
  booktitle	= {Proceedings of WSOM'99 Workshop on Self-Organizing Maps},
  year		= {1999},
  publisher	= {Elsevier},
  address	= {New York},
  dbinsdate	= {oldtimer}
}

@InCollection{	  miikkulainen99c,
  author	= {R. Miikkulainen and J. A. Bednar and Y. Choe and J.
		  Sirosh},
  title		= {Modeling Self-Organization in the Visual Cortex},
  booktitle	= {Kohonen Maps},
  pages		= {243--252},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: visual cortex, computational neuroscience,
		  biological modeling, lateral connections, spiking neurons},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mikami00a,
  author	= {Mikami, Dan and Hagiwara, Masafumi},
  title		= {Self-growing learning vector quantization with additional
		  learning and rule extraction abilities},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {2895--2900},
  organization	= {Keio Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {In this paper, we propose a self-growing learning vector
		  quantization (SGLVQ). The proposed SGLVQ is constructed
		  based on the self-organizing map (SOM) and the learning
		  vector quantization (LVQ). Learning of the SGLVQ consists
		  of 3 steps: SOM step, LVQ step, and rule extraction step.
		  In the LVQ step, neurons are incremented and the size of
		  the network is adjusted automatically. The incrementation
		  of neurons enables additional learning and contributes to
		  obtain high recognition ability. In the rule extraction
		  step, rules can be extracted. Computer experiments show the
		  improvement of the recognition rate, the ability of
		  additional learning and extraction of the rules.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  mikami01a,
  author	= {Mikami, T. and Wada, M.},
  title		= {Data visualization method for growing self-organizing
		  networks with ant clustering algorithm},
  booktitle	= {Advances in Artificial Life. 6th European Conference, ECAL
		  2001. Proceedings (Lecture Notes in Artificial Intelligence
		  Vol.2159). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {623--6},
  abstract	= {The growing self-organizing networks are useful tools
		  suitable for data analysis in which networks learn the
		  topology of the high-dimensional data by inserting/deleting
		  neurons. However, these methods cannot represent the
		  high-dimensional clusters on the lower-dimensional
		  intuitive space. We proposed a visualization method by ant
		  clustering to construct a two-dimensional feature map for
		  growing self-organizing networks.},
  dbinsdate	= {2002/1}
}

@Article{	  mikami01b,
  author	= {Mikami, D. and Hagiwara, M.},
  title		= {Self-growing learning vector quantization with additional
		  learning ability},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-C}
		  ,
  year		= {2001},
  volume	= {121},
  pages		= {1620--6},
  abstract	= {We propose a self-growing learning vector quantization
		  with additional learning (SGLVQ). It has a high pattern
		  recognition ability and an additional learning is possible.
		  The proposed SGLVQ is constructed based on the
		  self-organizing map (SOM) and learning vector quantization
		  (LVQ) and is composed of 2 layers: an input/output layer
		  and a rule layer. There are full connections between the
		  input/output layer and rule layer. Learning of the SGLVQ
		  consists of 2 steps: the SOM step and LVQ step. In the SOM
		  step, similar inputs are mapped in the neighboring area
		  using the SOM algorithm. In the LVQ step, neurons are
		  incremented and the size of the network is adjusted
		  automatically. The number of neurons increases when
		  fault-recognition occurs. The increment of neurons enables
		  additional learning and leads to a high recognition
		  ability.},
  dbinsdate	= {2002/1}
}

@InCollection{	  mikami95a,
  author	= {S. Mikami and M. Wada and T. C. Fogarty},
  title		= {Learning to achieve co-operation by temporal-spatial
		  fitness sharing},
  booktitle	= {1995 IEEE International Conference on Evolutionary
		  Computation},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {803--7},
  dbinsdate	= {oldtimer}
}

@Article{	  mikhael01a,
  author	= {Mikhael, W. B. and Krishnan, V.},
  title		= {Energy-based split vector quantizer employing signal
		  representation in multiple transform domains},
  journal	= {Digital Signal Processing: A Review Journal},
  year		= {2001},
  volume	= {11},
  number	= {4},
  month		= {October },
  pages		= {359--370},
  organization	= {School of Engineering and Comp. Sci., University of
		  Central Florida},
  publisher	= {},
  address	= {},
  abstract	= {Vector quantization schemes are widely used for waveform
		  coding of one- and multidimensional signals. In this
		  contribution, a novel energy-based, split vector
		  quantization technique is presented, which represents
		  digital signals efficiently as measured by the number of
		  bits per sample for a predetermined signal reconstruction
		  quality. In this approach, each signal vector is projected
		  into multiple transform domains. In the learning mode, for
		  a given transform domain representation, the transformed
		  vector is split into subvectors (subbands) of equal average
		  energy estimated from the transformed training vector
		  ensemble. An equal number of bits is assigned to each
		  subvector. A codebook is then designed for each equal
		  energy subband of each transform domain representation. In
		  the running mode, the coder selects codes from the domain
		  that best represents the signal vector. The proposed
		  multiple transform, split vector quantizer is developed and
		  its performance is evaluated for both single-stage and
		  multistage implementations. Several single transform vector
		  quantizers for waveform coding exist, some of which employ
		  energy-based bit allocation. Sample results using
		  one-dimensional speech signals confirm the superior
		  performance of the proposed scheme over existing single
		  transform vector quantizers for waveform coding.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  miki99a,
  author	= {Tsutomu Miki and Shino Izumi and Tekeeshi Yamakawa},
  title		= {Self-Organizing Method with Input Vector Transformation
		  and Its Application to Shape Detection},
  booktitle	= {15th Fuzzy System Symposium (Osaka, June 2--5, 1999)},
  year		= {1999},
  pages		= {305--308},
  note		= {in Japanese},
  abstract	= {In this paper, we propose a new method of the shape
		  detection by using self-organizing method with an input
		  vector transformation. It is hard to detect shapes from
		  noisy images by the conventional Hough transformation. The
		  proposed method enables to detect the shape automatically.
		  Two SOMs are used in the proposed method. The first SOM
		  determines the number of shapes included in the image and
		  the second determines the parameter of the shapes. The
		  performances of the proposed method is confirmed by the
		  detection of lines and circles from their noisy images.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  miki99b,
  author	= {Tsutomu Miki and Youhei Oosako and Takeshi Yamakawa},
  title		= {A new Interpolation Algorithm Employing Self-organizing
		  Map},
  booktitle	= {15th Fuzzy System Symposium (Osaka, June 2--5, 1999)},
  year		= {1999},
  pages		= {311--314},
  note		= {in Japanese},
  abstract	= {A new interpoltaion method employing the trangent
		  characteristics of a Self-Organizing Map (SOM) is proposed
		  in this paper. We focuse on the special feature that the
		  units in the competitive layer are arranged for
		  interpolation between them in early learning. THe
		  performance of the proposed method is confirmed by the
		  interpolation results in 2-dimension and 3-dimension
		  spaces.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  miki99c,
  author	= {Tsutomu Miki and Shunsuke Iwasaki and Keiichi Horio and
		  Tekeshi Yamakawa},
  title		= {Non-linear Quantization Method Depending upon Appearance
		  Frequency by Using Self-Organising Map},
  booktitle	= {15th Fuzzy System Symposium (Osaka June 2--5, 1999)},
  year		= {1999},
  pages		= {315--318},
  note		= {in Japanese},
  abstract	= {In this paper, a new nonlinear quantization method, in
		  which the quantization levels can be decided corresponding
		  to the appearance frequency of the sample signal, is
		  proposed. The Self-Organizing Map (SOM) is used to extract
		  the distribution of signals. Futhermore, the new learning
		  algorithm suitable for the proposed method is described.
		  The effectiveness of the proposed method is verified by
		  applying to voice and music signals.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  mikrut01a,
  author	= {Mikrut, Z.},
  title		= {Recognition of objects normalized in log-polar space using
		  Kohonen networks},
  booktitle	= {ISPA 2001. Proceedings of the 2nd International Symposium
		  on Image and Signal Processing and Analysis. In conjunction
		  with 23rd International Conference on Information
		  Technology Interfaces. Univ. Zagreb, Zagreb, Croatia},
  year		= {2001},
  volume	= {},
  pages		= {308--12},
  abstract	= {In the paper, an algorithm is presented for the
		  construction of representations of 18 object classes, which
		  can be later recognized by a hybrid neural network. The
		  preprocessing took place in log-polar space and it
		  included: object centering, binarization, edge detection,
		  normalization of angular position and scaling. After the
		  normalization and log-Hough transformation, the maxima have
		  been projected onto respective axes. In the paper, results
		  have been discussed of neural network learning using such
		  constructed representations, and the map of features for
		  the Kohonen layer has been analyzed.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  milano01a,
  author	= {Milano, M. and Schmidhuber, J. and Koumoutsakos, P.},
  title		= {Active learning with adaptive grids},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {436--442},
  abstract	= {Given some optimization problem and a series of typically
		  expensive trials of solution candidates taken from a search
		  space, how can we efficiently select the next candidate? We
		  address this fundamental problem using adaptive grids
		  inspired by Kohonen's self-organizing map. Initially the
		  grid divides the search space into equal simplexes. To
		  select a candidate we uniform randomly first select a
		  simplex, then a point within the simplex. Grid nodes are
		  attracted by candidates that lead to improved evaluations.
		  This quickly biases the active data selection process
		  towards promising regions, without loss of ability to deal
		  with "surprising" global optima in other areas. On standard
		  benchmark functions the technique performs more reliably
		  than the widely used covariance matrix adaptation evolution
		  strategy.},
  dbinsdate	= {2002/1}
}

@Article{	  millan02a,
  author	= {Millan, J. R. and Posenato, D. and Dedieu, E.},
  title		= {Continuous-action Q-learning},
  journal	= {Machine-Learning},
  year		= {2002},
  volume	= {49},
  pages		= {247--65},
  abstract	= {This paper presents a Q-learning method that works in
		  continuous domains. Other characteristics of the approach
		  are the use of an incremental topology preserving map
		  (ITPM) to partition the input space, and the incorporation
		  of bias to initialize the learning process. A unit of the
		  ITPM represents a limited region of the input space and
		  maps it onto the Q-values of M possible discrete actions.
		  The resulting continuous action is an average of the
		  discrete actions of the "winning unit" weighted by their
		  Q-values. Then, TD( lambda ) updates the Q-values of the
		  discrete actions according to their contribution. Units are
		  created incrementally and their associated Q-values are
		  initialized by means of domain knowledge. Experimental
		  results in robotics domains show the superiority of the
		  proposed continuous-action Q-learning over the standard
		  discrete-action version in terms of both asymptotic
		  performance and speed of learning. The paper also reports a
		  comparison of discounted-reward against average-reward
		  Q-learning in an infinite horizon robotics task.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  miller95a,
  author	= {David Miller and Ajit Rao and Kenneth Rose and Allen
		  Gersho},
  title		= {A Maximum Entropy Approach for Optimal Statistical
		  Classification},
  booktitle	= {Proc. NNSP'95, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1995},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {58--66},
  annote	= {LVQ comparison},
  dbinsdate	= {oldtimer}
}

@Article{	  miller96a,
  author	= {A. Miller and M. Coe},
  title		= {Star/galaxy classification using {K}ohonen
		  \mbox{self-organizing} maps},
  journal	= {Royal Astronomical Society, Monthly Notices},
  year		= {1996},
  volume	= {20},
  number	= {1},
  pages		= {293--300},
  dbinsdate	= {oldtimer}
}

@Article{	  miller96b,
  author	= {D. Miller and A. V. Rao and K. Rose and A. Gersho},
  title		= {A global optimization technique for statistical classifier
		  design},
  journal	= {IEEE Transactions on Signal Processing},
  year		= {1996},
  volume	= {44},
  number	= {12},
  pages		= {3108--22},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  min-kyu01a,
  author	= {Min-Kyu, Shon and Murata, Junichi and Hirasawa, Kotaro},
  title		= {Function approximation using {LVQ} and fuzzy sets},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {1442--1447},
  organization	= {Dept. of Elec./Electron. Sys. Eng., Grad. Sch. of Info.
		  Sci./Elec. Eng., Kyushu University},
  publisher	= {},
  address	= {},
  abstract	= {Neural networks with local activation functions, for
		  example RBFNs (Radial Basis Function Networks), have a
		  merit of excellent generalization abilities. When this type
		  of network is used in function approximation, it is very
		  important to determine the proper division of the input
		  space into local regions to each of which a local
		  activation function is assigned. In RBFNs, this is
		  equivalent to determination of the locations and the
		  numbers of its RBFs, which is generally done based on the
		  distribution of input data. But, in function approximation,
		  the output information (the value of the function to be
		  approximated) must be considered in determination of the
		  local regions. A new method is proposed that uses LVQ
		  network to approximate the functions based on the output
		  information. It divides the input space into regions with a
		  prototype vector at the center of each region. The ordinary
		  LVQ, however, outputs discrete values only, and therefore
		  can not approximate continuous functions. In this paper,
		  fuzzy sets are employed in both of learning and output
		  calculation. Finally, the proposed method uses the
		  back-propagation algorithm for fine adjustment. An example
		  is provided to show the effectiveness of the proposed
		  method.},
  dbinsdate	= {2002/1}
}

@Article{	  min92a,
  author	= {Min, K. S. and Min, H. L. },
  title		= {Neural network based image compression using {AMT} {DAP}
		  610},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 1},
  pages		= {386--93},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  minamimoto95a,
  author	= {Kazuhiro Minamimoto and Kazushi Ikeda and Kenji Nakayama},
  title		= {Topology Analysis of Data Space Using Self-Organizing
		  Feature Maps},
  volume	= {II},
  pages		= {789--794},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ming00a,
  author	= {Ming, Hsuan Yang and Abuja, N. and Kriegman, D.},
  title		= {Face detection using mixtures of linear subspaces},
  booktitle	= {Proceedings Fourth IEEE International Conference on
		  Automatic Face and Gesture Recognition},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {70--6},
  abstract	= {We present two methods using mixtures of linear sub-spaces
		  for face detection in gray level images. One method uses a
		  mixture of factor analyzers to concurrently perform
		  clustering and, within each cluster, perform local
		  dimensionality reduction. The parameters of the mixture
		  model are estimated using an EM algorithm. A face is
		  detected if the probability of an input sample is above a
		  predefined threshold. The other mixture of subspaces method
		  uses Kohonen's self-organizing map for clustering and
		  Fisher linear discriminant to find the optimal projection
		  for pattern classification, and a Gaussian distribution to
		  model the class-conditioned density function of the
		  projected samples for each class. The parameters of the
		  class-conditioned density functions are maximum likelihood
		  estimates and the decision rule is also based on maximum
		  likelihood. A wide range of face images including ones in
		  different poses, with different expressions and under
		  different lighting conditions are used as the training set
		  to capture the variations of human faces. Our methods have
		  been tested on three sets of 225 images which contain 871
		  faces. Experimental results on the first two datasets show
		  that our methods perform as well as the best methods in the
		  literature, yet have fewer false detects.},
  dbinsdate	= {oldtimer}
}

@Article{	  ming00b,
  author	= {Ming, X. G. and Mak, K. L.},
  title		= {Intelligent setup planning in manufacturing by neural
		  networks based approach},
  journal	= {Journal of Intelligent Manufacturing},
  year		= {2000},
  volume	= {11},
  number	= {3},
  month		= {Jun},
  pages		= {311--331},
  organization	= {Univ of Hong Kong},
  publisher	= {Kluwer Academic Publishers},
  address	= {Dordrecht},
  abstract	= {Setup planning is considered the most significant but also
		  difficult activity in Computer Aided Process Planning
		  (CAPP), and has a strong impact on manufacturability,
		  product quality and production cost. Indeed, setup planning
		  activity deserves much attention in CAPP. The setup
		  planning in manufacturing consists mainly of three steps,
		  namely, setup generation, operation sequence, and setup
		  sequence. In this paper, the Kohonen self-organizing neural
		  networks and Hopfield networks are adopted to solve such
		  problems in setup planning efficiently. Kohonen
		  self-organizing neural networks are utilized, according to
		  the nature of the different steps in setup planning, to
		  generate setups in terms of the constraints of
		  fixtures/jigs, approach directions, feature precedence
		  relationships, and tolerance relationships. The operation
		  sequence problem and the setup sequence problem are mapped
		  onto the traveling salesman problem, and are solved by
		  Hopfield neural networks. This paper actually provides a
		  complete research basis to solve the setup planning problem
		  in CAPP, and also develops the most efficient neural
		  networks based approaches to solve the setup planning
		  problem in manufacturing. Indeed, the results of the
		  proposed approaches work towards the optimal solution to
		  the intelligent setup planning in manufacturing.},
  dbinsdate	= {2002/1}
}

@Article{	  ming01a,
  author	= {Ming Hsuan Yang and Kriegman, D. and Ahuja, N.},
  title		= {Face detection using multimodal density models},
  journal	= {Computer-Vision-and-Image-Understanding},
  year		= {2001},
  volume	= {84},
  pages		= {264--84},
  abstract	= {We present two methods using multimodal density models for
		  face detection in gray-level images. One generative method
		  uses a mixture of factor analyzers to concurrently perform
		  clustering and, within each cluster, perform local
		  dimensionality reduction. The parameters of the mixture
		  model are estimated using the EM algorithm. A face is
		  detected if the probability of an input sample is above a
		  predefined threshold. The other discriminative method uses
		  Kohonen's self-organizing map for clustering, Fisher's
		  linear discriminant to find an optimal projection for
		  pattern classification, and a Gaussian distribution to
		  model the class-conditional density function of the
		  projected samples for each class. The parameters of the
		  class-conditional density functions are maximum likelihood
		  estimates, and the decision rule is also based on maximum
		  likelihood. A wide range of face images including ones in
		  different poses, with different expressions and under
		  different lighting conditions, is used as the training set
		  to capture variations of the human face. Our methods have
		  been tested on three data sets with a total of 225 images
		  containing 871 faces. Experimental results on the first two
		  data sets show that our generative and discriminative
		  methods perform as well as the best methods in the
		  literature, yet have fewer false detections. Meanwhile,
		  both methods are able to detect faces of nonfrontal views
		  and under more extreme lighting in the third data set.},
  dbinsdate	= {2002/1}
}

@Article{	  ming94a,
  author	= {Chen Ming and Li Minghui},
  title		= {{K}ohonen neural network-based solution of {TSP}},
  journal	= {Mini-Micro Systems},
  year		= {1994},
  volume	= {15},
  number	= {11},
  pages		= {35--9},
  month		= {Nov},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ming99a,
  author	= {Ming, Hsuan Yang and Ahuja, N.},
  title		= {A data partition method for parallel
		  \mbox{self-organizing} map},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1929--33},
  abstract	= {We propose a method to partition training vectors into
		  clusters for a parallel implementation of self-organizing
		  map (SOM) algorithm. The proposed algorithm assigns a
		  cluster to a processor such that, in updating weights, the
		  neighbourhoods of a winning node in a cluster do not
		  overlap the neighboring nodes of some winning nodes in
		  other clusters. It reduces the overheads caused by
		  synchronization (i.e., maintaining coherency) of the weight
		  matrices in the processors since the proposed algorithm
		  allows multiple vectors to find their winning nodes and
		  update weights in parallel. Our experimental results show
		  that an average speedup of 3.15 for a parallel
		  implementation of a four processor simulation.},
  dbinsdate	= {oldtimer}
}

@Article{	  mirelli95a,
  author	= {V. Mirelli and D. Nguyen and N. M. Nasrabadi},
  title		= {Target recognition for {FLIR} imagery using learning
		  vector quantization and multilayer perceptrons},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2485},
  pages		= {110--22},
  note		= {(Automatic Object Recognition V Conf. Date: 19--21 April
		  1995 Conf. Loc: Orlando, FL, USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@Article{	  mitchison95a,
  author	= {Graeme Mitchison},
  title		= {A Type of Duality between {Self-Organizing} {M}aps and
		  Minimal Wiring},
  journal	= {Neural Computation},
  year		= {1995},
  volume	= {7},
  number	= {1},
  pages		= {25--35},
  dbinsdate	= {oldtimer}
}

@Article{	  mitchison99a,
  author	= {G. J. Mitchison and N. V. Swindale},
  title		= {Can Hebbian Volume Learning Explain Discontinuities in
		  Cortical Maps?},
  journal	= {Neural Computation},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@Article{	  mitiche01a,
  author	= {Mitiche, A. and Lebidoff, M.},
  title		= {Pattern classification by a condensed neural network},
  journal	= {Neural Networks},
  year		= {2001},
  volume	= {14},
  number	= {4--5},
  month		= {},
  pages		= {575--580},
  organization	= {INRS-Telecommunications},
  publisher	= {},
  address	= {},
  abstract	= {Neural networks have come to the fore as potent pattern
		  classifiers. More amenable to parallel computation, they
		  are much faster than the nearest neighbor classifier (NN),
		  which, however, has distinctly outperformed them in several
		  applications. The purpose of this study is to investigate a
		  condensed neural network that combines the classification
		  speed of neural networks and the low error rate of the
		  nearest neighbor classifier. This condensed network is a
		  fast, accurate classifier of simple architecture and
		  function: it consists of a set of generalized perceptrons
		  that draw maximal hyperspherical boundaries centered on
		  patterns of memory units, each circumscribing reference
		  patterns of a single category. The generalized perceptrons
		  carry out classification, assisted by sporadic nearest
		  neighbor matching to patterns of a small reference set. We
		  compare the condensed network to a high performance neural
		  network pattern classifier (Kohonen) and to NN in
		  experiments on hand-printed character recognition. },
  dbinsdate	= {2002/1}
}

@Article{	  mitiche96a,
  author	= {A. Mitiche and J. K. Aggarwal},
  title		= {Pattern category assignment by neural networks and nearest
		  neighbours rule: a synopsis and a characterization},
  journal	= {International Journal of Pattern Recognition and
		  Artificial Intelligence},
  year		= {1996},
  volume	= {10},
  number	= {5},
  pages		= {393--408},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mitihata00a,
  author	= {Masahiro Mitihata and Tsutomu Miyoshi and Hiroshi
		  Masuyama},
  title		= {A Consideration on the Labels of the Self-Organizing Map
		  After Refresh Learning},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {233--238},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@Article{	  mitra94a,
  author	= {Mitra, S. and Pal, S. K. },
  title		= {Self-organizing neural network as a fuzzy classifier},
  journal	= {IEEE Transactions on Systems, Man and Cybernetics},
  year		= {1994},
  volume	= {24},
  number	= {3},
  pages		= {385--99},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mitra94b,
  author	= {Mitra, S. },
  title		= {Fuzzy inferencing with ART networks},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {2},
  pages		= {1230--4},
  organization	= {Dept. of Electr. Eng. , Texas Tech. Univ. , Lubbock, TX,
		  USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  mitra96a,
  author	= {S. Mitra and S. K. Pal},
  title		= {Fuzzy self-organization, inferencing, and rule
		  generation},
  journal	= {IEEE Transactions on Systems, Man \& Cybernetics, Part A
		  [Systems \& Humans]},
  year		= {1996},
  volume	= {26},
  number	= {5},
  pages		= {608--20},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mitsukura00a,
  author	= {Mitsukura, Y. and Fukumi, M. and Akamatsu, N.},
  title		= {Design of genetic fog occurrence forecasting system by
		  using {LVQ} network},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {3678--3681},
  organization	= {Univ of Tokushima},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A transportation development in recent years is quite
		  remarkable. However, poor visibility often cause an
		  accident. Therefore, it is very important to forecast a fog
		  occurrence. In this paper, we propose a scheme to forecast
		  a fog occurrence by using the Learning Vector Quantization
		  (LVQ) and a Genetic Algorithm (GA). This scheme forecasts
		  the fog occurrence by the weather data which are provided
		  from the Japan Meteorological Agency. First, the provided
		  data formation are shown. Next, the prediction scheme is
		  described in detail. In this method, input attributes for a
		  LVQ network are selected by real-coded GA to improve
		  forecast accuracy. Furthermore, a partial selection
		  processing in the real-coded GA improves its convergence
		  properties. Finally, in order to show the effectiveness of
		  the proposed prediction scheme, computer simulations are
		  performed.},
  dbinsdate	= {2002/1}
}

@Article{	  mizoguchi01a,
  author	= {Mizoguchi, K. and Hagiwara, M.},
  title		= {A novel neural network for four-term analogy based on area
		  representation},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-C}
		  ,
  year		= {2001},
  volume	= {121},
  pages		= {1261--7},
  abstract	= {We propose a novel neural network for four-term analogy
		  based on area representation. It can deal with four-term
		  analogy such as "teacher: student=doctor :?". The proposed
		  network is composed of three map layers and an input layer.
		  The area representation method based on Kohonen feature map
		  is employed in order to represent knowledge, so that
		  similar concepts are mapped in the nearer area in the map
		  layer. The proposed mechanism in the map layer can realize
		  the movement of the excited area to the near area. We
		  carried out some computer simulations and confirmed as
		  follows: 1) similar concepts are mapped in the nearer area
		  in the map layer; 2) the excited area moves among similar
		  concepts; 3) the proposed network realizes four-term
		  analogy; and 4) the network is robust for the lack of
		  connections.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  mizoguchi99a,
  author	= {Mizoguchi, K. and Hagiwara, M.},
  title		= {A novel neural network for four-term analogy based on area
		  representation},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1144--9},
  abstract	= {We propose a novel neural network for four-term analogy
		  based on area representation. It can deal with four-term
		  analogy such as "teacher: student=doctor: ?". The proposed
		  network is composed of three map layers and an input layer.
		  The area representation method based on Kohonen feature map
		  (KFM) is employed in order to represent knowledge, so that
		  similar concepts are mapped in nearer area in the map
		  layer. The proposed mechanism in the map layer can realize
		  the movement of the excited area to the near area. We
		  carried out some computer simulations and confirmed as
		  follows: 1) similar concepts are mapped in the nearer area
		  in the map layer; 2) the excited area moves among similar
		  concepts; 3) the proposed network realizes four-term
		  analogy; and 4) the network is robust for the lack of
		  connections.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mizuta90a,
  author	= {Shinobu Mizuta and Kunio Nakajima},
  title		= {An Optimal Discriminative Training Method for Continuous
		  Mixture Density {HMM}s},
  booktitle	= {Proc. ICSLP, International Conference on Spoken Language
		  Processing},
  year		= {1990},
  publisher	= {University of Alberta},
  address	= {Edmonton, Alberta, Canada},
  volume	= {1},
  pages		= {245--248},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mocofan00a,
  author	= {Mocofan, M. and Caleanu, C. and Lacrama, D. and Alexa,
		  F.},
  title		= {Unsupervised texture image segmentation},
  booktitle	= {Proceedings of the 5th Seminar on Neural Network
		  Applications in Electrical Engineering. NEUREL 2000. IEEE
		  \& Academic Mind, Piscataway, NJ, USA \& Belgrade,
		  Yugoslavia},
  year		= {2000},
  volume	= {},
  pages		= {101--4},
  abstract	= {This paper is focused on a hierarchical structure of
		  modular self-organizing neural networks for unsupervised
		  texture segmentation (sofm-nn). Input data consists of
		  local information regarding textures (cooccurrence matrix
		  elements) and the texture image itself. An unsupervised
		  segmentation is done using a sofm-nn network and then the
		  final segmentation is performed by another sofm-nn neural
		  network using the previously obtained results. Experimental
		  results show the efficiency of the proposed method.},
  dbinsdate	= {2002/1}
}

@Article{	  mohsenian92a,
  author	= {Mohsenian, N. and Nasrabadi, N. M. },
  title		= {A neural net approach to predictive vector quantization},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1818},
  number	= {pt. 2},
  pages		= {476--87},
  annote	= {A conference paper in journal},
  abstract	= {A new predictive vector quantization (PVQ) technique,
		  capable of exploring the nonlinear dependencies in addition
		  to the linear dependencies that exist between adjacent
		  blocks of pixels, is introduced. Two different classes of
		  neural nets form the components of the PVQ scheme. A
		  multi-layer perceptron is embedded in the predictive
		  component of the compression system. This neural network,
		  using the non-linearity condition associated with its
		  processing units, can perform as a non-linear vector
		  predictor. The second component of the PVQ scheme vector
		  quantizes (VQ) the residual vector that is formed by
		  subtracting the output of the perceptron from the original
		  wave-pattern. Kohonen Self-Organizing Feature Map (KSOFM)
		  was utilized as a neural network clustering algorithm to
		  design the codebook for the VQ technique. Coding results
		  are presented for monochrome 'still' images.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mohsenian93a,
  author	= {Nader Mohsenian and Nasser M. Nasrabadi},
  title		= {Predictive Vector Quantization Using a Neural Network},
  booktitle	= {Proc. ICASSP-93, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1993},
  pages		= {V-245--248},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  mohsenian93b,
  author	= {Mohsenian, N. and Rizvi, S. A. and Nasrabadi, N. M. },
  title		= {Predictive vector quantization using a neural network
		  approach},
  journal	= {Optical Engineering},
  year		= {1993},
  volume	= {32},
  number	= {7},
  pages		= {1503--13},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  molander95a,
  author	= {Molander, S. },
  title		= {'{B}lob' analysis of biomedical image sequences: a
		  model-based and an inductive approach},
  booktitle	= {Analysis of Dynamical and Cognitive Systems. Advanced
		  Course. Proceedings},
  year		= {1995},
  editor	= {Andersson, S. I. },
  pages		= {169--87},
  organization	= {Dept. of Appl. Electron. , Chalmers Univ. of Technol. ,
		  Goteborg, Sweden},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  moldenhauer00a,
  author	= {Oliver Moldenhauer and Matthias K. B. L\"{u}deke},
  title		= {{NNN}---A neural network model for net primary production
		  of plants on a global scale},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  molina00a,
  author	= {Molina, J. M. and Isasi, P. and Berlanga, A. and Sanchis,
		  A.},
  title		= {Hydroelectric power plant management relying on neural
		  networks and expert system integration},
  journal	= {Engineering Applications of Artificial Intelligence},
  year		= {2000},
  volume	= {13},
  number	= {3},
  month		= {Jun},
  pages		= {357--369},
  organization	= {Universidad Carlos III de Madrid},
  publisher	= {Elsevier Science Ltd},
  address	= {},
  abstract	= {The use of Neural Networks (NN) is a novel approach that
		  can help in taking decisions when integrated in a more
		  general system, in particular with expert systems. In this
		  paper, an architecture for the management of hydroelectric
		  power plants is introduced. This relies on monitoring a
		  large number of signals, representing the technical
		  parameters of the real plant. The general architecture is
		  composed of an Expert System and two NN modules: Acoustic
		  Prediction (NNAP) and Predictive Maintenance (NNPM). The
		  NNAP is based on Kohonen Learning Vector Quantization (LVQ)
		  Networks in order to distinguish the sounds emitted by
		  electricity-generating machine groups. The NNPM uses an
		  ART-MAP to identify different situations from the plant
		  state variables, in order to prevent future malfunctions.
		  In addition, a special process to generate a complete
		  training set has been designed for the ART-MAP module. This
		  process has been developed to deal with the absence of data
		  about abnormal plant situations, and is based on neural
		  nets trained with the backpropagation algorithm.},
  dbinsdate	= {2002/1}
}

@Article{	  moll97a,
  author	= {Mark Moll and Risto Miikkulainen},
  title		= {Convergence-Zone Episodic Memory: Analysis and
		  Simulations},
  journal	= {Neural Networks},
  year		= 1997,
  volume	= 10,
  pages		= {1017--1036},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  moller00a,
  author	= {Moller, D. P. F. and Berger, A.},
  title		= {Discrete event simulation based on an artificial neural
		  network simulator},
  booktitle	= {Simulation and Modelling. Enablers for a Better Quality of
		  Life. 14th European Simulation. Multiconference 2000. SCS,
		  San Diego, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {686--90},
  abstract	= {The application of neural networks in a discrete event
		  simulator for sleep stage analysis is shown. The area of
		  sleep analysis deals with the detection of EEG
		  (electroencephalography) and the corresponding map in a
		  neural network simulator based on Kohonen maps.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  moller93a,
  author	= {Knut M{\"{o}}ller},
  title		= {A Multiassociative Memory for Control},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {593--596},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  monakhov96a,
  author	= {O. G. Monakhov and O. Y. Chunikhin},
  title		= {Parallel mapping of program graphs into parallel computers
		  by self-organization algorithm},
  booktitle	= {Applied Parallel Computing. Industrial Computation and
		  Optimization. Third International Workshop, PARA '96
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {J. Wasniewski and J. Dongarra and K. Madsen and D.
		  Olesen},
  address	= {Berlin, Germany},
  pages		= {525--8},
  dbinsdate	= {oldtimer}
}

@Article{	  mongini00a,
  author	= {Mongini, F. and Italiano, M. and Raviola, F. and Mossolov,
		  A.},
  title		= {The McGill Pain Questionnaire in patients with {TMJ} pain
		  and with facial pain as a somatoform disorder},
  journal	= {CRANIO-THE JOURNAL OF CRANIOMANDIBULAR PRACTICE},
  year		= {2000},
  volume	= {18},
  number	= {4},
  month		= {OCT},
  pages		= {249--256},
  abstract	= {The purpose of this study was to assess the discriminative
		  capacity of the McGill Pain Questionnaire (MPQ) in patients
		  with temporomandibular joint disorders (TMD) or with facial
		  pain disorder as somatoform disorder (referred to as
		  "atypical facial pain") (FP), The MPQ was administered to
		  57 TMD and 34 FP patients. Weighted MPO item scores,
		  subscale Pain Rating indexes (PRI), and total Pain Rating
		  Index were tested for significant differences (Student's
		  t-test), and the frequency of descriptor choice was also
		  analyzed. Furthermore, the data were processed through two
		  systems based on a counter- propagation neural network: the
		  Self-Organizing Map (SOM) system and a cluster-like
		  analysis, In the FP group eleven MPQ item scores and five
		  PRI scores were significantly higher than those of the TMJ
		  group, There was a considerable difference in descriptor
		  choice between the groups. SOM analysis and cluster- like
		  analysis correctly discriminated 85% or more of the
		  patients. In conclusion, the MPQ showed a consistent
		  discriminative capacity between TMD and FP patients.},
  dbinsdate	= {2002/1}
}

@Article{	  mongini01a,
  author	= {Mongini, F. and Italiano, M.},
  title		= {{TMJ} disorders and myogenic facial pain: a discriminative
		  analysis using the McGill Pain Questionnaire},
  journal	= {PAIN},
  year		= {2001},
  volume	= {91},
  number	= {3},
  month		= {APR},
  pages		= {323--330},
  abstract	= {Our aim was to assess the discriminative capacity of the
		  McGill Pain Questionnaire (MPQ) in patients with
		  temporomandibular joint (TMJ) disorders or with myogenous
		  facial pain (MP). The MPQ was administered to 57 TMJ and 28
		  MP patients who were also asked to assess the level of pain
		  using the Visual Analog Scale (VAS). Weighted MPQ item
		  scores, subscale Pain Raring Indexes (PRI). total PRI and
		  the number of words chosen were calculated. Mean scores
		  were tested for significant differences (Student's t-test).
		  and the frequency with which each descriptor was chosen by
		  the patients in both groups was analyzed. Data were also
		  processed through two systems based on a
		  counter-propagation neural network: the Self-Organizing Map
		  (SOM) system, and a cluster-like analysis. In the MP group,
		  16 out of 20 mean MPQ item scores and all mean PRI and VAS
		  scores were significantly higher than those in the TMJ
		  group. There was a marked difference in descriptor choice.
		  In the TMJ group the following descriptors were chosen by
		  25% or more of the patients: tiring. troublesome, nagging.
		  sore, tender. and aching. In the MP group the descriptors
		  most frequently chosen were: 'exhausting' (57%),
		  'punishing' (50%), and pulling (47%). SOM analysis
		  distributed the two groups in the two halves of the map:
		  only two out of 28 MP cases (7%) and 12 out of 57 TMJ casts
		  (21%) were misplaced. The cluster-like analysis based on
		  the 20 MPQ item scores correctly recognized 94.73% of TMJ
		  patients and 89.28% of MP patients. In conclusion, the MPQ
		  consistently discriminated between TMJ and MP patients.
		  Although the higher affective scores in the MP patients may
		  be partly induced by higher levels of anxiety in these
		  patients. the data convincingly show that the system's
		  discriminative capacity relates to all MPQ subscores and to
		  the majority of the MPQ items. Moreover, within the same
		  item. the choice of verbal descriptors varies consistently
		  between the two groups of patients. },
  dbinsdate	= {2002/1}
}

@TechReport{	  monnerjahn94a,
  author	= {J{\"u}rgen Monnerjahn},
  title		= {Visuomotorische Robotersteuereung mit
		  selbstorganisierenden Karten},
  institution	= {Zentrum f{\"u}r Kognitionswissenschaften, Universit{\"a}t
		  Bremen},
  year		= 1994,
  type		= {ZKW Bericht},
  number	= {7/94},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  monnerjahn94b,
  author	= {J{\"{u}}rgen Monnerjahn},
  title		= {Speeding-up {S}elf-organizing {M}aps: The Quick Reaction},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {326--329},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  monnerjahn94c,
  author	= {Monnerjahn, J. },
  title		= {Efficient motor learning by \mbox{self-organizing} maps
		  and implicit linear transformations},
  booktitle	= {Proceedings. From Perception to Action Conference},
  year		= {1994},
  editor	= {Gaussier, P. and Nicoud, J. -D. },
  pages		= {416--19},
  organization	= {Zentrum fur Kognitionswissenschaften, Bremen Univ. ,
		  Germany},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@TechReport{	  monnerjahn96a,
  author	= {J{\"u}rgen Monnerjahn},
  title		= {Rectangular Self-Organizing Maps with Flexible Network
		  Size},
  institution	= {Zentrum f{\"u}r Kognitionswissenschaften, Universit{\"a}t
		  Bremen},
  year		= 1996,
  type		= {ZKW Bericht},
  number	= {4/96},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  monostori92a,
  author	= {L. Monostori and A. Bothe},
  title		= {Convergence behaviour of connectionist models in large
		  scale diagnostic problems},
  booktitle	= {Industrial and Engineering Applications of Artificial
		  Intelligence and Expert Systems. 5th International
		  Conference , IEA/AIE-92},
  year		= {1992},
  editor	= {F. Belli and F. J. Radermacher},
  pages		= {113--122},
  organization	= {Univ. Paderborn; Southwest Texas State Univ; et al},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  x		= {Some network models isomorphic to conventional pattern
		  recognition techniques are also enumerated, with special
		  emphasis on the condensed nearest neighbour network (CNNN),
		  which is a new, self-organizing network model with
		  supervised learning ability. . . . Thesaurus: . . .
		  Self-organizing feature maps},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  monte91a,
  author	= {E. Monte and J. B. Marino},
  title		= {A speech recognition system that integrates neural nets
		  and {HMM}},
  booktitle	= {Proc. IWANN'91, Int. Workshop on Artificial Neural
		  Networks},
  year		= {1991},
  editor	= {A. Prieto},
  pages		= {370--376},
  publisher	= {Springer},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  monte92a,
  author	= {E. Monte and J. B. Mari{\~{n}}o and E. L. Leida},
  title		= {Smoothing {Hidden Markov Models} by means of a
		  {Self-Organizing Feature Map}},
  booktitle	= {Proc. ICSLP'92, International Conference on Spoken
		  Language Processing},
  year		= {1992},
  publisher	= {University of Alberta},
  address	= {Edmonton, Alberta, Canada},
  month		= {},
  monthf	= {Lokakuu},
  volume	= {1},
  pages		= {551--554},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  monte94a,
  author	= {Monte, E. and Hernando, J. },
  title		= {A self organizing feature map based on the fisher
		  discriminant},
  booktitle	= {ICSLP 94. 1994 International Conference on Spoken Language
		  Processing},
  year		= {1994},
  volume	= {3},
  pages		= {1535--7},
  organization	= {ETSI Telecomm. , Barcelona, Spain},
  publisher	= {Acoustical Soc. Japan},
  address	= {Tokyo, Japan},
  dbinsdate	= {oldtimer}
}

@InCollection{	  monte96a,
  author	= {E. Monte and J. Hernando and X. Miro and A. Adolf},
  title		= {Text independent speaker identification on noisy
		  environments by means of self organizing maps},
  booktitle	= {Proceedings ICSLP 96. Fourth International Conference on
		  Spoken Language Processing},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  editor	= {H. T. Bunnell and W. Idsardi},
  address	= {New York, NY, USA},
  pages		= {1804--7},
  dbinsdate	= {oldtimer}
}

@InBook{	  moolman95a,
  author	= {D. W. Moolman and C. Aldrict and J. S. J. {van Deventer}},
  title		= {Neural Networks for Chemical Engineers},
  chapter	= {21, The videographic characterization of flotation froths
		  using neural networks},
  publisher	= {Elsevier},
  year		= {1995},
  pages		= {535},
  volume	= {6},
  series	= {Computer-Aided Chemical Engineering},
  address	= {Amsterdam},
  dbinsdate	= {oldtimer}
}

@Article{	  moon95a,
  author	= {Moon, Young B and Janowski, Rick},
  title		= {Neural network approach for smoothing and categorizing
		  noisy data},
  journal	= {Computers in Industry},
  year		= {1995},
  number	= {1},
  volume	= {26},
  pages		= {23--39},
  abstract	= {This paper describes an approach to smoothing and
		  categorizing noisy data, using neural networks. The
		  specific example is that of interpreting product order data
		  as a preliminary step to forecasting future orders.
		  However, the technique is quite general and can be applied
		  to noisy data in a wide variety of systems, including
		  natural, social and artificial ones. In recent years, a
		  major characteristic of computer and electronic products
		  has been their short life cycles. This creates
		  unprecedented challenges, particularly for the task of
		  sales volume forecasting in manufacturing enterprises.
		  Owing to the short history of available past order data,
		  traditional forecasting methods are not adequate. In this
		  paper, a robust back-propagation neural network is adopted
		  to extract an underlying shape for the order pattern of
		  each product. The self-organizing map neural network is
		  used to categorize products according to these shapes. The
		  categorization results will establish a basis for further
		  investigation toward new production introduction
		  analysis.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  moonasar01a,
  author	= {Moonasar, V. and Venayagamoorthy, G. K.},
  title		= {A committee of neural networks for automatic speaker
		  recognition ({ASR}) systems},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2936--2940},
  organization	= {Dept. of Electronics Engineering, M L Sultan Technikon},
  publisher	= {},
  address	= {},
  abstract	= {This paper describes how the results of speaker
		  verification systems can be improved and made robust with
		  the use of a committee of neural networks for pattern
		  recognition rather than the conventional single-network
		  decision system. It illustrates the use of a supervised
		  Learning Vector Quantization (LVQ) neural network as the
		  pattern classifier. Linear Predictive Coding (LPC) and
		  Cepstral signal processing techniques are utilized to form
		  hybrid feature parameter vectors to combat the effect of
		  decreased recognition success with increased group size
		  (number of speakers to be recognized).},
  dbinsdate	= {2002/1}
}

@InProceedings{	  moonasar99a,
  author	= {Moonasar, V. and Venayagamoorthy, G. K.},
  title		= {Speaker identification using a combination of different
		  parameters as feature inputs to an artificial neural
		  network classifier},
  booktitle	= {1999 IEEE Africon. 5th Africon Conference in Africa.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {1},
  pages		= {189--94},
  abstract	= {This paper presents a technique using artificial neural
		  networks (ANNs) for speaker identification that results in
		  a better success rate compared to other techniques. The
		  technique used in this paper uses both power spectral
		  densities (PSDs) and linear prediction coefficients (LPCs)
		  as feature inputs to a self organizing feature map to
		  achieve a better identification performance. Results for
		  speaker identification with different methods are presented
		  and compared.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morabito91a,
  author	= {M. Morabito and A. Macerata and A. Taddei and C.
		  Marchesi},
  title		= {{QRS} morphological classification using artificial neural
		  networks},
  booktitle	= {Proc. Computers in Cardiology},
  year		= {1991},
  pages		= {181--184},
  organization	= {Nat. Res. Council of Italy; Eur. Soc. Cardiology; Nat.
		  Inst. Health},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  moran00a,
  author	= {Moran, A. W. and O'Reilly P and Irwin, G. W.},
  title		= {Probability estimation algorithms for self-validating
		  sensors},
  booktitle	= {Algorithms and Architectures for Real-Time Control 2000,
		  (AARTC'2000). Proceedings volume from the 6th IFAC
		  Workshop. Elsevier Science, Kidlington, UK},
  year		= {2000},
  volume	= {},
  pages		= {},
  abstract	= {Three alternative approaches are investigated for
		  probability estimation for use in a self-validating sensor.
		  The three methods are: stochastic approximation, a reduced
		  bias estimate of this same approach by Naim and Kam (1994),
		  and a method based on the Bayesian self-organising map
		  (BSOM). Simulation studies show that the BSOM based method
		  gives superior results when compared to the NK algorithm.
		  It is also demonstrated that the BSOM method is more
		  computationally efficient and requires storage space for
		  fewer variables.},
  dbinsdate	= {2002/1}
}

@Article{	  moran01a,
  author	= {Moran, A. W. and O'Reilly, P. G. and Irwin, G. W.},
  title		= {Probability estimation algorithms for self-validating
		  sensors},
  journal	= {Control Engineering Practice},
  year		= {2001},
  volume	= {9},
  number	= {4},
  month		= {April},
  pages		= {425--438},
  organization	= {Intelligent Systems/Control Group, Sch. of
		  Electrical/Electronic Eng., Queens University of Belfast},
  publisher	= {},
  address	= {},
  abstract	= {Three alternative approaches are investigated for
		  probability estimation for use in a self-validating sensor.
		  The three methods arc Stochastic Approximation (SA), a
		  Reduced Bias Estimate (RBE) of this same approach and a
		  method based on the Bayesian Self-Organising Map using
		  Gaussian Kernels (GK). Simulation studies show that the
		  GK-based method gives superior results when compared to the
		  RBE algorithm. It has also been demonstrated that the GK
		  method is more computationally efficient and requires
		  storage space for fewer variables. The techniques are
		  demonstrated using data from a thermocouple sensor
		  experiencing a change in time constant. },
  dbinsdate	= {2002/1}
}

@Article{	  morasso89a,
  author	= {Morasso, P. and {Mussa Ivaldi}, F. A. and Vercelli, G. and
		  Zaccaria, R.},
  title		= {A connectionist formulation of motor planning},
  journal	= {Connectionism in Perspective},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  year		= {1989},
  volume	= {},
  pages		= {413--20},
  abstract	= {Redundancy and concurrency in motor control are basic
		  problems for multi-joint, multi-limb humanoids. The
		  viscous-elastic properties of muscular actuators provide an
		  organizing factor because they define equilibrium postures
		  that influence body movements as 'postural attractors'.
		  From this it is possible to derive a planning approach
		  which is based on the internal simulation of passive
		  movement. The authors show how this approach can be
		  formulated in connectionist terms as a multiple constraints
		  satisfaction network, driven by a potential function,
		  similarly to a Hopfield network. The authors call this
		  model 'motor relaxation network'.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso89b,
  author	= {Morasso, P.},
  title		= {Neural models of cursive script handwriting},
  booktitle	= {IJCNN: International Joint Conference on Neural Networks.
		  IEEE TAB Neural Network Committee, New York, NY, USA},
  year		= {1989},
  volume	= {2},
  pages		= {539--42},
  abstract	= {The main characteristics of cursive script handwriting are
		  reviewed, and a basic coding scheme is described. Two types
		  of neural networks are described: (i) a self-organizing
		  network, similar to that used by T. Kohonen for his
		  phonetic typewriter (see IEEE Comput., p.11--22, 1988),
		  which is able to discover Graphotopic Maps; (ii) a
		  three-layer perceptron called NetWrite in analogy with the
		  NeTalk architecture developed by C.R. Rosemberg and T.
		  Sejnowksi (see Complex Syst., vol.1, p.145--68, 1987), that
		  can recognize digraphs. Pros and cons are discussed, as
		  well as an integration proposal.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso90a,
  author	= {Morasso, P. and Vercelli, G. and Zaccaria, R.},
  title		= {Neuro-computing aspects in motor planning and control},
  booktitle	= {Neurocomputing, Algorithms, Architectures and
		  Applications. Proceedings of the NATO Advanced Research
		  Workshop},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1990},
  volume	= {},
  pages		= {},
  abstract	= {Planning robot movements in an unstructured environment is
		  investigated from the point of view of common sense
		  reasoning as the simulation of a complex dynamical system.
		  The authors first show how motor redundancy can be solved
		  by simulating passive motion. They then extend this concept
		  in terms of abstract force fields, that allow to express a
		  variety of problems in addition to trajectory formation
		  (force/position control, obstacle avoidance, concurrent
		  tasks, etc.). They show the analogy between this approach
		  and Hopfield nets. They define the notion of hybrid motor
		  schema as the composite ensemble of an analogic component
		  (which consists of the 'mental' simulation of model
		  dynamics) and a symbolic component (which contains rules
		  and methods for instantiating the analogic component in a
		  given context: goal: environment, expectations,
		  constraints, . . .). Simulation results of preliminary
		  implementations are presented.},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso90b,
  author	= {Morasso, P.},
  title		= {Neural representation of motor synergies},
  journal	= {Advanced Neural Computers},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  year		= {1990},
  volume	= {},
  pages		= {51--9},
  abstract	= {The generation of synergies of motor commands and of the
		  predicted patterns of their motor consequences are two
		  complementary aspects at the core of motor control. The
		  connectionist framework that is proposed is based on the
		  concept that the elastic properties of the human
		  musculature not only represent a significant low-level
		  feature of the motor system, but can also provide an
		  organising principle for the global computational
		  architecture. A principle has been formulated (Mussa
		  Ivaldi, Morasso and Zaccaria, 1988) according to which
		  synergies of motor commands can be generated via the
		  internal simulation of passive movements, i.e. movements
		  driven by external disturbances and constrained by the
		  optimal distribution of elastic energy to the different
		  muscles. A parallel and distributed computational
		  architecture was preliminarily proposed (Morasso et al,
		  1989) which operates via relaxation in a similar way to a
		  Hopfield net. The model is called M-Net (Motor relaxation
		  Network) and is driven by a computational energy which
		  mirrors the elastic potential energy of the muscles. The
		  model has a somatotopic structure and is constructed as a
		  network of units which correspond to the different
		  constituent parts of the musculo-skeletal system: S-units
		  (skeletal segments), M-units (muscles either single-joint
		  or multiple-joint), and L-units (ligaments of the joints).
		  The units exchange force and displacement signals and the
		  dynamics of the network is automatically seeking an
		  equilibrium configuration.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso90c,
  author	= {Morasso, P. and Kennedy, J. and Antonj, E. and {Di Marco},
		  S. and Dordoni, M.},
  title		= {Self-organisation of an allograph lexicon},
  booktitle	= {INNC 90 Paris. International Neural Network Conference},
  year		= {1990},
  volume	= {1},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  pages		= {141--4},
  abstract	= {In the framework of a project which aims at the
		  recognition of dynamically acquired cursive handwriting by
		  means of concepts of neural computation, this paper
		  presents results as regards the automatic construction of
		  an allograph lexicon through self-organisation. Allographs
		  are the graphic equivalent of allophons in speech, i.e.
		  they are alternative graphical structures that correspond
		  to the same symbol; the identification of a dictionary of
		  allographs, specific to a given individual, is a necessary
		  step toward the recognition of cursive script. The method
		  proposed for the automatic construction of an allograph
		  lexicon uses as array of T. Kohonen's self-organising
		  networks (1984). Each network stores allographs of a
		  specific number of strokes, the number ranging from 2 to 7.
		  The networks are trained with a test-text, about a page
		  long, which is pre-processed and segmented with a heuristic
		  procedure. After training, the different networks contain
		  clusters of allographs with a similar topological
		  structure.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso91a,
  author	= {P. Morasso and S. Pagliano},
  title		= {A neural architecture for the recognition of cursive
		  handwriting},
  booktitle	= {Fourth Italian Workshop. Parallel Architectures and Neural
		  Networks},
  year		= {1991},
  editor	= {E. R. Caianiello},
  pages		= {250--254},
  organization	= {Univ. Salerno; Inst. Italiano di Studi Filosofici},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morasso91b,
  author	= {Morasso, P. and Sanguineti, V.},
  title		= {Neurocomputing concepts in motor control},
  booktitle	= {Brain and Space},
  publisher	= {Oxford University Press},
  year		= {1991},
  editor	= {J. Paillard},
  address	= {Oxford},
  pages		= {404--432},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso91c,
  author	= {Pietro Morasso},
  title		= {Self-Organizing Feature Maps for Cursive Script
		  Recognition},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1323--1326},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  abstract	= {In the framework of a project which aims at the
		  recognition of dynamically acquired cursive handwriting by
		  means of concepts of neural computation, this paper
		  presents preliminary recognition results obtained from the
		  application of an allograph lexicon constructed by means of
		  an array of self-organising feature maps.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso91d,
  author	= {Morasso, P. and Solari, M.},
  title		= {A neural implementation of analogic planning methods},
  booktitle	= {Artificial Neural Networks. Proceedings of the 1991
		  International Conference. ICANN-91},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  year		= {1991},
  volume	= {2},
  pages		= {1281--4},
  abstract	= {A fruitful formulation of many different aspects of robot
		  planning uses a analogic approach based on the simulation
		  of vector fields instead of the classical methods of
		  symbolic AI. The authors used force fields, among other
		  things, for the inverse kinematics of redundant robots, for
		  the avoidance of obstacles and joint limits, and for the
		  representation of flexible objects. A natural formulation
		  of these concepts in neural terms is obtained by means of
		  generalised Hopfield networks whose units correspond to
		  physical entities such as parts of the manipulator or of
		  the environments, and the connections among them propagate
		  interaction forces and mutual displacements. An
		  implementation with the Rochester connectionist simulator
		  is shown.},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso92a,
  author	= {Morasso, P. and V. Sanguineti},
  title		= {Neurocomputing aspects in modelling cursive handwriting},
  journal	= {Acta Psychologica},
  year		= {1992},
  volume	= {82},
  pages		= {213--235},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso92b,
  author	= {Morasso, P. and V. Sanguineti},
  title		= {Equilibrium-point and self-organization},
  journal	= {Behavioral and Brain Sciences},
  year		= {1992},
  volume	= {15},
  pages		= {781--782},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morasso92c,
  author	= {Morasso, P. and V. Sanguineti},
  title		= {Neural models of distributed motor control},
  booktitle	= {Tutorials in Motor Behavior II},
  publisher	= {Elsevier Science Publishers B.V.},
  year		= {1992},
  editor	= {G. E. Stelmach and J. Requin},
  address	= {Amsterdam},
  pages		= {3--30},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morasso92d,
  author	= {Morasso, P. and V. Sanguineti},
  title		= {{SOBoS}---A self-organized body-schema.},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {1},
  publisher	= {North-Holland},
  address	= {Amsterdam},
  pages		= {487--490},
  abstract	= {A neural framework for the generation of motor synergies
		  is described, based on self-organization principles and on
		  the concept of internal model; the emergent population
		  coding is exploited during activation in order to
		  incorporate task constraints, by a relaxation dynamics.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morasso92e,
  author	= {Morasso, P. and Pareto, A. and V. Sanguineti},
  title		= {{SOC}---A \mbox{self-organizing} classifier},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {2},
  publisher	= {North-Holland},
  address	= {Amsterdam},
  pages		= {1223--1226},
  abstract	= {SOC is a self-organizing classifier which is characterized
		  by an original combination of several features (dynamic
		  size, dynamic connectivity, and boundary focusing) that are
		  ideal for applications that require incremental learning.},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso92f,
  author	= {Morasso, P.},
  title		= {Neural mechanisms of synergy formation},
  journal	= {Human Movement Science},
  year		= {1992},
  volume	= {11},
  pages		= {169--80},
  abstract	= {Discusses a neural modelling approach to synergy formation
		  which is based on the concept that the elastic properties
		  of the human musculature not only represent a significant
		  low-level feature of the motor system, but can also provide
		  an organizing principle for the global computational
		  architecture. The approach is based, on the one hand, on
		  'equilibrium-point models' and, on the other, on the
		  dynamics of 'relaxation networks'.},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso92g,
  author	= {Morasso, P. and D'Alessio, P. and V. Sanguineti},
  title		= {Distributed models of motor control},
  journal	= {Functional Neurology},
  year		= {1992},
  volume	= {4},
  pages		= {23--30},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morasso92h,
  author	= {Morasso, P. and Pagliano, S. and A. Pareto},
  title		= {Neural models for handwriting recognition},
  booktitle	= {From Pixels to Features III},
  publisher	= {Elsevier},
  year		= {1992},
  editor	= {S. Impedovo and J. C. Simon},
  address	= {Amsterdam},
  pages		= {423--440},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso93a,
  author	= {Morasso, P. and Barberis and L. Pagliano, S. and D.
		  Vergano},
  title		= {Recognition Experiments of Cursive Dynamic Handwriting
		  with Self-Organizing Networks},
  journal	= {Pattern Recognition},
  year		= {1993},
  month		= {March},
  volume	= {26},
  number	= {3},
  pages		= {451--460},
  abstract	= {Assesses the feasibility of using self-organizing methods
		  for the development of a recognition system of cursive,
		  dynamic handwriting for interactive applications with a
		  large dictionary. A prototype system based on segmentation
		  into motor-based strokes and concurrent
		  segmentation/classification of allographs by means of a set
		  of allographic maps is developed. In the initial
		  implementation, based on Kohonen's self-organized maps, a
		  70% user-specific word recognition rate with a 4 k-words
		  dictionary is approached. From this, indications are
		  derived for a modified neural recognizer (self-organized
		  classifier) that is still based on self-organization but is
		  more flexible and can support incremental learning. The new
		  model, together with improved pre-processing methods, could
		  overcome the 80% mark in a pilot study with three
		  subjects.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso93b,
  author	= {Pietro Morasso and Alberto Pareto and Vittorio
		  Sanguineti},
  title		= {Incremental Category Formation},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {III},
  pages		= {372--375},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso93c,
  author	= {Pietro Morasso and Alberto Pareto and Stefano Pagliano and
		  Vittorio Sanguineti},
  title		= {Self-Organizing Neural Network for Diagnosis},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {806--809},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso93d,
  author	= {P. Morasso and G. Vercelli and R. Zaccaria},
  title		= {A Hybrid Architecture for Robot Navigation},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1875--1878},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso93e,
  author	= {P. Morasso and L. Gismondi and E. Musante and A. Pareto},
  title		= {A Hybrid Neural Architecture for On-Line Recognition of
		  Cursive Handwriting},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {III},
  pages		= {71--74},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso93f,
  author	= {Pietro Morasso and Vittorio Sanguineti},
  title		= {Coordinating Multiple Joints},
  volume	= {II},
  pages		= {71--78},
  booktitle	= {Proc. Conf. on Prerational Intelligence---Phenomenology of
		  Complexity Emerging in Systems of Agents Interagtion Using
		  Simple Rules},
  year		= {1993},
  address	= {Center for Interdisciplinary Research, University of
		  Bielefeld},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso94a,
  author	= {Morasso, P. and Sanguineti, V. },
  title		= {Models of self-organized topographic maps},
  booktitle	= {Neural Networks in Biomedicine. Proceedings of the
		  Advanced School of the Italian Biomedical Physics
		  Association},
  year		= {1994},
  editor	= {Masulli, F. and Morasso, P. G. and Schenone, A. },
  pages		= {89--112},
  organization	= {DIST, Genova Univ. , Italy},
  publisher	= {World Scientific},
  address	= {Singapore},
  abstract	= {Self organized topographic maps (SOM) are a class of
		  neural models, typically trained in an unsupervised way,
		  which represent a tabular approach to the solution of a
		  wide variety of problems: data compression, dimensionality
		  reduction, classification, regression, clustering, etc. The
		  basic idea is the optimal allocation of a database of
		  prototype or reference vectors in the problem space via a
		  (parallel and distributed) mechanism of competition
		  cooperation.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso94b,
  author	= {Morasso, P. and Vercelli, G. and Zaccaria, R. },
  title		= {Self-organizing navigation: a hybrid framework for robot
		  motion planning},
  booktitle	= {Neural Nets Wirn Vietri 93---Proceedings of the 5th
		  Italian Workshop on Neural Nets},
  year		= {1994},
  editor	= {Caianiello, E. R. },
  organization	= {DIST, Genoa Univ. , Italy},
  publisher	= {World Scientific},
  address	= {Singapore},
  abstract	= {In the context of the approach to intelligent autonomous
		  systems based on the subsumption architectural concept, we
		  describe a hybrid model of the navigation skill, to be
		  considered as one of the many skills or behaviours that
		  allow an autonomous agent to survive in an unknown/hostile
		  environment. The hybrid navigation behaviour consists of
		  three main functions that operate in parallel on the same
		  set of input/output data: WRA (Wild Rover Algorithm); SON
		  (Self-Organized Navigator); SEA (Symbolic Environment
		  Analysis). The term "hybrid" here refers to the cooperation
		  between a logic-based representation formalism and a neural
		  model. Starting from rough sensorial data given by WRA, the
		  knowledge about the explored environment of a mobile robot
		  can be incrementally organized by means of self-organizing
		  maps (SON) and the set of heuristic rules (SEA).},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso94c,
  author	= {P. Morasso and V. Sanguineti},
  title		= {Cortical Representation of External Space},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1247--1252},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, internal representation},
  abstract	= {The topographic organization of the cerebral cortex,
		  particularly in primary sensory areas, is very well known
		  and self-organized maps (SOM) (Kohonen, 1982) are a good
		  paradigm to model its genesis, as a process of learning
		  which integrates the sensory signals coming from the
		  periphery. On the other hand, experiments on trajectory
		  formation (Morasso, 1981) suggest that motor plans might
		  require an internal representation of external space
		  somehow compatible with Euclidean geometry and the paper
		  addresses the question of how such representation might
		  possibly arise, in spite of the fact that Euclidean space
		  is an abstraction because it is not supported by any
		  Euclidean, sensory modality. It is shown that an extension
		  of the classic SOM model allows to capture a biologically
		  plausible solution and it is suggested that the trained
		  computational lattice of neurons can be considered as a
		  cellular automaton (CA), yielding a combined SOM.CA model,
		  capable of learning and complex dynamics. Thus, the
		  cortical representation of external space is modeled as a
		  SOM.CA network and dynamic interaction rules are shown for
		  generating simple trajectories with shape and timing
		  constraints.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morasso94d,
  author	= {Morasso, P. and Sanguineti, V.},
  title		= {Self-organizing topographic maps and motor learning},
  booktitle	= {From Animals to Animats 3},
  publisher	= {MIT Press},
  year		= {1994},
  editor	= {D. Cliff and P. Husbands and J.~A. Meyer and S.~W.
		  Wilson},
  pages		= {214--220},
  abstract	= {The topic of interest is the coordination of multiple
		  joints and, in particular, the motor and task planning
		  processes which are capable to transform a concise motor
		  intention into a detailed activation flow. We propose to
		  look at this topic as a pattern completion problem, driven
		  by a selective attention paradigm. We investigate
		  self-organizing topographic maps for addressing both
		  elements and we focus our attention on perfectly-topology
		  preserving maps, based on competitive learning at the level
		  of both the input weight vectors and the lateral
		  connectivity. We show how an internal representation of
		  Euclidean space can emerge from this process and we discuss
		  a general way of linking self-organizing maps with cellular
		  automata, demonstrating its usefulness for carrying out
		  pattern completion tasks.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morasso95a,
  author	= {Morasso, P. and Sanguineti, V. and Spada, G.},
  title		= {Neocortical dynamics in sensorimotor control},
  booktitle	= {Brain Processes, Theories and Models},
  publisher	= {MIT Press},
  year		= {1995},
  editor	= {J. Mira},
  address	= {Cambridge, Mass, USA},
  pages		= {503--512},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso95b,
  author	= {Morasso, P. and Sanguineti, V. and and G. Spada},
  title		= {Neuromorphic planning and control with a mixture of cost
		  functions},
  booktitle	= {Proceedings of the Third European Congress on Intelligent
		  Techniques and Soft Computing (EUFIT'95 Aachen, Germany,
		  August 28--31)},
  volume	= {3},
  year		= {1995},
  pages		= {1586--1590},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso96a,
  author	= {Morasso, P. and V. Sanguineti},
  title		= {How the brain can discover the existence of external
		  egocentric space.},
  journal	= {Neurocomputing},
  year		= {1996},
  volume	= {12},
  pages		= {289--310},
  abstract	= {The neurobiological problem is addressed of how the brain
		  might build an internal representation of external
		  egocentric space by fusing the information from different
		  sensory modalities. The proposed model is based on
		  self-organizing topology representing networks, activated
		  by multi-modal sensory vectors. The learnt representation
		  is invariant to coordinate transformations and can support
		  an active planning function by exploiting the lateral
		  connectivity of the network.},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso96b,
  author	= {Morasso, P. and V. Sanguineti},
  title		= {Self-organizing body-schema for motor planning.},
  journal	= {Journal Motor Behavior},
  year		= {1996},
  volume	= {26},
  pages		= {131--148},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morasso97a,
  author	= {Morasso, P.},
  title		= {Active perception and representation of space.},
  booktitle	= {Artificial and natural perception},
  publisher	= {World Scientific},
  year		= {1997},
  editor	= {C. {Di Natale}, A. D'Amico and F. Davide},
  address	= {Singapore},
  pages		= {22--28},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso97b,
  author	= {Morasso, P. and Sanguineti, V. and Spada, G.},
  title		= {A computational theory of targeting movements based on
		  force fields and topology representing networks},
  journal	= {Neurocomputing},
  year		= {1997},
  volume	= {15},
  pages		= {414--434},
  abstract	= {A computational theory of the targeting problem in
		  reaching movements is presented, which is based on
		  interacting force fields, in the proximal and distance
		  spaces, respectively. A distributed implementation of the
		  theory is then analyzed, which uses a pair of interacting
		  topology-representing networks. In particular, the intra
		  and inter-connections of the two networks support a
		  relaxation process, with terminal attractor dynamics, which
		  implements the flow-line tracking in the force fields.},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso97c,
  author	= {Morasso, P. and V. Sanguineti},
  title		= {Learning tidal waves vs learning sensorimotor mappings},
  journal	= {Behavioral and Brain Sciences},
  year		= {1997},
  volume	= {20},
  pages		= {260--261},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morasso97d,
  author	= {Morasso, P. and Sanguineti, V. and F. Frisone},
  title		= {A principled approach to a theory of self-organization in
		  cortical maps based on EM-learning},
  booktitle	= {Progress in Connectionist-based Information Systems},
  publisher	= {Springer Verlag},
  year		= {1997},
  editor	= {N. Kasabov},
  volume	= {1},
  pages		= {166--169},
  abstract	= {Starting from the problem of density estimation, it is
		  shown that expectation-maximization (EM) learning can be
		  considered as a Hebbian mechanism. From this it is possible
		  to outline a theory of self-organization of cortical maps
		  which is based on a well defined optimization process and
		  still preserves biologically desirable characteristics:
		  local computation and uniform treatment of input and
		  lateral connections.},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso97e,
  author	= {Morasso, P. and Sanguineti, V. and F. Frisone},
  title		= {Topologic organization of context fields for sensorimotor
		  coordination},
  journal	= {Behavioral and Brain Sciences},
  year		= {1997},
  volume	= {21},
  number	= {412--413},
  dbinsdate	= {oldtimer}
}

@Book{		  morasso97f,
  author	= {},
  title		= {Self-organization, Cortical Maps and Motor Control.},
  publisher	= {Elsevier Science Publ.},
  year		= {1997},
  editor	= {Morasso, P. and Sanguineti, V.},
  address	= {Amsterdam},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso98a,
  author	= {Morasso, P. and Sanguineti, V. and Frisone, F. and Perico,
		  L.},
  title		= {Coordinate-free sensorimotor processing: computing with
		  population codes},
  journal	= {Neural Networks},
  year		= {1998},
  volume	= {11},
  pages		= {1417--1428},
  abstract	= {This paper outlines a computational architecture for the
		  intelligent processing of sensorimotor patterns. The focus
		  is on the nature of the internal representations of the
		  outside world which are necessary for planning and other
		  goal-oriented functions. A model of cortical map dynamics
		  and self-organization is proposed that integrates a number
		  of concepts and methods partly explored in the field. The
		  novelty and the biological plausibility is related to the
		  global architecture which allows one to deal with
		  sensorimotor patterns in a coordinate-free way, using
		  population codes as distributed internal representations of
		  external variables and the coupled dynamics of cortical
		  maps as a general tool of trajectory formation. The basic
		  computational features of the model are demonstrated in the
		  case of articulatory speech synthesis and some of the
		  metric properties are evaluated by means of simple
		  simulation studies.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso98b,
  author	= {Morasso, P. and Frisone, F. and Perico, L.},
  title		= {Self-organization and cortical dynamics.},
  booktitle	= {Perspective in Neural Computing (ICANN'98)},
  editor	= {L. Niklasson, M. Boden and T. Ziemke},
  volume	= {I},
  year		= {1998},
  publisher	= {Springer},
  address	= {Heidelberg},
  pages		= {373--377},
  abstract	= {Expectation maximization (EM) learning can be considered
		  as a Hebbian mechanism. From this, it is possible to
		  outline a theory of self-organisation of cortical maps
		  which is based on a well-defined optimisation process and
		  which still preserves biologically desirable
		  characteristics: local computation and uniform treatment of
		  input and lateral connections. In this paper, we explore
		  the issue of implementing such a learning paradigm in a
		  cortical map, taking into account the cortical dynamics,
		  modelled according to an extension of the TRN
		  (topology-representing network) model.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso98c,
  author	= {Morasso, P. and Frisone, F. and Bruni, S.},
  title		= {Fast learning of dynamic compensation in motor control. },
  booktitle	= {Perspective in Neural Computing (ICANN'98)},
  editor	= {L. Niklasson, M. Boden and T. Ziemke},
  volume	= {II},
  year		= {1998},
  publisher	= {Springer},
  address	= {Heidelberg},
  pages		= {985--990},
  abstract	= {In the framework of the theory of equilibrium-point
		  control, a model for learning the compensation of dynamic
		  loads is presented. It is self-supervised but non-Hebbian
		  and can compensate unexpected load variations in 1--2
		  repeated trials. A preliminary study is presented as
		  regards the generalisation across tasks and the role of the
		  cerebellar circuitry is discussed as a dynamic co-processor
		  capable of implementing part of the required
		  computations.},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso99a,
  author	= {Morasso, P. G. and Sanguineti, V.},
  title		= {Cerebellar versus stiffness control},
  journal	= {IEE Colloquium on Self-Learning Robots III Brianstyle
		  Robotics: The Cerebellum Beyond Function Approximation},
  year		= {1999},
  volume	= {28},
  pages		= {1--3},
  abstract	= {The mechanical properties of the muscular actuators are
		  important for dynamics (compensation of internal/external
		  loads and disturbances) as well as for spatial cognition.
		  Consider the so called lambda -model (Feldman and Levin,
		  (1995)). The key point, from the point of view of space
		  representation, is that that the controlled variable has
		  the same dimensions of the control variable. The
		  proprioceptive space can be defined as a lower-dimensional
		  manifold in the set of all possible configurations and can
		  be represented in a distributed way in terms of a
		  self-organising cortical map. Its dimensionality and
		  geometry is coded into the pattern of lateral connections
		  that are excitatory and recurrent. These connections can be
		  adapted by means of unsupervised Hebbian learning within a
		  (probably innate) behavioural strategy of circular
		  reaction. The same strategy and unsupervised learning
		  paradigm allow the emergence of an internal distributed
		  representation of the exteroceptive space and the nonlinear
		  mapping between the two spaces, exploiting a side-effect of
		  circular reaction.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morasso99b,
  author	= {P. G. Morasso and V. Sanguineti and F. Frisone},
  title		= {Advances in modeling cortical maps},
  booktitle	= {Kohonen Maps},
  pages		= {267--278},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: Neurobiological models, Cortical maps,
		  Population code},
  dbinsdate	= {oldtimer}
}

@Article{	  morasso99c,
  author	= {Morasso, P. and Baratto, L. and Capra, R. and Spada, G. },
  title		= {Internal models in the control of posture.},
  journal	= {Neural Networks},
  year		= {1999},
  volume	= {12},
  pages		= {1173--1180},
  abstract	= {The aim of the paper is to investigate the application of
		  control schemes based on "internal models" to the
		  stabilization of the standing posture. The computational
		  complexities of the control problems are analyzed, showing
		  that muscle stiffness alone is insufficient to carry out
		  the task. The paper also re-visits the concept of the
		  cerebellum as a Smith's predictor.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morasso99d,
  author	= {Morasso, P. and Baratto, L. and Capra, R. and Re, C. and
		  Spada, G.},
  title		= {Use of neural networks for the evaluation of
		  classification power of different posturographic
		  parameters.},
  booktitle	= {Proceed. EMBEC'99, Vienna},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@Article{	  morawski94a,
  author	= {Morawski, M. },
  title		= {A new method of recognition of distorted objects on binary
		  images},
  journal	= {Prace Instytutu Elektrotechniki},
  year		= {1994},
  volume	= {42},
  number	= {179},
  pages		= {59--71},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mori91a,
  author	= {H. Mori and Y. Tamaru and S. Tsuzuki},
  title		= {An artificial neural-net based technique for power system
		  dynamic stability with the {K}ohonen model},
  booktitle	= {Conf. Papers. 1991 Power Industry Computer Application
		  Conf. Seventeenth PICA Conf. },
  year		= {1991},
  pages		= {293--301},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  mori92a,
  author	= {H. Mori and Y. Tamaru and S. Tsuzuki},
  title		= {An artificial neural-net based technique for power system
		  dynamic stability with the {K}ohonen model},
  journal	= {IEEE Trans. Power Systems},
  year		= {1992},
  volume	= {7},
  number	= {2},
  pages		= {856--864},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mori92b,
  author	= {Mori, H. and Tamaru, Y. },
  title		= {Hybrid artificial neural networks for voltage instability
		  monitoring in electric power systems},
  booktitle	= {Proceedings of the 1992 IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {1992},
  volume	= {1},
  pages		= {151--6},
  organization	= {Dept. of Electr. Eng. , Meiji Univ. , Kawasaki, Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mori92c,
  author	= {Mori, H. and Miyamoto, H. and Tsuzuki, S. },
  title		= {Estimation of a voltage stability index with a {K}ohonen
		  neural network},
  booktitle	= {ICARCV '92. Second International Conference on Automation,
		  Robotics and Computer Vision},
  year		= {1992},
  volume	= {3},
  pages		= {INV-11. 5/1--5},
  organization	= {Dept. of Electr. Eng. , Meiji Univ. , Kawasaki, Japan},
  publisher	= {Nanyang Technol. Univ},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@Article{	  morita00a,
  author	= {Morita, Yoshinori and Obu-Cann, K. and Tokutaka, Heizo and
		  Fujimura, Kikuo and Yoshihara, Kazuhiro},
  title		= {Data mining of chemical analysis},
  journal	= {Shinku/Journal of the Vacuum Society of Japan},
  year		= {2000},
  volume	= {43},
  number	= {3},
  month		= {},
  pages		= {263--267},
  organization	= {Univ of Tottori},
  publisher	= {Nihon Shinku Kyokai},
  address	= {Tokyo},
  abstract	= {The Self-Organizing Map (SOM) developed by Teuvo Kohonen
		  is a powerful tool for Data Mining or knowledge discovery
		  and visualization of high dimensional data. The SOM is
		  being applied to problems of chemical analysis. It
		  simultaneously performs topology preservation of the data
		  space whiles quantizing the data space formed by the input
		  data. The compositions of the unlabeled spectra whose
		  compositions are unknown can be determined using the SOM
		  method which uses the labeled spectra whose compositions
		  are known. In this study, the data mining capabilities of
		  SOM are examined using data from Auger Electron
		  Spectroscopy (AES) and X-ray Photoelectric Spectroscopy
		  (XPS). The results obtained are compared to determine which
		  data is more adaptive to the SOM.},
  dbinsdate	= {2002/1}
}

@Article{	  morita01a,
  author	= {Morita, Y. and Tokutaka, H. and Fujimura, K. and
		  Yoshihara, K.},
  title		= {Chemical spectra analysis system using internet},
  journal	= {Shinku/Journal of the Vacuum Society of Japan},
  year		= {2001},
  volume	= {44},
  number	= {3},
  month		= {},
  pages		= {248--251},
  organization	= {Dept. of Elec. and Electron. Eng., University of Tottori},
  publisher	= {},
  address	= {},
  abstract	= {The Self-Organizing Map (SOM) developed by Teuvo Kohonen
		  is a powerful tool for Data Mining, knowledge discovery and
		  visualization of high dimensional data. The SOM is being
		  applied to problems of chemical analysis. The SOM
		  simultaneously performs topology preservation of the data
		  space whiles quantizing the data space formed by the input
		  data. In this paper, we will discuss Auger Electron
		  Spectroscopy (AES) Spectra Analysis System using SOM via
		  Internet (Web Chemical SOM system). We will also discuss
		  the X-ray Photoelectric Spectroscopy (XPS) chemical element
		  map and its verification.},
  dbinsdate	= {2002/1}
}

@InCollection{	  morlini98a,
  author	= {I. Morlini},
  title		= {{K}ohonen networks and the influence of training on data
		  structures},
  booktitle	= {Neural Networks for Signal Processing VIII. Proceedings of
		  the 1998 IEEE Signal Processing Society Workshop},
  publisher	= {IEEE},
  year		= {1998},
  editor	= {M. Niranjan and E. Wilson and T. Constantinides and S.- Y.
		  Kung},
  address	= {New York, NY, USA},
  pages		= {370--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morris01a,
  author	= {Morris, S. A. and Wu, Z. and Yen, G.},
  title		= {A {SOM} mapping technique for visualizing documents in a
		  database},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {1914--1919},
  organization	= {Sch. of Elec. and Comp. Engineering, Oklahoma State
		  University},
  publisher	= {},
  address	= {},
  abstract	= {A method is introduced for mapping documents, based on
		  document citations, on a two dimensional map for clustering
		  and visualization for the application of technology
		  forecasting. The citation data is used to build an
		  adjacency matrix which describes the document set as an
		  undirected graph. The dimensionality of the adjacency
		  matrix is reduced using principal components analysis. The
		  reduced dimension data is used to train a small rectangular
		  self organizing map (SOM). After training, each document's
		  input vector is premultiplied by the SOM weight matrix to
		  find a spatial response across the SOM and the centroid of
		  this response is used to map the document. The ordination
		  method is demonstrated on a synthetic data set with good
		  results. Further encouraging results using an actual 118
		  polymer document dataset are also shown.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  morris89a,
  author	= {R. J. T. Morris and L. D. Rubin and H. Tirri},
  title		= {A comparison of feedforward and \mbox{self-organizing}
		  approaches to the font orientation problems},
  booktitle	= {Proc. IJCNN'89 International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  pages		= {291--197},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  morris90a,
  author	= {Morris, R. J. T. and Rubin, L. D. and Tirri, H.},
  title		= {Neural network techniques for object orientation
		  detection: Solution by optimal feedforward network and
		  learning vector quantization approaches.},
  journal	= {IEEE Transactions on Pattern Analysis and Machine
		  Intelligence},
  year		= {1990},
  number	= {11},
  volume	= {12},
  pages		= {1107--1114},
  month		= {November},
  abstract	= {We examine a problem in computer vision, the determination
		  of object orientation from the consensus of orientations of
		  individual symbols or marks. The typical multilayer
		  threshold networks are argued to be unsuitable, and the
		  optimal Bayesian detector is derived and found to have the
		  highly parallel structure of a feedforward network. The
		  learning vector quantization (LVQ) neural network method of
		  T. Kohonen is also applied. Experimental results,
		  comparisons, and a complete implementation are described.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  morris94a,
  author	= {C. W. Morris and L. Boddy and M. F. Wilkins},
  title		= {Approaches to applying neural networks to the
		  identification of phytoplankton taxa from flow cytometry
		  data},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {619--27},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  morton91a,
  author	= {P. E. Morton and D. M. Tumey and D. F. Ingle and C. W.
		  Downey and J. H. Schnurer},
  title		= {Neural network classification of {EEG} data generated
		  through use of the audio oddball paradigm},
  booktitle	= {Proc. IEEE Seventeenth Annual Northeast Bioengineering
		  Conf. },
  year		= {1991},
  editor	= {M. D. Fox and M. A. F. Epstein and R. B. Davis and T. M.
		  Alward},
  pages		= {7--8},
  organization	= {IEEE; Univ. Connecticut; Trinity College/Hartford Graduate
		  Center; Whitaker Found},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Book{		  morton96a,
  author	= {Morton, P.~R. and Fix, E.~L. and Calhoun, G.~L.},
  title		= {Hand Gesture Recognition Using Neural Networks. Final
		  rept. Nov 93-Sep 95.},
  year		= {1996},
  abstract	= {Gestural interfaces have the potential of enhancing
		  control operations in numerous applications. For Air Force
		  systems, machine-recognition of whole-hand gestures may be
		  useful as an alternative controller, especially when
		  conventional controls are less accessible. The objective of
		  this effort was to explore the utility of a neural
		  network-based approach to the recognition of whole-hand
		  gestures. Using a fiber-optic instrumented glove, gesture
		  data were collected for a set of static gestures drawn from
		  the manual alphabet used by the deaf. Two types of neural
		  networks (multilayer perceptron and Kohonen self-organizing
		  feature map) were explored. Both showed promise, but the
		  perceptron model was quicker to implement and
		  classification is inherent in the model. The high gesture
		  recognition rates and quick network retraining times found
		  in the present study suggest that a neural network approach
		  to gesture recognition be further evaluated.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  moscinska93a,
  author	= {Moscinska, K. and Tyma, G. },
  title		= {Neural network based fingerprint classification},
  booktitle	= {Third International Conference on Artificial Neural
		  Networks},
  year		= {1993},
  pages		= {229--32},
  organization	= {Silesian Tech. Univ. , Gliwice, Poland},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  moshou00a,
  author	= {Moshou, D. and Ramon, H.},
  title		= {Wavelets and \mbox{self-organizing} maps in financial time
		  series analysis},
  journal	= {Neural Network World},
  year		= {2000},
  volume	= {10},
  pages		= {231--8},
  abstract	= {A methodology on how to combine wavelets with
		  self-organizing maps for financial time series
		  visualisation and interpretation is presented. Current
		  volatility modelling is introduced and compared with the
		  advantages the wavelet-based analysis offers over
		  conventional moving average based methods like the
		  Bollinger bands. The immunity of the wavelet based
		  de-noising to recording errors and transient shocks offers
		  important help in analysing the long and short-term
		  behaviour of financial data. The visualisation of transient
		  shocks like crashes, in higher order wavelet coefficients
		  is presented. The self-organising map neural network is
		  introduced to aid the visualisation of the behaviour of
		  indicator data and specifically the Dow-Jones industrial
		  average. The features that are used for the visualisation
		  are the approximation coefficients of 32-day trading
		  periods with daily sampling of the closing value. The
		  trajectory formed at different component levels shows the
		  evolution of the indicator data from its beginning until
		  September 1999.},
  dbinsdate	= {oldtimer}
}

@Article{	  moshou01a,
  author	= {Moshou, D. and Vrindts, E. and {De Ketelaere}, B. and {De
		  Baerdemaeker}, J. and Ramon, H.},
  title		= {A neural network based plant classifier},
  journal	= {Computers and Electronics in Agriculture},
  year		= {2001},
  volume	= {31},
  number	= {1},
  month		= {},
  pages		= {5--16},
  organization	= {Dept. of Agro-Engineering/Economics, Lab. for
		  Agro-Machinery/Processing, K.U. Leuven},
  publisher	= {Elsevier Science Publishers B.V.},
  address	= {Amsterdam},
  abstract	= {The Self-Organizing Map (SOM) neural network is used in a
		  supervised way for a classification task. The neurons of
		  the SOM are extended with local linear mappings. Error
		  information obtained during training is used in a novel
		  learning algorithm to train the classifier. The proposed
		  method achieves fast convergence and good generalization.
		  The classification method is then applied in a precision
		  farming application, the classification of crops and weeds
		  using spectral properties. The proposed method compares
		  favorably with an optimal Bayesian classifier that is
		  presented in the form of a probabilistic neural network.
		  The classification performance of the proposed method is
		  proven superior compared with other statistical and neural
		  classifiers.},
  dbinsdate	= {2002/1}
}

@InCollection{	  moshou97a,
  author	= {Dimitrios Moshou and Herman Ramon},
  title		= {Extended \mbox{self-organizing} maps with local linear
		  mappings for function approximation and system
		  identification},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {181--186},
  dbinsdate	= {oldtimer}
}

@Article{	  moshou97b,
  author	= {Moshou, D. and {De Ketelaere}, B. and Coucke, P. and {De
		  Baerdemacker}, J. and Ramon, H.},
  title		= {A hierarchical \mbox{self-organizing} map for egg breakage
		  classification},
  journal	= {Mathematics and Control Applications in Agriculture and
		  Horticulture},
  year		= {1997},
  publisher	= {Elsevier Science},
  address	= {Oxford, UK},
  volume	= {},
  pages		= {125--9},
  abstract	= {A hierarchical self-organizing map (SOM) has been
		  developed for solving classification problems, where
		  several measurements have been taken from one object. The
		  algorithm can be used to classify eggs according to their
		  shell state. Broken eggs can be separated from intact eggs.
		  The classification architecture consists of two different
		  SOMs. The first SOM clusters the data in an unsupervised
		  way. Afterwards, the ordered activations of each object are
		  collected and fed to the second SOM which associates them
		  with a class. This class-vector is assigned to every node
		  in the second map and it is learned with Kohonen's learning
		  rule.},
  dbinsdate	= {oldtimer}
}

@Article{	  moshou97c,
  author	= {Moshou, D. and Clijmaus, L. and Anthonis, J. and Kennes,
		  P. and Ramon, H.},
  title		= {Neural network based system identification of agricultural
		  machinery},
  journal	= {Mathematics and Control Applications in Agriculture and
		  Horticulture},
  year		= {1997},
  publisher	= {Elsevier Science},
  address	= {Oxford, UK},
  volume	= {},
  pages		= {151--6},
  abstract	= {A new method for online system identification based on the
		  self-organizing map (SOM) is presented. The standard SOM is
		  extended with local linear mappings. To every node in the
		  SOM along with the input weight two output weights are
		  assigned: one stores the output part of an input-output
		  pair, and the other stores the local gradient matrix
		  (Jacobian) that is calculated from the training pairs. A
		  training algorithm for the Jacobian matrices is derived.
		  The method is tested in system identification of two
		  agricultural machines: a flexible spray boom and a shaker
		  with a nonlinear spring.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  moshou98a,
  author	= {Moshou, D. and Anthonis, J. and Ramon, H.},
  title		= {An active suspension based on \mbox{self-organizing} maps},
  booktitle	= {Intelligent Components for Vehicles (ICV'98). Proceedings
		  volume from the IFAC Workshop.},
  year		= {1998},
  publisher	= {Elsevier Science},
  address	= {Kidlington, UK},
  volume	= {},
  pages		= {},
  abstract	= {The self-organizing map neural network is used in a
		  supervised way to represent a sensor-actuator mapping. The
		  learning of the controller assumes no prior information,
		  but only reward/failure signals that are produced by an
		  evaluation criterion. The evaluation criterion used is
		  based on the low-pass filtering of the gradient of a reward
		  function and the local storing of the filtered gradient
		  value. The control method is tested in vibration isolation
		  of a flexible spray boom used in agriculture for pesticide
		  application. The neural network learns to stabilise the
		  boom online without any prior information and with a very
		  high performance.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  moshou99a,
  author	= {Moshou, D. and {De Ketelaere}, B. and Vrindts, E. and
		  Kennes, P. and {De Baerdemaeker}, J. and Ramon, H.},
  title		= {Local linear mapping neural networks for pattern
		  recognition of plants},
  booktitle	= {Control Applications and Ergonomics in Agriculture
		  (CAEA'98). Proceedings volume from the IFAC Workshop.},
  year		= {1999},
  publisher	= {Elsevier Science},
  address	= {Kidlington, UK},
  volume	= {},
  pages		= {},
  abstract	= {A new neural network architecture for classification
		  purposes is proposed. The self-organizing map (SOM) neural
		  network is used in a supervised way for a classification
		  task. The neurons of the SOM are associated with local
		  linear mappings. Error information obtained during training
		  is used in a novel learning algorithm to train the
		  classifier. The proposed method achieves fast convergence
		  and good generalisation. The classification method is then
		  applied to the classification of crops and weeds using
		  spectral properties. The classification performance of the
		  proposed method is proven superior compared to other
		  statistical and neural classifiers.},
  dbinsdate	= {oldtimer}
}

@Article{	  moszczynski00a,
  author	= {Moszczynski, L.},
  title		= {Classification of measurement results of experiments using
		  statistical software and neural networks},
  journal	= {Pomiary-Automatyka-Kontrola. no.11; Nov. 2000; p.8--10},
  year		= {2000},
  volume	= {},
  pages		= {8--10},
  abstract	= {A dynamic growth of information technology increased
		  interest in the application of neural networks in data
		  classification. We investigate the use of the Kohonen
		  self-organising algorithm for simultaneous clustering and
		  dimensionality reduction. Two statistical programs (SPSS
		  and Stathgraphics) were compared with the competitive
		  network which adds "conscience" mechanism.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  motter00a,
  author	= {Motter, Mark A.},
  title		= {Predictive multiple model switching control with the
		  self-organizing map},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {317--322},
  organization	= {NASA Langley Research Cent},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A predictive, multiple model control strategy is developed
		  by extension of self-organizing map (SOM) local dynamic
		  modeling of nonlinear autonomous systems to a control
		  framework. Multiple SOMs collectively model the global
		  response of a nonautonomous system to a finite set of
		  representative prototype controls. Each SOM provides a
		  codebook representation of the dynamics corresponding to a
		  prototype control. Different dynamic regimes are organized
		  into topological neighborhoods where the adjacent entries
		  in the codebook represent the global minimization of a
		  similarity metric. The SOM is additionally employed to
		  identify the local dynamical regime, and consequently
		  implements a switching scheme that selects the best
		  available model for the applied control. SOM based linear
		  models are used to predict the response to a larger family
		  of control sequences which are clustered on the
		  representative prototypes. The control sequence which
		  corresponds to the prediction that best satisfies the
		  requirements on the system output is applied as the
		  external driving signal.},
  dbinsdate	= {2002/1}
}

@Book{		  motter98a,
  author	= {Motter, M. A.},
  title		= {Control of the {NASA} {L}angley 16-Foot Transonic Tunnel
		  with the Self-Organizing Feature Map. PhD Dissertation.},
  year		= {1998},
  abstract	= {A predictive, multiple model control strategy is developed
		  based on an ensemble of local linear models of the
		  nonlinear system dynamics for a transonic wind tunnel. The
		  local linear models are estimated directly from the weights
		  of a Self Organizing Feature Map (SOFM). Local linear
		  modeling of nonlinear autonomous systems with the SOFM is
		  extended to a control framework where the modeled system is
		  nonautonomous, driven by an exogenous input. This extension
		  to a control framework is based on the consideration of a
		  finite number of subregions in the control space. Multiple
		  self organizing feature maps collectively model the global
		  response of the wind tunnel to a finite set of
		  representative prototype controls. These prototype controls
		  partition the control space and incorporate experimental
		  knowledge gained from decades of operation. Each SOFM
		  models the combination of the tunnel with one of the
		  representative controls, over the entire range of
		  operation. The SOFM based linear models are used to predict
		  the tunnel response to a larger family of control sequences
		  which are clustered on the representative prototypes. The
		  control sequence which corresponds to the prediction that
		  best satisfies the requirements on the system output is
		  applied as the external driving signal. Each SOFM provides
		  a codebook representation of the tunnel dynamics
		  corresponding to a prototype control. Different dynamic
		  regimes are organized into topological neighborhoods where
		  the adjacent entries in the codebook represent the
		  minimization of a similarity metric which is the essence of
		  the self organizing feature of the map. Thus, the SOFM is
		  additionally employed to identify the local dynamical
		  regime, and consequently implements a switching scheme than
		  selects the best available model for the applied control.
		  Experimental results of controlling the wind tunnel, with
		  the proposed method, during operational runs where strict
		  research requirements on the control of the Mach number
		  were met, are presented. Comparison to similar runs under
		  the same conditions with the tunnel controlled by either
		  the existing controller or an expert operator indicate the
		  superiority of the method.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  motter99a,
  author	= {Motter, M. A.},
  title		= {Control of the {NASA} {L}angley 16-foot transonic tunnel
		  with the \mbox{self-organizing} map},
  booktitle	= {Proceedings of the 1999 American Control Conference.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1659--60},
  abstract	= {A predictive, multiple model control strategy is developed
		  based on an ensemble of local linear models of the
		  nonlinear system dynamics for a transonic wind tunnel. The
		  local linear models are estimated directly from the weights
		  of a self-organizing map (SOM). Multiple self-organizing
		  maps collectively model the global response of the wind
		  tunnel to a finite set of representative prototype
		  controls. These prototype controls partition the control
		  space and incorporate experiential knowledge gained from
		  decades of operation. Each SOM models the combination of
		  the tunnel with one of the representative controls, over
		  the entire range of operation. The SOM based linear models
		  are used to predict the tunnel response to a larger family
		  of control sequences which are on the representative
		  prototypes. The control which corresponds to the prediction
		  that best satisfies the requirements on the system output
		  is applied as the external driving signal. Each SOM
		  provides a codebook representation of the tunnel dynamics
		  corresponding to a prototype control. Different dynamic
		  regimes are organized into topological neighborhoods where
		  the adjacent entries in the codebook represent the
		  minimization of a similarity metric which is the essence of
		  the self organizing feature of the map. Thus, the SOM is
		  additionally employed to identify the local dynamical
		  regime, and consequently implements a switching scheme that
		  selects the best available model for the applied control.
		  Experimental results of controlling the wind tunnel, with
		  the proposed method, during operational runs where strict
		  research requirements on the control of the Mach number
		  were met, are presented.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mou94a,
  author	= {Kwok-Leung Mou and Dit-Yan Yeung},
  title		= {Gabriel Networks: {S}elf-{O}rganizing Neural Networks for
		  Adaptive Vector Quantization},
  booktitle	= {Proc. Int. Symp. on Speech, Image Processing and Neural
		  Networks},
  year		= {1994},
  volume	= {II},
  pages		= {658--661},
  organization	= {{IEEE} Hong Kong Chapter of Signal Processing},
  address	= {Hong Kong},
  annote	= {application, vector quantization, modification, adaptive
		  neighborhood},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mouravliansky99a,
  author	= {Mouravliansky, N. A. and Delibasis, K. K. and Matsopoulos,
		  G. K. and Nikita, K. S.},
  title		= {A new method for the elastic registration of {CT} and
		  {MRI} head images},
  booktitle	= {Proceedings of the First Joint BMES/EMBS Conference. 1999
		  IEEE Engineering in Medicine and Biology 21st Annual
		  Conference and the 1999 Annual Fall Meeting of the
		  Biomedical Engineering Society.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1156},
  abstract	= {A new method for optimizing the performance of CT and
		  {MRI} head registration techniques introducing elastic
		  deformation is presented. The method uses a Self Organizing
		  Map to define the node correspondence of the triangulated
		  external surfaces of both modalities. The two sets of
		  equivalent nodes are subsequently registered using
		  Thin-Plate Splines deformation.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  moya92a,
  author	= {Mary M. Moya and Mark W. Koch and R. Joe Fogler and Larry
		  D. Hostetler},
  title		= {One-class classifiers and their application to synthetic
		  aperture radar target recognition},
  institution	= {Sandia National Laboratories},
  year		= {1992},
  number	= {92--2104},
  address	= {Albuquerque, NM},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  moya93b,
  author	= {Mary M. Moya and Mark W. Koch and L. D. Hostetler},
  title		= {Ona-Class Classifier Networks for Target Recognition
		  Applications},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {III},
  pages		= {797--801},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mozayyani95a,
  author	= {N. Mozayyani and V. Alanou and J. F. Dreyfus and G.
		  Vaucher},
  title		= {A Spatio-Temporal Data-Coding Applied to {K}ohonen Maps},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {75--79},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Book{		  mueller95a,
  author	= {Mueller, B. and Reinhardt, J. and Strickland, M. T. },
  title		= {Neural networks 2. updated and corrected ed. . An
		  introduction},
  year		= {1995},
  publisher	= {Springer},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  mujunen93a,
  author	= {Riitta Mujunen and Lea Leinonen and Jari Kangas and Kari
		  Torkkola},
  title		= {Acoustic pattern recognition of /s/ misarticulation by the
		  Self-Organizing Map},
  journal	= {Folia Phoniatrica},
  year		= {1993},
  volume	= {45},
  pages		= {135--144},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  muknahallipatna94a,
  author	= {Muknahallipatna, S. and Chowdhury, B. H. },
  title		= {Identification of coherent generators during transient
		  stability studies by {K}ohonen network},
  booktitle	= {Proceedings of the Twenty-Sixth Annual North American
		  Power Symposium},
  year		= {1994},
  volume	= {1},
  pages		= {64--71},
  organization	= {Dept. of Electr. Eng. , Wyoming Univ. , Laramie, WY, USA},
  publisher	= {Kansas State Univ},
  address	= {Manhattan, KS, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  muknahallipatna96a,
  author	= {S. Muknahallipatna and B. H. Chowdhury},
  title		= {Determination, by {K}ohonen network, of the generator
		  coherency in dynamic studies},
  journal	= {Electric Machines and Power Systems},
  year		= {1996},
  volume	= {24},
  number	= {8},
  pages		= {869--82},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  mulier94a,
  author	= {Mulier, F. and Cherkassky, V. },
  title		= {Learning rate schedules for \mbox{self-organizing} maps},
  booktitle	= {Proceedings of the 12th IAPR International Conference on
		  Pattern Recognition},
  year		= {1994},
  volume	= {2},
  pages		= {224--8},
  organization	= {Dept. of Electr. Eng. , Minnesota Univ. , Minneapolis, MN,
		  USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  mulier95a,
  author	= {Filip Mulier and Vladmir Cherkassky},
  title		= {Self-Organization as an Iterative Kernel Smoothing
		  Process},
  journal	= {Neural Computation},
  year		= {1995},
  volume	= {7},
  number	= {6},
  pages		= {1165--1177},
  dbinsdate	= {oldtimer}
}

@Article{	  mulier95b,
  author	= {Filip M. Mulier and Vladmir S. Cherkassky},
  title		= {Statistical Analysis of Self-Organization},
  journal	= {Neural Networks},
  year		= {1995},
  volume	= {8},
  number	= {5},
  pages		= {717--727},
  publisher	= {Elsevier Science Ltd},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  muller93a,
  author	= {Muller, C. and Mangeas, M. },
  title		= {Neural networks and times series forecasting: a
		  theoretical approach},
  booktitle	= {Proceedings of the 1993 International Conference on
		  Systems, Man and Cybernetics. Systems Engineering in the
		  Service of Humans},
  year		= {1993},
  volume	= {2},
  pages		= {590--4},
  organization	= {Dept. of Res. \& Dev. , Electr. de France, Clamart,
		  France},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {This paper presents some results of our work about
		  forecasting by neural networks. We try to have an approach
		  as close as possible to the classical statistical one. In
		  this way, we show how multilayer perceptron could process
		  as classical model (ARMA), and we present an algorithm to
		  eliminate unnecessary connections of a network. Then, after
		  a brief presentation of our data and our forecasting
		  methodology, we give a typology of the daily electric
		  consumption curves which was done by a self-organizing
		  Kohonen map.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  muller94a,
  author	= {C. Muller and M. Cottrell and B. Girard and Y. Girard and
		  M. Mangeas},
  title		= {A Neural Network Tool for Forecasting Freach Electricity
		  Consumption},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {360--365},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, forecasting},
  dbinsdate	= {oldtimer}
}

@Article{	  muller97a,
  author	= {H. Muller and T. Kapetanovic},
  title		= {Power system security by neural networks},
  journal	= {Elektrotechnik und Informationstechnik},
  year		= {1997},
  volume	= {114},
  number	= {6},
  pages		= {304--7},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  muneesawang01a,
  author	= {Muneesawang, P. and Guan, L.},
  title		= {Automatic similarity learning using {SOTM} for {CBIR} of
		  the {WT}/{VQ} coded images},
  booktitle	= {IEEE International Conference on Image Processing},
  year		= {2001},
  editor	= {},
  volume	= {2},
  pages		= {749--752},
  organization	= {School of Elec. and Info. Engr., The University of
		  Sydney},
  publisher	= {},
  address	= {},
  abstract	= {The unsupervised learning network is explored to
		  incorporate self-learning capability into image retrieval
		  systems. More specifically, we propose the adoption of a
		  Self Organizing Tree Map (SOTM) to implement a
		  self-learning methodology that allows minimization of the
		  role of users in an effort to automate interactive
		  retrieval. This automatic learning modes is applied to
		  interactive retrieval strategies such as the radial basis
		  function method and the relevance feedback method. The
		  proposed method has beem applied to retrieve the images
		  compressed by wavelet transform and vector quantization
		  coders. Retrieval performances are compared with
		  conventional retrieval systems employing both non-iterative
		  and user controlled iteractive retrieval using the MIT
		  texture database. The results obtained are compared
		  favorably with preceding methods.},
  dbinsdate	= {2002/1}
}

@TechReport{	  munoz95a,
  author	= {Alberto Mu{\~n}oz and Jorge Muruz{\'a}bal},
  title		= {Self-Organizing Maps for Outlier Detection},
  institution	= {Universidad Carlos III de Madrid},
  year		= 1995,
  type		= {Statistics and Econometrics Series 19},
  number	= {95--53},
  dbinsdate	= {oldtimer}
}

@Article{	  munoz98a,
  author	= {Munoz, Alberto and Muruzabal, Jorge},
  title		= {Self-organizing maps for outlier detection},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {18},
  pages		= {33--60},
  abstract	= {In this paper we address the problem of multivariate
		  outlier detection using the (unsupervised) self-organizing
		  map (SOM) algorithm introduced by Kohonen. We examine a
		  number of techniques, based on summary statistics and
		  graphics derived from the trained SOM, and conclude that
		  they work well in cooperation with each other. Useful tools
		  include the median interneuron distance matrix and the
		  projection of the trained map (via Sammon's mapping). SOM
		  quantization errors provide an important complementary
		  source of information for certain type of outlying
		  behavior. Empirical results are reported on both artificial
		  and real data.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  murai00a,
  author	= {Murai, Hiroshi and Omatu, Sigeru and Oe, Shunichiro},
  title		= {Improvement of classification accuracy by two neural
		  networks and its application to land cover mapping},
  booktitle	= {International Geoscience and Remote Sensing Symposium
		  (IGARSS)},
  year		= {2000},
  editor	= {},
  volume	= {2},
  pages		= {685--687},
  organization	= {Shikoku Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {In the recent works, we have proposed a hybrid system
		  using a Kohonen's Self-Organization Feature Mapping
		  Preprocessor (SOM) and a multi-layered neural network
		  processor (BPM) to analyze remotely sensed data, and
		  demonstrated the significance of outputs of the SOM
		  preprocessor by a principal component analysis (PCA). In
		  this paper, we apply the proposed system on three images
		  taken by three optical sensors, namely, LANDSAT-TM,
		  JERS1-OPS and SPOT2-HRV, to investigate the applicability
		  of the system.},
  dbinsdate	= {2002/1}
}

@Article{	  murai95a,
  author	= {Murai, H. and Omatu, S. and Oe, S. },
  title		= {Principal component analysis for remotely sensed data
		  classified by {K}ohonen`s feature mapping preprocessor and
		  multi-layered neural network classifier},
  journal	= {IEICE Transactions on Communications},
  year		= {1995},
  volume	= {E78-B},
  number	= {12},
  pages		= {1604--10},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  murao93a,
  author	= {Hajime Murao and Ikuko Nishikawa and Shinzo Kitamura},
  title		= {A Hybrid Neural Network System for the Rainfall Estimation
		  using Satellite Imagery},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1211--1214},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {Hybrid neural network composed of a Self-Organizing Map
		  (SOM) and three-layered feedforward neural networks have
		  been developed and applied for rainfall estimation using
		  satellite imagery. The SOM classifies an input vector
		  extracted from satellite imagery, then one of the
		  feedforward neural networks is chosen according to the
		  class to give the rainfall estimation. In order to train
		  the hybrid neural network, adjoining seas of Japan were
		  selected as testing area. Hourly GMS infrared imagery data
		  and simultaneous ground truth data (the area average of
		  AMeDAS super(1) rainfall observations and radar/ raingage
		  composite data) were collected from AIP/1 super(2) data
		  sets. The SOM is trained to classify the textural feature
		  vectors extracted the imagery data, and tuned by Learning
		  Vector Quantization method. The feedforward neural networks
		  are trained to give the estimation by back propagation
		  algorithm. Fairly good correlation coefficients about 0.8
		  are obtained between the estimation and corresponding
		  ground truth for the unlearned test set. Furthermore, SOM
		  with a recurrent structure for processing the temporal
		  information has been proposed and tested.},
  dbinsdate	= {oldtimer}
}

@Article{	  murashima00a,
  author	= {Sadayuki Murashima and Masayuki Kashima and Takayasu
		  Fuchida},
  title		= {New Method for Measuring the Topology Preservation of
		  Self-organizing Feature Maps},
  journal	= {Australian Journal of Intelligent Information Processing
		  Systems},
  year		= {2000},
  key		= {},
  volume	= {6},
  number	= {2},
  pages		= {97--104},
  month		= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  murashima98a,
  author	= {S. Murashima and M. Kashima and T. Fuchida},
  title		= {Topology preservation of \mbox{self-organizing} maps},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1998},
  volume	= {J81D-II},
  number	= {10},
  pages		= {2457--66},
  dbinsdate	= {oldtimer}
}

@Article{	  murdoch96a,
  author	= {Murdoch, Tim and Ball, Nigel},
  title		= {Machine learning in configuration design},
  journal	= {Artificial Intelligence for Engineering Design, Analysis
		  and Manufacturing: AIEDAM},
  year		= {1996},
  number	= {2},
  volume	= {10},
  pages		= {101--113},
  abstract	= {New methods of configuration analysis have recently
		  emerged that are based on development trends characteristic
		  of many technical systems. It has been found that though
		  the development of any system aims to increase a
		  combination of the performance, reliability and economy,
		  actual design changes are frequently kept to a minimum to
		  reduce the risk of failure. However, a strategy of risk
		  reduction commits the designer to an existing configuration
		  and an approved set of components and materials. Therefore,
		  it is important to analyze the configurations, components,
		  and materials of past designs so that good aspects may be
		  reused and poor ones changed. A good configuration produces
		  the required performance and reliability with maximum
		  economy. These three evaluation criteria form the core of a
		  configuration optimization tool called KATE, where known
		  configurations are optimized producing a set of ranked
		  trial solutions. The authors suggest that this solution set
		  contains valuable design knowledge that can be reused. This
		  paper briefly introduces a generic method of configuration
		  evaluation and then describes the use of a self-organizing
		  neural network, the Kohonen Feature Map, to analyze
		  solution sets by performing an initial data reduction step,
		  producing archetype solutions, and supporting qualitative
		  clustering.},
  dbinsdate	= {oldtimer}
}

@Article{	  murtagh94a,
  author	= {F. Murtagh},
  title		= {Neural Networks and Related 'Massively Parallel' Methods
		  for Statistics: A Short Overview},
  journal	= {International Statistical Review},
  year		= {1994},
  volume	= {64},
  pages		= {275--288},
  dbinsdate	= {oldtimer}
}

@Article{	  murtagh95a,
  author	= {F. Murtagh},
  title		= {Interpreting the {K}ohonen \mbox{self-organizing} map
		  using contiguity-constrained clustering},
  journal	= {Pattern Recognition Letters},
  year		= {1995},
  volume	= {16},
  pages		= {399--408},
  dbinsdate	= {oldtimer}
}

@Article{	  murtagh95b,
  author	= {F. Murtagh and M. Hern{\'{a}}ndez-Pajares},
  title		= {Clustering Moderately-Sized Datasets using the {K}ohonen
		  map Approach},
  journal	= {Statistics in Transition---Journal of the Polish
		  Statistical Association},
  year		= {1995},
  volume	= {2},
  pages		= {151--162},
  dbinsdate	= {oldtimer}
}

@Article{	  murtagh95c,
  author	= {F. Murtagh and M. Hern{\'{a}}ndez-Pajares},
  title		= {The {K}ohonen Self-Organizing Map Method: An Assessment},
  journal	= {Journal of Classification},
  year		= 1995,
  volume	= 12,
  pages		= {165--190},
  dbinsdate	= {oldtimer}
}

@Article{	  murtagh95d,
  author	= {F. Murtagh},
  title		= {Unsupervised catalog classification},
  journal	= {Astronomical Society of the Pacific Conference Series},
  year		= {1995},
  volume	= {77},
  pages		= {264--7},
  note		= {(Astronomical Data Analysis Software and Systems IV
		  Meeting Conf. Date: 25--28 Sept. 1994 Conf. Loc: Baltimore,
		  MD, USA)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  murtagh96a,
  author	= {F. Murtagh and A. Aussem and O. J. W. F. Kardaun},
  title		= {The wavelet transform in multivariate data analysis},
  booktitle	= {COMPSTAT. Proceedings in Computational Statistics. 12th
		  Symposium},
  publisher	= {Physica-Verlag},
  year		= {1996},
  editor	= {A. Prat},
  address	= {Heidelberg, Germany},
  pages		= {397--402},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  muruzabal01a,
  author	= {Muruzabal, J.},
  title		= {Evolving high-posterior self-organizing maps},
  booktitle	= {Connectionist Models of Neurons, Learning Processes, and
		  Artificial Intelligence. 6th International Work-Conference
		  on Artificial and Natural Neural Networks, IWANN 2001.
		  Proceedings, Part I (Lecture Notes in Computer Science Vol.
		  2084). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {701--8},
  abstract	= {Bayesian inference for neural networks has received a good
		  deal of attention in recent years. Unlike standard methods,
		  the Bayesian approach provides the analyst with the
		  richness (and complexity) of a probability distribution
		  over the space of network weights (and possibly other
		  quantities of interest). These posterior distributions
		  prompt an optimization problem that may be suitable for
		  evolutionary algorithms. This possibility is obviously of
		  foremost interest when no alternative global functions are
		  available for optimization. Some preliminary results
		  related to one of such cases, namely, the self-organizing
		  map, are presented in this paper. Specifically, a familiar
		  "steady-state" diffusion genetic algorithm is described and
		  tested.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  muruzabal01b,
  author	= {Muruzabal, J.},
  title		= {On the emulation of Kohonen's self-organization via
		  single-map Metropolis-Hastings algorithms},
  booktitle	= {Computational Science---ICCS 2001. International
		  Conference. Proceedings, Part II (Lecture Notes in Computer
		  Science Vol.2074). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {346--55},
  abstract	= {As data sets get larger and larger, the need for
		  exploratory methods that allow some visualization of the
		  overall structure in the data is becoming more important.
		  The self-organizing map (SOM) introduced by Kohonen is a
		  powerful tool for precisely this purpose. In recent years,
		  SOM-based methodology has been refined and deployed with
		  success in various high-dimensional problems. Still, our
		  understanding of the properties of SOMs fitted by Kohonen's
		  original algorithm is not complete, and several statistical
		  models and alternative fitting algorithms have been devised
		  in the literature. This paper presents a new
		  Metropolis-Hastings Markov chain Monte Carlo algorithm
		  designed for SOM fitting. The method stems from both the
		  previous success of bayesian machinery in neural models and
		  the uprise of computer-intensive, simulation-based
		  algorithms in bayesian inference. Experimental results
		  suggest the feasibility as well as the limitations of the
		  approach in its current form. Since the method is based on
		  a few extremely simple chain transition kernels, the
		  framework may well accommodate the more sophisticated
		  constructs needed for a full emulation of the
		  self-organization treat.},
  dbinsdate	= {2002/1}
}

@Article{	  musil96a,
  author	= {M. Musil and A. Plesinger},
  title		= {Discrimination between local microearthquakes and quarry
		  blasts by multi-layer perceptrons and {K}ohonen maps},
  journal	= {Bulletin of the Seismological Society of America},
  year		= {1996},
  volume	= {86},
  number	= {4},
  pages		= {1077--90},
  dbinsdate	= {oldtimer}
}

@Article{	  mussa_ivaldi88a,
  author	= {Mussa Ivaldi, F. A. and Morasso, P. and Zaccaria, R.},
  title		= {Kinematic Networks. {A} Distributed Model for Representing
		  and Regularizing Motor Redundancy.},
  journal	= {Biological Cybernetics},
  year		= {1988},
  volume	= {60},
  pages		= {1--16},
  abstract	= {Motor control in primates relates to a system which is
		  highly redundant from the mechanical point of
		  view-redundancy coming from an imbalance between the set of
		  independently controllable variables and the set of system
		  variables. The consequence is the manifestation of a broad
		  class of ill-posed problems, problems for which it is
		  difficult to identify unique solutions. The authors propose
		  that the basic regularization mechanism is provided by the
		  potential fields generated by the elastic properties of
		  muscles, according to an organizational principle-'passive
		  motion paradigm'. The physiological basis of this
		  hypothesis is reviewed and a 'kinematic network' (K-net)
		  model is proposed that expresses the kinematic
		  transformations and the causal relations implied by
		  elasticity. Moreover, it is shown how K-nets can be
		  obtained from a kinematic 'body model', in the context of a
		  specific task.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  mussa_ivaldi89b,
  author	= {Mussa Ivaldi, F. A. and Morasso, P. and N. Hogan, N. and
		  Bizzi, E.},
  title		= {Network Models of Motor Systems with Many Degrees of
		  Freedom},
  booktitle	= {Advances in Control Networks and Large Scale Parallel
		  Distributed Processing Models},
  publisher	= {Ablex Publ. Corp.},
  year		= {1989},
  editor	= {M. D. Fraser},
  address	= {Norwood, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  myers00a,
  author	= {Myers, D. and Kok Wai Wong and Chun Che Fung},
  title		= {Self-organising maps use for intelligent data analysis},
  journal	= {Australian-Journal-of-Intelligent-Information-Processing-Systems}
		  ,
  year		= {2000},
  volume	= {6},
  pages		= {89--96},
  abstract	= {A neural network-based data analysis model for the
		  prediction and classification of field data has many
		  attractions. However, there are problems in ensuring the
		  generalisation capability of the data analysis model, in
		  measuring the similarity between the original training data
		  and the new unknown data, and in processing large data
		  volumes. This paper proposes the use of self-organising
		  maps (SOMs) to overcome these difficulties and illustrates
		  the utility of the approach though applications in the
		  agricultural, resource exploration and mineral processing
		  areas. In most SOM applications, its self organising and
		  clustering capabilities have always been the focus. In this
		  paper, SOM is used as an enhancement approach that can be
		  incorporated within another intelligent data analysis
		  approach.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  myklebust95a,
  author	= {Gaute Myklebust and Jon G. Solheim},
  title		= {Parallel \mbox{Self-organizing} Maps for actual
		  applications},
  volume	= {II},
  pages		= {1054--1059},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  myklebust95b,
  author	= {Myklebust, G. and Solheim, J. G. and Steen, E. },
  title		= {Speeding up small sized \mbox{self-organizing} maps for
		  use in visualization of multispectral medical images},
  booktitle	= {Proceedings of the Eighth IEEE Symposium on Computer-Based
		  Medical Systems},
  year		= {1995},
  pages		= {103--10},
  organization	= {Norwegian Inst. of Technol. , Trondheim, Norway},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  myllykoski97a,
  author	= {Myllykoski, P. and Wiklund, O.},
  title		= {Prediction of temper rolling force with neural networks},
  booktitle	= {Neural Networks in Engineering Systems. Proceedings of the
		  1997 International Conference on Engineering Applications
		  of Neural Networks},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1997},
  volume	= {1},
  pages		= {151--4},
  abstract	= {This study considers the use of measured process data for
		  developing a prediction model for temper rolling force. The
		  process data is from Rautaruukki's Raahe and Hameenlinna
		  works. The temper rolling force is modelled with a
		  multilayer perceptron (MLP) type neural network, using raw
		  process data with different weighting and selection methods
		  as well as using a SOM pre-processing of training data.
		  Different ways of evaluating the model performance are
		  discussed as well as methods of enhancing the accuracy. It
		  is concluded that MLP is a powerful tool for developing a
		  predictor for temper rolling force from process data.
		  However, if unevenly distributed data is used the
		  generality of a MLP model is worsened, and if trained, goes
		  to a very low RMS error even for an independent data set.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  nababhushana98a,
  author	= {T. N. Nababhushana and K. T. Veeramanju and Shivanna},
  title		= {Coherency identification using growing self organizing
		  feature maps (power system stability)},
  booktitle	= {Proceedings of EMPD '98. 1998 International Conference on
		  Energy Management and Power Delivery},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {113--16},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nag01a,
  author	= {Nag, A. K. and Mitra, A.},
  title		= {Recent patterns of economic development in the world: an
		  exploratory data analysis using self-organizing maps},
  booktitle	= {Proceedings of the IASTED International Conference.
		  Artificial Intelligence and Applications. ACTA Press,
		  Anaheim, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {115--20},
  abstract	= {In this paper, we perform exploratory data analysis to
		  detect recent patterns of economic development of various
		  world countries. We use the artificial intelligence
		  approach of self-organizing map (SOM) for the exploratory
		  analysis. Using information on different aspects of
		  economic development as input variables, we project the
		  multidimensional data onto the 2D SOM surface in such a way
		  that the nonlinear relations of the data are preserved
		  allowing easy visualization of the important features of
		  the data. The results of the study reveal interesting
		  aspects of the pattern of economic development in the
		  world. We also used different statistical clustering and
		  projection tools for comparison with the SOM results.},
  dbinsdate	= {2002/1}
}

@Article{	  nagino00a,
  author	= {Nagino, N. and Yamada, S.},
  title		= {Information gathering based on user's interest by multiple
		  Web robots},
  journal	= {Transactions-of-the-Institute-of-Electronics,-Information-and-Communication-Engineers-D-I}
		  ,
  year		= {2000},
  volume	= {},
  pages		= {780--8},
  abstract	= {The authors propose a PWM (Personal Web Map) system which
		  gathers information through the WWW depending on a user's
		  interests. The PWM is a database of relevant Web pages
		  constructed under his/her interactive control. For a user's
		  easy understanding of the gathering process, the system
		  controls Web robots to keep PWM as uniform as possible on
		  keywords. The gathering process is indicated using a 2D map
		  generated by SOM (self-organizing map), and a user gives
		  the system feedback through it. Finally, we conducted
		  various experiments, and proved that a PWM system was
		  promising for information gathering in the WWW.},
  dbinsdate	= {2002/1}
}

@InCollection{	  nagrath96a,
  author	= {I. J. Nagrath and L. Behera and K. M. Krishna and K. D.
		  Rajasekar},
  title		= {Real-time navigation of a mobile robot using {K}ohonen's
		  topology conserving neural network},
  booktitle	= {1997 8th International Conference on Advanced Robotics.
		  Proceedings. ICAR'97},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {B. Yuan and X. Tang},
  address	= {New York, NY, USA},
  pages		= {459--64},
  dbinsdate	= {oldtimer}
}

@Article{	  naim97a,
  author	= {A. Naim and K. U. Ratnatunga and R. E. Griffiths},
  title		= {Galaxy morphology without classification:
		  \mbox{self-organizing} maps},
  journal	= {Astrophysical Journal Supplement Series},
  year		= {1997},
  volume	= {111},
  number	= {2},
  pages		= {357--67},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  najafi93a,
  author	= {Hossein L. Najafi and Vladimir Cherkassky},
  title		= {Adaptive Knot Placement Based on Estimated Second
		  Derivative of Regression Surface},
  booktitle	= {Proc. NIPS'93, Neural Information Processing Systems},
  year		= {1993},
  editor	= {Jack D. Cowan and Gerald Tesauro and Joshua Alspector},
  pages		= {247--254},
  publisher	= {Morgan Kaufmann Publishers},
  address	= {San Francisco, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  najand92a,
  author	= {Shariar Najand and Zhen-Ping Lo and Behnam Bavarian},
  title		= {Using the {K}ohonen Topology Preserving Mapping Network
		  for Learning the minimal environment representation},
  booktitle	= {Proc. IJCNN'92, Int. Joint Conference on Neural Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {II},
  pages		= {87--93},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nakagawa90a,
  author	= {Seiichi Nakagawa and Yoshimitsu Hirata},
  title		= {Comparison Among Time-Delay Neural Networks, {LVQ2},
		  Discrete Parameter {HMM} and Continuous Parameter {HMM}},
  booktitle	= {Proc. ICASSP-90, International Conference on Acoustics
		  Speech and Signal Processing},
  year		= {1990},
  volume	= {1},
  pages		= {509--512},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nakagawa93a,
  author	= {Seiichi Nakagawa and Yoshiyuki Ono and Kangin Hur},
  title		= {Estimation of Probability Density Function and Evaluation
		  by Vowel Recognition},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2223--2226},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nakagawa93b,
  author	= {Nakagawa, T. and Ito, T. },
  title		= {Self-organizing feature map with position information and
		  spatial frequency information},
  booktitle	= {Neural Networks for Processing III Proceedings of the 1993
		  IEEE-SP Workshop},
  year		= {1993},
  editor	= {Kamm, C. A. and Kuhn, G. M. and Yoon, B. and Chellappa, R.
		  and Kung, S. Y. },
  pages		= {40--9},
  organization	= {NHK Sci. \& Tech. Res. Lab. , Tokyo, Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  nakagawa94a,
  author	= {Nakagawa, T. and Ito, T. },
  title		= {Self-organizing feature map with spatial position and
		  spatial frequency information},
  journal	= {NHK Laboratories Note},
  year		= {1994},
  volume	= {429},
  pages		= {1--15},
  month		= {Oct},
  dbinsdate	= {oldtimer}
}

@Article{	  nakajima98a,
  author	= {T. Nakajima and H. Takizawa and H. Kobayashi and T.
		  Nakamura},
  title		= {{K}ohonen learning with a mechanism, the law of the
		  jungle, capable of dealing with nonstationary probability
		  distribution functions},
  journal	= {IEICE Transactions on Information and Systems},
  year		= {1998},
  volume	= {E81-D},
  number	= {6},
  pages		= {584--91},
  dbinsdate	= {oldtimer}
}

@Article{	  nakajima99a,
  author	= {Nakajima, T. and Takizawa, H. and Kobayashi, H. and
		  Nakamura, T.},
  title		= {A topology preserving neural network for nonstationary
		  distributions},
  journal	= {IEICE Transactions on Information and Systems},
  year		= {1999},
  volume	= {},
  pages		= {1131--5},
  abstract	= {We propose a learning algorithm for self-organizing neural
		  networks to form a topology preserving map from an input
		  manifold whose topology may dynamically change.
		  Experimental results show that the network using the
		  proposed algorithm can rapidly adjust itself to represent
		  the topology of nonstationary input distributions.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nakamura91a,
  author	= {S. Nakamura and T. Akabane},
  title		= {A neural speaker model for speaker clustering},
  booktitle	= {ICASSP-91, International Conference on Acoustics, Speech
		  and Signal Processing},
  year		= {1991},
  volume	= {II},
  pages		= {853--856},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nakamura96a,
  author	= {Nakamura, T. and Ishida, Y. and Honda, T.},
  title		= {Spoken word recognition using {DP} matching based on SOM
		  clustering},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {273--6},
  abstract	= {This paper proposes a method of speech recognition using
		  DP matching based on Kohonen model self-organizing map
		  (SOM) clustering. The dynamic programming (DP) matching
		  method is effective in nonlinear time-normalization and has
		  good performance for the recognition of continuous speech.
		  On the other hand, SOM networks can automatically learn to
		  classify input spectral vectors in accordance with their
		  similarity, updating the weight of the winning neuron and
		  the neighbor neurons. We present a new method of making
		  standard patterns, in which each input spectral vector is
		  replaced with the most similar neuron weight vector
		  clustered by SOM. As a significant feature of this method,
		  it is easy to change the vocabulary because standard
		  patterns are derived from the neuron weight vectors trained
		  beforehand instead of learning new standard patterns
		  again.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  nakamura97a,
  author	= {M. Nakamura and I. Sugimoto and H. Kuwano},
  title		= {Pattern recognition of dynamic chemical-sensor responses
		  by using {LVQ} algorithm},
  booktitle	= {1997 IEEE International Conference on Systems, Man, and
		  Cybernetics. Computational Cybernetics and Simulation},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {4},
  editor	= {P. Thorburn and J. Quaicoe},
  address	= {New York, NY, USA},
  pages		= {3036--41},
  dbinsdate	= {oldtimer}
}

@Article{	  nakamura98a,
  author	= {M. Nakamura and I. Sugimoto and H. Kuwano},
  title		= {Pattern recognition of {QCM} chemical sensor responses
		  using {LVQ}},
  journal	= {Transactions of the Society of Instrument and Control
		  Engineers},
  year		= {1998},
  volume	= {34},
  number	= {8},
  pages		= {877--83},
  dbinsdate	= {oldtimer}
}

@Article{	  nakamura99a,
  author	= {Nakamura, K. and Yamamoto, S. and Itoh, T.},
  title		= {Document image segmentation using neural networks},
  journal	= {Journal of the Institute of Image Electronics Engineers of
		  Japan},
  year		= {1999},
  volume	= {28},
  pages		= {106--15},
  abstract	= {We propose an image segmentation method by neural networks
		  (NN) to extract text, continuous-tone and screened-halftone
		  region in a document's image. This method is composed of 3
		  stages. In the feature extraction stage, a image feature
		  extractor is constructed by the Kohonen map, which converts
		  all of the document's images into feature parameters. We
		  call this feature extractor the feature extraction map
		  (FEM). The classification stage is composed of a 3-layered
		  NN, which is based on the multilayered perceptron (MLP).
		  The classifier uses feature parameters, which are applied
		  from the FEM. We call this the classification NN (CNN). The
		  post-processing stage is composed of the Boltzmann machine
		  which is one of the recurrent NN. Therefore, the feature
		  extraction stage, classification stage and post-processing
		  stage are constructed with neural networks. The
		  segmentation system classifies each region automatically.
		  Experimental results show this method is successful in
		  segmenting text, continuous-tone and screened-halftone
		  regions.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nakatsuji94a,
  author	= {Nakatsuji, T. and Seki, S. and Shibuya, S. and Kaku, T. },
  title		= {Artificial intelligence approach for optimizing traffic
		  signal timings on urban road network},
  booktitle	= {1994 Vehicle Navigation and Information Systems Conference
		  Proceedings},
  year		= {1994},
  pages		= {199--202},
  organization	= {Dept. of Civil Eng. , Hokkaido Univ. , Sapporo, Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  nakatsuji94b,
  author	= {Nakatsuji, T. and Seki, S. and Shibuya, S. and Kaku, T. },
  title		= {Artificial intelligence approach for optimizing traffic
		  signal timing on an urban road network},
  journal	= {Transactions of the Institute of Systems, Control and
		  Information Engineers},
  year		= {1994},
  volume	= {7},
  number	= {11},
  pages		= {470--8},
  month		= {Nov},
  dbinsdate	= {oldtimer}
}

@Article{	  nakayama00a,
  author	= {Nakayama, M. and Sanematsu, H. and Shimizu, Y.},
  title		= {A document indexing and retrieval method based on a
		  teaching guideline for keyword searching educational
		  information},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers D I},
  year		= {2000},
  volume	= {},
  pages		= {225--33},
  abstract	= {To retrieve and categorize educational information, the
		  relationships between subjects and terms and between two
		  terms were examined by analyzing the frequency of terms
		  that appeared in the teaching guidelines of elementary
		  schools. Using the singular value decomposition method,
		  feature vectors were obtained from the number of
		  occurrences of the terms that appeared in the teaching
		  guidelines. Using these feature vectors, terms and subject
		  areas that contained them were analyzed to indicate the
		  relationships between them. Codebook vectors, that were
		  learned by a self-organizing algorithm with feature
		  vectors, then put the terms and subjects into a
		  2-dimensional map that described their relationship.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nakayama92a,
  author	= {K. Nakayama and Y. Chigawa and O. Hasegawa},
  title		= {Handwritten Alphabet and Digit Character Recognition Using
		  Feature Extracting Neural Network and Modified
		  Self-Organizing Map},
  booktitle	= {Proc. IJCNN'92, of the International Joint Conference on
		  Neural Networks},
  year		= {1992},
  volume	= {IV},
  pages		= {235--240},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {Recognition of handwritten characters. The preset approach
		  sounds healthy. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nakayama92b,
  author	= {Nakayama, K. and Chigawa, Y. },
  title		= {Japanese {K}anji character recognition using cellular
		  neural networks and modified \mbox{self-organizing} feature
		  map},
  booktitle	= {CNNA'92 Proceedings. Second International Workshop on
		  Cellular Neural Networks and their Applications},
  year		= {1992},
  pages		= {191--6},
  organization	= {Dept. of Electr. \& Comput. Eng. , Kanazawa Univ. , Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  namba96a,
  author	= {Namba, M. and Ishida, Y.},
  title		= {Speaker identification using the combination of
		  {DP}-matching and {LVQ} clustering techniques},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {261--4},
  abstract	= {Automatic speaker identification and verification are
		  subjects of speech processing science. The individual
		  identities in utterances are too ambiguous to establish
		  phonemic division and hidden {M}arkov models are not
		  necessarily required for this purpose. Vector quantization
		  and DP-matching have been successfully applied to this
		  problem, however each has its own weakness. We propose a
		  hybrid method with the combination of DP-matching and LVQ
		  techniques. Because of its generalization capability in
		  both time and frequency domains, we can expect to obtain a
		  higher identification rate.},
  dbinsdate	= {oldtimer}
}


@Article{	  namba97a,
  author	= {M. Namba and H. Kamata and Y. Ishida},
  title		= {An approach to speaker identification using {DP}-matched
		  {LVQ} neural networks},
  journal	= {Journal of the Acoustical Society of Japan [E]},
  year		= {1997},
  volume	= {18},
  number	= {2},
  pages		= {81--8},
  dbinsdate	= {oldtimer}
}

@Article{	  nanning95a,
  author	= {Zheng Nanning and Liu Jianqing},
  title		= {An adaptive approach to image segmentation based on region
		  features},
  journal	= {Acta Electronica Sinica},
  year		= {1995},
  volume	= {23},
  number	= {7},
  pages		= {98--101},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@Article{	  nasrabadi88a,
  author	= {N. M. Nasrabadi and Yushu Feng},
  title		= {Vector quantization of images based upon a neural-network
		  clustering algorithm},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1988},
  volume	= {1001},
  number	= {pt. 1},
  pages		= {207--213},
  annote	= {Conf. paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nasrabadi88b,
  author	= {Nasser M. Nasrabadi and Yushu Feng},
  title		= {Vector quantization of images based upon the {K}ohonen
		  \mbox{self-organizing} feature maps},
  booktitle	= {Proc. ICNN'88, International Conference on Neural
		  Networks},
  year		= {1988},
  volume	= {I},
  pages		= {101--108},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  nasrabadi88c,
  author	= {Nasser M. Nasrabadi and Yushu Feng},
  title		= {Vector quantization of images based upon the {K}ohonen
		  self-organization feature maps},
  journal	= {Neural Networks},
  year		= {1988},
  volume	= {1},
  number	= {1 SUPPL},
  pages		= {518},
  dbinsdate	= {oldtimer}
}

@Article{	  nassery00a,
  author	= {Nassery, Payam and Faez, Karim},
  title		= {Seismic events discrimination using a new {FLVQ}
		  clustering model},
  journal	= {IEICE Transactions on Information and Systems},
  year		= {2000},
  number	= {7},
  volume	= {},
  pages		= {1533--1539},
  abstract	= {In this paper, the LVQ (Learning Vector Quantization)
		  model and its variants are regarded as the clustering tools
		  to discriminate the natural seismic events (earthquakes)
		  from the artificial ones (nuclear explosions). The study is
		  based on the six spectral features of the P-wave spectra
		  computed from the short period teleseismic recordings. The
		  conventional LVQ proposed by Kohenen and also the Fuzzy LVQ
		  (FLVQ) models proposed by Sakuraba and Bezdek are all
		  tested on a set of 26 earthquakes and 24 nuclear explosions
		  using the leave-one-out testing strategy. The primary
		  experimental results have shown that the shapes, the number
		  and also the overlaps of the clusters play an important
		  role in seismic classification. The results also showed how
		  an improper feature space partitioning would strongly
		  weaken both the clustering and recognition phases. To
		  improve the numerical results, a new combined FLVQ
		  algorithm is employed in this paper. The algorithm is
		  composed of two nested sub-algorithms. The inner
		  sub-algorithm tries to generate a well-defined fuzzy
		  partitioning with the fuzzy reference vectors in the
		  feature space. To achieve this goal, a cost function is
		  defined as a function of the number, the shapes and also
		  the overlaps of the fuzzy reference vectors. The update
		  rule tries to minimize this cost function in a stepwise
		  learning algorithm. On the other hand, the outer
		  sub-algorithm tries to find an optimum value for the number
		  of the clusters, in each step. For this optimization in the
		  outer loop, we have used two different criteria. In the
		  first criterion, the newly defined 'fuzzy entropy' is used
		  while in the second criterion, a performance index is
		  employed by generalizing the Huntsberger formula for the
		  learning rate, using the concept of fuzzy distance. The
		  experimental results of the new model show a promising
		  improvement in the error rate, an acceptable convergence
		  time, and also more flexibility in boundary decision
		  making.},
  dbinsdate	= {2002/1}
}

@Article{	  nassery00b,
  author	= {Nassery, Payam and Faez, Karim},
  title		= {Dynamic model for the seismic signals processing and
		  application in seismic prediction and discrimination},
  journal	= {IEICE Transactions on Information and Systems},
  year		= {2000},
  volume	= {E83-D},
  number	= {12},
  month		= {Dec},
  pages		= {2098--2106},
  organization	= {Amirkabir Univ of Technology},
  publisher	= {IEICE of Japan},
  address	= {Tokyo},
  abstract	= {In this paper we have presented a new method for seismic
		  signal analysis, based on the ARMA modeling and a fuzzy LVQ
		  clustering method. The objective achieved in this work is
		  to sense the changes made naturally or artificially on the
		  seismogram signal, and to detect the sources, which caused
		  these changes (seismic classification). During the study,
		  we have also found out that the model is sometimes capable
		  to alarm the further seismic events just a little time
		  before the onset of those events (seismic prediction). So
		  the application of the proposed method both in seismic
		  classification and seismic prediction are studied through
		  the experimental results. The study is based on the
		  background noise of the teleseismic short period
		  recordings. The ARMA model coefficients are derived for the
		  consecutive overlapped windows. A base model is then
		  generated by clustering the calculated model parameters,
		  using the fuzzy LVQ method proposed by Nassery \& Faez in
		  [19]. The time windows, which do not take part in model
		  generation process, are named as the test windows. The
		  model coefficients of the test windows are then compared to
		  the base model coefficients through some pre-defined
		  composition rules. The result of this comparison is a
		  normalized value generated as a measure of similarity. The
		  set of the consecutive similarity measures generate above,
		  produce a curve versus the time windows indices called as
		  the characteristic curves. The numerical results have shown
		  that the characteristic curves often contain much vital
		  seismological information and can be used for source
		  classification and prediction purposes.},
  dbinsdate	= {2002/1}
}

@Article{	  nassery98a,
  author	= {Nassery, Payam and Faez, Karim},
  title		= {Signature pattern recognition using moments invariant and
		  a new fuzzy {LVQ} model},
  journal	= {IEICE Transactions on Information and Systems, IMAGE
		  Signal Proc},
  year		= {1998},
  number	= {12},
  volume	= {},
  pages		= {1483--1493},
  abstract	= {In this paper we have introduced a new method for
		  signature pattern recognition, taking advantage of some
		  image moment transformations combined with fuzzy logic
		  approach. For this purpose first we tried to model the
		  noise embedded in signature patterns inherently and
		  separate it from environmental effects. Based on the first
		  step results, we have performed a mapping into the unit
		  circle using the error least mean square (LMS) error
		  criterion, to get ride of the variations caused by shifting
		  or scaling. Then we derived some orientation invariant
		  moments introduced in former reports and studied their
		  statistical properties in our special input space. Later we
		  defined a fuzzy complex space and also a fuzzy complex
		  similarity measure in this space and constructed a new
		  training algorithm based on fuzzy learning vector
		  quantization (FLVQ) method. A comparison method has also
		  been proposed so that any input pattern could be compared
		  to the learned prototypes through the pre-defined fuzzy
		  similarity measure. Each set of the above image moments
		  were used by the fuzzy classifier separately and the
		  mis-classifications were detected as a measure of error
		  magnitude. The efficiency of the proposed FLVQ model has
		  been numerically shown compared to the conventional FLVQs
		  reported so far. Finally some satisfactory results are
		  derived and also a comparison is made between the above
		  considered image transformations.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nathan91a,
  author	= {K. S. Nathan and H. F. Silverman. },
  title		= {Classification of unvoiced stops based on formant
		  transitions prior to release},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {I},
  pages		= {445--448},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {. . . The performance of three learning vector classifier,
		  is compared with standard K-means and LVQ2 (learning vector
		  quantization-2) classifiers. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  natori95a,
  author	= {Naotake Natori and Kazuo Nishimura},
  title		= {A Practical Neural Network for Handwritten Character
		  Recognition Built by Dynamics-Based Active Learning and
		  Self-Organization of Feedback Mechanism},
  volume	= {VI},
  pages		= {3089--3094},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  natowicz93a,
  author	= {Ren{\'{e}} Natowicz and Fabrizio Bosio and Serge Sean},
  title		= {Segmentation of Image Sequences Using Self-Organizing
		  Feature Maps},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {1002--1005},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  natowicz93b,
  author	= {Natowicz, R. and Sokol, R. },
  title		= {Self-organizing feature maps for image segmentation},
  booktitle	= {New Trends in Neural Computation. International Workshop
		  on Artificial Neural Networks. IWANN '93 Proceedings},
  year		= {1993},
  editor	= {Mira, J. and Cabestany, J. and Prieto, A. },
  pages		= {626--31},
  organization	= {ESIEE Lab. de Traitement de l'Inf. et des Syst. , Noisy le
		  Grand, France},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  natowicz94a,
  author	= {Natowicz, R. and {Alves de Barros}, M. and Akil, M. and
		  Bosio, F. },
  title		= {Real time segmentation of image sequences by
		  \mbox{self-organizing} feature map: method and
		  reconfigurable architecture},
  journal	= {IFIP Transactions A [Computer Science and Technology]},
  year		= {1994},
  volume	= {A-44},
  pages		= {267--76},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  natowicz95a,
  author	= {R. Natowicz},
  title		= {{K}ohonen`s \mbox{self-organizing} maps for contour
		  segmentation of gray level and color images},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {890--7},
  dbinsdate	= {oldtimer}
}

@InCollection{	  natowicz95b,
  author	= {R. Natowicz and L. Bergen and B. Gas},
  title		= {{K}ohonen's maps for contour and 'region-like'
		  segmentation of gray level and color images},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {D. W. Pearson and N. C. Steele and R. F. Albrecht},
  address	= {Vienna, Austria},
  pages		= {360--3},
  dbinsdate	= {oldtimer}
}

@InBook{	  natschlager02a,
  author	= {Thomas Natschl{\"a}ger and Berthold Ruf and Michael
		  Schmitt},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Unsupervised Learning and Selg-Organization in Networks of
		  Spiking Neurons},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  naude00a,
  author	= {Naude, J. and Nel, N. and Kruger, J.},
  title		= {Initial results of stick-slip modelling and
		  characterization by Self-organizing Map ({SOM}) to improve
		  machine tool design},
  journal	= {SOUTH AFRICAN JOURNAL OF SCIENCE},
  year		= {2000},
  volume	= {96},
  number	= {9--10},
  month		= {SEP-OCT},
  pages		= {499--504},
  abstract	= {In designing machine toots, the absence of a detailed
		  procedure for selecting friction material on guideways
		  often results in the stick-slip phenomenon, causing
		  tolerance defects and undesirable motion properties. This
		  paper describes the use of Self-organizing Maps (SOMs) to
		  capture the tendencies of the stick-slip phenomenon,
		  according to predefined parameters, from experimental data.
		  These tendencies are presented graphically to the designer,
		  as a result of which one can analyse and predict the
		  stick-slip properties for a given guideway set-up. Owing to
		  the intelligent data grouping properties of the SOM, a
		  graphical interface produces areas of 'acceptabte' and
		  'unacceptable' stick-slip properties according to the
		  designer's criteria for the guideway. This assists in the
		  correct selection of friction material and guideway surface
		  finish for a certain stress on the friction surface. The
		  SOM modelling used, when compared to experience in
		  industry, captures the basic characteristics of different
		  combinations of guideway and friction material.},
  dbinsdate	= {2002/1}
}

@Article{	  naude00b,
  author	= {Naude, J. and Nel, N. and Kruger, J.},
  title		= {Stick-slip modelling by {SOM} (self-organising map):
		  towards the intelligent design and selection of friction
		  surface and material in machine tool design},
  journal	= {Prace-Naukowe-Instytutu-Technologii-Maszyn-i-Automatyzacji-Politechniki-Wroclawskiej,-Seria:-Konferencje.}
		  ,
  year		= {2000},
  volume	= {},
  pages		= {69--80},
  abstract	= {In the designing of machine tools the absence of a
		  detailed design procedure for selecting friction material
		  on slideways often results in the stick-slip phenomena
		  causing tolerance defects and undesirable motion
		  properties. The paper describes the use of self-organising
		  maps (SOM) to capture the tendencies of the stick-slip
		  phenomena, according to predefined parameters, from
		  experimental data. These tendencies are presented
		  graphically to the designer through which the designer can
		  analyse and predict the stick-slip properties for a given
		  slideway set-up. Due to the intelligent data grouping
		  properties of the SOM the graphical interface produces
		  areas of "acceptable" and "unacceptable" stick-slip
		  properties according to the designer's stick-slip criteria
		  for the current slideway design. The above results in the
		  correct friction material selection and slideway surface
		  finish for a certain stress on the friction material. The
		  system tested in industry proved to be very successful and
		  is currently under further development to include an expert
		  system for more detailed design by various graphical
		  interfaces.},
  dbinsdate	= {2002/1}
}

@Article{	  naylor88a,
  author	= {Naylor, J. and Higgins, A. and Li, K. P. and Schmoldt,
		  D.},
  title		= {Speaker recognition using {K}ohonen's
		  \mbox{self-organizing} feature map algorithm.},
  journal	= {Neural Networks},
  year		= {1988},
  number	= {},
  volume	= {1},
  pages		= {311},
  abstract	= {We used the Kohonen self-organizing feature mapping
		  algorithm to derive speech templates for text-independent
		  automatic speaker recognition. The speaker recognition
		  algorithm is based on template matching and is described
		  elsewhere. The Kohonen method of deriving templates was
		  compared with an alternate method based on cluster
		  averaging. We found the recognition performance of the two
		  methods to be about the same, given equal computation.
		  However, the Kohonen method has a practical advantage that
		  the desired number of templates is specified in advance.
		  This advantage can be significant, particularly for noisy
		  or distorted speech data, because it avoids the need for
		  the operator to "tune" the system for the input data.},
  dbinsdate	= {oldtimer}
}

@Article{	  naylor88b,
  author	= {Naylor, J. and Li, K. P.},
  title		= {Analysis of a neural network algorithm for vector
		  quantization of speech parameters.},
  journal	= {Neural Networks},
  year		= {1988},
  number	= {},
  volume	= {1},
  pages		= {310},
  abstract	= {This work presents an analysis of a speech vector
		  quantization (VQ) process based on Kohonen's self
		  organizing feature map algorithm. Performance of this
		  algorithm is compared to that of a previously developed
		  speech VQ algorithm based on covering followed by K-means
		  clustering. Two different criteria were used to analyze
		  algorithm performance. One criteria was the maximization of
		  entropy, a measure of the distribution of codebook element
		  usage. A nearly uniform distribution indicates that the
		  distribution of the training data is well modeled by the
		  codebook. The second criteria was the average distortion
		  between input vectors and their coded values. Low
		  distortion is an important goal for most VQ applications,
		  such as coding. The analyses show that the Kohonen
		  algorithm provided VQ codebooks superior to those created
		  by the clustering algorithm without increasing the amount
		  of processing required for training.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  naylor90a,
  author	= {J. A. Naylor},
  title		= {A neural network algorithm for enhancing delta
		  modulation/{LPC} tandem connections},
  booktitle	= {Proc. ICASSP-90, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1990},
  volume	= {I},
  pages		= {211--224},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  naylor92a,
  author	= {Naylor, J. A. and Rossen, M. L. },
  title		= {Neural network word/false-alarm discriminators for
		  improved keyword spotting},
  booktitle	= {IJCNN International Joint Conference on Neural Networks},
  year		= {1992},
  volume	= {2},
  pages		= {296--301},
  organization	= {ITT ACD, San Diego, CA, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  naylor92b,
  author	= {Naylor, J. A. and Huang, W. Y. and Nguyen, M. and Li, K.
		  P. },
  title		= {The application of neural networks to wordspotting},
  booktitle	= {Conference Record of The Twenty-Sixth Asilomar Conference
		  on Signals, Systems and Computers},
  year		= {1992},
  editor	= {Singh, A. },
  volume	= {2},
  pages		= {1081--5},
  organization	= {ITT Aerosp. Commun. Div. , San Diego, CA, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nazlibilek92a,
  author	= {Nazlibilek, S. and Erkmen, A. and Demirekler, M. },
  title		= {A neural controller for local activation in fractal
		  information network},
  booktitle	= {Distributed Intelligence Systems. Selected Papers from the
		  IFAC Symposium},
  year		= {1992},
  editor	= {Levis, A. H. and Stephanou, H. E. },
  pages		= {153--8},
  organization	= {Dept. of Electr. Eng. , Middle East Tech. Univ. , Ankara,
		  Turkey},
  publisher	= {Pergamon},
  address	= {Oxford, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  neagoe96a,
  author	= {V. -E. Neagoe},
  title		= {A circular {K}ohonen network for image vector
		  quantization},
  booktitle	= {Parallel Computing: State-of-the-Art and Perspectives},
  publisher	= {Elsevier},
  year		= {1996},
  editor	= {E. D'Hollander and F. J. Peters and G. R. Jouber and D.
		  Trystram},
  address	= {Amsterdam, Netherlands},
  pages		= {677--80},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  neagoe99a,
  author	= {Neagoe, V. and Fratila, I.},
  title		= {A neural segmentation of multispectral satellite images},
  booktitle	= {Computational Intelligence. Theory and Applications.
		  International Conference, 6th Fuzzy Days. Proceedings},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {334--41},
  abstract	= {A neural model for segmentation of multispectral satellite
		  images is presented, implying the following processing
		  stages: (a) feature extraction using the 2-dimensional
		  discrete cosine transform (2d-DCT) applied on the image
		  segment centered in the current pixel, for each frame of
		  the spectral sequence; (b) a neural self-organizing map
		  having as input the concatenation of the feature vectors
		  assigned to the projections of the current pixel in all the
		  image bands (computed in stage (a)). The software
		  implementation of the model for multispectral satellite
		  images SPOT leads to interesting experimental results.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  negri01a,
  author	= {Negri, S. and Belanche, L. A.},
  title		= {Heterogeneous Kohonen networks},
  booktitle	= {Connectionist Models of Neurons, Learning Processes, and
		  Artificial Intelligence. 6th International Work-Conference
		  on Artificial and Natural Neural Networks, IWANN 2001.
		  Proceedings, Part I (Lecture Notes in Computer Science Vol.
		  2084). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {243--52},
  abstract	= {A large number of practical problems involves elements
		  that are described as a mixture of qualitative and
		  quantitative information, and whose description is probably
		  incomplete. The self-organizing map is an effective tool
		  for visualization of high-dimensional continuous data. We
		  extend the network and training algorithm to cope with
		  heterogeneous information, as well as missing values. The
		  classification performance on a collection of benchmarking
		  data-sets is compared in different configurations. Various
		  visualization methods are suggested to aid users interpret
		  post-training results.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  nehmzow00b,
  author	= {Nehmzow, U.},
  title		= {Map building through self-organisation for robot
		  navigation},
  booktitle	= {Advances in Robot Learning. 8th European Workshop on
		  Learning Robots, EWLR-8. Proceedings (Lecture Notes in
		  Artificial Intelligence Vol.1812). Springer-Verlag, Berlin,
		  Germany},
  year		= {2000},
  volume	= {},
  pages		= {1--22},
  abstract	= {The ability to navigate is arguably the most fundamental
		  competence of any mobile agent, besides the ability to
		  avoid basic environmental hazards (e.g. obstacle
		  avoidance). The simplest method to achieve navigation in
		  mobile robot is to use path integration. However, because
		  this method suffers from drift errors, it is not robust
		  enough for navigation over middle scale and large scale
		  distances. This paper gives an overview of research in
		  mobile robot navigation at Manchester University, using
		  mechanisms of self-organisation (artificial neural
		  networks) to identify perceptual landmarks in the robot's
		  environment, and to use such landmarks for route learning
		  and self-localisation, as well as the quantitative
		  assessment of the performance of such systems.},
  dbinsdate	= {2002/1}
}

@TechReport{	  nehmzow90a,
  author	= {Ulrich Nehmzow and Tim Smithers},
  title		= {Mapbuilding using Self-Organizing Networks in 'Really
		  Useful Robots'},
  institution	= {Department of Artificial Intelligence, University of
		  Edinburgh},
  year		= {1990},
  number	= {DAI-489},
  address	= {Edinburgh, Scotland},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nehmzow91a,
  author	= {U. Nehmzow and T. Smithers and J. Hallam},
  title		= {Location recognition in a mobile robot using
		  \mbox{self-organising} feature maps},
  booktitle	= {Information Processing in Autonomous Mobile Robots. Proc.
		  of the Int. Workshop},
  year		= {1991},
  editor	= {G. Schmidt},
  pages		= {267--277},
  organization	= {VDI/VDE; German Soc. Meas. Autom},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nehmzow92a,
  author	= {U. Nehmzow and T. Smithers},
  title		= {Using motor actions for location recognition},
  booktitle	= {Toward a Practice of Autonomous Systems. Proc. First
		  European Conf. on Artificial Life},
  year		= {1992},
  editor	= {F. J. Varela and P. Bourgine},
  pages		= {96--104},
  organization	= {Cite des Sci. Ind. ; Banque de France; Fondation de
		  France; Electr. France; CEMAGREF; CNR; AFCET; CREA;
		  OFFILIB; Sun Microsyst},
  publisher	= {MIT Press},
  address	= {Cambridge, MA, USA},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  nehmzow92b,
  author	= {Ulrich Nehmzow},
  title		= {Experiments in Competence Acquisition for Autonomous
		  Mobile Robots},
  school	= {University of Edinburgh, Department of Artificial
		  Intelligence, Edinburgh, UK},
  year		= {1992},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nehmzow95a,
  author	= {Nehmzow, U. },
  title		= {Some initial experiments in self-organization and dynamic
		  sensing},
  booktitle	= {IEE Colloquium on Design and Development of Autonomous
		  Agents (Digest No. 1995/211)},
  year		= {1995},
  pages		= {5/1--3},
  organization	= {Dept. of Comput. Sci. , Manchester Univ. , UK},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nelson92a,
  author	= {D. J. Nelson and Shwu-Jen Chang and Muhlin Chen},
  title		= {Modeling the time of occurrence of electric utility peak
		  loads},
  booktitle	= {Proc. 1992 Summer Computer Simulation Conference.
		  Twenty-Fourth Annual Computer Simulation Conference},
  year		= {1992},
  editor	= {P. Luker},
  pages		= {217--212},
  organization	= {SCS},
  publisher	= {SCS},
  address	= {San Diego, CA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  neto96a,
  author	= {J. S. Neto and S. doN. Neto and F. A. deO. Nascimento},
  title		= {Dynamic bit allocation in image coding using a
		  Self-Organizing Map with Learning Vector Quantization},
  booktitle	= {38th Midwest Symposium on Circuits and Systems.
		  Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  editor	= {L. P. Caloba and P. S. R. Diniz and A. C. M. {de Querioz}
		  and E. H. Watanabe},
  address	= {New York, NY, USA},
  pages		= {858--61},
  dbinsdate	= {oldtimer}
}

@InCollection{	  neto97a,
  author	= {Jo{\~a}o Souza Neto and Sebasti{\~a}o do Nascimento Neto
		  and Francisco Assis de O. Nascimento},
  title		= {Improved Dynamic Bit Allocation in Image Coding Using a
		  Self-Organizing Map with Learning Vector Quantization},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {III},
  pages		= {1501--1505},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  neumann90d,
  author	= {Eric K. Neumann and David A. Wheeler and Jamie W. Burnside
		  and Adam S. Bernstein and Jeffrey C. Hall},
  title		= {A Technique for the Classification and Analysis of Insect
		  Courtship Song},
  booktitle	= {Proc. of the IJCNN, Washington},
  year		= {1990},
  volume	= {2},
  pages		= {257--262},
  annote	= {application, sound processing, classification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  neuwirth98a,
  author	= {Neuwirth, S. and Tenhagen, A. and Lippe, W. M},
  title		= {{K}ohonen-{SOM} for the neuro-computer Synapse 1 {N}110},
  booktitle	= {Proceedings of the 5th International Conference on Soft
		  Computing and Information/Intelligent Systems.
		  Methodologies for the Conception, Design and Application of
		  Soft Computing},
  publisher	= {World Scientific},
  address	= {Singapore},
  year		= {1998},
  volume	= {1},
  pages		= {77--80},
  abstract	= {We used the matrix-based neuro-computer Synapse 1 N110,
		  built by Siemens-Nixdorf, for computing Kohonen SOM (Self
		  Organizing Map) neural networks. Therefore the SOM
		  algorithm is represented mainly with matrix operations
		  provided by the C++ nAPL library coming with Synapse 1. We
		  developed an expandable OO model for the SOM. One of the
		  main goals of implementing the model was to create an
		  implementation for workstations. Additionally, programs
		  using our model can be compiled for Synapse 1 and for the
		  workstation without changing the source code. Tests using
		  workstations and Synapse 1 for SOMs showed that Synapse 1
		  can speed up the simulation significantly.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ng00a,
  author	= {Ng, A. and Smith, K. A.},
  title		= {{LOGSOM}: Web usage mining by a self-organizing map},
  booktitle	= {Smart Engineering System Design: Neural Networks, Fuzzy
		  Logic, Evolutionary Programming, Data Mining, and Complex
		  Systems. Vol.10. Proceedings of the Artificial Neural
		  Networks in Engineering Conference (ANNIE 2000). ASME, New
		  York, NY, USA},
  year		= {2000},
  volume	= {},
  pages		= {495--500},
  abstract	= {The continuous growth in the size and use of the Internet
		  is creating difficulties in the search for information. A
		  sophisticated method to organize the layout of the
		  information and assist user navigation is therefore
		  particularly important. We evaluate the feasibility of
		  using a self-organizing map (SOM) to mine Web log data and
		  provide a visual tool to assist user navigation. We have
		  developed LOGSOM, a system that utilizes Kohonen's
		  self-organizing map to organize Web pages into a
		  two-dimensional map. The organization of the Web pages is
		  based solely on the users' navigation behaviour, rather
		  than content of the Web pages. The resulting map not only
		  provides a meaningful navigation tool (for Web users) that
		  is easily incorporated with Web browsers, but also serves
		  as a visual analyzing tool for Webmasters, to better
		  understand the characteristics and navigation behaviours of
		  Web users visiting their pages.},
  dbinsdate	= {2002/1}
}

@Article{	  ngan99a,
  author	= {Shing Chung Ngan and Xiaoping Hu},
  title		= {Analysis of functional magnetic resonance imaging data
		  using \mbox{self-organizing} mapping with spatial
		  connectivity},
  journal	= {Magnetic Resonance in Medicine},
  year		= {1999},
  volume	= {41},
  pages		= {939--46},
  abstract	= {Commonly used methods in analyzing functional magnetic
		  resonance imaging ({fMRI}) data, such as the Student's
		  t-test and cross-correlation analysis, are model-based
		  approaches. Although these methods are easy to implement
		  and are effective in analyzing data obtained with simple
		  paradigms, they are not applicable in situations in which
		  patterns of neuronal response are complicated and when
		  {fMRI} response is unknown. In this work, Kohonen's
		  self-organizing mapping (SOM), which is a model-free
		  approach, is adapted for analyzing {fMRI} data. Because
		  spatial connectivity is an important function in the
		  identification of activation sites in functional brain
		  imaging, it is incorporated into the SOM algorithm.
		  Receiver operating characteristic analysis on simulated
		  data shows that the new algorithm achieves measurable
		  improvement over the standard algorithm. The applicability
		  of the new algorithm is demonstrated on experimental
		  data.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nguyen98a,
  author	= {Nguyen, P. T. A. and Romagnoli, R. and Fekete, P. and
		  Arnison, M. R. and Ling Guan and Cogswell, C.},
  title		= {A \mbox{self-organizing} map for extracting features of
		  chromosomes in microscopy images},
  booktitle	= {Proceedings of the Ninth Australian Conference on Neural
		  Networks (ACNN'98). Univ. Queensland, Brisbane,
		  Qld.,Australia},
  year		= {1998},
  volume	= {},
  pages		= {158--62},
  abstract	= {An investigation of an image processing algorithm used to
		  extract features of chromosomes from three-dimensional (3D)
		  image datasets taken by a confocal light microscope is
		  presented. The use of this confocal light microscope allows
		  biologists to observe live (or preserved) dividing eels in
		  3D. The top and bottom surfaces of these images' features
		  are indistinct, therefore requiring feature extraction and
		  segmentation of the chromosomes. Kohonen's Self-Organizing
		  Map (SOM) is used to perform segmentation. The segmentation
		  algorithm is first developed to work on 2D dataset, based
		  on a projection of the three-dimensional dataset, and then
		  generalised to 3D cases. The 3D approach to segmenting
		  individual chromosome features preserves the 3D
		  orientations in relation to the surrounding cell volume.
		  SOM performs very satisfactorily in both 2D and 3D cases.
		  Examples are provided to demonstrate the performance of the
		  proposed method.},
  dbinsdate	= {oldtimer}
}

@Article{	  nie94a,
  author	= {Junhong Nie and Linkens, D. A. },
  title		= {Fast self-learning multivariable fuzzy controllers
		  constructed from a modified CPN network},
  journal	= {International Journal of Control},
  year		= {1994},
  volume	= {60},
  number	= {3},
  pages		= {369--93},
  month		= {Sept},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  niebur91a,
  author	= {D. Niebur and A. J. Germond},
  title		= {Power system static security assessment using the
		  {K}ohonen neural network classifier},
  booktitle	= {Conf. Papers. 1991 Power Industry Computer Application
		  Conference. Seventeenth PICA Conference. },
  year		= {1991},
  pages		= {270--277},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  niebur91b,
  author	= {D. Niebur and A. J. Germond},
  title		= {Power flow classification for static security assessment},
  booktitle	= {Proc. First Int. Forum on Applications of Neural Networks
		  to Power Systems},
  year		= {1991},
  editor	= {M. A. El-Sharkawi and R. J. Marks II},
  pages		= {83--88},
  organization	= {IEEE; NSF; Pugent Power \& Light; EPRI},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  niebur91c,
  author	= {Dagmar Niebur and Alain J. Germond},
  title		= {Unsupervised Neural Net Classification of Power System
		  Static Security States},
  booktitle	= {Proc. Third Symp. on Expert Systems Application to Power
		  Systems},
  year		= {1991},
  address	= {Tokyo \& Kobe},
  dbinsdate	= {oldtimer}
}

@Article{	  niebur92a,
  author	= {D. Niebur and A. J. Germond},
  title		= {Unsupervised neural net classification of power system
		  static security states},
  journal	= {Int. J. Electrical Power \& Energy Systems},
  year		= {1992},
  volume	= {14},
  number	= {2--3},
  pages		= {233--242},
  month		= {April-June},
  dbinsdate	= {oldtimer}
}

@Article{	  niebur92b,
  author	= {D. Niebur and A. J. Germond},
  title		= {Power system static security assessment using the
		  {K}ohonen neural network classifier},
  journal	= {IEEE Trans. Power Systems},
  year		= {1992},
  volume	= {7},
  number	= {2},
  pages		= {865--872},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nielsen00a,
  author	= {Nielsen, C. F. and Passmore, P. J.},
  title		= {A solution to the problem of segmentation near edges using
		  adaptable class-specific representation},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {1},
  pages		= {436--40},
  abstract	= {Accurate segmentation of pixels near edges is important in
		  applications where exact shape and size is critical. Image
		  sampling traditionally involves moving a sampling window of
		  fixed shape across an image. Mismatches in the spatial
		  frequency domain between templates and new images occur
		  when the sampling window contains an edge and more than one
		  true segment. This paper presents a novel algorithm, which
		  adapts the shape of the sampling window locally,
		  approximating to optimal class-specific representations.
		  Unique representations of the same pixel for different
		  segment classes are generated before evaluation by a set of
		  classifiers. The algorithm is not specific to a particular
		  type of classifier or encoding scheme. In this paper the
		  algorithm is demonstrated by shelving that it produces
		  accurate segmentation with minimal or no edge artefacts of
		  artificial and natural colour images using LVQ
		  classifiers.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  nielsen00b,
  author	= {Nielsen, C. F. and Passmore, P. J.},
  title		= {Achieving accurate colour image segmentation in 2D and 3D
		  with {LVQ} classifiers and partial adaptable class-specific
		  representation},
  booktitle	= {Proceedings Fifth IEEE Workshop on Applications of
		  Computer Vision. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {72--8},
  abstract	= {Adaptable Class-Specific Representation (ACSR) has
		  previously been used as a solution to the problem of
		  segmentation near edges in 2D colour images. Sampling
		  windows of fixed shape used in many segmentation approaches
		  cause misrepresentation of texture classes. ACSR greatly
		  reduces this problem, based on simple templates, resulting
		  in accurate semi-automatic segmentation. The price of
		  accuracy in ACSR is high processing overhead. We introduce
		  an initial segmentation step using a faster fixed-shape
		  window sampling and Learning Vector Quantization, and apply
		  ACSR only at edge point. Processing speed is significantly
		  increased without compromising segmentation accuracy. ACSR
		  segmentation is particularly interesting for medical
		  applications where correct shape and size is important. We
		  extend the ACSR framework to true 3D volume segmentation.
		  3D information is used for classification at all sampling
		  points, producing better results than per slice pseudo-3D
		  segmentation. Colour volumes based on the Visible Human
		  Project are used to demonstrate the approach. We conclude
		  that ACSR can produce accurate segmentation in colour 2D
		  images and 3D volumes, and that partial ACSR can
		  significantly reduce processing overhead without losing
		  segmentation quality.},
  dbinsdate	= {2002/1}
}

@Article{	  nightingale90a,
  author	= {Charles Nightingale and Robert A. Hutchinson},
  title		= {Artificial neural nets and their application to image
		  processing},
  journal	= {British Telecom Technology J. },
  year		= {1990},
  volume	= {8},
  number	= {3},
  pages		= {81--93},
  month		= {July},
  x		= {The paper briefly introduces the principles of artificial
		  neural nets and the reasons for their adoption for solving
		  certain types of problems. . . . DIALOG No: 02993782 EI
		  Monthly No: EI9012139148},
  dbinsdate	= {oldtimer}
}

@InCollection{	  niki97a,
  author	= {Kazuhisa Niki},
  title		= {Self-organizing Information Retrieval System on the Web:
		  {S}ir{W}eb},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {881--884},
  dbinsdate	= {oldtimer}
}

@Article{	  nines97a,
  author	= {E. L. Nines and J. W. Gardner and C. E. R. Potter},
  title		= {Olfactory feature maps from an electronic nose},
  journal	= {Measurement and Control},
  year		= {1997},
  volume	= {30},
  number	= {9},
  pages		= {262--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ning01a,
  author	= {Ning Li and Li, Y. F.},
  title		= {Feature encoding for color image segmentation},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.4550},
  year		= {2001},
  volume	= {4550},
  pages		= {127--31},
  abstract	= {An approach for color image segmentation is proposed based
		  on the contributions of color features to segmentation
		  rather than the choice of a particular color space. It is
		  different from the previous methods where SOFM is used to
		  construct the feature encoding so that the feature-encoding
		  can self-organize the effective features for different
		  color images. Fuzzy clustering is applied for the final
		  segmentation when the well-suited color features and the
		  initial parameter are available. The proposed method has
		  been applied in segmenting different types of color images
		  and the experimental results show that it outperforms the
		  classical clustering method. Our study shows that the
		  feature encoding approach offers great promise in
		  automating and optimizing color image segmentation.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  nishida01a,
  author	= {Nishida, T. and Kurogi, S. and Saeki, T.},
  title		= {An analysis of competitive and reinitialization learning
		  for adaptive vector quantization},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {2},
  pages		= {978--983},
  organization	= {Dept. of Control Engineering, Kyushu Institute of
		  Technology},
  publisher	= {},
  address	= {},
  abstract	= {This paper describes an analysis of competitive and
		  reinitialization learning (CRL) for adaptive vector
		  quantization (AVQ) which is a version of vector
		  quantization (VQ) in order for digital coding of signals to
		  adapt to changing statistics of the signal sources. The CRL
		  has been designed for achieving equidistortion or
		  asymptotically optimal quantization to overcome the
		  under-utilization problem or the local minimum problem of
		  vector quantization networks, while its performance in
		  adaptation speed and obtained distortion level has been
		  shown higher than the conventional AVQ algorithms such as
		  OPTM (optimal adaptive k-means algorithm) and DOCL-II
		  (diversity oriented competitive learning II). In this
		  paper, after reviewing the CRL algorithm, we examine how
		  the CRL algorithm works for various source signals such as
		  nonstationary 2 dimensional vectors and high dimensional
		  images. Furthermore, we compare the performance of the CRL
		  with the OPTM and the DOCL-II.},
  dbinsdate	= {2002/1}
}

@Article{	  nishida01b,
  author	= {Nishida, T. and Kurogi, S. and Saeki, T.},
  title		= {Adaptive vector quantization using re-initialization
		  method},
  journal	= {Transactions-of-the-Institute-of-Electronics,-Information-and-Communication-Engineers-D-II}
		  ,
  year		= {2001},
  volume	= {},
  pages		= {1503--11},
  abstract	= {The conventional vector quantization (VQ) algorithms
		  assume that the input signals follow time-invariant
		  probability distributions, however, in actual cases the
		  statistics of input signals, sensor, etc. is time-varying.
		  So far, several adaptive VQ algorithms for applying to such
		  cases have been presented, however, their adaptation speeds
		  are slow and they have many parameters to be adjusted. We
		  present a novel adaptive VQ algorithm using a
		  re-initialization method and the gradient method for
		  competitive learning of the neural networks. Moreover, we
		  verify the performance of the present algorithm through
		  several quantizer problems.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  nishina94a,
  author	= {Nishina, T. and Hagiwara, M. and Nakagawa, M. },
  title		= {Fuzzy inference neural networks which automatically
		  partition a pattern space and extract fuzzy if-then rules},
  booktitle	= {Proceedings of the Third IEEE Conference on Fuzzy Systems.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {2},
  pages		= {1314--19},
  organization	= {Dept. of Electr. Eng. , Keio Univ. , Yokohama, Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nissinen00a,
  author	= {Nissinen, Ari S. and Hyotyniemi, Heikki and Koivo,
		  Heikki},
  title		= {Evolutionary self-organizing model bank},
  booktitle	= {Control Systems, Preprints, Conference},
  year		= {2000},
  editor	= {},
  volume	= {},
  pages		= {267--270},
  organization	= {Helsinki Univ of Technology},
  publisher	= {TAPPI Press},
  address	= {Norcross, GA},
  abstract	= {The evolutionary self-organizing map (EVSOM) capable of
		  creating an organized model bank from a data set is
		  presented. The framework introduces a self-organizing
		  algorithm that enables identification and organization of
		  models having different structures. During the training,
		  behavioral clustering of data and model structure
		  identification is carried out simultaneously. The algorithm
		  is demonstrated with a process having a time variant
		  structure.},
  dbinsdate	= {2002/1}
}

@InCollection{	  nissinen98a,
  author	= {A. S. Nissinen and H. Hy\"otyniemi},
  title		= {Evolutionary training of behavior-based
		  \mbox{self-organizing} map},
  booktitle	= {1998 IEEE International Conference on Evolutionary
		  Computation Proceedings. IEEE World Congress on
		  Computational Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  address	= {New York, NY, USA},
  pages		= {660--5},
  abstract	= {This paper presents a novel idea of a behavior-based
		  self-organizing map. The self-organizing map (SOM) is
		  extended to cover `objects' that interact with their
		  environment. They are organized based on their behavior
		  instead of parameterized presentation. The original SOM
		  needs a metric to be defined, while in the new
		  self-organizing map no metric between the parameterized
		  presentations is needed. The neighborhood concept of SOM
		  algorithm is given a probability interpretation that is
		  suitable for evolutionary computing. The behavior based SOM
		  algorithm is presented, and the new concept is demonstrated
		  on linear time-series models, that are identified and
		  organized based on sample data from a simulated system.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nissinen98b,
  author	= {Nissinen, A. S. and Hy\"otyniemi, H.},
  title		= {Evolutionary \mbox{self-organizing} map},
  booktitle	= {6th European Congress on Intelligent Techniques and Soft
		  Computing. EUFIT '98},
  publisher	= {Verlag Mainz},
  address	= {Aachen, Germany},
  year		= {1998},
  volume	= {3},
  pages		= {1596--600},
  abstract	= {An evolutionary self-organizing map is presented. The
		  evolutionary training algorithm operates on a
		  two-dimensional population grid that has sample points to
		  guide the search. As a result of competition and locally
		  guided evolution the network is able to create organization
		  among individuals. For visual validation of the algorithm,
		  a two-dimensional data example is presented.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nogami97a,
  author	= {Y. Nogami and Y. Jyo and M. Yoshioka and S. Omatu},
  title		= {Remote sensing data analysis by {K}ohonen feature map and
		  competitive learning},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man, and Cybernetics},
  volume	= {1},
  year		= {1997},
  publisher	= {Institute of Electrical and Electronics Engineers, Inc.},
  address	= {Piscataway, NJ},
  pages		= {524--529},
  dbinsdate	= {oldtimer}
}

@Article{	  nogami98a,
  author	= {Y. Nogami and Y. Jyo and M. Yoshioka and S. Omatu},
  title		= {Remote sensing data analysis by using {K}ohonen feature
		  map and competitive learning},
  journal	= {Transactions of the Institute of Systems, Control and
		  Information Engineers},
  year		= {1998},
  volume	= {11},
  number	= {9},
  pages		= {508--13},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nolker00a,
  author	= {Nolker, C. and Ritter, H.},
  title		= {Parametrized {SOM}s for hand posture reconstruction},
  booktitle	= {Proceedings of the IEEE-INNS-ENNS International Joint
		  Conference on Neural Networks. IJCNN 2000. Neural
		  Computing: New Challenges and Perspectives for the New
		  Millennium. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {4},
  pages		= {139--44},
  abstract	= {This paper describes the use of neural network for gesture
		  recognition based on finger tips, a system that recognizes
		  continuous hand postures from video images. Our approach
		  yields a full identification of all finger joint angles.
		  This allows a full reconstruction of the 3D hand shape,
		  using an artificial hand model with 16 segments and 20
		  joint angles. The focus of the present paper is how to
		  employ a parametrised SOM neural network for the inverse
		  kinematics task to compute the angles of a hand model out
		  of 3D positions of the fingertips. We show that this type
		  of neural net does not only achieve excellent results from
		  very few training examples, but also can be applied to
		  uncommon data structures.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  nomura95a,
  author	= {Nomura, T. and Miyoshi, T. },
  title		= {An adaptive rule extraction with the fuzzy
		  \mbox{self-organizing} map and a comparison with other
		  methods},
  booktitle	= {Proceedings of ISUMA---NAFIPS '95 The Third International
		  Symposium on Uncertainty Modeling and Analysis and Annual
		  Conference of the North American Fuzzy Information
		  Processing Society},
  year		= {1995},
  pages		= {311--16},
  organization	= {Software Lab. , Sharp Corp. , Nara, Japan},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  abstract	= {For automatic rule extraction from a set of input-output
		  data examples, decision tree generating methods such as ID3
		  and Fuzzy ID3 play a major role. These methods, however,
		  are difficult to apply when there is a tendency for the
		  examples to change dynamically. This paper presents a new
		  method for adaptive rule extraction with the Fuzzy
		  Self-Organizing Map and the results of the simulations to
		  present the effectiveness by a comparison with other
		  methods such as RBF and GA. We got the result that our
		  method is superior to other methods for automatic and
		  adaptive rule extraction.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  nomura96a,
  author	= {T. Nomura and T. Miyoshi},
  title		= {An adaptive fuzzy rule extraction using hybrid model of
		  the fuzzy \mbox{self-organizing} map and the genetic
		  algorithm with numerical chromosomes},
  booktitle	= {Methodologies for the Conception, Design, and Application
		  of Intelligent Systems. Proceedings of the 4th
		  International Conference on Soft Computing},
  publisher	= {World Scientific},
  year		= {1996},
  volume	= {1},
  editor	= {T. Yamakawa and G. Matsumoto},
  address	= {Singapore},
  pages		= {70--3},
  dbinsdate	= {oldtimer}
}

@Article{	  nomura96b,
  author	= {T. Nomura and T. Miyoshi},
  title		= {An adaptive rule extraction with the fuzzy
		  \mbox{self-organizing} map and a comparison with other
		  methods},
  journal	= {Japanese Journal of Fuzzy Theory and Systems},
  year		= {1996},
  volume	= {8},
  number	= {2},
  pages		= {283--98},
  dbinsdate	= {oldtimer}
}

@Article{	  nor95a,
  author	= {Nor, K. B. M. },
  title		= {Neural networks based on simultaneous equations},
  journal	= {Malaysian Journal of Computer Science},
  year		= {1995},
  volume	= {8},
  number	= {1},
  pages		= {25--42},
  dbinsdate	= {oldtimer}
}

@Misc{		  norditadiku86,
  key		= {Nordita},
  title		= {Nordita-{DIKU} Conf. on Vision},
  howpublished	= {Conf. proceedings in journal Physica Scripta Vol. 39(1)},
  year		= {1989},
  month		= {January},
  annote	= {Conf. date: Aug. 1986},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nordstrom92a,
  author	= {Tomas Nordstr{\"{o}}m},
  title		= {Designing Parallel Computers for {S}elf {O}rganizing
		  {M}aps},
  booktitle	= {Proc. DSA-92, Fourth Swedish Workshop on Computer System
		  Artchitecture},
  year		= {1992},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  nordstrom95a,
  author	= {Tomas Nordstr{\"{o}}m},
  title		= {Highly Parallel Computers for Artificial Neural Networks},
  school	= {Lule{\aa} University of Technology},
  year		= {1995},
  address	= {Lule{\aa}, Sweden},
  dbinsdate	= {oldtimer}
}

@Article{	  nour96a,
  author	= {M. A. Nour and G. R. Madey},
  title		= {Heuristic and optimization approaches to extending the
		  {K}ohonen self organizing algorithm},
  journal	= {European Journal of Operational Research},
  year		= {1996},
  volume	= {93},
  number	= {2},
  pages		= {428--48},
  dbinsdate	= {oldtimer}
}

@Article{	  novic95a,
  author	= {Novic, M. and Zupan, J. },
  title		= {Investigation of infrared spectra-structure correlation
		  using {K}ohonen and counterpropagation neural network},
  journal	= {Journal of Chemical Information and Computer Sciences},
  year		= {1995},
  volume	= {35},
  number	= {3},
  pages		= {454--66},
  month		= {May-June},
  dbinsdate	= {oldtimer}
}

@Article{	  noyons98a,
  author	= {Noyons, E. C. M. and van Raan, A. F. J.},
  title		= {Monitoring scientific developments from a dynamic
		  perspective: self-organized structuring to map neural
		  network research},
  journal	= {Journal of the American Society for Information Science},
  year		= {1998},
  number	= {1},
  volume	= {49},
  pages		= {68--81},
  abstract	= {With the help of bibliometric mapping techniques, we have
		  developed a methodology of 'self-organized' structuring of
		  scientific fields. This methodology is applied to the field
		  of neural network research. We propose a field-definition
		  based on the present situation. This is done by letting the
		  data themselves generate a structure, and, with that,
		  define the subdivision of the research field into
		  meaningful subfields. In order to study the evolution over
		  time, the above 'self-organized' definition of the present
		  structure is taken as a framework for the past structure.
		  We explore this evolution by monitoring the interrelations
		  between subfields and by zooming into the internal
		  structure of each subfield. The overall ('coarse')
		  structure and the detailed subfield maps ('fine structure')
		  are used for monitoring the dynamical features of the
		  entire research field. Furthermore, by determining the
		  positions of the main actors on the map, these structures
		  can also be used to assess the activities of these main
		  actors (universities, firms, countries, etc.). Finally, we
		  'reverse' our approach by analyzing the developments based
		  on a structure generated in the past. Comparison of the
		  'real present' and the 'present constructed from the past'
		  may provide new insight into successful, as well as
		  unsuccessful, patterns and 'trajectories' of developments.
		  Thus, we explore the potential of our method to put the
		  observed 'actual' developments into a possible future
		  perspective.},
  dbinsdate	= {oldtimer}
}

@Article{	  nunes92a,
  author	= {J. F. Nunes and J. S. Marques},
  title		= {A comparison of two low bit rate image coders},
  journal	= {European Trans. on Telecommunications and Related
		  Technologies},
  year		= {1992},
  volume	= {3},
  number	= {6},
  pages		= {599--603},
  month		= {November-December},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  nunes_de_castro99a,
  author	= {{Nunes de Castro}, L. and {Von Zuben}, F. J.},
  title		= {An improving pruning technique with restart for the
		  {K}ohonen \mbox{self-organizing} feature map},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1916--19},
  abstract	= {Presents a pruning technique developed for the
		  one-dimensional Kohonen self-organizing feature map (SOM)
		  to be applied in clustering and classification problems.
		  Its innovative aspect is the combined proposition of a
		  penalty term, a clustering measure, a delayed pruning
		  activation and a restarting phase. The proposed algorithm
		  (PSOM) always guides to a reduced architecture capable of
		  representing the data set. We compare the PSOM with the
		  original SOM applying them to three different
		  classification problems. The results show that the PSOM is
		  able to present superior performance in all cases.},
  dbinsdate	= {oldtimer}
}

@Article{	  obach01a,
  author	= {Obach, M. and Wagner, R. and Werner, H. and Schmidt, H.
		  H.},
  title		= {Modelling population dynamics of aquatic insects with
		  artificial neural networks},
  journal	= {ECOLOGICAL MODELLING},
  year		= {2001},
  volume	= {146},
  number	= {1--3},
  month		= {DEC 1},
  pages		= {207--217},
  abstract	= {We modelled the total number of individuals of selected
		  water insects based on a 30-year data set of population
		  dynamics and environmental variables (discharge,
		  temperature, precipitation, abundance of parental
		  generation) in a small stream in central Germany. For data
		  exploration, visualisation of data, outlier detection,
		  hypothesis generation, and to detect basic patterns in the
		  data, we used Kohonen's self organizing maps (SOM). They
		  are comparable to statistical cluster analysis by
		  ordinating data into groups. Based on annual abundance
		  patterns of Ephemeroptera, Plecoptera and Trichoptera
		  (EPT), species groups with similar ecological requirements
		  were distinguished. Furthermore, we applied linear neural
		  networks, general regression neural networks, modified
		  multi-layer perceptrons, and radial basis function networks
		  combined with a SOM (RBFSOM) and successfully predicted the
		  annual abundance of selected species from environmental
		  variables. Results were visualised in three-dimensional
		  plots. Relevance detection methods were sensitivity
		  analysis, stepwise method and Genetic Algorithms. Instead
		  of a sliding windows approach we computed the in- and
		  output data of fixed periods for two caddis flies. In order
		  to assess the quality of the models we applied several
		  reliability measures and compared the generalisation error
		  with the long- term mean of the target variable. RBFSOMs
		  were used to denominate and visualise local and general
		  model accuracy. Results were interpreted on the basis of
		  known species traits. We conclude that it is possible to
		  predict the abundance of aquatic insects based on relevant
		  environmental factors using artificial neural networks. },
  dbinsdate	= {2002/1}
}

@InCollection{	  obaidat95a,
  author	= {M. S. Obaidat and O. Khalid},
  title		= {Performance evaluation of neural network paradigms for the
		  characterization of ultrasonic transducers},
  booktitle	= {ICECS '95. International Conference on Electronics,
		  Circuits and Systems. Proceedings},
  publisher	= {Higher Council for Sci. \& Technol},
  year		= {1995},
  address	= {Amman, Jordan},
  pages		= {370--6},
  dbinsdate	= {oldtimer}
}

@Article{	  obaidat97a,
  author	= {Obaidat, M. S. and Sadoun, Balqies},
  title		= {Verification of computer users using keystroke dynamics},
  journal	= {IEEE Transactions on Systems, Man, and Cybernetics. Part
		  B: Cybernetics},
  year		= {1997},
  number	= {2},
  volume	= {27},
  pages		= {261--269},
  abstract	= {This paper presents techniques to verify the identify of
		  computer users using the keystroke dynamics of computer
		  user's login string as characteristic patterns using
		  pattern recognition and neural network techniques. This
		  work is a continuation of our previous work [1]-[3] where
		  only interkey times were used as features for identifying
		  computer users. In this work we used the key hold times for
		  classification and then compared the performance with the
		  former interkey time-based technique. Then we use the
		  combined interkey and hold times for the identification
		  process. We applied several neural network and pattern
		  recognition algorithms for verifying computer users as they
		  type their password phrases. It was found that hold times
		  are more effective than interkey times and the best
		  identification performance was achieved by using both time
		  measurements. An identification accuracy of 100% was
		  achieved when the combined hold and interkey time-based
		  approach were considered as features using the fuzzy
		  ARTMAP, radial basis function networks (RBFN), and learning
		  vector quantization (LVQ) neural network paradigms. Other
		  neural network and classical pattern algorithms such as
		  backpropagation with a sigmoid transfer function (BP,
		  Sigm), hybrid sum-of-products (HSOP), sum-of-products
		  (SOP), potential function and Bayes' rule algorithms gave
		  moderate performance. Using the hold time as a feature for
		  identifying the computer user is novel.},
  dbinsdate	= {oldtimer}
}

@Article{	  obaidat98a,
  author	= {Obaidat, M. S. and Khalid, H. and Sadoun, Balqies},
  title		= {Ultrasonic transducer characterization by neural
		  networks},
  journal	= {Information Sciences},
  year		= {1998},
  number	= {1},
  volume	= {107},
  pages		= {195--215},
  abstract	= {This paper presents neural network-based system for the
		  characterization of ultrasonic transducers. An automated
		  system for characterizing ultrasonic transducers was
		  designed and built. Different characterizing algorithms
		  were applied and their performance was investigated and
		  compared. It was found that artificial neural network (ANN)
		  techniques, in general, provide better classification as
		  compared to the pattern recognition techniques we applied
		  earlier. The Moody-Darken Radial Basis Function network
		  (MD-RBFN), Learning Vector Quantization (LVQ) with 52
		  kohonen neurons, and Fuzzy ARTMAP classification network
		  are the neural networks (NNs) that provided us with a
		  classification accuracy of 100%. Several variants of
		  Backpropagation neural network (BPNN) were tested for this
		  application, and the classification results were seen to
		  vary in the range 6.55%-98.35%. The best performing
		  paradigm among several variants of the Modular Neural
		  Network (MNN), Reinforcement Neural Network (RNN),
		  Probabilistic Neural Network (PNN), and Counterpropagation
		  Neural Network (CPNN) produced a classification accuracy of
		  78.85%, 31.42%, 45.21%, and 79.28%, respectively. The
		  competitive learning (CL) technique provided poor results
		  as compared to the Self-Organizing-Map (SOM) for
		  pre-clustering.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  obermayer90a,
  author	= {Klaus Obermayer and Helmut Heller and Helge Ritter and
		  Klaus Schulten},
  title		= {Simulation of Self-Organizing Neural Nets: A Comparision
		  Between a Transputer Ring and a {C}onnection {M}achine
		  {CM-2}},
  booktitle	= {{NATUG~3}: Transputer Res. and Applications~3},
  organization	= {North American Transputer Users Group},
  publisher	= {{IOS} Press},
  address	= {Amsterdam, Netherlands},
  editor	= {Alan S. Wagner},
  year		= {1990},
  pages		= {95--106},
  annotes	= {Proc. of the Third Conf. of the North American Transputer
		  Users Group, April~26--27, 1990---Sunnyvale, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  obermayer90b,
  author	= {K. Obermayer and H. Ritter and K. Schulten},
  journal	= {Parallel Computing},
  volume	= {14},
  year		= {1990},
  pages		= {381--404},
  title		= {Large-Scale Simulations of Self-Organizing Neural Networks
		  on Parallel Computers: Application to Biological
		  Modelling},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  obermayer90c,
  author	= {K. Obermayer and H. Ritter and K. Schulten},
  title		= {A Neural Network Model for the Formation of Topographic
		  Maps in the {CNS}: Development of Receptive Fields},
  booktitle	= {Proc. IJCNN-90, International Joint Conference of Neural
		  Networks, Washington, DC},
  year		= {1990},
  pages		= {423--429},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  obermayer90d,
  author	= {K. Obermayer and H. J. Ritter and K. J. Schulten},
  title		= {A Principle for the Formation of the Spatial Structure of
		  Cortical Feature Maps},
  journal	= {Proc. Natl Acad. of Sci. , {USA}},
  year		= {1990},
  volume	= {87},
  pages		= {8345--8349},
  month		= {November},
  annote	= {A dot-product SOM. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  obermayer90e,
  author	= {K. Obermayer and H. Ritter and K. Schulten},
  title		= {Large-Scale Simulation of a \mbox{Self-organizing} Neural
		  Network: Formation of a {SOM} totopic Map},
  booktitle	= {Parallel Processing in Neural Systems and Computers},
  year		= {1990},
  editor	= {R. Eckmiller and G. Hartmann and G. Hauske},
  pages		= {71--74},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  annote	= {Dot product as a similarity measure; otherwise like normal
		  SOM. },
  dbinsdate	= {oldtimer}
}

@Article{	  obermayer91a,
  author	= {Klaus Obermayer and Helge Ritter and Klaus Schulten},
  title		= {A Model for the Development of the Spatial Structure of
		  Retinotopic Maps and Orientation Columns},
  journal	= {{IEICE} Trans. Fund. Electr. Comm. Comp. Sci. },
  volume	= {{E75-A}},
  number	= 5,
  month		= may,
  year		= 1992,
  pages		= {537--545},
  note		= {Reprinted in {\em The Principles of Organization in
		  Organisms---{S}anta {F}e {I}nstitute Studies in the
		  Sciences of Complexity, Vol. ~{XII}. } A. \ Baskin and J. \
		  Mittenthal, Eds. (Addison Wesley, 1991)},
  annote	= {In the book version, the pages are 141--166. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  obermayer91b,
  author	= {Klaus Obermayer and Gary G. Blasdel and Klaus Schulten},
  title		= {A neural network model for the formation and for the
		  spatial structure of retinotopic maps, orientation-and
		  ocular dominance columns},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {Kohonen, Teuvo and M{\"{a}}kisara, Kai and Simula, Olli
		  and Kangas, Jari},
  pages		= {505--511},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  obermayer91c,
  author	= {K. Obermayer and H. Ritter and K. Schulten},
  title		= {Development and Spatial Structure of Cortical Feature
		  Maps: A Model Study},
  booktitle	= {Advances in Neural Information Processing Systems 3},
  publisher	= {Morgan Kaufmann},
  year		= {1991},
  editor	= {Richard P. Lippmann and John E. Moody and David S.
		  Touretzky},
  address	= {San Mateo, CA},
  pages		= {11--17},
  dbinsdate	= {oldtimer}
}

@Article{	  obermayer92a,
  author	= {K. Obermayer and G. G. Blasdel and K. Schulten},
  journal	= {Physical Review A [Statistical Physics, Plasmas, Fluids,
		  and Related Interdisciplinary Topics]},
  year		= {1992},
  volume	= 45,
  number	= 10,
  pages		= {7568--7589},
  title		= {A Statistical Mechanical Analysis of Self-Organization and
		  Pattern Formation during the Development of Visual Maps},
  dbinsdate	= {oldtimer}
}

@Article{	  obermayer92b,
  author	= {K. Obermayer},
  journal	= {Annales du Groupe CARNAC},
  year		= {1992},
  volume	= 5,
  pages		= {91--104},
  title		= {Neural pattern formation and \mbox{self-organizing} maps},
  dbinsdate	= {oldtimer}
}

@InCollection{	  obermayer92c,
  author	= {K. Obermayer and K. Schulten and G. G. Blasdel},
  title		= {A Comparison of a Neural Network Model for the Formation
		  of Brain Maps with Experimental Data},
  booktitle	= {Advances in Neural Information Processing Systems 4},
  publisher	= {Morgan Kaufmann},
  year		= {1992},
  editor	= {John E. Moody and Stephen J. Hanson and Richard P.
		  Lippmann},
  address	= {San Mateo, CA},
  pages		= {83--90},
  dbinsdate	= {oldtimer}
}

@Book{		  obermayer93a,
  author	= {K. Obermayer},
  title		= {Adaptive neuronale Netze und ihre Anwendung als Modelle
		  der Entwicklung kortikaler Karten},
  publisher	= {Infix Verlag},
  address	= {Sankt Augustin, Germany},
  year		= {1993},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  obermayer93b,
  author	= {Klaus Obermayer},
  title		= {Modelling the Formation of Sensory Representation in the
		  Brain},
  volume	= {I},
  pages		= {117--135},
  booktitle	= {Proc. Conf. on Prerational Intelligence---Phenomenology of
		  Complexity Emerging in Systems of Agents Interagtion Using
		  Simple Rules},
  year		= {1993},
  address	= {Center for Interdisciplinary Research, University of
		  Bielefeld},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  obrien93a,
  author	= {Jane O'Brien and Colin Reeves},
  title		= {Comparison of Neural Network Paradigms for Condition
		  Monitoring},
  booktitle	= {Proc. 5th Int. Congress on Condition Monitoring and
		  Diagnostic Engineering Management},
  year		= {1993},
  editor	= {Raj B. K. N. Rao and G. J. Trmal},
  pages		= {395--400},
  organization	= {University of the West of England},
  address	= {Bristol. UK},
  dbinsdate	= {oldtimer}
}

@Article{	  obu-cann00a,
  author	= {Obu-Cann, K. and Fujimura, K. and Tokutaka, H. and
		  Yoshihara, K.},
  title		= {Data mining from chemical spectra data using
		  \mbox{self-organising} maps},
  journal	= {Neural Network World},
  year		= {2000},
  volume	= {10},
  pages		= {217--30},
  abstract	= {The self-organising map (SOM) being one of the most widely
		  used ANNs, is a powerful tool for data mining or knowledge
		  discovery and visualisation of high dimensional data. It
		  simultaneously performs topology preservation of the data
		  space while quantizing the data space formed by the input
		  data. Data is useless to mankind if no meaningful
		  information can be derived from it. In this work, SOM is
		  applied to chemical spectral data from auger electron
		  spectroscopy (AES), X-ray photoelectron spectroscopy (XPS)
		  and a combination of data from both AES and XPS. This paper
		  also builds a SOM of elements in the periodic table. By use
		  of this map, any element can be analysed. In topology
		  preservation, similar input patterns that are close to each
		  other in the input data space are correspondingly located
		  close to each other on the map. This paper looks at
		  clustering using the minimal spanning tree (MST).},
  dbinsdate	= {oldtimer}
}

@Article{	  obu-cann00b,
  author	= {Obu-Cann, K. and Tokutaka, H. and Fujimura, K. and
		  Yoshihara, K.},
  title		= {Chemical analysis using {XPS} data and self-organizing
		  maps},
  journal	= {Surface and Interface Analysis},
  year		= {2000},
  volume	= {30},
  number	= {1},
  month		= {Aug},
  pages		= {181--184},
  organization	= {Univ of Tottori},
  publisher	= {John Wiley \& Sons Ltd},
  address	= {Chichester},
  abstract	= {This paper reports on the application of self-organizing
		  maps (SOM) to the chemical analysis of FeNi alloys by XPS.
		  During the course of the experiment, either by human or
		  mechanical error, the conditions for the experiment may not
		  remain the same. Thus, the results obtained may be slightly
		  different from expected. Because the compositions of Fe and
		  Ni in the alloys are anti-correlated, the XPS signals from
		  Fe2p can be used to minimize the error margin of the XPS
		  signals from Ni2p, and vice versa, depending on the most
		  sensitive of the two. The SOM method developed by Kohonen
		  was first applied to information processing. Currently, it
		  has been applied to some problems of chemical analysis
		  using AES, XPS and x-ray Diffraction data. One
		  characteristic of the SOM is its ability to make
		  multidimensional data visible on a two-dimensional map.
		  Using a two-dimensional SOM, the items that are described
		  qualitatively by linguistic expressions can be explained
		  more quantitatively by the position of the spectral data on
		  the SOM, together with a grey-level expression.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  obu-cann00c,
  author	= {K. Obu-Cann and Y. Morita and K. Fujimura and H. Tokutaka
		  and M. Ohkita and M. Inui},
  title		= {Data Mining of Power Transformer Database using Self
		  Organising Maps},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  crossref	= {},
  key		= {},
  pages		= {201--6},
  year		= {2000},
  editor	= {},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  obu-cann00d,
  author	= {K. Obu-Cann and K. Fujimura and H. Tokutaka and K.
		  Yoshihara and Metals Materials Group of SASJ},
  title		= {Application of Self-Organizing Maps to Data Mining using
		  Chamical Spectra},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {311--8},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@Article{	  obu-cann00e,
  author	= {Obu-Cann, K. and Fujimura, K. and Tokutaka, H.},
  title		= {Clustering by {SOM} (self-organising maps), {MST} (minimal
		  spanning tree) and {MCP} (modified counter propagation)},
  journal	= {Australian-Journal-of-Intelligent-Information-Processing-Systems}
		  ,
  year		= {2000},
  volume	= {6},
  pages		= {72--81},
  abstract	= {Evaluation of the cluster classification generated by the
		  self-organising maps (SOM) is usually done by the human
		  eye. Due to the qualitative nature of this experiment, in a
		  dense input data space, the evaluator may either
		  overestimate or underestimate the number of clusters formed
		  on the map. With this approach, the exact number of
		  clusters generated by the map cannot be confirmed because
		  of the misinterpretation of the grey level expression. This
		  paper presents the application of SOM to chemical spectral
		  analysis and the use of the minimal spanning tree (MST) and
		  the modified counter propagation (MCP) algorithms in
		  cluster classification.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  obu-cann01a,
  author	= {K. Obu-Cann and K. Fujimura and H. Tokutaka and M. Ohkita
		  and M. Inui and S. Yamada},
  title		= {Exploring power transformer database using self-organising
		  maps ({SOM}) and minimal spanning tree ({MST})},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {132--9},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  obu-cann01b,
  author	= {Obu-Cann, Kwaw and Kikuo, F. and Heizo, T. and Masaaki, O.
		  and Masahiro I.},
  title		= {{SOM} an approach to data mining of power transformer
		  database},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-C}
		  ,
  year		= {2001},
  volume	= {121},
  pages		= {1126--32},
  abstract	= {Data mining is part of a larger area of recent research in
		  artificial intelligence and information processing and
		  management otherwise known as knowledge discovery in
		  database (KDD). The main aim here is to identify new
		  information or knowledge from database in which the
		  dimensionality or amount of data is so large that it is
		  beyond human comprehension. Self-organising map is used to
		  analyse power transformer database from one of the electric
		  energy providers in Japan. Furthermore, the regression
		  aspect of SOM is also tested. Regression is achieved by
		  searching for the best matching unit (BMU) using the known
		  vector components.},
  dbinsdate	= {2002/1}
}

@Article{	  obu-cann02a,
  author	= {Obu Cann, K. and Fujimura, K. and Tokutaka, H. and Ohkita,
		  M. and Inui M. and Yamada S.},
  title		= {Data mining with self-organising maps ({SOM}) and minimal
		  spanning tree ({MST})},
  journal	= {International-Journal-of-Knowledge-Based-Intelligent-Engineering-Systems}
		  ,
  year		= {2002},
  volume	= {6},
  pages		= {40--7},
  abstract	= {data mining or exploration is part of a large area of
		  recent research in artificial intelligence and information
		  processing and management otherwise known as knowledge
		  discovery in database (KDD). The main aim here is to
		  identify new information or knowledge from database in
		  which the dimensionality or amount of data is so large that
		  it is beyond human comprehension. Self-organising map and
		  minimal spanning tree are used to analyse power transformer
		  database from one of the electric energy providers in
		  Japan. Evaluation of the clusters generated by SOM is
		  usually done by human eye. Due to its qualitative nature,
		  the evaluator may either overestimate or underestimate the
		  number of clusters formed on the map. With this approach,
		  the exact number of clusters generated by the map cannot be
		  confirmed because of the misinterpretation of the grey
		  level expression. This paper looks at clustering with
		  minimal spanning tree (MST).},
  dbinsdate	= {2002/1}
}

@Article{	  obu-cann99a,
  author	= {K. Obu-Cann and H. Tokutaka and K. Fujimura and K.
		  Yoshihara},
  title		= {Chemical Analysis of {AES}, {XPS} and {XRD} Data using
		  Self-Organising Maps},
  journal	= {Journal of Surface Analysis},
  year		= {1999},
  volume	= {5},
  number	= {1},
  pages		= {208--211},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oddo00a,
  author	= {Oddo, L. A. and Doucette, P. and Agouris, P.},
  title		= {Automated road extraction via the hybridization of
		  self-organization and model based techniques},
  booktitle	= {Proceedings 29th Applied Imagery Pattern Recognition
		  Workshop. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {},
  abstract	= {A novel approach to automated road extraction using a
		  hybrid combination of self-organization and model-based
		  extraction techniques is introduced. We use a
		  self-organized mapping technique to first delineate the
		  medial axis topology of road features. This is accomplished
		  through a local clustering of class-binarized spatial
		  information provided from a region segmentation. Since the
		  cluster analysis exemplifies a center-of-gravity solution,
		  it is not sensitive to edge definition. Topological
		  structure is subsequently derived through the application
		  of a graph-theoretic approach to link convergent cluster
		  centers. Taking initialization cues from the centerline
		  extraction results of self-organization, a model-based
		  fitting algorithm is then applied to robustly delineate
		  road segment orientations and widths. Preliminary results
		  demonstrate the ability of this approach to automatically
		  extract road centerline position as well as road segment
		  width and orientation in high spatial resolution urban
		  imagery.},
  dbinsdate	= {2002/1}
}

@Article{	  odorico96a,
  author	= {Odorico, R.},
  title		= {Neural 2.00---a program for neural net and statistical
		  pattern recognition},
  journal	= {Computer Physics Communications},
  year		= {1996},
  number	= {2},
  volume	= {96},
  pages		= {314--330},
  abstract	= {A neural net program for pattern classification is
		  presented, which includes: i) an improved version of
		  Kohonen's learning vector quantization (LVQ with mining
		  count); ii) feed-forward neural networks with
		  back-propagation training; iii) Gaussian (or Mahalanobis
		  distance) classification; iv) Fisher linear discrimination.
		  Back-prop trainings with emulations of Intel's ETANN and
		  Siemens' MA16 neural chips are available as options. The
		  program has been developed for high energy physics
		  applications.},
  dbinsdate	= {oldtimer}
}

@Article{	  odorico97a,
  author	= {R. Odorico},
  title		= {Learning vector quantization with training count {(LVQ
		  TC)}},
  journal	= {Neural Networks},
  year		= {1997},
  volume	= {10},
  number	= {6},
  pages		= {1083--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oe93a,
  author	= {Shunichiro Oe and Masaharu Hashida and Yasuori Shinohara},
  title		= {A Segmentation Method of Texture Image by Using Neural
		  Network},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {I},
  pages		= {189--192},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oe94a,
  author	= {Shunichiro Oe and Masaharu Hashida and Masaki Enokihara
		  and Yasunori Shinohara},
  title		= {A Texture Segmentation Method Using Unsupervised and
		  Supervised Neural Networks},
  pages		= {2415--2418},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {texture segmentation, application, comparison},
  dbinsdate	= {oldtimer}
}

@Article{	  oehler95a,
  author	= {Karen L. Oehler and Robert M. Gray},
  title		= {Combining Image Compression and Classification Using
		  Vector Quantization},
  journal	= {IEEE Transactions on Pattern Analysis and Machine
		  Intelligence},
  year		= 1995,
  volume	= 17,
  pages		= {461--473},
  dbinsdate	= {oldtimer}
}

@Article{	  ogi92a,
  author	= {H. Ogi and Y. Izui and S. Kobayashi},
  title		= {Application of neural networks to fault detection systems
		  for gas-insulated switchgear},
  journal	= {Mitsubishi Denki Giho},
  year		= {1992},
  volume	= {66},
  number	= {12},
  pages		= {63--67},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@Article{	  oh01a,
  author	= {Oh, K. -S. and Kaneko, K. and Makinouchi, A.},
  title		= {A method of highspeed similarity retrieval based on
		  self-organizing maps},
  journal	= {Research Reports on Information Science and Electrical
		  Engineering of Kyushu University},
  year		= {2001},
  volume	= {6},
  number	= {1},
  month		= {March 2001},
  pages		= {77--82},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {Feature-based similarity retrieval become an important
		  research issue in image database systems. The features of
		  image data are useful to discrimination of images. In this
		  paper, we propose the highspeed k-Nearest Neighbor
		  algorithm based on Self-Organizing Maps. Self-Organizing
		  Maps(SOM) provides a mapping from high dimensional feature
		  vectors onto a two-dimensional space. The mapping preserves
		  the topology of the feature vectors. The map is called
		  topological feature map. A topological feature map
		  preserves the mutual relations (similarity) in feature
		  spaces of input data, and clusters mutually similar feature
		  vectors in a neighboring nodes. Each node of the
		  topological feature map holds a node vector and similar
		  images that is the closest to each node vector. In
		  topological feature map, there are empty nodes in which no
		  image is classified. We experiment on the performance of
		  our algorithm using color feature vectors extracted from
		  images. Promising results have been obtained in
		  experiments.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  oh01b,
  author	= {Oh, S. -K. and Kim, M. -S. and Lee, J. -J.},
  title		= {Adapting the migration topology of macro-micro
		  evolutionary algorithm by clustering the individuals using
		  self-organizing map},
  booktitle	= {IEEE International Symposium on Industrial Electronics},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {308--311},
  organization	= {Dept. of Elec. Eng. and Comp. Sci., Korea Adv. Inst. Sci.
		  and Technol.},
  publisher	= {},
  address	= {},
  abstract	= {In this paper, we propose a self-adaptive migration rule
		  for Macro-micro Evolutionary Algorithm which was proposed
		  to find several local optima for multi-modal optimization
		  problems. The algorithm consists of two evolutionary
		  algorithms which control global species and local
		  individuals respectively. To keep the diversity explicitly,
		  we incorporate a clustering method to divide individuals to
		  several species. Clustering method based on Self-Organizing
		  Map (SOM) can divide individuals to several species and
		  determine the neighboring topology information which
		  defines the migration topology between species. To examine
		  the computational effectiveness of proposed algorithm, we
		  apply the algorithm to standard benchmark problems for
		  numerical optimization.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  oh01c,
  author	= {Kun Seok Oh and Yaokai Feng and Kaneko, K. and Makinouchi,
		  A. and Sang Hyun Bae},
  title		= {{SOM}-based R*-tree for similarity retrieval},
  booktitle	= {Proceedings Seventh International Conference on Database
		  Systems for Advanced Applications. DASFAA 2001. IEEE
		  Comput. Soc, Los Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {182--9},
  abstract	= {Feature-based similarity retrieval has become an important
		  research issue in multimedia database systems. The features
		  of multimedia data are useful for discriminating between
		  multimedia objects (e.g., documents, images, video, music
		  score, etc.). For example, images are represented by their
		  color histograms, texture vectors, and shape descriptors. A
		  feature vector is a vector that represents a set of
		  features, and are usually high-dimensional data. The
		  performance of conventional multidimensional data
		  structures (e.g., R-tree family K-D-B tree, grid file,
		  TV-tree) tends to deteriorate as the number of dimensions
		  of feature vectors increases. The R*-tree is the most
		  successful variant of the R-tree. We propose a SOM-based
		  R*-tree as a new indexing method for high-dimensional
		  feature vectors. The SOM-based R*-tree combines SOM and
		  R*-tree to achieve search performance more scalable to high
		  dimensionalities. Self-organizing maps (SOMs) provide
		  mapping from high-dimensional feature vectors onto a
		  two-dimensional space. The mapping preserves the topology
		  of the feature vectors. The map is called a topological
		  feature map, and preserves the mutual relationships
		  (similarity) in the feature spaces of input data,
		  clustering mutually similar feature vectors in neighboring
		  nodes. We experimentally compare the retrieval time cost of
		  a SOM-based R*-tree with that of an SOM and an R*-tree
		  using color feature vectors extracted from 40,000 images.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from Internet.}
}

@Article{	  oh90a,
  author	= {Se-Young Oh and Jae-Myeong Song},
  title		= {A dynamically reconfiguring backpropagation neural network
		  and its application to the inverse kinematic solution of
		  robot manipulators},
  journal	= {Trans. of the Korean Inst. of Electrical Engineers},
  year		= {1990},
  volume	= {39},
  number	= {9},
  pages		= {985--996},
  month		= {September},
  note		= {(in Korean)},
  x		= {. . . A new algorithm named dynamically reconfiguring BP
		  is proposed to improve its learning speed. It uses a
		  modified version of Kohonen's self-organizing feature map
		  (SOFM) to partition the input space and for each input
		  point, select a subset of the hidden processing elements or
		  neurons. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oh91a,
  author	= {S. -Y. Oh and I. -S. Yi},
  title		= {A backpropagation neural networks with locally activated
		  hidden layer for fast and accurate mapping},
  booktitle	= {IJCNN-91, International Joint Conference on Neural
		  Networks, Seattle},
  year		= {1991},
  volume	= {II},
  pages		= {1000},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {. . . It uses a Kohonen self-organizing feature map front
		  end},
  dbinsdate	= {oldtimer}
}

@Article{	  oh95a,
  author	= {Se-Young Oh and Doo-Hyun Choi and In-Sook Lee},
  title		= {A hybrid learning neural network architecture with locally
		  activated hidden layer for fast and accurate mapping},
  journal	= {Neurocomputing},
  year		= {1995},
  volume	= {7},
  number	= {3},
  pages		= {211--24},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@Article{	  oh96a,
  author	= {Kyuhwan Oh and Soo-Ik Chae},
  title		= {Incremental adaptive learning algorithm with initial
		  generic knowledge},
  journal	= {Journal of the Korean Institute of Telematics and
		  Electronics},
  year		= {1996},
  volume	= {33B},
  number	= {2},
  pages		= {187--96},
  dbinsdate	= {oldtimer}
}

@Article{	  ohberg96a,
  author	= {F. Ohberg and K. Johansson and M. Bergenheim and J.
		  Pedersen and M. Djupsjobacka},
  title		= {A neural network approach to real-time spike
		  discrimination during simultaneous recording from several
		  multi-unit nerve filaments},
  journal	= {Journal of Neuroscience Methods},
  year		= {1996},
  volume	= {64},
  number	= {2},
  pages		= {181--7},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ohta01a,
  author	= {Ryuji Ohta and Toshimichi Saito},
  title		= {A Growing Self-Organnizing Algorithm for Dynamic
		  Clustering},
  booktitle	= {International Joint Conference on Neural Networks,
		  Washington, DC, USA, July 15--19},
  pages		= {},
  year		= {2001},
  month		= {July},
  note		= {CD-ROM},
  dbinsdate	= {2002/1}
}

@Article{	  oiming93a,
  author	= {Cheng Oiming and Zhang Shujing},
  title		= {Adaptive segmenting and clustering of quasi-stationary
		  signal},
  journal	= {Acta Electronica Sinica},
  year		= {1993},
  volume	= {21},
  number	= {6},
  pages		= {51--8},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@InCollection{	  oja02a,
  author	= {Merja Oja and Janne Nikkil{\"a} and Petri T{\"o}r{\"o}nen
		  and Garry Wong and Eero Castr{\'e}n and Samuel Kaski},
  title		= {Exploratory Clustering of Gene Expression Profiles of
		  Mutated Yeast Strains},
  booktitle	= {Computational And Statistical Approaches To Genomics},
  pages		= {},
  publisher	= {Kluwer Acadmic Publishers},
  year		= {2002},
  editor	= {Wei Zhang and Ilya Shmulevich},
  note		= {In press},
  dbinsdate	= {2002/1}
}

@InCollection{	  oja84a,
  author	= {Erkki Oja},
  title		= {NEW ASPECTS ON THE SUBSPACE METHODS OF PATTERN
		  RECOGNITION},
  booktitle	= {Electron. Electr. Eng. Res. Stud. Pattern Recognition and
		  Image Processing Ser. 5},
  publisher	= {Letchworth},
  year		= {1984},
  pages		= {55--64},
  address	= {UK},
  x		= {DIALOG No: 02306042 EI Monthly No: EI8709093281 'Type: MC;
		  (Monograph Chapter)' Hm. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oja90a,
  author	= {E. Oja and L. Xu and P. Kultanen},
  title		= {Curve detection by an extended \mbox{self-organizing} map
		  and the related {RHT} method},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conference},
  year		= {1990},
  volume	= {I},
  pages		= {27--30},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oja91a,
  author	= {E. Oja},
  title		= {Neural networks in image processing and analysis},
  booktitle	= {Proc. Symp. on Image Sensing and Processing in Industry},
  year		= {1991},
  pages		= {143---152},
  publisher	= {Pattern Recognition Society of Japan},
  address	= {Tokyo, Japan},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oja92a,
  author	= {E. Oja},
  title		= {Neural computing},
  booktitle	= {Proc. NORDDATA},
  year		= {1992},
  pages		= {306---316},
  publisher	= {Tietojenk{\"{a}}sittelyliitto},
  address	= {Helsinki, Finland},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  oja92b,
  author	= {E. Oja},
  title		= {Self-organizing maps and computer vision},
  booktitle	= {Neural Networks for Perception, vol. 1: Human and Machine
		  Perception},
  publisher	= {Academic Press},
  year		= {1992},
  editor	= {Harry Wechsler},
  chapter	= {},
  pages		= {368--385},
  address	= {New York, NY},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oja94a,
  author	= {Erkki Oja},
  title		= {Neural networks---advantages and applications},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conf. Proc. of Finnish Artificial Intelligence Society},
  pages		= {2--8},
  booktitle	= {Proc. Conf. on Artificial Intelligence Res. in Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {review, tutorial},
  dbinsdate	= {oldtimer}
}

@InBook{	  oja95a,
  author	= {Erkki Oja},
  title		= {Neural Networks for Chemical Engineers},
  chapter	= {2, Unsupervised neural learning},
  publisher	= {Elsevier},
  year		= {1995},
  volume	= {6},
  series	= {Computer-Aided Chemical Engineering},
  address	= {Amsterdam},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oja95b,
  author	= {Erkki Oja and Kimmo Valkealahti},
  title		= {Compressing higher-order co-occurrences for texture
		  analtsis using the Self-Organizing Map},
  volume	= {II},
  pages		= {1160--1164},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  oja96a,
  author	= {E. Oja and K. Valkealahti},
  title		= {Co-occurrence map: quantizing multidimensional texture
		  histograms},
  journal	= {Pattern Recognition Letters},
  year		= {1996},
  volume	= {17},
  number	= {7},
  pages		= {723--30},
  dbinsdate	= {oldtimer}
}

@Article{	  oja96c,
  author	= {E. Oja and L. Wang},
  title		= {Robust Fitting by Nonlinear Neural Units},
  journal	= {Neural Networks},
  year		= {1996},
  volume	= {9},
  pages		= {435--444},
  dbinsdate	= {oldtimer}
}

@Article{	  oja96d,
  author	= {E. Oja and L. Wang},
  title		= {Neural Fitting: Robustness by Anti-{H}ebbian Learning},
  journal	= {Neurocomputing},
  year		= {1996},
  volume	= {12},
  pages		= {155--170},
  dbinsdate	= {oldtimer}
}

@InCollection{	  oja97a,
  author	= {E. Oja and K. Valkealahti},
  title		= {Local independent component analysis by the
		  \mbox{self-organizing} map},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {553--8},
  dbinsdate	= {oldtimer}
}

@Book{		  oja99a,
  author	= {Oja, E. and Kaski, S.},
  editor	= {},
  title		= {{K}ohonen Maps},
  publisher	= {Elsevier},
  year		= {1999},
  address	= {Amsterdam},
  dbinsdate	= {oldtimer}
}

@InCollection{	  oja99b,
  author	= {E. Oja and J. Laaksonen and M. Koskela and S. Brandt},
  title		= {Self-Organizing Maps for Content-Based Image Database
		  Retrieval},
  booktitle	= {Kohonen Maps},
  pages		= {349--362},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: content-based image retrieval, image databases,,
		  adaptive systems, neural networks, self-organising map},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ojala95a,
  author	= {T. Ojala and V. T. Ruoppila and P. Vuorimaa},
  title		= {Identification of fuzzy {ARX} model},
  booktitle	= {WCNN '95. World Congress on Neural Networks. 1995
		  International Neural Network Society Annual Meeting},
  publisher	= {MIT Press},
  year		= {1995},
  volume	= {2},
  editor	= {D. S. Touretzky and M. C. Mozer and M. E. Hasselmo},
  address	= {Cambridge, MA, USA},
  pages		= {713--16},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ojala95b,
  author	= {T. Ojala and P. Vuorimaa},
  title		= {Modified {K}ohonen's learning laws for {RBF} network},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {D. W. Pearson and N. C. Steele and R. F. Albrecht},
  address	= {Vienna, Austria},
  pages		= {356--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oka00a,
  author	= {Oka, S. and Takefuji, Y. and Suzuki, T.},
  title		= {Feature extraction of {IKONOS} images by self-organization
		  topological map},
  booktitle	= {Proceedings of the International Conference on Imaging
		  Science, Systems, and Technology. CISST'2000. CSREA Press -
		  Univ. Georgia, Athens, GA, USA},
  year		= {2000},
  volume	= {2},
  pages		= {687--91},
  abstract	= {A new algorithm for feature extraction in hyper-spectral
		  images is proposed. To assign an appropriate visible color
		  to each pixel from RGB color space, we use the
		  self-organization topological map (SOM) algorithm. The SOM
		  algorithm assigns various colors to each pixel so that
		  similar features can be represented by similar colors
		  respectively, and different features by different colors in
		  hyper-spectral images. As a result, invisible features can
		  be extracted and identified visually by color variations.
		  By using the proposed algorithm, we show a breakthrough for
		  the clustering analysis of satellite images. The simulation
		  data is captured by a satellite IKONOS with high resolution
		  1m * 1m. The simulation result shows that final color image
		  is able to determine the distance of the wave spectral
		  reflectance, and display the difference with variations in
		  colors. The most important advantage of the proposed
		  algorithm is that the significant features are
		  automatically emphasized with remarkable colors even when
		  we do not know the best number of clusters previously. The
		  proposed algorithm can be applied to other various field
		  applications by extracting features from hyper-spectral
		  images.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  okada01a,
  author	= {Okada, N. and Minamoto, K. and Kondo, E.},
  title		= {Collision avoidance for a visuo-motor system with a
		  redundant manipulator using a self-organizing visuo-motor
		  map},
  booktitle	= {Proceedings of the IEEE International Symposium on
		  Assembly and Task Planning},
  year		= {2001},
  editor	= {},
  volume	= {},
  pages		= {104--109},
  organization	= {Graduate Sch. of Eng., Kyushu University},
  publisher	= {},
  address	= {},
  abstract	= {Collision avoidance for a visuo-motor system with a
		  redundant manipulator is described. A self-organizing map
		  is used to resolve the inverse kinematics of the
		  manipulator. The map determines the joint angles of a
		  redundant manipulator so that the end effector of the
		  manipulator reaches a target given in the image space. In
		  our past study, although the map can make the manipulator
		  take obstacle free poses, it is not considered if the
		  trajectory of the manipulator avoids collisions. In this
		  paper, we added a path planning system to the visuo-motor
		  system. By combining the path planning and the
		  self-organizing map, the system accomplished collision
		  avoidance. Simulation and experimental results showed that
		  the self-organizing map learned well mapping, and that by
		  using the map the manipulator can avoid collisions.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  okada99a,
  author	= {Okada, N. and Maruki, Y. and Yoshida, A. and Shimizu, Y.
		  and Kondo E.},
  title		= {A self-organizing visuo-motor map for a redundant
		  manipulator in environments with obstacles},
  booktitle	= {Proceedings of the Ninth International Conference on
		  Advanced Robotics. 99 ICAR. Japan Robot Assoc, Tokyo,
		  Japan},
  year		= {1999},
  volume	= {},
  pages		= {517--22},
  abstract	= {A self-organizing visuo-motor map that maps from an image
		  space to joint angles space is described. It determines the
		  joint angles of a manipulator so that the end effector of
		  the manipulator moves to a target position, given in the
		  image space. It also determines them so that a redundant
		  manipulator achieves high manipulability and that the
		  manipulator takes an obstacle-free pose. Simulation and
		  experimental results show that the self-organizing map
		  learned well mapping.},
  dbinsdate	= {2002/1}
}

@Article{	  olafsson92a,
  author	= {S. Olafsson},
  title		= {Dynamical neural networks for speech recognition},
  journal	= {BT Technology J. },
  year		= {1992},
  volume	= {10},
  number	= {3},
  pages		= {48--58},
  month		= {July},
  x		= {. . . The results are compared with those arrived at by
		  using other techniques, such as . . . Kohonen networks and
		  . . . },
  dbinsdate	= {oldtimer}
}

@Article{	  olbert95a,
  author	= {Olbert, C. and Schaale, M. and Furrer, R. },
  title		= {Mapping of forest fire damages using imaging
		  spectroscopy},
  journal	= {Advances in Space Research},
  year		= {1995},
  volume	= {15},
  number	= {11},
  pages		= {115--22},
  month		= {June},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  ollikainen02a,
  author	= {Ollikainen, V. and Backstrom, C. and Kaski, S.},
  title		= {Electronic editor: automatic content-based sequential
		  compilation of newspaper articles},
  journal	= {NEUROCOMPUTING},
  year		= {2002},
  volume	= {43},
  month		= {MAR},
  pages		= {91--106},
  abstract	= {New information carriers, such as electronic books and MP3
		  players, can be utilized for displaying customized content.
		  Using these carriers, however, only browsing forwards and
		  backwards is easy. The crucial question in making these
		  carriers user-friendly is then to construct an order of
		  presentation that enhances readability. We have developed a
		  tool that uses the self-organizing map algorithm of Kohonen
		  to automatically organize a collection of text articles
		  into a meaningful content-based sequential order. The
		  article sequence constructed by the system was compared to
		  the sequences made by 21 humans, and in our small-scale
		  case study they were comparable. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  omatu01a,
  author	= {Omatu, S. and Fujinaka, T. and Kosaka, T. and Yanagimoto,
		  H. and Yoshioka, M.},
  title		= {Italian Lira classification by {LVQ}},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2947--2951},
  organization	= {Osaka Prefecture University},
  publisher	= {},
  address	= {},
  abstract	= {Bill money classification by transaction machines has
		  become important to make progress in the office automation.
		  In this paper, a new method to classify the Italian Liras
		  by using the learning vector quatization (LVQ) is proposed.
		  The Italian Liras of 8 kinds, 1,000, 2,000, 5,000, 10,000,
		  50,000 (new), 50,000 (old), 100,000 (new), 100,000 (old)
		  Liras with four directions A, B, C, and D are used, where A
		  and B mean the normal direction and the upside down
		  direction and C and D mean the reverse version of A and B.
		  The original image with 128 by 64 pixels is observed at the
		  transaction machine in which rotation and shift are
		  included. After correction of these effects, we select a
		  suitable area which shows the bill image and feed the image
		  with 64 by 15 pixels to a neural network. Although the
		  neural network of the LVQ type can process any order of the
		  dimension of the input data, the smaller size is better to
		  achieve faster convergence result.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  omatu91a,
  author	= {S. Omatu and T. Yosida},
  title		= {Pattern classification for remote sensing using neural
		  network},
  booktitle	= {1991 IEEE International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {653--658},
  organization	= {IEEE; Int. Neural Networks Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  omatu93a,
  author	= {Omatu, S. and Yoshida, T. },
  title		= {Pattern classification for remote sensing using neural
		  network},
  booktitle	= {IGARSS '93. 1993 International Geoscience and Remote
		  Sensing Symposium (IGARSS'93). Better Understanding of
		  Earth Environment},
  year		= {1993},
  editor	= {Fujimura, S. },
  volume	= {2},
  pages		= {899--901},
  organization	= {Dept. of Inf. Sci. \& Intelligent Syst. , Tokushima Univ.
		  , Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  ong02a,
  author	= {Ong, S. H. and Yeo, N. C. and Lee, K. H. and Venkatesh, Y.
		  V. and Cao, D. M.},
  title		= {Segmentation of color images using a two-stage
		  self-organizing network},
  journal	= {Image and Vision Computing},
  year		= {2002},
  volume	= {20},
  number	= {4},
  month		= {Apr 1 },
  pages		= {279--289},
  organization	= {Department of Elec. and Comp. Eng., National University of
		  Singapore},
  publisher	= {},
  address	= {},
  abstract	= {We propose a two-stage hierarchical artificial neural
		  network for the segmentation of color images based on the
		  Kohonen self-organizing map (SOM). The first stage of the
		  network employs a fixed-size two-dimensional feature map
		  that captures the dominant colors of an image in an
		  unsupervised mode. The second stage combines a
		  variable-sized one-dimensional feature map and color
		  merging to control the number of color clusters that is
		  used for segmentation. A post-processing noise-filtering
		  stage is applied to improve segmentation quality.
		  Experiments confirm that the self-learning ability, fault
		  tolerance and adaptability of the two-stage SOM lead to a
		  good segmentation results. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  onodera90a,
  author	= {H. Onodera and K. Takeshita and K. Tamaru},
  title		= {Hardware architecture for {K}ohonen network},
  booktitle	= {1990 IEEE Int. Symp. on Circuits and Systems},
  year		= {1990},
  volume	= {II},
  pages		= {1073--1077},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {A fully digital architecture for Kohonen networks suitable
		  for VLSI implementation is proposed. The architecture has a
		  structure similar to a content addressable memory (CAM). .
		  . . },
  dbinsdate	= {oldtimer}
}

@Article{	  onodera93a,
  author	= {Onodera, H. and Takeshita, K. and Tamaru, K. },
  title		= {Hardware architecture for {K}ohonen network},
  journal	= {IEICE Transactions on Electronics},
  year		= {1993},
  volume	= {E76-C},
  number	= {7},
  pages		= {1159--66},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@Article{	  onwubolu98a,
  author	= {Onwubolu, G. C.},
  title		= {Artificial neural network-based approach for design of
		  parts for cellular manufacturing},
  journal	= {Artificial Intelligence in Design '98. Kluwer Academic
		  Publishers, Dordrecht, Netherlands; 1998; x+679
		  pp.p.661--78},
  year		= {1998},
  volume	= {},
  pages		= {661--78},
  abstract	= {An artificial neural network approach is applied to the
		  problem of integrating design and manufacturing
		  engineering. The self-organising map neural network
		  recognises products and parts which are modelled as
		  boundary representation solids using a modified face
		  complexity code scheme that forms the necessary feature
		  families. Based on the part features, machines, tools and
		  fixtures are selected. This information is then fed into a
		  four-layer feedforward neural network that provides the
		  designer with the desired features that meet the current
		  manufacturing constraints for the design of a new product
		  or part. The proposed methodology does not involve training
		  of the neural networks used, and is seen to be a
		  significant potential for application in concurrent
		  engineering, where design and manufacturing are integrated.
		  The main advantages of the neural network approach over
		  rule-based systems are high recognition speed, ease of
		  computation, minimal memory storage and the ability to
		  recognise partial and complex features that are encountered
		  in design.},
  dbinsdate	= {2002/1}
}

@Article{	  onwubolu99a,
  author	= {Onwubolu, G. C.},
  title		= {Design of parts for cellular manufacturing using neural
		  network-based approach},
  journal	= {Journal of Intelligent Manufacturing},
  year		= {1999},
  volume	= {10},
  pages		= {251--65},
  abstract	= {A neural network approach is applied to the problem of
		  integrating design and manufacturing engineering. The self
		  organising map (SOM) neural network recognizes products and
		  parts which are modeled as boundary representation (B-rep)
		  solids using a modified face complexity code scheme
		  adopted, and forms the necessary feature families. Based on
		  the part features, machines, tools and fixtures are
		  selected. These information are then fed into a four layer
		  feedforward neural network that provides a designer with
		  the desired features that meet the current manufacturing
		  constraints for design of a new product or part. The
		  proposed methodology does not involve training of the
		  neural networks used and is seen to be a significant
		  potential for application in concurrent engineering where
		  design and manufacturing are integrated.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  oommen95a,
  author	= {B. John Oommen and I. Kuban Altinel and Necati Aras},
  title		= {Arbitrary Distance Function Estimation Using Vector
		  Quantization},
  volume	= {VI},
  pages		= {3062--3067},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {In this paper we shall utilize the concepts of Vector
		  Quantization (VQ) for the computation of arbitrary distance
		  functions---a problem which has been receiving much
		  attention in the Operations Research and Location Analysis
		  community. The input to our problem is the set of
		  coordinates of a large number of nodes whose inter-node
		  arbitrary 'distances' have to be estimated. Unlike
		  traditional Operations Research methods, which use
		  parametric functional estimators, we have utilized VQ
		  principles to first adaptively polarize the nodes into
		  sub-regions according to Kohonen's Self-Organizing Map
		  (SOM). Subsequently, the parameters characterizing the
		  sub-regions are learnt by using a variety of methods.},
  dbinsdate	= {oldtimer}
}

@Article{	  openshaw96a,
  author	= {S. Openshaw and I. Turton},
  title		= {A parallel {K}ohonen algorithm for the classification of
		  large spatial datasets},
  journal	= {Computers \& Geosciences},
  year		= {1996},
  volume	= {22},
  number	= {9},
  pages		= {1019--26},
  dbinsdate	= {oldtimer}
}

@Article{	  oravec01a,
  author	= {Oravec, M.},
  title		= {Multilayer perceptron, radial basis function network, and
		  self-organizing map in the problem of face recognition},
  journal	= {Journal-of-Electrical-Engineering},
  year		= {2001},
  volume	= {52},
  pages		= {284--8},
  abstract	= {In this contribution, one and two-stage neural networks
		  methods for face recognition are presented. For two-stage
		  systems, the Kohonen self-organizing map is used as a
		  feature extractor and multiplayer perceptron (MLP) or
		  radial basis function (RBF) networks are used as
		  classifiers. The results of such recognition are compared
		  with face recognition using a one-stage multilayer
		  perceptron and radial basis function network classifiers.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  oravec01b,
  author	= {Oravec, M. and Jurica, P.},
  title		= {Face recognition based on feature-extraction by
		  self-organizing map and classification by {RBF} networks},
  booktitle	= {Proceedings VIPromCom-2001. 3rd International Symposium on
		  Video Processing and Multimedia Communications. Croatian
		  Soc. Electron. Marine---ELMAR, Zadar, Croatia},
  year		= {2001},
  volume	= {},
  pages		= {87--90},
  abstract	= {In this contribution, a two-stage method for face
		  recognition is presented. A Kohonen self-organising map is
		  used as a feature extractor. A radial basis function (RBF)
		  network is used as a classifier. The results of such
		  recognition are compared to face recognition using a
		  one-stage radial basis function network classifier and
		  principal component analysis results.},
  dbinsdate	= {2002/1}
}

@Article{	  oravec94a,
  author	= {Oravec, M. },
  title		= {{K}ohonen and {G}rossberg learning in neural networks for
		  image compression},
  journal	= {Journal on Communications},
  year		= {1994},
  volume	= {45},
  pages		= {77--9},
  month		= {July-Aug},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  oravec95a,
  author	= {Oravec, M. and Podhradsky, P. },
  title		= {Image compression using neural networks},
  journal	= {Journal of Electrical Engineering},
  year		= {1995},
  volume	= {46},
  number	= {9},
  pages		= {309--17},
  dbinsdate	= {oldtimer}
}

@InCollection{	  oravec97a,
  author	= {M. Oravec},
  title		= {Experiments with neural networks for compression of
		  medical X-ray images},
  booktitle	= {DSP '97. 3rd International Conference on Digital Signal
		  Processing. Proceedings of the Conference},
  publisher	= {Tech. Univ. Kosice},
  year		= {1997},
  editor	= {D. Kocur and D. Levicky and S. Marchevsky},
  address	= {Kosice, Slovakia},
  pages		= {177--80},
  dbinsdate	= {oldtimer}
}

@Article{	  oreski01a,
  author	= {Oreski, S. and Zupan, J. and Glavic, P.},
  title		= {Neural network classification of phase equilibrium
		  methods},
  journal	= {CHEMICAL AND BIOCHEMICAL ENGINEERING QUARTERLY},
  year		= {2001},
  volume	= {15},
  number	= {1},
  month		= {MAR},
  pages		= {3--12},
  abstract	= {In the paper Kohonen neural network is described as an
		  alternative tool for a fast selection of the most suitable
		  physical property estimation method to be used in efficient
		  chemical process design and simulation. Kohonen neural
		  networks are trained to suggest the appropriate method of
		  phase equilibrium estimation on the basis of known physical
		  properties of samples (objects of the study). In other
		  words, they classify the objects into none, one or more
		  possible classes (possible methods of phase equilibrium)
		  and estimate the reliability of the proposed classes
		  (adequacy of different methods of phase equilibrium).
		  Kohonen map with almost clearly separated clusters of
		  vapor, vapor/liquid and liquid phase regions and 15
		  probability maps for each of the specific phase equilibrium
		  method, were obtained. The analysis of the results
		  confirmed the hypothesis that the use of Kohonen neural
		  networks for separation of the classes was correct.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  orlando90a,
  author	= {J. Orlando and R. Mann and S. Haykin},
  title		= {Radar Classification of Sea-Ice using Traditional and
		  Neural Classifiers},
  booktitle	= {Proc. IJCNN-90, International Joint Conference of Neural
		  Networks, Washington, DC},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  year		= 1990,
  pages		= {263--266},
  dbinsdate	= {oldtimer}
}

@Article{	  orlando90b,
  author	= {J. R. Orlando and R. Mann and S. Haykin},
  title		= {Classification of sea-ice images using a dual-polarized
		  radar},
  journal	= {IEEE J. Oceanic Engineering},
  year		= {1990},
  volume	= {15},
  number	= {3},
  pages		= {228--237},
  x		= {Aihe sama kuin Orlando90:ssa},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ornes97a,
  author	= {Chester Ornes and Jack Sklansky},
  title		= {A Visual Multi-Expert Neural Classifier},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {III},
  pages		= {1448--1453},
  dbinsdate	= {oldtimer}
}

@Article{	  ornes98a,
  author	= {Ornes, Chester and Sklansky, Jack},
  title		= {Visual neural classifier},
  journal	= {IEEE Transactions on Systems, Man, and Cybernetics. Part
		  B: Cybernetics},
  year		= {1998},
  number	= {4},
  volume	= {28},
  pages		= {620--625},
  abstract	= {A new neural classifier allows visualization of the
		  training set and decision regions, providing benefits for
		  both the designer and the user. We demonstrate the
		  visualization capabilities of this visual neural classifier
		  using synthetic data, and compare the visualization
		  performance to Kohonen's self-organizing map. We show in
		  applications to image segmentation and medical diagnosis
		  that visualization enables a designer to refine the
		  classifier to achieve low error rates and enhances a user's
		  ability to make classifier-assisted decisions.},
  dbinsdate	= {oldtimer}
}

@Article{	  ortega00a,
  author	= {Ortega, Arturo and Marco, Santiago and Sundic, Teodor and
		  Samitier, Josep},
  title		= {New pattern recognition systems designed for electronic
		  noses},
  journal	= {Sensors and Actuators, B: Chemical},
  year		= {2000},
  volume	= {69},
  number	= {3},
  month		= {Oct},
  pages		= {302--307},
  organization	= {Universitat de Barcelona},
  publisher	= {Elsevier Sequoia SA},
  address	= {Lausanne},
  abstract	= {Electronic noses represent a big challenge for the pattern
		  recognition (PARC) systems due to several particular
		  problems they involve. The work presented in this paper is
		  targeted to develop specific methods for these kinds of
		  problems. One of the main issues to deal with, is the
		  concentration variation, as a main cause of pattern
		  dispersion in aroma/gas recognition. Such dispersion
		  hinders easy cluster separation, specially for small aroma
		  intensities. Specific algorithms for gas identification are
		  introduced. They cope with the usual elongated cluster
		  structure found in electronic noses. The PARC systems
		  combine self-organising maps (SOM) and minimum spanning
		  tree (MST) to build curvilinear prototypes. The method is
		  exemplified with a minimal tin dioxide sensor array chosen
		  for CO and CH<sub>4</sub> detection in domestic premises.},
  dbinsdate	= {2002/1}
}

@Article{	  ortega01a,
  author	= {Ortega, A. and Marco, S. and Perera, A. and Sundic, T. and
		  Pardo, A. and Samitier, J.},
  title		= {An intelligent detector based on temperature modulation of
		  a gas sensor with a digital signal processor},
  journal	= {Sensors and Actuators, B: Chemical},
  year		= {2001},
  volume	= {78},
  number	= {1--3},
  month		= {Aug 30 },
  pages		= {32--39},
  organization	= {Instrumentation Laboratory, University of Barcelona},
  publisher	= {},
  address	= {},
  abstract	= {An intelligent detector based on a hot-plate gas sensor
		  and a digital signal processor (DSP) is presented. The work
		  comprises sensor measurements and gas identification with a
		  pattern recognition (PARC) system along with a systematic
		  verification of both stages, thanks to clustering validity
		  methods and performance tests. Commercial silicon
		  micromachined tin-oxide sensors have been used to capture
		  dynamic measurements modulating the sensor heater at
		  different temperatures, waveforms and frequencies. Feature
		  extraction is based on the spectral and transient analysis
		  of the sensor output signals. The PARC systems are based on
		  self-organizing maps (SOM) and recent variations of these
		  well-known neural networks. The proposed hardware is in
		  charge of the whole system: the sensor temperature
		  modulation and signal processing. },
  dbinsdate	= {2002/1}
}

@InCollection{	  orwell97a,
  author	= {James Orwell and Ram{\'o}n Turnes and Mar{\'i}a Jos{\'e}
		  Carreira and Diego Cabello and James Boyce},
  title		= {Towards self-organized feature maps from {G}abor filter
		  responses},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {220--226},
  dbinsdate	= {oldtimer}
}

@Article{	  orwig97a,
  author	= {R. E. Orwig and Hsinchun Chen and Jr. J. F. Nunamaker},
  title		= {A graphical, \mbox{self-organizing} approach to
		  classifying electronic meeting output},
  journal	= {Journal of the American Society for Information Science},
  year		= {1997},
  volume	= {48},
  number	= {2},
  pages		= {157--70},
  dbinsdate	= {oldtimer}
}

@InCollection{	  oshima97a,
  author	= {N. Oshima and T. Ogawa and Y. Takefuji},
  title		= {Airport allocation problems in Mongolia using neural
		  networks},
  booktitle	= {Proceedings of the Eighth Australian Conference on Neural
		  Networks (ACNN'97)},
  publisher	= {Telstra Res. Lab},
  year		= {1997},
  editor	= {M. Dale and A. Kowalczyk and R. Slaviero and J.
		  Szymanski},
  address	= {Clayton, Vic. , Australia},
  pages		= {197--201},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  osogami96a,
  author	= {Osogami, Y. and Ishida, Y.},
  title		= {Speech synthesis using zero-phase impulse responses
		  clustered by SOM},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {281--4},
  abstract	= {This paper introduces a low-bit-rate speech synthesizer
		  using systematic synthesizing with LPC analysis and
		  self-organizing map (SOM). Many compression techniques have
		  been developed and used in various fields of information
		  processing. This research achieved a higher compression
		  rate for speech signals compared with conventional methods.
		  Synthesized speech signals with our methods have been very
		  close to original voice.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  osowski00a,
  author	= {Osowski, S. and Linh, Tran Hoai},
  title		= {Fuzzy clustering neural network for classification of
		  {ECG} beats},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {26--30},
  organization	= {Warsaw Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The paper presents the application of fuzzy selforganizing
		  neural network and higher order statistics for ECG beat
		  classification. The new classification algorithm of the ECG
		  beats, applying the higher order statistics and fuzzy
		  selforganizing neural classifier has been proposed in the
		  paper. The cumulants of the second, third and fourth orders
		  have been used for the feature selection. The GK algorithm
		  for selforganization of the neural network has been
		  applied. The results of experiments have confirmed good
		  efficiency of the proposed solution. The investigations
		  show that the method may find practical application in the
		  recognition of beats. The main features of the proposed
		  method are the good efficiency and real time performance.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  osowski00b,
  author	= {Osowski, S. and Nghia, D. D.},
  title		= {Neural networks for classification of 2-D patterns},
  booktitle	= {WCC 2000---ICSP 2000. 2000 5th International Conference on
		  Signal Processing Proceedings. 16th World Computer Congress
		  2000. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {3},
  pages		= {1568--71},
  abstract	= {The paper presents the application of three different
		  types of neural networks to the 2D pattern recognition on
		  the basis of its shape. They include the multilayer
		  perceptron (MLP), Kohonen self-organizing network and
		  hybrid structure composed of the self-organizing layer and
		  the MLP subnetwork connected in cascade. The recognition is
		  based on the features extracted from the Fourier transform
		  of the data describing the shape of the pattern.
		  Application of different neural network structure results
		  in different accuracy of recognition and classification.
		  The numerical experiments performed for the recognition of
		  the shapes of airplanes have shown the superiority of the
		  hybrid structure.},
  dbinsdate	= {2002/1}
}

@Article{	  osowski00c,
  author	= {Osowski, Stanislaw and Brudzewski, Kazimierz},
  title		= {Fuzzy self-organizing hybrid neural network for gas
		  analysis system},
  journal	= {IEEE Transactions on Instrumentation and Measurement},
  year		= {2000},
  volume	= {49},
  number	= {2},
  month		= {},
  pages		= {424--428},
  organization	= {Warsaw Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The paper presents the gas analysis system applying the
		  self-organizing fuzzy hybrid neural network. The network is
		  composed of the self-organizing competitive fuzzy layer and
		  the supervised multilayer perceptron (MLP) subnetwork,
		  connected in cascade. The characteristic features of this
		  network structure for gas analysis systems are discussed
		  and the results of experiments compared to standard neural
		  solutions based on MLP or classical hybrid network
		  employing the Kohonen layer.},
  dbinsdate	= {2002/1}
}

@Article{	  osowski95a,
  author	= {Stanislaw Osowski and Jeanny Herault and Pierre
		  Demartines},
  title		= {Fault Localization in Analogue Circuits Using {K}ohonen
		  Neural Network},
  journal	= {Bulletin of the Polish Academy of Sciences. Technical
		  Sciences},
  year		= 1995,
  volume	= 43,
  number	= 1,
  pages		= {111--124},
  dbinsdate	= {oldtimer}
}

@Book{		  osowski96a,
  author	= {Stanislaw Osowski},
  title		= {Sieci Neuronowe. W ujeciu algorytmicznym},
  publisher	= {Wydawnictwa Naukowo-Techniczne},
  year		= 1996,
  address	= {Warszawa, Poland},
  dbinsdate	= {oldtimer}
}

@InCollection{	  osowski97a,
  author	= {Stanislaw Osowski and Krzysztof Siwek},
  title		= {{K}ohonen neural network for load forecasting in power
		  system},
  booktitle	= {Proceedings of the XXth National Conference on Circuit
		  Theory and Electronic Networks, Kolobrzeg, Poland, October
		  21--24},
  publisher	= {Technical University of Koszalin, Department of
		  Electronics},
  year		= 1997,
  volume	= 2,
  address	= {Kolobrzeg, Poland},
  pages		= {611--616},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ossen93a,
  author	= {Arnfried Ossen},
  title		= {Learning Topology-Preserving Maps Using Self-Supervised
		  Backpropagation},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {586--591},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  otte97a,
  author	= {R. Otte and K. Goser},
  title		= {New approaches of process visualization and process
		  analysis},
  journal	= {Automatisierungstechnische Praxis},
  year		= {1997},
  volume	= {39},
  number	= {12},
  pages		= {28, 31--2, 35--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  otte97b,
  author	= {Ralf Otte and Karl Goser},
  title		= {New approaches of process visualization and analysis in
		  power plants},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {44--50},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  otte98a,
  author	= {R. Otte},
  title		= {Selbstorganisierende {M}erkmalskarten zur multivariaten
		  {D}atenanalyse komplexer technischer {P}rozesse},
  school	= {Universit\"at Dortmund},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ouellette00a,
  author	= {Ouellette, R. and Noda, H. and Niimi, M. and Kawaguchi,
		  E.},
  title		= {Topological ordered color table for {BPCS}-steganography
		  using indexed color images},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.3971},
  year		= {2000},
  volume	= {3971},
  pages		= {502--9},
  abstract	= {Image index tables values generally give the best possible
		  representation of the color information of the image.
		  However, no consideration is given to the arrangement of
		  the color table itself. Thus, depending on the image,
		  pixels with similar colors may have different index values
		  and can therefore have considerably different index binary
		  make-ups. BPCS image steganography hides information in
		  images based on the principle that if regions in a bitplane
		  are noise-like, those regions can be replaced with
		  noise-like secret data. Therefore, applying traditional
		  BPCS steganography to indexed image data results in drastic
		  visible changes to the image. To overcome this problem, we
		  used a self-organizing neural network to reorder the index
		  table, based on samples from the image, such that similar
		  colors in the index table are near each other with respect
		  to their index values. As a result, regions with similar
		  color information have only slight binary differences at
		  the bitplane level, whereas regions with mixed color
		  information will have considerable binary differences.
		  Using this technique, we can embed secret data that is 15
		  to 35% the size of the image with little or no noticeable
		  degradation in the image.},
  dbinsdate	= {2002/1}
}

@Article{	  ouellette01a,
  author	= {Ouellette, R. and Noda, H. and Niimi, M. and Kawaguchi,
		  E.},
  title		= {Topological ordered color table for {BPCS} steganography
		  using indexed color images},
  journal	= {Transactions-of-the-Information-Processing-Society-of-Japan}
		  ,
  year		= {2001},
  volume	= {42},
  pages		= {110--13},
  abstract	= {A new method is presented for applying bit-plane
		  complexity segmentation (BPCS) steganography to 8-bit color
		  images. By reorganizing the indices of 8-bit indexed color
		  files using a Kohonen self-organizing neural network, we
		  have created a topological ordered color table in which
		  nearby indices have similar color components. Consequently,
		  we have been able to embed data whose sizes are 20--40% of
		  the dummy files, with very little degradation in the dummy
		  image.},
  dbinsdate	= {2002/1}
}

@InCollection{	  outten96a,
  author	= {A. G. Outten and S. J. Roberts and M. J. Stokes},
  title		= {Analysis of human muscle activity},
  booktitle	= {IEE Colloquium on Artificial Intelligence Methods for
		  Biomedical Data Processing (Ref. No. 1996/100)},
  publisher	= {IEE},
  year		= {1996},
  address	= {London, UK},
  pages		= {7/1--6},
  dbinsdate	= {oldtimer}
}

@Article{	  ouzounov96a,
  author	= {A. P. Ouzounov},
  title		= {Text-independent speaker identification using a hybrid
		  neural network},
  journal	= {Problemy na Tekhnicheskata Kibernetika i Robotikata},
  year		= {1996},
  volume	= {44},
  pages		= {28--35},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ouzounov97a,
  author	= {A. Ouzounov and L. Spirov},
  title		= {An experimental comparative study of two approaches for
		  text-independent speaker identification},
  booktitle	= {Applications of Computer Systems. Proceedings of the
		  Fourth International Conference},
  publisher	= {Wydwnictwo i Drukarnia Inst. Inf. Polytech.
		  Szczecinskiej},
  year		= {1997},
  editor	= {J. Soldek},
  address	= {Szezecin, Poland},
  pages		= {86--91},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ouzounov97b,
  author	= {Atanas Ouzounov},
  title		= {Text-Independent Speaker Identification Using a Hybrid
		  Neural Network and Conformity Approach},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {2098--2102},
  dbinsdate	= {oldtimer}
}

@InCollection{	  owechko95a,
  author	= {Y. Owechko and B. H. Soffer},
  title		= {An optical neural network based on distributed holographic
		  gratings for {ATR}},
  booktitle	= {1995 IEEE International Conference on Neural Networks
		  Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {2450--5},
  dbinsdate	= {oldtimer}
}

@Article{	  owechko95b,
  author	= {Y. Owechko and B. H. Soffer},
  title		= {Holographic neurocomputer utilizing laser-diode light
		  source},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2565},
  pages		= {12--19},
  note		= {(Optical Implementation of Information Processing Conf.
		  Date: 10--11 July 1995 Conf. Loc: San Diego, CA, USA Conf.
		  Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@Book{		  owechko96a,
  author	= {Owechko, Y.},
  title		= {Optical Neural Networks Based on Distributed Holographic
		  Gratings. Final rept. 30 Sep 92--28 Feb 96.},
  year		= {1996},
  abstract	= {This final report describes research in optical neural
		  networks performed at Hughes Research Laboratories under a
		  three year DARPA sponsored contract the advantages of
		  optics for neural network implementations, including high
		  storage capacity, connectivity, and very fine grained
		  parallelism, was demonstrated. The optical neurocomputer
		  developed under this program is based on a new type of
		  holography which we call multiple grating holography, in
		  which this approach reduces crosstalk and improves the
		  utilization of the optical input device. In addition, this
		  optical neurocomputer is the first and, to the best of our
		  knowledge, the only one which is programmable and capable
		  of implementing a wide variety of neural network models
		  without any hardware adjustments. Successfully implemented
		  neural networks included the Perceptron, Bidirectional
		  Associative Memory, Kohonen, and backpropagation neural
		  networks. Up to 10(4) neurons, 2 x 10(7) weights, and
		  processing rates of 10(8) connection updates per second
		  were achieved. Under this contract, we built an optical
		  neurocomputer which utilizes a laser diode light source
		  operating at 830 nm. This allowed us to reduce the size of
		  the system. We also developed a new method for representing
		  bipolar neural weights using coherent detection.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  owens00a,
  author	= {Owens, J. and Hunter, A.},
  title		= {Application of the self-organising map to trajectory
		  classification},
  booktitle	= {Proceedings Third IEEE International Workshop on Visual
		  Surveillance. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {77--83},
  abstract	= {This paper presents an approach to the problem of
		  automatically classifying events detected by video
		  surveillance systems; specifically, of detecting unusual or
		  suspicious movements. Approaches to this problem typically
		  involve building complex 3D-models in real-world
		  coordinates to provide trajectory information for the
		  classifier. We show that analysis of trajectories may be
		  carried out in a model-free fashion, using self-organising
		  feature map neutral networks to learn the characteristics
		  of normal trajectories, and to detect novel ones.
		  Trajectories are represented in 2D image coordinates. First
		  and second order motion information is also generated, with
		  moving-average smoothing. This allows novelty detection to
		  be applied on a point-by-point basis in real time, and
		  permits both instantaneous motion and whole trajectory
		  motion to be subjected to novelty detection.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  owsley93a,
  author	= {Lane Owsley and Les Atlas},
  title		= {Ordered Vector Quantization for Neural Network Pattern
		  Classification},
  booktitle	= {Neural Networks for Signal Processing 3---Proceedings of
		  the 1993 IEEE Workshop},
  year		= {1993},
  editor	= {Kamm, C. A. and Kung, S. Y. and Yoon, B. and Chellappa, R.
		  and Kung, S. Y. },
  pages		= {141--150},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, New Jersey, USA},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  owsley95a,
  author	= {Owsley, L. and Atlas, L. and Bernard, G. },
  title		= {Feature extraction networks for dull tool monitoring},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  year		= {1995},
  volume	= {5},
  pages		= {3355--8},
  organization	= {Dept. of Electr. Eng. , Washington Univ. , Seattle, WA,
		  USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  owsley96a,
  author	= {L. Owsley and L. Atlas and G. Bernard},
  title		= {Self-organizing feature maps with perfect organization},
  booktitle	= {1996 IEEE International Conference on Acoustics, Speech,
		  and Signal Processing Conference Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {6},
  address	= {New York, NY, USA},
  pages		= {3557--60},
  dbinsdate	= {oldtimer}
}

@Article{	  owsley97a,
  author	= {L. M. D. Owsley and L. E. Atlas and G. D. Bernard},
  title		= {Self-organizing feature maps and hidden {M}arkov models
		  for machine-tool monitoring},
  journal	= {IEEE Transactions on Signal Processing},
  year		= {1997},
  volume	= {45},
  number	= {11},
  pages		= {2787--98},
  dbinsdate	= {oldtimer}
}

@Article{	  ozdemir01a,
  author	= {Ozdemir, D. and Akarun, L.},
  title		= {Fuzzy algorithms for combined quantization and dithering},
  journal	= {IEEE TRANSACTIONS ON IMAGE PROCESSING},
  year		= {2001},
  volume	= {10},
  number	= {6},
  month		= {JUN},
  pages		= {923--931},
  abstract	= {Color quantization reduces the number of the colors in a
		  color image, while the subsequent dithering operation
		  attempts to create the illusion of more colors with this
		  reduced palette, In quantization, the palette is designed
		  to minimize the mean squared error (MSE), However, the
		  dithering that follows enhances the color appearance at the
		  expense of increasing the MSE, We introduce three joint
		  quantization and dithering algorithms to overcome this
		  contradiction. The basic idea is the same in two of the
		  approaches: introducing the dithering error to the
		  quantizer in the training phase, The fuzzy C-means (FCM)
		  and the fuzzy learning vector quantization (FLVQ)
		  algorithms are used to develop two combined mechanisms. In
		  the third algorithm, we minimize an objective function
		  including an inter-cluster separation (ICS) term to obtain
		  a color palette which is more suitable for dithering, The
		  goal is to enlarge the convex hull of the quantization
		  colors to obtain the illusion of more colors after error
		  diffusion, The color contrasts of images are also enhanced
		  with the proposed algorithm, We test the results of these
		  three new algorithms using quality metrics which model the
		  perception of the human visual system and illustrate that
		  substantial improvements are achieved after dithering.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ozdemir93a,
  author	= {Kadir Ozdemir and Aydan M. Erkmen},
  title		= {A Modified {K}ohonen's Neural Network Algorithm},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {513--516},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  padgett95a,
  author	= {Padgett, M. L. and Josephson, E. M. and White, C. R. and
		  Duffield, D. W. },
  title		= {Clustering, simulation and neural networks in real-world
		  applications},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2492},
  number	= {pt. 1},
  pages		= {562--72},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  padgett97a,
  author	= {Mary Lou Padgett and Paul J. Werbos and Teuvo Kohonen},
  title		= {Strategies and Tactics for the Application of Neural
		  Networks to Industrial Electronics},
  booktitle	= {The Industrial Electronics Handbook},
  publisher	= {CRC Press},
  year		= 1997,
  editor	= {J. David Irwin},
  pages		= {835--852},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pages93a,
  author	= {Gilles Pag{\`{e}}s},
  title		= {Vorono{\"{\i}} tesselation, space quantization algorithms
		  and numerical integration},
  editor	= {Micle Verleysen},
  pages		= {221--228},
  booktitle	= {Proc. ESANN'93, European Symp. on Artificial Neural
		  Networks},
  year		= {1993},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pahalawatta00a,
  author	= {Pahalawatta, P. V. and Jouny, I.},
  title		= {Web-based handwritten character recognition system},
  booktitle	= {Proceedings of the IASTED International Conference. Signal
		  and Image Processing. IASTED/ACTA Press, Anaheim, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {179--85},
  abstract	= {This paper presents a study of the components necessary to
		  create a WWW-based handwriting recognition system. A system
		  ranging from data input to pattern classification was
		  developed that could be implemented over the Web using a
		  Perl-based programming language that could handle fast data
		  manipulation. Pattern recognition methods such as
		  artificial neural networks, distance-based classifiers and
		  statistical classifiers were tested for accuracy and
		  efficiency. A distance-based classifier using a Hough
		  transform of the input pattern proved to be the most
		  successful with a 96% success rate. A Hough transform in
		  conjunction with a neural network that used learning vector
		  quantization had a success rate of 94%. Image preprocessing
		  techniques were also investigated in order to normalize the
		  image with respect to slant and size prior to
		  classification.},
  dbinsdate	= {2002/1}
}

@Article{	  pai00a,
  author	= {Pai, Ping-Feng},
  title		= {Neuro-fuzzy approach in parts clustering},
  journal	= {Annual Conference of the North American Fuzzy Information
		  Processing Society---NAFIPS},
  year		= {2000},
  volume	= {},
  number	= {},
  month		= {},
  pages		= {138--142},
  organization	= {Minghsin Inst of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {In the feature-based clustering system, conversion from
		  part feature to crisp codes is a conventional procedure in
		  part clustering. However, part characteristics is reduced
		  in the procedure especially for fuzzy and interval
		  attributes. To remedy the shortages, a self organizing maps
		  network with fuzzy weights is proposed. By taking the
		  linguistic representation capabilities of fuzzy theory and
		  the clustering abilities of self-organizing maps (SOM)
		  networks, the proposed approach is able to deal with not
		  only crisp attributes but also interval attributes as well
		  as fuzzy attributes. Due to the fuzzy data and fuzzy
		  weights between input layer and output layer, a learning
		  rule is presented. The influence of three parameters in the
		  network is discussed in the paper.},
  dbinsdate	= {2002/1}
}

@Article{	  pai01a,
  author	= {Pai, P. -F. and Lee, E. S.},
  title		= {Parts clustering by self-organizing map neural network in
		  a fuzzy environment},
  journal	= {Computers and Mathematics with Applications},
  year		= {2001},
  volume	= {42},
  number	= {1--2},
  month		= {July },
  pages		= {179--188},
  organization	= {Dept. of Indust. and Mfg. Syst. Eng., Kansas State
		  University},
  publisher	= {},
  address	= {},
  abstract	= {The description of the attributes or characteristics of
		  the individual parts in a feature-based clustering system
		  is frequently vague, and linguistic, fuzzy number or fuzzy
		  coding is ideally suited to represent these attributes.
		  However, due to the vagueness of the description, the
		  resulting fuzzy membership functions are usually very
		  approximate. Neural network learning to improve the fuzzy
		  representation was used in this investigation to overcome
		  these difficulties. In particular, Kohonen's
		  self-organizing map network combined with fuzzy membership
		  functions was used to classify the different parts based on
		  their various attributes. The network can simultaneously
		  deal with crisp attributes, interval attributes, and fuzzy
		  attributes. Due to the fuzzy input and fuzzy weights, a
		  revised weight updating rule was proposed. Various
		  approaches have been proposed to define the distance or
		  ranking of fuzzy numbers, which is essential in order to
		  use the Kohonen map. The overall existence measurement was
		  used in the present investigation. To illustrate the
		  approach, parts based on two attributes were classified and
		  discussed. },
  dbinsdate	= {2002/1}
}

@Article{	  pajares98a,
  author	= {G. Pajares and J. M. Cruz and J. Aranda},
  title		= {Stereo matching based on the \mbox{self-organizing}
		  \mbox{\mbox{feature-mapping}} algorithm},
  journal	= {Pattern Recognition Letters},
  year		= {1998},
  volume	= {19},
  number	= {3--4},
  pages		= {319--30},
  dbinsdate	= {oldtimer}
}

@Article{	  pajares98b,
  author	= {Pajares, G. and {de la Cruz}, J. M. and {Lopez Orozco}, J.
		  A.},
  title		= {Improving stereovision matching through supervised
		  learning},
  journal	= {Pattern Analysis and Applications},
  year		= {1998},
  volume	= {1},
  pages		= {105--20},
  abstract	= {Most classical local stereovision matching algorithms use
		  features representing objects in both images and compute
		  the minimum difference attribute values. We have verified
		  that the differences in attributes for the true matches
		  cluster in a cloud around a centre. The correspondence is
		  established on the basis of the minimum squared Mahalanobis
		  distance between the difference of the attributes for a
		  current pair of features and the cluster centre (similarity
		  constraint). We introduce a new supervised learning
		  strategy derived from the learning vector quantization
		  (LVQ) approach to get the best cluster centre.
		  Additionally, we obtain the contribution or specific weight
		  of each attribute for matching. We improve the learning law
		  introducing a variable learning rate. The supervised
		  learning and the improved learning law are the most
		  important findings, which are justified by the computed
		  better results compared with classical stereovision
		  matching methods without learning and with other learning
		  strategies. The method is illustrated with 47 pairs of
		  stereo images.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pajunen96a,
  author	= {P. Pajunen},
  title		= {Nonlinear independent component analysis by
		  \mbox{self-organizing} maps},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {815--20},
  dbinsdate	= {oldtimer}
}

@TechReport{	  pajunen96b,
  author	= {P. Pajunen},
  title		= {An Algorithm for Binary Blind Source Separation},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1996},
  number	= {A36},
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pajunen96c,
  author	= {P. Pajunen and A. Hyv\"arinen and J. Karhunen},
  title		= {Nonlinear Blind Source Separation by Self-Organizing
		  Maps},
  booktitle	= {Proc. of the 1996 International Conference on Neural
		  Information Processing (ICONIP'96)},
  editors	= {S.-I. Amari and L. Xu and L.-W. Chan and I. King and K.-S.
		  Leung},
  volume	= {2},
  year		= {1996},
  publisher	= {Springer-Verlang},
  pages		= {1207--1210},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pajunen97a,
  author	= {Petteri Pajunen and Juha Karhunen},
  title		= {Self-organizing maps for independent component analysis},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {96--99},
  dbinsdate	= {oldtimer}
}

@Article{	  pal00a,
  author	= {Pal, N. R. and Laha, A.},
  title		= {A multi-prototype classifier and its application to
		  remotely sensed image analysis},
  journal	= {Australian-Journal-of-Intelligent-Information-Processing-Systems}
		  ,
  year		= {2000},
  volume	= {6},
  pages		= {110--18},
  abstract	= {Designing a prototype-based classifier involves three
		  issues: how to generate the prototypes, how many prototypes
		  to generate and how to use these prototypes for
		  classification. We propose a comprehensive scheme for
		  designing a prototype-based classifier. The self-organizing
		  feature map (SOFM) is used to generate an initial set of
		  prototypes. Starting with this initial set a tuning
		  algorithm produces a set containing an adequate number of
		  prototypes. The tuning algorithm evaluates in each
		  iterative step the classification performance of the
		  prototypes using them in a nearest-prototype classifier.
		  Based on their performance prototypes may be deleted,
		  merged, or split resulting in a new set of prototypes. The
		  new set of prototypes is retrained using the SOFM algorithm
		  with winner-only update. The final set of prototypes is
		  used to design a nearest-prototype classifier. We tested
		  our algorithm on several well known data sets and
		  performance is found to be quite good. We applied our
		  algorithm for classification of remotely sensed images and
		  also obtained very good results.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  pal92a,
  author	= {Nikhil R. Pal and James C. Bezdek and Eric C. K. Tsao},
  title		= {Improving convergence and performance of {K}ohonen's
		  \mbox{self-organizing} sceme},
  booktitle	= {SPIE Vol. 1710, Science of Artificial Neural Networks},
  year		= {1992},
  pages		= {500--509},
  organization	= {SPIE},
  address	= {Bellingham, WA},
  dbinsdate	= {oldtimer}
}

@Article{	  pal93a,
  author	= {Pal, Nikhil R and Bezdek, James C and Tsao, Eric C K},
  title		= {Generalized clustering networks and {K}ohonen's
		  \mbox{self-organizing} scheme.},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1993},
  number	= {4},
  volume	= {4},
  pages		= {549--557},
  abstract	= {This paper first discusses the relationship between the
		  sequential hard c-means (SHCM) and learning vector
		  quantization (LVQ) clustering algorithms. These methods
		  suffer from several major problems. For example, they
		  depend heavily on initialization. If the initial values of
		  the cluster centers are outside the convex hull of the
		  input data, such algorithms, even if they terminate, may
		  not produce meaningful results in terms of prototypes for
		  clustering. This is due in part to the fact that they
		  update only the winning prototype for every input vector.
		  We also discuss the impact and interaction of these two
		  families of methods with Kohonen's self-organizing feature
		  mapping (SOFM), which is not a clustering method, but which
		  often lends ideas to clustering algorithms. Finally, we
		  propose a generalization of LVQ which (may) update all
		  nodes for a given input vector. Moreover, our network
		  attempts to find a minimum of a well-defined objective
		  function. The learning rules depend on the degree of
		  distance match to the winner node; the lesser the degree of
		  match with the winner, the more is the impact on nonwinner
		  nodes. Numerical results indicate that the terminal
		  prototypes generated by this modification of LVQ are
		  generally insensitive to initialization and independent of
		  any choice of learning coefficient(s). We use Anderson's
		  IRIS data to illustrate our method; and we compare our
		  results with the standard LVQ approach.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pal93b,
  author	= {Nikhil R. Pal and James C. Bezdek},
  title		= {Extensions of Self-Organizing Feature Maps for Improved
		  Visual Displays},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2441--2447},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {This paper addresses the problem of visual assessment of
		  clustering tendency in p-dimensional data using two
		  extensions of Kohonen's self-organizing feature map (SOFM).
		  We show that SOFM cell displays generally do not produce
		  visual evidence that leads to good guesses about cluster
		  substructure or data density even for 2-dimensional data.
		  The two proposed extensions of SOFM improve the quality of
		  displays and enable us to make better guesses about the
		  existence of substructure in data.},
  dbinsdate	= {oldtimer}
}

@Article{	  pal94a,
  author	= {Pal, S. K. and Mitra, S. },
  title		= {Fuzzy versions of {K}ohonen's net and {MLP}-based
		  classification: performance evaluation for certain
		  nonconvex decision regions},
  journal	= {Information Sciences},
  year		= {1994},
  volume	= {76},
  number	= {3--4},
  pages		= {297--337},
  dbinsdate	= {oldtimer}
}

@Article{	  pal95a,
  author	= {N. R. Pal and J. C. Bezdek and E. C. -K. Tsao},
  title		= {Errata to {Generalized} Clustering Networks and
		  {{K}ohonen}'s Self-Organizing Scheme},
  journal	= {IEEE Transactions on Neural Networks},
  type		= {Letter},
  year		= 1995,
  volume	= 6,
  number	= 2,
  pages		= {521--521},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  pal96a,
  author	= {Pal, Nikhil R and Bezdek, James C and Hathaway, Richard
		  J},
  title		= {Sequential competitive learning and the fuzzy c-means
		  clustering algorithms},
  journal	= {Neural Networks},
  year		= {1996},
  number	= {5},
  volume	= {9},
  pages		= {787--796},
  abstract	= {Several recent papers have described sequential
		  competitive learning algorithms that are curious hybrids of
		  algorithms used to optimize the fuzzy c-means (FCM) and
		  learning vector quantization (LVQ) models. First, we show
		  that these hybrids do not optimize the FCM functional. Then
		  we show that the gradient descent conditions they use are
		  not necessary conditions for optimization of a sequential
		  version of the FCM functional. We give a numerical example
		  that demonstrates some weaknesses of the sequential scheme
		  proposed by Chung and Lee. And finally, we explain why
		  these algorithms may work at times, by exhibiting the
		  stochastic approximation problem that they unknowingly
		  attempt to solve.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pal97a,
  author	= {Nikhil R. Pal and E. Vijaya Kumar},
  title		= {Neural Networks for Dimensionality Reduction},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {1},
  address	= {Singapore},
  pages		= {221--224},
  dbinsdate	= {oldtimer}
}

@Article{	  pal98a,
  author	= {Pal, Nikhil R and Eluri, Vijay Kumar},
  title		= {Two efficient connectionist schemes for structure
		  preserving dimensionality reduction},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1998},
  number	= {6},
  volume	= {9},
  pages		= {1142--1154},
  abstract	= {We propose two neural-net-based methods for structure
		  preserving dimensionality reduction. Method 1 selects a
		  small representative sample and applies Sammon's method to
		  project it. This projected data set is then used to train
		  an MLP. Method 2 uses Kohonen's self-organizing feature map
		  (SOFM) to generate a small set of prototypes which is then
		  projected by Sammon's method. This projected data set is
		  then used to train an MLP. Both schemes are quite effective
		  in terms of computation time and quality of output, and
		  both outperform methods of Jain and Mao on the data sets
		  tried.},
  dbinsdate	= {oldtimer}
}

@Article{	  palakal95a,
  author	= {Palakal, M. J. and Murthy, U. and Chittajallu, S. K. and
		  Wong, D. },
  title		= {Tonotopic representation of auditory responses using
		  \mbox{self-organizing} maps},
  journal	= {Mathematical and Computer Modelling},
  year		= {1995},
  volume	= {22},
  number	= {2},
  pages		= {7--21},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  palisson94a,
  author	= {Palisson, P. and Zegadi, N. and Peyrin, F. and
		  Unterreiner, R. },
  title		= {Unsupervised multiresolution texture segmentation using
		  wavelet decomposition},
  booktitle	= {Proceedings ICIP-94},
  year		= {1994},
  volume	= {2},
  pages		= {625--9},
  organization	= {CNRS, Inst. Nat. des Sci. Appliquees, Villeurbanne,
		  France},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  palmieri94a,
  author	= {Palmieri, F. },
  title		= {Hebbian learning and self-association in nonlinear neural
		  networks},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {2},
  pages		= {1258--63},
  organization	= {Connecticut Univ. , CT, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  pan92a,
  author	= {Huang-Luang Pan and Yung-Chang Chen},
  title		= {Liver Tissues classification by artificial neural
		  networks},
  journal	= {Pattern Recognition Letters},
  year		= {1992},
  volume	= {13},
  number	= {5},
  pages		= {355--368},
  month		= {May},
  annote	= {Some (very heavy) modifications of SOM and LVQ are used
		  for classification of ultrasonic images},
  dbinsdate	= {oldtimer}
}

@Article{	  panayiotopoulos01a,
  author	= {Panayiotopoulos, T. and Zacharis, N. Z.},
  title		= {Machine learning and intelligent agents},
  journal	= {Machine learning and its applications. Advanced lectures.
		  Springer-Verlag, Berlin, Germany; 2001; viii+324
		  pp.p.281--5},
  year		= {2001},
  volume	= {},
  pages		= {281--5},
  abstract	= {Provides an introduction to the field of machine learning
		  techniques for intelligent agents. Machine Learning in
		  single and multi-agent systems is a relatively new but
		  significant and promising topic in Artificial Intelligence.
		  Intelligent agents and agent-based computer systems
		  represent an important, fundamentally new way of dealing
		  with many important software application problems for which
		  mainstream computer science techniques offer no obvious
		  solution. Many of these new problems are due to the recent
		  dynamic and distributed nature of both data and
		  applications. Topics covered in this chapter concern the
		  development of intelligent agent architectures which
		  exhibit machine learning capabilities. These architectures
		  apply some learning strategy, learning from examples,
		  evolutionary learning etc. and some learning feedback
		  method, supervised, reward and punishment, unsupervised
		  learning, etc. Various approaches and algorithms with
		  comparative studies have also been concerned for
		  classification problems such as k-nearest neighbor, naive
		  Bayes artificial neural networks with back propagation
		  learning, Kohonen maps, decision rulesets, genetic
		  algorithms, etc.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  panayiotou00a,
  author	= {Panayiotou, Panayiotis A. and Pattichis, Constantinos and
		  Jenkins, David and Plimmer, Frances},
  title		= {Modular artificial neural network valuation system},
  booktitle	= {Proceedings of the Mediterranean Electrotechnical
		  Conference---MELECON},
  year		= {2000},
  editor	= {},
  volume	= {2},
  pages		= {457--460},
  organization	= {Land Information Cent},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The objective of this study was to design, develop and
		  test a Modular Artificial Neural Network Valuation system
		  (MANN) incorporating Multiple Regression Analysis (MRA) for
		  the assessment of houses and apartments in Strovolos
		  municipality in Cyprus for taxation purposes. The system
		  combined the assessment results of three neural network
		  assessors: i) back-propagation (BP), ii) probabilistic
		  network (PNN) iii) self-organising feature map (SOFM); and
		  iv) MRA. Features include age, size, plot size, date of
		  sale etc. The mean absolute percentage error (MAPE) and the
		  coefficient of dispersion (COD) of the MANN system for
		  houses were 10.67% and 10.57% respectively. The MAPE and
		  the COD for apartments were 8.68% and 8.41% respectively.
		  These findings compare favourably with other studies and
		  satisfy International Association of Assessing Officers
		  (IAAO) criteria for mass appraisal techniques.},
  dbinsdate	= {2002/1}
}

@Article{	  panchanathan92a,
  author	= {Panchanathan, S. and Yeap, T. H. and Pilache, B. },
  title		= {A neural network for image compression},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 1},
  pages		= {376--85},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pang90a,
  author	= {V. Pang and M. Palaniswami},
  title		= {Pattern classification using a \mbox{self-organizing}
		  neural network},
  booktitle	= {IEEE TENCON'90: 1990 IEEE Region 10 Conf. on Computer and
		  Communication Systems},
  year		= {1990},
  volume	= {II},
  pages		= {562--566},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  x		= {. . . The network is based on a laterally inhibited neural
		  network model developed by Kohonen (1988). },
  dbinsdate	= {oldtimer}
}

@Book{		  pao89a,
  author	= {Yoh-Han Pao},
  title		= {Adaptive Pattern Recognition and Neural Networks},
  booktitle	= {Adaptive Pattern Recognition and Neural Networks},
  publisher	= {Addison-Wesley},
  year		= {1989},
  address	= {Reading, MA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  paoloni90a,
  author	= {Andrea Paoloni},
  title		= {Neural Networks for Speech Recognition},
  editor	= {Andrea Paoloni},
  pages		= {5--17},
  booktitle	= {Proc. 1st Workshop on Neural Networks and Speech
		  Processing, November 89, Roma. },
  year		= {1990},
  dbinsdate	= {oldtimer}
}

@Article{	  papadimitriou01a,
  author	= {Papadimitriou, S. and Mavroudi, S. and Vladutu, L. and
		  Bezerianos, A.},
  title		= {Ischemia detection with a self-organizing map supplemented
		  by supervised learning},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2001},
  volume	= {12},
  number	= {3},
  month		= {May },
  pages		= {503--515},
  organization	= {Medical Physics Department, Medical School, University of
		  Patras},
  publisher	= {},
  address	= {},
  abstract	= {The problem of maximizing the performance of the detection
		  of ischemia episodes is a difficult pattern classification
		  problem. The state space for this problem consists of
		  regions that lie near class separation boundaries and
		  require the construction of complex discriminants while for
		  the rest regions the classification task is significantly
		  simpler. The motivation for developing the supervising
		  network self-organizing map (sNet-SOM) model is to exploit
		  this fact for designing computationally effective solutions
		  both for the particular ischemic detection problem and for
		  other applications that share similar characteristics.
		  Specifically, the sNet-SOM utilizes unsupervised learning
		  for the "simple" regions and supervised for the "difficult"
		  ones in a two stage learning process. The unsupervised
		  learning approach extends and adapts the self-organizing
		  map (SOM) algorithm of Kohonen. The basic SOM is modified
		  with a dynamic expansion process controlled with an entropy
		  based criterion that allows the adaptive formation of the
		  proper SOM structure. This extension proceeds until the
		  total number of training patterns that are mapped to
		  neurons with high entropy (and therefore with ambiguous
		  classification) reduces to a size manageable numerically
		  with a capable supervised model. The second learning phase
		  (the supervised training) has the objective of constructing
		  better decision boundaries at the ambiguous regions. At
		  this phase, a special supervised network is trained for the
		  computationally reduced task of performing the
		  classification at the ambiguous regions only. The
		  utilization of sNet-SOM with supervised learning based on
		  the radial basis functions and support vector machines has
		  resulted in an improved accuracy of ischemia detection
		  especially in the last case. The highly disciplined design
		  of the generalization performance of the support vector
		  machine allows designing the proper model for the number of
		  patterns transferred to the supervised expert.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  papadourakis90a,
  author	= {G. M. Papadourakis and G. N. Bebis and M. Georgiopoulos},
  title		= {Machine printed character recognition using artificial
		  neural networks},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  volume	= {I},
  pages		= {392},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  x		= {. . . The authors describe the particular, the ART1, the
		  Kohonen and two back-propagation algorithms are considered.
		  . . . },
  dbinsdate	= {oldtimer}
}

@Article{	  papadourakis96a,
  author	= {G. Papadourakis and M. Vourkas and S. Micheloyannis and B.
		  Jervis},
  title		= {Use of artificial neural networks for clinical diagnosis},
  journal	= {Mathematics and Computers in Simulation},
  year		= {1996},
  volume	= {40},
  number	= {5--6},
  pages		= {623--35},
  dbinsdate	= {oldtimer}
}

@Article{	  papamarkos00a,
  author	= {Papamarkos, N. and Strouthopoulos, C. and Andreadis, I.},
  title		= {Multithresholding of color and gray-level images through a
		  neural network technique},
  journal	= {Image and Vision Computing},
  year		= {2000},
  volume	= {18},
  pages		= {213--22},
  abstract	= {One of the most frequently used methods in image
		  processing is thresholding. This can be a highly efficient
		  means of aiding the interpretation of images. A new
		  technique suitable for segmenting both gray-level and color
		  images is presented in this paper. The proposed approach is
		  a multithresholding technique implemented by a Principal
		  Component Analyzer (PCA) and a Kohonen Self-Organized
		  Feature Map (SOFM) neural network. To speedup the entire
		  multithresholding algorithm and reduce the memory
		  requirements, a sub-sampling technique can be used. Several
		  experimental and comparative results exhibiting the
		  performance of the proposed technique are presented.},
  dbinsdate	= {oldtimer}
}

@Article{	  papamarkos00b,
  author	= {Papamarkos, Nikos and Atsalakis, Antonios},
  title		= {Gray-level reduction using local spatial features},
  journal	= {Computer Vision and Image Understanding},
  year		= {2000},
  volume	= {78},
  number	= {3},
  month		= {},
  pages		= {336--350},
  organization	= {Democritus Univ of Thrace},
  publisher	= {Acad Press Inc},
  address	= {Orlando, FL},
  abstract	= {This paper proposes a new method for reduction of the
		  number of gray-levels in an image. The proposed approach
		  achieves gray-level reduction using both the image
		  gray-levels and additional local spatial features. Both
		  gray-level and local feature values feed a self-organized
		  neural network classifier. After training, the neurons of
		  the output competition layer of the SOFM define the
		  gray-level classes. The final image has not only the
		  dominant image gray-levels, but also has a texture
		  approaching the image local characteristics used. To split
		  the initial classes further, the proposed technique can be
		  used in an adaptive mode. To speed up the entire
		  multithresholding algorithm and reduce memory requirements,
		  a fractal scanning subsampling technique is adopted. The
		  method is applicable to any type of gray-level image and
		  can be easily modified to accommodate any type of spatial
		  characteristic. Several experimental and comparative
		  results, exhibiting the performance of the proposed
		  technique, are presented.},
  dbinsdate	= {2002/1}
}

@Article{	  papamarkos02a,
  author	= {Papamarkos, Nikos and Atsalakis, Antonis E. and
		  Strouthopoulos, Charalampos P.},
  title		= {Adaptive color reduction},
  journal	= {IEEE Transactions on Systems, Man, and Cybernetics, Part
		  B: Cybernetics},
  year		= {2002},
  volume	= {32},
  number	= {1},
  month		= {February },
  pages		= {44--56},
  organization	= {Electric Circuits Analysis Lab., Department of Electrical
		  Comp., Democritus Univ. of Thrace},
  publisher	= {Institute of Electrical and Electronics Engineers Inc.},
  address	= {},
  abstract	= {This paper proposes a new algorithm for the reduction of
		  the number of colors in an image. The proposed adaptive
		  color reduction (ACR) technique achieves color reduction
		  using a tree clustering procedure. In each node of the
		  tree, a self-organized neural network classifier (NNC) is
		  used which is fed by image color values and additional
		  local spatial features. The NNC consists of a principal
		  component analyzer (PCA) and a Kohonen self-organized
		  feature map (SOFM) neural network (NN). The output neurons
		  of the NNC define the color classes for each node. The
		  final image not only has the dominant image colors, but its
		  texture also approaches the image local characteristics
		  used. Using the adaptive procedure and different local
		  features for each level of the tree, the initial color
		  classes can be split even more. For better classification,
		  split and merging conditions are used in order to define if
		  color classes must be split or merged. To speed up the
		  entire algorithm and reduce memory requirements, a fractal
		  scanning subsampling technique is used. The method is
		  independent of the color scheme, it is applicable to any
		  type of color images, and it can be easily modified to
		  accommodate any type of spatial features and any type of
		  tree structure. Several experimental and comparative
		  results, exhibiting the performance of the proposed
		  technique, are presented.},
  dbinsdate	= {2002/1}
}

@Article{	  papamarkos99a,
  author	= {Papamarkos, Nikos},
  title		= {Color reduction using local features and a {K}ohonen
		  self-organized feature map neural network},
  journal	= {International Journal of Imaging Systems and Technology },
  year		= {1999},
  number	= {5},
  volume	= {10},
  pages		= {404--409},
  abstract	= {This paper proposes a new method for reducing the number
		  of colors in an image. The proposed approach uses both the
		  image color components and local image characteristics to
		  feed a Kohonen self-organized feature map (SOFM) neural
		  network. After training, the neurons of the output
		  competition layer define the proper color classes. The
		  final image has the dominant image colors and its texture
		  approaches the image local characteristics used. To speed
		  up the entire algorithm and reduce memory requirements, a
		  fractal scanning subsampling technique can be used. The
		  method is applicable to all types of color images and can
		  be easily extended to accommodate any type of spatial
		  characteristics. Several experimental and comparative
		  results are presented.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  paquier98a,
  author	= {W. Paquier and M. Ibnkahla},
  title		= {\mbox{Self-organizing} maps for rapidly fading nonlinear
		  channel equalization},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {865--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  paradis94a,
  author	= {Rose Paradis and Eric Dietrich},
  title		= {Concept Development in a Scaffolded Neural Network},
  pages		= {2339--2343},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {hybrid, semantic analysis},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  paradis94b,
  author	= {Rose Paradis and Eric Dietrich},
  title		= {Cumulative Learning in a Scaffolded Neural Network},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {II},
  pages		= {775--780},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {modification, extension},
  dbinsdate	= {oldtimer}
}

@Article{	  parhi94a,
  author	= {Parhi, K. K. and Wu, F. H. and Genesan, K. },
  title		= {Sequential and parallel neural network vector quantizers},
  journal	= {IEEE Transactions on Computers},
  year		= {1994},
  volume	= {43},
  number	= {1},
  pages		= {104--9},
  month		= {Jan},
  dbinsdate	= {oldtimer}
}

@Article{	  parikh92a,
  author	= {Parikh, J. A. and DaPonte, J. S. and DiNicola, E. G. and
		  Pedersen, R. A. },
  title		= {Selective detection of linear features in geological
		  remote sensing data},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 2},
  pages		= {963--72},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  park00a,
  author	= {Dong Chul Park},
  title		= {Centroid neural network for unsupervised competitive
		  learning},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  volume	= {11},
  pages		= {520--8},
  abstract	= {An unsupervised competitive learning algorithm based on
		  the classical k-means clustering algorithm is proposed. The
		  proposed learning algorithm called the centroid neural
		  network (CNN) estimates centroids of the related cluster
		  groups in training date. This paper also explains
		  algorithmic relationships among the CNN and some of the
		  conventional unsupervised competitive learning algorithms
		  including Kohonen's self-organizing map and Kosko's
		  differential competitive learning algorithm. The CNN
		  algorithm requires neither a predetermined schedule for
		  learning coefficient nor a total number of iterations for
		  clustering. The simulation results on clustering problems
		  and image compression problems show that CNN converges much
		  faster than conventional algorithms with compatible
		  clustering quality while other algorithms may give unstable
		  results depending on the initial values of the learning
		  coefficient and the total number of iterations.},
  dbinsdate	= {oldtimer}
}

@Article{	  park00b,
  author	= {Park, Gwang Hoon and Lee, Yoon Jin},
  title		= {Rate control algorithm using {SOFM}-based neural
		  classifier},
  journal	= {Electronics Letters},
  year		= {2000},
  volume	= {36},
  number	= {12},
  month		= {},
  pages		= {1041--1042},
  organization	= {Yonsei Univ},
  publisher	= {IEE},
  address	= {Stevenage},
  abstract	= {A neural-net based rate control algorithm for MPEG
		  encoders is introduced. It is based on the global
		  rate-distortion model constructed by a self-organizing
		  feature map. The performances of the proposed algorithm are
		  better than those for MPEG-4 VM5+ frame-based rate control,
		  based on the criteria of the average number of bits per
		  frame and PSNR.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  park00c,
  author	= {Park, Y. G. and Lee, H. K. and Kim, W. S. and Lim, K. J.
		  and Kang, S.H. and Ree, J.H. and Kim, B.H.},
  title		= {Classification of defects in solid insulation material by
		  {PD} methods},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Properties and Applications of Dielectric Materials},
  year		= {2000},
  editor	= {},
  volume	= {2},
  pages		= {749--752},
  organization	= {Chungbuk Natl Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Solid insulators have good insulation properties. However
		  in case that they have void or crack in bulk or at surface
		  of them, electric field is concentrated and insulation
		  material is degraded at this point. Defects of solid
		  insulation system act as the source of PD. PD is very
		  harmful since it often leads to deterioration of insulation
		  by the combined action of the discharge ions bombarding the
		  surface and the action of chemical compounds that are
		  formed by the discharge. PD can indicate incipient failure,
		  so it has been used to determine degradation of insulation.
		  In this paper, we investigated the classification of PD in
		  defects of solid insulation by using statistical method and
		  classified patterns of PD due to surface discharge,
		  electrical tree and void discharge by using Kohonen
		  network. To analysis PD we used to distribution of Hqn(q)
		  and parameters such as peak charge, average discharge
		  power, average discharge current, and repetition rate.},
  dbinsdate	= {2002/1}
}

@Article{	  park00d,
  author	= {Young Guk Park and Kwang Woo Lee and Dong Uk Jang and
		  Seong Hwa Kang and Kwang Ho Jeong and Wan Su Kim and Yong
		  Hee Lee and Kee Joe Lim},
  title		= {Properties and classification of patterns of air
		  discharges},
  journal	= {Transactions-of-the-Korean-Institute-of-Electrical-Engineers,-C}
		  ,
  year		= {2000},
  volume	= {49},
  pages		= {19--23},
  abstract	= {Partial discharges (PD) in air-insulated electric power
		  apparatus often lead to deterioration of solid insulation
		  by electron bombardments and electrochemical reaction. The
		  PD caused to reduce the life time of power apparatus and to
		  increase power losses. Thus understanding and
		  classification of PD patterns in air are very important to
		  discern sources of PD. In this paper, PD in air by using
		  statistical methods was investigated. The authors
		  classified air discharges, corona, surface discharges and
		  cavity discharges by using a Kohonen network. For
		  classification of PD patterns, they used statistical
		  operators and parameters such as skewness, kurtosis, mean
		  phase, cross-correlation factor and asymmetry derived from
		  the mean pulse-height phase distribution, the max
		  pulse-height phase distribution, the pulse count phase
		  distribution and the pulse height vs. repetition rate.},
  dbinsdate	= {2002/1},
  merjanote     = {last name assumed}
}

@Article{	  park01a,
  author	= {Park, D. -C. and Woo, Y. -J.},
  title		= {Weighted centroid neural network for edge preserving image
		  compression},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2001},
  volume	= {12},
  number	= {5},
  month		= {September },
  pages		= {1134--1146},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {An edge preserving image compression algorithm based on an
		  unsupervised competitive neural network is proposed in this
		  paper. The proposed unsupervised competitive neural
		  network, called weighted centroid neural network (WCNN),
		  utilizes the characteristics of image blocks from edge
		  areas. The mean/residual vector quantization (M/RVQ) scheme
		  is utilized in this proposed approach as the framework of
		  the proposed algorithm. The edge strength of image block
		  data is utilized as a tool to allocate the proper
		  codevectors in the proposed WCNN. The WCNN successfully
		  allocates more codevectors to the image block data from
		  edge area while it allocates less codevectors to the image
		  block data from shade or nonedge area when compared to
		  conventional neural networks based on VQ algorithm. As a
		  result, a simple application of WCNN to an image
		  compression problem gives improved edge characteristics in
		  reconstructed images over conventional neural network based
		  on VQ algorithms such as self-organizing map (SOM) and
		  adaptive SOM.},
  dbinsdate	= {2002/1}
}

@Article{	  park92a,
  author	= {Sang-Tae Park and Seung-Yang Bang},
  title		= {Neural networks-an introduction},
  journal	= {Korea Information Science Society Rev. },
  year		= {1992},
  volume	= {10},
  number	= {2},
  pages		= {5--14},
  note		= {(in Korean)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  park94a,
  author	= {Htiung-Gweon Park and Se-Young Oh},
  title		= {A Neural Network Based Real-Time Robot Tracking Controller
		  Using Position Sensitive Detectors},
  pages		= {2754--2758},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, data clustering},
  dbinsdate	= {oldtimer}
}

@Article{	  park95a,
  author	= {Young-Moon Park and Gwang-Won Kim},
  title		= {Power system transient stability analysis using boundary
		  searching algorithm},
  journal	= {Transactions of the Korean Institute of Electrical
		  Engineers},
  year		= {1995},
  volume	= {44},
  number	= {5},
  pages		= {549--57},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InCollection{	  park95b,
  author	= {Yonug-Moon Park and Gwang-Won Kim and K. Y. Lee},
  title		= {Power system transient stability analysis using {K}ohonen
		  layer},
  booktitle	= {Stockholm Power Tech International Symposium on Electric
		  Power Engineering},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {308--13},
  dbinsdate	= {oldtimer}
}

@InCollection{	  park96a,
  author	= {Cheol Hoon Park and Jung Pil Yu and Lae-Jeohg Park and
		  Sangbong Park},
  title		= {A new neural network construction algorithm using a pool
		  of hidden candidates},
  booktitle	= {Methodologies for the Conception, Design, and Application
		  of Intelligent Systems. Proceedings of the 4th
		  International Conference on Soft Computing},
  publisher	= {World Scientific},
  year		= {1996},
  volume	= {2},
  editor	= {T. Yamakawa and G. Matsumoto},
  address	= {Singapore},
  pages		= {654--7},
  dbinsdate	= {oldtimer}
}

@Article{	  park97a,
  author	= {Chan Ho Park and Hyon Soo Lee},
  title		= {Hybrid multiple component neural network design and
		  learning by efficient pattern partitioning method},
  journal	= {Journal of the Korea Institute of Telematics and
		  Electronics C},
  year		= {1997},
  volume	= {34-C},
  number	= {7},
  pages		= {70--81},
  dbinsdate	= {oldtimer}
}

@Article{	  park97b,
  author	= {Park, Young Moon and Kim, Gwang Won and Cho, Hong Shik and
		  Lee, Kwang Y},
  title		= {New algorithm for {K}ohonen layer learning with
		  application to power system stability analysis},
  journal	= {IEEE Transactions on Systems, Man, and Cybernetics. Part
		  B: Cybernetics},
  year		= {1997},
  number	= {6},
  volume	= {27},
  pages		= {1030--1034},
  abstract	= {In certain classification problems, input patterns are not
		  distributed in a clustering manner but distributed
		  uniformly in an input space and there exist certain
		  critical hyperplanes called decision boundaries. Since
		  learning vector quantization (LVQ) classifies an input
		  vector based on the nearest neighbor, the codebook vectors
		  away from the decision boundaries are redundant. This paper
		  presents an alternative algorithm called boundary search
		  algorithm (BSA) for the purpose of solving this redundancy
		  problem. The BSA finds a fixed number of codebook vectors
		  near decision boundaries by selecting appropriate training
		  vectors. It is found to be more efficient compared with LVQ
		  and its validity is demonstrated with satisfaction in the
		  transient stability analysis of a power system.},
  dbinsdate	= {oldtimer}
}

@Article{	  parkinson98a,
  author	= {Alan M. Parkinson and Dawood Y. Parpia},
  title		= {Intensity encoding in unsupervised neural nets},
  journal	= {Neural Networks},
  year		= 1998,
  volume	= 11,
  pages		= {723--730},
  dbinsdate	= {oldtimer}
}

@InCollection{	  parsiani96a,
  author	= {H. Parsiani and O. Misla},
  title		= {Fuzzy class learning vector quantizer in image
		  compression},
  booktitle	= {Proceedings of the 39th Midwest Symposium on Circuits and
		  Systems},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  editor	= {G. Cameron and M. Hassoun and A. Jerdee and C. Melvin},
  address	= {New York, NY, USA},
  pages		= {579--82},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  partsinevelos01a,
  author	= {Partsinevelos, P. and Stefanidis, A. and Agouris, P.},
  title		= {Automated spatiotemporal scaling for video
		  generalization},
  booktitle	= {IEEE International Conference on Image Processing},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {177--180},
  organization	= {Dept. of Spatial Information Eng., Natl. Ctr. Geogr. Info.
		  and Analysis, University of Maine},
  publisher	= {},
  address	= {},
  abstract	= {In this paper we present a technique for the summarization
		  and spatiotemporal scaling of video content. A Self
		  Organizing Map (SOM) neural network can be used or acquire
		  a rough generalization of the spatiotemporal trajectories
		  of moving objects, in the form of few selected nodes along
		  these trajectories. We introduce a hybrid technique,
		  combining SOM with geometric analysis to properly densify
		  these nodes, to better represent the spatiotemporal
		  behavior of objects. This allows us to bypass problems
		  inherently associated with parameter selection in SOM. We
		  also demonstrate how spatio-temporal scaling supports the
		  analysis of behavioral patterns. The paper shows that our
		  novel technique is a powerful tool for the extraction of
		  generalized information from complex trajectories,
		  displaying high invariance to noise and information gaps in
		  the video stream. Experimental results demonstrate the
		  accuracy potential of our generalization technique.},
  dbinsdate	= {2002/1}
}

@Article{	  parui95a,
  author	= {Parui, S. K. and Datta, A. and Pal, T. },
  title		= {Shape approximation of arc patterns using dynamic neural
		  networks},
  journal	= {Signal Processing},
  year		= {1995},
  volume	= {42},
  number	= {2},
  pages		= {221--5},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  pascual-marqui01a,
  author	= {Pascual-Marqui, R. D. and Pascual-Montano, A. D. and
		  Kochi, K. and Carazo J. M.},
  title		= {Smoothly distributed fuzzy c-means: A new self-organizing
		  map},
  journal	= {Pattern Recognition},
  year		= {2001},
  volume	= {34},
  number	= {12},
  month		= {December},
  pages		= {2395--2402},
  organization	= {The Key Institute Brain-Mind Res., University Hospital of
		  Psychiatry},
  publisher	= {},
  address	= {},
  abstract	= {This paper presents a new self-organizing map algorithm.
		  Unlike the well-known method of Kohonen, the new algorithm
		  corresponds to the optimization of an unambiguously defined
		  cost function. It consists of a modified version of the
		  widely used fuzzy c-means functional, where the code
		  vectors are distributed on a regular low-dimensional grid,
		  and a penalization term is added in order to guarantee a
		  smooth distribution for the values of the code vectors on
		  the grid. The mapping properties of the new method, similar
		  to those of Kohonen's algorithm, are illustrated with
		  several data sets. Computer programs (source code and
		  executables) and data are available upon request to the
		  authors. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  pascual00a,
  author	= {A. Pascual and M. Barc\'{e}na and J. J. Merelo and J. -M.
		  Carazo},
  title		= {Self-Organizing Networks for Mapping and Clustering
		  Biological Macromolecules Images},
  booktitle	= {Artificial Neural Networks in Medicine and Biology,
		  Prodeedings of the ANNIMAB-1 COnference, Göteborg, Sweden,
		  13--16 May 2000},
  pages		= {283--288},
  year		= {2000},
  editor	= {H. Malmgren and M. Boga and L. Niklasson},
  abstract	= {In this work we study the effectiveness of the Fuzzy
		  Kohonen Clustering Network (FKCN) in the unsupervised
		  classification of electron microscopic images of biological
		  macromolecules. The algorithm combines Kohonen's
		  Self-Organizing Feature Map (SOM) and Fuzzy c-means
		  klustering technique (FCM) in order to obtain a clustering
		  technique that inherits their best properties. Two
		  different data sets obtained from the G40P helicase from B.
		  Subtlis bacteriophage SPP1 have been used for testing the
		  proposed method, one composed by 388 images from the same
		  macromolecule. Results of FKCN are compared with
		  Self-Organizing Maps (SOM) and manual classification.
		  Experimental results have proved that this new technique is
		  suitable for working with large, high dimensional and noisy
		  data sets.},
  dbinsdate	= {oldtimer}
}

@Article{	  pascual00b,
  author	= {Pascual, Alberto and Barcena, Montserrat and Merelo, J. J.
		  and Carazo, Jose-Maria},
  title		= {Mapping and fuzzy classification of macromolecular images
		  using self-organizing neural networks},
  journal	= {Ultramicroscopy},
  year		= {2000},
  volume	= {84},
  number	= {1},
  month		= {},
  pages		= {85--99},
  organization	= {Universidad Autonoma},
  publisher	= {Elsevier Science Publishers B.V.},
  address	= {Amsterdam},
  abstract	= {In this work the effectiveness of the fuzzy kohonen
		  clustering network (FKCN) in the unsupervised
		  classification of electron microscopic images of biological
		  macromolecules is studied. The algorithm combines Kohonen's
		  self-organizing feature maps (SOFM) and Fuzzy c-means (FCM)
		  in order to obtain a powerful clustering technique with the
		  best properties inherited from both. Exploratory data
		  analysis using SOFM is also presented as a step previous to
		  final clustering. Two different data sets obtained from the
		  G40P helicase from B. Subtilis bacteriophage SPP1 have been
		  used for testing the proposed method, one composed of 2458
		  rotational power spectra of individual images and the other
		  composed by 338 images from the same macromolecule. Results
		  of FKCN are compared with self-organizing feature maps
		  (SOFM) and manual classification. Experimental results
		  prove that this new technique is suitable for working with
		  large, high-dimensional and noisy data sets and, thus, it
		  is proposed to be used as a classification tool in electron
		  microscopy.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  pascual99a,
  author	= {Pascual, A. and Barcena, M. and Merelo, J. J. and Carazo,
		  J. M.},
  title		= {Application of the fuzzy {K}ohonen clustering network to
		  biological macromolecules images classification},
  booktitle	= {Engineering Applications of Bio-Inspired Artificial Neural
		  Networks. International Work-Conference on Artificial and
		  Natural Neural Networks, IWANN'99. Proceedings, (Lecture
		  Notes in Computer Science Vol.1607)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {2},
  pages		= {331--40},
  abstract	= {In this work we study the effectiveness of the fuzzy
		  Kohonen clustering network (FKCN) in the unsupervised
		  classification of electron microscopic images of biological
		  macromolecules. The algorithm combines Kohonen's
		  self-organizing feature maps (SOM) and fuzzy c-means
		  clustering technique (FCM) in order to obtain a powerful
		  clustering technique that inherits their best properties.
		  Two different data sets obtained from the G40P helicase
		  from B. Subtilis bacteriophage SPP1 have been used for
		  testing the proposed method, one composed of 2458
		  rotational power spectra of individual images and the other
		  composed by 338 images from the same macromolecule. Results
		  of FKCN are compared with SOM and manual classification.
		  Experimental results have proved that this new technique is
		  suitable for working with large, high dimensional and noisy
		  data sets. This method is proposed to be used as a
		  classification tool in electron microscopy.},
  dbinsdate	= {oldtimer}
}

@Article{	  pasian97a,
  author	= {F. Pasian and R. Smareglia and P. Hantzios and A.
		  Dapergolas and I. Bellas-Velidis},
  title		= {Automated objective prism spectral classification using
		  neural networks},
  journal	= {Astrophysics and Space Science Library},
  year		= {1997},
  volume	= {212},
  pages		= {103--8},
  note		= {(Wide-Field Spectroscopy. 2nd Conference of the Working
		  Group of IAU Commission 9 on `Wide-Field Imaging' Conf.
		  Date: 20--25 May 1996 Conf. Loc: Athens, Greece)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  patel93a,
  author	= {Patel, S. and Mahers, E. and Ashton, M. },
  title		= {Measuring the size distribution of emulsion droplets in an
		  image using Kohenen's \mbox{self-organising} feature map},
  booktitle	= {Techniques and Applications of Neural Networks},
  year		= {1993},
  editor	= {Taylor, M. and Lisboa, P. },
  pages		= {219--33},
  organization	= {Unilever Res. Lab. , Port Sunlight, UK},
  publisher	= {Ellis Horwood},
  address	= {Hemel Hempstead, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  patel94a,
  author	= {D. Patel and I. Hannah and E. R. Davies},
  title		= {Foreign object detection using a unsupervised neural
		  network},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {631--635},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, texture analysis, pattern recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  patrick00a,
  author	= {Patrick, R.},
  title		= {Curve forecast with the {SOM} algorithm: using a tool to
		  follow the time on a Kohonen map},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {353--8},
  abstract	= {To forecast a complete curve, we propose a method that
		  consists of predicting from a rule based on a
		  classification, which takes the present time class into
		  account. This technique is simpler than a vectorial
		  prediction and solves some problems of long-term
		  forecasting. A type of error which belongs to this method
		  is imposed in order to determine a way to deal with it. A
		  self-organizing map (SOM) and a visualization tool that
		  permits one to follow the time on that kind of
		  classification give us a way to control such errors. An
		  application to Polish electricity consumption is
		  presented.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  pattichis01a,
  author	= {Pattichis, C. S. and Christodoulou, C. I. and Pattichis,
		  M.S. and Pantziaris, M. and Nicolaides, A.},
  title		= {An integrated system for the assessment of ultrasonic
		  imaging atherosclerotic carotid plaques},
  booktitle	= {IEEE International Conference on Image Processing},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {325--328},
  organization	= {Department of Computer Science, University of Cyprus},
  publisher	= {},
  address	= {},
  abstract	= {The objective of this work is to develop a system that
		  will facilitate the automated characterization of
		  ultrasonic imaging carotid plaques for the identification
		  of individuals with asymptomatic carotid stenosis at risk
		  of stroke. A total of 166 images were collected which were
		  classified into: symptomatic because of ipsilateral
		  hemispheric symptoms, or asymptomatic because they were not
		  connected with ipsilateral hemisphere events. Ten different
		  texture feature sets were extracted: first order
		  statistics, spatial gray level dependence matrices, gray
		  level difference statistics, neighbourhood gray tone
		  difference matrix, statistical feature matrix, Laws texture
		  energy measures, fractal dimension texture analysis,
		  Fourier power spectrum and shape parameters. A modular
		  neural network classifier was developed composed of
		  self-organizing map (SOM) classifiers, achieving an overall
		  diagnostic yield of 76.4%. The results of this work show
		  that it is possible to identify a group of patients at risk
		  of stroke based on texture features.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  pattichis94a,
  author	= {Pattichis, C. S. and Schizas, C. N. and Sergiou, A. and
		  Schnorrenberg, F. },
  title		= {A hybrid neural network electromyographic system:
		  incorporating the {WISARD} net},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {6},
  pages		= {3478--83},
  organization	= {Dept. of Comput. Sci. , Cyprus Univ. , Nicosia, Cyprus},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  pattichis95a,
  author	= {Pattichis, C. S. and Schizas, C. N. and Middleton, L. T.
		  },
  title		= {Neural network models in {EMG} diagnosis},
  journal	= {IEEE Transactions on Biomedical Engineering},
  year		= {1995},
  volume	= {42},
  number	= {5},
  pages		= {486--96},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  patton01a,
  author	= {Patton, R. and Webb, M. and Gaj, R.},
  title		= {Covert operations detection for maritime applications},
  journal	= {Canadian Journal of Remote Sensing},
  year		= {2001},
  volume	= {27},
  number	= {4},
  month		= {August },
  pages		= {306--319},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {This paper describes the development and demonstration of
		  a Covert Operations Detection (COD) processor designed to
		  provide an automated capability for detecting anomalous
		  and/or threatening behaviour in complex environments. The
		  current version utilizes data on ship tracks, obtained from
		  a recent Fleet Battle Experiment, to demonstrate the
		  ability to identify different ships based on track data
		  alone and to rapidly detect anomalous behaviour, including
		  simulated mine laying, with high statistical confidence.
		  The algorithm development utilizes a non-template-based
		  technique designed to offer robust anomaly detection in
		  changing conditions. The processor is based on an
		  unsupervised clustering algorithm (Self-Organizing Map or
		  SOM') developed by Kohonen (1997)<sup>2</sup>. Features of
		  the track data, such as the distribution of speeds over a
		  short interval of time, are used to define clusters
		  corresponding to characteristic behaviours. The locations
		  of the clusters in multi-dimensional feature space are
		  projected onto a lower dimensional lattice (e.g., 2-D) for
		  efficient identification of cluster groups. Data associated
		  with isolated and sparsely populated clusters represent
		  possible anomalous behaviour. Suspicious contacts can be
		  automatically flagged for further investigation or action.
		  Efforts are under, ray to transition an operational
		  capability to the fleet. The automated classification
		  capability of the COD will allow the commander to
		  efficiently deploy his resources to prosecute, monitor,
		  avoid or ignore the multiple contacts encountered in the
		  battlespace. The current focus is on covert mine-laying and
		  other suspicious behaviour in the littoral, but the
		  technique can be extended to a broad range of applications
		  including global ship tracking, time critical targeting,
		  fisheries enforcement and drug interdiction.},
  dbinsdate	= {2002/1}
}

@Article{	  pavlides02a,
  author	= {Pavlides, G. and Papadimitriou, Stergios and Mavroudi,
		  Seferina and Vladutu, Liviu and Bezerianos, Anastasios},
  title		= {The supervised network self-organizing map for
		  classification of large data sets},
  journal	= {Applied Intelligence},
  year		= {2002},
  volume	= {16},
  number	= {3},
  month		= {May/June },
  pages		= {185--203},
  organization	= {Department of Medical Physics, School of Medicine,
		  University of Patras},
  publisher	= {Kluwer Academic Publishers},
  address	= {},
  abstract	= {Complex application domains involve difficult pattern
		  classification problems. The state space of these problems
		  consists of regions that lie near class separation
		  boundaries and require the construction of complex
		  discriminants while for the rest regions the classification
		  task is significantly simpler. The motivation for
		  developing the Supervised Network Self-Organizing Map
		  (SNet-SOM) model is to exploit this fact for designing
		  computationally effective solutions. Specifically, the
		  SNet-SOM utilizes unsupervised learning for classifying at
		  the simple regions and supervised learning for the
		  difficult ones in a two stage learning process. The
		  unsupervised learning approach is based on the
		  Self-Organizing Map (SOM) of Kohonen. The basic SOM is
		  modified with a dynamic node insertion/deletion process
		  controlled with an entropy based criterion that allows an
		  adaptive extension of the SOM. This extension proceeds
		  until the total number of training patterns that are mapped
		  to neurons with high entropy (and therefore with ambiguous
		  classification) reduces to a size manageable numerically
		  with a capable supervised model. The second learning phase
		  (the supervised training) has the objective of constructing
		  better decision boundaries at the ambiguous regions. At
		  this phase, a special supervised network is trained for the
		  computationally reduced task of performing the
		  classification at the ambiguous regions only. The
		  performance of the SNet-SOM has been evaluated on both
		  synthetic data and on an ischemia detection application
		  with data extracted from the European ST-T database. In all
		  cases, the utilization of SNet-SOM with supervised learning
		  based on both Radial Basis Functions and Support Vector
		  Machines has improved the results significantly related to
		  those obtained with the unsupervised SOM and has enhanced
		  the scalability of the supervised learning schemes. The
		  highly disciplined design of the generalization performance
		  of the Support Vector Machine allows to design the proper
		  model for the particular training set.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  pedotti94a,
  author	= {Pedotti, A. and Ferrigno, G. and Redolfi, M. },
  title		= {Neural network in multimedia speech recognition},
  booktitle	= {Proceedings of the International Conference on Neural
		  Networks and Expert Systems in Medicine and Healthcare},
  year		= {1994},
  editor	= {Ifeachor, E. C. and Rosen, K. G. },
  pages		= {167--73},
  organization	= {Centro di Bioingegneria, Politecnico di Milano, Italy},
  publisher	= {Univ. Plymouth},
  address	= {Plymouth, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pedrazzi94a,
  author	= {Pedrazzi, P. },
  title		= {On \mbox{self-organizing} neural character recognizers},
  booktitle	= {Neural Nets Wirn Vietri 93---Proceedings of the 5th
		  Italian Workshop on Neural Nets},
  year		= {1994},
  editor	= {Caianiello, E. R. },
  organization	= {Elsag Bailey, Genova, Italy},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pedrycz01a,
  author	= {Pedrycz, W. and Succi, G. and Reformat, M. and Musilek, P.
		  and Bai, X.},
  title		= {Self organizing maps as a tool for software analysis},
  booktitle	= {Canadian Conference on Electrical and Computer
		  Engineering},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {93--98},
  organization	= {Dept. of Elec. and Computer Eng., University of Alberta},
  publisher	= {},
  address	= {},
  abstract	= {Software measures (metrics) are indicators describing
		  complexity of software products and processes. By their
		  very nature, software measures give rise to a number of
		  complex and highly dimensional data (patterns) that attempt
		  to provide some useful insights into the very nature of the
		  software systems. Such findings help to investigate and
		  quantify the key properties of the systems such as their
		  reliability, maintainability, readability, etc. In this
		  study, self-organizing maps (SOMs) are considered as a
		  vehicle for analysis of multidimensional data. From the
		  functional point of view, SOMs are neural networks that map
		  highly dimensional data into low dimensional (usually
		  two-dimensional) space in such a way that the topology of
		  the data is preserved. The construction of a map is
		  realized through a process of unsupervised learning. Owing
		  to the visualization capabilities arising in the
		  two-dimensional space, one can visualize a structure in the
		  original data and identify potential clusters as well as
		  their size (compactness) and mutual distribution in the
		  map. In this study, analysis of software data concerning
		  JAVA classes is being carried out.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  pedrycz92a,
  author	= {W. Pedrycz and H. C. Card},
  title		= {Linguistic interpretation of \mbox{self-organizing} maps},
  booktitle	= {IEEE International Conference on Fuzzy Systems},
  year		= {1992},
  pages		= {371--378},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {An unconventional interpretation of Kohonen
		  self-organizing maps is presented. It employs linguistic
		  variables and mechanisms of fuzzy decision theory to
		  quantitatively reveal pattern structure in the map
		  following self-organization. A simple example of pattern
		  clustering is provided.},
  dbinsdate	= {oldtimer}
}

@Article{	  pedrycz97a,
  author	= {Pedrycz, Witold and Waletzky, James},
  title		= {Fuzzy clustering in software reusability},
  journal	= {Software---Practice and Experience},
  year		= {1997},
  number	= {3},
  volume	= {27},
  pages		= {245--270},
  abstract	= {This paper is concerned with the organization and
		  retrieval of reusable software components with the aid of
		  unsupervised learning. The methods considered of
		  unsupervised learning include FUZZY ISODATA and Kohonen
		  self-organizing maps. The key issues addressed in the study
		  include information retrieval in the presence of incomplete
		  information, and domain specific enhancements of
		  unsupervised learning, including those of partial
		  supervision. The primary intention is to reveal how the
		  learning mechanism can accommodate individual preferences
		  (profile) of the users viewed as a significant component of
		  organization and retrieval algorithms. Numerical examples
		  use a set of MS-DOS system commands and a collection of
		  reusable C++ classes.},
  dbinsdate	= {oldtimer}
}

@Article{	  pedrycz97b,
  author	= {W. Pedrycz and J. Waletzky},
  title		= {Neural-network front ends in unsupervised learning},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  volume	= {8},
  number	= {2},
  pages		= {390--401},
  dbinsdate	= {oldtimer}
}

@Article{	  pei98a,
  author	= {Soo-Chang Pei and You-Shen Lo},
  title		= {Color image compression and limited display using self-
		  organization {K}ohonen map},
  journal	= {IEEE Transactions on Circuits and Systems for Video
		  Technology},
  year		= {1998},
  volume	= {8},
  number	= {2},
  pages		= {191--205},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  peiris89a,
  author	= {V. Peiris and B. Hochet and G. Corbaz and M. Declercq and
		  S. Piguet},
  title		= {A versatile numerical circuit for the simulation of neural
		  networks},
  booktitle	= {Proc. Journees d'Electronique 1989. Artificial Neural
		  Networks},
  year		= {1989},
  pages		= {313--322},
  publisher	= {Presses Polytechniques Romandes},
  address	= {Lausanne, Switzerland},
  note		= {(in French)},
  x		= { Describes a versatile digital building block dedicated to
		  the assembly of various neural networks (Hopfield,
		  Multilayers, Kohonen) for fast simulation purposes. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  peiris91a,
  author	= {V. Peiris and B. Hochet and S. Abdo and M. Declercq},
  title		= {Implementation of a {K}ohonen map with learning
		  capabilities},
  booktitle	= {Int. Symp. on Circuits and Systems},
  year		= {1991},
  volume	= {III},
  pages		= {1501--1504},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  peiris92a,
  author	= {V. Peiris and B. Hochet and T. Creasy and M. Declercq},
  title		= {Implementation of a {K}ohonen network with learning
		  faculties},
  journal	= {Bull. des Schweizerischen Elektrotechnischen Vereins \&
		  des Verbandes Schweizerischer Elektrizitaetswerke},
  year		= {1992},
  volume	= {83},
  number	= {5},
  pages		= {41--43},
  note		= {(in English)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  peiris94a,
  author	= {Vincent Peiris and Bertrand Hochet and Michel Declercq},
  title		= {Implementation of a Fully Parallel {K}ohonen Map: A Mixed
		  Analog Digital Approach},
  pages		= {2064--2069},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {implementation},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pelikan96a,
  author	= {E. Pelikan and P. Matejka and M. Slama and K. Vinkler},
  title		= {Interactive forecasting of the electric load using
		  {K}ohonen \mbox{self-organizing} feature maps},
  booktitle	= {WCNN'96. World Congress on Neural Networks. International
		  Neural Network Society 1996 Annual Meeting},
  publisher	= {Lawrence Erlbaum Associates},
  year		= {1996},
  address	= {Mahwah, NJ, USA},
  pages		= {443--6},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  peltoranta92a,
  author	= {Mauri Peltoranta},
  title		= {Methods for classification of non-averaged {EEG} responses
		  using autoregressive model based features},
  school	= {Graz University of Technology},
  year		= {1992},
  address	= {Graz, Austria},
  month		= {May},
  annote	= {Compares classifiers: SOM, K-means, LVQ, Backprop,
		  discriminant functions. },
  dbinsdate	= {oldtimer}
}

@Article{	  peltoranta94a,
  author	= {Peltoranta, M. and Pfurtscheller, G.},
  title		= {Neural network based classification of non-averaged event
		  related-EEG responses},
  journal	= {Medical \& Biological Engineering \& Computing},
  year		= {1994},
  number	= {2},
  volume	= {32},
  pages		= {189--196},
  month		= {March},
  abstract	= {Classification of non-averaged task-related EG responses
		  with different types of classifier, including
		  self-organising feature map and learning vector quantiser,
		  K-mean, back-propagation and a combination of the last two,
		  is reported. EEG data are collected from approximately one
		  second periods prior to movement of the right or left index
		  finger. A cue stimulus indicating which hand to use is
		  employed. Feature vectors are formed by concatenating
		  spatial information from the EEg electrodes and temporal
		  information from different time incidents during the
		  planning of hand movement. Power values of the most
		  reactive frequencies within the extended alpha-band (5--16
		  Hz) are used as features. The features are derived from an
		  autoregressive model fitted to the EEG signals. The
		  performance of the classifier and their ability to learn
		  and generalise is tested with 200 arbitrarily selected
		  event-related EEG data from a normal subject.
		  Classification accuracies as high as 85--90% are achieved
		  with the methods described here. A comparison of the
		  classifier is made.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pendock94a,
  author	= {Pendock, N. },
  title		= {Signal segmentation using \mbox{self-organizing} maps},
  booktitle	= {Proceedings of the 1993 IEEE South African Symposium on
		  Communications and Signal Processing},
  year		= {1994},
  pages		= {218--23},
  organization	= {Dept. of Comput. \& Appl. Math. , Univ. of the
		  Witwatersrand, Johannesburg, South Africa},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  peng91a,
  author	= {M. Peng and C. L. Nikias and J. G. Proakis},
  title		= {Adaptive equalization for {PAM} and {QAM} signals with
		  neural networks},
  booktitle	= {Conf. Record of the Twenty-Fifth Asilomar Conf. on
		  Signals, Systems and Computers},
  year		= {1991},
  volume	= {I},
  pages		= {496--500},
  organization	= {IEEE; Naval Postgraduate School; San Jose State Univ},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  penman94a,
  author	= {Penman, J. and Yin, C. M. },
  title		= {Feasibility of using unsupervised learning, artificial
		  neural networks for the condition monitoring of electrical
		  machines},
  journal	= {IEE Proceedings-Electric Power Applications},
  year		= {1994},
  volume	= {141},
  number	= {6},
  pages		= {317--22},
  month		= {Nov},
  dbinsdate	= {oldtimer}
}

@Article{	  peper93a,
  author	= {Ferdinand Peper and Mehdi N. Shirazi and Hideki Noda},
  title		= {A noise suppressing distance measure for competitive
		  learning neural networks},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1993},
  volume	= {4},
  pages		= {151--153},
  month		= {January},
  x		= {as6 / 1 AST},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  peper93b,
  author	= {Ferdinand Peper and Bijng Zhang and Hideki Noda},
  title		= {A Comparative Study of {ART-2} and the Self-Organizing
		  Feature Map},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1425--1429},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {This paper compares the ART2-A model and the
		  Self-Organizing Feature Map. The two models are applied to
		  the classification of feature vectors extracted from
		  texture images. Simulation shows that ART2-A performs best
		  when its noise-reduction/contrast-enhancement mechanism is
		  switched off. In this mode it performs better than the
		  Self-Organizing Feature Map. Experiments known from
		  literature show that a Back Propagation network performs
		  only slightly better than ART2-A for the same texture
		  classification task.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  perez01a,
  author	= {Perez, Claudio A. and Salinas, Cristian A. and Estevez,
		  Pablo},
  title		= {Designing biologically inspired receptive fields for
		  neural pattern recognition technology},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {58--63},
  organization	= {Department of Electrical Engineering, Universidad de
		  Chile},
  publisher	= {},
  address	= {},
  abstract	= {The paper proposes a new method to incorporate
		  biologically inspired receptive fields in feed forward
		  neural networks to enhance pattern recognition performance.
		  It is proposed a neural architecture composed of two
		  networks in cascade: a feature extraction network followed
		  by a neural classifier. A genetic algorithm is used to
		  search for the receptive field configuration in the problem
		  of handwritten digit recognition. The proposed network,
		  with receptive fields properly designed, shows an
		  improvement in the classification performance relative to
		  other neural network models with fully connected
		  architectures where receptive fields are not explicitly
		  defined. A Self Organizing Map (SOM) is used to show the
		  relative distance among patterns before and after the
		  transformation performed by the network with biologically
		  inspired receptive fields.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  perez93a,
  author	= {Juan-Carlos Perez and Enrique Vidal},
  title		= {Constructive Design of {LVQ} and {DSM} Classifiers},
  booktitle	= {New Trends in Neural Computation, Lecture Notes in
		  Computer Science No. 686},
  publisher	= {Springer},
  year		= {1993},
  editor	= {J. Mira and J. Cabestany and A. Prieto},
  pages		= {335--339},
  dbinsdate	= {oldtimer}
}

@InCollection{	  perez95a,
  author	= {M. Jara Perez and W. Machaca Luque and F. Damiani},
  title		= {Design of a 4*4 {K}ohonen neural net-VHDL description},
  booktitle	= {Proceedings of the 1995 First IEEE International Caracas
		  Conference on Devices, Circuits and Systems},
  publisher	= {IEEE},
  year		= {1995},
  address	= {New York, NY, USA},
  pages		= {135--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  perez99a,
  author	= {Perez, C. A. and Held, C. M. and Mollinger, P. R.},
  title		= {Handwritten digit recognition based on prototypes created
		  by Euclidean distance},
  booktitle	= {Proceedings 1999 International Conference on Information
		  Intelligence and Systems},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {320--3},
  abstract	= {Handwritten digits are recognized using prototypes created
		  by a training algorithm based on the Euclidean distance.
		  The subsequent classification of a handwritten digit is
		  based on criteria considering the Euclidean distance to the
		  prototypes. A training set of 2361 patterns is used to
		  create the prototypes and a separate set of 1320 patterns
		  is used to test the proposed method. The system performance
		  is compared to two other known classification algorithms: a
		  MLP (multilayer perceptron network), and SOM
		  (self-organizing map) plus LVQ1 (a linear vector
		  quantization algorithm). The proposed method reached a
		  recognition rate of 93.5% when using the nearest-prototype
		  criterion, and raised to 94.8% when using a
		  nearest-prototype-voting criterion. It compared favorably
		  with the MLP (91.8%) and SOM+LVQ1 (91.5%).},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  perlmutter94a,
  author	= {Perlmutter, K. O. and Nash, C. L. and Gray, R. M. },
  title		= {A comparison of {B}ayes risk weighted vector quantization
		  with posterior estimation with other {VQ}-based
		  classifiers},
  booktitle	= {Proceedings ICIP-94},
  year		= {1994},
  volume	= {2},
  pages		= {217--21},
  organization	= {Dept. of Electr. Eng. , Stanford Univ. , CA, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  perlmutter96a,
  author	= {Keren O. Perlmutter and Sharon M. Perlmutter and Robert M.
		  Gray and Richard A. Olshen and Karen L. Oehler},
  title		= {{B}ayes Risk Weighted Vector Quantization with Posterior
		  Estimation for Image Compression and Classification},
  journal	= {IEEE Trans. on Image Processing},
  year		= {1996},
  volume	= {5},
  number	= {2},
  pages		= {347--360},
  month		= {February},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  perrone94a,
  author	= {Antonio L. Perrone and Gianfranco Basti},
  title		= {Computation and Reversibility in a Chaotic System Modelled
		  by a {T}uring Machine. An Application to Contextual Pattern
		  Recognition},
  pages		= {501--504},
  booktitle	= {Proc. 3rd International Conference on Fuzzy Logic, Neural
		  Nets and Soft Computing},
  year		= {1994},
  publisher	= {Fuzzy Logic Systems Institute},
  address	= {Iizuka, Japan},
  annote	= {application, hybrid},
  dbinsdate	= {oldtimer}
}

@Article{	  perus01a,
  author	= {Perus, M.},
  title		= {Multi-level synergetic computation in the brain},
  journal	= {Nonlinear-Phenomena-in-Complex-Systems},
  year		= {2001},
  volume	= {4},
  pages		= {157--93},
  abstract	= {Patterns of activities of neurons serve as attractors,
		  since they are those neuronal configurations which
		  correspond to minimal 'free energy' of the whole system.
		  Namely, they realize maximal possible agreement among
		  constitutive neurons and are most-strongly correlated with
		  some environmental pattern. Neuronal
		  patterns-qua-attractors have both a material and a virtual
		  aspect. As neuronal patterns, on the one hand,
		  patterns-qua-attractors are explicit carriers of
		  informational contents. As attractors, on the other hand,
		  patterns-qua-attractors are implicit mental representations
		  which acquire a meaning in contextual relations to other
		  possible patterns. Recognition of an external pattern is
		  explained as a (re) construction of the pattern which is
		  the most relevant and similar to a given environmental
		  pattern. The identity of the processes of pattern
		  construction, re-construction and Hebbian short-term
		  storage is realized in a net. Perceptual processes are here
		  modeled using Kohonen's topology-preserving feature mapping
		  onto the cortex where further associative processing is
		  continued. To model stratification of associative
		  processing because of the influence from higher brain
		  areas, Haken's multi-level synergetic network is found to
		  be appropriate. The hierarchy of brain processes is of
		  "software"-type, i.e. virtual, as well as it is of
		  "hardware"-type, i.e. physiological. It is shown that
		  synergetic and attractor dynamics can characterize not only
		  neural networks, but also underlying quantum networks.
		  Neural nets are alone not sufficient for consciousness, but
		  interaction with the quantum level might provide effects
		  necessary for consciousness, like, for instance, ultimate
		  binding of perceptual features into an unified experience.
		  It is mathematically demonstrated that associative neural
		  networks realize information processing analogous to the
		  quantum dynamics. Parallels in the formalism of neural
		  models and quantum theory are listed. Basic elements of the
		  quantum versus neural system (modeled by formal neurons and
		  connections) are very different, but their collective
		  processes obey similar laws. Specifically, it is shown that
		  neuron's weighted spatio-temporal integration of signals
		  corresponds to the Feynman's version of the Schrodinger
		  equation. In the first case weights are synaptic strengths
		  determined by the Hebb or delta correlation rule; in the
		  second case weights are Green functions or density
		  matrices. In both cases encodings of pattern-correlations
		  represent memory. (Re) construction of a neuronal
		  pattern-qua-attractor is analogous to the "wave-function
		  collapse". Transformations of memory (or sub-conscious)
		  representations to a conscious representation is modeled in
		  the same way. The found mathematical analogies allow
		  translation of the neural-net "algorithm", which in
		  author's simulations works very well, into a quantum one.
		  This indicates how such quantum networks, which might be
		  exploited by the sub-cellular levels of brain, could
		  process information efficiently and also make it
		  conscious.},
  dbinsdate	= {2002/1}
}

@Article{	  pesonen96a,
  author	= {Pesonen, E. and Eskelinen, M. and Juhola, M. },
  title		= {Comparison of different neural network algorithms in the
		  diagnosis of acute appendicitis},
  journal	= {International Journal of Bio-Medical Computing},
  year		= {1996},
  volume	= {40},
  number	= {3},
  pages		= {227--33},
  dbinsdate	= {oldtimer}
}

@Article{	  pesonen98a,
  author	= {Pesonen,E. and Ohmann,C. and Eskelinen,M. and Juhola,M. },
  title		= {Diagnosis of Acute Appendicitis in 2 Databases Evaluation
		  of Different Neighborhoods with an {LVQ} Neural Network},
  journal	= {Methods Inform. Med. },
  year		= {1998},
  pages		= {59--63},
  volume	= {37},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pessa96a,
  author	= {E. Pessa and M. P. Penna},
  title		= {Can learning process in neural networks be considered as a
		  phase transition?},
  booktitle	= {Proceedings of the 7th Italian Workshop on Neural Nets.
		  Neural Nets. WIRN Vietri-95},
  publisher	= {World Scientific},
  year		= {1996},
  editor	= {M. Marinaro and R. Tagliaferri},
  address	= {Singapore},
  pages		= {123--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pessi95a,
  author	= {T. Pessi and J. Kangas and O. Simula},
  title		= {Patient Grouping Using Self-Organizing Map},
  booktitle	= {Proc. International Conference on Artificial Neural
		  Networks (ICANN'95), Industrial Session 5 (Medicine)},
  year		= {1995},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pesu96a,
  author	= {L. Pesu and E. Ademovic and J. -C. Pesquet and P.
		  Helisto},
  title		= {Wavelet packet based respiratory sound classification},
  booktitle	= {Proceedings of the IEEE-SP International Symposium on
		  Time-Frequency and Time-Scale Analysis},
  publisher	= {IEEE},
  year		= {1996},
  address	= {New York, NY, USA},
  pages		= {377--80},
  dbinsdate	= {oldtimer}
}

@Article{	  petersohn98a,
  author	= {H. Petersohn},
  title		= {Assessment of cluster analysis and \mbox{self-organizing}
		  maps},
  journal	= {International Journal of Uncertainty, Fuzziness and
		  Knowledge-Based Systems},
  year		= {1998},
  volume	= {6},
  number	= {2},
  pages		= {139--49},
  dbinsdate	= {oldtimer}
}

@Article{	  petrescu98a,
  author	= {A. Petrescu and N. Tomescu},
  title		= {{K}ohonen neural nets in colours reduction of images},
  journal	= {Electrotehnica, Electronica si Automatica},
  year		= {1998},
  volume	= {46},
  number	= {5--6},
  pages		= {22--4},
  dbinsdate	= {oldtimer}
}

@InCollection{	  petroni97a,
  author	= {N. C. Petroni and M. Tricarico},
  title		= {Self-organizing neural nets and the perceptual origin of
		  the circle of fifths},
  booktitle	= {Music, Gestalt, and Computing. Studies in Cognitive and
		  Systematic Musicology},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {M. Leman},
  address	= {Berlin, Germany},
  pages		= {169--80},
  dbinsdate	= {oldtimer}
}

@Article{	  peura01a,
  author	= {Peura, M.},
  title		= {Attribute trees as adaptive object models in image
		  analysis},
  journal	= {Acta-Polytechnica-Scandinavica,-Mathematics-and-Computing-Series.
		  no.Ma113; 2001; p.1--80},
  year		= {2001},
  volume	= {},
  pages		= {1--80},
  abstract	= {This thesis focuses on the analysis of irregular
		  hierarchical visual objects. The main approach involves
		  modelling the objects as unordered attribute trees. A tree
		  presents the overall organization, or topology, of an
		  object, while the vertex attributes describe further visual
		  properties such as intensity, color, or size. Techniques
		  for extracting, matching, comparing, and interpolating
		  attribute trees are presented, principally aiming at
		  systems that can learn to recognize objects. Analysis of
		  weather radar images has been the pilot application for
		  this study, but the main ideas are applicable in structural
		  pattern recognition more generally. The central original
		  contribution of this thesis is the Self-Organizing Map of
		  Attribute Trees (SOM-AT) which demonstrates hen; the
		  proposed tree processing techniques-tree indexing:
		  matching, distance functions: and mixtures-can be embedded
		  in a learning system, the SOM-AT is a variant of the
		  Self-Organizing Map (SOM), the neural network model
		  invented by Prof. Teuvo Kohonen. The SOM is especially
		  suited to visualizing distributions of objects, classifying
		  objects and monitoring changes in objects. Hence, the
		  SOM-AT can be applied in the respective tasks involving
		  hierarchical objects. More generally, the proposed ideas
		  are applicable in learning systems involving comparisons
		  and interpolations of trees. The suggested heuristic
		  index-based tree matching scheme is another independent
		  contribution. The basic idea of the heuristic is to divide
		  trees to subtrees and match the subtrees recursively by
		  means of topological indices. Given two attribute trees,
		  the larger of which has N vertices, and the maximal child
		  count (out-degree) is D vertices: the complexity of the
		  scheme is only O(N log D) operations while exact matching
		  schemes seem to have at least quadratic complexity: O(N/sup
		  2.5/) operations in checking isomorphisms and O(N/sup 3/)
		  operations in matching attribute trees. The proposed scheme
		  is efficient also in terms of its "hit rate", the number of
		  successfully matched vertices. In matching two random trees
		  of N<or=10 vertices, the number of heuristically matched
		  vertices is on average 99% of the optimum, and the accuracy
		  for trees of N<or=500 vertices is still above 90%. The
		  feasibility of the proposed techniques is demonstrated by
		  database querying, monitoring, and clustering of weather
		  radar images. A related tracking scheme is outlined as
		  well. In addition to weather radar images, a case study is
		  presented on Northern light (Aurora Borealis) images. Due
		  to the generic approach, there are also medical and
		  geographical applications in view.},
  dbinsdate	= {2002/1}
}

@Article{	  peura98a,
  author	= {M. Peura},
  title		= {The Self-Organizing Map of Trees},
  journal	= {Neural Processing Letters},
  year		= {1998},
  volume	= {8},
  number	= {2},
  pages		= {155--162},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  peura99a,
  author	= {Peura, Markus},
  title		= {Self-Organizing Map of attribute trees},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  publisher	= {IEE},
  address	= {London, UK},
  year		= {1999},
  volume	= {1},
  pages		= {168--173},
  abstract	= {The standard version of the Self-Organizing Map applies
		  vector data. This paper explains how attribute trees can be
		  used as the learning medium in the Self-Organizing Map. As
		  a data structure, a tree is an optimal presentation of many
		  hierarchical and dynamical objects appearing in natural
		  phenomena and human activities. The proposed approach is
		  based on introducing a distance metric and adjusting
		  schemes for attribute trees. The trees are assumed to be
		  rooted and unordered. The key idea is in heuristic matching
		  which provides approximate results but above all avoids the
		  exponential complexity of exact matching. The feasibility
		  of the suggested methods is demonstrated with an experiment
		  on weather radar imagery.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  peura99b,
  author	= {Peura, M.},
  title		= {Attribute Trees in Image Analysis---Heuristic Matching and
		  Learning Techniques.},
  booktitle	= {Proc. of International Conference on Image Analysis and
		  Processing (ICIAP'99), Venice, Italy, September 27--29},
  pages		= {1160--1165},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@Article{	  pfurtscheller92a,
  author	= {G. Pfurtscheller and W. Klimesch},
  title		= {Functional topography during a visuoverbal judgment task
		  studied with event-related desynchronization mapping},
  journal	= {J. Clin. Neurophysiol. },
  year		= {1992},
  volume	= {9},
  number	= {1},
  pages		= {120--131},
  month		= {January},
  dbinsdate	= {oldtimer}
}

@Article{	  pfurtscheller92b,
  author	= {G. Pfurtscheller and D. Flotzinger and K. Matuschik},
  title		= {Sleep classification in infants based on artificial neural
		  networks},
  journal	= {Biomedizinische Technik},
  year		= {1992},
  volume	= {37},
  number	= {6},
  pages		= {122--130},
  month		= {June},
  note		= {(in German)},
  dbinsdate	= {oldtimer}
}

@Article{	  pfurtscheller92c,
  author	= {G. Pfurtscheller and D. Flotzinger and W. Mohl and M.
		  Peltoranta},
  title		= {Prediction of the side of hand movements from single-trial
		  multi-channel {EEG} data using neural networks},
  journal	= {Electroencephalography and Clinical Neurophysiology},
  year		= {1992},
  volume	= {82},
  number	= {4},
  pages		= {313--315},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@Article{	  pfurtscheller96a,
  author	= {G. Pfurtscheller and J. Kalcher and Ch. Neuper and D.
		  Flotzinger and M. Pregenzer},
  title		= {On-line {EEG} classification during externally-paced hand
		  movements using a neural network-based classifier},
  journal	= {Electroencephalography and Clinical Neurophysiology},
  year		= {1996},
  volume	= {99},
  number	= {5},
  pages		= {416--25},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pfurtscheller99a,
  author	= {G. Pfurtscheller and M. Pregenzer},
  title		= { {LVQ} and single trial EEG classification},
  booktitle	= {Kohonen Maps},
  pages		= {317--328},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: LVQ, DSLVQ, EEG, classification, feature
		  selection },
  dbinsdate	= {oldtimer}
}

@Article{	  phaf01a,
  author	= {Phaf, R. H. and Den Dulk, P. and Tijsseling, A. and
		  Lebert, E.},
  title		= {Novelty-dependent learning and topological mapping},
  journal	= {CONNECTION SCIENCE},
  year		= {2001},
  volume	= {13},
  number	= {4},
  month		= {DEC},
  pages		= {293--321},
  abstract	= {Unsupervised topological ordering, similar to Kohonen's
		  (1982, Biological Cybernetics, 43: 59--69) self-organizing
		  feature map, was achieved in a connectionist module for
		  competitive learning (a CALM Map) by internally regulating
		  the learning rate and the size of the active neighbourhood
		  on the basis of input novelty. In this module,
		  winner-take-all competition and the 'activity bubble' are
		  due to graded lateral inhibition between units. It tends to
		  separate representations as far apart as possible, which
		  leads to interpolation abilities and an absence of
		  catastrophic interference when the interfering set of
		  patterns forms an interpolated set of the initial data set.
		  More than the Kohonen maps, these maps provide an
		  opportunity for building psychologically and
		  neurophysiologically motivated multimodular connectionist
		  models. As an example, the dual pathway connectionist model
		  for fear conditioning by Armony et al. (1997, Trends in
		  Cognitive Science, 1: 28--34) was rebuilt and extended with
		  CALM Maps. If the detection of novelty enhances memory
		  encoding in a canonical circuit, such as the CALM Map, this
		  could explain the finding of large distributed networks for
		  novelty detection (e.g. Knight and Scabini, 1998, Journal
		  of Clinical Neurophysiology, 15: 3--13) in the brain.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  pham00a,
  author	= {Pham, D. T. and Sagiroglu, S.},
  title		= {Neural network classification of defects in veneer
		  boards},
  booktitle	= {Proceedings of the Institution of Mechanical Engineers,
		  Part B (Journal of Engineering Manufacture)},
  year		= {2000},
  volume	= {214},
  pages		= {255--8},
  abstract	= {Learning vector quantization (LVQ) networks are known good
		  neural classifiers which provide fast and accurate results
		  for many applications. The aim of this work was to test if
		  this network paradigm could be employed for the
		  classification of wood sheet defects. Experiments conducted
		  with LVQ networks have shown that they provide a high
		  degree of discrimination between the different types of
		  defects and potentially can perform defect classification
		  in real time.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pham00b,
  author	= {D. T. Pham and H. A. Awad},
  title		= {Function approximation using fuzzy {K}ohonen networks},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Intelligent Systems Laboratory, Systems Division, School
		  of Engineering, University of Wales Cardiff},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  pham94a,
  author	= {Pham, D. T. and Bayro-Corrochano, E. J. },
  title		= {Self-organizing neural-network-based pattern clustering
		  method with fuzzy outputs},
  journal	= {Pattern Recognition},
  year		= {1994},
  volume	= {27},
  number	= {8},
  pages		= {1103--10},
  month		= {Aug},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pham98a,
  author	= {Pham, D. T. and Chan, A. B.},
  title		= {A novel firing rule for training {K}ohonen
		  \mbox{self-organising} maps},
  booktitle	= {Thirteenth International Conference on Applications of
		  Artificial Intelligence in Engineering AIENG XIII},
  publisher	= {Computational Mechanics Publications},
  address	= {Southampton, UK},
  year		= {1998},
  volume	= {},
  pages		= {101--4},
  abstract	= {Statistical process control charts can exhibit six
		  principal types of patterns: Normal, Cyclic, Increasing
		  Trend, Decreasing Trend, Upward Shift and Downward Shift.
		  All except Normal patterns indicate abnormalities in the
		  process that must be corrected. Accurate and speedy
		  detection of such patterns is important to achieving tight
		  control of the process and ensuring good product quality.
		  The paper describes an implementation of the Kohonen
		  self-organising map which employs the Euclidean distance as
		  the firing rule for control chart pattern recognition.
		  First, the structure of the network is outlined and the
		  equations which govern its dynamics are given. Then, the
		  learning mechanism of the network is explained. The effects
		  of different combinations of network parameters on
		  classification accuracy are discussed. A novel firing rule
		  for the Kohonen self-organising map is proposed. This rule
		  involves component-by-component comparison between the
		  input pattern and the established class templates. When an
		  input vector is presented, it is compared with the class
		  templates in all the neurons in turn. The neuron containing
		  the class template that best matches the input vector will
		  subsequently fire. This approach is intended to enhance the
		  generalisation capability and accuracy of the Kohonen
		  self-organising map. The paper gives a comparison of the
		  results obtained using the Euclidean distance and the
		  proposed firing rule.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pican97a,
  author	= {N. Pican},
  title		= {Contextual {K}ohonen {SOM} with orthogonal weight
		  estimator principle},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {667--72},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pico95a,
  author	= {F. Ibarra Pico and D. Asensi Munoz and A. Almagro Leon and
		  J. M. Garcia-Chamizo},
  title		= {Segmentation of defect in textile fabric using semi-cover
		  vector and self-organization},
  booktitle	= {QCAV 95. 1995 International Conference on Quality Control
		  by Artificial Vision},
  publisher	= {Univ. Bourgogne},
  year		= {1995},
  address	= {Le Creusot, France},
  pages		= {58--65},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  picton91a,
  author	= {P. D. Picton},
  title		= {The relationship between {K}ohonen learning and {K}alman
		  filters},
  booktitle	= {IEE Colloquium on 'Adaptive Filtering, Non-Linear Dynamics
		  and Neural Networks' (Digest No. 176)},
  year		= {1991},
  pages		= {7/1--5},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  piepenbrock00a,
  author	= {Piepenbrock, C. and Obermayer, K.},
  title		= {The effect of intracortical competition on the formation
		  of topographic maps in models of {H}ebbian learning},
  journal	= {Biological Cybernetics},
  year		= {2000},
  volume	= {82},
  pages		= {345--53},
  abstract	= {Correlation-based learning (CBL) models and
		  self-organizing maps (SOM) are two classes of Hebbian
		  models that have both been proposed to explain the
		  activity-driven formation of cortical maps. Both models
		  differ significantly in the way lateral cortical
		  interactions are treated, leading to different predictions
		  for the formation of receptive fields. The linear CBL
		  models predict that receptive field profiles are determined
		  by the average values and the spatial correlations of the
		  second order of the afferent activity patterns, whereas SOM
		  models map stimulus features. Here, we investigate a class
		  of models which are characterized by a variable degree of
		  lateral competition and which have the CBL and SOM models
		  as limit cases. We show that there exists a critical value
		  for intracortical competition below which the model
		  exhibits CBL properties and above which feature mapping
		  sets in. The class of models is then analyzed with respect
		  to the formation of topographic maps between two layers of
		  neurons. For Gaussian input stimuli we find that localized
		  receptive fields and topographic maps emerge above the
		  critical value for intracortical competition, and we
		  calculate this value as a function of the size of the input
		  stimuli and the range of the lateral interaction function.
		  Additionally, we show that the learning rule can be derived
		  via the optimization of a global cost function in a
		  framework of probabilistic output neurons which represent a
		  set of input stimuli by a sparse code.},
  dbinsdate	= {oldtimer}
}

@Article{	  piepenbrock99a,
  author	= {Piepenbrock, C. and Obermayer, K.},
  title		= {Effects of lateral competition in the primary visual
		  cortex on the development of topographic projections and
		  ocular dominance maps},
  journal	= {Neurocomputing},
  year		= {1999},
  volume	= {26},
  pages		= {477--82},
  abstract	= {We present a Hebbian model for the development of cortical
		  maps in the striate cortex that includes a parameter which
		  represents the degree of lateral competition for activity
		  between neurons. It has two well-known models as limiting
		  cases: for weak competition we obtain a correlation-based
		  learning (CBL) model and for strong lateral competition we
		  recover the self-organizing map (SOM). We show that
		  increasing the competition for positively correlated
		  localized stimuli leads to a sharp transition from an
		  unorganized map to a topographic projection and
		  subsequently to a topographic map with OD and locally
		  magnified OD stripes.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pilla00a,
  author	= {Pilla V., Jr. and Lopes, H. S.},
  title		= {Detection of movement-related desynchronization of the
		  {EEG} using neural networks},
  booktitle	= {Annual International Conference of the IEEE Engineering in
		  Medicine and Biology---Proceedings},
  year		= {2000},
  editor	= {Enderle, J.D},
  volume	= {2},
  pages		= {1372--1376},
  organization	= {Department of Electronics, CEFET-PR},
  publisher	= {},
  address	= {},
  abstract	= {In this work, we aimed to detect the non-averaged MRD of
		  the mu rhythm from the background activity of the EEG. The
		  MRD was produced in the contralateral sensorimotor cortex
		  by means of a movement of the right hand. Thirteen
		  volunteers took part in the experiments. They were all
		  right-handed and had ages between 22 and 37 years old. The
		  volunteers were asked to do one of two tasks: (a) just one
		  movement by trial; (b) repetition of movements. The EEG
		  signal was taken in the scalp near to C3 lead. The signal
		  was amplified, sampled and digitally filtered off-line. The
		  time response of the mu band was computed with the FFT.
		  Windows of the time response was used as predicates in the
		  training and testing of a neural network classifier.
		  Representative samples of MRD and background activity was
		  taken for each class. The neural networks used were LVQ and
		  they were trained by session, by task and by volunteer. The
		  performance was measured by the geometric mean of the
		  sensibility and specificity indexes, calculated for every
		  training epoch over test data. The best detection
		  performance was 88% for the single movement task and 78%
		  for repetition of movements, with an average of 66% for
		  both tasks. For the LVQ, only 3 subclasses for every class
		  were sufficient, and a constant learning rate of 0.01 with
		  the number of training epochs calculated by the relation of
		  250 \times (total number of subclasses) was enough to get
		  the best results.},
  dbinsdate	= {2002/1}
}

@InCollection{	  pilot96a,
  author	= {T. Pilot and R. Knosala},
  title		= {The neural network application in the group technology},
  booktitle	= {III Konferencja Naukowa Komputerowe Wspomaganie Prac
		  Inzynierskich (III Conference on Computer Aided Engineering
		  Practice)},
  publisher	= {ASME},
  year		= {1996},
  editor	= {K. Stelson and F. Oba},
  address	= {New York, NY, USA},
  pages		= {443--54},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pino94a,
  author	= {Pino, B. and Pelayo, F. J. and Prieto, A. },
  title		= {A digital implementation of \mbox{self-organizing} maps},
  booktitle	= {Proceedings of the Fourth International Conference on
		  Microelectronics for Neural Networks and Fuzzy Systems},
  year		= {1994},
  pages		= {260--7},
  organization	= {Dept. de Electron. y Tecnologia de Computadores, Granada
		  Univ. , Spain},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  pintore01a,
  author	= {Pintore, M. and Taboureau, O. and Ros, F. and Chretien, J.
		  R.},
  title		= {Database mining applied to central nervous system ({CNS})
		  activity},
  journal	= {EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY},
  year		= {2001},
  volume	= {36},
  number	= {4},
  month		= {APR},
  pages		= {349--359},
  abstract	= {A data set of 389 compounds. active in the central nervous
		  system (CNS) and divided into eight classes according to
		  the receptor type, was extracted from the RBI (C) database
		  and analyzed by Self-Organizing Maps (SOM), also known as
		  Kohonen Artificial Neural Networks. This method gives a 2D
		  representation of the distribution of the compounds in the
		  hyperspace derived from their molecular descriptors. As SOM
		  belongs to the category of unsupervised techniques, it has
		  to be combined with another method in order to generate
		  classification models with predictive ability. The fuzzy
		  clustering (FC) approach seems to be particularly suitable
		  to delineate clusters in a rational way from SOM and to get
		  an automatic objective map interpretation. Maps derived by
		  SOM showed specific regions associated with a unique
		  receptor type and zones in which two or more activity
		  classes are nested. Then, the modeling ability of the
		  proposed SOM.:FC Hybrid System tools applied simultaneously
		  to right activity classes was validated after dividing the
		  389 compounds into a training set and a test set, including
		  259 and 130 molecules, respectively. The proper
		  experimental activity class, among the eight possible ones,
		  was predicted simultaneously and correctly for 81% of the
		  test set compounds.},
  dbinsdate	= {2002/1}
}

@InCollection{	  piras97a,
  author	= {Antonio Piras and Alain Germond},
  title		= {Local linear correlation analysis with the {SOM}},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {203--208},
  dbinsdate	= {oldtimer}
}

@Article{	  piras98a,
  author	= {Piras, Antonio and Germond, Alain},
  title		= {Local linear correlation analysis with the SOM},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {79--90},
  abstract	= {The purpose of this paper is to illustrate a method which
		  be used to select relevant input variables for non-linear
		  regression. The proposed method is an extension to the
		  concept of SOM such that the linear correlation coefficient
		  is computed over a whole data manifold in neighbour
		  subspaces. Using the topographic properties of the usual
		  SOM a localized correlation coefficient may be obtained by
		  a modified Kohonen learning. The graphical ordered plot of
		  the obtained local correlation allows to study the
		  non-linear dependencies of variables.},
  dbinsdate	= {oldtimer}
}

@Article{	  pitas96a,
  author	= {Pitas, I. and Kotropoulos, C. and Nikolaidis, N. and Yang,
		  R. and Gabbouj, M},
  title		= {Order statistics learning vector quantizer},
  journal	= {IEEE Transactions on Image Processing},
  year		= {1996},
  number	= {6},
  volume	= {5},
  pages		= {1048--1053},
  abstract	= {In this correspondence, we propose a novel class of
		  learning vector quantizers (LVQ's) based on multivariate
		  data ordering principles. A special case of the novel LVQ
		  class is the median LVQ, which uses either the marginal
		  median or the vector median as a multivariate estimator of
		  location. The performance of the proposed marginal median
		  LVQ in color image quantization is demonstrated by
		  experiments.},
  dbinsdate	= {oldtimer}
}

@Article{	  pitas97a,
  author	= {I. Pitas and C. Kotropoulos and N. Nikolaidis and A. G.
		  Bors},
  title		= {Robust and Adaptive Techniques in Self Organizing Neural
		  Networks},
  journal	= {Nonlinear Analysis, Theory, Methods \& Applications},
  volume	= {30},
  pages		= {4517--4528},
  year		= {1997},
  dbinsdate	= {oldtimer}
}

@Article{	  platero96a,
  author	= {C. Platero and C. Fernandez and P. Campoy and R. Aracil},
  title		= {Surface analysis of cast aluminum by means of artificial
		  vision and {AI} based techniques},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2665},
  pages		= {36--46},
  note		= {(Machine Vision Applications in Industrial Inspection IV
		  Conf. Date: 31 Jan. -1 Feb. 1996 Conf. Loc: San Jose, CA,
		  USA Conf. Sponsor: SPIE; Soc. Imaging Sci. \& Technol)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  platt87a,
  author	= {John C. Platt and Alan H. Barr},
  title		= {Constrained Differential Optimization},
  booktitle	= {Neural Information Processing Systems},
  editor	= {Dana Z. Anderson},
  address	= {New York, NY},
  year		= {1987},
  pages		= {612--621},
  publisher	= {American Inst. of Physics},
  dbinsdate	= {oldtimer}
}

@Article{	  plebe02a,
  author	= {Plebe, A. and Anile, A. M.},
  title		= {A neural-network-based approach to the double traveling
		  salesman problem},
  journal	= {NEURAL COMPUTATION},
  year		= {2002},
  volume	= {14},
  number	= {2},
  month		= {FEB},
  pages		= {437--471},
  abstract	= {The double traveling salesman problem is a variation of
		  the basic traveling salesman problem where targets can be
		  reached by two salespersons operating in parallel. The real
		  problem addressed by this work concerns the optimization of
		  the harvest sequence for the two independent arms of a
		  fruit-harvesting robot. This application poses further
		  constraints, like a collision-avoidance function. The
		  proposed solution is based on a self-organizing map
		  structure, initialized with as many artificial neurons as
		  the number of targets to be reached. One of the key
		  components of the process is the combination of competitive
		  relaxation with a mechanism for deleting and creating
		  artificial neurons. Moreover, in the competitive relaxation
		  process, information about the trajectory connecting the
		  neurons is combined with the distance of neurons from the
		  target. This strategy prevents tangles in the trajectory
		  and collisions between the two tours. Results of tests
		  indicate that the proposed approach is efficient and
		  reliable for harvest sequence planning. Moreover, the
		  enhancements added to the pure self-organizing map concept
		  are of wider importance, as proved by a traveling salesman
		  problem version of the program, simplified from the double
		  version for comparison.},
  dbinsdate	= {2002/1}
}

@Article{	  pleshko01a,
  author	= {Pleshko, V. V. and Ermakov, A. E. and Lipinskii, G. V.},
  title		= {Top{SOM}: visualisation of document collections by means
		  of self-organising maps of topics},
  journal	= {Informatsionnye-Tekhnologii. no.8; 2001; p.8--11},
  year		= {2001},
  volume	= {},
  pages		= {8--11},
  abstract	= {A modification of the WebSOM method, which is widely used
		  for visual representation of full-text document
		  collections, was developed. The method proposed is called
		  TopSOM and differs from the original WebSOM method in the
		  way semantic document space is formed. Instead of a word
		  category map, a new approach is used to extract the most
		  meaningful topics (words and phrases) from documents.},
  dbinsdate	= {2002/1}
}

@Article{	  plesinger00a,
  author	= {Plesinger, A. and Ruzek, B. and Bouskova, A.},
  title		= {Statistical interpretation of {WEBNET} seismograms by
		  artificial neural nets},
  journal	= {STUDIA GEOPHYSICA ET GEODAETICA},
  year		= {2000},
  volume	= {44},
  number	= {2},
  pages		= {251--271},
  abstract	= {We employed multilayer perceptrons (MLP), self organizing
		  feature maps (SOFM), and learning vector quantization (LVQ)
		  to reveal and interpret statistically significant features
		  of different categories of waveform parameter vectors
		  extracted from three-component WEBNET velocigrams. In this
		  contribution we present and discuss in a summarizing manner
		  the results of (i) SOFM classification and MLP
		  discrimination between microearthquakes and explosions on
		  the basis of single-station spectral and amplitude
		  parameter vectors, (ii) SOFM/LVQ recognition of initial
		  onset polarities from PV-waveforms, and (iii) a source
		  mechanism study of the January 1997 microearthquake swarm
		  based on SOFM classification of combined multi-station
		  PV-onset polarity and SH/PV amplitude ratio (CPA) data.
		  Unsupervised SOFM classification of 497 NKC seismograms
		  revealed that the best discriminants are pure spectral
		  parameter vectors for the recognition of microearthquakes
		  (reliability 95% with 30 spectral parameters), and mixed
		  amplitude a,Ed spectral parameter vectors for the
		  recognition of explosions (reliability 98% with 41
		  amplitude and 30 spectral parameters). The optimal MLP,
		  trained with the standard backpropagation error method by
		  one randomly selected half of a set of 312 mixed (7
		  amplitude and 7 spectral) single- station (NKC)
		  microearthquake and explosion parameter vectors and tested
		  by the other half-set, and vice versa, correctly
		  classified, on average, 99% of all events. From a set of
		  NKC PV-waveform vectors for 375 events, the optimal LVQ net
		  correctly classified, on average, 98% of all up and 97% of
		  all down onsets, and assigned the likely correct polarity
		  to 85% of the onsets that were visually classified as
		  uncertain. Optimal SOFM architectures categorized the CPA
		  parameter vector sets for 145 January 97 events
		  individually for each of five stations (KOC, KRC, SKC, NKC,
		  LAG) quire unambiguously and stable into three
		  statistically significant classes. The nature of the
		  coincidence of these classes among the stations that
		  provided most reliable mechanism-relevant information (KOC,
		  KRC SKC) points at the occurence of further seven
		  statistically significant subclassses of mechanisms during
		  the swarm. The ten 'neural' classes of focal mechanisms
		  coincide fairly well with those obtained by moment tensor
		  inversion of P and SH polarities and amplitudes extracted
		  fp om the seismograms interactively. The obtained results,
		  together with those of refined hypocenter location, imply
		  that the focal area consisted of three dominant faults and
		  at least seven subfaults within a volume of riot more than
		  1 km in diameter that likely were seismically activated by
		  vertical stress from underneath.},
  dbinsdate	= {2002/1}
}

@Article{	  pletnev02a,
  author	= {Pletnev, I. V. and Zernov, V. V.},
  title		= {Classification of metal ions according to their complexing
		  properties: a data-driven approach},
  journal	= {ANALYTICA CHIMICA ACTA},
  year		= {2002},
  volume	= {455},
  number	= {1},
  month		= {MAR 18},
  pages		= {131--142},
  abstract	= {Factor, cluster and self-organizing map analyses were
		  performed for the stability constants of complexes of 24
		  metal ions and hydrogen with 3960 ligands (15606 values of
		  log K-1). Five factors reproduce 89% of data variability.
		  Both direct clusterization and clusterization on the basis
		  of factor analysis established the existence of six
		  different classes of similar cations. The similarity series
		  for metal ions and relative similarity of several ions are
		  discussed and the Kohonen two-dimensional map, which
		  visually represents the similarity, is presented.},
  dbinsdate	= {2002/1}
}

@Article{	  plummer93a,
  author	= {Plummer, J. },
  title		= {Tighter process control with neural networks},
  journal	= {AI Expert},
  year		= {1993},
  volume	= {8},
  number	= {10},
  pages		= {49--55},
  month		= {Oct},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  podgornik96a,
  author	= {Podgornik, P. and Dobnikar, A.},
  title		= {Modified ART for character recognition},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {289--92},
  abstract	= {A modular ART neural network (MART) is introduced in order
		  to increase the probability of correct optical character
		  recognition. Both layers, as well as the reset module, are
		  modified. A new learning algorithm combined with adaptive
		  LVQ is introduced in order to increase recognition
		  reliability. 3 different fonts were used in exhaustive
		  testing with MLP and FLVQ methods used as references.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  poechmueller93a,
  author	= {W. Poechmueller and M. Glesner and H. Juergs},
  title		= {Is {LVQ} Really Good for Classification?---An Interesting
		  Alternative},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1207--1212},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  poggi93a,
  author	= {Giovanni Poggi and Elvira Sasso},
  title		= {Codebook Ordering Techniques for Address-Predictive {VQ}},
  booktitle	= {Proc. ICASSP-93, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1993},
  volume	= {V},
  pages		= {586--589},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  poggi95a,
  author	= {Poggi, G. },
  title		= {Applications of the {K}ohonen algorithm in vector
		  quantization},
  journal	= {European Transactions on Telecommunications and Related
		  Technologies},
  year		= {1995},
  volume	= {6},
  number	= {2},
  pages		= {191--202},
  month		= {March-April},
  dbinsdate	= {oldtimer}
}

@Article{	  poggi96a,
  author	= {Giovanni Poggi},
  title		= {Generalized-Cost-Measure-Base Address-Predictive Vector
		  Quantization},
  journal	= {IEEE Trans. on Image Processing},
  year		= {1996},
  volume	= {5},
  number	= {1},
  pages		= {49--55},
  month		= {January},
  dbinsdate	= {oldtimer}
}

@Article{	  poincot97a,
  author	= {Philippe Poin\c{c}ot and Soizick Lesteven and Fionn
		  Murtagh},
  title		= {A Spatial User Interface to the Astronomical Literature},
  journal	= {Astronomy and Astrophysics},
  year		= {1997},
  note		= {Accepted for publication},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  poirier91a,
  author	= {F. Poirier},
  title		= {Improving the training and testing speed and the ability
		  of generalization in learning vector quantization-{DVQ}},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {I},
  pages		= {649--652},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  poirier91b,
  author	= {F. Poirier},
  title		= {{DVQ} : dynamic vector quantization application to speech
		  processing},
  booktitle	= {Proc. EUROSPEECH-91, 2nd European Conf. on Speech
		  Communication and Technology},
  year		= {1991},
  volume	= {II},
  pages		= {1003--1006},
  organization	= {Assoc. Belge Acoust. ; Assoc. Italiana di Acustica; CEC;
		  et al},
  publisher	= {Istituto Int. Comunicazioni},
  address	= {Genova, Italy},
  x		= {Sama asia kuin Poirier91b eri kustantajalta},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  poirier91c,
  author	= {F. Poirier and A. Ferrieux},
  title		= {{DVQ}: dynamic vector quantization-an incremental {LVQ}},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1333--1336},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  x		= {. . . In this paper the authors present and evaluate a new
		  version of the LVQ algorithm called dynamic vector
		  quantization (DVQ). },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pok98a,
  author	= {Gouchol Pok and Jyh Charn Liu},
  title		= {Texture classification by a two-level hybrid scheme},
  booktitle	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1998},
  volume	= {3656},
  pages		= {614--22},
  abstract	= {In this paper, we propose a novel feature extraction
		  scheme for texture classification, in which the texture
		  features are extracted by a two-level hybrid scheme, by
		  integrating two statistical techniques of texture analysis.
		  In the first step, the low level features are extracted by
		  the Gabor filters, and they are encoded with the feature
		  map indices, using Kohonen's SOFM algorithm. In the next
		  step, the encoded feature images are processed by the Gabor
		  filters, Gaussian {M}arkov random fields (GMRF), and grey
		  level co-occurrence matrix (GLCM) methods to extract the
		  high level features. By integrating two methods of texture
		  analysis in a cascaded manner, we obtained the texture
		  features which achieved a high accuracy for the
		  classification of texture patterns. The proposed schemes
		  were tested on the real microtextures, and the Gabor-GMRF
		  scheme achieved 10% increase of the recognition rate,
		  compared to the result obtained by the simple Gabor
		  filtering.},
  dbinsdate	= {oldtimer}
}

@Article{	  pok99a,
  author	= {Pok, Gouchol and Liu, Jyh Charn},
  title		= {Unsupervised texture segmentation based on histogram of
		  encoded {G}abor features and {MRF} model},
  journal	= {IEEE International Conference on Image Processing},
  year		= {1999},
  number	= {},
  volume	= {3},
  pages		= {208--211},
  abstract	= {In this paper, we propose an unsupervised texture
		  segmentation scheme in which Gabor transforms and GMRF
		  model are integrated. The Gabor filters are used to extract
		  low-level textural features. The Gabor feature vectors are
		  mapped to an 1-D space using the Kohnen's SOFM algorithm,
		  and then encoded by the feature map indices. The histogram
		  of encoded features over a small window are used to
		  determine the regions of homogeneous textures. From these
		  regions, class-specific parameters for GMRF model are
		  estimated and used to detect exact boundaries of different
		  textures.},
  dbinsdate	= {oldtimer}
}

@Article{	  polanco01a,
  author	= {Polanco, X. and Francois, C. and Lamirel, J. C.},
  title		= {Using artificial neural networks for mapping of science
		  and technology: A multi-self-organizing-maps approach},
  journal	= {SCIENTOMETRICS},
  year		= {2001},
  volume	= {51},
  number	= {1},
  month		= {MAY},
  pages		= {267--292},
  abstract	= {We argue in favour of artificial neural networks for
		  exploratory data analysis, clustering and mapping. We
		  propose the Kohonen self-organizing map (SOM) for
		  clustering and mapping according to a multi-maps extension.
		  It is consequently called Multi-SOM. Firstly the Kohonen
		  SOM algorithm is presented. Then the following improvements
		  are detailed: the way of naming the clusters, the map
		  division into logical areas, and the map generalization
		  mechanism. The multi-map display founded on the inter-maps
		  communication mechanism is exposed, and the notion of the
		  viewpoint is introduced. The interest of Multi-SOM is
		  presented for visualization, exploration or browsing, and
		  moreover for scientific and technical information analysis.
		  A case study in patent analysis on transgenic plants
		  illustrates the use of the Multi-SOM. We also show that the
		  inter-map communication mechanism provides support for
		  watching the plants on which patented genetic technology
		  works. It is the first map. The other four related maps
		  provide information about the plant parts that are
		  concerned, the target pathology, the transgenic techniques
		  used for making these plants resistant, and finally the
		  firms involved in genetic engineering and patenting. A
		  method of analysis is also proposed in the use of this
		  computer-based multi-maps environment. Finally, we discuss
		  some critical remarks about the proposed approach at its
		  current state. And we conclude about the advantages that it
		  provides for a knowledge-oriented watching analysis on
		  science and technology. In relation with this remark we
		  introduce in conclusion the notion of knowledge
		  indicators.},
  dbinsdate	= {2002/1}
}

@InBook{	  polani02a,
  author	= {Daniel Polani},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Measures for the Organization of Self-Organizing Maps},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {13--44},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  polani92a,
  author	= {D. Polani and T. Uthmann},
  title		= {Adaptation of {K}ohonen feature map topologies by genetic
		  algorithms},
  booktitle	= "Parallel Problem Solving from Nature, 2",
  year		= "1992",
  editor	= {R. M\"anner and B. Manderick},
  pages		= {421--429},
  publisher	= {Elsevier Science Publishers B.V.},
  month		= {September 28--30},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  polani93a,
  author	= {Polani, D. and Uthmann, T. },
  title		= {Training {K}ohonen feature maps in different topologies:
		  an analysis using genetic algorithms},
  booktitle	= {Proceedings of the Fifth International Conference on
		  Genetic Algorithms},
  year		= {1993},
  editor	= {Forrest, S. },
  pages		= {326--33},
  organization	= {Inst. fur Inf. , Johannes Gutenberg-Univ. , Mainz,
		  Germany},
  publisher	= {Morgan Kaufmann},
  address	= {San Mateo, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  polani97a,
  author	= {Daniel Polani},
  title		= {Fitness Functions for the Optimization of
		  \mbox{Self-Organizing} Maps},
  booktitle	= {Proceedings of the Seventh International Conference on
		  Genetic Algorithms},
  editor	= {Thomas B{\"a}ck},
  year		= {1997},
  publisher	= {Morgan Kaufmann},
  pages		= {776--783},
  dbinsdate	= {oldtimer}
}

@InCollection{	  polani97b,
  author	= {Daniel Polani and Johannes Gutenberg},
  title		= {Organization measures for \mbox{self-organizing} maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {280--285},
  dbinsdate	= {oldtimer}
}

@InCollection{	  polani99a,
  author	= {D. Polani},
  title		= {On the Optimization of Self-Organizing Maps by Genetic
		  Algorithms},
  booktitle	= {Kohonen Maps},
  pages		= {157--170},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: Genetic, Evolution, Topology, Learning,
		  Transcription},
  dbinsdate	= {oldtimer}
}

@Article{	  polanski00a,
  author	= {Polanski, J. and Walczak, B.},
  title		= {Comparative molecular surface analysis ({COMSA}): A novel
		  tool for molecular design},
  journal	= {Computers and Chemistry},
  year		= {2000},
  volume	= {24},
  number	= {5},
  month		= {},
  pages		= {615--625},
  organization	= {Univ of Silesia},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {A new method allowing for 3-D quantitative
		  structure-activity relationship (QSAR) analysis and the
		  prediction of biological activity is presented. A Kohonen
		  self-organizing neural network and partial least square
		  (PLS) analysis are used for performing such an operation.
		  The series of steroids complexing the corticosteroid (CBG)
		  and testosterone (TBG) globulins and a series of benzoic
		  acids is used for testing the method. The method can be
		  used efficiently to evaluate the responses determined both
		  by the combination of electrostatic and steric effects or
		  by electrostatic effects alone. Comparison from a
		  comparative molecular field analysis proves that the method
		  is at least as effective for the responses limited by
		  electrostatic effects.},
  dbinsdate	= {2002/1}
}

@Article{	  polanski96a,
  author	= {J. Polanski},
  title		= {Neural nets for the simulation of molecular recognition
		  within {MS-Windows} environment},
  journal	= {Journal of Chemical Information and Computer Sciences},
  year		= {1996},
  volume	= {36},
  number	= {4},
  pages		= {694--705},
  dbinsdate	= {oldtimer}
}

@Article{	  polanski97a,
  author	= {J. Polanski},
  title		= {The receptor-like neural network for modeling
		  corticosteroid and testosterone binding globulins},
  journal	= {Journal of Chemical Information and Computer Sciences},
  year		= {1997},
  volume	= {37},
  number	= {3},
  pages		= {553--61},
  dbinsdate	= {oldtimer}
}

@Article{	  polanski97b,
  author	= {J. Polanski and A. Ratajczak and J. Gasteiger and Z.
		  Galdecki and E. Galdecka},
  title		= {Molecular modeling and X-ray analysis for a structure-
		  taste study of alpha -arylsulfonylalkanoic acids},
  journal	= {Journal of Molecular Structure},
  year		= {1997},
  volume	= {407},
  number	= {1},
  pages		= {71--80},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  polishchuk00a,
  author	= {V. Polishchuk and M. Kanevski},
  title		= {Comparison of Unsupervised and Supervised Training of
		  {RBF} Neural Networks. {C}ase Study: Mapping of
		  Contamination Data},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Institute of Nuclear Safety},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InCollection{	  polze95a,
  author	= {A. Polze and M. Malek},
  title		= {Parallel computing in a world of workstations},
  booktitle	= {Proceedings of the Seventh IASTED/ISMM International
		  Conference Parallel and Distributed Computing and Systems},
  publisher	= {IASTED-ACTA Press},
  year		= {1995},
  editor	= {M. H. Hamza},
  address	= {Anaheim, CA, USA},
  pages		= {72--4},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pomierski93a,
  author	= {T. Pomierski and H. M. Gross and D. Wendt},
  title		= {A Distributed Multicolumnar System for Primary Cortical
		  Analysis of Real-World Scenes},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {142--147},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pomplun94a,
  author	= {Pomplun, M. and Velichkovsky, B. and Ritter, H. },
  title		= {An artificial neural network for high precision eye
		  movement tracking},
  booktitle	= {KI-94: Advances in Artificial Intelligence. 18th German
		  Annual Conference on Artificial Intelligence. Proceedings},
  year		= {1994},
  editor	= {Nebel, B. and Dreschler-Fischer, L. },
  pages		= {63--9},
  organization	= {Dept. of Inf. Sci. , Bielefeld Univ. , Germany},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  ponthieux01a,
  author	= {Ponthieux, S. and Cottrell, M.},
  title		= {Living conditions: classification of households using the
		  Kohonen algorithm},
  journal	= {European-Journal-of-Economic-and-Social-Systems},
  year		= {2001},
  volume	= {15},
  pages		= {69--84},
  abstract	= {In the analysis of poverty and social exclusion,
		  indicators of living conditions are some interesting
		  non-monetary complements to the usual measurements in terms
		  of current or annual income. Living conditions depend in
		  fact on longer-term factors than income, and provide
		  further information on households' actual resources that
		  allow us to compare more accurately between living
		  standards, But in counterpart, a difficulty comes from the
		  qualitative nature of the information, and the large number
		  of dimensions and items that may be taken into account; in
		  other words, living conditions are difficult to "measure".
		  A consequence is that very often, the information is either
		  used only partly, or reduced into a global score of (bad)
		  living conditions, that results from counting "negative"
		  items, and the qualitative dimension is lost. We propose to
		  use the Kohonen algorithm first to describe how the
		  elements of living conditions are combined, and secondly to
		  classify households according to their living conditions.
		  The main interest of a classification is to make appear not
		  only quantitative differences in the "levels" of living
		  conditions, but also qualitative differences within similar
		  "levels".},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ponthieux01b,
  author	= {Ponthieux, S. and Cottrell, M.},
  title		= {Neural classification and "traditional" data analysis: an
		  application to households' living conditions},
  booktitle	= {Bio-Inspired Applications of Connectionism. 6th
		  International Work-Conference on Artificial and Natural
		  Neural Networks, IWANN 2001. Proceedings, Part II. (Lecture
		  Notes in Computer Science Vol.2085). Springer-Verlag,
		  Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {738--45},
  abstract	= {The description, classification and "measurement" of
		  living conditions present many difficulties. A very
		  important one comes from the qualitative nature of the
		  data, and the large number of characteristics that may be
		  taken into account. For this reason, it is difficult to
		  obtain a description that could give an overall view of the
		  arrangements between the modalities, and be usable to
		  breakdown the observations into a reasonable number of
		  classes. We propose several examples of the use of neural
		  network techniques, precisely the Kohonen algorithm, to
		  classify a population of households according to their
		  characteristics in terms of living conditions.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  pope89a,
  author	= {C. Pope and L. Atlas and C. Nelson},
  title		= {A comparison between neural network and conventional
		  vector quantization codebook algorithms},
  booktitle	= { Proc. IEEE Pacific Rim Conf. on Communications, Computers
		  and Signal Processing. },
  year		= {1989},
  pages		= {521--524},
  organization	= {IEEE; Univ. Victoria},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  porrmann99a,
  author	= {Porrmann, M. and Ruping, S. and Ruckert, U.},
  title		= {SOM hardware with acceleration module for graphical
		  representation of the learning process},
  booktitle	= {Proceedings of the Seventh International Conference on
		  Microelectronics for Neural, Fuzzy and Bio-Inspired
		  Systems},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {380--6},
  abstract	= {A digital hardware implementation of self-organizing maps
		  is presented. Dedicated hardware is implemented that allows
		  the on-line visualization of the map during learning. The
		  use of a scalable parallel architecture enables the
		  realization of large scale high performance maps. Fist
		  silicon was produced in a 0.8 mu m, 2 metal layer CMOS
		  technology, implementing about 161,800 transistors on a die
		  size of 28.58 mm/sup 2/. Experimental results are
		  presented, that prove the functionality of the design up to
		  a clock frequency of 40 MHz. A classification rate of
		  250,000 vectors per second and an adaptation rate of 94,000
		  vectors per second can be guaranteed, independent from the
		  size of the network.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  portin93a,
  author	= {K. Portin and R. Salmelin and S. Kaski},
  title		= {Analysis of magnetoencephalographic data with
		  \mbox{self-organizing} maps},
  booktitle	= {Proc. XXVII Annual Conf. of the Finnish Physical Society,
		  Turku, Finland},
  year		= {1993},
  editor	= {T. Kuusela},
  pages		= {15. 2},
  publisher	= {Finnish Physical Society},
  address	= {Helsinki, Finland},
  annote	= {Analyzing the direction of movement from MEG. },
  dbinsdate	= {oldtimer}
}

@Article{	  portin96a,
  author	= {K. Portin and M. Kajola and R. Salmelin},
  title		= {Neural net identification of thumb movement using spectral
		  characteristics of magnetic cortical rhythms},
  journal	= {Electroencephalography and Clinical Neurophysiology},
  year		= {1996},
  volume	= {98},
  number	= {4},
  pages		= {273--80},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  portin98a,
  author	= {Karin Portin},
  title		= {Analysis of neuromagnetic oscillatory cortical activity
		  and visual evoked responses},
  school	= {Helsinki University of Technology},
  year		= 1998,
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InCollection{	  portugali97a,
  author	= {J. Portugali},
  title		= {Self-organization, cities, cognitive maps and information
		  systems},
  booktitle	= {Spatial Information Theory, A Theoretical Basis for GIS.
		  International Conference COSIT '97 Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {S. C. Hirtle and A. U. Frank},
  address	= {Berlin, Germany},
  pages		= {329--46},
  dbinsdate	= {oldtimer}
}

@Article{	  postula93a,
  author	= {Postula, A. and Hemani, A. and Hungenahally, S. },
  title		= {Self organisation based scheduling and binding algorithm
		  for high level synthesis of digital circuits},
  journal	= {Australian Computer Science Communications},
  year		= {1993},
  volume	= {15},
  number	= {1,},
  pages		= {pt. A},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  potlapalli96a,
  author	= {H. Potlapalli and R. C. Luo},
  title		= {Projection learning for \mbox{self-organizing} neural
		  networks},
  journal	= {IEEE Transactions on Industrial Electronics},
  year		= {1996},
  volume	= {43},
  number	= {4},
  pages		= {485--91},
  dbinsdate	= {oldtimer}
}

@Article{	  potvin93a,
  author	= {Potvin, J. -Y. },
  title		= {The traveling salesman problem: a neural network
		  perspective},
  journal	= {ORSA Journal on Computing},
  year		= {1993},
  volume	= {5},
  number	= {4},
  pages		= {328--48},
  month		= {Fall},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  poulos99a,
  author	= {Poulos, M. and Rangoussi, M. and Alexandris, N.},
  title		= {Neural network based person identification using {EEG}
		  features},
  booktitle	= {1999 IEEE International Conference on Acoustics, Speech,
		  and Signal Processing. Proceedings. ICASSP99},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1117--20},
  abstract	= {A direct connection between the electroencephalogram (EEG)
		  and the genetic information of an individual has been
		  suspected and investigated by neurophysiologists and
		  psychiatrists since 1960. However, most of this early as
		  well as more recent research focuses on the classification
		  of pathological EEG cases, aiming to construct tests for
		  purposes of diagnosis. On the contrary, our work focuses on
		  healthy individuals and aims to establish an one-to-one
		  correspondence between the genetic information of the
		  individual and certain features of his/her EEG, as an
		  intermediate step towards the further goal of developing a
		  test for person identification based on features extracted
		  from the EEG. Potential applications include, among others,
		  information encoding and decoding and access to secure
		  information. At the present stage the proposed method uses
		  spectral information extracted from the EEG
		  non-parametrically via the FFT and employs a neural network
		  (a learning vector quantizer-LVQ) to classify unknown EEGs
		  as belonging to one of a finite number of individuals.
		  Correct classification scores ranging from 80% to 100% in
		  experiments conducted on real data, show evidence that the
		  EEG indeed carries genetic information and that the
		  proposed method can be used to construct person
		  identification tests based on EEG features.},
  dbinsdate	= {oldtimer}
}

@Article{	  poulton92a,
  author	= {Poulton, M. M. and Sternberg, B. K. and Glass, C. E. },
  title		= {Location of subsurface targets in geophysical data using
		  neural networks},
  journal	= {Geophysics},
  year		= {1992},
  volume	= {57},
  number	= {12},
  pages		= {1534--44},
  month		= {Dec},
  dbinsdate	= {oldtimer}
}

@Article{	  pradhan96a,
  author	= {N. Pradhan and P. K. Sadasivan and G. R. Arunodaya},
  title		= {Detection of seizure activity in {EEG} by an artificial
		  neural network: a preliminary study},
  journal	= {Computers and Biomedical Research},
  year		= {1996},
  volume	= {29},
  number	= {4},
  pages		= {303--13},
  abstract	= {Neural networks, inspired by the organizational principles
		  of the human brain, have recently been used in various
		  fields of application such as pattern recognition,
		  identification, classification, speech, vision, signal
		  processing, and control systems. In this study, a
		  two-layered neural network has been trained for the
		  recognition of temporal patterns of the
		  electroencephalogram (EEG). This network is called a
		  Learning Vector Quantization (LVQ) neural network since it
		  learns the characteristics of the signal presented to it as
		  a vector. The first layer is a competitive layer which
		  learns to classify the input vectors. The second, linear,
		  layer transforms the output of the competitive layer to
		  target classes defined by the user. We have tested and
		  evaluated the LVQ network. The network successfully detects
		  epileptiform discharges (EDs) when trained using EEG
		  records scored by a neurologist. Epochs of EEG containing
		  EDs from one subject have been used for training the
		  network, and EEGs of other subjects have been used for
		  testing the network. The results demonstrate that the LVQ
		  detector can generalize the learning to previously "unseen"
		  records of subjects. This study shows that the LVQ network
		  offers a practical solution for ED detection which is
		  easily adjusted to an individual neurologist's style and is
		  as sensitive and specific as an expert visual analysis.},
  dbinsdate	= {oldtimer}
}

@Article{	  pregenzer94a,
  author	= {Pregenzer, M. and Pfurtscheller, G. and Flotzinger, D.},
  title		= {Selection of electrode positions for an {EEG}-based brain
		  computer interface ({BC}l) super(1))},
  journal	= {Biomedizinische Technik},
  year		= {1994},
  number	= {10},
  volume	= {39},
  pages		= {264--269},
  abstract	= {One major question in designing an EEG-based Brain
		  Computer Interface to bypass the normal motor pathways is
		  the selection of proper electrode positions. This study
		  investigates electrode selection with a Distinction
		  Sensitive Learning Vector Quantizer (DSLVQ). DSLVQ is an
		  extended Learning Vector Quantizer (LVQ) which employs a
		  weighted distance function for dynamical scaling and
		  feature selection. The data analysed and classified were
		  56-channel EEG recordings over sensorimotor areas during
		  preparation for discrete left or right index finger
		  flexions. Data from 3 subjects are reported. It was found
		  by DSLVQ that the most important electrode positions for
		  differentiation between planning of left and right finger
		  movement overlie cortical finger/hand areas over both
		  hemispheres.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pregenzer94b,
  author	= {M. Pregenzer and D. Flotzinger and G. Pfurtscheller},
  title		= {Distinction Sensitive {L}earning {V}ector {Q}uatization
		  for Automated Feature Selection},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1075--1078},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, feature selection, optimization},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pregenzer94c,
  author	= {M. Pregenzer and D. Flotzinger and G. Pfurtscheller},
  title		= {Distinction Sensitive Learning Vector Quantization---A new
		  noise-insensitive classification method},
  pages		= {2890--2894},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {pattern recognition, modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pregenzer95a,
  author	= {M. Pregenzer and G. Pfurtscheller and C. Andrew},
  title		= {Improvement of {EEG} classification with a subject
		  specific feature selection},
  booktitle	= {Proc. ESANN'95, European Symp. on Artificial Neural
		  Networks},
  year		= {1995},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {247--252},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pregenzer95b,
  author	= {M. Pregenzer and G. Pfurtscheller},
  title		= {Distinction Sensitive Learning Vector Quantization ({DS
		  {LVQ} }) application as a classifier based feature
		  selection method for a Brain Computer Interface},
  booktitle	= {Fourth International Conference on `Artificial Neural
		  Networks`},
  publisher	= {IEE},
  year		= {1995},
  address	= {London, UK},
  pages		= {433--6},
  dbinsdate	= {oldtimer}
}

@Article{	  pregenzer96a,
  author	= {Pregenzer, M. and Pfurtscheller, G. and Flotzinger, D.},
  title		= {Automated feature selection with a distinction sensitive
		  learning vector quantizer},
  journal	= {Neurocomputing},
  year		= {1996},
  number	= {1},
  volume	= {11},
  pages		= {19--29},
  abstract	= {An extended version of Kohonen's Learning Vector
		  Quantization (LVQ) algorithm, called Distinction Sensitive
		  Learning Vector Quantization (DSLVQ), is introduced which
		  overcomes a major problem of LVQ, the dependency on proper
		  pre-processing methods for scaling and feature selection.
		  The algorithm employs a weighted distance function and
		  adapts the metric with learning. Highest weights are
		  assigned to components in the input vectors which are most
		  informative for classification; non-informative components
		  are discarded. The algorithm is applied to the analyses of
		  multi-channel EEG data and compared with experienced
		  methods.},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  pregenzer97a,
  author	= {Martin Pregenzer},
  title		= {Distinction Sensitive Learning Vector Quantization ({DS
		  {LVQ} })},
  school	= {Graz University of Technology},
  year		= 1997,
  address	= {Graz},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  pregenzer99a,
  author	= {M. Pregenzer and G. Pfurtscheller},
  title		= {Frequency Component Selection for an {{EEG}}-Based Brain
		  Computer Interface ({{BCI}})},
  journal	= {IEEE Transactions on Rehabilitation Engineering},
  year		= {1999},
  volume	= {7},
  number	= {3},
  dbinsdate	= {oldtimer}
}

@InCollection{	  prem95a,
  author	= {E. Prem},
  title		= {Dynamic symbol grounding, state construction and the
		  problem of teleology},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {619--26},
  dbinsdate	= {oldtimer}
}

@InCollection{	  presedo96a,
  author	= {J. Presedo and E. A. Fernandez and J. Vila and S. Barro},
  title		= {Cycles of {ECG} parameter evolution during ischemic
		  episodes},
  booktitle	= {Computers in Cardiology 1996},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {A. Murray and R. Arzbaecher},
  address	= {New York, NY, USA},
  pages		= {489--92},
  dbinsdate	= {oldtimer}
}

@Book{  	  priddy01a,
  title		= {Proceedings of {SPIE}---The International Society for
		  Optical Engineering},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Priddy, K. L. and Keller, P. E. and Angeline, P. J.},
  volume	= {4390},
  pages		= {},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {The proceedings contains 31 papers from SPIE 2001
		  Conference on Applications and Science of Computational
		  Intelligence. Topics discussed include: neural network
		  inverse models for propulsion vibrating diagnostics;
		  parental population manipulation in evolution strategies;
		  hybrid evolutionary computing model for mobile agents for
		  wireless internet multimedia; adaptive critic design for
		  computer intrusion detection system; fuzzy neural bang-bang
		  controller for satellite attitude control; dynamic
		  optimization for optimal control of water distribution
		  systems and self-organizing map with fuzzy class
		  memberships.},
  dbinsdate	= {2002/1}
}

@Article{	  prigent01a,
  author	= {Prigent, C. and Aires, F. and Rossow, W. and Matthews,
		  E.},
  title		= {Joint characterization of vegetation by satellite
		  observations from visible to microwave wavelengths: A
		  sensitivity analysis},
  journal	= {Journal-of-Geophysical-Research},
  year		= {2001},
  volume	= {106},
  pages		= {20665--85},
  abstract	= {This study presents an evaluation and comparison of
		  visible, near-infrared, passive and active microwave
		  observations for vegetation characterization, on a global
		  basis, for a year, with spatial resolution compatible with
		  climatological studies. Visible and near-infrared
		  observations along with the Normalized Difference
		  Vegetation Index come from the Advanced Very High
		  Resolution Radiometer. An atlas of monthly mean microwave
		  land surface emissivities from 19 to 85 GHz has been
		  calculated from the Special Sensor Microwave/Imager for a
		  year, suppressing the atmospheric problems encountered with
		  the use of simple channel combinations. The active
		  microwave measurements are provided by the ERS-1
		  scatterometer at 5.25 GHz. The capacity to discriminate
		  between vegetation types and to detect the vegetation
		  phenology is assessed in the context of a vegetation
		  classification obtained from in situ observations. A
		  clustering technique derived from the Kohonen topological
		  maps is used to merge the three data sets and interpret
		  their relative variations. NDVI varies with vegetation
		  density but is not very sensitive in semi-arid environments
		  and in forested areas. Spurious seasonal cycles and large
		  spatial variability in several areas suggest that
		  atmospheric contamination and/or solar zenith angle drift
		  still affect the NDVI. Passive and active microwave
		  observations are sensitive to overall vegetation structure:
		  they respond to absorption, emission, and scattering by
		  vegetation elements, including woody parts.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  principe95a,
  author	= {Jose C. Principe and Ludong Wang},
  title		= {Non-Linear Time Series Modeling with {S}elf-{O}rganization
		  {F}eature {M}aps},
  booktitle	= {Proc. NNSP'95, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1995},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {11--20},
  annote	= {data analysis},
  dbinsdate	= {oldtimer}
}

@Article{	  principe98a,
  author	= {J. C. Principe and Ludong Wang and M. A. Motter},
  title		= {Local dynamic modeling with \mbox{self-organizing} maps
		  and applications to nonlinear system identification and
		  control},
  journal	= {Proceedings of the IEEE},
  year		= {1998},
  volume	= {86},
  number	= {11},
  pages		= {2240--58},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  privitera93a,
  author	= {C. M. Privitera and P. Morasso},
  title		= {A new approach to stroring temporal sequences},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2745--2748},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  privitera94a,
  author	= {Privitera, C. M. and Morasso, P.},
  title		= {A neural model for the execution of symbolic motor
		  programs},
  booktitle	= {International Conference on Artificial Neural Networks},
  publisher	= {Springer-Verlag},
  year		= {1994},
  editor	= {M. Marinaro and Morasso, P.},
  address	= {London,},
  pages		= {254--257},
  abstract	= {The generation of a motor plan is a complex process which
		  transforms a syntactical and synthetical input
		  representation in the corresponding time sequence of
		  patterns defined in the motor space R/sup n/. Moreover, the
		  elaboration of sensorial information is essential for the
		  correct adjustment of the plan execution to the casual
		  changing of the application environment. Speech production
		  is a clear instance of this process. The problem to define
		  neural models for storing and controlling temporal sequence
		  generation appears to be quite complex, not only as regards
		  the complexity of the learning phase and the limits about
		  storing capability of neural networks, but especially
		  because it turns out very difficult to contemporaneously
		  obtain the diversified characteristics at the base of
		  biological serial order in behavior by a single neural
		  architecture. We already mentioned the compensation
		  phenomenon that is in other words the capability to perform
		  a specific motor plan in diversified environment conditions
		  (i.e. the production of bite block phonemes (T. Gay,
		  1981)). Another fundamental point is the so called
		  ambiguousness problem that is when the appearance of a
		  specific temporal pattern during the plan generation can be
		  followed every time by different patterns (S. Keele et al.,
		  1990). Finally we underline the need to control the
		  velocity of the sequence generation, both during the
		  transition between two states of the plan, and during the
		  stabilization of the plan in one of its own steps.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  privitera94b,
  author	= {Privitera, C. M. and Morasso, P.},
  title		= {The analysis of continuous temporal sequences by a map of
		  sequential leaky integrators},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  volume	= {5},
  pages		= {3127--30},
  abstract	= {The problem to detect and recognize the occurrence of
		  specific events in a continually evolving environment, is
		  particularly important in many fields, starting from motor
		  planning. In this paper, the authors propose a
		  two-dimensional map, where the processing elements
		  correspond to specific instances of leaky integrators whose
		  parameters (or tops) are learned in a self-organizing
		  manner: in this way the map becomes a topologic
		  representation of temporal sequences whose presence in a
		  continuous temporal data flow is detectable by means of the
		  activation level of the corresponding neurons.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  privitera95a,
  author	= {Claudio M. Privitera and R{\'{e}}jean Plamondon},
  title		= {A \mbox{self-organizing} neural network for learning and
		  generating sequences of traget-directed movements in the
		  context of a delta-lognormal synergy},
  volume	= {IV},
  pages		= {1999--2004},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Book{		  proriol93a,
  author	= {Proriol, J.},
  title		= {{MLP}-{RBF}. {A} cooperative multi-modular neural network
		  application in high-energy physics.},
  year		= {1993},
  abstract	= {A cooperative multi-modular neural network architecture is
		  presented: a Multi-Layer Perceptron (MLP), followed by a
		  Radial Basis Function network (RBF). It is shown that, in
		  the LEP experiment of electron-positron collision run at
		  CERN, this architecture was able to outperform both a
		  simple multi-layer perceptron, a multi-modular MLP+LVQ
		  (LVQ: Learning Vector Quantization) and MLP+RBF trained
		  sequentially and a conventional technique (Discriminant
		  Analysis). (author). 10 refs., 2 figs. (Atomindex citation
		  25:073617)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  puechmorel95a,
  author	= {S. Puechmorel and E. Gaubet},
  title		= {Time-frequency feature maps},
  volume	= {I},
  pages		= {532--535},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InCollection{	  puechmorel96a,
  author	= {S. Puechmorel and M. Ibnkahla},
  title		= {Manifold {K}ohonen maps},
  booktitle	= {WCNN'96. World Congress on Neural Networks. International
		  Neural Network Society 1996 Annual Meeting},
  publisher	= {Lawrence Erlbaum Associates},
  year		= {1996},
  address	= {Mahwah, NJ, USA},
  pages		= {995--8},
  dbinsdate	= {oldtimer}
}

@Article{	  pulakka98a,
  author	= {K. Pulakka and V. Kujanpa},
  title		= {Rough level path planning method for a robot using {SOFM}
		  neural network},
  journal	= {Robotica},
  year		= {1998},
  volume	= {16},
  number	= {pt.4},
  pages		= {415--23},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pulice94a,
  author	= {Pulice, W. M. },
  title		= {Naming the unmeasurable using a neural-fuzzy approach},
  booktitle	= {World Congress on Neural Networks-San Diego. 1994
		  International Neural Network Society Annual Meeting},
  year		= {1994},
  volume	= {1},
  pages		= {I/853--6},
  publisher	= {Lawrence Erlbaum Associates},
  address	= {Hillsdale, NJ, USA},
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  pulkki94a,
  author	= {Ville Pulkki},
  title		= {Er{\"{a}}it{\"{a}} itseorganisoivan kartan digitaalisia
		  toteutuksia {SOM} digital implementations of the
		  \mbox{self-organizing} map)},
  school	= {Helsinki University of Technology},
  year		= {1994},
  address	= {Espoo, Finland},
  note		= {(in finnish)},
  dbinsdate	= {oldtimer}
}

@TechReport{	  pulkki95a,
  author	= {Ville Pulkki},
  title		= {Data Averaging inside Categories with the Self-Organizing
		  Map},
  institution	= {Helsinki Univ. of Technology, Laboratory of Computer and
		  Information Science},
  year		= {1995},
  type		= {Report},
  number	= {A27},
  address	= {Espoo, Finland},
  abstract	= {If the samples in a data set can be categorized, then the
		  computational load in representing the data set can be
		  reduced significantly by averaging the samples inside each
		  category. At the same time, the frequencies of accurance of
		  samples of different categories can be balanced. If the
		  averaging is done with the Self-Organizing Map (SOM), the
		  structures in the categories can be preserved while still
		  reducing the computational load. This is especially useful
		  when processing natural language, where the words may have
		  multiple meanings. In the report, a method of averaging
		  inside categories using SOM is presented and is applied in
		  a natural language processing task.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  pulkki96a,
  author	= {V. Pulkki and T. Harju},
  title		= {An implementation of the \mbox{self-organizing} map on the
		  {CNAPS} neurocomputer},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1345--9},
  abstract	= {We present an implementation of the Self-Organizing Map on
		  the CNAPS neurocomputer. We briefly discuss the structure
		  of the hardware used and describe how it is utilized in a
		  very fast realization of the algorithm. We also compare our
		  implementation to a previous one reported in the
		  literature, and find that the presented method is
		  significantly more efficient.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  pullwitt01a,
  author	= {R. Pullwitt and R. Der},
  title		= {Integrating contextual information into text document
		  clustering with self-organising maps},
  booktitle	= {Advances in Self-Organising Maps},
  crossref	= {},
  key		= {},
  pages		= {54--60},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {Springer},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  purucker96a,
  author	= {M. C. Purucker},
  title		= {Neural network quarterbacking},
  journal	= {IEEE Potentials},
  year		= {1996},
  volume	= {15},
  number	= {3},
  pages		= {9--15},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  purwins00a,
  author	= {Purwins, Hendrik and Blankertz, Benjamin and Obermayer,
		  Klaus},
  title		= {New method for tracking modulations in tonal music in
		  audio data format},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {270--275},
  organization	= {CCRMA},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Cq-profiles are 12-dimensional vectors, each component
		  referring to a pitch class. They can be employed to
		  represent keys. Cq-profiles are calculated with the
		  constant Q filter bank. They have the following advantages:
		  (i) They correspond to probe tone ratings. (ii) Calculation
		  is possible in real-time. (iii) Stability is obtained with
		  respect to sound quality. (iv) They are transposable. By
		  using the cq-profile technique as a simple auditory model
		  in combination with the SOM an arrangement of keys emerges,
		  that resembles results from psychological experiments, and
		  from music theory. Cq-profiles are reliably applied to
		  modulation tracking by introducing a special distance
		  measure.},
  dbinsdate	= {2002/1}
}

@Article{	  qiao01a,
  author	= {Qiao, X. B. and Jiang, B. and Hou, T. J. and Xu, X. J.},
  title		= {Representation of molecular electrostatic potentials of
		  biopolymer by self-organizing feature map},
  journal	= {CHINESE JOURNAL OF CHEMISTRY},
  year		= {2001},
  volume	= {19},
  number	= {12},
  month		= {DEC},
  pages		= {1172--1178},
  abstract	= {The Kohonen self-organizing map was introduced to map the
		  protein molecular surface features. The protein or
		  polypeptide properties, such as shape and molecular
		  electrostatic potential, can be visualized by
		  self-organizing map, which was trained by the 3D surface
		  coordinates. Such maps allow the visual comparison of
		  molecular properties between proteins having common
		  topological or chemical features.},
  dbinsdate	= {2002/1}
}

@Article{	  qing01a,
  author	= {Qing Ma and Kanzaki, K. and Murata, M. and Uchimoto, K.
		  and Isahara H.},
  title		= {Self-organizing semantic map of Japanese nouns},
  journal	= {Transactions-of-the-Information-Processing-Society-of-Japan}
		  ,
  year		= {2001},
  volume	= {42},
  pages		= {2379--91},
  abstract	= {A method is described for automatically constructing a
		  Semantic map, a visible and continuous representation in
		  which Japanese nouns with similar meanings are placed at
		  the same or neighboring points so that the distance between
		  them represents Semantic similarity. This is done by using
		  the self-organizing neural network, SOM. From the point of
		  view of common adnominal constituents, we first manually
		  gather noun phrases whose adnominal constituents concretely
		  describe the contents of head nouns from newspapers and
		  construct a semantic map of the nouns using these noun
		  phrases. Such types of noun phrases are thought to be
		  effective for self-organizing a semantic map. Because it is
		  indispensable to gather data automatically for constructing
		  a large semantic map, we then construct a semantic map of
		  the nouns using the noun phrases that consist of nouns and
		  their co-occuring adjectives and nominal adjectivals. They
		  are gathered automatically from newspapers in the order of
		  the frequency of their co-occurrent words. Examination of
		  semantic maps obtained in computer experiments showed that
		  the nouns were mapped to the points corresponding to the
		  training data. And, to objectively evaluate the SOM's
		  ability in semantic classification, the semantic maps are
		  compared to the results of classification by hierarchical
		  clustering, which cannot give results with visible and
		  continuous representation. Further, it is clarified that
		  the multivariate statistical analysis such as principle
		  component analysis and factor analysis cannot be used to
		  construct semantic maps which reinforces the necessity of
		  the proposed method for this task.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  qingyu00a,
  author	= {Qingyu Xiong and Hirasawa, K. and Jinglu Hu and Murata,
		  J.},
  title		= {Growing {RBF} structures using self-organizing maps},
  booktitle	= {Proceedings 9th IEEE International Workshop on Robot and
		  Human Interactive Communication. IEEE RO-MAN 2000. IEEE,
		  Piscataway, NJ, USA},
  year		= {2000},
  volume	= {},
  pages		= {107--11},
  abstract	= {We present a novel growing RBF network structure using SOM
		  in this paper. It consists of SOM and RBF networks
		  respectively. The SOM performs unsupervised learning and
		  also the weight vectors belonging to its output nodes are
		  transmitted to the hidden nodes in the RBF networks as the
		  centers of RBF activation functions, as a result one to one
		  correspondence relationship is realised between the output
		  nodes in SOM and the hidden nodes in RBF networks. The RBF
		  networks perform supervised training using delta rule.
		  Therefore, the current output errors in the RBF networks
		  can be used to determine where to insert a new SOM unit
		  according to the rule. This also makes it possible to make
		  the RBF networks grow until a performance criterion is
		  fulfilled or until a desired network size is obtained. The
		  simulations on the two-spirals benchmark are shown to prove
		  the proposed networks have good performance.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  qiong01a,
  author	= {Qiong Liu and Levinson, S. and Ying Wu and Huang, T.},
  title		= {Robot speech learning via entropy guided {LVQ} and memory
		  association},
  booktitle	= {IJCNN'01. International Joint Conference on Neural
		  Networks. Proceedings. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {3},
  pages		= {2176--81},
  abstract	= {The goal of this project is to teach a computer-robot
		  system to understand human speech through natural
		  human-computer interaction. To achieve this goal, we
		  develop an interactive and incremental learning algorithm
		  based on entropy-guided learning vector quantisation (LVQ)
		  and memory association. Supported by this algorithm, the
		  robot has the potential to learn unlimited sounds
		  progressively. Experimental results of a multilingual
		  short-speech learning task are given after the presentation
		  of the learning system. Further investigation of this
		  learning system will include human-computer interactions
		  that involve more modalities, and applications that use the
		  proposed idea to train home appliances.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  qiong99a,
  author	= {Qiong, Liu and Ray, S. and Levinson, S. and Huang, T. and
		  Huang, J.},
  title		= {Temporal sequence learning and recognition with dynamic
		  SOM},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {2970--5},
  abstract	= {The purpose of the paper is to propose a map-like
		  artificial neural network for temporal sequence pattern
		  clustering. The map construction in our presentation is
		  related to the self-organizing map (SOM) idea. The SOM idea
		  was originally designed for static pattern learning and
		  recognition. It has been found efficient for organizing
		  high dimensional data sets. One of the biggest limitations
		  of the traditional SOM technique is caused by its static
		  characteristics. We propose a new neural network
		  construction model and its corresponding training algorithm
		  based on traditional SOM training technology and
		  backpropagation training technology. It overcomes the
		  static limitation of traditional SOM and tries to reach a
		  new stage for dynamic pattern clustering, and recognition.
		  At the end of the paper, we give some experimental results
		  for testing this proposed method on real speech data.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  qiu96a,
  author	= {Guoping Qiu and A. W. Booth},
  title		= {Frequency sensitive {H}ebbian learning},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {143--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  qixiu97a,
  author	= {Hu Qixiu and Pan Yue},
  title		= {Neural net approach for speaker sensitive measure
		  analysis},
  booktitle	= {1997 IEEE 6th International Conference on Emerging
		  Technologies and Factory Automation Proceedings},
  publisher	= {Poznan Univ. Technol},
  year		= {1997},
  editor	= {M. Domanski and R. Stasinski},
  address	= {Poznan, Poland},
  pages		= {365--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  quek00a,
  author	= {Quek, C. and Wahab, Abdul and Aarit, S.},
  title		= {{POP}-Yager: A novel self-organizing fuzzy neural network
		  based on the Yager inference},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {4120},
  pages		= {14--25},
  organization	= {Nanyang Technological Univ},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {A Pseudo-Outer Product based Fuzzy Neural Network using
		  the Yager Rule of Inference called the POP-Yager FNN is
		  proposed in this paper. The proposed POP-Yager FNN training
		  consists of two phases; namely: the fuzzy membership
		  derivation phase using the Modified Learning Vector
		  Quantization (MLVQ) method; and the rule identification
		  phase using the novel one-pass LazyPOP learning algorithm.
		  The proposed two-phase learning process effectively
		  constructs the membership functions and identifies the
		  fuzzy rules. Extensive experimental results based on the
		  classification performance of the POP-Yager FNN using the
		  Anderson's Iris data are presented for discussion. Results
		  show that the POP-Yager FNN possesses excellent recall and
		  generalization abilities.},
  dbinsdate	= {2002/1}
}

@Article{	  quittek95a,
  author	= {J. W. Quittek},
  title		= {Optimizing parallel program execution by
		  \mbox{self-organizing} maps},
  journal	= {Journal of Artificial Neural Networks},
  year		= {1995},
  volume	= {2},
  number	= {4},
  pages		= {365--80},
  dbinsdate	= {oldtimer}
}

@Article{	  raekelboom98a,
  author	= {S. Raekelboom and M. M. {van Hulle}},
  title		= {The {S}oftmap algorithm},
  journal	= {Neural Processing Letters},
  year		= {1998},
  volume	= {8},
  pages		= {181--192},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  raffo92a,
  author	= {L. Raffo and D. D. Caviglia and G. M. Bisio},
  title		= {Neural Clustering Algorithms for Classification and
		  Pre-placement of {VLSI} Cells},
  booktitle	= {Proc. COMPEURO'92, The Hague, Netherlands, May 4--8},
  year		= {1992},
  pages		= {556--561},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  raghu93a,
  author	= {P. P. Raghu and R. Poongodi and B. Yegnanarayana},
  title		= {Texture Classification Using a Two-stage Neural Network
		  Approach},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2195--2198},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {In this article, we present a two stage neural network
		  structure which combines the self-organizing map (SOM) and
		  the multilayer perceptron (MLP) for the problem of texture
		  classification. The texture features are extracted using a
		  multichannel approach. These channels comprise of a set of
		  Gabor filters having different sizes, orientations and
		  frequencies to constitute N-dimensional feature vectors.
		  The SOM acts as a clustering mechanism to map these
		  N-dimensional feature vectors onto a 2-dimensional space.
		  This in turn forms the feature space to feed into MLP for
		  training and subsequent classification. It is shown that
		  this mechanism increases the inter-class separation and
		  decreases the intra-class distance in the feature space,
		  hence reduces the classification complexity. Also, the
		  reduction in the dimensionality of the feature space
		  results in reduction of the learning time of the MLP.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  raghu94a,
  author	= {Raghu, P. P. and Poongodi, R. and Yegnanarayana, B. },
  title		= {Texture classification using a combined
		  \mbox{self-organizing} map and multilayer perceptron},
  booktitle	= {Computer Systems and Education. Proceedings of the
		  International Conference on Computer Systems and Education
		  in Honour of Prof. V. Rajaraman},
  year		= {1994},
  editor	= {Balakrishnan, N. and Radhakrishnan, T. and Sampath, D. and
		  Sundaram, S. },
  pages		= {145--53},
  organization	= {Dept. of Comput. Sci. \& Eng. , Indian Inst. of Technol. ,
		  Madras, India},
  publisher	= {Tata McGraw-Hill},
  address	= {New Delhi, India},
  dbinsdate	= {oldtimer}
}

@Article{	  raghu95a,
  author	= {Raghu, P. P. and Poongodi, R. and Yegnanarayana, B. },
  title		= {A combined neural network approach for texture
		  classification},
  journal	= {Neural Networks},
  year		= {1995},
  volume	= {8},
  number	= {6},
  pages		= {975--87},
  dbinsdate	= {oldtimer}
}

@InCollection{	  raghu96a,
  author	= {P. P. Raghu and B. Yegnanarayana},
  title		= {Texture Classification using a Probabilistic Neural
		  Network and Constraint Satisfaction Model},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {424--429},
  dbinsdate	= {oldtimer}
}

@Article{	  rahman95a,
  author	= {Rahman, M. and Zhou, Q. and Hong, G. S. },
  title		= {Application of {K}ohonen neural network for tool condition
		  monitoring},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2620},
  pages		= {422--8},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rahman97a,
  author	= {S. M. Monzurur Rahman and Xinghuo Yu and Geoff Martin},
  title		= {Neural Network Approach for Data Mining},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {851--854},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rahman98a,
  author	= {S. M. Rahman and G. C. Karmakar and B. Bignall},
  title		= {\mbox{Self-organising} map for shape-based image
		  classification},
  booktitle	= {13th International Conference on Computers and Their
		  Applications},
  publisher	= {International Society for Computers and Their
		  Applications---ISCA},
  year		= {1998},
  address	= {Cary, NC, USA},
  pages		= {291--4},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rahman99a,
  author	= {Rahman, S. M. and Karmaker, G. C. and Bignall, R. J.},
  title		= {Improving image classification using extended run length
		  features},
  booktitle	= {Visual Information and Information Systems. Third
		  International Conference, VISUAL'99. Proceedings (Lecture
		  Notes in Computer Science Vol.1614)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {475--82},
  abstract	= {In this paper we evaluate the performance of
		  self-organising maps (SOM) for image classification using
		  invariant features based on run length alone and also on
		  run length plus run length totals, for horizontal runs.
		  Objects were manually separated from an experimental set of
		  natural images. Object classification performance was
		  evaluated by comparing the SOM classifications
		  independently with a manual classification for both of the
		  feature extraction methods. The experimental results showed
		  that image classification using the run length method that
		  included run length totals achieved a recognition rate that
		  was, on average, 4.65 percentage points higher that the
		  recognition rate achieved with the normal run length
		  method. Thus the extended method is promising for practical
		  applications.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rahman99b,
  author	= {Rahman, M. and Xinghuo Yu and Srinivasan, B.},
  title		= {A neural networks based approach for fast mining
		  characteristic rules},
  booktitle	= {Advanced Topics in Artificial Intelligence. 12th
		  Australian Joint Conference on Artificial Intelligence,
		  AI'99. Proceedings (Lecture Notes in Artificial
		  Intelligence Vol.1747). Springer-Verlag, Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {36--47},
  abstract	= {Data mining is about extracting hidden information from a
		  large data set. One task of data mining is to describe the
		  characteristics of the data set using attributes in the
		  form of rules. This paper aims to develop a neural networks
		  based framework for the fast mining of characteristic
		  rules. The idea is to first use the Kohonen map to cluster
		  the data set into groups with common similar features. Then
		  use a set of single-layer supervised neural networks to
		  model each of the groups so that the significant attributes
		  characterizing the data set can be extracted. An
		  incremental algorithm combining these two steps is proposed
		  to derive the characteristic rules for the data set with
		  nonlinear relations. The framework is tested using a large
		  size problem of forensic data of heart patients. Its
		  effectiveness is demonstrated.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rahmel94a,
  author	= {Rahmel, J. and {von Wangenheim}, A. },
  title		= {The {KoDiag} system: case-based diagnosis with {K}ohonen
		  networks},
  booktitle	= {Proceedings of the Workshop on Neural Network Applications
		  and Tools},
  year		= {1994},
  editor	= {Lisboa, P. J. G. and Taylor, M. J. },
  pages		= {82--8},
  organization	= {Dept. of Comput. Sci. , Kaiserslautern Univ. , Germany},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rahmel96a,
  author	= {J. Rahmel},
  title		= {SplitNet: a dynamic hierarchical network model},
  booktitle	= {Proceedings of the Thirteenth National Conference on
		  Artificial Intelligence and the Eighth Innovative
		  Applications of Artificial Intelligence Conference},
  publisher	= {Springer-Verlag},
  year		= {1996},
  volume	= {2},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {1404},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rahmel96b,
  author	= {J. Rahmel},
  title		= {SplitNet: learning of tree structured {K}ohonen chains},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1221--6},
  abstract	= {This work introduces a tree structured neural network
		  model for topology preserving vector quantization with
		  one-dimensional Kohonen chains. The leaves of the tree are
		  the chains, each of which quantizes a subspace of the input
		  space. Topological defects can effectively be detected and
		  splitting of the chain at that location results in a
		  growing of the tree structure and increase of topology
		  preservation. Additionally, the chains are able to grow and
		  shrink in order to approximate user defined criteria.
		  Advantages over existing dynamic network models are the
		  flexible tree structure, the total lack of global
		  parameters or calculations as well as the simulation and
		  retrieval speed due to the network structure. Different
		  levels of generalization and prototypicality are naturally
		  observed.},
  dbinsdate	= {oldtimer}
}

@Article{	  raiche91a,
  author	= {A. Raiche},
  title		= {A pattern recognition approach to geophysical inversion
		  using neural nets},
  journal	= {Geophysical J. International},
  year		= {1991},
  volume	= {105},
  number	= {3},
  pages		= {629--648},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  raivio00a,
  author	= {Ossi Raivio and Janne Riihij\"{a}rvi and Petri
		  M\"{a}h\"{o}nen},
  title		= {Classifying and Clustering the {I}nternet Traffic by
		  {K}ohonen Network},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {VTT, Networking Research, Wireless Internet Laboratory},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  raivio01a,
  author	= {Raivio, K. and Simula, O. and Laiho, J.},
  title		= {Neural analysis of mobile radio access network},
  booktitle	= {Proceedings 2001 IEEE International Conference on Data
		  Mining. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {457--64},
  abstract	= {The self-organizing map (SOM) is an efficient tool for
		  visualization and clustering of multidimensional data. It
		  transforms the input vectors on two-dimensional grid of
		  prototype vectors and orders them. The ordered prototype
		  vectors are easier to visualize and explore than the
		  original data. Mobile networks produce a huge amount of
		  spatiotemporal data. The data consists of parameters of
		  base stations (BS) and quality information of calls. There
		  are two alternatives in starting the data analysis. We can
		  build either a general one-cell-model trained using state
		  vectors from all cells, or a model of the network using
		  state vectors with parameters from all mobile cells. In
		  both methods, further analysis is needed to understand the
		  reasons for various operational states of the entire
		  network.},
  dbinsdate	= {2002/1}
}

@Article{	  raivio91a,
  author	= {Kimmo Raivio and Olli Simula and Jukka Henriksson},
  title		= {Improving Decision Feedback Equaliser Performance Using
		  Neural Networks},
  journal	= {Electronics Letters},
  volume	= {27},
  number	= {23},
  year		= {1991},
  pages		= {2151--2153},
  month		= {November},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  raivio93a,
  author	= {Kimmo Raivio and Teuvo Kohonen},
  title		= {Detection of Nonlinearly Distorted and Two-Path Propagated
		  Signals Using a Neural Network Based Equalizer},
  booktitle	= {XIX Convention on Radio Science, Abstracts of Papers},
  year		= {1993},
  editor	= {Veikko Porra and Petteri Alinikula},
  pages		= {11--12},
  publisher	= {Helsinki University of Technology, Electronic Circuit
		  Design Laboratory},
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  raivio94a,
  author	= {Kimmo Raivio and Teuvo Kohonen},
  title		= {Detection of Nonlinearly Distorted and Two-Path Propagated
		  Signals using {SOM}-Based Equalizers},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1037--1040},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, signal processing, pattern recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  raivio95a,
  author	= {Kimmo Raivio and Jukka Henriksson and Olli Simula},
  title		= {Neural Detection of {QAM} Modulation in the Precence of
		  Interference},
  volume	= {IV},
  pages		= {1566--1569},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  raivio95b,
  author	= {K. Raivio and J. Henriksson and O. Simula},
  title		= {Interference Cancellation for {PAM} Modulation using
		  Neural Networks},
  booktitle	= {Proc. of the Finnish Signal Processing Symposium},
  year		= {1995},
  pages		= {50--54},
  dbinsdate	= {oldtimer}
}

@InCollection{	  raivio97a,
  author	= {Kimmo Raivio and Jukka Henriksson and Olli Simula},
  title		= {Neural detection of {QAM} signal with strongly nonlinear
		  receiver},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {20--25},
  dbinsdate	= {oldtimer}
}

@InCollection{	  raivio97b,
  author	= {Kimmo Raivio and Ari H{\"a}m{\"a}l{\"a}inen and Jukka
		  Henriksson and Olli Simula},
  title		= {Performance of Two Neural Receiver Structures in the
		  Presence of Co-Channer Interference},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {2080--2084},
  dbinsdate	= {oldtimer}
}

@Article{	  raivio98a,
  author	= {Raivio, Kimmo and Henriksson, Jukka and Simula, Olli},
  title		= {Neural detection of QAM signal with strongly nonlinear
		  receiver},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {159--171},
  abstract	= {Neural receiver structures have been developed for
		  adaptive discrete-signal detection in telecommunication
		  applications. Neural networks combined with conventional
		  equalizers improve the performance especially in
		  compensating for nonlinear distortions. These distortions
		  may result, for instance, from nonlinear amplification
		  implemented for reducing the power consumption. In this
		  paper, the behavior of the neural receiver in multipath
		  channel with additive white Gaussian noise has been
		  investigated. The transmitted signal is quadrature
		  amplitude modulated (QAM). A receiver structure based on
		  self-organizing map (SOM) is compared with a conventional
		  decision feedback equalizer (DFE).},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  raivio99a,
  author	= {Kimmo Raivio},
  title		= {Receiver Structures Based on Self-Organizing Maps},
  school	= {Helsinki University of Technology},
  year		= 1999,
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@Article{	  rajaniemi02a,
  author	= {Rajaniemi, H. J. and Mahonen, P.},
  title		= {Classifying gamma-ray bursts using self-organizing maps},
  journal	= {ASTROPHYSICAL JOURNAL},
  year		= {2002},
  volume	= {566},
  number	= {1},
  month		= {FEB 10},
  pages		= {202--209},
  abstract	= {Using the self-organizing map (SOM) algorithm developed by
		  Kohonen in 1982, we investigate the three gamma-ray burst
		  classes discovered by Mukherjee et al. in 1998 and classify
		  the bursts in the current BATSE catalog. The validity of
		  the classification and the isotropy of the burst classes is
		  examined using well-known statistical methods. We are able
		  to show by this independent unsupervised classifier that
		  two previously observed burst classes are indeed present in
		  BATSE data. We also support the 2000 conclusion of Hakkila
		  et al. that there is no strong statistical signal for the
		  existence of the third class, which might actually be due
		  to an instrumental bias. This result is obtained through
		  our independent analysis method, which is not related to
		  the 2000 analysis of Hakkila et al.},
  dbinsdate	= {2002/1}
}

@InCollection{	  ralli96a,
  author	= {E. Ralli and G. Hirzinger},
  title		= {A global and resolution complete path planner for up to
		  {6DOF} robot manipulators},
  booktitle	= {Proceedings of the 1996 IEEE International Conference on
		  Robotics and Automation},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {4},
  address	= {New York, NY, USA},
  pages		= {3295--302},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ralli97a,
  author	= {E. Ralli and G. Hirzinger},
  title		= {Robot path planning using {K}ohonen maps},
  booktitle	= {Proceedings of the 1997 IEEE/RSJ International Conference
		  on Intelligent Robot and Systems. Innovative Robotics for
		  Real-World Applications. IROS '97},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1224--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ramesh91a,
  author	= {P. Ramesh and Shigeru Katagiri and Chin-Hui Lee},
  title		= {A New Connected Word Recognition Algorithm Based on
		  {HMM}/{LVQ} Segmentation and {LVQ} Classification},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {I},
  pages		= {113--116},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ramos95a,
  author	= {Ant{\^{o}}nio Rog{\'{e}}rio Machado Ramos and Dante
		  Augusto Couto Barone},
  title		= {Presentation of a Hybrid Evolutionary Classifier System},
  volume	= {I},
  pages		= {770--773},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ramsay92a,
  author	= {Ramsay, C. S. and Sutherland, K. and Renshaw, D. and
		  Denyer, P. B. },
  title		= {A comparison of vector quantization codebook generation
		  algorithms applied to automatic face recognition},
  booktitle	= {BMVC92. Proceedings of the British Machine Vision
		  Conference},
  year		= {1992},
  editor	= {Hogg, D. and Boyle, R. },
  pages		= {508--17},
  organization	= {Integrated Syst. Group, Edinburgh Univ. , UK},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  rana00a,
  author	= {Rana, O. F.},
  title		= {Automating parallel implementation of neural learning
		  algorithms},
  journal	= {International-Journal-of-Neural-Systems},
  year		= {2000},
  volume	= {10},
  pages		= {227--41},
  abstract	= {A design scheme is described for translating a neural
		  learning algorithm from inception to implementation on a
		  parallel machine using PVM or MPI libraries, or onto
		  programmable logic such as FPGAs. A designer must first
		  describe the algorithm using a specialised neural language,
		  from which a Petri net (PN) model is constructed
		  automatically for verification, and building a performance
		  model. The PN model can be used to study issues such as
		  synchronisation points, resource sharing and concurrency
		  within a learning rule. Specialised constructs are provided
		  to enable a designer to express various aspects of a
		  learning rule, such as the number and connectivity of
		  neural nodes, the interconnection strategies, and
		  information flows required by the learning algorithm. A
		  scheduling and mapping strategy is then used to translate
		  this PN model onto a multiprocessor template. We
		  demonstrate our technique using a Kohonen and
		  backpropagation learning rules, implemented on a loosely
		  coupled workstation cluster, and a dedicated parallel
		  machine, with PVM libraries.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rangoussi95a,
  author	= {Rangoussi, M. and Delopoulos, A. },
  title		= {Recognition of unvoiced stops from their time-frequency
		  representation},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  year		= {1995},
  volume	= {1},
  pages		= {792--5},
  organization	= {Dept. of Electr. Eng. , Nat. Tech. Univ. of Athens,
		  Greece},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {Recognition of the unvoiced stop sounds /k/, /p/ and /t/
		  in a speech signal is an interesting problem, due to the
		  irregular, aperiodic, nonstationary nature of the
		  corresponding signals. Their spotting is much easier,
		  however, thanks to the characteristic silence interval they
		  include. Classification of these three phonemes is
		  therefore proposed in the present paper, based on patterns
		  extracted from their time---frequency representation. This
		  is possible because the different articulation points of
		  /k/, /p/ and /t/ are reflected into distinct patterns of
		  evolution of their spectral contents with time. These
		  patterns can be obtained by suitable time---frequency
		  analysis, and then used for classification. The Wigner
		  distribution of the unvoiced stop signals, appropriately
		  smoothed and subsampled, is proposed here as the basic
		  classification pattern. Finally, for the classification
		  step, the Learning Vector Quantization (LVQ) classifier of
		  Kohonen is employed on a set of unvoiced stop signals
		  extracted from the TIMIT speech database, with encouraging
		  results under context- and speaker- independent testing
		  conditions.},
  dbinsdate	= {oldtimer}
}

@Article{	  rantanen01a,
  author	= {Rantanen, J. T. and Laine, S. J. and Antikainen, O. K. and
		  Mannermaa, J. P. and Simula, O. E. and Yliruusi, J. K.},
  title		= {Visualization of fluid-bed granulation with
		  self-organizing maps},
  journal	= {JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS},
  year		= {2001},
  volume	= {24},
  number	= {3},
  month		= {JAN},
  pages		= {343--352},
  abstract	= {The degree of the instrumentation of pharmaceutical unit
		  operations has increased. This instrumentation provides
		  information of the state of the process and can be used for
		  both process control and research. However, on-line process
		  data is usually multidimensional, and is difficult to study
		  with traditional trends and scatter plots. The
		  Self-Organizing Map (SOM) is a recognized tool for
		  dimension reduction and process state monitoring. The
		  basics of the SOM and the application to on-line data
		  collected from a fluid-bed granulation process are
		  presented. As a batch process, granulation traversed
		  through a number of process states, which was visualized
		  with SOM as a two-dimensional map. In addition, it is
		  demonstrated how the differences between granulation
		  batches can be studied. The results suggest that SOM
		  together with new in-line process analytical solutions
		  support the in- process control of the pharmaceutical unit
		  operations. Further, a novel research tool for
		  understanding the phenomena during processing is achieved.
		  },
  dbinsdate	= {2002/1}
}

@InProceedings{	  rao92a,
  author	= {Rao, V. and Moorthy, S. and Shihab, S. and Bates, I. },
  title		= {Application of neural network techniques to partial
		  discharge measurements of high voltage energy systems},
  booktitle	= {IEEE International Workshop on Emerging Technologies and
		  Factory Automation---Technology for the Intelligent Factory
		  ---Proceedings},
  year		= {1992},
  editor	= {Zurawski, R. and Dillon, T. S. },
  pages		= {441--5},
  organization	= {Dept. of Electr. \& Manuf. Syst. Eng. , R. Melbourne Inst.
		  of Technol. , Vic. , Australia},
  publisher	= {CRL Publishing},
  address	= {Aldershot, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rao94a,
  author	= {Rao, L. and He, B. and Yan, W. },
  title		= {A novel adaptive generator based on {K}ohonen's neural
		  network model and vector quantization},
  booktitle	= {Second International Conference on Computation in
		  Electromagnetics},
  year		= {1994},
  pages		= {193--7},
  organization	= {Heibei Inst. of Technol. , China},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  rape95a,
  author	= {Rape, R. and Fefer, D. and Jeglic, A. },
  title		= {Detection of Pc-2--5 groups of geomagnetic micropulsations
		  with neural networks},
  journal	= {Measurement},
  year		= {1995},
  volume	= {15},
  number	= {2},
  pages		= {103--17},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  rasanen90a,
  author	= {T. R{\"{a}}s{\"{a}}nen and S. K. Hakum{\"{a}}ki and E. Oja
		  and M. O. K. Hakum{\"{a}}ki},
  title		= {Analysis of r and s Disordes in Finnish by Using a
		  Laboratory Computer},
  journal	= {Folia Phoniatrica},
  year		= {1990},
  volume	= {42},
  pages		= {135--143},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rath95a,
  author	= {T. Rath},
  title		= {Artificial neural networks for plant classification with
		  image processing},
  booktitle	= {Artificial Intelligence in Agriculture. Postprint Volume
		  from the 2nd IFAC/IFIP/EurAgEng Workshop},
  publisher	= {Elsevier},
  year		= {1995},
  editor	= {A. J. Udink Ten Cate and R. Martin-Clouaire and A. A.
		  Dijkhuizen and C. Lokhorst},
  address	= {Oxford, UK},
  pages		= {183--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ratnasamy01a,
  author	= {Ratnasamy, S. and Francis, P. and Handley, M. and Karp, R.
		  and Shenker, S.},
  title		= {A scalable content-addressable network},
  booktitle	= {Computer Communication Review},
  year		= {2001},
  editor	= {},
  volume	= {31},
  pages		= {161--172},
  organization	= {Dept. of Elec. Eng. and Comp. Sci., University of
		  California, Berkeley},
  publisher	= {},
  address	= {},
  abstract	= {Hash tables---which map "keys" onto "values"---are an
		  essential building block in modern software systems. We
		  belive a similar functionality would be equally valuable to
		  large distributed systems. In this paper, we introduce the
		  concept of a Content-Addressable Network (CAN) as a
		  distributed infrastructure that provides hash table-like
		  functionality on Internet-like scales. The CAN is scalable,
		  fault-tolerant and completely self-organizing, and we
		  demonstrate its scalability, robustness and low-latency
		  properties through simulation.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rau99a,
  author	= {Jen Da Rau and Jung Hua Wang},
  title		= {A voting principle of multiple features for Chinese
		  character recognition system using neural network
		  classifiers},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {6},
  pages		= {874--8},
  abstract	= {We propose a modified SCONN (self creating and organising
		  neural network) classifier (MSC), which uses the algorithm
		  of learning vector quantization. We adopt two commonly used
		  features, namely the crossing-count feature and
		  contour-direction feature in our recognition system. The
		  experimental results show that MSC performs well and has
		  advantages of being simple in network structure and
		  efficient in computation time. A voting principle useful in
		  selecting candidates based on measurement values derived
		  from variable error distance is proposed. We test several
		  formulas for calculating the confidence level (ballots) of
		  candidates, and show that the proposed voting principle can
		  increase up to 10% in recognition accuracy than otherwise
		  using the MSC alone.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rauber00a,
  author	= {Rauber, Andreas and Tomsich, Philipp and Merkl, Dieter},
  title		= {<sub>par</sub>{SOM}: A parallel implementation of the
		  self-organizing map exploiting cache effects: Making the
		  {SOM} fit for interactive high-performance data analysis},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {177--182},
  organization	= {Vienna Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A large number of applications has shown, that the
		  self-organizing map is a prominent unsupervised neural
		  network model for high-dimensional data analysis. However,
		  the high execution times required to train the map put a
		  limit to its use in many application domains, where either
		  very large datasets are encountered and/or interactive
		  response times are required. In order to provide
		  interactive response times during data analysis we
		  developed the <sub>par</sub>SOM, a software-based parallel
		  implementation of the self-organizing map Parallel
		  execution reduces the training time to a large degree, with
		  an even higher speedup obtained by using the resulting
		  cache effects. We demonstrate the scalability of the
		  <sub>par</sub>SOM system and the speed-up obtained on
		  different architectures using an example from
		  high-dimensional text data classification.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rauber00b,
  author	= {Rauber, A. and Dittenbach, M. and Merkl, D.},
  title		= {Automatically detecting and organizing documents into
		  topic hierarchies: A neural network based approach to
		  bookshelf creation and arrangement},
  booktitle	= {RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES,
		  PROCEEDINGS},
  year		= {2000},
  pages		= {348--351},
  abstract	= {With the increasing amount of information available in
		  electronic document collections. methods for organizing
		  these collections to allow topic-oriented browsing and
		  orientation gain importance. The SOMLib Digital Library
		  System provides such an organization based on the
		  self-organizing map, a popular neural network model. In
		  this paper, we present the GHSOM, which, based on the same
		  concepts, allows an automatic hierarchical decomposition
		  and organization of documents, which very intuitively
		  reflects the organization typically found in (manually
		  organized) conventional libraries. We present a case study
		  based on a 3-month article collection from an Austrian
		  daily newspaper.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rauber00c,
  author	= {Rauber, A. and Merkl, D.},
  title		= {Providing topically sorted access to subsequently released
		  newspaper editions or: how to build your private digital
		  library},
  booktitle	= {Database and expert systems applications. 11th
		  International Conference, DEXA 2000. Proceedings (Lecture
		  Notes in Computer Science Vol.1873). Springer-Verlag,
		  Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {499--508},
  abstract	= {Self-organizing maps are a popular neural network model
		  for presenting high-dimensional input data on a
		  two-dimensional map, providing a particularly useful
		  interface to electronic document collections. However, as
		  the size of the training data increases, both the necessary
		  computational power as well as the training time required
		  exceed tolerable limits. Still more important, not all
		  training data may be available in one central location but
		  may rather be collected and managed at different
		  repositories or released in subsequent periods of time. The
		  paper describes an approach for combining independent,
		  distributed self-organizing maps to build a higher order
		  map, allowing the creation and maintenance of scalable,
		  independent map systems, which can be built to suit the
		  needs of individual users. This is achieved by training
		  higher order maps using the trained lower order maps as
		  input data. We demonstrate this approach by creating an
		  integrated view of subsequent releases of a newspaper
		  archive.},
  dbinsdate	= {2002/1}
}

@Article{	  rauber00d,
  author	= {Rauber, A. and Schweighofer, E. and Merkl, D.},
  title		= {Text classification and labelling of document clusters
		  with self-organising maps},
  journal	= {OEGAI-Journal},
  year		= {2000},
  volume	= {19},
  pages		= {17--23},
  abstract	= {The freely available law on the Internet could be one of
		  the best application areas of text classification and
		  labelling. The paper explores the high potential of the
		  self-organising map for information reconnaissance by
		  classifying and describing unknown legal text collections.
		  The maps can be seen as topic-oriented libraries that are
		  automatically created without intellectual input. The
		  clustered topics---units of the self-organising map---are
		  labelled with the most appropriate keywords. Extensive
		  tests have shown the potential of this approach.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rauber01a,
  author	= {Rauber, A. and Fruhwirth, M.},
  title		= {Automatically analyzing and organizing music archives},
  booktitle	= {Research and Advanced Technology for Digital Libraries.
		  5th European Conference, ECDL 2001. Proceedings (Lecture
		  Notes in Computer Science Vol.2163). Springer-Verlag,
		  Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {402--14},
  abstract	= {We are experiencing a tremendous increase in the amount of
		  music being made available in digital form. With the
		  creation of large multimedia collections, however, we need
		  to devise ways to make those collections accessible to the
		  users. While music repositories exist today, they mostly
		  limit access to their content to query-based retrieval of
		  their items based on textual meta-information, with some
		  advanced systems supporting acoustic queries. What we would
		  like to have additionally, is a way to facilitate
		  exploration of musical libraries. We thus need to
		  automatically organize music according to its sound
		  characteristics in such a way that we find similar pieces
		  of music grouped together, allowing us to find a classical
		  section, or a hard-rock section etc. in a music repository.
		  In this paper we present an approach to obtain such an
		  organization of music data based on an extension to our
		  SOMLib digital library system for text documents.
		  Particularly, we employ the Self-Organizing Map to create a
		  map of a musical archive, where pieces of music with
		  similar sound characteristics are organized next to each
		  other on the two-dimensional map display. Locating a piece
		  of music on the map then leaves you with related music next
		  to it, allowing intuitive exploration of a music archive.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rauber98a,
  author	= {Rauber, A. and Merkl, D.},
  title		= {Organization of distributed digital libraries: a neural
		  network-based approach},
  booktitle	= {Intelligent Data Engineering and Learning. Perspectives on
		  Financial Engineering and Data Mining. 1st International
		  Symposium. IDEAL'98.},
  publisher	= {Springer-Verlag},
  address	= {Singapore},
  year		= {1998},
  volume	= {},
  pages		= {283--8},
  abstract	= {Self-organizing maps are a popular neural network model
		  for mapping high-dimensional input data onto a
		  lower-dimensional output space. However, as the size of the
		  training data increases, both the necessary computational
		  power as well as the training time required exceed
		  tolerable limits. Still more important, not all training
		  data may be available in one central location but may
		  rather be collected and managed at different sites. This
		  paper describes an approach for combining independent,
		  distributed self-organizing maps to build a higher order
		  map, allowing the creation and maintenance of scalable,
		  independent map systems, which can be built to suit the
		  individual needs of the users. This is achieved by training
		  higher order maps using the trained lower order maps as
		  input data. We demonstrate the applicability of this
		  approach in the field of digital libraries.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rauber98b,
  author	= {Rauber, A. and Merkl, D.},
  title		= {Creating an order in distributed digital libraries by
		  integrating independent \mbox{self-organizing} maps},
  booktitle	= {ICANN 98. Proceedings of the 8th International Conference
		  on Artificial Neural Networks.},
  publisher	= {Springer},
  year		= {1998},
  volume	= {2},
  pages		= {773--8},
  address	= {London},
  abstract	= {Digital document libraries are an almost perfect
		  application arena for unsupervised neural networks. This
		  because many of the operations computers have to perform on
		  text documents are classification tasks based on "noisy"
		  input patterns. The "noise" arises because of the known
		  inaccuracy of mapping natural language to an indexing
		  vocabulary representing the contents of the documents. A
		  growing number of papers is dedicated to the usage of
		  self-organizing maps to organize the contents of such
		  digital libraries. These papers assume the central
		  availability of the data; an assumption that is
		  questionable given the massive amount of available
		  information. In this paper we describe an approach for
		  organizing distributed digital libraries based on a system
		  of independent self-organizing maps each of which
		  representing just a portion of the complete digital
		  library. Furthermore, we argue in favor of integrating
		  these independent maps in a hierarchical fashion, again by
		  means of self-organizing maps. The integration is based on
		  the trained low level maps.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rauber98c,
  author	= {Rauber, A. and Merkl, D.},
  title		= {Finding structure in text archives},
  booktitle	= {6th European Symposium on Artificial Neural Networks.
		  ESANN'98. Proceedings},
  publisher	= {D-Facto},
  address	= {Brussels, Belgium},
  year		= {1998},
  volume	= {},
  pages		= {179--84},
  abstract	= {With the advance and massive growth of electronic text
		  archives, the need for tools emerges, which help to gain
		  insight into the basic structure of the underlying digital
		  library. We present a neural network approach for the
		  analysis and exploration of text archives aiming at the
		  detection and visualization of the inherent structure of
		  the text collection. This cluster visualization technique
		  called Adaptive Coordinates is based on an extended
		  learning rule for the self-organizing map. It provides an
		  intuitive visualization by mapping clusters in a
		  high-dimensional input-space onto groups of nodes in a
		  2-dimensional output space. We further compare the results
		  of this mapping with another prominent cluster
		  visualization technique, namely Sammon's Mapping.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rauber99a,
  author	= {Rauber, A.},
  title		= {{L}abel{SOM}: on the labeling of \mbox{self-organizing}
		  maps},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3527--32},
  abstract	= {Self-organizing maps are a prominent unsupervised neural
		  network model providing cluster analysis of
		  high-dimensional input data. However, in spite of enhanced
		  visualization techniques for self-organizing maps,
		  interpreting a trained map proves to be difficult because
		  the features responsible for a specific cluster assignment
		  are not evident from the resulting map representation. In
		  this paper we present our LabelSOM approach for
		  automatically labeling a trained self-organizing map with
		  the features of the input data that are the most relevant
		  ones for the assignment of a set of input data to a
		  particular cluster. The resulting labeled map allows the
		  user to understand the structure and the information
		  available in the map and the reason for a specific map
		  organization, especially when only little prior information
		  on the data set and its characteristics is available. We
		  demonstrate the applicability of the LabelSOM method in the
		  field of data mining providing an example from real world
		  text mining.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rauber99b,
  author	= {Rauber, A. and Merkl, D.},
  title		= {Using \mbox{self-organizing} maps to organize document
		  archives and to characterize subject matter: how to make a
		  map tell the news of the world},
  booktitle	= {Database and Expert Systems Applications. 10th
		  International Conference, DEXA'99 (Lecture Notes in
		  Computer Science Vol.1677)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {302--11},
  abstract	= {While the focus of research concerning electronic document
		  archives still is on information retrieval, the importance
		  of interactive exploration has been realized and is gaining
		  importance. The map metaphor, where documents are organized
		  on a map according to their contents, has proven
		  particularly useful as an interface to such a collection.
		  The self-organizing map has shown to produce stable
		  topically ordered organizations of documents on such a
		  2-dimensional map display. However, the characteristics of
		  these topical clusters are not being made explicit. We
		  present the LabelSOM method which takes the applicability
		  of the self-organizing map for document archive
		  organization one step further by automatically labeling the
		  various topical clusters found in the map. This allows the
		  user to get an instant overview of the various topics
		  covered by a document collection.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rauber99c,
  author	= {Rauber, A. and Merkl, D.},
  title		= {Automatic labeling of \mbox{self-organizing} maps: making
		  a treasure-map reveal its secrets},
  booktitle	= {Methodologies for Knowledge Discovery and Data Mining.
		  Third Pacific-Asia Conference, PAKDD-99. Proceedings},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {228--37},
  abstract	= {The self-organizing map is an unsupervised neural network
		  model which lends itself to the cluster analysis of
		  high-dimensional input data. However, interpreting a
		  trained map proves to be difficult because the features
		  responsible for a specific cluster assignment are not
		  evident from the resulting map representation. We present
		  our LabelSOM approach for automatically labeling a trained
		  self-organizing map with the features of the input data
		  that are the most relevant ones for the assignment of a set
		  of input data to a particular cluster. The resulting
		  labeled map allows the user to better understand the
		  structure and the information available in the map and the
		  reason for a specific map organization, especially when
		  only little prior information on the data set and its
		  characteristics is available.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rauber99d,
  author	= {Rauber, A. and Merkl, D.},
  title		= {Mining text archives: Creating readable maps to structure
		  and describe document collections},
  booktitle	= {PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY},
  year		= {1999},
  pages		= {524--529},
  abstract	= {With the ever-growing amount of unstructured textual data
		  on the web, mining these text collections is of increasing
		  importance for the understanding of document archives.
		  Particularly the self-organizing map has shown to be very
		  well suited for this task. However, the interpretation of
		  the resulting document maps still requires a tremendous
		  effort, especially as far as the analysis of the features
		  learned and the characteristics of identified text clusters
		  are concerned. In this paper we present the LabelSOM method
		  which, based on the features learned by the map,
		  automatically assigns a set of keywords to the units of the
		  map to describe the concepts of the underlying text
		  clusters, thus making the characteristics of the various
		  topical areas on the map explicit.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rauber99e,
  author	= {Rauber, A. and Merkl, D.},
  title		= {{SOML}ib: a digital library system based on neural
		  networks},
  booktitle	= {Digital 99 Libraries. Fourth ACM Conference on Digital
		  Libraries. ACM, New York, NY, USA},
  year		= {1999},
  volume	= {},
  pages		= {240--1},
  abstract	= {Digital libraries have gained tremendous interest with
		  numerous research projects addressing the wealth of
		  challenges in this field. While computational intelligence
		  systems are being used for specific tasks in this arena,
		  the majority of projects relies on conventional techniques
		  for the basic structure of the library itself. With the
		  SOMLib project we create a digital library system that uses
		  a neural network-based core for library representation and
		  query processing. The self-organizing map, a popular
		  unsupervised neural network model, is used to automatically
		  structure a document collection. Based on this core,
		  additional modules integrate distributed libraries and
		  create an intuitive representation of the library,
		  automatically labeling the various topical sections in the
		  document collection.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rauber99f,
  author	= {Rauber, A. and Merkl, D.},
  title		= {The {SOML}ib digital library system},
  booktitle	= {RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES,
		  PROCEEDINGS},
  year		= {1999},
  pages		= {323--342},
  abstract	= {Digital Libraries have gained tremendous interest with
		  several research projects addressing the wealth of
		  challenges in this field. While computational intelligence
		  systems are being used for specific tasks in this arena,
		  the majority of projects relies on conventional techniques
		  for the basic structure of the library itself. With the
		  SOMLib project we created a digital library system that
		  uses a neural network-based core for the representation of
		  the library. The self-organizing map, a popular
		  unsupervised neural network model, is used to topically
		  structure a document collection similar to the organization
		  of real-world libraries. Based on this core, additional
		  modules provide information retrieval features, integrate
		  distributed libraries, and automatically label the various
		  topical sections in the document collection. A metaphor
		  graphics based interface further assists the user in
		  intuitively understanding the library providing an instant
		  overview.},
  dbinsdate	= {2002/1}
}

@Article{	  raviwongse00a,
  author	= {Raviwongse, R. and Allada, V. and {Sandidge Jr.}, T.},
  title		= {Plastic manufacturing process selection methodology using
		  \mbox{self-organizing} map ({SOM})/fuzzy analysis},
  journal	= {International Journal of Advanced Manufacturing
		  Technology},
  year		= {2000},
  number	= {3},
  volume	= {16},
  pages		= {155--161},
  abstract	= {For plastic products, one of the crucial decisions made
		  during the preliminary design stage is the selection of an
		  appropriate manufacturing process. In many industries, the
		  selection of the manufacturing process is primarily based
		  on the empirical knowledge and past experience of the
		  manufacturing personnel. This selection procedure may
		  result in inconsistent or poor choices if the manufacturing
		  personnel fail to map correctly the product characteristics
		  with the manufacturing efficacy of various manufacturing
		  processes. This paper presents an intelligent
		  self-organizing map (SOM)/fuzzy-based model to aid
		  designers in the selection of an appropriate plastic
		  manufacturing process. The plastic part attributes are
		  broadly classified into three main categories: part
		  characteristics, material type, and production
		  requirements. A fuzzy membership function is generated for
		  each of the attributes using the self-organizing map
		  paradigm. Fuzzy associative memories (FAMs) are used to
		  perform reasoning on these input fuzzy sets to derive the
		  output fuzzy sets. The output fuzzy sets are then
		  defuzzified to determine the process compatibility scores
		  (PCS). The working of the proposed model is demonstrated
		  using an example case.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ray01a,
  author	= {Ray, S. and Chan, A.},
  title		= {Automatic feature extraction from wavelet coefficients
		  using genetic algorithms},
  booktitle	= {Neural Networks for Signal Processing XI: Proceedings of
		  the 2001 IEEE Signal Processing Society Workshop. IEEE,
		  Piscataway, NJ, USA},
  year		= {2001},
  volume	= {},
  pages		= {233--41},
  abstract	= {Deciding what features can be effective for a signal
		  classification problem is often a nontrivial task. We
		  present a method that can be used for automatic extraction
		  of high quality features from wavelet coefficients without
		  a priori knowledge of features. Preprocessing of the
		  wavelet coefficients is necessary to obtain a measurable
		  set of features. The preprocessing is suitable for the
		  Morlet wavelet. Genetic algorithms are used in combination
		  with learning vector quantization neural networks to select
		  the relevant features from the processed wavelet
		  coefficients. A simple variation of the traditional feature
		  selection genetic algorithms is used as it applies to this
		  method. The method has been applied on different signals
		  for classification and has shown high classification rates
		  with a small number of features. Results from different
		  signal classification problems are also presented.},
  dbinsdate	= {2002/1}
}

@InCollection{	  raychaudhuri95a,
  author	= {T. RayChaudhuri and J. C. H. Yeh and G. C. Hamey and S. K.
		  Y. Sung and T. Westcott},
  title		= {A connectionist approach to quality assessment of food
		  products},
  booktitle	= {Eighth Australian Joint Conference on Artificial
		  Intelligence},
  publisher	= {World Scientific},
  year		= {1995},
  editor	= {X. Yao},
  address	= {Singapore},
  pages		= {435--41},
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  recla89a,
  author	= {W. F. Recla},
  title		= {Study in Speech Recognition Using a {K}ohonen Neural
		  Network Dynamic Programming and Multi-Feature Fusion},
  school	= {Air Force Inst. of Tech. },
  year		= {1989},
  address	= {Wright-Patterson AFB, OH},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@Article{	  recproc01a,
  author	= {},
  title		= {Proceedings {IEEE} {ICCV} Workshop on Recognition,
		  Analysis, and Tracking of Faces and Gestures in Real-Time
		  Systems},
  journal	= {IEEE Comput. Soc, Los Alamitos, CA, USA; 2001; viii+181
		  pp.},
  year		= {2001},
  volume	= {},
  pages		= {},
  abstract	= {The following topics were covered: sample-based synthesis
		  of talking heads; reconstruction of movies of facial
		  expressions; integrated approach to 3D face model
		  reconstruction from video; real-time 3D hand posture
		  estimation based on 2D appearance retrieval using monocular
		  camera; tracking with attributes using probabilistic
		  techniques; automatic learning of appearance face models;
		  video-based online face recognition using identity
		  surfaces; nonlinear mapping of multi-view face patterns to
		  a Gaussian distribution in a low dimensional space;
		  real-time stereo tracking of multiple moving heads; head
		  gestures for computer control; active appearance algorithm
		  for face and facial feature tracking; head and hands 3D
		  tracking; dynamic time warping for off-line recognition of
		  a small gesture vocabulary; signer-independent sign
		  language recognition based on SOFM/HMM; boosting for fast
		  face recognition; vision-based microphone switch for speech
		  intent detection; face detection utilizing audio and video
		  cues; real-time face tracking in wavelet subspace; GMM
		  hand-color classification and mean shift tracking;
		  view-subspace analysis of multi-view face patterns; fast
		  hand gesture recognition for real-time teleconferencing
		  applications; learning visual models of social engagement;
		  hybrid face recognition systems for profile views using the
		  MUGSHOT database; auto clustering for unsupervised learning
		  of atomic gesture components using minimum description
		  length; facial expression recognition using continuous
		  dynamic programming; robust facial feature point detection
		  under nonlinear illuminations; and stabilized adaptive
		  appearance changes model for 3D head tracking.},
  dbinsdate	= {2002/1}
}

@InCollection{	  reddy96a,
  author	= {N. V. S. Reddy and P. Nagabhushan and K. C. Gowda},
  title		= {A neural network based expert system model for conflict
		  resolution},
  booktitle	= {1996 Australian New Zealand Conference on Intelligent
		  Information Systems. Proceedings. ANZIIS 96},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {V. L. Narasimhan and L. C. Jain},
  address	= {New York, NY, USA},
  pages		= {229--32},
  dbinsdate	= {oldtimer}
}

@Article{	  reddy97a,
  author	= {N. V. S. Reddy and P. Nagabhushan},
  title		= {A multi-stage neural network model for unconstrained
		  handwritten numeral recognition},
  journal	= {Vivek},
  year		= {1997},
  volume	= {10},
  number	= {2},
  pages		= {3--11},
  dbinsdate	= {oldtimer}
}

@Article{	  redlich92a,
  author	= {A. N. Redlich},
  title		= {Redundancy reduction as the basis for visual signal
		  processing},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1992},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  volume	= {1710},
  number	= {pt. 1},
  pages		= {201--210},
  annote	= {Conf. paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rehtanz98a,
  author	= {Rehtanz, C. and Kuhlmann, D.},
  title		= {Application of the \mbox{self-organizing} map in electric
		  power systems},
  booktitle	= {6th European Congress on Intelligent Techniques and Soft
		  Computing. EUFIT '98},
  publisher	= {Verlag Mainz},
  address	= {Aachen, Germany},
  year		= {1998},
  volume	= {1},
  pages		= {195--9},
  abstract	= {Information technology offers a lot of data which requires
		  an intelligent processing to get a precise view of the
		  effects in electric power systems. In the paper
		  self-organizing maps (SOM) are developed for the indication
		  and visualization of voltage stability of large electric
		  power systems. The necessity of tools for indication and
		  visualization is pointed out. On this basis the application
		  of a SOM is worked out bypassing the disabilities of
		  standard voltage stability indicators. In addition, the
		  application of SOM can be used for the analysis of a power
		  system and the development of stabilizing measures. All
		  examples are calculated using a model of a real power
		  transmission system.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rehtanz99a,
  author	= {Rehtanz, C.},
  title		= {Visualisation of voltage stability in large electric power
		  systems},
  booktitle	= {IEE Proceedings Generation, Transmission and
		  Distribution},
  year		= {1999},
  volume	= {146},
  number	= {6},
  pages		= {573--6},
  abstract	= {Information technology offers much data from electric
		  power systems that can be condensed into high-quality
		  information with the techniques of computational
		  intelligence to enable a precise assessment of the system's
		  condition. A self-organising feature map (SOM) is used both
		  for the indication and visualisation of voltage stability
		  in large electric power systems. A conclusive mathematical
		  description and selection of input data for the assessment
		  of voltage stability is given. The application of a SOM is
		  worked out bypassing the disadvantages of standard voltage
		  stability indicators. The SOM is applicable to the analysis
		  of power systems and the development of stabilising
		  measures. The method is tested on a model of a real
		  voltage-critical transmission system.},
  dbinsdate	= {oldtimer}
}

@Article{	  reinders92a,
  author	= {Reinders, A. and {de Vink}, P. J. F. },
  title		= {Classification of {IR}-spectra with back propagation and
		  {K}ohonen networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 2},
  pages		= {855--65},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  reinhardt97a,
  author	= {Lutz Reinhardt and Riikka Vesanto and Juha Montonen and
		  Thomas Fetsch and Markku M{\"a}kij{\"a}rvi and Gilberto
		  Sierra and Toivo Katila and G{\"u}nter Breithardt},
  title		= {Location of Myocardial Infarction Based on Learning Vector
		  Quantization Networks Applied to {ST} Elevations of the
		  12-Lead {ECG}},
  journal	= {Annals of Noninvasive Electrocardiology},
  year		= 1997,
  volume	= 2,
  number	= 4,
  pages		= {331--337},
  dbinsdate	= {oldtimer}
}

@InCollection{	  reinhardt97b,
  author	= {L. Reinhardt and R. Vesanto and J. Montonen and T. Fetsch
		  and M. M\"akij\"arvi and G. Sierra and G. Breithardt},
  title		= {Application of learning vector quantization for
		  localization of myocardial infarction},
  booktitle	= {Proceedings of the 18th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  `Bridging Disciplines for Biomedicine'},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {3},
  editor	= {H. Boom and C. Robinson and W. Rutten and M. Neuman and H.
		  Wijkstra},
  address	= {New York, NY, USA},
  pages		= {921--2},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  reinhardt98a,
  author	= {Reinhardt, L. and Simelius, K. and Jokiniemi, T. and
		  Nenonen, J. and Tierala, I. and Toivonen, L. and Katila,
		  T.},
  title		= {Classification of body surface potential map sequences
		  during ventricular activation using {K}ohonen networks},
  booktitle	= {Proceedings of the 20th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  Vol.20 Biomedical Engineering Towards the Year 2000 and
		  Beyond.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  volume	= {3},
  pages		= {1344--7},
  abstract	= {The authors present a new method based on Kohonen networks
		  for the analysis and classification of body surface
		  potential map (BSPM) sequences. First, BSPM sequences
		  obtained from a time interval of the cardiac cycle (e.g.
		  QRS, ST) are presented to an untrained Self-Organizing Map
		  (SOM). During the learning process the SOM units organize
		  in such a way that similar BSPMs are represented in
		  particular areas of the SOM. Time traces from the cardiac
		  activation are then created on the trained SOM and
		  forwarded to a Learning Vector Quantization network for
		  final classification. In this paper the method was applied
		  to BSPM sequences obtained during catheter pace mappings
		  with the aim to noninvasively localize sources of
		  ventricular tachycardia.},
  dbinsdate	= {oldtimer}
}

@Article{	  reinhardt99a,
  author	= {Reinhardt, L. and Simelius, K. and Nenonen, J. and
		  Tierala, I. and M\"akij\"arvi, M. and Toivonen, L. and
		  Katila, T.},
  title		= {Source localization of ventricular arrhythmias using
		  \mbox{self-organizing} neural networks},
  journal	= {Computers in Cardiology},
  year		= {1999},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {26},
  pages		= {331--4},
  abstract	= {Body surface potential mapping (BSPM) data obtained during
		  endocardial stimulation at multiple ventricular pacing
		  sites show a broad spectrum of potential distributions. In
		  this study, BSPM sequences are analysed using a neural
		  network approach based on self-organisation that provides a
		  noninvasive estimation of the site of origin of stimulated
		  ventricular activation. The Self-Organizing Map (SOM)
		  network used in this study is arranged as a two-dimensional
		  lattice of neurons, each of them representing a particular
		  distribution of body surface potentials. For the training
		  of the SOM network, 123-channel BSPM recordings were
		  obtained from 86 endocardial pacing locations in 19
		  patients with a previous myocardial infarction. Ventricular
		  activation patterns from different pacing sites are
		  visualized as time traces on the trained SOM.
		  Classification of the activation patterns with respect to
		  the endocardial pacing location is performed by Learning
		  Vector quantization. The localisation results are
		  visualized on a realistic model of the endocardial surfaces
		  of the right and left ventricles.},
  dbinsdate	= {oldtimer}
}

@Article{	  reissman97a,
  author	= {P. -J. Reissman and I. E. Magnin},
  title		= {Modeling {{3D}} deformable object with the active
		  pyramid},
  journal	= {International Journal of Pattern Recognition and
		  Artificial Intelligence},
  year		= {1997},
  volume	= {11},
  number	= {7},
  pages		= {1129--39},
  note		= {(Parallel Image Analysis. International Workshop Conf.
		  Date: 7--8 Dec. 1995 Conf. Loc: Lyon, France)},
  dbinsdate	= {oldtimer}
}

@Article{	  ren96a,
  author	= {C. Ren and R. Means and P. McCabe},
  title		= {Image Content Addressable Retrieval System ({ICARS}) using
		  context vector approach},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2670},
  pages		= {450--60},
  note		= {(Storage and Retrieval for Still Image and Video Databases
		  IV Conf. Date: 1--2 Feb. 1996 Conf. Loc: San Jose, CA, USA
		  Conf. Sponsor: SPIE; Soc. Imaging Sci. \& Technol)},
  dbinsdate	= {oldtimer}
}

@Article{	  ren98a,
  author	= {Ren, Shougang and Araki, Yosuke and Uchino, Yoshitaka and
		  Kurogi, Shuichi},
  title		= {Learning algorithms using firing numbers of weight vectors
		  for WTA networks in rotation invariant pattern
		  classification},
  journal	= {IEICE Transactions on Fundamentals of Electronics,
		  Communications and Computer Sciences},
  year		= {1998},
  number	= {1},
  volume	= {E81-A},
  pages		= {175--182},
  abstract	= {Competitive learning algorithms for winner-take-all (WTA)
		  networks which perform rotation invariant pattern
		  classification are presented. Two algorithms are developed
		  for learning vectors in classes LVC1 and LVC2, to
		  effectively memorize input patterns or the vectors to be
		  classified. The cells in the network memorizes not only
		  weight vectors but also their firing numbers as statistical
		  values of the vectors.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rendon97a,
  author	= {E. Rendon and L. Salgado and J. M. Menendez and N.
		  Garcia},
  title		= {Adaptive palette determination for color images based on
		  {K}ohonen networks},
  booktitle	= {Proceedings of the International Conference on Image
		  Processing},
  publisher	= {IEEE Computer Society},
  year		= {1997},
  volume	= {1},
  address	= {Los Alamitos, CA, USA},
  pages		= {830--3},
  dbinsdate	= {oldtimer}
}

@Article{	  resta00a,
  author	= {Resta, Marina},
  title		= {{ATA}: The artificial technical analyst building intra-day
		  market strategies},
  journal	= {International Conference on Knowledge-Based Intelligent
		  Electronic Systems, Proceedings, KES},
  year		= {2000},
  volume	= {2},
  number	= {},
  month		= {},
  pages		= {729--732},
  organization	= {Univ of Genova},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A trading system based on artificial neural networks is
		  therein presented: the Artificial Technical Analyst (ATA)
		  is part of a project focusing on the capabilities of
		  variants of Self-Organizing Maps (SOMs) as pattern
		  recognition tools, as well as on their perspective use as
		  forecasters. The basic idea is to take advantage by the
		  analogies shared by SOMs and human technical traders, that
		  is their common search for known patterns, through which
		  extrapolating useful knowledge to forecast future moves of
		  prices. The project is here depicted in its guidelines,
		  discussing experimental results over S\&P500 intra-day
		  futures market contracts prices.},
  dbinsdate	= {2002/1}
}

@InBook{	  resta02a,
  author	= {Marina Resta},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Self-Organizing Maps and Financial Forecasting: an
		  Application},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {185--216},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InCollection{	  resta97a,
  author	= {Marina Resta},
  title		= {Self organizing evolutionary models in financial markets
		  forecasting},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {187--190},
  dbinsdate	= {oldtimer}
}

@InCollection{	  resta98a,
  author	= {M. Resta},
  title		= {A Hybrid Neural Network System for Trading Financial
		  Markets},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {106--116},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  resta99a,
  author	= {Resta, M.},
  title		= {Hybrid neural networks vs nonlinear time series models in
		  financial forecasting},
  booktitle	= {Proceedings of the International ICSC Congress on
		  Computational Intelligence Methods and Applications. ICSC
		  Academic Press, Zurich, Switzerland},
  year		= {1999},
  volume	= {},
  pages		= {},
  abstract	= {We discuss some results we have obtained in financial
		  indexes forecasting by means of neural networks. In this
		  paper we focus on the use of hybrid neural models which
		  combine the approach of both Self Organizing Features Maps
		  and Genetic Algorithms, and we show empirically that the
		  idea of neighborhood preservation can be applied to built
		  efficient trading strategies in the market. In particular,
		  we test such efficiency on the Italian official market
		  index (Mib30), comparing our results to that of a
		  "traditional" Kohonen map, and to the ones obtained by
		  nonlinear stochastic time series models (ARCH-GARCH and
		  EGARCH).},
  dbinsdate	= {2002/1}
}

@Article{	  reutterer00a,
  author	= {Reutterer, Thomas and Natter, Martin},
  title		= {Segmentation-based competitive analysis with {MULTICLUS}
		  and topology representing networks},
  journal	= {Computers and Operations Research},
  year		= {2000},
  volume	= {27},
  number	= {11},
  month		= {},
  pages		= {1227--1247},
  organization	= {Vienna Univ of Economics and Business Administration},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {Two neural network approaches, Kohonen's self-organizing
		  (feature) map (SOM) and the topology representing network
		  (TRN) of Martinetz and Schulten are employed in the context
		  of competitive market structuring and segmentation
		  analysis. In an empirical study using brands preferences
		  derived from household panel data, we compare the SOM and
		  TRN approach to MULTICLUS, a parametric latent vector
		  multi-dimensional scaling (MDS) model approach which also
		  simultaneously solves the market structuring and
		  segmentation problem. Our empirical analysis shows several
		  benefits and shortcomings of the three methodologies under
		  investigation. As compared to MULTICLUS, we find that the
		  non-parametric neural network approaches show a higher
		  robustness against any kind of data preprocessing and a
		  higher stability of partitioning results. As compared to
		  SOM, we find advantages of TRN which uses a more flexible
		  concept of adjacency structure. In TRN, no rigid grid of
		  units must be prespecified. A further advantage of TRN lies
		  in the possibility to exploit the information of the
		  neighborhood graph for adjacent prototypes which supports
		  ex-post decisions about the segment configuration at both
		  the micro and the macro level. However, SOM and TRN also
		  have some drawbacks as compared to MULTICLUS. The network
		  approaches are, for instance, not directly accessible to
		  inferential statistics. Our empirical study indicates that
		  especially TRN may represent a useful expansion of the
		  marketing analyst's tool box.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  reynolds01a,
  author	= {Robert Reynolds and Habtom Resssom and Mohamad Musavi},
  title		= {Use of Clustering to Improve Performance in Fuzzy Gene
		  Expression Analysis},
  booktitle	= {International Joint Conference on Neural Networks,
		  Washington, DC, USA, July 15--19},
  pages		= {},
  year		= {2001},
  month		= {July},
  note		= {CD-ROM},
  dbinsdate	= {2002/1}
}

@TechReport{	  reynolds92a,
  author	= {J. Reynolds},
  title		= {Visual feedback therapy with the visual ear},
  institution	= {Univ. Oxford},
  year		= {1992},
  type		= {Report},
  number	= {OUEL 1914/92},
  address	= {Oxford, UK},
  month		= {January},
  dbinsdate	= {oldtimer}
}

@Article{	  reynolds93a,
  author	= {Jake Reynolds and Lionel Tarassenko},
  title		= {Learning Pronunciation with the Visual Ear},
  journal	= {Neural Computing \& Application},
  year		= {1993},
  volume	= {1},
  number	= {3},
  pages		= {169--175},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rezai_rad97a,
  author	= {{Rezai Rad}, G. A. and Green, R. J.},
  title		= {A competitive learning algorithm for non-zero memory
		  codebook design in encoding of {CT} sequences},
  booktitle	= {Proceedings of the 19th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  `Magnificent Milestones and Emerging Opportunities in
		  Medical Engineering'.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1997},
  volume	= {3},
  pages		= {1342--6},
  abstract	= {Implementation of Artificial Neural Network (ANN) in
		  various aspects is increased day by day. One of the major
		  applications is in compression of images. Here an algorithm
		  has been developed for use in encoding of Computed
		  Tomography (CT) image sequences. The method is based on
		  application of ANN distributed system which classifies all
		  possible m*m (here 4*4) blocks into a smaller number well
		  distinct classes of vectors. In an extension of Kohonen
		  self organising net called Frequency Sensitive Competitive
		  Learning (FSCL) algorithm, the required time for obtaining
		  an ignorable error will depend on both distortion and the
		  number of iterations which, are more or less equal for all
		  units. Application of ANN to Vector Quantisation (VQ) stems
		  from this major concept that in usual methods the error
		  between each input pattern and a pattern of the codebook
		  (word), is calculated without regarding the weight of each
		  pixel value in entire pattern. A proper ANN exploits this
		  concept in an efficient classification of various patterns
		  in an image and/or sequence of images. This significantly
		  decreases artefact, such as blocking effect which normally
		  appears in ordinary VQ reconstructed images in a low
		  bitrate. In the case of sequences interframes correlation
		  is exploited in provision of a common codebook for highly
		  correlated frames. Further redundancy is decreased by
		  optimal decomposition of the sequence into most correlated
		  subsequences.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rhee94a,
  author	= {Hyun-Sook Rhee and Kyung-Whan Oh},
  title		= {Unsupervised Fuzzy Clustering Model with Optimal
		  Clusters},
  pages		= {335--336},
  booktitle	= {Proc. 3rd International Conference on Fuzzy Logic, Neural
		  Nets and Soft Computing},
  year		= {1994},
  publisher	= {Fuzzy Logic Systems Institute},
  address	= {Iizuka, Japan},
  annote	= {comparison, clustering, hybrid},
  dbinsdate	= {oldtimer}
}

@Article{	  riadokoro00a,
  author	= {Riadokoro, H. and Sato, K. and Ishii, M.},
  title		= {Acquisition of world image and self-localization using
		  sequential view images},
  journal	= {Transactions-of-the-Institute-of-Electronics,-Information-and-Communication-Engineers-D-II}
		  ,
  year		= {2000},
  volume	= {},
  pages		= {2587--96},
  abstract	= {We propose a new self-localization method for an
		  autonomous mobile robot. The robot can recognize
		  self-location in an environment using sequential view
		  images without landmarks. First, we construct "concept
		  patterns" of sequential view images using a self-organizing
		  map (SOM). The concept patterns show change of view images,
		  and the mean "world image". Next, the concept patterns are
		  integrated as locational information using a hierarchical
		  SOM (HSOM). Experimental results indicate that the robot
		  can obtain its own concept patterns using sequential view
		  images, and can autonomously recognize self-location in an
		  environment.},
  dbinsdate	= {2002/1}
}

@Article{	  ribes01a,
  author	= {Ribes, J. M. M. and Simon, B. and Macq, B.},
  title		= {Combined Kohonen neural networks and discrete cosine
		  transform method for iterated transformation theory},
  journal	= {SIGNAL PROCESSING-IMAGE COMMUNICATION},
  year		= {2001},
  volume	= {16},
  number	= {7},
  month		= {APR},
  pages		= {643--656},
  abstract	= {Iterated transformation theory (ITT) coding, also known as
		  fractal coding, in its original form, allows fast decoding
		  but suffers from long encoding times. During the encoding
		  step, a large number of block best-matching searches have
		  to be performed which leads to a computationally expensive
		  process. Because of that, most of the research efforts
		  carried on this held are focused on speeding up the
		  encoding algorithm. Many different methods and algorithms
		  have been proposed, from simple classifying methods to
		  multi-dimensional nearest key search. We present in this
		  paper a new method that significantly reduces the
		  computational load of ITT-based image coding. Both domain
		  and range blocks of the image are transformed into the
		  frequency domain (which has proven to be more appropriate
		  for ITT coding). Domain blocks are then used to train a
		  two-dimensional Kohonen neural network (KNN) forming a
		  codebook similar to vector quantization coding. The
		  property of KNN land self-organizing feature maps in
		  general) which maintains the input space (transformed
		  domain blocks) topology allows to perform a neighboring
		  search to find the piecewise transformation between domain
		  and range blocks. },
  dbinsdate	= {2002/1}
}

@InCollection{	  rieger97a,
  author	= {B. B. Rieger},
  title		= {Dynamic word meaning representations and the notion of
		  granularity. Text understanding as meaning constitution by
		  SCIPS},
  booktitle	= {Proceedings of the 1997 International Conference on
		  Intelligent Systems and Semiotics: A Learning Perspective.
		  ISAS '97 (NIST-SP 918)},
  publisher	= {NIST},
  year		= {1997},
  editor	= {A. M. Meystel},
  address	= {Gaithersburg, MD, USA},
  pages		= {331--2},
  dbinsdate	= {oldtimer}
}

@Article{	  riesenhuber96a,
  author	= {M. Riesenhuber and H. -U. Bauer and T. Geisel},
  title		= {Analyzing phase transitions in high-dimensional
		  \mbox{self-organizing} maps},
  journal	= {Biological Cybernetics},
  year		= {1996},
  volume	= {75},
  number	= {5},
  pages		= {397--407},
  dbinsdate	= {oldtimer}
}

@InCollection{	  riesenhuber96b,
  author	= {M. Riesenhuber and H. -U. Bauer and T. Geisel},
  title		= {Analyzing the formation of structure in high-dimensional
		  \mbox{self-organizing} maps reveals differences to feature
		  map models},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {409--14},
  dbinsdate	= {oldtimer}
}

@Article{	  riesenhuber98a,
  author	= {M. Riesenhuber and H. -U. Bauer and D. Brockmann and T.
		  Geisel},
  title		= {Breaking rotational symmetry in a \mbox{self-organizing}
		  map model for orientation map development},
  journal	= {Neural Computation},
  year		= {1998},
  volume	= {10},
  number	= {3},
  pages		= {717--30},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rigoll90a,
  author	= {G. Rigoll},
  title		= {Information theory principles for the design of
		  \mbox{self-organizing} maps in combination with hidden
		  {M}arkov modeling for continuous speech recognition},
  booktitle	= {Proc. IJCNN'90, International Joint Conference on Neural
		  Networks, San Diego},
  year		= {1990},
  volume	= {I},
  pages		= {569--574},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rigoll90b,
  author	= {G. Rigoll},
  title		= {Neural network based continuous speech recognition by
		  combining self organizing feature maps and hidden {M}arkov
		  modeling},
  booktitle	= {Neural Networks. EURASIP Workshop 1990 Proceedings},
  year		= {1990},
  editor	= {L. B. Almeida and C. J. Wellekens},
  pages		= {205--214},
  organization	= {Eur. Assoc. Signal Process},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rigoll91a,
  author	= {G. Rigoll},
  title		= {Information theory-based supervised learning methods for
		  \mbox{self-organizing} maps in combination with hidden
		  {M}arkov modeling},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {I},
  pages		= {65--68},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  rihkanen94a,
  author	= {H. Rihkanen and L. Leinonen and T. Hiltunen and J.
		  Kangas},
  title		= {Spectral Pattern Recognition of Improved Voice Quality},
  journal	= {Journal of Voice},
  year		= {1994},
  volume	= {8},
  pages		= {320--326},
  dbinsdate	= {oldtimer}
}

@Book{		  ripley96a,
  author	= {B. D. Ripley},
  title		= {Pattern Recognition and Neural Networks},
  publisher	= {Cambridge University Press},
  year		= 1996,
  address	= {Cambridge, Great Britain},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  riqueline00a,
  author	= {Riqueline, J. and Martinez, J. L. and Gomez, A. and Goma,
		  D. C.},
  title		= {Possibilities of artificial neural networks in short-term
		  load forecasting},
  booktitle	= {Proceedings of the IASTED International Conference Power
		  and Energy Systems. IASTED/ACTA Press, Anaheim, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {165--70},
  abstract	= {In this paper, the possibilities of ANNs in hourly load
		  forecasting are studied. First, a Kohonen ANN is used to
		  identify daily patterns of the load curve. Then, a
		  comparative study between ARIMA and ANN techniques to
		  predict the hourly load of working days is presented and
		  discussed. Finally, an improved, recurrent multi-layer
		  perceptron is developed and applied to the same problem,
		  providing forecasting errors below 2%. The different models
		  have been applied to the 1996 hourly demand of an energy
		  supply utility with a peak load of 4162 MW.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  riskin91a,
  author	= {E. A. Riskin and L. E. Atlas and S. -R. Lay},
  title		= {Ordered neural maps and their applications to data
		  compression},
  booktitle	= {Proc. Workshop on Neural Networks for Signal Processing},
  year		= {1991},
  editor	= {B. H. Juang and S. Y. Kung and C. A. Kamm},
  pages		= {543--551},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter86a,
  author	= {H. Ritter and K. Schulten},
  title		= {Topology conserving mappings for learning motor tasks},
  booktitle	= {Neural Networks for Computing, AIP Conference Proc. 151,
		  Snowbird, Utah},
  year		= {1986},
  editor	= {J. S. Denker},
  pages		= {376--380},
  publisher	= {American Inst. of Phys. },
  address	= {New York, NY},
  dbinsdate	= {oldtimer}
}

@Article{	  ritter86b,
  author	= {H. Ritter and K. Schulten},
  title		= {On the Stationary State of {{K}ohonen's} Self-Organizing
		  Sensory Mapping},
  journal	= {Biol. Cyb. },
  year		= {1986},
  volume	= {54},
  pages		= {99--106},
  annote	= {The equation for the stationary state is derived for the
		  one-and two-dimensional case. The local magnification
		  factor can be solved in special cases. },
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  ritter88a,
  author	= {H. Ritter},
  school	= {Technische Universit\"at M\"unchen},
  title		= {Selbstorganisierende Neuronale Karten},
  address	= {Munich, Germany},
  year		= 1988,
  dbinsdate	= {oldtimer}
}

@InCollection{	  ritter88b,
  author	= {Helge Ritter and Klaus Schulten},
  title		= {Extending {{K}ohonen's} Self-Organizing Mapping Algorithm
		  to Learn Ballistic Movements},
  booktitle	= {Neural Computers},
  publisher	= {Springer},
  year		= {1988},
  editor	= {Eckmiller, R. and Malsburg, Ch. v. d. },
  pages		= {393--406},
  address	= {Berlin, Heidelberg},
  note		= {NATO ASI Series, Vol. F41},
  dbinsdate	= {oldtimer}
}

@Article{	  ritter88c,
  author	= {H. Ritter and K. Schulten},
  title		= {Convergence properties of {K}ohonen's topology preserving
		  maps: fluctuations, stability, and dimension selection},
  journal	= {Biol. Cyb. },
  year		= {1988},
  volume	= {60},
  number	= {1},
  pages		= {59--71},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter88d,
  author	= {H. Ritter and K. Schulten},
  title		= {{K}ohonen \mbox{self-organizing} maps: exploring their
		  computational capabilities},
  booktitle	= {Proc. ICNN'88 International Conference on Neural
		  Networks},
  year		= {1988},
  pages		= {109--116},
  volume	= {I},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter89a,
  author	= {H. J. Ritter},
  title		= {Combining Self-Organizing Maps},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks, Washington DC},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1989},
  volume	= {II},
  pages		= {499--502 },
  dbinsdate	= {oldtimer}
}

@TechReport{	  ritter89b,
  author	= {H. Ritter and T. Kohonen},
  title		= {Self-Organizing Semantic Maps},
  institution	= {Helsinki Univ. of Technology, Lab. of Computer and
		  Information Science},
  year		= {1989},
  type		= {Report},
  address	= {Espoo, Finland},
  x		= {AN ACCESSION NUMBER: PB89212609XSP Jos taman olisi pitanyt
		  olla mukana, niin miksi se ei ollut? Artikkeliversio
		  Ritter89bc on mukana. },
  dbinsdate	= {oldtimer}
}

@Article{	  ritter89c,
  author	= {Helge Ritter and Thomas Martinetz and Klaus Schulten},
  title		= {Wie neuronale Netze Roboter steuern k\"{o}nnen},
  journal	= {{MC-Computermagazin}},
  volume	= 2,
  pages		= {48--61},
  year		= 1989,
  dbinsdate	= {oldtimer}
}

@Article{	  ritter89d,
  author	= {Helge Ritter and Teuvo Kohonen},
  title		= {Self-organizing semantic maps},
  journal	= {Biol. Cyb. },
  year		= {1989},
  volume	= {61},
  number	= {4},
  pages		= {241--254},
  dbinsdate	= {oldtimer}
}

@Article{	  ritter89e,
  author	= {Ritter, H. J. and Martinetz, T. M. and Schulten, K. J.},
  title		= {Topology-conserving maps for learning
		  visuo-motor-coordination.},
  journal	= {Neural Networks},
  year		= {1989},
  number	= {3},
  volume	= {2},
  pages		= {159--168},
  abstract	= {The authors investigate the application of an extension of
		  Kohonen's self-organizing mapping algorithm to the learning
		  of visuo-motor-coordination of a simulated robot arm and
		  show that both arm kinematics and arm dynamics can be
		  learned, if a suitable representation for the map output it
		  used. Due to the topology-conserving property of the map
		  spatially neighboring neurons can learn cooperatively,
		  which greatly improves the robustness and the convergence
		  properties of the algorithm.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ritter89f,
  author	= {H. Ritter and T. Martinetz and K. Schulten},
  title		= {Topology Conserving Maps for Motor Control},
  booktitle	= {Neural Networks, from Models to Applications},
  publisher	= {{EZIDET}},
  year		= {1989},
  editor	= {L. Personnaz and G. Dreyfus},
  pages		= {579--591},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@TechReport{	  ritter89g,
  author	= {Helge Ritter},
  title		= {Asymptotic Level Density for a Class of Vector
		  Quantization Processes},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1989},
  type		= {Report},
  number	= {A9},
  address	= {Espoo, Finland},
  annote	= {An approximate expression of the point density of model
		  vectors in an 1D map. },
  dbinsdate	= {oldtimer}
}

@Book{		  ritter90a,
  author	= {H. J. Ritter and T. M. Martinetz and K. J. Schulten},
  title		= {Neuronale Netze: Eine Einf{\"{u}}hrung in die
		  Neuroinformatik selbstorganisierender Abbildungen},
  year		= {1990},
  address	= {Munich, Germany},
  publisher	= {Addison-Wesley},
  pages		= {258 },
  dbinsdate	= {oldtimer}
}

@InCollection{	  ritter90b,
  author	= {H. Ritter},
  title		= {Motor Learning by 'Charge' Placement with
		  \mbox{Self-organizing} Maps},
  booktitle	= {Neural Networks for Sensory and Motor Systems},
  publisher	= {Elsevier},
  year		= 1990,
  editor	= {R. Eckmiller},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter90c,
  author	= {Helge Ritter and Teuvo Kohonen},
  title		= {Learning 'Semantotopic Maps' from Context},
  booktitle	= {Proc. IJCNN'90, International Joint Conference on Neural
		  Networks, Washington DC},
  year		= 1990,
  volume	= {I},
  pages		= {23--26},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ritter90d,
  author	= {H. Ritter},
  title		= {Motor Learning by 'Charge' Placement with
		  \mbox{Self-organizing} Maps},
  booktitle	= {Advanced Neural Computers},
  publisher	= {Elsevier},
  year		= {1990},
  editor	= {R. Eckmiller},
  pages		= {381--388},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter90e,
  author	= {H. Ritter},
  title		= {Modular Networks of Multiple Maps},
  booktitle	= {Proc. COGNITIVA'90},
  year		= {1990},
  volume	= {II},
  pages		= {105--116},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter90f,
  author	= {H. Ritter},
  title		= {A Spatial Approach to Feature Linking},
  booktitle	= {Proc. INNC'90 Int. Neural Network Conf. },
  year		= {1990},
  pages		= {898},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  ritter90g,
  author	= {Helge J. Ritter},
  title		= {Self-Organizing Maps for Internal Representations},
  journal	= {Psych. Res. },
  year		= {1990},
  volume	= {52},
  pages		= {128--136},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ritter91a,
  author	= {Helge Ritter and Klaus Obermayer and Klaus Schulten and
		  Jeanette Rubner},
  title		= {Self-Organizing Maps and Adaptive Filters},
  booktitle	= {Models of Neural Networks},
  series	= {Physics of Neural Networks},
  publisher	= {Springer},
  address	= {New York, NY},
  editor	= {J. Leo {von Hemmen} and Eytan Domany and Klaus Schulten},
  year		= 1991,
  pages		= {281--307},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter91b,
  author	= {Helge Ritter},
  title		= {Learning with the Self-Organizing map},
  booktitle	= {Artificial Neural Networks. },
  year		= {1991},
  editor	= {Kohonen, Teuvo and M{\"{a}}kisara, Kai and Simula, Olli
		  and Kangas, Jari},
  pages		= {379--384},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  month		= {},
  annote	= {Discusses networks with random topology and SOM as a
		  controller of weights of a second, simpler network. },
  dbinsdate	= {oldtimer}
}

@Article{	  ritter91c,
  author	= {Helge Ritter},
  title		= {Asymptotic level density for a class of vector
		  quantization processes},
  journal	= {IEEE Trans. on Neural Networks},
  year		= {1991},
  volume	= {2},
  number	= {1},
  pages		= {173--175},
  month		= {January},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter91d,
  author	= {H. Ritter},
  title		= {The \mbox{Self-organizing} Map},
  booktitle	= {Proc. NOLTA, 2nd Symp. on Nonlinear Theory and its
		  Applications},
  year		= {1991},
  pages		= {5--8},
  address	= {Fukuoka, Japan},
  dbinsdate	= {oldtimer}
}

@Book{		  ritter92a,
  author	= {Helge Ritter and Thomas Martinetz and Klaus Schulten},
  title		= {Neural {C}omputation and {S}elf-{O}rganizing {M}aps: {A}n
		  {I}ntroduction},
  booktitle	= {Neural {C}omputation and {S}elf-{O}rganizing {M}aps: {A}n
		  {I}ntroduction},
  publisher	= {Addison-Wesley},
  year		= {1992},
  address	= {Reading, MA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter93a,
  author	= {Helge Ritter},
  title		= {Parametrized Self-Organizing Maps},
  booktitle	= {Proc. ICANN'93 International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {568--575},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter94a,
  author	= {Helge Ritter},
  title		= {Parametrized {S}elf-{O}rganizing {M}aps for Vision
		  Learning Tasks},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {803--810},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {apllication, vision task, modification},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ritter97a,
  author	= {H. Ritter},
  title		= {\mbox{Self-organizing} maps for robot control},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {675--84},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ritter97b,
  author	= {Helge Ritter},
  title		= {Learning with the parameterized \mbox{self-organizing}
		  map},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= 1,
  dbinsdate	= {oldtimer}
}

@InCollection{	  ritter97c,
  author	= {Helge Ritter},
  title		= {Self-Organizing Maps for Robot Control},
  booktitle	= {Proc. ICANN'97, 7th International Conference on Artificial
		  Neural Networks},
  publisher	= {Springer},
  year		= 1997,
  volume	= 1327,
  series	= {Lecture Notes in Computer Science},
  address	= {Berlin},
  pages		= {675--684},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ritter98a,
  author	= {Helge Ritter},
  title		= {Applications of the Self-Organizing Map in Electric Power
		  Systems},
  booktitle	= {Proc. EUFIT'98, 6th European Congress on Intelligent
		  Techniques \& Soft Computing},
  publisher	= {ELITE Foundation},
  year		= 1998,
  volume	= 1,
  address	= {Aachen, Germany},
  pages		= {200--204},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ritter98b,
  author	= {Ritter, H.},
  title		= {Robotics applications of the \mbox{self-organizing} map},
  booktitle	= {6th European Congress on Intelligent Techniques and Soft
		  Computing. EUFIT '98},
  publisher	= {Verlag Mainz},
  address	= {Aachen, Germany},
  year		= {1998},
  volume	= {1},
  pages		= {200--4},
  abstract	= {The capability to approximate data manifolds makes the
		  self-organizing map a useful tool in robotics. However,
		  most situations call for maps of dimensionality higher than
		  two. Parametrized self-organizing maps (PSOMs) can overcome
		  the "curse of dimensionality" by using an interpolation
		  approach, leading to a "continuous associative memory" that
		  allows us to flexibly represent many of the continuous
		  constraints that are typically found in robotics.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ritter99a,
  author	= {H. Ritter},
  title		= {Self-Organizing Maps on non-euclidean Spaces},
  booktitle	= {Kohonen Maps},
  pages		= {97--110},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: Self-Organising Map, non-euclidean space,
		  hyperbolic geometry, hyperbolic tesselation, data
		  visualization},
  dbinsdate	= {oldtimer}
}

@Article{	  rizvi00a,
  author	= {Rizvi, S. A. and Saadawi, T. N. and Nasrabadi, N. M.},
  title		= {A clutter rejection technique for {FLIR} imagery using
		  region based principal component analysis},
  journal	= {Pattern-Recognition},
  year		= {2000},
  volume	= {33},
  pages		= {1931--3},
  abstract	= {In automatic target recognition using forward-looking
		  infrared (FLIR) imagery, the false alarms produced by the
		  target-detection stage must be discarded at the clutter
		  rejection stage. We present a clutter rejection technique
		  that uses region-based PCA. We propose to categorize all
		  target images by clustering together the target images with
		  respect to their similar sizes and shapes in order to form
		  a group. Each group is further divided in to several
		  regions, and a PCA is performed for each region in a
		  particular group to extract feature vectors. One can then
		  use these feature vectors to decide whether a potential
		  target is a clutter or a real target. The proposed
		  technique is based on learning vector quantization that
		  generates codebooks of the most representative feature
		  vectors for each region in a particular group. The decision
		  about a potential target is then made based on the
		  similarity between the extracted feature vectors and the
		  corresponding regional representative feature vectors, as
		  well as the number of similar feature vectors found.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rizvi01a,
  author	= {Rizvi, S. A. and Nasrabadi, N. M.},
  title		= {Clutter-rejection technique for {FLIR} imagery using
		  dynamic {ROI} extraction},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Nasrabadi, N. M. and Katsaggelos, A. K.},
  volume	= {4305},
  pages		= {1--10},
  organization	= {Dept. of Eng. Science and Physics, College of Staten
		  Island, University of New York},
  publisher	= {},
  address	= {},
  abstract	= {A modular clutter-rejection technique that uses
		  region-based principal component analysis (PCA) is
		  proposed. A major problem in FLIR ATR is the poorly
		  centered targets generated by the preprocessing stage. Our
		  modular clutter-rejection system uses static as well as
		  dynamic region of interest (ROI) extraction to overcome the
		  problem of poorly centered targets. In static ROI
		  extraction, the center of the representative ROI coincides
		  with the center of the potential target image. In dynamic
		  ROI extraction, a representative ROI is moved in several
		  directions with respect to the center of the potential
		  target image to extract a number of ROIs. Each module in
		  the proposed system applies region-based PCA to generate
		  the feature vectors, which are subsequently used to make a
		  decision about the identity of the potential target.
		  Region-based PCA uses topological features of the targets
		  to reject false alarms. In this technique, a potential
		  target is divided into several regions and a PCA is
		  performed on each region to extract regional feature
		  vectors. We propose using regional feature vectors of
		  arbitrary shapes and dimensions that are optimized for the
		  topology of a target in a particular region. These regional
		  feature vectors are then used by a two-class classifier
		  based on the learning vector quantization to decide whether
		  a potential target is a false alarm or a real target. We
		  also present experimental results using real-life data to
		  evaluate and compare the performance of the
		  clutter-rejection systems with static and dynamic ROI
		  extraction.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rizvi01b,
  author	= {Rizvi, S. A. and Nasrabadi, N. M.},
  title		= {Neural network algorithms for automatic target recognition
		  using forward-looking infra-red imagery: a survey},
  booktitle	= {PINSA-A-(Proceedings-of-the-Indian-National-Science-Academy)-Part-A-(Physical-Sciences).
		  vol.67, no.2},
  year		= {2001},
  volume	= {67},
  pages		= {243--76},
  abstract	= {Automatic target recognition is a multistage process.
		  Initially, a preprocessing stage operates on the entire
		  image and extracts regions containing potential targets. A
		  sophisticated classification technique (clutter rejection)
		  is then applied to reject false alarms from the potential
		  targets. Finally, a classifier operates on target and
		  clutter regions that have been selected as targets from the
		  clutter rejection stage to classify the target into one of
		  a number of pre-defined classes. We present a survey of
		  neural network algorithms for automatic target recognition
		  (ATR) using forward-looking infrared (FLIR) imagery. We
		  focus on ATR algorithms developed at the U.S. Army Research
		  Laboratory using real-life FLIR data. This data contains
		  ten vehicles commonly used by the military. Specifically,
		  we discuss three main neural network architectures that
		  have been utilized in a number of different configurations
		  for both target detection and classification in FLIR
		  imagery. The neural network algorithms covered include: (1)
		  multilayer convolution neural network; (2) learning vector
		  quantization; and (3) modular neural network. We also
		  present experimental results and performance evaluation of
		  the ATR systems based on above mentioned neural network
		  algorithms.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rizvi99a,
  author	= {Rizvi, Syed A. and Nasrabadi, Nasser M.},
  title		= {Neural networks for image coding: A survey},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3647},
  pages		= {46--57},
  abstract	= {Neural networks are highly parallel architectures, which
		  have been used successfully in pattern matching,
		  clustering, and image coding applications. In this paper,
		  we review neural network based techniques that have been
		  used in image coding applications. The neural networks
		  covered in this paper include multilayer perceptron (MLP),
		  competitive neural network (CNN), frequency sensitive
		  competitive neural network (FS-CNN), and self-organizing
		  feature map network (SOFM). All of the above mentioned
		  neural networks except MLP are trained using competitive
		  learning and used for designing the vector quantizer (VQ)
		  codebook. The major problem with the competitive learning
		  is that some of the neurons may get a little or no chance
		  at all to win the competition. This may lead to a codebook
		  containing several untrained codevectors or the codevectors
		  that have not been trained enough (under-utilized neuron).
		  There are several possible ways to solve this problem.
		  FS-CNN and SOFM offer solution to under-utilization of
		  neurons. We present design algorithms for above mentioned
		  neural networks as well as evaluate and compare their
		  performance on several standard monochrome images.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rizvi99b,
  author	= {Rizvi, S. A. and Nasrabadi, N. M. and Der, S. Z.},
  title		= {A clutter rejection technique for {FLIR} imagery using
		  region-based principal component analysis},
  booktitle	= {Proceedings 1999 International Conference on Image
		  Processing},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {415--19},
  abstract	= {The preprocessing stage of an automatic target recognition
		  system extracts areas containing potential targets from a
		  battlefield scene. These potential target images are then
		  sent to the classification stage to identify the targets.
		  It is highly desirable at the preprocessing stage to
		  minimize the incorrect rejection rate. This, however,
		  results in a high false alarm rate. The high false alarm
		  rate, in turn, makes subsequent target classification
		  decisions unreliable. We present a new technique to reject
		  false alarms (clutter images) produced by the preprocessing
		  stage. Our technique, which we call region-based principal
		  component analysis (PCA), uses topological features of the
		  targets to reject false alarms. In this technique a
		  potential target is divided into several regions and a PCA
		  is performed on each region to extract regional feature
		  vectors. We propose to use regional feature vectors of
		  arbitrary shapes and dimensions that are optimized for the
		  topology of a target in a particular region. These regional
		  feature vectors are then used by a two-class classifier
		  based on the learning vector quantization to decide whether
		  a potential target is a false alarm or a real target.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rizzo01a,
  author	= {Rizzo, R.},
  title		= {{LBG}-m: A modified {LBG} architecture to extract
		  high-order neural structures},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {2},
  pages		= {779--783},
  organization	= {CNR, ITDF},
  publisher	= {},
  address	= {},
  abstract	= {Neural networks that learn in an unsupervised way and
		  generate their topology during learning can be useful to
		  build topology representing structures. These networks can
		  be used for vector quantization and clustering each time it
		  is necessary to characterize the topology of the underlying
		  data distribution. A drawback of these networks is that the
		  structures created have the same complexity as the input
		  data, so a simplification of the structure is needed to
		  allow the user to visualize and manipulate these
		  representations. The aim of the proposed algorithm is to
		  simplify the graph structure created by these kinds of
		  neural networks. The LBG-m algorithm takes the position of
		  the nodes and the adjacency matrix of the graph as input
		  and builds an over-imposed graph that clusterizes the graph
		  nodes and tries to reproduce the "shape" of the input
		  graph.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rizzo99a,
  author	= {Rizzo, R. and Allegra, M. and Fulantelli, G.},
  title		= {Hypertext-like structures through a {SOM} network},
  booktitle	= {Hypertext '99. Returning to our Diverse Roots. The 10th
		  ACM Conference on Hypertext and Hypermedia. ACM, New York,
		  NY, USA},
  year		= {1999},
  volume	= {},
  pages		= {71--2},
  abstract	= {The authors describe a system whose main aim is supporting
		  a hypertext author in classifying and organizing a large
		  amount of documents. The system allows the author to have
		  access to the documents with hypertext features, providing
		  some access points and suggesting, for each document, the
		  related ones. The system is an interesting application of
		  the Self Organizing Map network, a neural network widely
		  used to organize multidimensional data; specifically, it is
		  based on two SOM networks, the first one is aimed at
		  organizing collections of documents in "information maps"
		  that display the relations between the content of the
		  documents; the second one identifies access points and
		  splits the maps into meaningful areas. Finally the author
		  can edit both the list of access points and the map through
		  a Web page editor, thus moving the misclassified documents
		  to the right area.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  roberts91a,
  author	= {S. Roberts and L. Tarassenko},
  title		= {{EEG} analysis using self-organisation},
  booktitle	= {Proc. Second International Conference on Artificial Neural
		  Networks},
  year		= {1991},
  pages		= {210--213},
  organization	= {IEE},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  roberts92a,
  author	= {S. Roberts and L. Tarassenko},
  title		= {Analysis of the sleep {EEG} using a multilayer network
		  with spatial organisation},
  journal	= {IEE Proc. F [Radar and Signal Processing]},
  year		= {1992},
  volume	= {139},
  number	= {6},
  pages		= {420--425},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  roberts92b,
  author	= {S. Roberts and L. Tarassenko},
  title		= {Analysis of the human {EEG} using \mbox{self-organising}
		  neural nets},
  booktitle	= {IEE Colloquium on 'Neurological Signal Processing' (Digest
		  No. 069)},
  year		= {1992},
  pages		= {6/1--3},
  organization	= {IEE},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  roberts92c,
  author	= {S. Roberts and L. Tarassenko},
  title		= {New method of automated sleep quantification},
  journal	= {Med. {\&} Biol. Eng. {\&} Comput. },
  year		= {1992},
  volume	= {30},
  number	= {5},
  pages		= {509--517},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rodrigues90a,
  author	= {J. S. Rodrigues and L. B. Almeida},
  title		= {Improving the Learning Speed in Topological Maps of
		  Patterns},
  booktitle	= {Proc. INNC'90, Int. Neural Networks Conference},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  pages		= {813--816},
  year		= {1990},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rodrigues91a,
  author	= {J. S. Rodrigues and L. B. Almeida},
  title		= {Improving the convergence in {K}ohonen topological maps},
  booktitle	= {Neural Networks: Advances and Applications},
  publisher	= {North-Holland},
  year		= {1991},
  editor	= {E. Gelenbe},
  pages		= {63--78},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rodriguez-fonollosa90a,
  author	= {J. A. Rodriguez-Fonollosa and E. Masgrau and A. Moreno},
  title		= {Robust {LPC} vector quantization based on {K}ohonen's
		  design algorithm},
  booktitle	= {Signal Processing V. Theories and Applications. Proc. of
		  EUSIPCO-90, Fifth European SignalProcessing Conference},
  year		= {1990},
  editor	= {Torres, L. and Masgrau, E. and Lagunas, M. A},
  volume	= {II},
  pages		= {1303--1306},
  organization	= {CIDEM; CIRIT; IBM; et al},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rodriguez93a,
  author	= {Rodriguez, M. J. and {del Pozo}, F. and Arredondo, M. T.
		  },
  title		= {Use of unsupervised neural networks for classification of
		  blood pressure time series},
  booktitle	= {New Trends in Neural Computation. International Workshop
		  on Artificial Neural Networks. IWANN '93 Proceedings},
  year		= {1993},
  editor	= {Mira, J. and Cabestany, J. and Prieto, A. },
  pages		= {536--41},
  organization	= {ETSI de Telecommunicaion, Univ. Politecnica de Madrid,
		  Spain},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rodriguez93b,
  author	= {Rodriguez, M. J. and {del Pozo}, F. and Arredondo, M. T.
		  and Gomez, E. },
  title		= {Use of unsupervised neural networks for blood pressure
		  profile classification},
  booktitle	= {Proceedings. Computers in Cardiology 1993},
  year		= {1993},
  pages		= {225--8},
  organization	= {Grupo de Bioingenieria, ETSI Telecom. , Madrid, Spain},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rodriguez_arroyo01a,
  author	= {Jose Miguel {Rodriguez Arroyo} and Nigel Allinson and
		  Andrew J. Beddoes},
  title		= {Self-Organising Maps for the Condition Monitoring of
		  11k{V} Paper Insulated Cables},
  booktitle	= {International Joint Conference on Neural Networks,
		  Washington, DC, USA, July 15--19},
  pages		= {},
  year		= {2001},
  month		= {July},
  note		= {CD-ROM},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rodriguez_arroyo01b,
  author	= {J. M. {Rodr{\'i}guez Arroyo} and A. J. Boddoes and N. M.
		  Allinson},
  title		= {Self-organising maps for condition assessment of paper
		  isunlated cables},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {253--8},
  year		= {2001},
  editor	= {Nigel Allison and Hujun Yin and Lesley Allison and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  rodriquez93a,
  author	= {Mari{\'{a}} Jos{\'{e}} Rodr{\'{i}}quez and Francisco{ del
		  Pozo} and Mar{\'{i}}a Teresa Arredondo},
  title		= {Use of Unsupervised Neural Networks for Classification of
		  Blood Pressure Time Series},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {469--472},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rofer94a,
  author	= {Thomas R{\"{o}}fer},
  title		= {Vier{LING}---Quadruped with Integrated Neural Balance
		  Control},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1311--1314},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, robot control},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rofer95a,
  author	= {Thomas R{\"{o}}fer},
  title		= {Image-based Homing Using a Self-Organizing Feature Map},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {I},
  pages		= {475--480},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rogers89a,
  author	= {Steven K. Rogers and Matthew Kabrisky},
  title		= {1988 {AFIT} neural network research},
  booktitle	= {Proc. IEEE National Aerospace and Electronics Conf. },
  year		= {1989},
  pages		= {688--694},
  organization	= {IEEE, Dayton Section, Dayton, OH, USA; IEEE, Aerospace and
		  Electronic Systems Soc, New York, NY, USA},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  rognvaldsson93a,
  author	= {T. R{\"{o}}gnvaldsson},
  title		= {Pattern Discrimination Using Feedforward Networks: A
		  Benchmark Study of Scaling Behavior},
  journal	= {Neural Computation},
  year		= {1993},
  volume	= {5},
  number	= {},
  pages		= {483--491},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rogozan98a,
  author	= {Rogozan, A. and Deleglise, P.},
  title		= {Hybrid hidden {M}arkov model/neural network models for
		  speechreading},
  booktitle	= {6th European Symposium on Artificial Neural Networks.
		  ESANN'98. Proceedings},
  publisher	= {D-Facto},
  address	= {Brussels, Belgium},
  year		= {1998},
  volume	= {},
  pages		= {377--82},
  abstract	= {Describes an approach for visual speech recognition (also
		  called speechreading) using hybrid HMM/NN models. First, we
		  use the self-organising map (SOM) to merge phonemes that
		  appear visually similar into visemes. Then we develop a
		  hybrid speechreading system with two communicating
		  components: HMM and NN, to take advantage from the
		  qualities of both. The first component is a classical
		  continuous HMM, while the second one is the time delay
		  neural network (TDNN) or the Jordan partially recurrent
		  neural network (JNN). At the beginning of the recognition
		  process the HMM component segments and labels the visual
		  data. In the case of visemes which are often confused by
		  using the HMM, but rarely with the NN, we use the NN
		  component to label the corresponding boundaries. For the
		  other visemes, the final response is given by the HMM
		  component. Finally, we evaluate the hybrid system on a
		  continuously spelling task and we show that it outperforms
		  an HMM system and a NN one.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  roh01a,
  author	= {Roh, Young Jun and Cho, Hyung Suck},
  title		= {Design and analysis of x-ray digital tomosynthesis
		  system},
  booktitle	= {Proceedings of the SICE Annual Conference},
  year		= {2001},
  editor	= {},
  volume	= {},
  pages		= {252--258},
  organization	= {Department of Mechanical Engineering, Korea Adv. Inst.
		  Sci. and Technology},
  publisher	= {},
  address	= {},
  abstract	= {X-ray digital tomosynthesis(DT) that can form a
		  cross-sectional image of 3-D objects promise to be one of
		  good solutions for inspecting interior or occluded defects
		  of industrial products. To acquire an image of an arbitrary
		  plane of objects in this x-ray system, 8 images or more
		  images should be synthesized. In this paper, we propose a
		  developed x-ray DT system which consists of a scanning
		  x-ray tube, an image intensifier as an x-ray imaging
		  device, a rotating prism and a CCD camera. In the DT
		  method, the system parameters such as geometric
		  magnification and x-ray projection angle can be the main
		  factors to be considered to design the system. In achieving
		  the larger magnification and projection angle together,
		  there are some restrictions due to the geometric
		  limitations. The parametric relations are investigated
		  based on a numerical model of the system. In the system,
		  images are distorted due to the curved surface of the x-ray
		  imaging device and a set of the optical components in the
		  system. This distortion has been a limiting factor to
		  acquire accurate cross-sectional image of 3-D object is
		  practice. In this work, a method to correct the distorted
		  x-ray images in intensity and shape as well is proposed.
		  The method utilizes a polynomial model from a known
		  reference grid pattern. The feature point extraction in the
		  irregularly distorted image is achieved from edge image
		  with the help of a self-organizing feature mapping (SOFM)
		  network. By the proposed method, the distortion-correcting
		  model can be made without any a priori-knowledge about the
		  characteristics of the distortion such as directional or
		  local elongation. As an application of the proposed DT
		  system, a series of experimental images of PCB boards will
		  be illustrated and their image quality will be discussed in
		  detail.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  roh99a,
  author	= {Roh, Y. J. and Ko, K. W. and Cho, H. S. and Kim, H. C. and
		  Joo, H. N. and Kim, S. K.},
  title		= {Inspection of ball grid array ({BGA}) solder joints using
		  X-ray cross-sectional images},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3836},
  pages		= {168--178},
  abstract	= {The ball grid array(BGA) chip is widely used in high
		  density printed circuit board(PCB). However, inspection of
		  defects in the solder joints is difficult by visual or a
		  normal x-ray imaging method, because unlike conventional
		  packages with gullwing type leads, solder joints of the BGA
		  are located underneath its own package and ball type leads.
		  Therefore, x-ray digital tomosynthesis(DT), which form a
		  cross-sectional image of 3-D objects, is needed to image
		  and inspect the solder joints of BGA. In this paper, we
		  propose a series of algorithms for inspecting the solder
		  joints of BGA by using x-ray cross-sectional images that
		  are acquired from the developed DT system. BGA solder
		  joints are examined to check the alignment between the chip
		  and pad on a PCB, bridge(electrically short), adequate
		  solder volume. The volume of the solder joint is
		  represented by a gray level in the x-ray images: thus
		  solder joints can be examined by use of the gray-level
		  profiles of each joint. To inspect and classify various
		  defects, pattern classification method using a learning
		  vector quantization(LVQ) neural network and a look up
		  table(LUT) is proposed. The clusters into which a
		  gray-level profile is classified are generated by the
		  learning process of the network by using a number of
		  sampled gray-level profiles. A series of these developed
		  algorithms for inspecting and classifying defects were
		  tested on a number of BGA solder joints. The experimental
		  results show that the proposed method yields satisfactory
		  solutions for inspection based on x-ray cross-sectional
		  images.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  romero01a,
  author	= {Romero, G. and Castillo, P. A. and Merelo, J. J. and
		  Prieto, A.},
  title		= {Using {SOM} for neural network visualization},
  booktitle	= {Connectionist Models of Neurons, Learning Processes, and
		  Artificial Intelligence. 6th International Work-Conference
		  on Artificial and Natural Neural Networks, IWANN 2001.
		  Proceedings, Part I (Lecture Notes in Computer Science Vol.
		  2084). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {629--36},
  abstract	= {Software visualization is an area of computer science
		  devoted to supporting the understanding and effective use
		  of algorithms. The application of software visualization to
		  evolutionary computation has been receiving increasing
		  attention. We apply a visualization technique to an
		  evolutionary algorithm for multilayer perceptron training.
		  Our goal is to better understand its internal behavior in
		  order to improve the evolutionary part of the method. The
		  effect of several genetic operators are compared and the
		  difference with a fitness sharing version of the algorithm
		  is considered.},
  dbinsdate	= {2002/1}
}

@InCollection{	  rong94a,
  author	= {S. Rong and B. Bhanu},
  title		= {Characterizing natural backgrounds for target detection},
  booktitle	= {Image Understanding Workshop. Proceedings},
  publisher	= {Morgan Kaufmann Publishers},
  year		= {1994},
  volume	= {1},
  address	= {San Francisco, CA, USA},
  pages		= {501--4},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rong95a,
  author	= {Songnian Rong and Bir Bhanu},
  title		= {Enhancing A {S}elf-{O}rganizing {M}ap Through Near-Miss
		  Injection},
  volume	= {I},
  pages		= {552--556},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rong96a,
  author	= {S. Rong and B. Bhanu},
  title		= {Enhancing a \mbox{self-organizing} map through near-miss
		  injection},
  booktitle	= {WCNN '95. World Congress on Neural Networks. 1995
		  International Neural Network Society Annual Meeting},
  publisher	= {Univ. Vaasa},
  year		= {1996},
  volume	= {1},
  editor	= {J. Alander and T. Honkela and M. Jakobsson},
  address	= {Vaasa, Finland},
  pages		= {552--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  roobaert98a,
  author	= {D. Roobaert and M. M. {van Hulle}},
  title		= {The {SIM} neural module: self-organized learning of {2D}
		  invariant representations},
  booktitle	= {Proceedings of SSAB98},
  year		= {1998},
  address	= {Uppsala, Sweden},
  pages		= {153--156},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ropke94a,
  author	= {K. R{\"{o}}pke and D. Filbert},
  title		= {Unsupervised Classification of Universal Motors using
		  Modern Clustering Algorithms},
  booktitle	= {Proc. SAFEPROCESS'94, IFAC Symp. on Fault Detection,
		  Supervision and Technical Processes},
  year		= {1994},
  volume	= {II},
  pages		= {720--725},
  dbinsdate	= {oldtimer}
}

@Article{	  ros00a,
  author	= {Ros, F. and Audouze, K. and Pintore, M. and Chretien, J.
		  R.},
  title		= {Hybrid systems for virtual screening: Interest of fuzzy
		  clustering applied to olfaction},
  journal	= {SAR AND QSAR IN ENVIRONMENTAL RESEARCH},
  year		= {2000},
  volume	= {11},
  number	= {3--4},
  pages		= {281--+},
  abstract	= {Kohonen neural networks, also known as Self Organizing Map
		  (SOM), offer a useful 2D representation of the compound
		  distribution inside a large chemical database. This
		  distribution results from the compound organization in a
		  molecular diversity hyperspace derived from a large set of
		  molecular descriptors. Fuzzy techniques based on the
		  "concept of partial truth" reveal to be also a valuable
		  tool for the direct exploitation of chemical databases or
		  SOM. In such cases a fuzzy clustering algorithm is used. In
		  this paper, a complete hybrid system, combining SOM and
		  fuzzy clustering, is applied. As example, a series of
		  olfactory compounds was selected. The complexity of such
		  information is that a same compound may exhibit different
		  odors. It is shown how fuzzy logic helps to have a better
		  understanding of the organization of the compounds. These
		  hybrid systems, using simultaneously SOM and fuzzy
		  clustering, are foreseen as powerful tools for "virtual
		  pre-screening".},
  dbinsdate	= {2002/1}
}

@InCollection{	  rosandich95a,
  author	= {R. G. Rosandich},
  title		= {Implementation of competitive learning in the HAVNET
		  neural network},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks. Vol. 5. Fuzzy Logic and Evolutionary Programming.
		  Proceedings of the Artificial Neural Networks in
		  Engineering (ANNIE'95)},
  publisher	= {ASME Press},
  year		= {1995},
  editor	= {C. H. Dagli and M. Akay and C. L. P. Chen and B. R.
		  Fernandez and J. Ghosh},
  address	= {New York, NY, USA},
  pages		= {173--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rosario98a,
  author	= {B. Rosario and D. R. Lovell and M. Niranjan and R. W.
		  Prager and K. J. Dalton and R. Derom},
  title		= {Self-organization with a large medical database: using
		  {GTM} for prediction and clustering},
  booktitle	= {Neural Nets WIRN-VIETRI-97. Proceedings of the 9th Italian
		  Workshop on Neural Nets},
  publisher	= {Springer-Verlag London},
  year		= {1998},
  editor	= {M. Marinaro and R. Tagliaferri},
  address	= {London, UK},
  pages		= {257--62},
  dbinsdate	= {oldtimer}
}

@Article{	  rose91a,
  title		= {An Application of Unsupervised Neural Network Methodology
		  ({K}ohonen Topology-Preserving Mapping) to {QSAR}
		  Analysis},
  journal	= {Quant. Struct. Act. Relat. },
  volume	= 10,
  author	= {Valerie S. Rose and Ian F. Croall and Halliday J. H.
		  MacFie},
  number	= 6,
  year		= 1991,
  pages		= {6--15},
  dbinsdate	= {oldtimer}
}

@Article{	  rosqvist95a,
  author	= {Rosqvist, T. and Paajanen, E. and Kallio, K. and Rajala,
		  H. -M. and Katila, T. and Piirila, P. and Malmberg, P. and
		  Sovijarvi, A. },
  title		= {Toolkit for lung sound analysis},
  journal	= {Medical \& Biological Engineering \& Computing},
  year		= {1995},
  volume	= {33},
  number	= {2},
  pages		= {190--5},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rousset01a,
  author	= {Rousset, P. and Guinot, C.},
  title		= {Distance between Kohonen classes visualization tool to use
		  {SOM} in data set analysis and representation},
  booktitle	= {Bio-Inspired Applications of Connectionism. 6th
		  International Work-Conference on Artificial and Natural
		  Neural Networks, IWANN 2001. Proceedings, Part II. (Lecture
		  Notes in Computer Science Vol.2085). Springer-Verlag,
		  Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {119--26},
  abstract	= {We have presented a method to visualize the input data set
		  structure with Kohonen maps that is able to substitute
		  linear graphical displays when these ones are
		  unsatisfactory. While the Kohonen map refers to the
		  neighborhood structure between the k-classes produced by
		  this algorithm, a new tool that represents any distance
		  between k-classes centroids allows for some of properties
		  in the input space. This new technique can be applied to a
		  larger domain than the interpretation of the k-classes. In
		  particular in data analysis, it looks very well adapted to
		  some applications such as the visualization of any
		  c-clustering result. In that context, the many charts
		  associated to the Kohonen algorithm became also graphical
		  displays of the data set or the clusters properties. As a
		  perspective, it can probably be used to visualize some
		  adjustment of the data set with non-linear surfaces.},
  dbinsdate	= {2002/1}
}

@Article{	  roussinov98a,
  author	= {D. G. Roussinov and H. Chen},
  title		= {A scalable \mbox{self-organizing} map algorithm for
		  textual classification: a neural network approach to
		  thesaurus generation},
  journal	= {CC-AI, The Journal for the Integrated Study of Artificial
		  Intelligence, Cognitive Science and Applied Epistemology},
  year		= {1998},
  volume	= {15},
  number	= {1--2},
  pages		= {81--111},
  dbinsdate	= {oldtimer}
}

@Article{	  roussinov99a,
  author	= {Roussinov, Dmitri G. and Chen, Hsinchun},
  title		= {Document clustering for electronic meetings: An
		  experimental comparison of two techniques},
  journal	= {Decision Support Systems},
  year		= {1999},
  number	= {1},
  volume	= {27},
  pages		= {67--79},
  abstract	= {In this article, we report our implementation and
		  comparison of two text clustering techniques. One is based
		  on Ward's clustering and the other on Kohonen's
		  Self-organizing Maps. We have evaluated how closely
		  clusters produced by a computer resemble those created by
		  human experts. We have also measured the time that it takes
		  for an expert to `clean up' the automatically produced
		  clusters. The technique based on Ward's clustering was
		  found to be more precise. Both techniques have worked
		  equally well in detecting associations between text
		  documents. We used text messages obtained from group
		  brainstorming meetings.},
  dbinsdate	= {oldtimer}
}

@Article{	  roverso00a,
  author	= {Roverso, Davide},
  title		= {Soft computing tools for transient classification},
  journal	= {Information Sciences},
  year		= {2000},
  volume	= {127},
  number	= {3},
  month		= {Aug},
  pages		= {137--156},
  organization	= {OECD Halden Reactor Project},
  publisher	= {Elsevier Science Inc},
  address	= {New York, NY},
  abstract	= {Any action taken on a process, for example in response to
		  an abnormal situation or in reaction to unsafe conditions,
		  relies on the ability to identify the state of operation or
		  the events that are occurring. Although there might be
		  hundreds or even thousands of measurements in a process,
		  there are generally few events occurring. The data from
		  these measurements must then be mapped into appropriate
		  descriptions of the occurring event(s), which in most cases
		  is a difficult task. A systematic study was carried out
		  with the aim of comparing alternative neural network
		  designs and models for performing this mapping task. Four
		  main approaches have been investigated. Radial basis
		  function (RBF) neural networks and cascade-RBF neural
		  networks combined with fuzzy clustering, self-organizing
		  map neural networks, and recurrent neural networks. The
		  main evaluation criteria adopted were identification
		  accuracy, reliability (i.e., correct recognition of an
		  unknown event as such), robustness (to noise and to
		  changing initial conditions), and real-time performance.
		  Additionally, in this paper we describe how ensembles of
		  recurrent neural networks can overcome some of the
		  limitations encountered in these early prototypes, and give
		  an example involving the identification of anomalous events
		  in a PWR 900 MW nuclear power plant.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  roverso98a,
  author	= {Roverso, D. and Fantoni, P. F.},
  title		= {{ALADDIN}: a neural classifier of fast transients for
		  alarm filtering in nuclear power plants},
  booktitle	= {Fuzzy Logic and Intelligent Technologies for Nuclear
		  Science and Industry. Proceedings of the 3rd International
		  FLINS Workshop},
  publisher	= {World Scientific},
  address	= {Singapore},
  year		= {1998},
  volume	= {},
  pages		= {},
  abstract	= {Events and faults in a nuclear power plant (NPP) generate
		  transients which can activate a large number of alarms
		  presented in rapid sequence to the operator. A filtering of
		  the less important alarms or a prioritization of the alarms
		  can contribute to minimize the potential of human error in
		  these stressful situations. In this work we have developed
		  a neural network based system which aims at providing a
		  fast classification of the occurring transient to an alarm
		  handling system which can then use this information to
		  perform event-driven alarm filtering. A systematic study
		  was carried out with the aim of comparing alternative
		  neural network designs and models. Four main approaches
		  have been investigated: radial basis function (RBF) neural
		  networks and cascade-RBF neural networks combined with
		  fuzzy clustering, self-organizing map neural networks, and
		  recurrent neural networks. The main evaluation criteria
		  adopted were: identification accuracy, reliability (i.e.
		  correct recognition of an unknown event as such),
		  robustness to noise and to changing initial conditions, and
		  real time performance.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rovetta95a,
  author	= {Rovetta, S. and Zunino, R. and Buffrini, L. and Rovetta,
		  G. },
  title		= {Prototyping neural networks learn Lyme borreliosis},
  booktitle	= {Proceedings of the Eighth IEEE Symposium on Computer-Based
		  Medical Systems},
  year		= {1995},
  pages		= {111--17},
  organization	= {Fac. of Eng. , Genova Univ. , Italy},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rozgonyi94a,
  author	= {Rozgonyi, T. and Fomin, T. and Lorincz, A. },
  title		= {Self-organizing scaling filters for image segmentation},
  booktitle	= {1994 IEEE International Conference on Neural Networks.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {7},
  pages		= {4380--3},
  organization	= {Dept. of Photophys. , Inst. of Isotopes, Hungarian Acad.
		  of Sci. , Budapest, Hungary},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rozmus95a,
  author	= {Rozmus, J. M. },
  title		= {Information retrieval by \mbox{self-organizing} maps},
  booktitle	= {16th National Online Meeting Proceedings---1995},
  year		= {1995},
  editor	= {Williams, M. E. },
  pages		= {349--54},
  organization	= {Smart Syst. , USA},
  publisher	= {Learned Inf},
  address	= {Medford, NJ, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rozmus96a,
  author	= {J. M. Rozmus},
  title		= {The density-tracking \mbox{self-organizing} map},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {44--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rubner90a,
  author	= {Jeanette Rubner and Klaus Schulten and Paul Tavan},
  title		= {A Self-Organizing Network for Complete Feature
		  Extraction},
  booktitle	= {Proc. International Conference on Parallel Processing in
		  Neural Systems and Computers {(ICNC)}, D\"usseldorf},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  year		= 1990,
  pages		= {365--368},
  dbinsdate	= {oldtimer}
}

@Article{	  rubner90b,
  author	= {J. Rubner and K. J. Schulten},
  title		= {Development of Feature Detectors by Self-Organization},
  journal	= {Biol. Cyb. },
  year		= {1990},
  volume	= {62},
  pages		= {193--199},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ruckert89a,
  author	= {U. Ruckert and I. Kreuzer and V. Tryba and K. Goser},
  title		= {Fault-tolerance of associative memories based on neural
		  networks},
  booktitle	= {Proceedings. VLSI and Computer Peripherals. VLSI and
		  Microelectronic Applications in Intelligent Peripherals and
		  their Interconnection Networks},
  year		= {1989},
  volume	= {I},
  pages		= {52--55},
  organization	= {IEEE; Gesellschaft fur Inf. ; Verband Deutscher
		  Elektrotech},
  publisher	= {IEEE Computer Society Press},
  address	= {Washington, DC, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  rueping94a,
  author	= {Rueping, S. and Goser, K. and Rueckert, U. },
  title		= {A chip for \mbox{self-organizing} feature maps},
  booktitle	= {Proceedings of the Fourth International Conference on
		  Microelectronics for Neural Networks and Fuzzy Systems},
  year		= {1994},
  pages		= {26--33},
  organization	= {Bauelemente der Elektrotech. , Dortmund Univ. , Germany},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  rueping98a,
  author	= {Rueping, S. and Porrmann, M. and Rueckert, U.},
  title		= {SOM accelerator system},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {31--50},
  abstract	= {Many applications of self-organizing maps (SOM) require
		  high computing performance in order to be efficient.
		  Because of the regular and modular structure of SOMs, a
		  custom hardware realization is obvious. Based on the idea
		  of a massively parallel system, several chips have been
		  designed, manufactured and tested by the authors. In this
		  article a high-performance system with the latest NBISOM_25
		  chips is presented. The NBISOM_25 integrated circuit (ES2
		  1.0 mu m CMOS) contains 25 processing elements in a 5x5
		  array. Due to the scalability of the chips a VMEbus board
		  was built with 16 ICs on it. The controllers for the VMEbus
		  and the SOM hardware are realized using FPGAs. The system
		  runs SOM applications with up to 400 elements in parallel
		  mode (20x20 map). Each model vector can have up to 64
		  weights of 8 bit accuracy. The maximum performance of the
		  board-system is 4.1 GCPS (recall) and 2.4 GCUPS (learning).
		  It is integrated in a simulation framework for neural
		  networks, that contains software tools for self-organizing
		  maps as well as for neural associative memories, tools for
		  pre- and postprocessing and tools for graphical analysis of
		  the simulation results.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ruf97a,
  author	= {B. Ruf and M. Schmitt},
  title		= {Unsupervised learning in networks of spiking neurons using
		  temporal coding},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference on Artificial Neural Networks Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {361--6},
  dbinsdate	= {oldtimer}
}

@Article{	  ruf98a,
  author	= {Ruf, Berthold and Schmitt, Michael},
  title		= {Self-organization of spiking neurons using action
		  potential timing},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1998},
  number	= {3},
  volume	= {9},
  pages		= {575--578},
  abstract	= {We propose a mechanism for unsupervised learning in
		  networks of spiking neurons which is based on the timing of
		  single firing events. Our results show that a topology
		  preserving behavior quite similar to that of Kohonen's
		  self-organizing map can be achieved using temporal coding.
		  In contrast to previous approaches, which use rate coding,
		  the winner among competing neurons can be determined fast
		  and locally. Our model is a further step toward a more
		  realistic description of unsupervised learning in
		  biological neural systems. Furthermore, it may provide a
		  basis for fast implementations in pulsed VLSI (very large
		  scale integration).},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ruf98b,
  author	= {Berthold Ruf and Michael Schmitt},
  title		= {Self-organizing maps of spiking neurons using temporal
		  coding},
  booktitle	= {Computational Neuroscience: Trends in Research, 1998},
  editor	= {James M. Bower},
  address	= {Plenum Press, New York},
  year		= 1998,
  pages		= {509--514},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ruggeri94a,
  author	= {A. Ruggeri and G. Danzi},
  title		= {Artificial neural networks for the classification of
		  electrophoretic patterns},
  booktitle	= {1995 IEEE Engineering in Medicine and Biology 17th Annual
		  Conference and 21 Canadian Medical and Biological
		  Engineering Conference},
  publisher	= {IASTED},
  year		= {1994},
  volume	= {1},
  editor	= {M. H. Hamza},
  address	= {Anaheim, CA, USA},
  pages		= {825--6},
  dbinsdate	= {oldtimer}
}

@Article{	  ruisanchez96a,
  author	= {I. Ruisanchez and P. Potokar and J. Zupan and V. Smolej},
  title		= {Classification of energy dispersion {X}-ray spectra of
		  mineralogical samples by artificial neural networks},
  journal	= {Journal of Chemical Information and Computer Sciences},
  year		= {1996},
  volume	= {36},
  number	= {2},
  pages		= {214--20},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ruiz-de-angulo96a,
  author	= {V. Ruiz-De-Angulo and C. Torras},
  title		= {Automatic recalibration of a space robot: an industrial
		  prototype},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {635--40},
  dbinsdate	= {oldtimer}
}

@Article{	  ruiz-de-angulo97a,
  author	= {Vicente Ruiz-De-Angulo and Carme Torras},
  title		= {Self-Calibration of a Space Robot},
  journal	= {IEEE Transactions on Neural Networks},
  year		= 1997,
  volume	= 8,
  pages		= {951--963},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ruiz-del-solar00a,
  author	= {Ruiz-del-Solar, J. and Kottow, D.},
  title		= {Bio-inspired texture segmentation architectures},
  booktitle	= {BIOLOGICALLY MOTIVATED COMPUTER VISION, PROCEEDING},
  year		= {2000},
  pages		= {444--452},
  abstract	= {This article describes three bio-inspired Texture
		  Segmentation Architectures that are based on the use of
		  Joint Spatial/Frequency analysis methods. In all these
		  architectures the bank of oriented filters is automatically
		  generated using adaptive-subspace self-organizing maps. The
		  automatic generation of the filters overcomes some
		  drawbacks of similar architectures, such as the large size
		  of the filter bank and the necessity of a priori knowledge
		  to determine the filters' parameters. Taking as starting
		  point the ASSOM (Adaptive- Subspace SOM) proposed by
		  Kohonen, three growing self- organizing networks based on
		  adaptive-subspace are proposed. The advantage of this new
		  kind of adaptive-subspace networks with respect to ASSOM is
		  that they overcome problems like the a priori information
		  necessary to choose a suitable network size (the number of
		  Biters) and topology in advance.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ruiz-del-solar00b,
  author	= {{Ruiz del Solar}, J and Kottow, D.},
  title		= {Neural-based architectures for the segmentation of
		  textures},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {3},
  pages		= {1080--3},
  abstract	= {An essential task in almost any pattern recognition system
		  is the extraction of feature vectors, which are then used
		  to perform a classification. Depending on the context of
		  this classification, these feature vectors are expected to
		  present invariance under basic transformations such as
		  translation, scaling or rotation. Thus, every problem needs
		  a careful selection of feature variables, which so far is
		  mostly done by hand. Neural networks have been used
		  successfully as classifiers for a long time, but only
		  recently they have begun to be employed for automatic
		  selection of feature variables. The ASSOM, ASGCS and ASGFC
		  neural models are able to automatically select features
		  variables (filters) for the segmentation of textures. In
		  the paper three different texture segmentation
		  architectures TEXSOM, TEXGFC and TEXSGFC, which are based
		  on the mentioned neural models, are described.},
  dbinsdate	= {2002/1}
}

@InCollection{	  ruiz-del-solar96a,
  author	= {Ruiz-Del-Solar, Javier and Koeppen, Mario},
  title		= {Automatic generation of oriented filters for texture
		  segmentation},
  booktitle	= {Proceedings International Workshop on Neural Networks for
		  Identification, Control, Robotics, and Signal/Image
		  Processing},
  year		= {1996},
  publisher	= {IEEE},
  address	= {Los Alamitos, CA, USA},
  number	= {},
  volume	= {},
  pages		= {212--220},
  abstract	= {This paper describes a new Texture Segmentation
		  architecture based on the Joint Spatial/Spatial-Frequency
		  paradigm, whose oriented filters are automatically
		  generated using the Adaptive-Subspace Self Organizing Map
		  (ASSOM) or the Supervised ASSOM (SASSOM) neural models. The
		  automatic filter generation overcomes some drawbacks of
		  similar architectures, such as the large size of the filter
		  bank and the necessity of a priori knowledge to determine
		  the filters' parameters.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ruiz-del-solar97a,
  author	= {Javier Ruiz-Del-Solar},
  title		= {{TEXSOM}: A new architecture for texture segmentation},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {227--232},
  dbinsdate	= {oldtimer}
}

@Article{	  ruiz-del-solar98a,
  author	= {Ruiz-Del-Solar, Javier},
  title		= {TEXSOM: Texture segmentation using \mbox{self-organizing}
		  maps},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {7--18},
  abstract	= {This article describes the so-called TEXSOM-architecture,
		  a texture segmentation architecture based on the joint
		  spatial /spatial-frequency paradigm. In this architecture
		  the oriented filters are automatically generated using the
		  adaptive-subspace self-organizing map (ASSOM) or the
		  supervised ASSOM (SASSOM) neural models. The automatic
		  filter generation overcomes some drawbacks of similar
		  architectures, such as the large size of the filter bank
		  and the necessity of a priori knowledge to determine the
		  filters' parameters. The quality of the segmentation
		  process is improved by applying median filtering and the
		  watershed transformation over the pre-segmented images. The
		  proposed architecture is also suitable to perform defect
		  identification on textured images.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ruiz-del-solar99a,
  author	= {{Ruiz-Del-Solar}, J and Kottow, D.},
  title		= {{ASGCS}: a new \mbox{self-organizing} network for
		  automatic selection of feature variables},
  booktitle	= {Engineering Applications of Bio-Inspired Artificial Neural
		  Networks. International Work-Conference on Artificial and
		  Natural Neural Networks, IWANN'99. Proceedings, (Lecture
		  Notes in Computer Science Vol.1607)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {2},
  pages		= {805--13},
  abstract	= {The automatic selection of invariant feature variables is
		  very important in pattern recognition systems. Recently,
		  neural models have begun to be employed for this task.
		  Among other models the adaptive-subspace self-organizing
		  map (ASSOM) stands out because of its simplicity and
		  biological plausibility. However, the main drawback of the
		  application of the ASSOM in image processing systems is
		  that a priori information is necessary to choose a suitable
		  network size and topology in advance. The main purpose of
		  this article is to present the adaptive-subspace growing
		  cell structures (ASGCS) network, which corresponds to a
		  further improvement of the ASSOM that overcomes its main
		  drawbacks. The ASGCS network introduces some GCS (growing
		  cell structures) concepts into the ASSOM model, The ASGCS
		  network is described and some examples of automatic
		  Gabor-like feature filter generation are given.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ruoppila93a,
  author	= {Vesa T. Ruoppila and Timo Sorsa and Heikki N. Koivo},
  title		= {Recursive Least-Squares Approach to Self-Organizing Maps},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1480--1485},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ruping93a,
  author	= {Ruping, S. and Ruckert, U. and Goser, K. },
  title		= {Hardware design for self organizing feature maps with
		  binary input vectors},
  booktitle	= {New Trends in Neural Computation. International Workshop
		  on Artificial Neural Networks. IWANN '93 Proceedings},
  year		= {1993},
  editor	= {Mira, J. and Cabestany, J. and Prieto, A. },
  pages		= {488--93},
  organization	= {Dept. of Electr. Eng. , Dortmund Univ. , Germany},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ruping96a,
  author	= {S. R\"uping and U. R\"uckert and K. Goser and M. Hartung},
  title		= {Diagnosis-system with \mbox{self-organizing} feature maps
		  and fuzzy-logic},
  booktitle	= {Neural Networks and Their Applications. Conference
		  Proceedings},
  publisher	= {Domaine Univ. Saint-Jerome},
  year		= {1996},
  address	= {Marseille, France},
  pages		= {251--8},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ruping97a,
  author	= {S. Ruping and M. Porrmann and U. Ruckert},
  title		= {A high performance {SOFM} hardware-system},
  booktitle	= {Biological and Artificial Computation: From Neuroscience
		  to Technology. International Work Conference on Artificial
		  and Natural Neural Networks, IWANN'97. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {J. Mira and R. Moreno-Diaz and J. Cabestany},
  address	= {Berlin, Germany},
  pages		= {772--81},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ruping97b,
  author	= {S. R{\"u}ping and M. Porrman and U. R{\"u}ckert},
  title		= {{SOM} hardware-accelerator},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {136--141},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ruping99a,
  author	= {Ruping, S. and Muller, J.},
  title		= {Analysis of {IC} fabrication processes using
		  \mbox{self-organizing} maps},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  publisher	= {IEE},
  address	= {London, UK},
  year		= {1999},
  volume	= {2},
  pages		= {631--6},
  abstract	= {The analysis of integrated circuit (IC) fabrication
		  processes is an important task in order to optimize the
		  yield and to detect problems in very early state. Neural
		  networks seem to be a promising approach to data analysis,
		  especially when there is a large amount of data with many
		  nonlinearities. In this paper we describe the use of
		  self-organizing maps (SOM) for visualization and analysis
		  of data derived from a running IC production of the Robert
		  Bosch GmbH Waferfab at Reutlingen, Germany. The main
		  aspects are techniques for pressure sensor trimming and
		  online process monitoring for in-process data, parameters
		  and the process status. After a short description of
		  visualization techniques for SOM, we present the results
		  based on the real world data from the Waferfab.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  rushmeier97a,
  author	= {H. Rushmeier and R. Lawrence and G. Almasi},
  title		= {Case study: visualizing customer segmentations produced by
		  self organizing maps},
  booktitle	= {Proceedings. Visualization '97},
  publisher	= {IEEE},
  year		= {1997},
  editor	= {R. Yagel and H. Hagen},
  address	= {New York, NY, USA},
  pages		= {463--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  russel93a,
  author	= {A. J. M. Russel and Th. E. Schouten},
  title		= {{FIELDNET}, A Dynamic Network For Pattern Classification},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {456--459},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  ruwisch93a,
  author	= {Ruwisch, D. and Bode, M. and Purwins, H. -G. },
  title		= {Parallel hardware implementation of {K}ohonen's algorithm
		  with an active medium},
  journal	= {Neural Networks},
  year		= {1993},
  volume	= {6},
  number	= {8},
  pages		= {1147--57},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ruwisch94a,
  author	= {D. Ruwisch and M. Bode and H. -G. Purwins},
  title		= {Parallel {K}ohonen Hardware with Low Connectivity based on
		  Active Media},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1335--1338},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {implementation, hardware},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ruwisch97a,
  author	= {D. Ruwisch and B. Dobrzewski and M. Bode},
  title		= {Wave propagation as a neural coupling mechanism: hardware
		  for \mbox{self-organizing} feature maps and the
		  representation of temporal sequences},
  booktitle	= {Neural Networks for Signal Processing VII. Proceedings of
		  the 1997 IEEE Signal Processing Society Workshop},
  publisher	= {IEEE},
  year		= {1997},
  editor	= {J. Principe and L. Gile and N. Morgan and E. Wilson},
  address	= {New York, NY, USA},
  pages		= {306--15},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ruzicka93a,
  author	= {Ruzicka, P. and Hrycej, D. },
  title		= {Topological maps for invariant features representation and
		  analysis of their self-organization},
  booktitle	= {Sixth International Conference. Neural Networks and their
		  Industrial and Cognitive Applications. NEURO-NIMES 93
		  Conference Proceedings and Exhibition Catalog},
  year		= {1993},
  pages		= {435--44},
  organization	= {Inst. of Comput. Sci. , Acad. of Sci. , Prague, Czech
		  Republic},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Article{	  ryan88a,
  author	= {Ryan, T. W. and Cotter, C. A.},
  title		= {Vector quantization training by a \mbox{self-organizing}
		  neural network.},
  journal	= {Recent advances in sensors, radiometry and data processing
		  for remote sensing},
  year		= {1988},
  number	= {},
  volume	= {924},
  pages		= {312--320},
  abstract	= {This paper presents work in progress on the application of
		  a self-organizing neural network to vector quantization
		  (VQ) training. A modified version of Kohonen's
		  self-organizing feature map algorithm was applied to
		  simulated data and to digitized Synthetic Aperture Radar
		  image data. Preliminary results indicate that the
		  network-based algorithm is potentially more robust than the
		  traditional LBG training algorithm, especially when
		  confronted with multimodal input data distributions.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ryu01a,
  author	= {Ryu, J. and Cho, S. -B.},
  title		= {Gender recognition of human behaviors using neural
		  ensembles},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {1},
  pages		= {571--576},
  organization	= {Yonsei University, Department of Computer Science},
  publisher	= {},
  address	= {},
  abstract	= {In this paper, we have developed two ensembles of neural
		  network classifiers in order to recognize actors' gender
		  from their biological movements. One is the ensemble of
		  modular MLPs (experts), the other is the ensemble of
		  modular MLPs and an inductive decision tree which combines
		  the output of experts. The human movement database consists
		  of 13 males' and 13 females' movements, and contains 10
		  repetitions of knocking, waving and lifting movements both
		  in neutral and angry style. Features have been extracted
		  with 4 different representations such as the 2D and 3D
		  velocities and positions, recorded from 6 point lights
		  attached on body. We have compared the results of ensembles
		  to the regular classifiers such as MLP, decision tree,
		  self-organizing map and support vector machine.
		  Furthermore, the discriminability and efficiency have been
		  calculated for the comparison with the human performance
		  that has been obtained with the same experiment. Our
		  experimental results indicate that the ensemble models are
		  superior to the conventional classifiers and human
		  participants.},
  dbinsdate	= {2002/1}
}

@Article{	  saarinen85a,
  author	= {Jukka Saarinen and Teuvo Kohonen},
  title		= {Self-Organized Formation of Colour Maps in a Model
		  Cortex},
  journal	= {Perception},
  year		= {1985},
  volume	= {14},
  number	= {},
  pages		= {711--719},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  saarinen91a,
  author	= {Jukka Saarinen},
  title		= {Studies of Parallel Processing Systems for Computationally
		  Intensive Scientific Simulations},
  school	= {Tampere University of Technology},
  year		= {1991},
  address	= {Tampere, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  saarinen92a,
  author	= {Saarinen, J. and Lindroos, M. and Tomberg, J. and Kaski,
		  K. },
  title		= {Parallel coprocessor for {K}ohonen's
		  \mbox{self-organizing} neural network},
  booktitle	= {Proceedings of the Sixth International Parallel Processing
		  Symposium},
  year		= {1992},
  editor	= {Prasanna, V. K. and Canter, L. H. },
  pages		= {537--42},
  organization	= {Microelectron. Lab. , Tampere Univ. of Technol. ,
		  Finland},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  abstract	= {A new efficient integrated circuit implementation of the
		  Self-Organizing Feature Map algorithm is described. The
		  fully digital hardware is designed for high speed parallel
		  processing and modular expandability. The hardware
		  implementation acts as a neural coprocessor which uses
		  synchronous, bit-serial arithmetic. It includes functional
		  units which perform the Euclidean distance computation, the
		  minimum distance search, the memory controlling, and the
		  updating function. The on-chip learning facilitates fully
		  autonomous operation.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  saarinen99a,
  author	= {Saarinen, J. and Kallioniemi, I. and Niinisto, A. and
		  Friberg A. T.},
  title		= {Surface roughness measurement with optical scatterometry
		  [and neural network model]},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.3897},
  year		= {1999},
  volume	= {3897},
  pages		= {570--7},
  abstract	= {Scattering of light by random rough surfaces can be
		  numerically simulated by using an exact electromagnetic
		  scattering theory. Unfortunately, the characterization of
		  surfaces is almost impossible owing to the nonuniqueness of
		  the inverse scattering problem and highly nonlinear
		  relationship between the surface parameters and the
		  scattering. Thus, existing practical methods for
		  qualitative or quantitative characterization are almost
		  entirely experimental. Here we apply neural networks for
		  estimating statistically the surface parameters.
		  Previously, we have successfully demonstrated that a neural
		  network as a statistical estimator for optical
		  scatterometry is an efficient tool for characterizing
		  periodic microstructures. We generate numerically random
		  surfaces, which are characterized with the degree of
		  roughness, i.e., root-mean-square (RMS) amplitude of the
		  roughness and correlation length. We are mainly interested
		  in the most demanding region of the RMS amplitude in the
		  so-called resonance domain, corresponding to height
		  fluctuations and correlations up to 5 times the wavelength
		  of light. The neural network model, which is a
		  self-organizing map, is first trained and calibrated with
		  the known surface parameter and scattering data pairs. At
		  the characterization stage, using only measured intensity
		  distributions, the neural network theory classifies surface
		  parameters into discrete classes of the RMS amplitude and
		  the correlation length. For most cases the classification
		  result deviates at most one class, corresponding to 0.5
		  wavelengths, from the correct values.},
  dbinsdate	= {2002/1}
}

@Article{	  saban01a,
  author	= {Saban, M. R. and Hellmich, H. and Nguyen, N. B. and
		  Winston, J. and Hammond, T. G. and Saban, R.},
  title		= {Time course of {LPS}- induced gene expression in a mouse
		  model of genitourinary inflammation},
  journal	= {PHYSIOLOGICAL GENOMICS},
  year		= {2001},
  volume	= {5},
  number	= {3},
  month		= {APR 2},
  pages		= {147--160},
  abstract	= {In this study, self-organizing map (SOM) gene cluster
		  techniques are applied to the analysis of cDNA microarray
		  analysis of gene expression changes occurring in the early
		  stages of genitourinary inflammation. We determined the
		  time course of lipopolysaccharide (LPS) induced gene
		  expression in experimental cystitis. Mice were euthanized
		  0.5, 1, 4, and 24 h after LPS instillation into the urinary
		  bladder, and gene expression was determined using four
		  replicate Atlas mouse cDNA expression arrays containing 588
		  known genes at each time point. SOM gene cluster analysis,
		  performed without preconditions, identified functionally
		  significant gene clusters based on the kinetics of change
		  in gene expression. Genes were classified as follows: 1)
		  expressed at time 0; 2) early genes (peak expression
		  between 0.5 and 1 h); and 3) late genes (peak expression
		  between 4 and 24 h). One gene cluster maintained a constant
		  level of expression during the entire time period studied.
		  In contrast, LPS treatment downregulated the expression of
		  some genes expressed at time 0, in a cluster including
		  transcription factors, protooncogenes, apoptosis- related
		  proteins (cysteine protease), intracellular kinases, and
		  growth factors. Gene upregulation in response to LPS was
		  observed as early as 0.5 h in a cluster including the
		  interleukin-6 (IL-6) receptor, alpha- and beta -nerve
		  growth factor (alpha- and beta -NGF), vascular endothelial
		  growth factor receptor-1 (VEGF R1), C-C chemokine receptor,
		  and P- selectin. Another tight cluster of genes with marked
		  expression at 1 h after LPS and insignificant expression at
		  all other time points studied included the protooncogenes
		  c-Fos, Fos-B, Fra-2, Jun-B, Jun-D, and Egr-1. Almost all
		  interleukin genes were upregulated as early as 1 h after
		  stimulation with LPS. Nuclear factor-kappaB (NF-kappaB)
		  pathway genes collected in a single cluster with a peak
		  expression 4 h after LPS stimulation. In contrast, most of
		  the interleukin receptors and chemokine receptors presented
		  a late peak of expression 24 h after LPS coinciding with
		  the peak of neutrophil infiltration into the bladder wall.
		  Selected cDNA microarray observations were confirmed by
		  RNase protection assay. In conclusion, the cDNA array
		  experimental approach provided a global profile of gene
		  expression changes in bladder tissue after stimulation with
		  LPS. SOM techniques identified functionally significant
		  gene clusters, providing a powerful technical basis for
		  future analysis of mechanisms of bladder inflammation.},
  dbinsdate	= {2002/1}
}

@InCollection{	  sabisch97a,
  author	= {Theo Sabisch and Alistair Ferguson and Hamid Bolouri},
  title		= {Rotation, translation, and scaling tolerant recognition of
		  complex shapes using a hierarchical \mbox{self-organizing}
		  neural network},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {1174--1178},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sabourin93a,
  author	= {Sabourin, R. and Cheriet, M. and Genest, G. },
  title		= {An extended-shadow-code based approach for off-line
		  signature verification},
  booktitle	= {Proceedings of the Second International Conference on
		  Document Analysis and Recognition},
  year		= {1993},
  pages		= {1--5},
  organization	= {Dept. de Genie de la Production Automatisee, Ecole de
		  Technol. Superiere, Montreal, Que. , Canada},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sadananda93a,
  author	= {R. Sadananda and A. Shestra},
  title		= {Topological Maps for {VLSI} Placement},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1955--1958},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sadananda94a,
  author	= {Sadananda, R. and Shrestha, A. },
  title		= {A \mbox{self-organizing} scheme for {VLSI} placement},
  booktitle	= {Moving Towards Expert Systems Globally in the 21st
		  Century},
  year		= {1994},
  editor	= {Liebowitz, J. },
  pages		= {1280--7},
  organization	= {Div. of Comput. Sci. , Asian Inst. of Technol. , Bangkok,
		  Thailand},
  publisher	= {Cognizant Commun. Corp},
  address	= {Elmsford, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  sadeghi01a,
  author	= {Sadeghi, A. A.},
  title		= {Convergence in distribution of the multi-dimensional
		  Kohonen algorithm},
  journal	= {JOURNAL OF APPLIED PROBABILITY},
  year		= {2001},
  volume	= {38},
  number	= {1},
  month		= {MAR},
  pages		= {136--151},
  abstract	= {Here we consider the Kohonen algorithm with a constant
		  learning rate as a Markov process evolving in a topological
		  space. Despite the fact that the algorithm is not weak
		  Feller, we show that it is a T-chain, regardless of the
		  dimensionalities of both data space and network and the
		  special shape of the neighborhood function. In addition for
		  the practically important case of the multi-dimensional
		  setting, it is shown that the chain is psi -irreducible and
		  aperiodic, We show that these imply the validity of
		  Doeblin's condition, which in turn ensures the convergence
		  in distribution of the process to an invariant probability
		  measure with a geometric rate. Furthermore, it is shown
		  that the process is positive Harris recurrent, which
		  enables us to use statistical devices to measure the
		  centrality and variability of the invariant probability
		  measure. Our results cover a wide class of neighborhood
		  functions.},
  dbinsdate	= {2002/1}
}

@TechReport{	  sadeghi96a,
  author	= {Ali A. Sadeghi},
  title		= {Asymptotic Behaviour of Self-Organizing Maps with
		  Non-Uniform Stimuli Distribution},
  institution	= {Universit{\"a}t Kaiserslautern, Fachbereich Mathematik},
  year		= 1996,
  number	= 166,
  address	= {Kaiserslautern, Germany},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@TechReport{	  sadeghi97a,
  author	= {Ali A. Sadeghi},
  title		= {Self-Organization Property of {K}ohonen's Map with General
		  Type of Stimuli DIstribution},
  institution	= {Universit{\"a}t Kaiserslautern, Fachbereich Mathematik},
  year		= 1997,
  number	= 181,
  address	= {Kaiserslautern, Germany},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@Article{	  sadeghi98a,
  author	= {Sadeghi, Ali A},
  title		= {Self-organization property of {K}ohonen's map with general
		  type of stimuli distribution},
  journal	= {Neural Networks, Signal Processing [IEE Proc VISION IMAGE
		  Signal Proc]},
  year		= {1998},
  number	= {9},
  volume	= {11},
  pages		= {1637--1643},
  abstract	= {Here the self-organization property of one-dimensional
		  Kohonen's algorithm in its 2k---neighbor setting with a
		  general type of stimuli distribution and non-increasing
		  learning rate is considered. A new definition of the winner
		  is given, which coincides with the usual definition in
		  implementations of the algorithm. We prove that the
		  probability of self-organization for all initial weights of
		  neurons is uniformly positive. For the special case of a
		  constant learning rate, it implies that the algorithm
		  self-organizes with probability one. The conditions imposed
		  on the neighborhood function, stimuli distribution and
		  learning rate are quite general.},
  dbinsdate	= {oldtimer}
}

@Article{	  sadeghi98b,
  author	= {A. A. Sadeghi},
  title		= {Asymptotic Behavior of Self Organizing Maps with
		  Nonuniform Stimuli Distribution},
  journal	= {Annals of Applied Probability},
  volume	= {8},
  pages		= {281--299},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  sagar01a,
  author	= {Sagar, B. S. D.},
  title		= {Generation of self organized critical connectivity network
		  map ({SOCCNM}) of randomly situated water bodies during
		  flooding process},
  journal	= {DISCRETE DYNAMICS IN NATURE AND SOCIETY},
  year		= {2001},
  volume	= {6},
  number	= {3},
  pages		= {225--228},
  abstract	= {This letter presents a brief framework based on nonlinear
		  morphological transformations to generate a self organized
		  critical connectivity network map (SOCCNM) in 2-dimensional
		  space. This simple and elegant framework is implemented on
		  a section that contains a few simulated water bodies to
		  generate SOCCNM. This is based on a postulate that the
		  randomly situated surface water bodies of various sizes and
		  shapes self organize during flooding process.},
  dbinsdate	= {2002/1}
}

@Article{	  saggaf00a,
  author	= {Saggaf, M. M. and Nebrija, Ed L.},
  title		= {Estimation of lithologies and depositional facies from
		  wire-line logs},
  journal	= {AAPG Bulletin (American Association of Petroleum
		  Geologists)},
  year		= {2000},
  volume	= {84},
  number	= {10},
  month		= {Oct},
  pages		= {1633--1646},
  organization	= {Massachusetts Inst of Technology},
  publisher	= {AAPG},
  address	= {Tulsa, OK},
  abstract	= {We approach the problem of identifying facies from well
		  logs through the use of neural networks that perform vector
		  quantization of input data by competitive learning. The
		  method can be used in either an unsupervised or supervised
		  manner. Unsupervised analysis is used to segregate a well
		  into distinct facies classes based on the log behavior.
		  Supervised analysis is used to identify the facies types
		  present in a certain well by making use of the facies
		  identified from cores in a nearby well. The method is
		  suitable for analyzing lithologies and depositional facies
		  of horizontal wells, which are almost never cored,
		  especially if core data is available for nearby vertical
		  wells. Both modes are implemented and used for the
		  automatic facies analysis of horizontal wells in Saudi
		  Arabia. In addition to the identification of facies, the
		  method is also able to calculate, for each analysis,
		  confidence measures that are indicative of how well the
		  analysis procedure can identify those facies given
		  uncertainties in the data. Moreover, we can apply
		  constraints derived from human experience and geologic
		  principles to guide the inference process.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  saheb_zamani95a,
  author	= {M. {Saheb Zamani} and G. R. Hellestrand},
  title		= {The Floorplanning of Hierarchical Design Using
		  Self-Organizing Neural Networks},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {279--282},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  saheb_zamani95c,
  author	= {{Saheb Zamani}, M. and Hellestrand, G. R. },
  title		= {A new neural network approach to the floorplanning of
		  hierarchical {VLSI} designs},
  booktitle	= {Proceedings of Neural, Parallel and Scientific
		  Computations. Vol. 1. Proceedings of the First
		  International Conference},
  year		= {1995},
  editor	= {Aityan, S. K. and Grujic, L. T. and Hathaway, R. J. and
		  Ladde, G. S. and Medhin, N. and Sambandham, M. },
  pages		= {399--402},
  organization	= {Sch. of Comput. Sci. \& Eng. , New South Wales Univ. ,
		  Sydney, NSW, Australia},
  publisher	= {Dynamic Publishers},
  address	= {Atlanta, GA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  saheb_zamani95d,
  author	= {{Saheb Zamani}, M. and Hellestrand, G. R. },
  title		= {A neural network approach to the placement problem},
  booktitle	= {Proceedings of the ASP-DAC`95/CHDL`95/VLSI`95. Asia and
		  South Pacific Design Automation Conference. IFIP
		  International Conference on Computer Hardware Description
		  Languages and their Applications. IFIP Interntional
		  Conference on Very Large Scale Integration},
  year		= {1995},
  pages		= {413--16},
  publisher	= {Nihon Gakkai Jimu Senta},
  address	= {Tokyo, Japan},
  abstract	= {In this paper, we introduce a new neural network approach
		  to the placement of gate array designs. The network used is
		  a Kohonen self-organising map. An abstract specification of
		  the design is converted to a set of appropriate input
		  vectors fed to the network at random. At the end of the
		  process, the map shows a 2-dimensional plane of the design
		  in which the modules with higher connectivity are placed
		  adjacent to each other, hence minimising total connection
		  length in the design. The approach can consider external
		  connections and is able to place modules in a rectilinear
		  boundary. These features makes the approach capable of
		  being used in hierarchical floorplanning algorithms.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sahep_zamani95b,
  author	= {M. {Sahep Zamani} and G. R. Hellestrand},
  title		= {Placement with Self-Organizing Neural Networks},
  volume	= {V},
  pages		= {2185--2189},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {This paper introduces a neural network approach to node
		  placement in an arbitrarily shaped rectilinear boundary
		  based on self-organizing principle. An abstract
		  specification of the design is converted to a set of
		  appropriate input vectors fed to the network at random. At
		  the end of the process, the map shows the rectilinear shape
		  2-dimensional plane of the design in which the modules with
		  higher connectivity to each other and also to some external
		  ports are placed close to each other, hence minimizing
		  total connection length in the design.},
  dbinsdate	= {oldtimer}
}

@Article{	  sakamoto00a,
  author	= {Sakamoto, S. and Kobuchi, Y.},
  title		= {Convergence property of topographic mapping formation from
		  cell layer to cell layer through correlation learning
		  rule},
  journal	= {Neural Networks},
  year		= {2000},
  volume	= {13},
  number	= {7},
  month		= {Sep},
  pages		= {709--718},
  organization	= {Ryukoku Univ},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {To elucidate the mechanism of topographic organization, we
		  propose a simple topographic mapping formation model from
		  one-dimensional cell layer to one-dimensional cell layer.
		  In our model, each cell takes a binary state value and we
		  consider several learning principles which are extensions
		  of Hebb's rule. We pay special attention to a correlation
		  learning rule where a synaptic weight value is increased if
		  pre- and post-synaptic cells' state values are the same.
		  First, we show that under a certain network size condition,
		  a mapping is stable with respect to the correlation
		  learning if and only if it is topographic. Second, we
		  introduce a special class of weight matrices called band
		  type and show that the set of band type weight matrices is
		  strongly closed and such a weight matrix cannot yield a
		  topographic mapping. Third, we show that any mapping, if it
		  is defined by a non band type weight matrix, converges to a
		  topographic mapping. The proof method is intrinsically of
		  combinatorial nature in a framework of Markov process.},
  dbinsdate	= {2002/1}
}

@Article{	  sakar93a,
  author	= {A. Sakar and R. J. Mammone},
  title		= {Growing and pruning neural tree networks},
  journal	= {IEEE Trans. on Computers},
  year		= {1993},
  volume	= {42},
  number	= {3},
  pages		= {291--299},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sako94a,
  author	= {Hiroshi Sako},
  title		= {Pattern Identification using Line-Codebooks},
  pages		= {3072--3077},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {vector quantization, modification},
  dbinsdate	= {oldtimer}
}

@Article{	  sakuraba90a,
  author	= {Y. Sakuraba and T. Nakamoto and T. Moriizumi},
  title		= {New method of learning vector quantization using fuzzy
		  theory},
  journal	= {Trans. Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1990},
  volume	= {J73D-II},
  number	= {11},
  pages		= {1863--1871},
  month		= {November},
  dbinsdate	= {oldtimer}
}

@Article{	  sakuraba91a,
  author	= {Y. Sakuraba and T. Nakamoto and T. Moriizumi},
  title		= {New method of learning vector quantization using fuzzy
		  theory},
  journal	= {Systems and Computers in Japan},
  year		= {1991},
  volume	= {22},
  number	= {13},
  pages		= {93--103},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sakuraba92a,
  author	= {Yuuichi Sakuraba and Takamichi Nakamoto and Toyosaka
		  Moriizumi},
  title		= {Expression of Odor Sensory Quantity by Neural Network},
  booktitle	= {Proc. 7'th Symp. on Biological and Physiological
		  Engineering},
  year		= {1992},
  pages		= {115--120},
  publisher	= {Toyohashi University of Technology},
  address	= {Toyohashi, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sallis99a,
  author	= {Sallis, P. and Hill, L. and Janee, G. and Lovette, K. and
		  Masi, C.},
  title		= {A methodology for profiling users of large interactive
		  systems incorporating neural network data mining
		  techniques},
  booktitle	= {Managing Information Technology Resources in Organizations
		  in the Next Millennium. 1999 Information Resources
		  Management Association International Conference},
  publisher	= {Idea Group Publishing},
  address	= {Hershey, PA, USA},
  year		= {1999},
  volume	= {},
  pages		= {994--8},
  abstract	= {The objective of the research is to analyze system-user
		  interaction as part of an evaluation framework based on
		  individual profiles of use. In particular, it describes a
		  methodology being developed in connection with the
		  Alexandria digital library (ADL) based at the University of
		  California Santa Barbara. It is suggested that the
		  methodology outlined in this paper could be used generally
		  for any large system evaluation project. There is no point
		  in speculating about actual use patterns emerging from the
		  data at this time because most of the current output is
		  related to system testing and does not reflect `real' use.
		  Once the system is operational, use patterns can be derived
		  and the results reported. The data mining capabilities of
		  software products such as Viscovery enable us to determine
		  the relative proximity of user and use attributes as they
		  inter-relate and form clusters to show how they combine to
		  produce a typical user at any snapshot of time based on the
		  incremental data at hand. The graphical output from
		  Viscovery is particularly advantageous for interactive data
		  mining visualization. By comparing the output from standard
		  statistical clustering with that of Viscovery, it can be
		  seen that SOM provide a rich alternative for conducting
		  `let's see' and `what if' analyses of user and use
		  patterns. The connectionist approach using artificial
		  neural networks also enhances the ease of profiling system
		  user behavior and can be capitalized upon by publication of
		  the summative results by way of a Web page, which in turn
		  facilitates an enriched user-feedback environment.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  salmela96a,
  author	= {P. Salmela and S. Kuusisto and J. Saarinen and K. Laurila
		  and P. Haavisto},
  title		= {Isolated spoken number recognition with hybrid of
		  \mbox{self-organizing} map and multilayer perceptron},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {4},
  address	= {New York, NY, USA},
  pages		= {1912--17},
  abstract	= {In this paper a neural network, which is capable of
		  recognizing isolated spoken numbers speaker independently
		  will be described. The recognition system is a hybrid of
		  self-organizing map (SOM) and multilayer perceptron (MLP).
		  The SOM maps the feature vectors of a word in a constant
		  dimension binary matrix, which is classified by an MLP. The
		  decision borders of the SOM were fine-tuned with LVQ1
		  algorithm, with which the hybrid achieved over 99%
		  recognition out of 1232 test set samples. The training
		  convergence of the MLP was tested with two different
		  initialization methods.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  salmela96b,
  author	= {P. Salmela and S. Kuusisto and J. Saarinen and K. Laurila
		  and P. Haavisto},
  title		= {The hybrid of \mbox{self-organizing} map and multilayer
		  perceptron in isolated spoken number recognition},
  booktitle	= {WCNN'96. World Congress on Neural Networks. International
		  Neural Network Society 1996 Annual Meeting},
  publisher	= {Lawrence Erlbaum Associates},
  year		= {1996},
  address	= {Mahwah, NJ, USA},
  pages		= {63--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  salvini00a,
  author	= {Salvini, R. L. and {de Carvalho}, L. A.},
  title		= {Elastic neural net algorithm for cluster analysis},
  booktitle	= {Proceedings. Vol.1. Sixth Brazilian Symposium on Neural
		  Networks. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {191--5},
  abstract	= {Proposes a method for data clustering in a n-dimensional
		  space using the elastic net algorithm which is a variant of
		  the Kohonen topographic map learning algorithm. The elastic
		  net algorithm is a mechanical metaphor in which an elastic
		  ring is attracted by points in a bi-dimensional space while
		  their internal elastic forces try to shun the elastic
		  expansion. The different weights associated with these two
		  kinds of forces lead the elastic to a gradual expansion in
		  the direction of the bi-dimensional points. In this method,
		  the elastic net algorithm is employed with the help of a
		  heuristic framework that improves its performance for
		  application in the n-dimensional space of cluster analysis.
		  Tests were made with two types of data sets: (1) simulated
		  data sets with up to 1000 points randomly generated in
		  groups linearly separable with up to dimension 10 and (2)
		  the Fisher Iris Plant database, a well-known database
		  referred to in the pattern recognition literature. The
		  advantages of the method presented are its simplicity, its
		  fast and stable convergence, beyond efficiency in cluster
		  analysis.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  samad91a,
  author	= {T. Samad and S. A. Harp},
  title		= {Feature map learning with partial training data},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {II},
  pages		= {949},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  samad92a,
  author	= {Samad, T. and Harp, S. A.},
  title		= {Self-organization with partial data.},
  journal	= {Network: Computation in Neural Systems},
  year		= {1992},
  number	= {2},
  volume	= {3},
  pages		= {205--212},
  month		= {May},
  abstract	= {We show how the Kohonen self-organizing feature map model
		  can be extended so that partial training data can be
		  utilized. Given input stimuli in which values for some
		  elements or features are absent, the match computation and
		  the weight updates are performed in the input subspace
		  defined by the available input values. Three examples,
		  including an application to student modelling for
		  intelligent systems in which data is inherently incomplete,
		  demonstrate the effectiveness of the extension.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  samarabandu90a,
  author	= {Jagath K. Samarabandu and Oleg G. Jakubowicz},
  title		= {Principles of Sequential Feature Maps in Multi-level
		  Problems},
  booktitle	= {Proc. IJCNN-90, Int. Joint Conference on Neural Networks,
		  Washington, DC},
  year		= {1990},
  volume	= {II},
  pages		= {683--686},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  samuel01a,
  author	= {Samuel, Paul D. and Pines, Darryll J.},
  title		= {Classifying helicopter gearbox faults using a normalized
		  energy metric},
  journal	= {Smart Materials and Structures},
  year		= {2001},
  volume	= {10},
  number	= {1},
  month		= {Feb},
  pages		= {145--153},
  organization	= {Univ of Maryland},
  publisher	= {IOP},
  address	= {Bristol},
  abstract	= {A normalized energy metric is used to classify seeded
		  faults of the OH-58A main transmission. This gearbox
		  comprises a two-stage transmission with an overall
		  reduction of 17.44:1. Loaded gearbox test runs are used to
		  evaluate the sensitivity of a non-stationary fault metric
		  for early fault detection and classification. The
		  non-stationary fault metric consists of a simple normalized
		  energy index developed to account for a redistribution of
		  sideband energy of the dominant mesh frequency and its
		  harmonics in the presence of actual gearbox faults. This
		  index is used to qualitatively assess the presence, type
		  and location of gearbox faults. In this work, elements of
		  the normalized energy metric are assembled into a feature
		  vector to serve as input into a self-organizing Kohonen
		  neural network classifier. This classifier maps vibration
		  features onto a two-dimensional grid. A feedforward back
		  propagation neural network is then used to classify
		  different faults according to how they cluster on the
		  two-dimensional self-organizing map. Gearbox faults of
		  OH-58A main transmission considered in this study include
		  sun gear spalling and spiral bevel gear scoring. Results
		  from the classification suggest that the normalized energy
		  metric is reasonably robust against false alarms for
		  certain geartrain faults.},
  dbinsdate	= {2002/1}
}

@Article{	  sanchez00a,
  author	= {Sanchez, J. S. and Pla, F. and Ferri, F. J.},
  title		= {Adaptive learning from nearest centroid neighbours for the
		  nearest neighbour rule},
  journal	= {Pattern Recognition and Applications (Frontiers in
		  Artificial Intelligence and Applications Vol.56). IOS
		  Press, Amsterdam, Netherlands; 2000; viii+287 pp.p.29--36},
  year		= {2000},
  volume	= {},
  pages		= {29--36},
  abstract	= {Introduces an adaptive algorithm for competitive training
		  of a nearest neighbour (NN) classifier. Unlike the
		  well-known LVQ method, the scheme proposed selects k
		  codebook vectors that are surrounding an input sample. The
		  focus of the work is on improving the performance of the
		  standard LVQ algorithms when using a small codebook. An
		  empirical analysis of the classification accuracies
		  achieved by different learning techniques is also provided,
		  demonstrating the efficiency of the new algorithm.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  sanchez99a,
  author	= {Sanchez, J. S. and Pla, F. and Ferri, F. J.},
  title		= {Learning vector quantization with alternative distance
		  criteria},
  booktitle	= {Proceedings 10th International Conference on Image
		  Analysis and Processing},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {84--9},
  abstract	= {An adaptive algorithm for training of a nearest neighbour
		  (NN) classifier is developed in this paper. This learning
		  rule has some similarity to the well-known LVQ method, but
		  uses the nearest centroid neighbourhood concept to estimate
		  optimal locations of the codebook vectors. The aim of this
		  approach is to improve the performance of the standard LVQ
		  algorithms when using a very small codebook. The behaviour
		  of the learning technique proposed here is experimentally
		  compared to those of the plain k-NN decision rule and the
		  LVQ algorithms.},
  dbinsdate	= {oldtimer}
}

@Article{	  sandidge97a,
  author	= {Sandidge, Thomas E. and Dagli, Cihan H.},
  title		= {Derivation of fuzzy membership functions using
		  \mbox{one-dimensional} \mbox{self-organizing} maps},
  journal	= {Proceedings of IEEE International Conference on Systems,
		  Man, and Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1997},
  number	= {},
  volume	= {2},
  pages		= {995--998},
  abstract	= {This paper discusses a system of self-organizing maps that
		  approximate the fuzzy membership function for an arbitrary
		  number of fuzzy classes. This is done through the ordering
		  and clustering properties of one-dimensional
		  self-organizing maps and iterative approximation of
		  conditional probabilities of nodes in one map being the
		  winner given that a node in the other map is the winner.
		  Application of this system reduces fuzzy membership design
		  time to that required to train the system of
		  self-organizing maps.},
  dbinsdate	= {oldtimer}
}

@Article{	  sandidge98a,
  author	= {T. E. Sandidge and C. H. Dagli},
  title		= {Construction of fuzzy membership functions using
		  interactive \mbox{self-organizing} maps},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1998},
  volume	= {3390},
  pages		= {282--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sang01a,
  author	= {Sang Keon Oh and Min Soeng Kim and Ju Jang Lee},
  title		= {Adapting the migration topology of macro-micro
		  evolutionary algorithm by clustering the individuals using
		  self-organizing map},
  booktitle	= {ISIE 2001. 2001 IEEE International Symposium on Industrial
		  Electronics Proceedings. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {1},
  pages		= {308--11},
  abstract	= {In this paper, we propose a self-adaptive migration rule
		  for macro-micro evolutionary algorithm which was proposed
		  to find several local optima for multi-model optimization
		  problems. The algorithm consists of two evolutionary
		  algorithms which control global species and local
		  individuals respectively. To keep the diversity explicitly,
		  we incorporate a clustering method to divide individuals to
		  several species. Clustering method based on self-organizing
		  map (SOM) can divide individuals to several species and
		  determine the neighboring topology information which
		  defines the migration topology between species. To examine
		  the computational effectiveness of proposed algorithm, we
		  apply the algorithm to standard benchmark problems for
		  numerical optimization.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  sanguineti93a,
  author	= {Sanguineti, V. and Morasso, P. and T. Tsuji},
  title		= {Run-time robot planning},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks (IJCNN'93-Nagoya 25--29 october '93)},
  volume	= {2},
  year		= {1993},
  pages		= {2815--2818},
  abstract	= {We (1993) have developed a neural network architecture
		  which learns a forward model of a redundant manipulator
		  (via self-supervised training) as a map of normalized
		  radial basis neurons and inverts the model by means of
		  run-time gradient descent of a task-related potential
		  field. In this paper, we propose a distributed model for
		  the computation of the field, which is consistent with the
		  model-inversion map; and we discuss the problem of
		  self-synchronization between the gradient-descent process
		  and a process for the generation of virtual trajectories of
		  the end-effector.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sanguineti93b,
  author	= {Sanguineti, V. and Tsuji, T. and P. Morasso},
  title		= {A dynamical model for the generation of curved
		  trajectories.},
  booktitle	= {Proceedings of ICANN'93 (Amsterdam, September 13--16)},
  editor	= {S. Gielen and B. Kappen},
  year		= {1993},
  publisher	= {Springer Verlag},
  address	= {London},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sanguineti94a,
  author	= {Sanguineti, V. and Morasso, P.},
  title		= {Self-organization of an equilibrium-point motor
		  controller},
  booktitle	= {International Conference on Artificial Neural Networks},
  publisher	= {Springer-Verlag},
  year		= {1994},
  editor	= {M. Marinaro and Morasso, P.},
  volume	= {1},
  address	= {London},
  pages		= {86--89},
  abstract	= {One of the main problems in motor control is how the
		  central nervous system (CNS) can deal with the multiplicity
		  of muscles and the complex geometry of the human body. The
		  equilibrium point hypothesis states that the generation of
		  movements is made by the CNS in terms of the equilibrium or
		  virtual trajectory, which is only influenced by the static
		  components of the involved mechanical structures, whereas
		  viscous and inertial effects act as perturbations, so that
		  real trajectories may differ from virtual ones. The lambda
		  -model reduces the problem of generating the set of muscle
		  activations to a coordinate transformation from
		  configuration space to muscle activation space. The most
		  interesting feature of this approach is that it allows one
		  to deal simultaneously with the muscle-joint configurations
		  and the level of coactivation. In fact, a number of
		  neurophysiological studies hypothesize distinct channels
		  and cortical areas, responsible for separately specifying
		  these quantities; moreover, the FLETE (Factorization of
		  LEngth and TEnsion) model is also based on integrating two
		  different kinds of central commands, one specifying the
		  equilibrium posture and the other related to force
		  magnitudes. After a review of the computational aspects of
		  an extended formulation of the lambda -model, this paper
		  proposes a new learning schema for training a feedforward
		  controller in order to compute the muscle activations
		  corresponding to a desired equilibrium configuration of the
		  body.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sanguineti95a,
  author	= {Sanguineti, V. and Spada, G. and P. Morasso},
  title		= {Function approximation by interconnected distributed
		  representations},
  booktitle	= {Proceedings of the International Conference on Artificial
		  Neural Networks (ICANN'95---Paris October 9--13)},
  editor	= {F. Fogelman},
  volume	= {2},
  year		= {1995},
  pages		= {87--91},
  abstract	= {A function approximation scheme is described which is
		  based on (i) the independent quantization of the domain and
		  co-domain, trained by soft-competitive learning, and (ii)
		  the inter-connection of the two codebooks, established via
		  Hebbian learning, thus extending the concept underlying the
		  topology representing networks-model. With respect to the
		  classical RBF schemes, it is more compact and easy to
		  train.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sankaran95a,
  author	= {Vijay Sankaran and Mark J. Embrechts and Lars-Erik Harsson
		  and Russell P. Kraft},
  title		= {Back-propagation Applications in Electronics Manufacturing
		  ---Solder Joint Classification},
  volume	= {II},
  pages		= {642--645},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sano94a,
  author	= {Hideki Sano and Yuji Iwahori and Naohiro Ishii},
  title		= {Attention to Feature Region in Neural Network},
  pages		= {1537--1541},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, hybrid},
  dbinsdate	= {oldtimer}
}

@Article{	  santini96a,
  author	= {S. Santini},
  title		= {The \mbox{self-organizing} field},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1996},
  volume	= {7},
  number	= {6},
  pages		= {1415--23},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  santos_andre99a,
  author	= {{Santos Andre}, T. C. S. and {da Silva}, A. C. R.},
  title		= {A neural network made of a {K}ohonen's SOM coupled to a
		  {MLP} trained via backpropagation for the diagnosis of
		  malignant breast cancer from digital mammograms},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3647--50},
  abstract	= {A system was built entirely based on artificial neural
		  networks to be used as an aiding tool in the analysis of
		  mammograms for the diagnosis of breast cancer. The system
		  receives a mammogram as input and gives as output one of
		  three possible answers: suspicious of malignant breast
		  cancer, suspicious of benign breast cancer, and without
		  suspicion of breast cancer. The system uses two different
		  kinds of neural networks: Kohonen's self-organizing map and
		  multilayer perceptron trained with the backpropagation
		  algorithm. The system's performance was evaluated by its
		  ability to generalize after training. The true-positive
		  fraction or sensitivity (fraction of actually malignant
		  cases that was correctly classified) was 0.50, and the
		  false-positive fraction (fraction of actually benign or
		  normal cases that was incorrectly classified) was 0.12. On
		  the other hand, the fraction of actually malignant or
		  benign cases that was correctly classified was 0.75 and the
		  fraction of normal cases that was incorrectly classified
		  either as benign or malignant was 0.38.},
  dbinsdate	= {oldtimer}
}

@Article{	  saraceno98a,
  author	= {Saraceno, C. and Leonardi, R.},
  title		= {Identification of visual correlations between
		  non-consecutive shots in digital image sequences},
  journal	= {VLBV98. Univ. Illinois, Urbana, IL, USA},
  year		= {1998},
  volume	= {},
  pages		= {65--8},
  abstract	= {A number of automated methods for indexing audio-visual
		  sequences have been developed. Typically, processing starts
		  with a low-level segmentation of a sequence of images so as
		  to identify a series of shots (i.e., continuous camera
		  records). To reach a higher level of description, patterns
		  must be identified in the flow of consecutive shots. In
		  this work, three different techniques for measuring visual
		  correlations among non-consecutive shots are proposed and
		  compared. Two methods measure the visual correlation among
		  shots by analyzing the respective K-frames. In particular,
		  they compare K-frames based either on a low-resolution DC
		  JPEG representation or on color and spatial organization of
		  the spatial information. The third technique measures the
		  similarity between shots by comparing their associated
		  codebooks, which are obtained using the learning vector
		  quantization approach. Simulations show that the learning
		  vector quantization approach leads to the best
		  performance.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sardy01a,
  author	= {Sardy, S. and Wihartini and Jatno, S.},
  title		= {The area change detection on synthetic aperture radar
		  images by using wavelet transform and neural networks},
  booktitle	= {Proceedings of the IASTED International Conference.
		  Artificial Intelligence and Applications. ACTA Press,
		  Anaheim, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {148--52},
  abstract	= {Discusses area change detection in Central Kalimantan, by
		  using multi-temporal synthetic aperture radar (SAR) images.
		  The use of SAR images to observe the Earth's surface-where
		  cloud and haze cover are the main problems-has a
		  potentiality to monitor the area. The main work of this
		  research is to minimize the speckle noise with a trous
		  algorithm wavelet transformation. Gaussian modelling with
		  multiresolution support is used for the statistical
		  significance test of wavelet coefficients. In order to
		  analyze the area change detection, it is combined
		  clustering techniques using a self-organizing map and image
		  substruction. It is shown that the denoising with a trous
		  algorithm multiresolution wavelet transform has shown a
		  good performance and suitable for such application, because
		  the area change is observable at the different scales
		  within a constant image size. The use of the
		  multiresolution wavelet transform simplifies the
		  observation of image structure at different resolutions,
		  therefore the area change appears more clearly.},
  dbinsdate	= {2002/1}
}

@Article{	  sardy96a,
  author	= {S. Sardy and L. Ibrahim},
  title		= {Experimental medical and industrial applications of neural
		  networks to image inspection using an inexpensive personal
		  computer},
  journal	= {Optical Engineering},
  year		= {1996},
  volume	= {35},
  number	= {8},
  pages		= {2182--7},
  dbinsdate	= {oldtimer}
}

@Article{	  sarkaria92a,
  author	= {Sarkaria, S. S. and Harget, A. J. and Claridge, E.},
  title		= {Shape recognition using the {K}ohonen
		  \mbox{self-organising} feature map.},
  journal	= {Pattern Recognition Letters},
  year		= {1992},
  number	= {3},
  volume	= {13},
  pages		= {189--194},
  month		= {March},
  abstract	= {This paper presents an account of a study which shows that
		  the Kohonen self-organising algorithm can be applied in the
		  area of shape recognition. The results show that
		  unsupervised training produces a Kohonen feature map
		  capable of recognition and generalisation.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sarukkai95a,
  author	= {R. R. Sarukkai},
  title		= {Solving XOR with a single layered perceptron by supervised
		  self-organization of multiple output labels per class},
  booktitle	= {1995 IEEE International Conference on Neural Networks
		  Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {2807--10},
  abstract	= {Popular neural network learning algorithms such as
		  Kohonen's LVQ handle non-linearity by assigning multiple
		  codebook vectors per class. However, the architectural
		  constraint requires the output units to activate in a
		  winner-take-all fashion. In this paper, clustering of
		  output projections developed with traditional discriminant
		  analysis networks is achieved by allowing multiple output
		  labels for every class: the key to such a formulation lies
		  in the supervised self-organization algorithm which enables
		  conventional feed-forward networks to self-organize their
		  own output labels given class information. The idea of
		  supervised self-organization of multiple output labels has
		  been demonstrated by implementing the XOR problem with a
		  single layer Perceptron network.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sarzeaud00a,
  author	= {Sarzeaud, Olivier and Stephan, Yann},
  title		= {Data interpolation using Kohonen networks},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {197--202},
  organization	= {ECTIA},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {An original method for optimal interpolation which relies
		  on some basic modification of the standard Kohonen
		  algorithm is presented. The method has been applied on
		  several synthetic and actual data sets. In every case, the
		  results compare perfectly well with those obtained by
		  kriging. Other uses of the Kohonen algorithm for data
		  fusion and assimilation are discussed.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  sarzeaud00b,
  author	= {Sarzeaud, O. and Stephan, Y.},
  title		= {Fast interpolation using Kohonen self-organizing neural
		  networks},
  booktitle	= {Theoretical Computer Science. Exploring New Frontiers of
		  Theoretical Informatics. International Conference IFIP TCS
		  2000. Proceedings (Lecture Notes in Computer Science
		  Vol.1872). Springer-Verlag, Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {126--39},
  abstract	= {This paper proposes a new interpolation method based on
		  Kohonen self-organizing networks. This method performs very
		  well, combining an accuracy comparable with usual optimal
		  methods (kriging) with a shorter computing time, and is
		  especially efficient when a great amount of data is
		  available. Under some hypothesis similar to those used for
		  kriging, unbiasness and optimality of neural interpolation
		  can be demonstrated. A real world problem is finally
		  considered: building a map of surface-temperature
		  climatology in the Mediterranean Sea. This example
		  emphasizes the abilities of the method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  sarzeaud90a,
  author	= {O. Sarzeaud and Y. Stephan and C. Touzet},
  title		= {Application of self organising maps to the generation of
		  finite element meshes},
  booktitle	= {Neuro-N\^{i}mes '90. Third Int. Workshop. Neural Networks
		  and Their Applications},
  year		= {1990},
  pages		= {81--96},
  organization	= {ARC; JSAI; SEE},
  publisher	= {EC2},
  address	= {Nanterre, France},
  note		= {(in French)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sarzeaud91a,
  author	= {Olivier Sarzeaud and Yann Stephan and Claude Touzet},
  title		= {Finite Element Meshing using {K}ohonen's Self-Organizing
		  Maps},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1313--1317},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sarzeaud94a,
  author	= {Sarzeaud, O. and Stephan, Y. and Le Corte, F. and
		  Kerleguer, L. },
  title		= {Neural meshing of a geographical space in regard to
		  oceanographic data location},
  booktitle	= {OCEANS 94. Oceans Engineering for Today's Technology and
		  Tomorrow's Preservation. Proceedings},
  year		= {1994},
  volume	= {1},
  pages		= {I/335--9},
  organization	= {CETIIS, Aux-en-Provence, France},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sasaki00a,
  author	= {Naohiro Sasaki and Matsumi Ishikawa},
  title		= {Gesture Recognition for Dynamic Scene Images},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {221--6},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@Article{	  sase92a,
  author	= {M. Sase and T. Hirano and T. Beppu and Y. Kosugi},
  title		= {Dimension reduction of working space by neural networks},
  journal	= {Robot},
  year		= {1992},
  volume	= {84},
  pages		= {106--110},
  month		= {January},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sato00a,
  author	= {Sato, A.},
  title		= {A learning method for definite canonicalization based on
		  minimum classification error},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {2},
  pages		= {199--202},
  abstract	= {This paper presents a novel learning method for definite
		  canonicalization (DC) based on minimum classification error
		  (MCE). It is shown that DC is identical to normalized
		  cross-correlation, and that the complementary similarity
		  measure is derived from DC for binary patterns. The
		  proposed learning method is derived from the framework of
		  generalized learning vector quantization (GLVQ), which is
		  one of the discriminative learning methods based on MCE.
		  Experimental results obtained for machine-printed Kanji
		  character recognition reveal that the proposed method
		  achieves high performance recognition of low-quality
		  patterns.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  sato01a,
  author	= {Sato, A.},
  title		= {Discriminative dimensionality reduction based on
		  generalized {LVQ}},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {65--72},
  abstract	= {In this paper, a method for the dimensionality reduction,
		  based on generalized learning vector quantization (GLVQ),
		  is applied to handwritten digit recognition. GLVQ is a
		  general framework for classifier design based on the
		  minimum classification error criterion, and it is easy to
		  apply it to dimensionality reduction in feature extraction.
		  Experimental results reveal that the training of both a
		  feature transformation matrix and reference vectors by GLVQ
		  is superior to that by principal component analysis in
		  terms of dimensionality reduction.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  sato93a,
  author	= {Atsushi Sato and Keiji Yamada and Jun Tsukumo},
  title		= {A Multi-Template Learning Method Based on {LVQ}},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {II},
  pages		= {632--637},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sato94a,
  author	= {Atsushi Sato and Jun Tsukumo},
  title		= {A Criterion for Training Reference Vectors and Improved
		  Vector Quantization},
  pages		= {161--166},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {vector quantization, modification, comparison},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sato96a,
  author	= {A. Sato and K. Yamada},
  title		= {Generalized learning vector quantization},
  booktitle	= {Advances in Neural Information Processing Systems 8.
		  Proceedings of the 1995 Conference},
  publisher	= {MIT Press},
  year		= {1996},
  editor	= {D. S. Touretzky and M. C. Mozer and M. E. Hasselmo},
  address	= {Cambridge, MA, USA},
  pages		= {423--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sato98a,
  author	= {A. Sato and K. Yamada},
  title		= {A formulation of learning vector quantization using a new
		  misclassification measure},
  booktitle	= {Proceedings. Fourteenth International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society},
  year		= {1998},
  volume	= {1},
  editor	= {A. K. Jain and S. Venkatesh and B. C. Lovell},
  address	= {Los Alamitos, CA, USA},
  pages		= {322--5},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sato98b,
  author	= {Atsushi Sato and Kenji Yamada},
  title		= {An Analysis of Convergence in Generalized {LVQ} },
  booktitle	= {Proceedings of ICANN98, the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer},
  year		= 1998,
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= 1,
  address	= {London},
  pages		= {170--176},
  abstract	= {We have proposed a new formulation of learning vector
		  quantization (LVQ) called "generalized LVQ" based on
		  minimum classification error (MCE). In this paper, we
		  attempt to clarify the convergence property of reference
		  vectors in our formulation. We discuss the equilibrium in a
		  dynamical system for two-class classification, and prove
		  that equilibrium states exist in our formulation, while
		  they do not exist in LVQ2.1 or Juang and Katagiri's
		  formulation (1992) based on MCE.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sato99a,
  author	= {Sato, Atsushi},
  title		= {Analysis of initial state dependence in Generalized {LVQ}
		  },
  booktitle	= {IEE Conference Publication},
  year		= {1999},
  volume	= {2},
  pages		= {928--933},
  abstract	= {The author has proposed a new formulation of LVQ called
		  Generalized LVQ (GLVQ) based on the Minimum Classification
		  Error criterion. In this paper, the initial state
		  dependence in GLVQ is discussed, and it is clarified that
		  the learning rule should be modified to make it insensitive
		  to the initial values of reference vectors. The robustness
		  of the modified GLVQ for the initial state is demonstrated
		  through simulation experiments and compared with
		  Generalized Probabilistic Descent.},
  dbinsdate	= {oldtimer}
}

@Article{	  sato99b,
  author	= {Sato, A. and Yamada, K.},
  title		= {A formulation of learning vector quantization using a new
		  misclassification measure},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers D II},
  year		= {1999},
  volume	= {},
  pages		= {650--9},
  abstract	= {We have proposed a formulation of learning vector
		  quantization (LVQ) based on minimum classification error
		  (MCE) using a new misclassification measure. We attempt to
		  clarify the convergence property of reference vectors
		  mathematically from a dynamical point of view. We discuss
		  the equilibrium states in a dynamical system for two-class
		  classification, and prove that equilibrium points exist in
		  the proposed formulation, while they do not exist in LVQ
		  2.1 or the conventional formulation using the generalized
		  probabilistic descent method based on MCE. Simulation
		  experiments for artificial data reveal that reference
		  vectors are well converged in the proposed formulation.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  satonaka97a,
  author	= {T. Satonaka and T. Baba and T. Chikamura and T. Otsuki and
		  T. H. Meng},
  title		= {A DCT-based adaptive metric learning model using
		  asymptotic local information measure},
  booktitle	= {Neural Networks for Signal Processing VII. Proceedings of
		  the 1997 IEEE Signal Processing Society Workshop},
  publisher	= {IEEE},
  year		= {1997},
  editor	= {J. Principe and L. Gile and N. Morgan and E. Wilson},
  address	= {New York, NY, USA},
  pages		= {521--30},
  dbinsdate	= {oldtimer}
}

@Article{	  satoshi00a,
  author	= {Satoshi, K. and Nakamoto, T. and Moriizumi, T.},
  title		= {Study of {LSI} circuit for learning and odor recognition
		  using 1 bit data stream signal processing method},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-E}
		  ,
  year		= {2000},
  volume	= {120},
  pages		= {204--10},
  abstract	= {A digital LSI circuit for learning and odor recognition
		  using 1 bit-stream data processing method is proposed. The
		  output pattern of the QCM sensor array is measured and is
		  classified by a circuit based upon the LVQ algorithm. Only
		  a small amount of circuitry and wiring are required when
		  the 1-bit data processing method is adopted. The whole
		  circuit was realized utilizing FPGAs, and odor recognition
		  was successfully performed.},
  dbinsdate	= {2002/1}
}

@InCollection{	  sauvage97a,
  author	= {V. Sauvage},
  title		= {The {T-SOM} ({T}ree-{SOM})},
  booktitle	= {Advanced Topics in Artificial Intelligence. 10th
		  Australian Joint Conference on Artificial Intelligence,
		  AI'97. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {A. Sattar},
  address	= {Berlin, Germany},
  pages		= {389--97},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  saveliev99a,
  author	= {Saveliev, A. A. and Dobrinin, D. V.},
  title		= {Hierarchical multispectral image classification based on
		  self organized maps},
  booktitle	= {IEEE 1999 International Geoscience and Remote Sensing
		  Symposium. IGARSS'99.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {2510--12},
  abstract	= {One of the problems in the thematic interpretation of the
		  remote sensor (RS) data is the processing of the sets of
		  multispectral, multidate images. The problem is that when
		  we try to compare two and more RS image, we have to rectify
		  their geometry and correct atmospheric effects. While the
		  geometric correction could be done with any precision, the
		  atmospheric correction for a set of images is a very
		  complex task, and it could not be solved in a common case.
		  The authors propose a new approach, based on the artificial
		  neural networks, for a stable RS images classification and
		  interpretation without the atmospheric correction. That
		  approach, using the Kohonen's self-organized maps (SOM),
		  has been realized as a part of the ScanEx image processing
		  technology in a computer program NeRIS (Neural Raster
		  Interpretation System). The Sammon's mapping of that SOM
		  classification from the p-dimensional input image space to
		  the 2-dimensional points on a plane (whereby the distances
		  between the mapped vectors tend to approximate to distances
		  of the input vectors), was used for hierarchical
		  classification and stable thematic interpretation of the RS
		  images.},
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  saxon91a,
  author	= {James Bennett Saxon},
  title		= {Simulating Sensorimotor Systems with Cortical Topology},
  school	= {Texas A\&M University, Computer Science Department},
  year		= {1991},
  address	= {College Station, Texas},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sbarbaro95a,
  author	= {Sbarbaro, D. and Bassi, D. },
  title		= {A nonlinear controller based on \mbox{self-organizing}
		  maps},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {2},
  pages		= {1774--7},
  organization	= {Dept. de Ingegneria Electr. , Univ. de Concepcion, Chile},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  scabai92a,
  author	= {I. Scabai and F. Czak{\'{o}} and Z. Fodor},
  title		= {Combined Neural Network---{QCD} Classifier for Quark and
		  Gluon Jet Separation},
  journal	= {Nuclear Physics},
  year		= {1992},
  volume	= {B374},
  number	= {},
  pages		= {288--308},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scaglione94a,
  author	= {Lois Jean Scaglione},
  title		= {Neural Network Application to Particle Impact Noise
		  Detection},
  pages		= {3415--3419},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {pattern recognition, hybrid},
  dbinsdate	= {oldtimer}
}

@Article{	  scheffer01a,
  author	= {Scheffer, C. and Heyns, P. S.},
  title		= {Wear monitoring in turning operations using vibration and
		  strain measurements},
  journal	= {Mechanical Systems and Signal Processing},
  year		= {2001},
  volume	= {15},
  number	= {6},
  month		= {November },
  pages		= {1185--1202},
  organization	= {Dept. of Mech. and Aeronautical Eng., University of
		  Pretoria},
  publisher	= {},
  address	= {},
  abstract	= {For the efficient and reliable operation of automated
		  machining processes, the implementation of suitable tool
		  condition monitoring (TCM) strategy is required. Various
		  monitoring systems, utilising sophisticated signal
		  processing techniques, have been widely researched for a
		  number of different processes. Most monitoring systems
		  developed up to date employ force, acoustic emission and
		  vibration, or a combination of these and other techniques
		  with a sensor integration strategy. With this work, the
		  implementation of a monitoring system utilising
		  simultaneous vibration and strain measurements on the tool
		  tip, is investigated for the wear of synthetic diamond
		  tools which are specifically used for the manufacturing of
		  aluminium pistons. Contrary to many of the earlier
		  investigations, this work was conducted in a manufacturing
		  environment, with the associated constraints such as the
		  impracticality of direct measurement of the wear. Data from
		  the manufacturing process was recorded with two
		  piezoelectric strain sensors and an accelerometer, each
		  coupled to a DSPT Siglab analyser. A large number of
		  features indicative of tool wear were automatically
		  extracted from different parts of the original signals.
		  These included features from the time and frequency
		  domains, time-series model coefficients (as features) and
		  features extracted from wavelet packet analysis. A
		  correlation coefficient approach was used to automatically
		  select the best features indicative of the progressive wear
		  of the diamond tools. The self-organising map (SOM) was
		  employed to identify the tool state. The SOM is a type of
		  neural network based on unsupervised learning. A near 100%
		  correct classification of the tool wear data was obtained
		  by training the SOM with two independent data sets, and
		  testing it with a third independent data set.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  scherf94a,
  author	= {O. Scherf and K. Pawelzik and F. Wolf and T. Geisel},
  title		= {Unification of Complementary Feature Map Models},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {338--341},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {analysis, comparison, extension},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scherf95a,
  author	= {O. Scherf and K. Pawelzik and T. Geisel},
  title		= {From Elastic Net to {SOFM}: the Energy Function of the
		  Convolution Model},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {39--43},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scheunders00a,
  author	= {Scheunders, P.},
  title		= {Multiscale edge representation applied to image fusion},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.4119, pt.1--2},
  year		= {2000},
  volume	= {4119},
  pages		= {894--901},
  abstract	= {In this paper the fusion of multimodal images into one
		  grey-level image is aimed at. A multiresolution technique,
		  based on the wavelet multiscale edge representation is
		  applied. The fusion consists of retaining only the modulus
		  maxima of the wavelet coefficients from the different bands
		  and combining them. After reconstruction, a synthetic image
		  is obtained that contains the edge information from all
		  bands simultaneously. Noise reduction is applied by
		  removing the noise-related modulus maxima. In several
		  experiments on test images and multispectral satellite
		  images, we demonstrate that the proposed technique
		  outperforms mapping techniques, such as principal component
		  analysis (PCA) and self-organizing maps (SOM) and other
		  wavelet-based fusion techniques.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  scheunders00b,
  author	= {Scheunders, P.},
  title		= {Multispectral image fusion using local mapping
		  techniques},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {2},
  pages		= {311--14},
  abstract	= {In this paper, fusion of multispectral images for
		  visualization is aimed at, based on the projection of the
		  scatter-diagrams onto a one-dimensional space. Linear as
		  well as nonlinear projection techniques are used. In
		  contrast with existing mapping techniques which work
		  globally, a local mapping technique is constructed. In this
		  technique, the images are subdivided into blocks, where
		  each block of pixels is visualized through a different map.
		  Then, for each pixel, a locally adapted map is created by
		  weighting the maps of the surrounding blocks using
		  Euclidean distance measure. A linear local mapping, based
		  on local PCA and a nonlinear local mapping, based on
		  Kohonen's SOM map are generated and compared to the global
		  procedures. Experiments are conducted on multispectral
		  LANDSAT imagery.},
  dbinsdate	= {2002/1}
}

@Article{	  scheunders01a,
  author	= {Scheunders, P.},
  title		= {Local mapping for multispectral image visualization},
  journal	= {Image and Vision Computing},
  year		= {2001},
  volume	= {19},
  number	= {13},
  month		= {Nov 1 },
  pages		= {971--978},
  organization	= {Department of Physics, Vision Laboratory, University of
		  Antwerp},
  publisher	= {},
  address	= {},
  abstract	= {In this paper, fusion of multispectral images for
		  visualization is aimed at. The techniques that are studied
		  are mappings of the scattergram onto a one-dimensional
		  feature space. Linear Principal Component Analysis (PCA) as
		  well as non-linear Self-Organizing Maps (SOM) are
		  discussed. In this paper, local mappings are studied. Two
		  spatially local techniques are proposed and discussed: a
		  pixel-based and a block-based technique, where a local map
		  of the scattergram is calculated for each pixel and for a
		  block of pixels, respectively. In the experimental section,
		  the proposed techniques are applied to PCA and SOM. For a
		  test image, they are compared to each other and to global
		  PCA and SOM.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  schiel93a,
  author	= {Florian Schiel},
  title		= {A Comparative Study of Speaker Adaptation under Realistic
		  Conditions},
  booktitle	= {Proc. EUROSPEECH-93, 3rd European Conf. on Speech,
		  Communication and Technology},
  year		= {1993},
  volume	= {III},
  pages		= {2271--2274},
  publisher	= {ESCA},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InCollection{	  schikuta97a,
  author	= {Erich Schikuta and Claus Weidmann},
  title		= {Data parallel simulation of \mbox{self-organizing} maps on
		  hypercube architectures},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {142--147},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schill01a,
  author	= {Schill, K. and Baier, V. and Rohrbein, F. and Brauer, W.},
  title		= {A hierarchical network model for the analysis of human
		  spatio-temporal information processing},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Rogowitz, B. E. and Pappas, T. N.},
  volume	= {4299},
  pages		= {615--621},
  organization	= {Inst. fur Medizinische Psychologie,
		  Ludwig-Maximilians-Universitat},
  publisher	= {},
  address	= {},
  abstract	= {The perception of spatio-temporal pattern is a fundamental
		  part of visual cognition. In order to understand more about
		  the principles behind these biological processes, we are
		  analysing and modeling the representation of
		  spatio-temporal structures on different levels of
		  abstraction. For the low-level processing of motion
		  information we have argued for the existence of a
		  spatio-temporal memory in early vision. The basic
		  properties of this structure are reflected in a neural
		  network model which is currently developed. Here we discuss
		  major architectural features of this network which is based
		  on Kohonens SOMs (self organizing maps). In order to enable
		  the representation, processing and prediction of
		  spatio-temporal pattern on different levels of granularity
		  and abstraction the SOM's are organized in a hierarchical
		  manner. The model has the advantage of a "self-teaching"
		  learning algorithm and stores temporal information by local
		  feedback in each computational layer. The constraints for
		  the neural modeling and the data sets for training the
		  neural network are obtained by psychophysical experiments
		  where human subjects' abilities for dealing with
		  spatio-temporal information is investigated.},
  dbinsdate	= {2002/1}
}

@InBook{	  schizas91a,
  author	= {C. N. Schizas and C. S. Pattichis and R. R. Livesay and I.
		  S. Schofield and K. X. Lazarou and L. T. Middleton},
  title		= {Computer-Based Medical Systems},
  chapter	= {9. 2, Unsupervised Learning in Computer Aided Macro
		  Electromyography},
  publisher	= {{IEEE} Computer Soc. Press},
  year		= {1991},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schizas92a,
  author	= {Schizas, C. N. and Pattichis, C. S. and Middleton, L. T.
		  },
  title		= {A new approach to medical diagnosis},
  booktitle	= {Proceedings of the 1992 International Biomedical
		  Engineering Days},
  year		= {1992},
  editor	= {Ulgen, Y. },
  pages		= {207--12},
  organization	= {Dept. of Comput Sci. , Cyprus Univ. , Nicosia, Cyprus},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schlang92a,
  author	= {Martin F. Schlang and Volker Tresp and Klaus Abraham-Fuchs
		  and Wolfgang H{\"{a}}rer and P. Weism{\"{u}}ller},
  title		= {Neural networks for segmentation and clustering of
		  biomagnetic signals},
  editor	= {S. Y. Kung and F. Fallside and J. Aa. Sorenson and C. A.
		  Kamm},
  pages		= {343--349},
  booktitle	= {Neural Networks for Signal Processing II, Proc. of the
		  1992 IEEE-SP Workshop},
  year		= 1992,
  dbinsdate	= {oldtimer}
}

@InCollection{	  schmitt98a,
  author	= {B. Schmitt and G. Duboeck},
  title		= {Differential Patterns in Consumer Purchase Preferences
		  using Self-Organizing Maps: A Case Study of {C}hina},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {141--156},
  dbinsdate	= {oldtimer}
}

@Article{	  schmitter95a,
  author	= {Schmitter, Ernst Dieter},
  title		= {Neural nets---types, configurations and pitfalls},
  journal	= {Steel Research},
  year		= {1995},
  number	= {10},
  volume	= {66},
  pages		= {444--448},
  month		= {Oct},
  abstract	= {Fuzzy logic, neural nets and genetic algorithms form the
		  core of soft computing methods. They are useful when there
		  is no possibility to compute an exact mathematical model
		  (hard computing). Neural nets have the ability to learn by
		  example. This advantage is exploited by a lot of
		  applications and many software packages make it quite easy
		  to use neural nets. A stage is reached, where some critical
		  remarks should be made in order to avoid disappointments.
		  Some frequently used net types (backpropagation, LVQ, SOM)
		  are discussed together with configuration and training
		  problems. Important topics are the avoidance of under- and
		  overfit and the remark that neural nets produce correct
		  outputs only if the inputs lie in the part of the feature
		  space, the net was trained for. Therefore, a detailed
		  analysis of the training data set should be made. In the
		  context of safety relevant applications the missing
		  interpretability of neural net outputs is often criticized.
		  Fuzzy-neuro-systems try to improve this situation.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schmitz94a,
  author	= {Schmitz, G. and Ermert, H. and Senge, T. },
  title		= {Tissue characterization of the prostate using
		  {K}ohonen-maps},
  booktitle	= {Proceedings of the 1994 IEEE Ultrasonics Symposium},
  year		= {1994},
  editor	= {Levy, M. and Schneider, S. C. and McAvoy, B. R. },
  volume	= {3},
  pages		= {1487--90},
  organization	= {Inst. fur Hochfrequenztech. , Ruhr-Univ. , Bochum,
		  Germany},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {Although transrectal ultrasound is one of the most
		  important tools in the diagnosis and early detection of
		  prostatic cancer, the sensitivity and specificity of the
		  standard sonographic methods are still insufficient. We
		  describe a method which provides the clinician with
		  additional information in form of color-coded tissue
		  characterization images based on the learning vector
		  quantization (LVQ) algorithm proposed by Kohonen [6].},
  dbinsdate	= {oldtimer}
}

@Article{	  schneider00a,
  author	= {Schneider, G.},
  title		= {Neural networks are useful tools for drug design},
  journal	= {Neural Networks},
  year		= {2000},
  volume	= {13},
  number	= {1},
  month		= {},
  pages		= {15--16},
  organization	= {F. Hoffmann-La Roche Ltd},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {A profound understanding of molecular recognition
		  processes and the underlying molecular interaction patterns
		  is a prerequisite for future progress and success in
		  rational drug design. Neural networks could play an
		  important role in guiding Drug Discovery process through
		  the extraction of relevant molecular features. Most
		  frequently applied are multilayered feedforward networks
		  trained by the backpropagation-of-errors algorithm, Kohonen
		  networks, and counterpopagation systems.},
  dbinsdate	= {2002/1}
}

@Article{	  schneider98a,
  author	= {Schneider, G.},
  title		= {Feature-extraction from endopeptidase cleavage sites in
		  mitochondrial targeting peptides},
  journal	= {Proteins},
  year		= {1998},
  volume	= {30},
  number	= {1},
  month		= {Jan},
  pages		= {49--50},
  dbinsdate	= {oldtimer}
}

@Article{	  schnettler93a,
  author	= {A. Schnettler and V. Tryba},
  title		= {Artificial \mbox{self-organizing} neural network for
		  partial discharge source recognition},
  journal	= {Archiv f{\"{u}}r Elektrotechnik},
  year		= {1993},
  volume	= {76},
  pages		= {149--154},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schnettler93b,
  author	= {Armin Schnettler and Michael Kurrat},
  title		= {Partial Discharge Diagnosis Using an Artificial Neural
		  Network},
  booktitle	= {Proc. 8th Int. Symp. on High Voltage Engineering,
		  Yokohama},
  year		= {1993},
  pages		= {57--60},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes90a,
  author	= {J. C. Scholtes},
  title		= {Trends in Neurolinguistics},
  booktitle	= {Proc. IEEE Symp. on Neural Networks, Delft, Netherlands,
		  June 21st},
  year		= {1990},
  pages		= {69--86},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes91a,
  author	= {J. C. Scholtes},
  title		= {Unsupervised learning and the information retrieval
		  problem},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {95--100},
  organization	= {IEEE; Int. Neural Networks Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes91b,
  author	= {J. C. Scholtes},
  title		= {{K}ohonen Feature Maps in Full-Text Data Bases: A Case
		  Study of the 1987 {P}ravda},
  booktitle	= {Proc. Informatiewetenschap 1991, Nijmegen},
  year		= {1991},
  pages		= {203--220},
  publisher	= {STINFON},
  address	= {Nijmegen, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes91c,
  author	= {J. C. Scholtes},
  title		= {Using Extended {K}ohonen-Feature Maps in a Language
		  Acquisition Model},
  booktitle	= {Proc. 2nd Australian Conf. on Neural Nets},
  year		= {1991},
  pages		= {38--43},
  publisher	= {University of Sydney},
  address	= {Sydney, Australia},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes91d,
  author	= {J. C. Scholtes},
  title		= {Learning Simple Semantics by Self-Organization},
  booktitle	= {Worknotes of the AAAI Spring Symp. Series on Machine
		  Learning of Natural Language and Ontology, Palo Alto, CA,
		  March 26--29},
  year		= {1991},
  pages		= {146--151},
  publisher	= {American Association for Artificial Intelligence},
  annote	= {page numbers may be incorrect},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes91e,
  author	= {J. C. Scholtes},
  title		= {Unsupervised Context Learning in Natural Language
		  Processing},
  booktitle	= {Proc. IJCNN'91, International Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {107--112},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  abstract	= {By generalizing over contextual information, amazing
		  results were obtained in connectionist language processing.
		  Normally, these contexts are added manually to the system
		  or deducted by using a supervised learning algorithm. Here,
		  a recurrent self-organizing model, capable to derive
		  context from scratch, is presented. Syntactic features and
		  structures are learned in an unsupervised way from flat
		  sentences. The model forms a two layer extension of the
		  Kohonen feature map, provided with additional recurrent
		  fibres which are responsible for the automatic
		  determination of word contexts, thus resulting in an
		  unsupervised recurrent learning algorithm. After a short
		  introduction and a formal description of the model, the
		  experimental results will be presented, followed by a small
		  discussion.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes91f,
  author	= {J. C. Scholtes},
  title		= {{K}ohonen's Self-Organizing Map in Natural Language
		  Processing},
  booktitle	= {Proc. SNN Symposium},
  year		= {1991},
  pages		= {64},
  publisher	= {STINFON},
  address	= {Nijmegen, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes91g,
  author	= {J. C. Scholtes},
  title		= {{K}ohonen's Self-Organizing Map Applied Towards Natural
		  Language Processing},
  booktitle	= {Proc. CUNY 1991 Conf. on Sentence Processing, Rochester,
		  NY, May 12--14},
  year		= {1991},
  pages		= {10},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes91h,
  author	= {J. C. Scholtes},
  title		= {Self-Organized Language Learning. },
  booktitle	= {The Annual Conf. on Cybernetics: Its Evolution and Its
		  Praxis, Amherst, MA, July 17--21},
  year		= {1991},
  pages		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes91j,
  author	= {J. C. Scholtes},
  title		= {Filtering the {P}ravda with a Self-Organizing Neural Net},
  booktitle	= {Worknotes of the Bellcore Workshop on High Performance
		  Information Filtering},
  year		= {1991},
  pages		= {},
  publisher	= {Bellcore},
  address	= {Chester, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes91k,
  author	= {J. C. Scholtes},
  title		= {Recurrent {K}ohonen Self-Organization in Natural Language
		  Processing},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1751--1754},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@TechReport{	  scholtes91l,
  author	= {J. C. Scholtes},
  title		= {{{K}ohonen} Feature Maps in Natural Language Processing},
  institution	= {Department of Computational Linguistics, University of
		  Amsterdam},
  year		= {1991},
  address	= {Amsterdam, Netherlands},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@TechReport{	  scholtes91m,
  author	= {J. C. Scholtes},
  title		= {Neural Nets and Their Relevance for Information
		  Retrieval},
  institution	= {University of Amsterdam},
  year		= {1991},
  type		= {ITLI Prepublication Series for Computational Linguistics},
  number	= {CL-91--02},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes92a,
  author	= {J. C. Scholtes},
  title		= {Neural Nets versus Statistics in Information Retrieval.
		  {A} Case Study of the 1987 {P}ravda},
  booktitle	= {Proc. SPIE Conf. on Applications of Artificial Neural
		  Networks III, Orlando, Florida, April 20--24},
  year		= {1992},
  pages		= {},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes92b,
  author	= {J. C. Scholtes},
  title		= {Neural Nets for Free-Text Information Filtering},
  booktitle	= {Proc. 3rd Australian Conf. on Neural Nets, Canberra,
		  Australia, February 3--5},
  year		= {1992},
  pages		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes92c,
  author	= {J. C. Scholtes},
  title		= {Filtering the {P}ravda with a Self-Organizing Neural Net},
  booktitle	= {Proc. Symp. on Document Analysis and Information
		  Retrieval, Las Vegas, NV, March 16--18},
  year		= {1992},
  pages		= {151--161},
  publisher	= {UNLV Publ. },
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes92d,
  author	= {J. C. Scholtes},
  title		= {Filtering the {P}ravda with a Self-Organizing Neural Net},
  booktitle	= {Proc. First {SHOE} Workshop, Tilburg, Netherlands,
		  February 27--28},
  year		= {1992},
  pages		= {267--277},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes92e,
  author	= {J. Scholtes},
  title		= {Neural Data Oriented Parsing},
  booktitle	= {Proc. 3rd Twente Workshop on Language Technology},
  year		= {1992},
  pages		= {},
  publisher	= {University of Twente},
  address	= {Twente, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes92f,
  author	= {J. C. Scholtes},
  title		= {Neural Data Oriented Parsing},
  booktitle	= {Proc. 2nd SNN, Nijmegen, The Netherlands, April 14--15},
  year		= {1992},
  pages		= {86},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes92g,
  author	= {J. C. Scholtes},
  title		= {Resolving Linguistic Ambiguities with a Neural
		  Data-Oriented Parsing ({DOP}) System},
  booktitle	= {Proc. First {SHOE} Workshop},
  year		= {1992},
  pages		= {279--282},
  publisher	= {University of Tilburg},
  address	= {Tilburg, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes92h,
  author	= {J. C. Scholtes and S. Bloembergen},
  title		= {The Design of a Neural Data-Oriented Parsing ({DOP})
		  Model},
  booktitle	= {Proc. IJCNN-92-Baltimore, International Joint Conference
		  on Neural Networks},
  year		= {1992},
  volume	= {II},
  pages		= {69--72},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes92i,
  author	= {J. C. Scholtes and S. Bloembergen},
  title		= {Corpus Based Parsing with a Self-Organizing Neural Net},
  booktitle	= {Proc. IJCNN-92-Beijing, International Joint Conference on
		  Neural Networks},
  year		= {1992},
  pages		= {},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  scholtes92j,
  author	= {J. C. Scholtes},
  title		= {Resolving Linguistic Ambiguities with a Neural
		  Data-Oriented Parsing {(DOP)} System},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1347--1350},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  scholtes92k,
  author	= {Scholtes, J. C. },
  title		= {Neural nets in information retrieval. {A} case study of
		  the 1987 {P}ravda},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1710},
  number	= {pt. 1},
  pages		= {631--41},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  scholtes93a,
  author	= {Johannes Cornelis Scholtes},
  title		= {Neural Networks in Natural Language Processing and
		  Information Retrieval},
  school	= {Universiteit van Amsterdam},
  year		= {1993},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  schomaker93a,
  author	= {L. Schomaker},
  title		= {Using stroke-or character-based \mbox{self-organizing}
		  maps in the recognition of on-line, connected cursive
		  script},
  journal	= {Pattern Recognition},
  year		= {1993},
  volume	= {26},
  number	= {3},
  pages		= {443--450},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schomaker94a,
  author	= {Schomaker, L. and Abbink, G. and Selen, S. },
  title		= {Writer and writing-style classification in the recognition
		  of online handwriting},
  booktitle	= {IEE European Workshop on Handwriting Analysis and
		  Recognition: A European Perspective (Digest No. 1994/123)},
  year		= {1994},
  pages		= {1/1--4},
  organization	= {Inst. for Cognition \& Inf. , Nijmegen Univ. ,
		  Netherlands},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  schon00a,
  author	= {Schon, P. C. and Puppe, B. and Manteuffel, G.},
  title		= {Classification of stress calls of the domestic pig (Sus
		  scrofa) using {LPC}-Analysis and a self organizing neuronal
		  network},
  journal	= {ARCHIV FUR TIERZUCHT-ARCHIVES OF ANIMAL BREEDING},
  year		= {2000},
  volume	= {43},
  pages		= {177--183},
  abstract	= {In the last years sound analysis has become an
		  increasingly important tool to interpret the behaviour, the
		  health condition, and the well-being of animals. The paper
		  presents a procedure that allows to characterize, classify
		  and visualize stress calls of domestic pigs (Sus scrofa) in
		  three steps. (I) Starting from the acoustic model of sound
		  production features are extracted from the call using the
		  linear prediction method. This procedure, linear prediction
		  coding (LPC), delivers an extremely compact short time
		  representation of the call with a relatively low effort of
		  calculation and a low number of features. (2) A neuronal
		  network was trained such that topological relations of the
		  neurons represent the input vector space of the determined
		  LPC-coefficients. This resulted in a feature map, where the
		  positions of the neurons allow conclusions about the
		  structure of the input data. (3) Visualizations of the
		  clustering structure of the calls were performed using
		  various types of representations. The procedure now allows
		  the development of online monitoring of stress calls in
		  farming environments.},
  dbinsdate	= {2002/1}
}

@Article{	  schon01a,
  author	= {Schon, P. C. and Puppe, B. and Manteuffel, G.},
  title		= {Linear prediction coding analysis and self-organizing
		  feature map as tools to classify stress calls of domestic
		  pigs (Sus scrofa)},
  journal	= {JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA},
  year		= {2001},
  volume	= {110},
  number	= {3},
  month		= {SEP},
  pages		= {1425--1431},
  abstract	= {It is assumed that calls may give information about the
		  inner (emotional) state of an animal. Hence, in the last
		  years sound analysis has become an increasingly important
		  tool for the interpretation of the behavior, the health
		  condition, and the well-being of animals. A procedure was
		  developed that allows the characterization, classification,
		  and visualization of the cluster structures of stress calls
		  of domestic pigs (Sus scrofa). Based on the acoustic model
		  of the sound production the extraction of features from
		  calls was performed with linear prediction coding (LPC). A
		  vector-based self-organizing neuronal network was trained
		  with the determined LPC coefficients, resulting in a
		  feature map. The cluster structure of the calls was then
		  visualized with a unified matrix and the neurons were
		  labeled for their input origin. The basic applicability of
		  the procedure was tested by using two examples which were
		  of special interest for a possible evaluation of the normal
		  farming practice. The procedure worked well both in
		  discriminating individual piglets by their scream
		  characteristics and in classifying pig stress calls vs
		  other calls and noise occurring under normal farming
		  conditions. },
  dbinsdate	= {2002/1}
}

@Article{	  schonweiler00a,
  author	= {Schonweiler, R. and Hess, M. and Wubbelt, P. and Ptok,
		  M.},
  title		= {Novel approach to acoustical voice analysis using
		  artificial neural networks},
  journal	= {JARO},
  year		= {2000},
  volume	= {1},
  number	= {4},
  month		= {DEC},
  pages		= {270--282},
  abstract	= {Perceptual rating scales are widely used for the
		  assessment of voice quality. These ratings may be
		  influenced by the individual experience of the listener.
		  Thus, researchers have turned to acoustical measures which
		  may eventually correlate with voice quality. In this study
		  we tested whether multivariate statistics, combined with
		  artificial neural networks, could identify patterns of
		  acoustic voice parameters corresponding to a widely used
		  perceptual rating scale. In a multicenter study with 31
		  raters, voice samples of 117 individuals with or without
		  voice disorders were perceptually rated. The RBH index,
		  consisting of a 4-point scale of roughness, breathiness,
		  and hoarseness, was used. Voice samples were then analyzed
		  with an acoustical feature extraction and classified using
		  amultivariate regression tree analysis with the perceptual
		  ratings as a priori information. Artificial neural networks
		  were trained to selected acoustic parameters having high
		  "relative importance" in the regression trees. Mean
		  classification accuracies were around 30% with topographic
		  feature maps (trained with Learning Vector Quantization
		  algorithm) and 65--85% with feedforward networks (trained
		  with RProp algorithm). Based on the best-fitting results
		  with feedforward networks, a classification system
		  (computer program) consisting of 50 simultaneous working
		  networks was developed. Using this program, the
		  classification matched 40% of the a priori values in both R
		  and B domains. In 65% they matched at least in one domain.
		  These accuracies are within the range reported by other
		  authors using artificial neural networks in biology and
		  clinical medicine. Thus, the results encourage further
		  research of feedforward networks for acoustic voice
		  analysis.},
  dbinsdate	= {2002/1}
}

@Article{	  schonweiler00b,
  author	= {Schonweiler, R. and Wubbelt, P. and Tolloczko, R. and
		  Rose, C. and Ptok, M.},
  title		= {Classification of passive auditory event-related
		  potentials using discriminant analysis and self-organizing
		  feature maps},
  journal	= {AUDIOLOGY AND NEURO-OTOLOGY},
  year		= {2000},
  volume	= {5},
  number	= {2},
  month		= {MAR-APR},
  pages		= {69--82},
  abstract	= {Discriminant analysis (DA) and self-organizing feature
		  maps (SOFM) were used to classify passively evoked auditory
		  event- related potentials (ERP) P-1, N-1, P-2 and N-2
		  Responses from 16 children with severe behavioral auditory
		  perception deficits, 16 children with marked behavioral
		  auditory perception deficits, and 14 controls were
		  examined. Eighteen ERP amplitude parameters were selected
		  for examination of statistical differences between the
		  groups, Different DA methods and SOFM configurations were
		  trained to the values, SOFM had better classification
		  results than DA methods. Subsequently, measures on another
		  37 subjects that were unknown for the trained SOFM were
		  used to test the reliability of the system. With
		  10-dimensional vectors, reliable classifications were
		  obtained that matched behavioral auditory perception
		  deficits in 96%, implying central auditory processing
		  disorder (CAPD). The results also support the assumption
		  that CARD includes a 'non-peripheral' auditory processing
		  deficit. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  schoonees88a,
  author	= {J. A. Schoonees},
  title		= {Parallel distributed processing: practical applications of
		  neural networks in signal processing},
  booktitle	= {Proc. COMSIG'88, Southern African Conf. on Communications
		  and Signal Processing},
  year		= {1988},
  pages		= {76--80},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schouten93a,
  author	= {Schouten, Th. E. and {klein Gebbinck}, M. and Thijssen, J.
		  M. and Verhoeven, J. T. M. },
  title		= {Ultrasonic tissue characterisation using neural networks},
  booktitle	= {Third International Conference on Artificial Neural
		  Networks},
  year		= {1993},
  pages		= {110--2},
  organization	= {Dept. of Informatics, Katholieke Univ. , Nijmegen,
		  Netherlands},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  schuchhardt96a,
  author	= {Schuchhardt, J.},
  title		= {Local structural motifs of protein backbones are
		  classified by \mbox{self-organizing} neural networks},
  journal	= {Protein Engineering},
  year		= {1996},
  volume	= {9},
  number	= {10},
  month		= {Oct},
  pages		= {833--842},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schuemie99a,
  author	= {Martijn Schuemie and Jan van den Berg},
  title		= {Information Retrieval Systems using an Associative
		  Conceptual Space and Self-Organising Maps},
  booktitle	= {Accepted for presentation at the BNAIC'99 Conference,
		  November 1999, Maastricht},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@Article{	  schultz93a,
  author	= {Schultz, Abraham},
  title		= {Collective recall via the brain-state-in-a-box network.},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1993},
  number	= {4},
  volume	= {4},
  pages		= {580--587},
  abstract	= {A number of approaches to pattern recognition employ
		  variants of nearest neighbor recall. This procedure can be
		  described as using a number of prototypes of known class
		  and identifying an unknown pattern vector according to the
		  prototype it is nearest to. A recall criterion of this type
		  that depends on the relation of the unknown to a single
		  prototype is a nonsmooth function and leads to a decision
		  boundary that is a jagged, piecewise linear hypersurface.
		  Decision boundaries generated by k-nearest neighbors also
		  have this property. This paper develops a pattern
		  recognition method based on a smooth nearness measure of
		  the unknown to all the prototypes and is called 'collective
		  recall.' The prototypes are represented as cells in a
		  brain-state-in-a-box (BSB) network. Cells that represents
		  the same pattern class are linked by positive weights and
		  cells representing different pattern classes are linked by
		  negative weights. The initial state of the network is a
		  vector whose components are measures of nearness of the
		  unknown pattern vector to each of the prototypes. Competing
		  coalitions of cells drive the network to a vertex point of
		  the BSB. This final state of the network classifies the
		  unknown and is one in which all the prototypes associated
		  with a class are 'turned on' and with all other cells
		  'turned off.' A new version of the BSB is introduced that
		  has an attractor at the origin. Pattern vectors that lie
		  near the boundary of two pattern classes tend to converge
		  to this attractor and by deleting these 'undecided cases'
		  one can get a significant improvement in performance for a
		  relatively small fraction of undecided cases. Analytic
		  solutions to the BSB eigenvalue problem are obtained and
		  allows one to select weights that generate flows that are
		  hyperbolic. Computer simulations of collective recall used
		  in conjunction with learning vector quantization (LVQ)
		  shows significant improvement in performance relative to
		  nearest neighbor recall for pattern classes defined by
		  nonspherically symmetric Gaussians.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schumann95a,
  author	= {Matthias Schumann and Ralf Retzko},
  title		= {Solving Vehicle Routing Problems with {S}elf {O}rganizing
		  {M}aps},
  volume	= {I},
  pages		= {189--192},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schumann95b,
  author	= {Matthias Schumann and Ralf Retzko},
  title		= {Self Organizing Maps for Vehicle Routing Problems -
		  minimizing an explicit cost function},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {401--406},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schunemann00,
  author	= {Stefan Sch\^{u}nemann and Bernd Michaelis},
  title		= {Special Algorithms for Analysis of Clusters with Different
		  Feature Density Using Self-Organizing Maps},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Otto von Guericke University of Magdeburg, Institute for
		  Electronics, Signal Processing and Communications},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InCollection{	  schunemann96a,
  author	= {S. Schunemann and B. Michaelis},
  title		= {A \mbox{self-organizing} map for analysis of high
		  dimensional feature spaces with clusters of highly
		  differing feature density},
  booktitle	= {4th European Symposium on Artificial Neural Networks,
		  ESANN '96. Proceedings},
  publisher	= {D Facto},
  year		= {1996},
  editor	= {M. Verleysen},
  address	= {Brussels, Belgium},
  pages		= {79--84},
  dbinsdate	= {oldtimer}
}

@InCollection{	  schunemann96b,
  author	= {S. Schunemann and B. Michaelis and W. Schubert},
  title		= {Analysis of multi-fluorescence signals using a modified
		  \mbox{self-organizing} feature map},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {575--80},
  dbinsdate	= {oldtimer}
}

@InCollection{	  schunemann97a,
  author	= {Stefan Sch{\"u}nemann and Udo Seiffert and Bernd
		  Michaelis},
  title		= {Two more modifications of {SOM}s to handle signals with
		  special properties},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {292--297},
  dbinsdate	= {oldtimer}
}

@InCollection{	  schunemann98a,
  author	= {S. Schunemann and B. Michaelis},
  title		= {Data analysis of not well separable clusters of different
		  feature density with a two-layer classification system
		  comprised of a {SOM} and an {ART} 2-{A} network},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {707--12},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schunemann98b,
  author	= {Schunemann, S. and Michaelis, B. and Schubert, W.},
  title		= {Cluster analysis with {SOFM} for the detection of
		  different diseases in the human immune system},
  booktitle	= {6th European Congress on Intelligent Techniques and Soft
		  Computing. EUFIT '98},
  publisher	= {Verlag Mainz},
  address	= {Aachen, Germany},
  year		= {1998},
  volume	= {2},
  pages		= {1320--4},
  abstract	= {Introduces a procedure for the analysis of higher-level
		  combinatorial receptor patterns in the cellular immune
		  system, which were obtained using fluorescence
		  multi-epitope-imaging microscopy. For the cluster analysis
		  a self-organizing feature map (SOFM), a special
		  architecture of an artificial neural network, is used. The
		  smoothing of input vectors is a basic property of these
		  neural networks and leads to a relatively low sensitivity
		  to clusters of low feature density. But, in the case of the
		  described application the clusters of low feature density
		  represent significant qualities of the feature
		  distribution. A modification of the learning algorithm of
		  the SOFM, which improves a projection of low feature
		  density clusters on a SOFM, and an example for the cluster
		  analysis of three different diseases are described.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schunemann99a,
  author	= {Schunemann, S. and Michaelis, B.},
  title		= {A hierarchical \mbox{self-organizing} feature map for
		  analysis of not well separable clusters of different
		  feature density},
  booktitle	= {7th European Symposium on Artificial Neural Networks.
		  ESANN'99. Proceedings},
  publisher	= {D-Facto},
  address	= {Brussels, Belgium},
  year		= {1999},
  volume	= {},
  pages		= {13--18},
  abstract	= {Introduces a hierarchical self-organizing feature map
		  (SOFM). The partial maps consist of individual numbers of
		  neurons, which makes a cluster analysis with different
		  degrees of resolution possible. A definition of a special
		  Mahalanobis space of the data set during the learning
		  improves the properties concerning clusters with low
		  density.},
  dbinsdate	= {oldtimer}
}

@Article{	  schurer94a,
  author	= {Schurer, Tilo},
  title		= {Experimental comparison of different feature extraction
		  and classification methods for telephone speech},
  journal	= {Proceedings of Second IEEE Workshop on Interactive Voice
		  Technology for Telecommunications Applications},
  year		= {1994},
  number	= {},
  volume	= {},
  pages		= {93--96},
  abstract	= {Robust speech recognition over telephone lines severely
		  depends on the choice of the feature extraction and
		  classification methods. In order to get the highest
		  possible performance of the speech recognizer a number of
		  commonly used feature extraction methods (MFCC, LPC, PLP,
		  RASTA-PLP) and classification methods (MLP, LVQ, HMM) were
		  tested on the same telephone speech data. All combinations
		  of feature extraction and classification methods were
		  computed and several parameters of both methods where
		  changed in order to find a non-local maximum of recognition
		  accuracy. The paper does not describe a comparison of
		  classification but of feature extraction methods because it
		  is clear that an HMM would outperform both LVQ and MLP. The
		  big question is if the same feature extraction methods
		  always lead to the best results, no matter which classifier
		  is used!},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schutte97a,
  author	= {Schutte, F. and Beineke, S. and Grotstollen, H. and
		  Frohleke, N. and Witkowski, U. and Ruckert, U. and Ruping,
		  S.},
  title		= {Structure- and parameter identification for a two-mass
		  system with backlash and friction using a
		  \mbox{self-organizing} map},
  booktitle	= {EPE'97. 7th European Conference on Power Electronics and
		  Applications. EPE Assoc, Brussels, Belgium},
  year		= {1997},
  volume	= {3},
  pages		= {358--63},
  abstract	= {A self-commissioning system for high performance speed and
		  position control of electrical drives requires a structure
		  and parameter identification of a nonlinear mechanic as
		  basic building block. This self-commisioning system in
		  combination with a new identification scheme is presented
		  here. The identification is based on extraction of
		  characteristic features from the system response and
		  evaluation of these features by self-organizing neural
		  network, especially the self-organizing feature map
		  (SOM).},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schweighofer99a,
  author	= {Schweighofer, E. and Merkl, D.},
  title		= {Data mining in law with adaptive learning techniques},
  booktitle	= {Proceedings. Tenth International Workshop on Database and
		  Expert Systems Applications. DEXA 99},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {799--803},
  abstract	= {The freely available law on the Internet could be one of
		  the best applications of data mining. The growing
		  complexity of legal rules and the necessary adaptation to
		  user needs requires better instruments than manual browsing
		  and searching interfaces. Our intelligent agent offers
		  information reconnaissance by classifying and describing
		  unknown text corpora of search results. Based on standard
		  agent technology, our research on self-organising maps
		  focuses on adaptive learning techniques for information
		  reconnaissance. The units of the self-organising map are
		  labeled with the most appropriate keywords. The user can
		  choose between the various units in order to refine the
		  next step of research. First results have shown the
		  potential of this approach.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schweizer91a,
  author	= {L. Schweizer and G. Parladori and G. L. Sicuranza and S.
		  Marsi},
  title		= {A Fully Neural Approach to Image Compression},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {815--820},
  publisher	= {North-Holland},
  address	= {Amsterdam},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schweizer92a,
  author	= {Schweizer, L. and Parladori, G. and Sicuranza, G. L. },
  title		= {Globally trained neural network architecture for image
		  compression},
  booktitle	= {Neural Networks for Signal Processing II. Proceedings of
		  the IEEE-SP Workshop},
  year		= {1992},
  pages		= {289--95},
  organization	= {Alcatel Italia-Telettra Spa, Milano, Italy},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InBook{	  schwenker02a,
  author	= {Friedhelm Schwenker and Hans A. Kestler and G{\"u}nther
		  Palm},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Unsupervised and Supervised Learning in
		  Radial-Basis-Function Networks},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {217--43},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InBook{	  schwenker02b,
  author	= {Friedhelm Schwenker and Hans A. Kestler and G{\"u}nther
		  Palm},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Algorithms for the Visualization of Large and Multivariate
		  Data Sets},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {165--184},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  schwenker94a,
  author	= {Schwenker, F. and Kestler, H. A. and Palm, G. and Hoeher,
		  M.},
  title		= {Similarities of {LVQ} and {RBF} learning---a survey of
		  learning rules and the application to the classification of
		  signals from high-resolution electrocardiography},
  journal	= {Proceedings of IEEE International Conference on Systems,
		  Man, and Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1994},
  number	= {},
  volume	= {1},
  pages		= {646--651},
  abstract	= {In this paper algorithms for neural network training are
		  described. We discuss the apparant similarities of LVQ and
		  RBF classification which motivate us to combine the two
		  approaches. The resulting algorithm is then tested on
		  features extracted from signals from high-resolution
		  electrocardiography.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schwenker98a,
  author	= {Schwenker, F. and Kestler, H. and Palm, G.},
  title		= {Adaptive clustering and multidimensional scaling of large
		  and high-dimensional data sets},
  booktitle	= {ICANN 98. Proceedings of the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer-Verlag London},
  address	= {London, UK},
  year		= {1998},
  volume	= {2},
  pages		= {911--16},
  abstract	= {We describe a algorithm for exploratory data analysis
		  which combines the adaptive c-means clustering and the
		  multidimensional scaling procedure (ACMMDS). ACMMDS is an
		  algorithm for the online visualization of clustering
		  processes and may be considered as a alternative approach
		  to Kohonen's self organizing feature (SOM). Whereas SOM is
		  a heuristic neural network algorithm, ACMMDS is derived
		  from multivariate statistic algorithms. The possible
		  implications of ACMMDS are illustrated through two
		  different data sets.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schyns90a,
  author	= {P. G. Schyns},
  title		= {Expertise Acquisition through the Refinement of Conceptual
		  Representation in a Self-Organizing Architecture},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  volume	= {I},
  pages		= {236--239 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  schyns90b,
  author	= {P. G. Schyns},
  title		= {A Modular Neural Network Model of the Acquisition of
		  Category Names in Children},
  booktitle	= {Connectionist Models: Proc. of the 1990 Summer School},
  year		= {1990},
  pages		= {228--235},
  publisher	= {Morgan-Kaufmann},
  address	= {San Mateo, CA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  schyns91a,
  author	= {P. G. Schyns},
  title		= {A Modular Neural Network Model of Concept Acquisition},
  journal	= {Cognitive Science},
  year		= {1991},
  volume	= {15},
  number	= {},
  pages		= {461--508},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  searle95a,
  author	= {Searle, I. and Ziola, S. and Rutherford, P. },
  title		= {Crack detection in lap-joints using acoustic emission},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2444},
  pages		= {212--23},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  see94a,
  author	= {Ng Geok See and Chew Wei Yih},
  title		= {Isolated, speaker-independent spoken {C}hinese digits
		  recognition using neural networks},
  booktitle	= {Proceedings of the Second Singapore International
		  Conference on Intelligent Systems. SPICIS `94},
  publisher	= {Japan-Singapore AI Centre},
  year		= {1994},
  address	= {Singapore},
  pages		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sehad94a,
  author	= {Sehad, S. and Touzet, C. },
  title		= {Self-organizing map for reinforcement learning:
		  obstacle-avoidance with {K}hepera},
  booktitle	= {Proceedings. From Perception to Action Conference},
  year		= {1994},
  editor	= {Gaussier, P. and Nicoud, J. -D. },
  pages		= {420--3},
  organization	= {LERI-EERIE, Nimes, France},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sehad95a,
  author	= {Samira Sehad and Claude Touzet},
  title		= {Neural Reinforcement Path Planning for the Miniature Robot
		  {K}hepera},
  volume	= {II},
  pages		= {350--354},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  seiffert01a,
  author	= {U. Seiffert and B. Michaelis},
  title		= {Multi-dimensional self-organising maps on massively
		  prallel hardware},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {160--6},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Book{		  seiffert02a,
  author	= {},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  address	= {},
  edition	= {},
  month		= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InBook{	  seiffert02b,
  author	= {Udo Seiffert},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Growing Multi-Dimensional Self-Organizing Maps for Motion
		  Detection},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {95--120},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InCollection{	  seiffert95a,
  author	= {U. Seiffert and B. Michaelis},
  title		= {Three-dimensional \mbox{self-organizing} maps for
		  classification of image properties},
  booktitle	= {Proceedings of the Second New Zealand International
		  Two-Stream Conference on Artificial Neural Networks and
		  Expert Systems},
  publisher	= {IEEE Computer Society Press},
  year		= {1995},
  editor	= {N. K. Kasabov and G. Coghill},
  address	= {Los Alamitos, CA, USA},
  pages		= {310--13},
  dbinsdate	= {oldtimer}
}

@InCollection{	  seiffert95b,
  author	= {Udo Seiffert and Bernd Michaelis},
  title		= {Classification of Image Properties for Motion Estimation
		  with 3-Dimensional Self-Organizing Maps},
  booktitle	= {Proc. SIP'95, International Conference on Signal and Image
		  Processing},
  publisher	= {IASTED/Acta Press},
  year		= 1995,
  address	= {Anaheim},
  pages		= {233--236},
  dbinsdate	= {oldtimer}
}

@InCollection{	  seiffert96a,
  author	= {Udo Seiffert and Bernd Michaelis},
  title		= {Growing {3D}-{SOM}'s with {2D}-Input Layer as a
		  Classification Tool in a Motion Detection System},
  booktitle	= {Proc. EANN `96, International Conference on Engineering
		  Applications of Neural Networks},
  publisher	= {Abo Akademis Tryckeri},
  year		= 1996,
  editor	= {A. B. Bulsari},
  address	= {Turku, Finland},
  pages		= {351--354},
  abstract	= {This paper introduces an adaptive growing
		  three-dimensional self-organizing map with the special
		  feature of a matrix input layer instead of a
		  one-dimensional vector. This is very important in order to
		  present matrix input functions to the net without losing
		  any neighbourhood relations. The network training runs in
		  two phases. First it grows from an initially small size
		  until a user-defined performance criterion is met. Then a
		  fine tuning procedure is performed with a decreasing
		  learning rate and several neighbourhood definitions.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  seiffert96b,
  author	= {U. Seiffert and B. Michaelis},
  title		= {Adaptive Three-Dimensional Self-Organizing Map with
		  Two-Dimensional Input Layer},
  booktitle	= {Proc. ANZIIS `96, the Australian New Zealand Conference on
		  Intelligent Information Systems},
  publisher	= {IEEE Press},
  year		= 1996,
  address	= {Piscataway, NJ},
  pages		= {258--263},
  dbinsdate	= {oldtimer}
}

@Article{	  seiffert97a,
  author	= {U. Seiffert and B. Michaelis},
  title		= {Growing {{3D}-SOM}s with {2D}-input layer as a
		  classification tool in a motion detection system},
  journal	= {International Journal of Neural Systems},
  year		= {1997},
  volume	= {8},
  number	= {1},
  pages		= {81--9},
  dbinsdate	= {oldtimer}
}

@Article{	  seiffert97b,
  author	= {Udo Seiffert and Bernd Michaelis},
  title		= {Estimating Motion Parameters with Three-Dimensional
		  Self-Organizing Maps},
  journal	= {Information Sciences},
  year		= 1997,
  volume	= 101,
  pages		= {187--201},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  seiffert98a,
  author	= {Seiffert, U. and Michaelis, B.},
  title		= {Quasi-four-dimensional-neuroncube and its application to
		  motion estimation},
  booktitle	= {Engineering Benefits from Neural Networks. Proceedings of
		  the International Conference EANN '98},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1998},
  volume	= {},
  pages		= {78--81},
  abstract	= {Motivated by a motion detection system, the application of
		  a new category of Kohonen's self-organizing map (SOM) is
		  presented. Based on earlier work on three-dimensional SOM,
		  a new implementation of growing multi-dimensional SOM, the
		  quasi-four-dimensional neuroncube (QFDN), has already been
		  introduced by U. Seiffert and B. Michaelis (1998). The
		  paper focuses on the application of the QFDN to motion
		  estimation.},
  dbinsdate	= {oldtimer}
}

@Article{	  seki02a,
  author	= {Seki, I. and Hori, Y.},
  title		= {Detection of abnormal action using image sequence for
		  monitoring system of aged people},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-D}
		  ,
  year		= {2002},
  volume	= {122},
  pages		= {182--8},
  abstract	= {Japan is now an aging society and this problem will be
		  more serious in the near future. Therefore, engineering
		  support for aged people is required. As an important
		  example of such support, a monitoring system for aged
		  people is proposed in this paper. This system automatically
		  learns aged people's usual actions in a few days and
		  detects his/her abnormal action by observing the room with
		  a simple camera such as a CCD camera. At the learning
		  stage, a self-organizing map (SOM) is used to realize
		  automatic learning. For the detection method, the
		  eigenspace method is used which is excellent in compression
		  of image data and calculation of the correlation among
		  images. The parametric eigenspace method (PEM) is also used
		  to detect abnormalities such as the speed of the action.
		  Some results show the effectiveness of the proposed
		  method.},
  dbinsdate	= {2002/1}
}

@Article{	  selb00a,
  author	= {Selb, A.},
  title		= {Comparison of vector quantization algorithms},
  journal	= {OEGAI-Journal},
  year		= {2000},
  volume	= {19},
  pages		= {8--14},
  abstract	= {Compares two algorithms which can be used to find the
		  optimal number of reference vectors for vector quantization
		  purposes. The MDL-based algorithm uses decisions based on
		  the minimum description length principle to reduce the
		  initial set of reference vectors until the optimal number
		  of reference vectors is reached. Furthermore this algorithm
		  can handle outliers, which makes it robust. The Growing
		  Neural Gas algorithm starts with a small set of initial
		  reference vectors and adds additional reference vectors
		  during training. It is also possible to improve the
		  MDL-based algorithm, if the two algorithms are combined to
		  form a new, computationally efficient algorithm. These
		  three approaches are explained and illustrated on the basis
		  of two-dimensional clustering problems.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  selouani01a,
  author	= {Selouani, S. A. and O'Shaughnessy D.},
  title		= {Hybrid architectures for complex phonetic features
		  classification: a unified approach},
  booktitle	= {Proceedings of the Sixth International Symposium on Signal
		  Processing and its Applications. IEEE, Piscataway, NJ,
		  USA},
  year		= {2001},
  volume	= {2},
  pages		= {719--22},
  abstract	= {This paper examines how to exploit the advantages of a
		  hybrid approach in order to overcome the drawbacks of
		  classic automatic speech recognition (ASR) systems faced
		  with complex phonetic features. The key idea consists of
		  'boosting' the capacity of a baseline ASR system to
		  identify features as subtle as emphasis, gemination or
		  relevant vowel lengthening. The 'booster' part is composed
		  of a mixture of time delay neural networks (TDNNs) using an
		  autoregressive version of the backpropagation algorithm. We
		  choose to carry out trials on the Arabic language, which is
		  characterized by the presence of complex features. We use
		  three baseline systems: hidden Markov models (HMM),
		  optimized version of learning vector quantization algorithm
		  (O/sup 2/LVQ1) and classical K-nearest neighbors'
		  classifier (KNN). The reported results showed clearly the
		  effectiveness of the approach since the three hybrid
		  systems (HMM/TDNN, O/sup 2/LVQ1/TDNN, KNN/TDNN) perform
		  significantly better than their corresponding baseline
		  systems.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  selouani99a,
  author	= {Selouani, S. A. and Caelen, J.},
  title		= {A hybrid learning vector quantization/time-delay neural
		  networks system for the recognition of Arabic speech},
  booktitle	= {Proceedings of the IEEE-EURASIP Workshop on Nonlinear
		  Signal and Image Processing (NSIP'99). Bogazici Univ,
		  Instanbul, Turkey},
  year		= {1999},
  volume	= {2},
  pages		= {709--13},
  abstract	= {In this paper, we present an approach which significantly
		  improves the performance of automatic speech recognition
		  systems (ASRS) dedicated to Arabic language. We propose to
		  combine a version of learning vector quantization (LVQ) and
		  time delay neural networks (TDNN) using an autoregressive
		  version (AR) of the backpropagation algorithm. The
		  underlying idea of this approach consists of the
		  incorporation of AR-TDNN in a hybrid structure in order to
		  give the LVQ-based system the ability to overcome failures
		  due to the language particularities such as emphasis,
		  gemination and vowel lengthening. The test corpus is
		  composed of subsets taken from an Arabic database. The
		  results show that the proposed LVQ/AR-TDNN system achieves
		  a high recognition rate compared to the baseline LVQ-based
		  system.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  senjyu00a,
  author	= {Senjyu, T. and Tamaki, Y. and Uezato, K.},
  title		= {Next day load curve forecasting using self organizing
		  map},
  booktitle	= {PowerCon 2000. 2000 International Conference on Power
		  System Technology. Proceedings. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {2},
  pages		= {1113--18},
  abstract	= {In this paper, we propose a new prediction scheme using
		  self organizing map for next day load curve forecasting. In
		  the proposed scheme, we select several similar days
		  corresponding to forecasted day using a Kohonen network
		  which is a representative of self organizing map, and we
		  forecast the next day load curve by averaging selected
		  similar days. Therefore, we do not need complex algorithm
		  and structure such as supervised neural network, genetic
		  algorithm (GA) and fuzzy inference, and we can forecast
		  next day load curve easily. The suitability of the proposed
		  approach is illustrated through an application to actual
		  load data of the Okinawa Electric Power Company in Japan.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  seo00a,
  author	= {Yeon-Guy Seo and Sung-Bae Cho},
  title		= {Self-Organizing Map for Optimal Audit Data Reduction in
		  Intrusion Detection System},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {195--200},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@Article{	  seo99a,
  author	= {Seok Bae Seo and Daijin Kim and Dae Seong Kang},
  title		= {{VQ} codebook design and feature extraction of image
		  information for multimedia information searching},
  journal	= {Journal of the Institute of Electronics Engineers of Korea
		  S},
  year		= {1999},
  volume	= {36},
  pages		= {101--12},
  abstract	= {In this paper, the codebook design method of VQ (vector
		  quantization) is proposed as a method to extract feature
		  data of image for multimedia information searching.
		  Conventional VQ codebook design methods are unsuitable to
		  extract the feature data of images because they have too
		  great a computation time, memory for vector decoding and
		  blocking effects like DCT (discrete cosine transform). The
		  proposed design method consists of the feature extraction
		  by WT (wavelet transform) and the data group divide method
		  by PCA (principal component analysis). WT is introduced to
		  remove the blocking effect of an image with high
		  compression ratio. Computer simulations show that the
		  proposed method has the performance in processing speed
		  than the VQ design method using SOM (self-organizing
		  map).},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sergi95a,
  author	= {Sergi, R. and Solaiman, B. and Mouchot, M. C. and
		  Pasquariello, G. and Posa, P. },
  title		= {{LANDSAT-TM} image classification using principal
		  components analysis and neural networks},
  booktitle	= {1995 International Geoscience and Remote Sensing
		  Symposium, IGARSS '95. Quantitative Remote Sensing for
		  Science and Applications},
  year		= {1995},
  editor	= {Stein, T. I. },
  volume	= {3},
  pages		= {1927--9},
  organization	= {Telecom Bretagne, Brest, France},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sergi96a,
  author	= {R. Sergi and G. Satalino and B. Solaiman and G.
		  Pasquariello},
  title		= {{SIR-C} polarimetric image segmentation by neural
		  network},
  booktitle	= {IGARSS '96. 1996 International Geoscience and Remote
		  Sensing Symposium. Remote Sensing for a Sustainable
		  Future},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  editor	= {T. I. Stein},
  address	= {New York, NY, USA},
  pages		= {1562--4},
  dbinsdate	= {oldtimer}
}

@TechReport{	  serrano-cinca94a,
  author	= {Carlos Serrano-Cinca},
  title		= {Beyond {Z}-Analysis: Self-Organizing Neural Networks for
		  Financial Diagnosis},
  institution	= {University of Southampton},
  year		= 1994,
  type		= {Discussion Papers in Accounting and Management Science},
  number	= {94--92},
  address	= {Southampton, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  serrano-cinca96a,
  author	= {Serrano-Cinca, Carlos},
  title		= {Self organizing neural networks for financial diagnosis},
  journal	= {Decision Support Systems},
  year		= {1996},
  number	= {3},
  volume	= {17},
  pages		= {227--238},
  abstract	= {A complete Decision Support System (DSS) for financial
		  diagnosis based on Self Organizing Feature Maps (SOFM) is
		  described. This is a neural network model which, on the
		  basis of the information contained in a multidimensional
		  space---in the case exposed, financial ratios---generates a
		  space of lesser dimensions. In this way, similar input
		  patterns---in the case exposed, companies---are represented
		  close to one another on a map. The neural network has been
		  complemented and compared with multivariate statistical
		  models such as Linear Discriminant Analysis (LDA), as well
		  as with neural models such as the Multilayer Perceptron
		  (MLP). As the principal advantage, this DSS provides a
		  complete analysis which goes beyond that of the traditional
		  models based on the construction of a solvency indicator
		  also known as Z score, without renouncing simplicity for
		  the final decision maker.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  serrano-cinca98a,
  author	= {C. Serrano-Cinca},
  title		= {Let Financial Data Speak for Themselves},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {3--23},
  dbinsdate	= {oldtimer}
}

@Article{	  serrano-cinca98b,
  author	= {Serrano-Cinca, C.~},
  title		= {From Financial Information to Strategic Groups: A
		  Self-Organizing Neural Network Approach},
  journal	= {Journal of Forecasting},
  year		= {1998},
  volume	= {17},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  serrano93a,
  author	= {Carlos Serrano and Bonifacio Mart{\'{\i}}n and Jos{\'{e}}
		  L. Gallizo},
  title		= {Artificial Neural Networks in Financial Statement
		  Analysis: Ratios versus Accounting Data},
  booktitle	= {Proc. 16th Annual Congress of the European Accounting
		  Associatian},
  year		= {1993},
  organization	= {European Accounting Association},
  dbinsdate	= {oldtimer}
}

@Article{	  severin01a,
  author	= {Severin, E.},
  title		= {Ownership structure and the performance of firms: evidence
		  from France},
  journal	= {European-Journal-of-Economic-and-Social-Systems},
  year		= {2001},
  volume	= {15},
  pages		= {85--107},
  abstract	= {Deals with the influence of ownership structure, variables
		  of external and organisational discipline on financial and
		  economic performance. By means of self-organising maps, in
		  particular Kohonen maps, we highlight three main results.
		  Firstly, the results obtained from a sample of French
		  companies are consistent with the findings of Morck,
		  Shleifer and Vishny (1988), McConnell and Servaes (1990)
		  and Short and Keasey ( 1999) and suggest a non-linear
		  relation between ownership structure and performance.
		  Secondly, the variables of external discipline, that is
		  leverage and stock-turnover, partly explain performance.
		  Although debt level negatively influences performance
		  (Opler and Titman, 1994), conversely, stock-turnover has a
		  beneficial impact on performance (Charreaux, 1997).
		  Finally, though the organisational variables seem to have
		  no significant impact on performance, corporate size has a
		  positive influence on performance.},
  dbinsdate	= {2002/1}
}

@Book{		  sforna95a,
  author	= {Sforna, M. and Lamedica, R. and Prudenzi, A. and Caciotta,
		  M. and Orsolini Cencelli, V.},
  title		= {Neutral network based technique for short-term forecasting
		  of anomalous load periods.},
  year		= {1995},
  abstract	= {The paper illustrates a part of the research activity
		  conducted by authors in the field of electric Short Term
		  Load Forecasting (STLF) based on Artificial Neural Network
		  (ANN) architectures. Previous experiences with basic ANN
		  architectures have shown that, even though these
		  architecture provide results comparable with those obtained
		  by human operators for most normal days, they evidence some
		  accuracy deficiencies when applied to 'anomalous' load
		  conditions occurring during holidays and long weekends. For
		  these periods a specific procedure based upon a combined
		  (unsupervised/supervised) approach has been proposed. The
		  unsupervised stage provides a preventive classification of
		  the historical load data by means of a Kohonen's Self
		  Organizing Map (SOM). The supervised stage, performing the
		  proper forecasting activity, is obtained by using a
		  multi-layer percept ron with a back propagation learning
		  algorithm similar to the ones above mentioned. The
		  unconventional use of information deriving from the
		  classification stage permits the proposed procedure to
		  obtain a relevant enhancement of the forecast accuracy for
		  anomalous load situations.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  shah_hosseini00a,
  author	= {{Shah Hosseini}, H. and Safabakhsh, R.},
  title		= {Pattern classification by the time adaptive
		  self-organizing map},
  booktitle	= {ICECS 2000. 7th IEEE International Conference on
		  Electronics, Circuits and Systems. IEEE, Piscataway, NJ,
		  USA},
  year		= {2000},
  volume	= {1},
  pages		= {495--8},
  abstract	= {The time adaptive SOM, or TASOM, is used to automatically
		  adjust learning rate and neighborhood size of each neuron
		  of the SOM network independently. Each neuron's learning
		  rate is determined by a function of the distance between an
		  input vector and its weight vector. The width of the
		  neighborhood function is updated by a function of the
		  distance between the weight vector of the neuron and the
		  weight vectors of neighboring neurons. Only one time
		  parameter initialization is sufficient throughout the
		  lifetime of TASOM to work in stationary and nonstationary
		  environments without retraining. In this paper, the TASOM
		  is tested with standard data sets including the iris plant,
		  breast cancer, and BUPA liver disease data for
		  classification of input vectors. The tests carried out in
		  stationary and nonstationary environments demonstrate that
		  the TASOM can work for classification without the need for
		  reinitializing the network parameters and weights.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  shah_hosseini01a,
  author	= {Shah-Hosseini, H. and Safabakhsh, R.},
  title		= {{TAPCA}: time adaptive self-organizing maps for adaptive
		  principal components analysis},
  booktitle	= {Proceedings 2001 International Conference on Image
		  Processing. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {1},
  pages		= {509--12},
  abstract	= {We propose a neural network called time adaptive principal
		  components analysis (TAPCA) which is composed of a number
		  of time adaptive self-organizing map (TASOM) networks. Each
		  TASOM in the TAPCA network estimates one eigenvector of the
		  correlation matrix of the input vectors entered so far,
		  without having to calculate the correlation matrix. This
		  estimation is done in an online fashion. The input
		  distribution can be nonstationary, too. The eigenvectors
		  appear in order of importance: the first TASOM calculates
		  the eigenvector corresponding to the largest eigenvalue of
		  the correlation matrix, and so on. The TAPCA network is
		  tested in stationary environments, and is compared with the
		  eigendecomposition (ED) method and generalized Hebbian
		  algorithm (GHA) network. It performs better than both
		  methods and needs fewer samples to converge. It is also
		  tested in nonstationary environments, where it
		  automatically tolerates translation, rotation, scaling, and
		  a change in the shape of the distribution.},
  dbinsdate	= {2002/1}
}

@Article{	  shaikh96a,
  author	= {M. A. Shaikh and B. Tian and M. R. Azimi-Sadjadi and K. E.
		  Eis and T. H. VonderHaar},
  title		= {An automatic neural network-based cloud
		  detection/classification scheme using multispectral and
		  textural features},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2758},
  pages		= {51--61},
  note		= {(Algorithms for Multispectral and Hyperspectral Imagery II
		  Conf. Date: 9--11 April 1996 Conf. Loc: Orlando, FL, USA
		  Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  shalash00a,
  author	= {Shalash, W. M. and Abou Chadi, F.},
  title		= {A fingerprint classification technique using multilayer
		  {SOM}},
  booktitle	= {Proceedings of the Seventeenth National Radio Science
		  Conference. 17th NRSC'2000. Minufiya Univ, Minufiya,
		  Egypt},
  year		= {2000},
  volume	= {},
  pages		= {},
  abstract	= {This paper presents an automatic fingerprint
		  classification technique similar to that reported by Ongun
		  and Halici (see Proc. of IEEE vol.84, no.10, p.1497--12,
		  1996) but, an inverse filtering technique was introduced to
		  restore the distorted parts of the images prior to the
		  feature extraction stage. The results have shown that
		  introducing the inverse filtering stage has improved the
		  percentage of correct classification. Typical
		  classification accuracy reaches 91% with no rejects, 98%
		  with 8.1% rejects compared to the 87% with no rejects, 95%
		  with 9.4% rejects obtained using the previously reported
		  technique.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  shan91a,
  author	= {Lin Shan},
  title		= {Comparison of {K}ohonen feature map against {K-mean}
		  clustering algorithm with application to reversible image
		  compression},
  booktitle	= {Proc. China 1991 International Conference on Circuits and
		  Systems},
  year		= {1991},
  volume	= {II},
  pages		= {808--811},
  organization	= {IEEE; Shenzhen Univ. , China; CIE Circuits {\&} Syst. Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  shanmukh99a,
  author	= {K. Shanmukh and C. N. S. {Ganesh Murthy} and Y. V.
		  Venkatesh},
  title		= {Applications of self-organization networks spatially {SOM}
		  rphic to patterns},
  journal	= {Information Sciences},
  year		= 1999,
  volume	= 114,
  pages		= {23--39},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  shao02a,
  author	= {Shao, J. F. and Han, Jiang},
  title		= {The application of {SOM} networks on rock blastability
		  classification},
  booktitle	= {Proceedings of the Annual Conference on Explosives and
		  Blasting Technique},
  year		= {2002},
  editor	= {},
  volume	= {I},
  pages		= {407--413},
  organization	= {Lab. of Mechanics of Lille, Univ. of Sci. and Technol. of
		  Lille},
  publisher	= {International Society of Explosives Engineers},
  address	= {},
  abstract	= {Based on the rock blasting engineering, The
		  Self-Organizing Map (SOM) network has been implemented for
		  the concept and method of rock blastability classification.
		  The Self-Organizing Map (SOM) is a neural network algorithm
		  which is especially suitable for the analysis and
		  visualization of high-dimensional data. It maps nonlinear
		  statistical relationships between high-dimensional input
		  data into simple geometric relationships, usually on a
		  two-dimensional grid. The mapping roughly preserves the
		  most important topological and metric relationships of the
		  original data elements and, thus, inherently clusters the
		  data. SOM allows easy data fusion enabling visualization
		  and analysis of large databases of rock blasting
		  engineering. As a case study, the classification technique
		  based on SOM has been used to classify the rock mass
		  blastability rank. It can be founded from this paper that
		  the SOM networks can be developed well based on a
		  comprehensive data set of rock blasting engineering.},
  dbinsdate	= {2002/1}
}

@Article{	  sharpe98a,
  author	= {P. K. Sharpe and P. Caleb},
  title		= {Self organising maps for the investigation of clinical
		  data: a case study},
  journal	= {Neural Computing \& Applications},
  year		= {1998},
  volume	= {7},
  number	= {1},
  pages		= {65--70},
  dbinsdate	= {oldtimer}
}

@Article{	  shawkey98a,
  author	= {Shawkey, H. and Elsimary, H. and Ragaai, H. and Haddara,
		  H.},
  title		= {Low power {VLSI} neural network based arrhythmia
		  classifier},
  journal	= {Proceedings of IEEE Symposium on Computer-based Medical
		  Systems},
  year		= {1998},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  number	= {},
  volume	= {},
  pages		= {282--288},
  abstract	= {The implantable cardioverter defibrillators (ICDs) detect
		  and treat dangerous cardiac arrhythmia. This paper
		  describes a low power VLSI neural network chip which acts
		  as an intracardiac tachycardia classification system. A
		  robust neural network reduces impact of noise, drift and
		  offsets inherent in analog applications. Our system uses a
		  Kohonen self organizing map, and has a typical power
		  consumption of few milliwatts.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  shawkey99a,
  author	= {Shawkey, H. and Elsimary, H. and Haddara, H. and Ragaie,
		  H. F.},
  title		= {Design of a {VLSI} neural network arrhythmia classifier},
  booktitle	= {Proceedings of the Sixteenth National Radio Science
		  Conference. NRSC'99. Ain Shams Univ, Cairo, Egypt},
  year		= {1999},
  volume	= {},
  pages		= {},
  abstract	= {Artificial neural networks are now attractive tools to
		  enhance and improve the efficiency, the capability and the
		  features of instrumentation in applications related to
		  measurements, system identification, and control. The aim
		  of this paper is to implement an implantable cardiverter
		  defibrillator (ICD) using an analog neural network. The
		  paper describes a VLSI neural network chip to be
		  implemented using 1.2 mu m CMOS technology, which acts as
		  an intracardiac tachycardia classification system. A robust
		  neural network is less sensitive to noise, drift and
		  offsets inherent in analog applications. The proposed
		  classifier uses two types of neural networks a Kohonen self
		  organizing map (KSOM) circuit and a winner take all (WTA)
		  circuit.},
  dbinsdate	= {oldtimer}
}

@Article{	  sheikhan97a,
  author	= {M. Sheikhan and M. Tebyani and M. Lotfizad},
  title		= {Continuous speech recognition and syntactic processing in
		  {IR}anian Farsi language},
  journal	= {International Journal of Speech Technology},
  year		= {1997},
  volume	= {1},
  number	= {2},
  pages		= {135--41},
  dbinsdate	= {oldtimer}
}

@Article{	  shen01a,
  author	= {Shen, Tao and Huang, Shuhong and Han, Shoumu and Liu,
		  Dechang},
  title		= {Multi-type information fusion and state identification
		  based {SOFM}},
  journal	= {Jixie Gongcheng Xuebao/Chinese Journal of Mechanical
		  Engineering},
  year		= {2001},
  volume	= {37},
  number	= {1},
  month		= {Jan},
  pages		= {37--41},
  organization	= {Huazhong Univ of Science and Technology},
  publisher	= {Chin Mech Eng Soc},
  address	= {Beijing},
  abstract	= {From the viewpoint of information source, the system of
		  monitoring and diagnosis for machinery is one of
		  multi-source information processing systems. Various
		  features can be reduced to three types: numeric, linguistic
		  and graphics. Through translating the non-numeric symptom
		  into numeric one, information of various types can be
		  denoted by multi-dimensions vector. So, the idea of
		  features fusion of various types is proposed through
		  information compression, and the method of how
		  self-organizing feature mapping (SOFM) network deals with
		  it is studied. With the trace of active nodes on output
		  layer, the underlying features varying of state represented
		  by multi-source information can be observed correctly and
		  visually, so occurrence and varying trend of faults can be
		  identified early. The high performance of this method
		  proposed is exemplified by handling fusion in experiments
		  and field work.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  shen92a,
  author	= {Tao Shen and Jun-Ren Gan and Lin-Sheng Yao},
  title		= {A Generalized Placement Algorithm Based on
		  Self-Organization Neural Network},
  booktitle	= {Proc. IJCNN'92, International Joint Conference on Neural
		  Networks},
  year		= {1992},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {761--766},
  dbinsdate	= {oldtimer}
}

@Article{	  shen92b,
  author	= {Tao Shen and Jun-Ren Gan and Lin-Sheng Yao},
  title		= {Application of self-organization neural network in {VLSI}
		  placement},
  journal	= {Chinese J. Computers},
  year		= {1992},
  volume	= {15},
  number	= {9},
  pages		= {648--654},
  note		= {(in Chinese)},
  dbinsdate	= {oldtimer}
}

@Article{	  shen92c,
  author	= {Tao Shen and Jun-Ren Gan and Lin-Sheng Yao},
  title		= {Application of fuzzy neural computing in circuit
		  partitioning},
  journal	= {Chinese J. Computers},
  year		= {1992},
  volume	= {15},
  number	= {9},
  pages		= {641--647},
  note		= {(in Chinese)},
  dbinsdate	= {oldtimer}
}

@Article{	  shen92d,
  author	= {Tao Shen and Jun-Ren Gan and Lin-Sheng Yao},
  title		= {A neural network approach to cell placement},
  journal	= {Acta Electronica Sinica},
  year		= {1992},
  volume	= {20},
  number	= {10},
  pages		= {100--105},
  month		= {October},
  note		= {(in Chinese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  shen92e,
  author	= {Tao Shen and Jun-Ren Gan and Lin-Sheng Yao},
  title		= {Application of fuzzy neural computing for partitioning
		  circuits},
  booktitle	= {Proceedings of the IEEE 1992 Custom Integrated Circuits
		  Conference},
  year		= {1992},
  pages		= {5. 3/1--4},
  organization	= {Shanghai Inst. of Metall. , Acad. Sinica, China},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  shen95a,
  author	= {Chang-Yun Shen and Yoh-Han Pao},
  title		= {'{L}et the Data Speak for Themselves': A Neural Net
		  Computing Approach to Information Management},
  volume	= {I},
  pages		= {142--145},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sheng01a,
  author	= {Sheng Tun Li and Tan Sheng Li},
  title		= {Interoperable Web-based data mining system by Java
		  distributed object computing},
  booktitle	= {Proceedings of the 34th Annual Hawaii International
		  Conference on System Sciences. IEEE Comput. Soc, Los
		  Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {},
  abstract	= {The development of Web-based data mining systems has
		  received a lot of attention in recent years. It plays the
		  key enabling role for competitive businesses in the
		  e-commerce era. A cost-effective and prompt approach for
		  this task is to integrate and coordinate existing data
		  mining applications in a seamless manner. In this paper, we
		  propose a new methodology for developing a Web-based data
		  mining system. This system relies on Java distributed
		  object computing to tackle the issues of interoperability
		  in heterogeneous environments, namely the language,
		  platform, visual object model and data access. The
		  effectiveness of the proposed system is demonstrated by
		  integrating two powerful data mining tools, SOM PAK and
		  Nenet, and an experiment on the Iris data. The methodology
		  can facilitate the collaboration of intelligent components
		  seamlessly in a "plug-N-work" manner, but without
		  re-engineering.},
  dbinsdate	= {2002/1}
}

@InCollection{	  sheng96a,
  author	= {W. Sheng and J. Rueda and D. Blight},
  title		= {Neural network-based {ATM} {QoS} estimation},
  booktitle	= {IEEE WESCANEX 97 Communications, Power and Computing.
		  Conference Proceedings},
  publisher	= {Pergamon},
  year		= {1996},
  editor	= {A. J. Morris and E. B. Martin},
  address	= {Oxford, UK},
  pages		= {1--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sheu92a,
  author	= {B. J. Sheu and J. Choi and C. F. Chang},
  title		= {An analog neural network processor for
		  \mbox{self-organizing} mapping},
  booktitle	= {1992 IEEE Int. Solid-State Circuits Conf. Digest of
		  Technical Papers. 39th ISSCC},
  year		= {1992},
  editor	= {J. H. Wuorinen},
  pages		= {136--137, 266},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  shi01a,
  author	= {Shi, Gui-Rong and Xing, Yuan and Zhang, Yong-Qing},
  title		= {Self-organizing feature map networks for segmentation of
		  point-cloud},
  journal	= {Journal-of-Shanghai-Jiaotong-University},
  year		= {2001},
  volume	= {35},
  pages		= {1093--6},
  abstract	= {Segmentation of point-cloud aims at classifying the
		  point-cloud into several subspaces and each can be fitted
		  to a surface. In this paper, a segmentation using
		  self-organizing feature map (SOFM) network was presented. A
		  six dimensional feature vector (3-dimensional coordinate
		  and 3-dimensional normal vector) was taken as input for
		  SOFM. Weighted input and weighted Euclidean distance were
		  adopted in the learning process of SOFM, which improves the
		  speed and exactness of the segmentation. The segmentation
		  using SOFM is robust to noise, and has no limitation for
		  surface type. The method is validated by the real scanned
		  point-cloud.},
  dbinsdate	= {2002/1},
  merjanote     = {last name assumed from small capitals (rong, qing)}
}

@InProceedings{	  shibata00a,
  author	= {Aiko Shibata and Yu Sakai},
  title		= {Budgetary Transfer Processe: Equity versus Efficiency},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {171--9},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@Article{	  shibata02a,
  author	= {Shibata, A. and Sakai, Y.},
  title		= {Budgetary transfer to local governments: equity,
		  efficiency and political influence},
  journal	= {International-Journal-of-Knowledge-Based-Intelligent-Engineering-Systems}
		  ,
  year		= {2002},
  volume	= {6},
  pages		= {23--30},
  abstract	= {Mechanisms of budgetary resource allocations from the
		  Japanese central government to local governments are
		  analyzed in this paper utilizing statistical methods and a
		  Self Organizing Map (SOM). All budgetary transfers to local
		  governments are said to be redistributed on an equitable
		  basis. As a result of the budgetary transfers to local
		  governments that have been carried out for a long time on
		  an equitable basis, we expect inefficient investment in
		  public goods. We also suspect political influences. This
		  paper tries to analyze the budgetary resource allocations
		  from three points of view---equity, efficiency and
		  political influences. Using a cross- sectional analysis of
		  fiscal year 1991 and panel data analyses of data from the
		  years 1977 through 1995, the following results were
		  obtained. With the national data, we found that our results
		  were as expected- namely, the budgetary resource
		  allocations are made on an equitable basis, but inefficient
		  public goods investment has taken place, and there is
		  political influence. However, when data from 46 prefectures
		  are clustered using SOM, and the two largest clusters are
		  analyzed further statistically, the results differ. We also
		  demonstrated that some variables have greater influences on
		  the budgetary transfers than the others and that SOM is
		  useful as a visual data mining method.},
  dbinsdate	= {2002/1}
}

@TechReport{	  shibata99a,
  author	= {T. Shibata and K. Miyagi and K. Fujimura and H. Tokutaka},
  title		= {Optimization of Surface Component Mounting on the Printed
		  Circuit Board using {SOM}-{TSP} Method},
  institution	= {IEICE},
  year		= {1999},
  note		= {(in Japanese)},
  key		= {NC98--135},
  dbinsdate	= {oldtimer}
}

@Article{	  shieh99a,
  author	= {J. S. Shieh and D. A. Linkens and J. E. Peacock},
  title		= {Hierarchical Rule Based and Self Organizing Fuzzy Logic
		  Control for Depth of Anesthesia},
  journal	= {IEEE Transactions on Systems, Man and Cybernetics Part C:
		  Applications and Reviews},
  volume	= {29},
  pages		= {98--109},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@Article{	  shihab00a,
  author	= {Shihab, K. I. and Ramadhan, H. A.},
  title		= {Clustering technique using dynamic filtering concepts and
		  its application to computer workload modeling},
  journal	= {Journal of Intelligent Systems},
  year		= {2000},
  volume	= {10},
  number	= {4},
  month		= {},
  pages		= {321--344},
  organization	= {Coll of Science},
  publisher	= {Freund Pettman Publ},
  address	= {Patrington},
  abstract	= {This work describes clustering methods for constructing a
		  reliable classification of data. The underlying scheme is
		  based on qualitative and quantitative measures of
		  similarity in order to create classes. The methods take
		  into consideration the objectives of the classification
		  study that have not been dealt with by previous work. They
		  are computationally efficient and have high classification
		  accuracy. The basic ideas of the presented methods are
		  simple yet effective. They use a control-generate-test
		  strategy, which involves the generation of a set of dynamic
		  filters (concepts) and then testing these concepts on given
		  data vectors (components) for detecting clusters of these
		  components. The SOM (self-organizing map) method and some
		  well-known clustering techniques are used to show the
		  clustering capability of these methods. In particular,
		  simulation results from the application of these methods to
		  computer workload characterization indicate that the
		  conceptual clustering has a higher potential for producing
		  meaningful categories of the workload components than the
		  traditional clustering methods.},
  dbinsdate	= {2002/1}
}

@InCollection{	  shihab95a,
  author	= {K. I. Shihab and J. A. Campbell},
  title		= {A conceptual clustering technique and its application to
		  computer workload characterisation},
  booktitle	= {Industrial and Engineering Applications of Artificial
		  Intelligence and Expert Systems. Proceedings of the Eighth
		  International Conference},
  publisher	= {Gordon \& Breach},
  year		= {1995},
  editor	= {G. F. Forsyth and M. Ali},
  address	= {Newark, NJ, USA},
  pages		= {289--94},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  shihab97a,
  author	= {Shihab, K. I. and Campbell, J. A.},
  title		= {Workload characterisation based on {AI} techniques: a
		  comparative study},
  booktitle	= {Simulation in Industry. 9th European Simulation Symposium
		  1997. ESS'97. SCS, San Diego, CA, USA},
  year		= {1997},
  volume	= {},
  pages		= {567--70},
  abstract	= {This paper describes conceptual clustering methods for
		  constructing a reliable classification of data. The
		  underlying scheme is based on qualitative and quantitative
		  measures of similarity in order to create classes. The
		  method takes into consideration the objectives of the
		  classification study that has not been dealt with by
		  previous work. It is computationally efficient and has high
		  classification accuracy. The SOM (self-organising map)
		  method is used to show the clustering capability of this
		  technique. Simulation results from the application of these
		  methods to computer workload characterisation indicate that
		  the conceptual clustering has more potential for producing
		  meaningful categories of the workload components than the
		  traditional clustering methods.},
  dbinsdate	= {2002/1}
}

@Article{	  shin00a,
  author	= {Shin, H. W. and Llobet, E. and Gardner, J. W. and Hines,
		  E.L. and Dow, C.S.},
  title		= {Classification of the strain and growth phase of
		  cyanobacteria in potable water using an electronic nose
		  system},
  journal	= {IEE Proceedings: Science, Measurement and Technology},
  year		= {2000},
  volume	= {147},
  number	= {4},
  month		= {},
  pages		= {158--164},
  organization	= {Univ of Warwick},
  publisher	= {IEE},
  address	= {Stevenage},
  abstract	= {An electronic nose comprising an array of six commercial
		  odour sensors has been used to monitor not only different
		  strains, but also the growth phase, of cyanobacteria which
		  is normally called blue green algal. A series of
		  experiments were carried out to analyze the nature of two
		  closely related strains of cyanobacteria, Microcystis
		  aeruginosa PCC 7806 that produces a toxin and PCC 7941 that
		  does not. The authors have constructed a measurement system
		  for the testing of the cyanobacteria in water over a period
		  of up to 40 days. After some pre-processing to remove the
		  variation associated with running the electronic nose in
		  ambient air, the two different strains, and their growth
		  phase, were classified with principal components analysis,
		  multilayer perceptron (MLP), learning vector quantization
		  (LVQ), and fuzzy ARTMAP. The optimal MLP network was found
		  to classify correctly 97.1% of unknown non-toxic and 100%
		  of unknown toxic cyanobacteria. The optimal LVQ and fuzzy
		  ARTMAP algorithms were able to classify 100% of both
		  strains of cyanobacteria. The accuracy of MLP, LVQ and
		  fuzzy ARTMAP algorithms with the four different growth
		  phases of toxic cyanobacteria was 92.3%, 95.1% and 92.3%,
		  respectively.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  shin91a,
  author	= {Y. H. Shin and C. -C. Lu},
  title		= {Image compression using vector quantization and artificial
		  neural networks},
  booktitle	= {Conf. Proc. 1991 IEEE International Conference on Systems,
		  Man, and Cybe. 'Decision Aiding for Complex Systems'},
  year		= {1991},
  volume	= {III},
  pages		= {1487--1491},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  shin92a,
  author	= {Yong Ho Shin and Cheng-Chang Lu},
  title		= {Neural networks for classified vector quantization of
		  images},
  journal	= {Proc. of the SPIE---The International Society for Optical
		  Engineering},
  year		= {1992},
  volume	= {1657},
  pages		= {100--105},
  dbinsdate	= {oldtimer}
}

@Article{	  shinichi99a,
  author	= {Shinichi, Y. and Shinji, D. and Shigeru, O.},
  title		= {Deterministic annealing method with equidistortion
		  principle for vector quantization},
  journal	= {Transactions of the Institute of Electrical Engineers of
		  Japan, Part C},
  year		= {1999},
  volume	= {119},
  pages		= {1010--17},
  abstract	= {The deterministic annealing (DA) method for vector
		  quantization can obtain better representative vectors than
		  the LBG, CL, FSCL and SOFM. But it has two problems. One is
		  that quality of the results is sensitive to the reduction
		  of parameter T which is a temperature in an annealing
		  operation. Another is that learning time is relatively
		  long. The equidistortion principle has been derived and it
		  is shown effective for vector quantization. In this paper,
		  to solve the above problems, a new algorithm named DAEP
		  (deterministic annealing method with equidistortion
		  principle) is proposed. In the DAEP, the idea of
		  equidistortion principle is introduced into the DA method.
		  In the annealing operation, location of representative
		  vectors are forced to move to the global optimum points
		  using the equidistortion principle. Judging from the
		  quality of results and learning time, it is shown that the
		  DAEP can obtain better results than the other methods.},
  dbinsdate	= {oldtimer}
}

@Article{	  shirakura01a,
  author	= {Shirakura, J. and Kurata, K.},
  title		= {Nonlinear principal component analysis by learning nerve
		  fields united by inhibitory connections},
  journal	= {Transactions-of-the-Institute-of-Electronics,-Information-and-Communication-Engineers-D-II}
		  ,
  year		= {2001},
  volume	= {},
  pages		= {549--58},
  abstract	= {The layered self-organizing map model is proposed to
		  detect from the first to the third nonlinear principal
		  components. The model is an advanced version of the
		  self-organizing overlapping maps model proposed and studied
		  in preceding papers. This model inherits its nature from
		  our overlapped mapping model and the resulting maps for the
		  second and the third nonlinear principal component can be
		  understood as the superposition of many correlating maps.},
  dbinsdate	= {2002/1}
}

@Article{	  shirakura98a,
  author	= {J. Shirakura and K. Kurata},
  title		= {Locking of \mbox{self-organizing} multiple maps by weak
		  similarity of input information},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1998},
  volume	= {J81D-II},
  number	= {10},
  pages		= {2421--9},
  dbinsdate	= {oldtimer}
}

@Article{	  shouhong01a,
  author	= {Shouhong Wang},
  title		= {Cluster analysis using a validated self-organizing method:
		  cases of problem identification},
  journal	= {International-Journal-of-Intelligent-Systems-in-Accounting,-Finance-and-Management}
		  ,
  year		= {2001},
  volume	= {10},
  pages		= {127--38},
  abstract	= {Kohonen's self-organizing feature maps (SOFMs) are
		  commonly used in cluster analysis for problem solving.
		  However, given a set of sample data, the cluster analysis
		  results obtained by using the standard SOFM method can vary
		  depending on the setting of the parameters of the SOFM. To
		  validate the cluster analysis results, information in
		  addition to the data for the SOFM is required. This paper
		  reports practical cases of cluster analysis using SOFMs in
		  conjunction with a measure of validation. In these cases,
		  multivariate survey data were used to identify problems
		  through validated SOFM cluster analyses.},
  dbinsdate	= {2002/1}
}

@Article{	  shoukry96a,
  author	= {S. N. Shoukry and D. Martinelli and S. T. Varadarajan and
		  U. B. Halabe},
  title		= {Radar signal interpretation using neural network for
		  defect detection in concrete},
  journal	= {Materials Evaluation},
  year		= {1996},
  volume	= {54},
  number	= {3},
  pages		= {393--7},
  dbinsdate	= {oldtimer}
}

@Article{	  shouno01a,
  author	= {Shouno, H. and Kurata, K.},
  title		= {Formation of a direction map by projection learning using
		  Kohonen's self-organization map},
  journal	= {BIOLOGICAL CYBERNETICS},
  year		= {2001},
  volume	= {85},
  number	= {4},
  month		= {OCT},
  pages		= {241--246},
  abstract	= {In this paper, we propose a modification of Kohonen's
		  self- organization map (SOM) algorithm. When the input
		  signal space is not convex, some reference vectors of SOM
		  can protrude from it. The input signal space must be convex
		  to keep all the reference vectors fixed on it for any
		  updates. Thus, we introduce a projection learning method
		  that fixes the reference vectors onto the input signal
		  space. This version of SOM can be applied to a non-convex
		  input signal space. We applied SOM with projection learning
		  to a direction map observed in the primary visual cortex of
		  area 17 of ferrets, and area 18 of cats. Neurons in those
		  areas responded selectively to the orientation of edges or
		  line segments, and their directions of motion. Some
		  iso-orientation domains were subdivided into selective
		  regions for the opposite direction of motion. The abstract
		  input signal space of the direction map described in the
		  manner proposed by Obermayer and Blasdel [(1993) J Neurosci
		  13: 4114--4129] is not convex. We successfully used SOM
		  with projection learning to reproduce a
		  direction-orientation joint map.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  shu_yue00a,
  author	= {Shu Yue, Joyce and Tsang, Eric and Yeung, Daniel and Shi,
		  Daming},
  title		= {Mining fuzzy association rules with weighted items},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2000},
  editor	= {},
  volume	= {3},
  pages		= {1906--1911},
  organization	= {Hong Kong Polytechnic Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {In most models of mining fuzzy association rules, the
		  items are considered to have equal importance. Due to the
		  diverse human's interestingness and preference to items,
		  such models do not work well in many situations. To improve
		  such models, we propose a method in this paper to mine
		  fuzzy association rules with weighted items. One of the
		  major problems in the research of data mining is the
		  development of good measures of interestingness of
		  discovered rules. In this paper, the weighted support and
		  weighted confidence for fuzzy association rules are
		  defined. The Kohonen self-organized mapping is used to
		  fuzzily the numerical attributes into linguistic terms. A
		  new fuzzy association rules mining algorithm, which
		  generalizes the popular Apriori Gen large itemset based
		  algorithm, is developed. The advantages of the new
		  algorithm are shown by testing it on a census database with
		  5000 transaction records.},
  dbinsdate	= {2002/1}
}

@Article{	  shubnikov97a,
  author	= {E. I. Shubnikov},
  title		= {The main models of neural networks},
  journal	= {Journal of Optical Technology},
  year		= {1997},
  volume	= {64},
  number	= {11},
  pages		= {989--1003},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  shumsky01a,
  author	= {S. Shumsky},
  title		= {Self-organising Internet semantic network},
  booktitle	= {Advances in Self-Organising Maps},
  crossref	= {},
  key		= {},
  pages		= {61--66},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {Springer},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InCollection{	  shumsky97a,
  author	= {S. A. Shumsky and A. V. Yarovoy},
  title		= {Neural network analysis of {R}ussian banks},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {351--355},
  dbinsdate	= {oldtimer}
}

@InCollection{	  shumsky98a,
  author	= {S. A. Shumsky and A. V. Yarovoy},
  title		= {Self-Organizing Atlas of Russian Banks},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {72--82},
  dbinsdate	= {oldtimer}
}

@InCollection{	  shumsky99a,
  author	= {S. A. Shumsky},
  title		= {NAVIGATION IN DATABASES USING SELF-ORGANISING MAPS},
  booktitle	= {Kohonen Maps},
  pages		= {197--206},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: self-organising, financial data, documents
		  categorisation},
  dbinsdate	= {oldtimer}
}

@Article{	  shuono01a,
  author	= {Shuono, H. and Kurata, K.},
  title		= {Formation of a direction map by projection learning using
		  Kohonen's self-organization map},
  journal	= {Biological-Cybernetics},
  year		= {2001},
  volume	= {85},
  pages		= {241--6},
  abstract	= {Proposes a modification of Kohonen's self-organization map
		  (SOM) algorithm. When the input signal space is not convex,
		  some reference vectors of SOM can protrude from it. The
		  input signal space must be convex to keep all the reference
		  vectors fixed on it for any updates. Thus, the authors
		  introduce a projection learning method that fixes the
		  reference vectors onto the input signal space. This version
		  of SOM call be applied to a non-convex input signal space.
		  The authors applied SOM with projection learning to a
		  direction map observed in the primary visual cortex of area
		  17 of ferrets, and area 18 of cats. Neurons in those areas
		  responded selectively to the orientation of edges or line
		  segments, and their directions of motion. Some
		  iso-orientation domains were subdivided into selective
		  regions for the opposite direction of motion. The abstract
		  input signal space of the direction map described in the
		  manner proposed by Obermayer and Blasdel [(1993) J Neurosci
		  13: 4114--4129] is not convex. The authors successfully
		  used SOM with projection learning to reproduce a
		  direction-orientation joint map.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  shwenker00a,
  author	= {Friedhelm Schwenker and Hans A. Kestler and Gunther Palm},
  title		= {Combination of Supervised and Unsupervised Learning for
		  Radial-Basis-Function Networks},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {University of Ulm, D-89069 Ulm, Germany},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@Article{	  shyamsunder00a,
  author	= {Shyamsunder, M. T. and Rajagopalan, C. and Raj, B. and
		  Dewangan, S. K. and Rao, B. P. C. and Ray, K. K.},
  title		= {Pattern recognition approaches for the detection and
		  characterization of discontinuities by eddy current
		  testing},
  journal	= {Materials Evaluation},
  year		= {2000},
  volume	= {58},
  number	= {1},
  month		= {Jan},
  pages		= {93--101},
  organization	= {},
  publisher	= {American Soc for Nondestructive Testing},
  address	= {Columbus, OH},
  abstract	= {Eddy current signals (ECS) generated under varied
		  experimental conditions from different types of
		  discontinuities like partial/through thickness holes and
		  notches of various dimensions, fatigue cracks, stress
		  corrosion cracks, etc. in AISI type 316 stainless steel
		  sheets/plates have been analyzed using pattern recognition
		  (PR) approaches to understand their quality of performance
		  for detection and characterization of several aspects of
		  the discontinuities. The PR analyses have been carried out
		  using linear discriminant (LD), minimum distance (MD),
		  empirical Bayesian (EB) and K-nearest neighbor (KNN)
		  statistical classifiers, and multilayered perceptron (MLP)
		  and Kohonen's artificial neural network (KANN). The MLP
		  approach has been extended to eddy current images also to
		  achieve deblurring. The practical feasibility and
		  application potential of ANNs is demonstrated through a
		  case study on nuclear fuel cladding tubes where both the
		  online and the offline approaches have been implemented.},
  dbinsdate	= {2002/1}
}

@Article{	  si00a,
  author	= {Si, J. and Lin, S. and Vuong, M. -A.},
  title		= {Dynamic topology representing networks},
  journal	= {Neural Networks},
  year		= {2000},
  volume	= {13},
  number	= {6},
  month		= {Jul},
  pages		= {617--627},
  organization	= {Arizona State Univ},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {In the present paper, we propose a new algorithm, namely
		  the Dynamic Topology Representing Networks (DTRN) for
		  learning both topology and clustering information from
		  input data. In contrast to other models with adaptive
		  architecture of this kind, the DTRN algorithm adaptively
		  grows the number of output nodes by applying a vigilance
		  test. The clustering procedure is based on a
		  winner-take-quota learning strategy in conjunction with an
		  annealing process in order to minimize the associated mean
		  square error. A competitive Hebbian rule is applied to
		  learn the global topology information concurrently with the
		  clustering process. The topology information learned is
		  also utilized for dynamically deleting the nodes and for
		  the annealing process. Properties of the DTRN algorithm
		  will be discussed. Extensive simulations will be provided
		  to characterize the effectiveness of the new algorithm in
		  topology preserving, learning speed, and classification
		  tasks as compared to other algorithms of the same nature.},
  dbinsdate	= {2002/1}
}

@Article{	  si00g,
  author	= {Si, Jie and Rawat, Banmali S.},
  title		= {New approach for virtual topology design in multihop
		  lightwave networks},
  journal	= {Journal of Optical Communications},
  year		= {2000},
  volume	= {21},
  number	= {3},
  month		= {},
  pages		= {94--100},
  organization	= {Univ of Nevada, Reno},
  publisher	= {Fachverlag Schiele \& Schoen GmbH},
  address	= {Berlin},
  abstract	= {The design of virtual topology of multihop lightwave
		  networks belongs to multivariable and multidimensional
		  optimization problem. It requires selection of the
		  connectivity diagram among all the communication nodes. In
		  this paper, this optimization problem is converted to a
		  problem of finding optimal path among all the nodes and
		  Kohonen's Self Organizing Feature Mapping (SOFM) algorithm
		  is used as the optimization approach. The main objective of
		  the design is to minimize a function which combines both
		  average delay and throughput. Simulation experiments are
		  carried out and results are obtained from 8-node to 20-node
		  optical multihop networks based on different fixed traffic
		  demands. The results obtained are in good agreement with
		  previous research work.},
  dbinsdate	= {2002/1}
}

@InCollection{	  sick97a,
  author	= {B. Sick},
  title		= {Classifying the wear of turning tools with neural
		  networks},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {1059--64},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sieben92d,
  author	= {G. Sieben and L. Vercauteren and M. Praet and G. Otte and
		  L. Boullart and L. Calliauw and L. Roels},
  title		= {The Application of Topological Mapping in the Study of
		  Human Cerebral Tumors},
  booktitle	= {Theory and Applications of Neural Networks},
  publisher	= {Springer},
  year		= {1992},
  editor	= {J. G. Taylor and C. L. T. Mannion},
  chapter	= {},
  pages		= {121--124},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  siegel92a,
  author	= {B. K. Siegel and K. J. Keller},
  title		= {Pilot task monitoring using neural networks},
  booktitle	= {Proceedings of the IEEE 1992 National Aerospace and
		  Electronics Conference},
  volume	= {2},
  year		= {1992},
  publisher	= {New York, Institute of Electrical and Electronics
		  Engineers, Inc.},
  address	= {Dayton, OH},
  pages		= {709--714},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  siemon90a,
  author	= {H. P. Siemon and A. Ultsch},
  title		= {{K}ohonen networks on transputers: implementation and
		  animation},
  booktitle	= {Proc. INNC-90 Int. Neural Network Conf. },
  year		= {1990},
  pages		= {643--646},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  siemon92a,
  author	= {H. P. Siemon},
  title		= {Selection of Optimal Parameters for {K}ohonen
		  Self-Organizing Feature Maps},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1573--1577},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sien97a,
  author	= {Swee {Sien Tan} and D. Srinivasan and C. S. Chang and
		  Minjun Yi and Eng Kiat Chan},
  title		= {Cascaded neural networks for accurate short-term load
		  forecasting},
  booktitle	= {ISAP '97 International Conference on Intelligent System
		  Application to Power Systems. Proceedings},
  publisher	= {Korean Inst. Electr. Eng},
  year		= {1997},
  editor	= {Y. -M. Park and J. -K. Park and K. Y. Lee},
  address	= {Seoul, South Korea},
  pages		= {357--61},
  dbinsdate	= {oldtimer}
}

@Article{	  silulwane01a,
  author	= {Silulwane, N. F. and Richardson, A. J. and Shillington, F.
		  A. and Mitchell-Innes, B. A.},
  title		= {Identification and classification of vertical chlorophyll
		  patterns in the Benguela upwelling system and
		  Angola-Benguela front using an artificial neural network},
  journal	= {SOUTH AFRICAN JOURNAL OF MARINE SCIENCE-SUID-AFRIKAANSE
		  TYDSKRIF VIR SEEWETENSKAP},
  year		= {2001},
  volume	= {23},
  pages		= {37--51},
  abstract	= {Information on the vertical chlorophyll structure in the
		  ocean is important for estimating integrated chlorophyll a
		  and primary production from satellite. For this study,
		  vertical chlorophyll profiles from the Benguela upwelling
		  system and the Angola-Benguela front were collected in
		  winter to identify characteristic profiles. A shifted
		  Gaussian model was fitted to each profile to estimate four
		  parameters that defined the shape of the curve: the
		  background chlorophyll concentration B-o), the height
		  parameter of the peak (h), the width of the peak (a) and
		  the depth of the chlorophyll peak (z(m)). A type of
		  artificial neural network called a self-organizing map
		  (SOM) was then used on these four parameters to identify
		  characteristic profiles. The analysis identified a
		  continuum of chlorophyll patterns, from those with large
		  surface peaks (>10 mg m(-3)) to those with smaller
		  near-surface peaks (< 2 mg m(- 3)). The frequency of
		  occurrence of each chlorophyll pattern identified by the
		  SOM showed that the most frequent pattern (12%) had a
		  near-surface peak and the least frequent pattern (2%) had a
		  large surface peak. These characteristic profile shapes
		  were then related to pertinent environmental variables such
		  as sea surface temperature, surface chlorophyll, mixed
		  layer depth and euphotic depth. Partitioning the SOM output
		  map into environmental categories showed large peaks of
		  surface chlorophyll dominating in water with cool
		  temperature, high surface chlorophyll concentration and
		  shallow mixed layer and euphotic depth. By contrast,
		  smaller peaks of subsurface chlorophyll were in water with
		  warmer temperature, lower surface chlorophyll
		  concentration, intermediate mixed layer and deep euphotic
		  depth. These relationships can be used semi-quantitatively
		  to predict profile shape under different environmental
		  conditions. The SOM analysis highlighted the large
		  variability in shape of vertical chlorophyll profiles in
		  the Benguela. This suggests that an ideal typical
		  chlorophyll profile, as used in the framework of
		  biogeochemical provinces, may not be applicable to this
		  dynamic upwelling system.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  sim97a,
  author	= {H. Sim and R. Damper},
  title		= {Two-dimensional object matching using {K}ohonen maps},
  booktitle	= {Proceedings of IEEE International Conference on Systems,
		  Man, and Cybernetics},
  volume	= {1},
  year		= {1997},
  publisher	= {Institute of Electrical and Electronics Engineers, Inc.},
  address	= {Piscataway, NJ},
  pages		= {620--25},
  dbinsdate	= {oldtimer}
}

@InCollection{	  simelius97a,
  author	= {K. Simelius and L. Reinhardt and J. Nenonen and I. Tierala
		  and L. Toivonen and T. Katila},
  title		= {Self-Organizing Maps in Arrhythmia Localization from Body
		  Surface Potential Mapping},
  booktitle	= {Proc. 19th Annual International Conference of the IEEE
		  Engineering in Medicine and Biology Society, Chicago},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  simula02a,
  author	= {Simula, O. and Hollmen, J. and Alhoniemi, E.},
  title		= {Models from data: analysis of industrial processes and
		  telecommunication systems},
  journal	= {Automazione-e-Strumentazione},
  year		= {2002},
  volume	= {50},
  pages		= {107--13},
  abstract	= {Modeling of systems is often based on the knowledge of the
		  physical phenomena in the system. When physical knowledge
		  is not available, or when modeling is too difficult due to
		  nonlinearities, operational measurement data from the
		  process may be used to build laws concerning the behavior
		  of such a system. In this paper, we review some data-driven
		  methods that have been successfully applied in industrial
		  settings and in telecommunications. In particular, we
		  present a neural network model-self-organizing map-that has
		  been applied in various projects.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  simula92a,
  author	= {Olli Simula and Ari Visa},
  title		= {Self-Organizing Feature Maps in Texture Classification and
		  Segmentation},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1621--1628},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  simula93a,
  author	= {Olli Simula and Ari Visa and Kimmo Valkealahti},
  title		= {Operational Cloud Classifier Based on the Topological
		  Feature Map},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {899--902},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InBook{	  simula95a,
  author	= {Olli Simula and Jari Kangas},
  title		= {Neural Networks for Chemical Engineers},
  chapter	= {14, Process monitoring and visualization using
		  self-organizing maps},
  publisher	= {Elsevier},
  year		= {1995},
  volume	= {6},
  series	= {Computer-Aided Chemical Engineering},
  address	= {Amsterdam},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  simula96a,
  author	= {O. Simula and E. Alhoniemi and J. Hollm\'en and J.
		  Vesanto},
  title		= {Monitoring and Modeling of Complex Processes Using
		  Hierarchical Self-Organizing Maps},
  booktitle	= {Proc. of 1996 IEEE International Symposium on Circuits and
		  Systems (ISCAS-96)},
  year		= {1996},
  volume	= {Supplement to vol. 4},
  pages		= {73--76},
  dbinsdate	= {oldtimer}
}

@InCollection{	  simula97a,
  author	= {Olli Simula and Esa Alhoniemi and Jaakko Hollm{\'e}n and
		  Juha Vesanto},
  title		= {Analysis of Complex Systems Using the Self-Organizing
		  Map},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {1313--1317},
  dbinsdate	= {oldtimer}
}

@InCollection{	  simula98a,
  author	= {O. Simula and J. Vesanto and P. Vasara},
  title		= {Analysis of Industrial Systems Using the Self-Organizing
		  Map},
  booktitle	= {Proceedings of 1998 Second International Conference on
		  Knowledge-Based Intelligent Engineering Systems (KES'98)},
  year		= {1998},
  address	= {Adelaide, Australia},
  pages		= {61--68},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  simula99a,
  author	= {Simula, O. and Alhoniemi, E.},
  title		= {SOM based analysis of pulping process data},
  booktitle	= {Engineering Applications of Bio-Inspired Artificial Neural
		  Networks. International Work-Conference on Artificial and
		  Natural Neural Networks, IWANN'99. Proceedings, (Lecture
		  Notes in Computer Science Vol.1607)},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  year		= {1999},
  volume	= {2},
  pages		= {567--77},
  abstract	= {Data driven analysis of complex systems or processes is
		  necessary in many practical applications where analytical
		  modeling is not possible. The self-organizing map (SOM) is
		  a neural network algorithm that has been widely applied in
		  analysis and visualization of high-dimensional data. It
		  carries out a nonlinear mapping of input data onto a
		  two-dimensional grid. The mapping preserves the most
		  important topological and metric relationships of the data.
		  The SOM has turned out to be an efficient tool in data
		  exploration tasks in various engineering applications:
		  process analysis in forest industry, steel production and
		  analysis of telecommunication networks and systems. In this
		  paper, SOM based analysis of complex process data is
		  discussed. As a case study, analysis of a continuous pulp
		  digester is presented. The SOM is used to form visual
		  presentations of the data. By interpreting the
		  visualizations, complex parameter dependencies can be
		  revealed. By concentrating on the significant measurements,
		  reasons for digester faults can be determined.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  simula99b,
  author	= {O. Simula and J. Ahola and E. Alhoniemi and J. Himberg and
		  J. Vesanto},
  title		= {Self-Organizing Map in Analysis of Large-Scale Industrial
		  Systems},
  booktitle	= {Kohonen Maps},
  pages		= {375--387},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: data mining, visualisation, system analysis},
  dbinsdate	= {oldtimer}
}

@InCollection{	  simula99c,
  author	= {Simula, O. and Vasara, P. and Vesanto, J. and Helminen,
		  R.-R.},
  title		= {The Self-Organizing Map in Industry Analysis.},
  booktitle	= {Intelligent Techniques in Industry.},
  pages		= {87--112},
  publisher	= {CRC Press LLC},
  year		= {1999},
  editor	= {Jain, L.C. and Vemuri, V.R.},
  chapter	= {4},
  dbinsdate	= {oldtimer}
}

@InCollection{	  simula99d,
  author	= {Simula, O. and Vesanto, J. and Alhoniemi, E. and Hollmén,
		  J.},
  title		= {Analysis and Modeling of Complex Systems Using the
		  Self-Organizing Map.},
  booktitle	= {Neuro-Fuzzy Techniques for Intelligent Information
		  Systems.},
  pages		= {3--22},
  publisher	= {Physica-Verlag},
  year		= {1999},
  editor	= {Kasabov, N. and Kozma, R.},
  dbinsdate	= {oldtimer}
}

@Article{	  sinesio00a,
  author	= {Sinesio, F. and {Di Natale}, C. and Quaglia, G. B. and
		  Bucarelli, F. M. and Moneta, E. and Macagnano, A. and
		  Paolesse, R. and D'Amico, A.},
  title		= {Use of electronic nose and trained sensory panel in the
		  evaluation of tomato quality},
  journal	= {JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE},
  year		= {2000},
  volume	= {80},
  number	= {1},
  month		= {JAN 1},
  pages		= {63--71},
  abstract	= {In this paper the performances of an electronic nose based
		  on metalloporphyrin-coated quartz microbalance sensors and
		  of an experienced panel of seven human assessors in the
		  evaluation of gases derived from degradation reactions in
		  tomatoes are presented and discussed. The performances are
		  measured in terms of the capability of both systems to
		  distinguish between samples of different quality coming
		  from conventional and organic production systems. The study
		  deals with the application of pattern recognition
		  techniques based on either multivariate statistical methods
		  (PCA, GPA) or artificial neural networks using a
		  self-organising map (SOM). The response pattern of the
		  sensor array and the sensory data are analysed and compared
		  using these methods. Similarities in the classification of
		  the data by electronic nose and human sensory profiling are
		  found. },
  dbinsdate	= {2002/1}
}

@Article{	  singer90a,
  author	= {Alexander Singer},
  title		= {Implementations of Artificial Neural Networks on the
		  Connection Machine},
  journal	= {Parallel Computing},
  volume	= 14,
  pages		= {305--315},
  year		= 1990,
  dbinsdate	= {oldtimer}
}

@Article{	  singh00a,
  author	= {Singh, R. and Cherkassky, V. and Papanikolopoulos, N.},
  title		= {Self-organizing maps for the skeletonization of sparse
		  shapes},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  volume	= {11},
  pages		= {241--8},
  abstract	= {This paper presents a method for computing the skeleton of
		  planar shapes and objects which exhibit sparseness (lack of
		  connectivity), within their image regions. Such sparseness
		  in images may occur due to poor lighting conditions,
		  incorrect thresholding or image sub-sampling. Furthermore,
		  in document image analysis, sparse shapes are
		  characteristic of texts faded due to aging and/or poor ink
		  quality. Given the pixel distribution for a shape, the
		  proposed method involves an iterative evolution of a
		  piecewise-linear approximation of the shape skeleton by
		  using a minimum spanning tree-based self-organizing map
		  (SOM). By constraining the SOM to lie on the edges of the
		  Delaunay triangulation of the shape distribution, the
		  adjacency relationships between regions in the shape are
		  detected and used in the evolution of the skeleton. The
		  SOM, on convergence, gives the final skeletal shape. The
		  skeletonization is invariant to Euclidean transformations.
		  The potential of the method is demonstrated on a variety of
		  sparse shapes from different application domains.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  singh01a,
  author	= {Singh, Kh. M. and Bora, P. K. and Mahanta, A.},
  title		= {Features preserving filters using fuzzy Kohonen clustering
		  network in detection of impulse noise},
  booktitle	= {Proceedings of IEEE Region 10 International Conference on
		  Electrical and Electronic Technology. TENCON 2001. IEEE,
		  Piscataway, NJ, USA},
  year		= {2001},
  volume	= {1},
  pages		= {420--3},
  abstract	= {It is always advisable to apply filtering only on
		  corrupted pixels of images leaving untouched the
		  uncorrupted ones to preserve image features and to avoid
		  blurring effects. We present an algorithm which can detect
		  the corrupted pixels in texture images. It uses a fuzzy
		  Kohonen clustering network that integrates with the fuzzy
		  c-means (FCM) model utilizing the updating strategies of
		  the first and the learning rate of the second.},
  dbinsdate	= {2002/1}
}

@InCollection{	  singh96a,
  author	= {R. Singh and V. Cherkassky and N. P. Papanikolopoulos},
  title		= {Determining the skeletal description of sparse shapes},
  booktitle	= {Proceedings. 1997 IEEE International Symposium on
		  Computational Intelligence in Robotics and Automation
		  CIRA'97. `Towards New Computational Principles for Robotics
		  and Automation'},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {H. T. Bunnell and W. Idsardi},
  address	= {New York, NY, USA},
  pages		= {368--73},
  dbinsdate	= {oldtimer}
}

@Article{	  singh97a,
  author	= {Singh, Rahul and Cherkassky, Vladimir and
		  Papanikolopoulos, Nikolaos P.},
  title		= {Determining the skeletal description of sparse shapes},
  journal	= {Proc. IEEE International Symposium on Computational
		  Intelligence in Robotics and Automation},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1997},
  number	= {},
  volume	= {},
  pages		= {368--373},
  abstract	= {A variety of techniques in machine vision involve
		  representation of objects by using their shape skeleton.
		  Many algorithms have been proposed to date for obtaining
		  the skeletal shape of digital images. The noise models
		  predominantly used in these techniques are restricted to
		  boundary noise. In particular, instances of noise occurring
		  inside object regions and causing their non-contiguity are
		  precluded. In this paper we present a method to obtain the
		  skeletal shape of binary images in the presence of both
		  boundary noise and noise occurring inside object regions.
		  We propose to obtain the skeletal shape of such images by a
		  modified version of the Kohonen self-organizing map,
		  implemented in a batch processing mode. The modifications
		  allow the map to adapt to the input shape distribution. At
		  each iteration, a competitive Hebbian rule is used to
		  progressively compute the Delaunay triangulation of the
		  shape. Information from the triangulation augments the map
		  topology to yield the final skeletal shape. The batch mode
		  implementation of the self-organizing process, allows our
		  approach to compare very favorably, in terms of
		  computational time, with the traditional flowthrough
		  implementations. Encouraging experimental performance has
		  been obtained on a variety of shapes under varying signal
		  to noise ratios.},
  dbinsdate	= {oldtimer}
}

@Article{	  sinha99a,
  author	= {Sinha, A. and Smith, A. D.},
  title		= {Self-organizing map ({SOM}) of space acceleration
		  measurement system ({SAMS}) data},
  journal	= {MICROGRAVITY SCIENCE AND TECHNOLOGY},
  year		= {1999},
  volume	= {12},
  number	= {2},
  pages		= {78--87},
  abstract	= {In this paper space acceleration measurement system (SAMS)
		  data have been classified using self-organizing map (SOM)
		  networks without any supervision; i.e., Ilo a priori
		  knowledge is assumed regarding input patterns belonging to
		  a certain class. Input patterns are created an the basis of
		  power spectral densities of SAMS data. Results for SAMS
		  data from STS-50 and STS-57 missions are presented
		  Following issues are discussed in details: impact of number
		  of neurons, global ordering of SOM weight vectors,
		  effectiveness of a SOM in data classification, and effects
		  of shifting time winnows in the generation of input
		  patterns. The concept of 'cascade of SOM networks' is also
		  developed and tested It has been found that a SOM network
		  can successfully classify SAMS data obtained during STS-50
		  and STS- 57 missions.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  siponen01a,
  author	= {M. Siponen and J. Vesanto and O. Simula and P. Vasara},
  title		= {An approach to automated interpretation of {SOM}},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {89--94},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  siqueira00a,
  author	= {Siqueira, Mozart L. and Gasperin, Caroline V. and
		  Scharcanski, Jacob and Zielinsky, Paulo and Navaux,
		  Philippe O. A.},
  title		= {Echocardiographic image sequence segmentation using
		  self-organizing maps},
  journal	= {Neural Networks for Signal Processing---Proceedings of the
		  IEEE Workshop},
  year		= {2000},
  volume	= {2},
  number	= {},
  month		= {},
  pages		= {594--603},
  organization	= {Federal Univ of Rio Grande do Sul},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper presents a new approach for echocardiographic
		  image sequence segmentation. The proposed method uses the
		  self-organizing map to approximate the probability density
		  function of the image patterns. The map is post-processed,
		  by the k-means clustering algorithm, in order to detect
		  groups of neurons whose weights are similar. Each segmented
		  image of the sequence is generated by correlation its
		  pixels and cluster found in the map. The best number of
		  clusters is dependent on the application To validate the
		  segmentation procedure, we used a segmented sequence to
		  measure successfully the variation of the interventricular
		  septum width.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  siqueira00b,
  author	= {Siqueira, M. and Drehmer, G. and Navaux, P.},
  title		= {Fetal left atrium segmentation using Kohonen maps to
		  measure the septum primum redundancy index},
  booktitle	= {Proceedings. Vol.1. Sixth Brazilian Symposium on Neural
		  Networks. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {287},
  abstract	= {Summary form only given. Echocardiographic images are used
		  by physicians in early detection of congenital heart
		  diseases. Ultrasonic imaging has been the basis of
		  noninvasive methods for early detection of fetal heart
		  diseases. However, echocardiographic images are
		  contaminated by speckle noise, and other imaging
		  disturbances, making it difficult to visualize important
		  heart structures. Usually the diagnosis is obtained by
		  measurements on the echocardiographic images. One important
		  measure is the redundancy index of the septum primum that
		  is associated with premature atrial contractions and the
		  thickness of septum interventricular that can indicate the
		  presence of myocardial hypertrophy in the fetus. The
		  redundancy index of septum primum was obtained by ratio
		  ledger between the maximum excursion of the septum primum
		  (SP) to inside of left atrium and the maximum diameter of
		  left atrium, both during diastole. For images of fetal
		  echocardiography exams, we use Kohonen self-organizing maps
		  (SOM) to segment and afterwards obtain measures that can
		  help the physicians in the analysis of several congenital
		  cardiopathies. The SOM organizes unknown data into groups
		  of similar patterns, according to a similarity criterion.
		  An important feature of this neural network is its ability
		  to process noisy data. For this reason, the SOM approach
		  has been recommended to process echocardiographic images.
		  In this work, random samples of gray tones means of the
		  images were used to train the map.},
  dbinsdate	= {2002/1}
}

@Article{	  sirat01a,
  author	= {Sirat, M. and Talbot, C. J.},
  title		= {Application of artificial neural networks to fracture
		  analysis at the Aspo {HRL}, Sweden: Fracture sets
		  classification},
  journal	= {International Journal of Rock Mechanics and Mining
		  Sciences},
  year		= {2001},
  volume	= {38},
  number	= {5},
  month		= {July },
  pages		= {621--639},
  organization	= {Department of Earth Sciences, Uppsala University, Hans
		  Ramberg Tectonic Lab.},
  publisher	= {},
  address	= {},
  abstract	= {This study investigates the potential of artificial neural
		  networks (ANNs) to recognize, classify and predict patterns
		  of different fracture sets in the top 450 m in crystalline
		  rocks at the Aspo Hard Rock Laboratory (HRL), Southeastern
		  Sweden. ANNs are computer systems composed of a number of
		  processing elements that are interconnected in a particular
		  topology which is problem dependent. ANNs have the ability
		  to learn from examples using different learning algorithms;
		  these involve incremental adjustment of a set of parameters
		  to minimize the error between the desired output and the
		  actual network output. Six fracture-sets with particular
		  ranges of strike and dip have been distinguished. A series
		  of trials were carried out using backpropagation (BP)
		  neural networks for supervised classification, and the BP
		  networks recognized different fracture sets accurately.
		  Self-organizing neural networks have been used for data
		  clustering analysis with supervised learning algorithms;
		  (competitive learning and learning vector quantization),
		  and unsupervised learning algorithms; (self-organizing
		  maps). The self-organizing networks adapted successfully to
		  different fracture clusters (sets). A set of trials has
		  been carried out to investigate the effect of changing the
		  network's topologies on the performance of the BP networks.
		  Using two hidden layers with tan-sigmoid and linear
		  transfer functions was beneficial for the performance of BP
		  classification. ANNs improved fracture sets classification
		  that was based on Kamb contouring method with constraint on
		  areas between fracture clusters. },
  dbinsdate	= {2002/1}
}

@InCollection{	  sirisena96a,
  author	= {H. R. Sirisena and G. L. Rule},
  title		= {Time optimal robot snatching control},
  booktitle	= {Proceedings of the Fourth IASTED International Conference
		  Robotics and Manufacturing},
  publisher	= {IASTED-Acta Press},
  year		= {1996},
  editor	= {R. V. Mayorga},
  address	= {Anaheim, CA, USA},
  pages		= {227--31},
  dbinsdate	= {oldtimer}
}

@TechReport{	  sirosh92a,
  author	= {Joseph Sirosh and Risto Miikkulainen},
  title		= {Self-Organization with Lateral Connections},
  institution	= {The University of Texas at Austin, Austin, TX},
  year		= {1992},
  number	= {AI92--191},
  annote	= {Like the ICANN91-paper of Miikkulainen, but also the
		  lateral connections are modifiable. The key idea is, that
		  weak connections will die. },
  dbinsdate	= {oldtimer}
}

@Article{	  sirosh93a,
  author	= {Joseph Sirosh and Risto Miikkulainen},
  title		= {Cooperative Self-Organization of Afferent and Lateral
		  Connections in Cortical Maps},
  journal	= {Biol. Cyb. },
  year		= {1994},
  volume	= {71},
  pages		= {65--78},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sirosh93b,
  author	= {Joseph Sirosh and Risto Miikkulainen},
  title		= {Self-Organizing Feature Maps with Lateral Connections:
		  Modeling Ocular Dominance},
  booktitle	= {Proc. 1993 Connectionist Models Summer School},
  year		= 1994,
  editor	= {M. C. Mozer and P. Smolensky and D. S. Touretzky and J. L.
		  Elman and A. S. Weigend},
  pages		= {31--38},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sirosh93c,
  author	= {Joseph Sirosh and Risto Miikkulainen},
  title		= {How Lateral Interaction Develops in a Self-Organizing
		  Feature Map},
  booktitle	= {Proc. ICNN'93 International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1360--1365},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sirosh95a,
  author	= {J. Sirosh and R. Miikkulainen},
  title		= {Modeling cortical plasticity based on adapting lateral
		  interaction},
  booktitle	= {Neurobiology of Computation. Proceedings of the Third
		  Annual Computation and Neural Systems Conference},
  publisher	= {Kluwer Academic Publishers},
  year		= {1995},
  editor	= {J. M. Bower},
  address	= {Norwell, MA, USA},
  pages		= {305--10},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sirosh95b,
  author	= {J. Sirosh and R. Miikkulainen},
  title		= {A Unified Neural Network Model for the Self-Organization
		  of Topographic Receptive Fields and Lateral Interaction},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms: Proceedings
		  of the International Conference in Ales, France
		  (ICANNGA95)},
  editor	= {D. W. Pearson and N. C. Steele and R. F. Albrecht},
  year		= {1995},
  publisher	= {Springer},
  address	= {New York},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sirosh95c,
  author	= {Joseph Sirosh and Risto Miikkulainen},
  title		= {Ocular Dominance and Patterned Lateral Connections in a
		  Self-Organizing Model of the Primary Visual Cortex},
  pages		= {109--116},
  editor	= {G. Tesauro and D. Touretzky and T. Leen},
  volume	= 7,
  booktitle	= {Advances in Neural Information Processing Systems},
  year		= 1995,
  publisher	= {The {MIT} Press},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  sirosh95d,
  author	= {Joseph Sirosh},
  title		= {A Self-Organizing Neural Network Model of the Primary
		  Visual Cortex},
  school	= {The University of Texas at Austin},
  year		= 1995,
  address	= {Austin, TX},
  dbinsdate	= {oldtimer}
}

@Article{	  sirosh96a,
  author	= {J. Sirosh and R. Miikkulainen},
  title		= {Self-organization and functional role of lateral
		  connections and multisize receptive fields in the primary
		  visual cortex},
  journal	= {Neural Processing Letters},
  year		= {1996},
  volume	= {3},
  number	= {1},
  pages		= {39--48},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sirosh96b,
  author	= {J. Sirosh and R. Miikkulainen},
  title		= {Multisize Receptive Fields and Lateral Connections
		  Self-Organize Like Ocular Dominance and Orientation Columns
		  in a {H}ebbian Model of the Visual Cortex},
  booktitle	= {Proceedings of the Eighteenth Annual Meeting of the
		  Cognitive Science Society (COGSCI-96)},
  year		= {1996},
  publisher	= {Erlbaum},
  address	= {Hillsdale, NJ},
  pages		= {430--435},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sirosh96c,
  author	= {Sirosh, J. and R. Miikkulainen},
  title		= {A Neural Network Model of Topographic Reorganization
		  Following Cortical Lesions},
  booktitle	= {Computational Medicine, Public Health and Biotechnology:
		  Building a Man in the Machine, Proceedings of the First
		  World Congress},
  editor	= {M. Witten},
  year		= {1996},
  publisher	= {World Scientific},
  address	= {Teaneck, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sirosh96d,
  author	= {J. Sirosh and R. Miikkulainen and J. A. Bednar},
  title		= {Self-Organization of Orientation Maps, Lateral
		  Connections, and Dynamic Receptive Fields in the Primary
		  Visual Cortex},
  booktitle	= {Lateral Interactions in the Cortex: Structure and
		  Function},
  publisher	= {The UTCS Neural Networks Research Group},
  year		= {1996},
  editor	= {J. Sirosh and R. Miikkulainen and Y. Choe},
  volume	= {Electronic book
		  http://www.cs.utexas.edu/users/nn/web-pubs/htmlbook96},
  address	= {Austin, TX},
  pages		= {420--423},
  dbinsdate	= {oldtimer}
}

@Article{	  sirosh97a,
  author	= {Joseph Sirosh and Risto Miikkulainen},
  title		= {Topographic Receptive Fields and Patterned Lateral
		  Interaction in a Self-Organizing Model of the Primary
		  Visual Cortex},
  journal	= {Neural Computation},
  volume	= 9,
  number	= 3,
  year		= 1997,
  pages		= {577--594},
  dbinsdate	= {oldtimer}
}

@InBook{	  skinnemoen93a,
  author	= {Harald Skinnemoen},
  title		= {New Advances and Trends in Speech Recognition and Coding},
  chapter	= {{MOR-VQ} for Speech Coding over Noisy Channels},
  publisher	= {Springer-Verlag},
  year		= {1993},
  series	= {NATO ASI Series F},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  skinnemoen94a,
  author	= {P{\aa}l Harald Skinnemoen},
  title		= {Robust Communication with Modulation Organized Vector
		  Quantization},
  school	= {The Norwegian Institute of Technology},
  year		= {1994},
  address	= {Trondheim, Norway},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  skinnemoen94b,
  author	= {Harald Skinnemoen and Andrew Perkis},
  title		= {Efficient Vector Quantizations of {LPC} Parameters for
		  Noisy Channels},
  booktitle	= {Proc. ICASSP'94 International Conference on Acoustics,
		  SPeech and Signal Processing},
  year		= {1994},
  volume	= {I},
  pages		= {497--500},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  skinnemoen94c,
  author	= {Harald Skinnemoen},
  title		= {Robust Communications with Modulation Organized Vector
		  Quantization ({MOR-VQ})},
  booktitle	= {Proc. NORSIG'94 Nordig Signal Processing Symposium},
  year		= {1994},
  pages		= {28--33},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  skinnemoen94d,
  author	= {Harald Skinnemoen},
  title		= {Modulation Organized Vector Quantization, {MOR-VQ}},
  booktitle	= {Proc. ISIT'94 IEEE Int. Symp. on Inf. Theory},
  year		= {1994},
  pages		= {238},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  skinnemoen94e,
  author	= {Harald Skinnemoen},
  title		= {Combined source-channel coding with modulation organized
		  vector quantization, {MOR-VQ}},
  booktitle	= {Proc. IEEE GLOBECOM},
  year		= {1994},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  skitt93a,
  author	= {P. J. C. Skitt and M. A. Javed and S. A. Sanders and A. M.
		  Higginson},
  title		= {Process monitoring using auto-associative, feed-forward
		  artificial neural networks},
  journal	= {J. Intelligent Manufacturing},
  year		= {1993},
  volume	= {4},
  number	= {1},
  pages		= {79--94},
  month		= {February},
  annote	= {Quality control applications using neural networks are
		  compared with conventional statistical approaches and
		  {SOFM}. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  skok00a,
  author	= {Skok, S. and Marusic, A.},
  title		= {Comparison of various neural network models applied to
		  adaptive distance protection},
  booktitle	= {DRPT2000. International Conference on Electric Utility
		  Deregulation and Restructuring and Power Technologies.
		  Proceedings. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {},
  pages		= {244--50},
  abstract	= {This paper elaborates settings and modification of the
		  operating characteristic of adaptive distance relays which
		  protect a transmission line between two terminals.
		  Operating characteristic should have such shape to satisfy
		  all conditions in the power system. However it is known
		  that there is no combination of characteristics which can
		  satisfy all possible conditions. So the only solution is
		  modification of the operating characteristic. Solution of
		  this problem involves two types of neural networks,
		  multilayered perceptron and the self-organizing neural
		  network, the so called Kohonen network. Here the authors
		  consider a two-terminal transmission line, confirm that
		  fault resistance and location of faults can produce
		  erroneous relay function and finally suggest ways to ensure
		  the generation of the correct signal for relay operation.
		  With the simple simulation, the authors present the
		  advantages of the particular models and the credibility of
		  the developed models.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  skok00b,
  author	= {Skok, S. and Martsic, A.},
  title		= {The self-organizing neural network applied to adaptive
		  distance protection},
  booktitle	= {2000 IEEE Power Engineering Society Winter Meeting.
		  Conference Proceedings. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {3},
  pages		= {1956--60},
  abstract	= {This paper elaborates settings and modification of the
		  operating characteristic of adaptive distance relay which
		  protects the transmission line between two terminals. The
		  operating characteristic should have such shape to satisfy
		  all conditions in power system. However it is known that
		  there is no combination of characteristics which can
		  satisfy all possible conditions. So the only solution is
		  modification of the operating characteristic. Solution of
		  this problem involves the self-organizing neural network,
		  the so called Kohonen network. Here, the authors consider a
		  two-terminal transmission line, confirm that fault
		  resistance and location of faults can produce erroneous
		  relay function and finally suggest ways to ensure the
		  generation of the correct signal for relay operation. With
		  the simple simulation it was presented advantages and the
		  credibility of the developed model.},
  dbinsdate	= {2002/1}
}

@Article{	  skok01a,
  author	= {Skok, Srdan and Marusic, Ante},
  title		= {Adaptive distance protection based on various neural
		  network models},
  journal	= {Ciencia and Engenharia/ Science and Engineering Journal},
  year		= {2001},
  volume	= {10},
  number	= {2},
  month		= {July/December },
  pages		= {39--46},
  organization	= {Faculty of Electrical Eng. and Comp., Department of Power
		  Systems},
  publisher	= {},
  address	= {},
  abstract	= {This paper elaborates settings and modification of the
		  operating characteristic of adaptive distance relay which
		  protects the transmission line between two terminals.
		  Operating characteristic should have such shape to satisfy
		  all conditions in power system. However it is known that
		  there is no combination of characteristics which can
		  satisfy all possible conditions. So the only solution is
		  modification of the operating characteristic. Solution of
		  this problem involves two types of neural networks,
		  multilayered perceptron and the self-organizing neural
		  network so called Kohonen network. Here we consider a
		  two-terminal transmission line, confirm that fault
		  resistance and location of faults can produce erroneous
		  relay function and finally suggest ways to ensure the
		  generation of the correct signal for relay operation. With
		  the simple simulation it was presented advantages of the
		  particular models and the credibility of the developed
		  models.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  skok01b,
  author	= {Skok, S. and Marusic, A.},
  title		= {Distance protection of a double-circuit line based on
		  Kohonen neural network considering different operation
		  modes},
  booktitle	= {2001 IEEE Porto Power Tech Proceedings. IEEE, Piscataway,
		  NJ, USA},
  year		= {2001},
  volume	= {4},
  pages		= {5},
  abstract	= {This paper deals with problems connected to distance
		  protection of a double-circuit transmission lines. Because
		  of the zero sequence mutual coupling of parallel circuits,
		  the relay "sees" incorrect impedance. Behavior of distance
		  relays, which protect multi-circuit lines, is that they
		  underreach when the parallel circuits are in service and
		  that they overreach when the parallel circuits are out of
		  service and earthed at both ends. In this paper an adaptive
		  model was developed which solves problems of
		  single-line-to-ground fault on a single line. From the
		  local measurements and from the transmission line theory
		  error factor can be calculated, which is the ratio of the
		  calculated impedance "seen" by relay and the desired
		  impedance at a fault distance. The aim of the adaptive
		  model is to improve accuracy of the distance relay (to
		  compensate error factor), according to the local
		  measurements using a self-organizing neural network or the
		  so called Kohonen network.},
  dbinsdate	= {2002/1}
}

@Article{	  skubalska00a,
  author	= {Skubalska Rafajlowicz, E.},
  title		= {One-dimensional Kohonen {LVQ} nets for multidimensional
		  pattern recognition},
  journal	= {International-Journal-of-Applied-Mathematics-and-Computer-Science}
		  ,
  year		= {2000},
  volume	= {10},
  pages		= {767--78},
  abstract	= {A new neural network based pattern recognition algorithm
		  is proposed. The method consists in preprocessing the
		  multidimensional data, using a space-filling curve based
		  transformation into the unit interval, and employing
		  Kohonen's vector quantization algorithms (of SOM and LVQ
		  types) in one dimension. The space-filling based
		  transformation preserves the theoretical Bayes risk.
		  Experiments show that such an approach can produce good or
		  even better error rates than the classical LVQ performed in
		  a multidimensional space.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  smart94a,
  author	= {William D. Smart and John Hallam},
  title		= {Location Recognition with Self-Ordering Networks},
  booktitle	= {Proc. IMACS Int. Symp. on Signal Processing, Robotics and
		  Neural Networks},
  year		= {1994},
  pages		= {449--453},
  publisher	= {IMACS},
  address	= {Lille, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  smith95a,
  author	= {Kate Smith},
  title		= {Solving the Generalised Quadratic Assignment Problem using
		  a Self-Organising Process},
  volume	= {IV},
  pages		= {1876--1879},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  smith96a,
  author	= {K. Smith and M. Palaniswami and M. Krishnamoorthy},
  title		= {A hybrid neural approach to combinatorial optimization},
  journal	= {Computers \& Operations Research},
  year		= {1996},
  volume	= {23},
  number	= {6},
  pages		= {597--610},
  dbinsdate	= {oldtimer}
}

@Article{	  smith97a,
  author	= {D. R. Smith and P. C. Parziale},
  title		= {Surface control and vibration suppression of a Large
		  Millimeter-Wave Telescope},
  journal	= {Optical Engineering},
  year		= {1997},
  volume	= {36},
  number	= {7},
  pages		= {1837--42},
  dbinsdate	= {oldtimer}
}

@Article{	  smith98a,
  author	= {Kate Smith and Marimuthu Palaniswami and Mohan
		  Krishnamoorthy},
  title		= {Neural Techniques for Combinatorial Optimization with
		  Applications},
  journal	= {IEEE Transactions on Neural Networks},
  year		= 1998,
  volume	= 9,
  pages		= {1301--1318},
  dbinsdate	= {oldtimer}
}

@Article{	  smola01a,
  author	= {Smola, A. J. and Mika, S. and Scholkopf, B. and
		  Williamson, R. C.},
  title		= {Regularized principal manifolds},
  journal	= {Journal-of-Machine-Learning-Research},
  year		= {2001},
  volume	= {1},
  pages		= {179--209},
  abstract	= {Many settings of unsupervised learning can be viewed as
		  quantization problems---the minimization of the expected
		  quantization error subject to some restrictions. This
		  allows the use of tools such as regularization, from the
		  theory of (supervised) risk minimization, for unsupervised
		  learning. This setting turns out to be closely related to
		  principal curves, the generative topographic map and robust
		  coding. We explore this connection in two ways: (1) we
		  propose an algorithm for finding principal manifolds that
		  can be regularized in a variety of ways; and (2) we derive
		  uniform convergence bounds and hence bounds on the learning
		  rates of the algorithm. In particular, we give bounds on
		  the covering numbers, which allows us to obtain
		  nearly-optimal learning rates for certain types of
		  regularization operators. Experimental results demonstrate
		  the feasibility of the approach.},
  dbinsdate	= {2002/1}
}

@InCollection{	  smolander97a,
  author	= {S. Smolander and J. Lampinen},
  title		= {Determining the optimal structure for multilayer
		  \mbox{self-organizing} map with genetic algorithm},
  booktitle	= {Proc. of the 10th Scandinavian Conference on Image
		  Analysis},
  year		= 1997,
  editor	= {J. Parkkinen and A. Visa},
  volume	= 1,
  pages		= {411--417},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  smolin91a,
  author	= {V. S. Smolin},
  title		= {Monitoring of input signals subspace location in sensory
		  space by neuronet inner layer neurons threshold value
		  adaptation},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1337--1340},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  x		= {Poistetaan. },
  dbinsdate	= {oldtimer}
}

@InCollection{	  snyder91a,
  author	= {W. Snyder and D. Nissman and D. {Van den Bout} and G.
		  Bilbro},
  title		= {{K}ohonen networks and clustering},
  pages		= {984--991},
  booktitle	= {Advances in Neural Information Processing Systems 3},
  editor	= {R. P. Lippmann and J. E. Moody and D. S. Touretzky},
  publisher	= {Morgan Kaufmann},
  address	= {San Mateo, CA},
  year		= {1991 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  so94a,
  author	= {Y. T. So and K. P. Chan},
  title		= {Topological Preserving Network by the Existence of Lateral
		  Feedback},
  pages		= {681--685},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {analysis, modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  so98a,
  author	= {So, P. and Liu, Z. Q},
  title		= {Adaptive subspace self-organising map for pattern
		  recognition},
  booktitle	= {IEEE ICIPS'98. Proceedings of the Second IEEE
		  International Conference on Intelligent Processing Systems.
		  IEEE, Piscataway, NJ, USA},
  year		= {1998},
  volume	= {},
  pages		= {149--53},
  abstract	= {The adaptive subspace self-organising map (ASSOM) was
		  proposed by Kohonen for the automatic extraction of
		  subspace detectors. However, one of the major problems that
		  prevent the ASSOM from being used for tasks that involve
		  compression and classification, especially of images, is
		  that it cannot compensate for the mean present in the data
		  set. The reason for this is that all subspaces represented
		  by neurons are constrained to intersecting with a common
		  origin. A more general approach, which substantially
		  increases the representational power of each neuron, is to
		  use affined subspaces, or complete linear manifolds. A new
		  map, known as the adaptive manifold self-organising map
		  (AMSOM), is thus proposed. The results obtained for the
		  AMSOM show that it performs several orders of magnitude
		  better than the ASSOM for some types of data. Also, since
		  the AMSOM extracts multiple manifolds from the data
		  automatically, the results in many cases are better than
		  those obtained using the standard principal component
		  analysis (PCA) method. The compression performance for face
		  images is presented.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  soffker00a,
  author	= {Soffker, D.},
  title		= {Human-machine interaction: modeling of individual
		  planning, cognition, representation and action},
  booktitle	= {7th IFAC Symposium on Automated Systems Based on Human
		  Skill. Joint Design of Technology and Organisation.
		  Preprints. VDI/VDE-GMA, Duesseldorf, Germany},
  year		= {2000},
  volume	= {},
  pages		= {123--6},
  abstract	= {The contribution introduces modeling of human interaction
		  with formalizable systems. Core of the work is an
		  engineering oriented modeling approach. The interaction is
		  modeled using a situation-operator modeling (SOM)
		  technique. The strong relation between human interaction,
		  planning, learning and human errors will be shown as well
		  as the connection to related mental representations.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  sohn01a,
  author	= {Sohn, S. and Dagli, C. H.},
  title		= {Advantages of using fuzzy class memberships in
		  self-organizing map and support vector machines},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {1886--1890},
  organization	= {Smart Engineering Systems Laboratory, Department of
		  Engineering Management, University of Missouri-Rolla},
  publisher	= {},
  address	= {},
  abstract	= {Self-organizing map (SOM) is naturally unsupervised
		  learning, but if a class label is known, it can be used as
		  the classifier. In SOM classifier, each neuron is assigned
		  a class label based on the maximum class frequency and
		  classified by a nearest neighbor strategy. The drawback
		  when using this strategy is that each pattern is treated by
		  equal importance in counting class frequency regardless of
		  its typicalness. For this reason, the fuzzy class
		  membership can be used instead of crisp class frequency and
		  this fuzzy-membership-label neuron provides another
		  perspective of a feature map. This fuzzy class membership
		  can be also used to select training samples in support
		  vector machines (SVM) classifier. This method allows us to
		  reduce the training set as well as support vectors without
		  significant loss of classification performance.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  sohn01b,
  author	= {Sohn, S. and Dagli, C. H.},
  title		= {Self-organizing map with fuzzy class memberships},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Priddy, K. L. and Keller, P. E. and Angeline, P. J.},
  volume	= {4390},
  pages		= {150--157},
  organization	= {Smart Engineering Systems Laboratory, Department of
		  Engineering Management, University of Missouri},
  publisher	= {},
  address	= {},
  abstract	= {Self-organizing maps (SOM) can be used as clustering
		  algorithm to discover structure and similarity in data and
		  to capture the descriptive aspect by repeated partitioning
		  and evaluating. It has the ability to represent
		  multidimensional data in topological mapping. If a class
		  label is known, self-organizing map can be also used by a
		  classifier. In this case, each neuron is assigned a class
		  label based on the maximum class frequency and classified
		  by a nearest neighbor strategy. The problem when using this
		  strategy is that each pattern is treated by equal
		  importance in counting class frequency regardless of its
		  typicalness. But, with known class label we can take an
		  advantage of this information by applying fuzzy set theory
		  and assigning the fuzzy class membership into each neuron.
		  In fact, the fuzzy-membership-label neuron gives us insight
		  of the degree of class typicalness and distinguishes itself
		  from a class cluster.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  solaiman94a,
  author	= {B. Solaiman and M. C. Mouchot and E. Maillard},
  title		= {A Hybrid Algorithm {(H {LVQ} )} Combining Unsupervised and
		  Supervised Learning Approaches},
  pages		= {1772--1776},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {modification, hybrid},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  solaiman94b,
  author	= {B. Solaiman and Y. Autret},
  title		= {Application of the {H {LVQ} } Neural Network to
		  Hand-Written Digit Recognition},
  booktitle	= {Proc. NNSP'94, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1994},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {384--393},
  annote	= {application, speech recognition},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  solaiman95a,
  author	= {Solaiman, B. and Maillard, E. P. },
  title		= {Image compression using {H {LVQ} } neural network},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  year		= {1995},
  volume	= {5},
  pages		= {3447--50},
  organization	= {ENST de Bretagne, Brest, France},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {We apply a new neural network: HLVQ combining supervised
		  and unsupervised learning to vector quantization. A
		  supervised learning based on Learning Vector Quantization 2
		  performs attention focusing over a background of a
		  Self-Organizing Feature Map algorithm. It exhibits the
		  salient features of both algorithms: the
		  topology-preserving mapping characteristic is acquired
		  through unsupervised learning while supervised learning
		  keeps the overlap between classes to a minimum. Pattern
		  labeling is carried out by a separate unsupervised network
		  taking as input the discrete cosine transform of a pattern.
		  First the labelling network is trained on the transform of
		  sub-images. Each neuron of this network is considered as
		  the prototype of one class. Once convergence is achieved,
		  HLVQ is trained. Each sub-images is input to the network.
		  The class of the input pattern is determined by the most
		  activated neuron of the labelling network on presentation
		  of the sub-image transform.},
  dbinsdate	= {oldtimer}
}

@Article{	  solaiman97a,
  author	= {B. Solaiman and R. Pyndiah and O. Aitsab and G. Cazuguel
		  and C. Roux},
  title		= {A hybrid fuzzy-neural approach for image
		  compression/transmission over noisy channels},
  journal	= {ITG-Fachberichte},
  year		= {1997},
  volume	= {9},
  number	= {143},
  pages		= {629--34},
  note		= {(Picture Coding Symposium. PCS 97 Conf. Date: 10--12 Sept.
		  1997 Conf. Loc: Berlin, Germany Conf. Sponsor: Deutsche
		  Telekom Bergkom; Heinrich-Hertz-Inst)},
  dbinsdate	= {oldtimer}
}

@Article{	  soliman00a,
  author	= {Soliman, H. and Abdelali, A.},
  title		= {Colored image compression using neural networks},
  journal	= {Parallel and Distributed Computing and Systems.
		  IASTED/ACTA Press, Anaheim, CA, USA; 2000; 2 vol},
  year		= {2000},
  volume	= {1},
  pages		= {229--31},
  abstract	= {We present new results of extending our SFSN neural model
		  (based on the Kohonen SOFM model) to the domain of colored
		  image compressing. The SFSN model has shown an improvement
		  of compression ratio and image quality, in some domains of
		  colored images, over the other non-neural network models
		  (e.g., JPEG, Wavelet, etc). We improved the technique of
		  computing, compressing, and shipping out the difference
		  between original and decompressed files (ghost image),
		  along with the compressed image. The result of using the
		  ghost image in the decompression process was a higher
		  quality decompressed colored images. In this paper we
		  present our neural compression algorithm and most recent
		  experimental results.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  soliman01a,
  author	= {Soliman, H.},
  title		= {Neural net simulation: {SFSN} model for image
		  compression},
  booktitle	= {Proceedings of the IEEE Annual Simulation Symposium},
  year		= {2001},
  editor	= {},
  volume	= {},
  pages		= {325--332},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {We present a recent simulation of our neural net model for
		  image compression (SFSN) which is based on the Kohonen SOFM
		  system. Our previous work was limited to a certain scope of
		  image' domains. Our updated simulator is meant to be very
		  general via a well constructed universal codebook for each
		  domain of images. It shows an improvement over the
		  traditional peer non-neural models (e.g., Wavelet and JPEG)
		  in some image domains. In this paper, we present our neural
		  compression simulator and our most recent results in some
		  important domains, such as satellite and documents
		  imaging.},
  dbinsdate	= {2002/1}
}

@Article{	  somayajula96a,
  author	= {S. A. S. Somayajula and E. Sanchez-Sinencio and J. {Pineda
		  de Gyvez}},
  title		= {Analog fault diagnosis based on ramping power supply
		  current signature clusters},
  journal	= {IEEE Transactions on Circuits and Systems II: Analog and
		  Digital Signal Processing},
  year		= {1996},
  volume	= {43},
  number	= {10},
  pages		= {703--12},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  somervuo00a,
  author	= {Somervuo, P. and Kohonen, T.},
  title		= {Clustering and visualization of large protein sequence
		  databases by means of an extension of the self-organizing
		  map},
  booktitle	= {Discovery Science. Third International Conference, DS
		  2000. Proceedings (Lecture Notes in Artificial Intelligence
		  Vol.1967). Springer-Verlag, Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {76--85},
  abstract	= {New, more effective software tools are needed for the
		  analysis and organization of the continually growing
		  biological databases. An extension of the Self-Organizing
		  Map (SOM) is used in this work for the clustering of all
		  the 77,977 protein sequences of the SWISS-PROT database,
		  release 37. In this method, unlike in some previous ones,
		  the data sequences are not converted into histogram vectors
		  in order to perform the clustering. Instead, a collection
		  of true representative model sequences that approximate the
		  contents of the database in a compact way is found
		  automatically, based on the concept of the generalized
		  median of symbol strings, after the user has defined any
		  proper similarity measure for the sequences such as
		  Smith-Waterman, BLAST, or FASTA. The FASTA method is used
		  in this work. The benefits of the SOM and also those of its
		  extension are fast computation,approximate representation
		  of the large database by means of a much smaller, fixed
		  number of model sequences, and an easy interpretation of
		  the clustering by means of visualization. The complete
		  sequence database is mapped onto a two-dimensional graphic
		  SOM display, and clusters of similar sequences are then
		  found and made visible by indicating the degree of
		  similarity of the adjacent model sequences by shades of
		  gray.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  somervuo00b,
  author	= {Somervuo, Panu},
  title		= {Competing hidden Markov models on the Self-Organizing
		  Map},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {3},
  pages		= {169--174},
  organization	= {Helsinki Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper presents an unsupervised segmentation method
		  for feature sequences based on competitive-learning hidden
		  Markov models. Models associated with the nodes of the
		  Self-Organizing Map learn to become selective to the
		  segments of temporal input sequences. Input sequences may
		  have arbitrary lengths. Segment models emerge then on the
		  map through an unsupervised learning process. The method
		  was tested in speech recognition, where the performance of
		  the emergent segment models was as good as the performance
		  of the traditionally used linguistic speech segment models.
		  The benefits of the proposed method are the use of
		  unsupervised learning for obtaining the state models for
		  temporal data and the convenient visualization of the state
		  space on the two-dimensional map.},
  dbinsdate	= {2002/1}
}

@Article{	  somervuo00c,
  author	= {Somervuo, P.},
  title		= {Self-organizing maps for signal and symbol sequences},
  journal	= {Acta-Polytechnica-Scandinavica,-Mathematics-and-Computing-Series.
		  no.Ma107; 2000; p.2--45},
  year		= {2000},
  volume	= {},
  pages		= {2--45},
  abstract	= {The topic of the article is the application of Kohonen's
		  self-organizing map (SOM) and learning vector quantization
		  (LVQ) algorithms to the processing of data sequences with
		  numeric or symbolic elements. Usually the models of the
		  data are fixed-dimensional feature vectors in these
		  algorithms. Other models are also experimented with, like
		  hidden Markov models, feature vector sequences, and symbol
		  sequences. The goal has been to utilize the principles of
		  the unsupervised learning of the SOM and the error
		  corrective learning of the LVQ whilst also taking into
		  account the sequential nature of the data. The developed
		  methods have been applied to unsupervised segmentation of
		  speech, generating prototype templates for variable-length
		  feature vector sequences, improving the performance of a
		  speech recognizer based on hidden Markov models,
		  constructing a data-driven pronunciation dictionary for
		  speech recognition, and clustering of all currently known
		  protein sequences.},
  dbinsdate	= {2002/1}
}

@Article{	  somervuo99a,
  author	= {Somervuo, Panu and Kohonen, Teuvo},
  title		= {Self-Organizing Maps and Learning Vector Quantization for
		  feature sequences},
  journal	= {Neural Processing Letters},
  year		= {1999},
  number	= {2},
  volume	= {10},
  pages		= {151--159},
  abstract	= {The Self-Organizing Map (SOM) and Learning Vector
		  Quantization (LVQ) algorithms are constructed in this work
		  for variable-length and warped feature sequences. The
		  novelty is to associate an entire feature vector sequence,
		  instead of a single feature vector, as a model with each
		  SOM node. Dynamic time warping is used to obtain
		  time-normalized distances between sequences with different
		  lengths. Starting with random initialization, ordered
		  feature sequence maps then ensue, and Learning Vector
		  Quantization can be used to fine tune the prototype
		  sequences for optimal class separation. The resulting SOM
		  models, the prototype sequences, can then be used for the
		  recognition as well as synthesis of patterns. Good results
		  have been obtained in speaker-independent speech
		  recognition.},
  dbinsdate	= {oldtimer}
}

@Article{	  somervuo99b,
  author	= {Somervuo, Panu},
  title		= {Redundant hash addressing of feature sequences using the
		  \mbox{self-organizing} map},
  journal	= {Neural Processing Letters},
  year		= {1999},
  number	= {1},
  volume	= {10},
  pages		= {25--34},
  abstract	= {Kohonen's Self-Organizing Map (SOM) is combined with the
		  Redundant Hash Addressing (RHA) principle. The SOM encodes
		  the input feature vector sequence into the sequence of
		  best-matching unit (BMU) indices and the RHA principle is
		  then used to associate the BMU index sequence with the
		  dictionary items. This provides a fast alternative for
		  dynamic programming (DP) based methods for comparing and
		  matching temporal sequences. Experiments include music
		  retrieval and speech recognition. The separation of the
		  classes can be improved by error-corrective learning.
		  Comparisons to DP-based methods are presented.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  somervuo99c,
  author	= {Somervuo, P.},
  title		= {Time topology for the \mbox{self-organizing} map},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1900--5},
  abstract	= {Time information of the input data is used for evaluating
		  the goodness of the self-organizing map to store and
		  represent temporal feature vector sequences. A new node
		  neighborhood is defined for the map which takes the
		  temporal order of the input samples into account. A
		  connection is created between those two map modes which are
		  the best-matching units for two successive input samples in
		  time. This results in the time-topology preserving
		  network.},
  dbinsdate	= {oldtimer}
}

@Article{	  sona00a,
  author	= {Sona, D. and Sperduti, A. and Starita, A.},
  title		= {Discriminant pattern recognition using
		  transformation-invariant neurons},
  journal	= {NEURAL COMPUTATION},
  year		= {2000},
  volume	= {12},
  number	= {6},
  month		= {JUN},
  pages		= {1355--1370},
  abstract	= {To overcome the problem of invariant pattern recognition,
		  Simard, LeCun, and Denker (1993) proposed a successful
		  nearest- neighbor approach based on tangent distance,
		  attaining state- of-the-art accuracy. Since this approach
		  needs great computational and memory effort, Hastie,
		  Simard, and Sackinger (1995) proposed an algorithm (HSS)
		  based on singular value decomposition (SVD), for the
		  generation of nondiscriminant tangent models. In this
		  article we propose a different approach, based on a
		  gradient-descent constructive algorithm, called TD-Neuron,
		  that develops discriminant models. We present as well
		  comparative results of our constructive algorithm versus
		  HSS and learning vector quantization (LVQ) algorithms.
		  Specifically, we tested the HSS algorithm using both the
		  original version based on the two-sided tangent distance
		  and a new version based on the one-sided tangent distance.
		  Empirical results over the NIST-3 database show that the
		  TD-Neuron is superior to both SVD- and LVQ-based
		  algorithms, since it reaches a better trade-off between
		  error and rejection.},
  dbinsdate	= {2002/1}
}

@Article{	  song00a,
  author	= {Song, Kai-Tai and Sheen, Liang-Hwang},
  title		= {Heuristic fuzzy-neuro network and its application to
		  reactive navigation of a mobile robot},
  journal	= {Fuzzy Sets and Systems},
  year		= {2000},
  volume	= {110},
  number	= {3},
  month		= {Mar},
  pages		= {331--340},
  organization	= {Natl Chiao Tung Univ},
  publisher	= {Elsevier Sci B.V.},
  address	= {Amsterdam},
  abstract	= {A novel pattern recognition approach to reactive
		  navigation of a mobile robot is presented in this paper. A
		  heuristic fuzzy-neuro network is developed for
		  pattern-mapping between quantized ultrasonic sensory data
		  and velocity commands to the robot. The design goal was to
		  enable an autonomous mobile robot to navigate safely and
		  efficiently to a target position in a previously unknown
		  environment. Useful heuristic rules were combined with the
		  fuzzy Kohonen clustering network (FKCN) to build the
		  desired mapping between perception and motion. This method
		  provides much faster response to unexpected events and is
		  less sensitive to sensor misreading than conventional
		  approaches. It allows continuous, fast motion of the mobile
		  robot without any need to stop for obstacles. The
		  effectiveness of the proposed method is demonstrated in a
		  series of practical tests on our experimental mobile
		  robot.},
  dbinsdate	= {2002/1}
}

@InCollection{	  song96a,
  author	= {Y. H. Song and H. B. Wan and A. T. Johns},
  title		= {Power system voltage stability assessment using a
		  \mbox{self-organizing} neural network classifier},
  booktitle	= {Fourth International Conference on Power System Control
		  and Management},
  publisher	= {IEE},
  year		= {1996},
  address	= {London, UK},
  pages		= {171--5},
  dbinsdate	= {oldtimer}
}

@Article{	  song96b,
  author	= {Song, Hee-Heon and Lee, Seong-Whan},
  title		= { {LVQ} combined with simulated annealing for optimal
		  design of large-set reference models},
  journal	= {Neural Networks},
  year		= {1996},
  number	= {2},
  volume	= {9},
  pages		= {329--336},
  abstract	= {Learning Vector Quantization (LVQ) has been intensively
		  studied to generate good reference models in pattern
		  recognition since 1986, and it has some nice theoretical
		  properties. However, the design of reference models based
		  on LVQ suffers from several major drawbacks for the
		  recognition of large-set patterns, in which good reference
		  models play an important role in achieving high
		  performance. They are due in large part to the following
		  facts: (1) it may not generate good reference models, if
		  the initial values of the reference models are outside the
		  convex hull of the input data, (2) it cannot guarantee
		  optimal reference models due to the strategy to accept new
		  reference models in each iteration step, and (3) it is apt
		  to get stuck at overtraining phenomenon. In this paper, we
		  first discuss the impact of these problems. And then, to
		  cope with these, we propose a new method for the optimal
		  design of large-set reference models using an improved LVQ3
		  combined with Simulated Annealing which has been proven to
		  be a useful technique in many areas of optimization
		  problems. Experimental results with large-set handwritten
		  characters reveal that the proposed method is superior to
		  the conventional method based on averaging and other
		  LVQ-based methods.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  song96c,
  author	= {Y. H. Song and Q. Y. Xuan and A. T. Johns},
  title		= {Comparison studies of five neural network based fault
		  classifiers for complex transmission lines},
  booktitle	= {Proceedings of the 1996 Canadian Conference on Electrical
		  and Computer Engineering. Theme: Glimpse into the 21st
		  Century},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  editor	= {T. J. Malkinson},
  address	= {New York, NY, USA},
  pages		= {745--9},
  abstract	= {The application of neural networks to power systems has
		  been extensively reported. In the field of protection,
		  neural network based protection techniques have been
		  proposed by a number of investigators including the
		  authors. However, almost all the studies have so far
		  employed the back-propagation neural network structure with
		  supervised learning. It is the purpose of this paper to
		  report some recent studies on different neural network
		  models, particularly those with combined
		  supervised/unsupervised learning applied to fault
		  classification for complex transmission lines. The neural
		  networks concerned here include: (i) back-propagation net
		  (BP); (ii) feature-map net (FM); (iii) radial basis
		  function net (RBF); (iv) counter-propagation net (CP) and
		  (v) learning vector quantization net (LVQ). Special
		  emphasis is placed on a comparison of the performance of
		  the five neural networks in terms of size of the neural
		  network, learning process, classification accuracy and
		  robustness. The outcome of the work serves and provides
		  guidelines on how to select a particular neural network
		  from a number of different neural networks for a specific
		  application.},
  dbinsdate	= {oldtimer}
}

@Article{	  song96d,
  author	= {X. H. Song and P. K. Hopke},
  title		= {{K}ohonen Neural-Network as a Pattern-Recognition Method},
  journal	= {Analytica Chimica Acta},
  year		= {1996},
  volume	= {334},
  number	= {1--2},
  pages		= {57--66},
  dbinsdate	= {oldtimer}
}

@InCollection{	  song96e,
  author	= {Wang Song and Shu Chang and Xia Shaowei},
  title		= {A hybrid approach to unconstrained handwritten numerals
		  recognition},
  booktitle	= {ICSP '96. 1996 3rd International Conference on Signal
		  Processing Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  editor	= {B. Yuan and X. Tang},
  address	= {New York, NY, USA},
  pages		= {1334--7},
  dbinsdate	= {oldtimer}
}

@Article{	  song97a,
  author	= {Y. H. Song and H. B. Wan and A. T. Johns},
  title		= {{K}ohonen neural network based approach to voltage weak
		  buses/areas identification},
  journal	= {IEE Proceedings-Generation, Transmission and
		  Distribution},
  year		= {1997},
  volume	= {144},
  number	= {3},
  pages		= {340--4},
  dbinsdate	= {oldtimer}
}

@Article{	  song97b,
  author	= {Hee-Heon Song and Seong-Whan Lee},
  title		= {A \mbox{self-organizing} neural tree for large-set pattern
		  classification},
  journal	= {Journal of KISS[B] [Software and Applications]},
  year		= {1997},
  volume	= {24},
  number	= {4},
  pages		= {422--31},
  dbinsdate	= {oldtimer}
}

@Article{	  song97c,
  author	= {Y. H. Song and Q. X. Xuan and A. T. Johns},
  title		= {Comparison studies of five neural network based fault
		  classifiers for complex transmission lines},
  journal	= {Electric Power Systems Research},
  year		= {1997},
  volume	= {43},
  number	= {2},
  pages		= {125--32},
  dbinsdate	= {oldtimer}
}

@InCollection{	  song97d,
  author	= {Wang Song and Ma Feng and Xia Shaowei and Su Hui},
  title		= {A Fault Tolerant {C}hinese Bank Check Recognition System
		  Based on {SOM} Neural Networks},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {2560--2565},
  dbinsdate	= {oldtimer}
}

@Article{	  song98a,
  author	= {Hee-Heon Song and Seong-Whan Lee},
  title		= {A Self-Organizing Neural Tree for Large-Set Pattern
		  Classification},
  journal	= {IEEE Transactions on Neural Networks},
  year		= 1998,
  volume	= 9,
  pages		= {369--380},
  dbinsdate	= {oldtimer}
}

@InCollection{	  sorhus94a,
  author	= {R. Sorhus and J. H. Husoy},
  title		= {Image subband coding with spatially adaptive {IIR} filter
		  banks: Automatic filter selection},
  booktitle	= {Signal Processing VII, Theories and Applications.
		  Proceedings of EUSIPCO-94. Seventh European Signal
		  Processing Conference},
  publisher	= {Eur. Assoc. Signal Process},
  year		= {1994},
  volume	= {2},
  editor	= {M. J. J. Holt and C. F. N. Cowan and P. M. Grant and W. A.
		  Sandham},
  address	= {Lausanne, Switzerland},
  pages		= {1230--3},
  dbinsdate	= {oldtimer}
}

@Article{	  soriano01a,
  author	= {Soriano, M. and Garcia, L. and Saloma, C.},
  title		= {Fluorescent image classification by major color histograms
		  and a neural network},
  journal	= {OPTICS EXPRESS},
  year		= {2001},
  volume	= {8},
  number	= {5},
  month		= {FEB 26},
  pages		= {271--277},
  abstract	= {Efficient image classification of microscopic fluorescent
		  spheres is demonstrated with a supervised backpropagation
		  neural network (NN) that uses as inputs the major color
		  histogram representation of the fluorescent image to be
		  classified. Two techniques are tested for the major color
		  search: (1) cluster mean (CM) and (2) Kohonen's
		  self-organizing feature map (SOFM). The method is shown to
		  have higher recognition rates than Swain and Ballard's
		  Color Indexing by histogram intersection. Classification
		  with SOFM-generated histograms as inputs to the classifier
		  NN achieved the best recognition rate (90%) for cases of
		  normal, scaled, defocused, photobleached, and combined
		  images of AMCA (7-Amino- 4Methylcoumarin-3-Acetic Acid) and
		  FITC (Fluorescein Isothiocynate) stained microspheres. },
  dbinsdate	= {2002/1}
}

@Article{	  sorsa91a,
  author	= {Timo Sorsa and Heikki N. Koivo and Hannu Koivisto},
  title		= {Neural Networks in Process Fault Diagnosis},
  journal	= {{IEEE} Trans. on Syst. , Man, and Cyb. },
  year		= {1991},
  volume	= {21},
  number	= {4},
  pages		= {815--825},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sorsa92a,
  author	= {T. Sorsa and H. N. Koivo and R. Korhonen},
  title		= {Application of Neural Network in the Detection of Breaks
		  in a Paper Machine},
  booktitle	= {Preprints of the IFAC Symp. on On-Line Fault Detection and
		  Supervision in the Chemical Process Industries, Newark,
		  Delaware, April 1992},
  year		= {1992},
  pages		= {162--167},
  dbinsdate	= {oldtimer}
}

@Article{	  sorsa93a,
  author	= {Timo Sorsa and Heikki N. Koivo},
  title		= {Application of Artificial Neural Networks in Process Fault
		  Diagnosis},
  journal	= {Automatica},
  year		= {1993},
  volume	= {29},
  number	= {4},
  pages		= {843--849},
  dbinsdate	= {oldtimer}
}

@Article{	  sotolongo01a,
  author	= {Sotolongo Aguilar, G. and Guzman Sanchez, M. V.},
  title		= {Application of neuronal networks. The case of
		  bibliometrics},
  journal	= {Ciencias-de-la-Informacion},
  year		= {2001},
  volume	= {32},
  pages		= {27--34},
  abstract	= {Artificial neural networks (ANNs) are applied in a wide
		  range of human activities. One of these applications is as
		  a tool for data analysis, especially within bibliometrics.
		  In this paper, an introduction to some special features of
		  ANNs is made, mainly those based on the Kohonen model
		  (self-organizing maps). The different elements that form
		  these networks are presented, and their working principle
		  is linked to that of bibliometrics. A software package
		  called Viscovery SOMine/sup (R)/ that takes, for its own
		  execution, the concepts and algorithms from self-organizing
		  maps is used and characterized. Finally, the use of ANNs in
		  bibliometrics is shown through different case studies.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  souza01a,
  author	= {Souza, S. X. and Doria Neto, A. D. and Costa, J. A. F. and
		  {De Andrade Netto}, M. L.},
  title		= {A neural hybrid system for large memory association},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {2},
  pages		= {1174--1179},
  organization	= {Univ. Federal do Rio Grande do Norte, Department of
		  Electrical Engineering, Lab. of Computer Eng. and
		  Automation},
  publisher	= {},
  address	= {},
  abstract	= {A neural hybrid system based on Kohonen and Hopfield
		  networks is proposed to memory association. It uses a
		  heuristic approach to split a total set of patterns into
		  various subsets with the aim to increase performance of the
		  Parallel Architecture of Hopfield Networks---PAHN. This
		  architecture avoids several spurious states enabling
		  pattern storage capacity very larger then permitted by a
		  typical Hopfield network. The strategy consists on a method
		  to sort patterns with the SOM algorithm and distribute them
		  into these subsets in such a way that the patterns of the
		  same subset are the more orthogonal as possible among
		  themselves. The results show that the strategy employed to
		  distribute patterns in subsets worked well when compared
		  with the random distributions and with the exhaustive
		  approach. The results also shows that the proposed
		  heuristic led to patterns subsets that enable more robust
		  memory retrieval.},
  dbinsdate	= {2002/1}
}

@Article{	  soylu00a,
  author	= {Soylu, Mustafa and Ozdemirel, Nur E. and Kayaligil,
		  Sinan},
  title		= {Self-organizing neural network approach for the single
		  {AGV} routing problem},
  journal	= {European Journal of Operational Research},
  year		= {2000},
  volume	= {121},
  number	= {1},
  month		= {Feb 15},
  pages		= {124--137},
  organization	= {Middle East Technical Univ},
  publisher	= {Elsevier Science B.V.},
  address	= {Amsterdam},
  abstract	= {In this research, a special form of Automated Guided
		  Vehicle (AGV) routing problem is investigated. The
		  objective is to find the shortest tour for a single,
		  free-ranging AGV that has to carry out multiple pick and
		  deliver (P\&D) requests. This problem is an incidence of
		  the asymmetric traveling salesman problem which is known to
		  be NP-complete. An artificial neural network algorithm
		  based on Kohonen's self-organizing feature maps is
		  developed to solve the problem, and several improvements on
		  the basic features of serf-organizing maps are proposed.
		  Performance of the algorithm is tested under various
		  parameter settings for different P\&D request patterns and
		  problem sizes, and compared with the optimal solution and
		  the nearest neighbor rule. Promising results are obtained
		  in terms of solution quality and computation time.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  speckmann92a,
  author	= {H. Speckmann and P. Thole and W. Rosentiel},
  title		= {Hardware Implementations of {K}ohonen's Self-Organizing
		  Feature Map},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1451--1454},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  speckmann93a,
  author	= {H. Speckmann and P. Thole and W. Rosenstiel},
  title		= {{COKOS}: A Coprocessor for {K}ohonen
		  \mbox{Self-organizing} Map},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {1040--1044},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  speckmann93b,
  author	= {Speckmann, H. and Thole, P. and Rosenstiel, W.},
  title		= {COprocessor for KOhonen's Selforganizing map (COKOS)},
  journal	= {Proc. IJCNN'93, International Joint Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1993},
  number	= {},
  volume	= {2},
  pages		= {1951--1954},
  abstract	= {In this paper we present a system which enables easy and
		  fast computation of Kohonen's selforganizing map (SOM). It
		  is a hardware supported system consisting of different
		  parts. A neural coprocessor (COKOS) is connected to a
		  personal computer (PC) by a special, asynchronous
		  interface. The neural coprocessor works
		  vector-component-parallel and processing-element-serial and
		  speeds up the performance of Kohonen's algorithm of the
		  selforganizing map by magnitude. An userfriendly interface
		  (ULTIKOS) supports the preprocessing of the input data and
		  the control and assessment of the learning process.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  speckmann93c,
  author	= {H. Speckmann and P. Thole and W. Rosentiel},
  title		= {Hardware Synthesis for Neural Networks from a Behavioral
		  Description with {VHDL}},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1983--1986},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  speckmann94a,
  author	= {H. Speckmann and G. Raddatz and W. Rosenstiel},
  title		= {Considerations of geometrical and fractal dimension of
		  {SOM} to get better learning results},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {342--345},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {analysis, dimensionality, dynamics},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  speckmann94b,
  author	= {H. Speckmann and P. Thole and M. Bogdan and W. Rosentiel},
  title		= {Coprocessor for special neural networks {KOKOS} and
		  {KOBOLD}},
  pages		= {1959--1962},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {implementation, hardware},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  speckmann94c,
  author	= {H. Speckmann and P. Thole and M. Bogdan and W.
		  Rosenstiel},
  title		= {Coprocessors for special neural networks {KOKOS} and
		  {KOBOLD}},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {II},
  pages		= {612--617},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {implementation, hardware},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  speckmann94d,
  author	= {Heike Speckmann and G{\"{u}}nter Raddatz and Wolfgang
		  Rosenstiel},
  title		= {Improvement of learning results of the
		  \mbox{self-organizing} map by calculating fractal
		  dimensions},
  booktitle	= {Proc. ESANN'94, European Symp. on Artificial Neural
		  Networks},
  year		= {1994},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  editor	= {M. Verleysen},
  pages		= {251--255},
  dbinsdate	= {oldtimer}
}

@Misc{		  speidel89a,
  author	= {S. L. Speidel},
  title		= {Signal Phase Pattern Sensitive Neural Network System and
		  Method},
  howpublished	= {U. S. Patent No. 5,146,541},
  year		= {1989},
  month		= {June},
  note		= {This {G}overnment-owned invention available for {U}. {S}.
		  licensing and, possibly, for foreign licensing. Copy of
		  patent available {C}ommissioner of {P}atents, {W}ashington,
		  {DC} 20231 {\$}1. 50},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  speidel91a,
  author	= {S. L. Speidel},
  title		= {Sonar scene analysis using neurobionic sound segregation},
  booktitle	= {IEEE Conf. on Neural Networks for Ocean Engineering},
  year		= {1991},
  pages		= {77--90},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  speidel92a,
  author	= {S. L. Speidel},
  title		= {Neural adaptive sensory processing for undersea sonar},
  journal	= {IEEE J. Oceanic Engineering},
  year		= {1992},
  volume	= {17},
  number	= {4},
  pages		= {341--350},
  month		= {October},
  dbinsdate	= {oldtimer}
}

@Article{	  spencer97a,
  author	= {R. G. Spencer and C. S. Lessard and F. Davila and B.
		  Etter},
  title		= {Self-organising discovery, recognition and prediction of
		  haemodynamic patterns in the intensive care unit},
  journal	= {Medical \& Biological Engineering \& Computing},
  year		= {1997},
  volume	= {35},
  number	= {2},
  pages		= {117--23},
  dbinsdate	= {oldtimer}
}

@Article{	  spiegel90a,
  author	= {J. {Van der Spiegel} and P. Mueller and D. Blackman and C.
		  Donham and R. Etienne-Cummings and P. Aziz and A. Choudhury
		  and L. Jones and J. Xin},
  title		= {Artificial neural networks: principles and {VLSI}
		  implementation},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1990},
  volume	= {1405},
  pages		= {184--197},
  x		= {Eri hermoverkkojen esittelya. },
  dbinsdate	= {oldtimer}
}

@Article{	  spitzer95a,
  author	= {Spitzer, M. and Bohler, P. and Weisbrod, M. and Kischka,
		  U. },
  title		= {A neural network model of phantom limbs},
  journal	= {Biological Cybernetics},
  year		= {1995},
  volume	= {72},
  number	= {3},
  pages		= {197--206},
  dbinsdate	= {oldtimer}
}

@Article{	  spitzer96a,
  author	= {M. Spitzer and M. Neumann},
  title		= {Noise in models of neurological and psychiatric
		  disorders},
  journal	= {International Journal of Neural Systems},
  year		= {1996},
  volume	= {7},
  number	= {4},
  pages		= {355--61},
  note		= {(Workshop on the Role and Control of Random Events in
		  Biological Systems Conf. Date: 4--9 Sept. 1995 Conf. Loc:
		  Sigtuna, Sweden)},
  dbinsdate	= {oldtimer}
}

@Article{	  spitzer97a,
  author	= {M. Spitzer},
  title		= {Noise-driven neuroplasticity in \mbox{self-organizing}
		  feature maps: a neurocomputational model of phantom limbs},
  journal	= {M. D. Computing},
  year		= {1997},
  volume	= {14},
  number	= {3},
  pages		= {192--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  spitzner98a,
  author	= {Spitzner, A. and Polani, D.},
  title		= {Order parameters for \mbox{self-organizing} maps},
  booktitle	= {ICANN 98. Proceedings of the 8th International Conference
		  on Artificial Neural Networks.},
  year		= {1998},
  volume	= {2},
  publisher	= {Springer-Verlag},
  pages		= {517--22},
  address	= {London, UK},
  abstract	= {We introduce and discuss different approaches to construct
		  order parameters for Kohonen's self-organizing maps. As one
		  approach the notion of an order parameter in the sense of
		  Haken's synergetics is studied and contrasted with
		  organization measures using SOM structure information.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sprekelmeyer00a,
  author	= {Sprekelmeyer, U. and Tenhagen, A. and Lippe, Wolfram-M.},
  title		= {Fuzzy Kohonen classifier},
  booktitle	= {IEEE International Conference on Fuzzy Systems},
  year		= {2000},
  editor	= {},
  volume	= {2},
  pages		= {572--576},
  organization	= {Westfaelische Wilhelms-Universitaet Muenster},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Several ways of combining concepts of fuzzy set theory
		  with connectionist methods are known. We focus on the use
		  of fuzzy numbers in neural networks. Our goal is to create
		  a fully fuzzified Kohonen-layer, which receives fuzzy
		  numbers as inputs and computes its output employing fuzzy
		  weights. The main problem is the determination of the
		  winning neuron by the exclusive use of special, `monotonic'
		  fuzzy operations, which guarantee a certain `goodness' of
		  the input/output behaviour. A selection-function is
		  introduced, solving this problem. Further on we formulate a
		  fuzzified version of the standard learning rule, that can
		  be applied on the fuzzified Kohonen neurons.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  srikanth93a,
  author	= {Srikanth, R. and Petry, F. E. and Koutsougeras, C. },
  title		= {Fuzzy elastic clustering},
  booktitle	= {Second IEEE International Conference on Fuzzy Systems},
  year		= {1993},
  volume	= {2},
  pages		= {1179--82},
  organization	= {Dept. of Comput. Sci. , Clark Atlanta Univ. , GA, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  srinivasa96a,
  author	= {N. Srinivasa and R. Sharma},
  title		= {A \mbox{self-organizing} invertible map for active vision
		  applications},
  booktitle	= {WCNN'96. World Congress on Neural Networks. International
		  Neural Network Society 1996 Annual Meeting},
  publisher	= {Lawrence Erlbaum Associates},
  year		= {1996},
  address	= {Mahwah, NJ, USA},
  pages		= {121--4},
  dbinsdate	= {oldtimer}
}

@Article{	  srinivasa97a,
  author	= {Srinivasa, Narayan and Sharma, Rajeev},
  title		= {SOIM: A \mbox{self-organizing} invertible map with
		  applications in active vision},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  number	= {3},
  volume	= {8},
  pages		= {758--773},
  abstract	= {We propose a novel neural network called the
		  self-organized invertible map (SOIM) that is capable of
		  learning many-to-one functionals mappings in a
		  self-organized and on-line fashion. The design and
		  performance of the SOIM are highlighted by learning a
		  many-to-one functional mapping that exists in active vision
		  for spatial representation of three-dimensional point
		  targets. The learned spatial representation is invariant to
		  changing camera configurations. The SOIM also possesses an
		  invertible property that can be exploited for active
		  vision. An efficient and experimentally feasible method was
		  devised for learning this representation on a real active
		  vision system. The proof of convergence during learning as
		  well as conditions for invariance of the learned spatial
		  representation are derived and then experimentally verified
		  using the active vision system. We also demonstrate various
		  active vision applications that benefit from the properties
		  of the mapping learned by SOIM.},
  dbinsdate	= {oldtimer}
}

@Article{	  srinivasa99a,
  author	= {Srinivasa, Narayan and Ahuja, Narendra},
  title		= {Topological and temporal correlator network for
		  spatiotemporal pattern learning, recognition, and recall},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1999},
  number	= {2},
  volume	= {10},
  pages		= {356--371},
  abstract	= {In this paper, we describe the design of an artificial
		  neural network for spatiotemporal pattern recognition and
		  recall. This network has a five-layered architecture and
		  operates in two modes: pattern learning and recognition
		  mode, and pattern recall mode. In pattern learning and
		  recognition mode, the network extracts a set of
		  topologically and temporally correlated features from each
		  spatiotemporal input pattern based on a variation of
		  Kohonen's self-organizing maps. These features are then
		  used to classify the input into categories based on the
		  fuzzy ART network. In the pattern recall mode, the network
		  can reconstruct any of the learned categories when the
		  appropriate category node is excited or probed. The network
		  performance was evaluated via computer simulations of
		  time-varying, two-dimensional and three-dimensional data.
		  The results show that the network is capable of both
		  recognition and recall of spatiotemporal data in an on-line
		  and self-organized fashion. The network can also classify
		  repeated events in the spatiotemporal input and is robust
		  to noise in the input such as distortions in the spatial
		  and temporal content.},
  dbinsdate	= {oldtimer}
}

@Article{	  srinivasan94a,
  author	= {Srinivasan, V. and Siang-Tiong Yeo and Chaturvedi, P. },
  title		= {Fringe processing and analysis with a neural network},
  journal	= {Optical Engineering},
  year		= {1994},
  volume	= {33},
  number	= {4},
  pages		= {1166--71},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InCollection{	  srinivasan96a,
  author	= {Dipti Srinivasan and C. S. Chang and Swee Sien Tan},
  title		= {One-day ahead electric load forecasting with hybrid
		  fuzzy-neural networks},
  booktitle	= {1996 Biennial Conference of the North American Fuzzy
		  Information Processing Society---NAFIPS},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {M. H. Smith and M. A. Lee and J. Keller and J. Yen},
  address	= {New York, NY, USA},
  pages		= {160--3},
  dbinsdate	= {oldtimer}
}

@Article{	  srinivasan99a,
  author	= {Srinivasan, D. and Swee Sien Tan and Cheng, C. S. and Eng
		  Kiat Chan},
  title		= {Parallel neural network-fuzzy expert system strategy for
		  short-term load forecasting: system implementation and
		  performance evaluation},
  journal	= {IEEE Transactions on Power Systems},
  year		= {1999},
  volume	= {14},
  pages		= {1100--6},
  abstract	= {The online implementation and results from a hybrid
		  short-term electrical load forecaster that is being
		  evaluated by a power utility are documented in this paper.
		  This forecaster employs a new approach involving a parallel
		  neural-fuzzy expert system, whereby Kohonen's
		  self-organizing feature map with unsupervised learning, is
		  used to classify daily load patterns. Post-processing of
		  the neural network outputs is performed with a fuzzy expert
		  system which successfully corrects the load deviations
		  caused by the effects of weather and holiday activity.
		  Being highly automated, little human interference is
		  required during the process of load forecasting. A
		  comparison made between this model and a regression-based
		  model currently being used in the control centre has shown
		  a marked improvement in load forecasting results.},
  dbinsdate	= {oldtimer}
}

@Article{	  srivastava98a,
  author	= {L. Srivastava and S. N. Singh and J. Sharma},
  title		= {Parallel Self Organizing Hierarchical Neural Network Based
		  Fast Voltage Estimation},
  journal	= {IEE Proceedings---Generation, Transmission and
		  Distribution},
  volume	= {145},
  pages		= {98--104},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  stacey00a,
  author	= {Stacey, D. A. and Kremer, S. C. and Dara, R.},
  title		= {A {SOM}/{MLP} hybrid network that uses unlabeled data to
		  improve classification performance},
  booktitle	= {Smart Engineering System Design: Neural Networks, Fuzzy
		  Logic, Evolutionary Programming, Data Mining, and Complex
		  Systems. Vol.10. Proceedings of the Artificial Neural
		  Networks in Engineering Conference (ANNIE 2000). ASME, New
		  York, NY, USA},
  year		= {2000},
  volume	= {},
  pages		= {179--84},
  abstract	= {The Guelph Cluster Class (GCC) system is an approach to
		  using unlabeled data to aid in the training of a supervised
		  neural network. A SOM picks out natural clusters in the
		  input data that are manipulated to correspond to various
		  sub-classes of the desired classification space. These
		  sub-class clusters are used to classify unlabeled data so
		  as to provide self-labeled data. The self-labeled data is
		  used as training data for a supervised system. Experiments
		  have demonstrated the increased classification power for
		  GCC especially when the initial amount of labeled data is
		  very small.},
  dbinsdate	= {2002/1}
}

@Article{	  stankevicius01a,
  author	= {Stankevicius, G.},
  title		= {Forming of the investment portfolio using the
		  self-organizing maps ({SOM})},
  journal	= {Informatica},
  year		= {2001},
  volume	= {12},
  pages		= {573--84},
  abstract	= {Considers the problem of the comparison of different
		  companies when looking for possible candidates for an
		  investment portfolio. Screening of companies, using
		  well-known trading strategy parameters, is one of the ways
		  to solve this problem. Actually, using this procedure many
		  more companies appear on the list than the trader is
		  willing to buy. To define the best companies or group of
		  the best companies a self-organizing (Kohonen's) map (SOM)
		  could be used. Using fundamental financial parameters as
		  inputs, the output of SOM forms the different groups of
		  companies located in a number of disjoint clusters. Then,
		  by the special averaging technique, the 3D map of the
		  quality of investment could be formed. Investing portfolios
		  also could be formed by a simple technical analysis
		  approach. The nonlinear ranging technique was applied as an
		  alternative to the self-organizing map procedure in this
		  paper. The certain meanings of weights were given to the
		  factors, which characterize the companies. Then, by
		  estimation of all weights, companies were assigned to their
		  place in the general listing. Four different portfolios
		  were formed as a result. The performance of these
		  portfolios showed which of the researched techniques gave
		  the better result. The real data from USA stock markets was
		  used for the realization of the whole idea.},
  dbinsdate	= {2002/1}
}

@Article{	  stanley01a,
  author	= {Stanley, K. and Wu, Q. M. J. and {de Silva} C. W. and
		  Gruver, W. A.},
  title		= {Modular neural-visual servoing with image compression
		  input},
  journal	= {Journal-of-Intelligent-\&-Fuzzy-Systems},
  year		= {2001},
  volume	= {10},
  pages		= {1--11},
  abstract	= {One of the essential problems of feature-based visual
		  servoing is calculating the inverse Jacobian, which relates
		  changes in features to changes in robot position. Neural
		  networks can approximate the inverse feature Jacobian.
		  Neural networks also allow other forms of vision input to
		  be easily used to position the robot. The vision system is
		  primarily responsible for reducing the dimensionality of
		  the input to reduce the size and therefore computational
		  load on the system. We develop a system which uses neural
		  networks to both encode the image and generate control
		  signals. In our system, the image dimensionality can be
		  reduced in four ways: feature extraction, averaging
		  compression, vector quantization, and principal component
		  expansion. We demonstrate that it is possible to use neural
		  networks for both image analysis and control of a vision
		  guided robot, with little loss of accuracy when compared to
		  feature based extraction.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  steffens95a,
  author	= {Steffens, J. and Kunze, M. },
  title		= {Implementation of the supervised growing cell structure on
		  the {CNAPS} neurocomputer},
  booktitle	= {ICANN `95. International Conference on Artificial Neural
		  Networks. Neuronimes `95 Scientific Conference},
  year		= {1995},
  editor	= {Fogelman-Soulie, F. and Gallinari, P. },
  volume	= {2},
  pages		= {51--6},
  organization	= {Inst. fur Experimentalphys. I, Ruhr-Univ. , Bochum,
		  Germany},
  publisher	= {EC2 \& Cie},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@InCollection{	  steinmetz95a,
  author	= {V. Steinmetz and G. Rabatel and M. Crochon and T. Talou
		  and B. Bourrounet},
  title		= {Sensor fusion for quality grading of melons},
  booktitle	= {Control Applications in Post-Harvest and Processing
		  Technology (CAPPT '95). A Postprint Volume from the 1st
		  IFAC/CIGR/EURAGENG/ISHS Workshop},
  publisher	= {Pergamon},
  year		= {1995},
  editor	= {J. D. Baerdemaeker and J. Vandewalle},
  address	= {Oxford, UK},
  pages		= {201--7},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  stephanidis95a,
  author	= {Stephanidis, C. N. and Cracknell, A. P. and Hayes, L. W.
		  B. },
  title		= {The implementation of self organised neural networks for
		  cloud classification in digital satellite images},
  booktitle	= {1995 International Geoscience and Remote Sensing
		  Symposium, IGARSS '95. Quantitative Remote Sensing for
		  Science and Applications},
  year		= {1995},
  editor	= {Stein, T. I. },
  volume	= {1},
  pages		= {455--7},
  organization	= {Dept. of Appl. Phys. \& Electron. \& Manuf. Eng. , Dundee
		  Univ. , UK},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ster96a,
  author	= {Ster, B. and Dobnikar, A.},
  title		= {Neural networks in medical diagnosis: comparison with
		  other methods},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {427--30},
  abstract	= {A number of classification systems were tested on
		  different medical data domains. Our goal was to obtain
		  comparative results for different methods conducted in
		  unified manner (10-fold cross-validation). The performance
		  of these methods can be evaluated on any data domain
		  described by known examples, consisting of a set of
		  attributes. Some medical databases are among the hardest
		  real-world data and can be useful for testing classifier
		  performance. The medical data used are from different
		  medical fields: coronary arterial disease, breast cancer,
		  hepatitis and diabetes. The neural network models used were
		  multilayer perceptron with backpropagation and LVQ. The
		  other methods were linear discriminant method (using
		  Fisher's criterion), linear discriminant by maximum
		  likelihood, quadratic discriminant by maximum likelihood,
		  K-nearest neighbour method, CART (decision trees),
		  lookahead feature construction (decision trees), Assistant,
		  Assistant-RELIEFF, naive Bayes and semi-naive Bayes. This
		  paper shows that, in terms of classification accuracy, the
		  neural network methods are among the best. In general, the
		  variation in accuracy across different classifiers is not
		  very large.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  steuer01a,
  author	= {Steuer, M. and Caleb Solly, P. and Smith, J.},
  title		= {An alternative approach for the evaluation of the
		  neocognitron},
  booktitle	= {9th European Symposium on Artificial Neural Networks.
		  ESANN'2001. Proceedings. D-Facto, Evere, Belgium},
  year		= {2001},
  volume	= {},
  pages		= {125--30},
  abstract	= {The necognitron neural network is analysed from the point
		  of view of the contribution of the different layers to the
		  final classification. A variation to the neocognitron which
		  gives improved performance is suggested. This variant
		  combines the low level feature extraction capabilities of
		  the initial layers with alternative classifiers such as LVQ
		  and Class Based Means Clustering. This is shown to give
		  performance which is superior to either of those
		  classifiers acting on their own, and to the neocognitron in
		  its standard form on two different instances of the letter
		  recognition problem.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  steven01a,
  author	= {Steven, G. and Anguera, R. and Egan, C. and Steven, F. and
		  Vintan L.},
  title		= {Dynamic branch prediction using neural networks},
  booktitle	= {Proceedings Euromicro Symposium on Digital Systems Design.
		  IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2001},
  volume	= {},
  pages		= {178--85},
  abstract	= {Dynamic branch prediction in high-performance processors
		  is a specific instance of a general time series prediction
		  problem that occurs in many areas of science. In contrast,
		  most branch prediction research focuses on two-level
		  adaptive branch prediction techniques, a very specific
		  solution to the branch prediction problem. An alternative
		  approach is to look to other application areas and fields
		  for novel solutions to the problem. In this paper, we
		  examine the application of neural networks to dynamic
		  branch prediction. Two neural networks are considered: a
		  lecturing vector quantisation (LVQ) Network and a
		  backpropagation network. We demonstrate that a neural
		  predictor can achieve misprediction rates comparable to
		  conventional two-level adaptive predictors and suggest that
		  neural predictors merit further investigation.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  stevens95a,
  author	= {Ronald H. Stevens and Peter Wang and Alina Lopo},
  title		= {Exploring the Medical Novice-Expert Performance Continuum
		  with Unsupervised Artificial Neural Networks},
  volume	= {II},
  pages		= {785--791},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@Article{	  stewart94a,
  author	= {Stewart, Clayton and Lu, Yi Chuan and Larson, Victor},
  title		= {Neural clustering approach for high resolution radar
		  target classification},
  journal	= {Pattern Recognition},
  year		= {1994},
  number	= {4},
  volume	= {27},
  pages		= {503--513},
  month		= {April},
  abstract	= {Neural learning techniques, the Self-Organizing Feature
		  Map and Learning Vector Quantization, have been applied to
		  the automatic target recognition (ATR) problem in the
		  presence of high range resolution radar target signatures.
		  The database is collected by placing the targets on a
		  rotary turntable and slowly turning them over a complete
		  360 degree azimuth while the radar signatures are
		  collected. Our pattern recognition system is composed of a
		  feature identifier and a classifier. A simple Euclidean
		  distance classifier using those identified features
		  provides a baseline of 97% mean probability of correct
		  classification.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  stinely93a,
  author	= {M. Stinely and P. Klinkhachorn and R. S. Nutter and R.
		  Kothari},
  title		= {Classification of Chest Radiographs for Pneumoconiosis
		  Using {L}earning {V}ector {C}lassification},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {I},
  pages		= {597--600},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  stocker96a,
  author	= {A. Stocker and O. Sipila and A. Visa and O. Salonen and T.
		  Katila},
  title		= {Stability study of {SOM} neural networks applied to tissue
		  characterization of brain magnetic resonance images},
  booktitle	= {Proceedings of the 13th International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  volume	= {4},
  address	= {Los Alamitos, CA, USA},
  pages		= {472--6},
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  stowe90a,
  author	= {F. S. Stowe},
  title		= {Speech Recognition Using {K}ohonen Neural Networks,
		  Dynamic Programming and Multi-Feature Fusion},
  school	= {Air Force Inst. of Tech. , School of Engineering},
  year		= {1990},
  address	= {Wright-Patterson AFB, OH},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  strickert01a,
  author	= {Strickert, M. and Bojer, T. and Hammer, B.},
  title		= {Generalized relevance {LVQ} for time series},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {677--683},
  abstract	= {An application of the recently proposed generalized
		  relevance learning vector quantization (GRLVQ) to the
		  analysis and modeling of time series data is presented. We
		  use GRLVQ for two tasks: first, for obtaining a phase space
		  embedding of a scalar time series, and second, for short
		  term and long term data prediction. The proposed embedding
		  method is tested with a signal from the well-known Lorenz
		  system. Afterwards, it is applied to daily lysimeter
		  observations of water runoff. A one- step prediction of the
		  runoff dynamic is obtained from the classification of high
		  dimensional subseries data vectors, from which a promising
		  technique for long term forecasts is derived.(1).},
  dbinsdate	= {2002/1}
}

@InProceedings{	  stroud93a,
  author	= {R. R. Stroud and S. Swallow and J. R. McCardle and K. T.
		  Burge},
  title		= {Controlling 1000 Amps using Neural Networks},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1857--1860},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  strouthopoulos97a,
  author	= {Strouthopoulos, C. and Papamarkos, N.},
  title		= {Document block identification using a neural network},
  journal	= {International Conference on Digital Signal Processing.
		  DSP},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1997},
  number	= {},
  volume	= {2},
  pages		= {999--1002},
  abstract	= {This paper describes a new method that clusters the
		  content of a mixed type document in text or non-text areas.
		  The proposed approach is based on a new set of textural
		  features combined with a two stage neural network
		  classifier. The neural network classifier consists of a
		  principal components analyzer and a Kohonen self organized
		  feature map. Document blocks are classified as text,
		  graphics and halftones or to secondary subclasses
		  corresponding to special cases of the primal classes. The
		  proposed method can identify text regions included in
		  graphics or even overlapped regions, that is, regions that
		  cannot be separated with horizontal and vertical cuts. The
		  performance of the method was extensively tested on a
		  variety of documents with very promising results.},
  dbinsdate	= {oldtimer}
}

@Article{	  strouthopoulos98a,
  author	= {Strouthopoulos, C. and Papamarkos, N.},
  title		= {Text identification for document image analysis using a
		  neural network},
  journal	= {Image and Vision Computing},
  year		= {1998},
  number	= {12},
  volume	= {16},
  pages		= {879--896},
  abstract	= {A new bottom-up method is described that clusters the
		  content of a mixed type document into text or non-text
		  areas. The proposed approach is based on a new set of
		  features combined with a self-organized neural network
		  classifier. The set of features corresponds to the contents
		  and the relationship of 3x3 masks, is selected by using a
		  statistical reduction procedure, and provides texture
		  information. Next, a Principal Components Analyzer (PCA) is
		  applied, which results in a reduced number of `effective'
		  features. The final set of features is then utilized as
		  input vector into a proper neural network to achieve the
		  classification goal. The neural network classifier is based
		  on a Kohonen Self Organized Feature Map (SOFM). Document
		  blocks are classified as text, graphics, and halftones or
		  to secondary subclasses corresponding to special cases of
		  the primal classes. The proposed method can identify text
		  regions included in graphics or even overlapped regions,
		  that is, regions that cannot be separated with horizontal
		  and vertical cuts. The performance of the method was
		  extensively tested on a variety of documents with very
		  promising results.},
  dbinsdate	= {oldtimer}
}

@Book{		  strube95a,
  author	= {Strube, H. W.},
  title		= {Sprachverstehen in neuronaler Architektur (SPINA).
		  Abschlussbericht. (Speech understanding in neural
		  architecture (SPINA). Final report).},
  year		= {1995},
  abstract	= {1. State of research. Classic and neural speech
		  recognition methods, non-binary neural networks of various
		  types, short-term analysis and feature extraction in the
		  human auditory system, vector quantization. 2.
		  Motivation/goals. In the framework of the
		  neurocomputational goal of a robot with multimodal
		  sensorium, the compound project 'SPINA' treated the
		  recognition of the meaning of spoken robot-command
		  sentences by means of a hierarchical neural system. 3.
		  Methods. Primary analysis: hearing-related short-term
		  spectral analysis. Higher features using correlation or
		  modulation analysis of critical-band channel signals, or
		  using self-organization by means of feature maps or fuzzy
		  vector quantization. Hypothesis formation (for phonemes or
		  words) by means of self-organized or perceptron-like nets,
		  TDNN. Word from phoneme hypotheses as well as
		  syntax-semantics-analysis with predominantly neural
		  associative memories. Speaker adaptation: by mapping of
		  patterns. Figure-ground separation: by means of correlated
		  activity. 4. Result. Several methods for word or phoneme
		  hypothesis formation in running speech. Assignment of
		  sentence meaning as robot action. Partial integration into
		  overall demo system, further partial demo systems. Fast
		  algorithms and learning methods. Theory of recurrent
		  associative memories. 5. Conclusion/possible applications.
		  The methods are largely practically applicable. Some
		  partial problems require further investigations. (orig.).
		  (Copyright (c) 1996 by FIZ. Citation no. 96:002590.)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  strupl97a,
  author	= {D. Strupl and R. Neruda},
  title		= {Parallelizing \mbox{self-organizing} maps},
  booktitle	= {SOFSEM '97: Theory and Practice of Informatics. 24th
		  Seminar on Current Trends in Theory and Practice of
		  Informatics. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {F. Plasil and K. G. Jeffery},
  address	= {Berlin, Germany},
  pages		= {563--70},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  su00a,
  author	= {Su, Mu-Chun and Chou, Chien-Hsing and Chang, Hsiao-Te},
  title		= {Adding a healing mechanism in the self-organizing feature
		  map algorithm},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {171--176},
  organization	= {Tamkang Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {It is often reported in the technique literature that the
		  success of the self-organizing feature map (SOM) formation
		  is critically dependent on the initial weights and the
		  selection of main parameters of the algorithm, namely, the
		  learning-rate parameter and the neighborhood set. In this
		  paper, we propose a healing mechanism to repair feature
		  maps that are not well topologically ordered. The healed
		  map is then further fine-tune by the SOM algorithm so as to
		  improve the accuracy of the map. Two data sets are tested
		  to illustrate the performance of the proposed method.},
  dbinsdate	= {2002/1}
}

@Article{	  su00b,
  author	= {Su, Mu-Chun and Chang, Hsiao-Te},
  title		= {Fast self-organizing feature map algorithm},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  volume	= {11},
  number	= {3},
  month		= {May},
  pages		= {721--733},
  organization	= {Tamkang Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {We present an efficient approach to forming feature maps.
		  The method involves three stages. In the first stage, we
		  use the K-means algorithm to select N<sup>2</sup> (i.e.,
		  the size of the feature map to be formed) cluster centers
		  from a data set. Then a heuristic assignment strategy is
		  employed to organize the N<sup>2</sup> selected data points
		  into an N \times N neural array so as to form an initial
		  feature map. If the initial map is not good enough, then it
		  will be fine-tuned by the traditional Kohonen
		  self-organizing feature map (SOM) algorithm under a fast
		  cooling regime in the third stage. By our three-stage
		  method, a topologically ordered feature map would be formed
		  very quickly instead of requiring a huge amount of
		  iterations to fine-tune the weights toward the density
		  distribution of the data points, which usually happened in
		  the conventional SOM algorithm. Three data sets are
		  utilized to illustrate the proposed method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  su00c,
  author	= {Su, Mu-Chun and Tew, Chee-Yuen},
  title		= {Self-organizing feature-map-based fuzzy system},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {20--25},
  organization	= {Tamkang Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper presents a neuro-fuzzy system by using the
		  Kohonen's self-organizing feature map algorithm, not only
		  for its vector quantitization feature, but also for its
		  topological property. This property prevents the proposed
		  neuro-fuzzy system from suffering from a drawback like any
		  of the conventional clustering-algorithm-based fuzzy
		  systems, i.e. the optimal number of clusters or some kind
		  of similarity threshold must be predetermined. Associated
		  with the self-organizing feature-map-based fuzzy system is
		  a hybrid learning algorithm, which is for initial
		  parameters setting and fine-tuning the parameters of the
		  system.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  su00d,
  author	= {Mu-Chun Su and Lai, E. and Chee-Yuen Tew},
  title		= {A {SOM}-based fuzzy system and its application in
		  handwritten digit recognition},
  booktitle	= {Proceedings International Symposium on Multimedia Software
		  Engineering. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {253--8},
  abstract	= {The paper presents a neuro-fuzzy system by using Kohonen's
		  self-organizing feature map algorithm, not only for its
		  vector quantization feature, but also for its topological
		  property. This property prevents the proposed neuro-fuzzy
		  system from suffering from a drawback like any of the
		  conventional clustering algorithm based fuzzy systems, i.e.
		  the optimal number of clusters or some kind of similarity
		  threshold must be predetermined. Associated with the
		  self-organizing feature map based fuzzy system is a hybrid
		  learning algorithm, which is for initial parameter setting
		  and fine-tuning the parameters of the system. Application
		  of the proposed fuzzy systems in optical handwritten digit
		  recognition is reported. High recognition rates can be
		  achieved.},
  dbinsdate	= {2002/1}
}

@Article{	  su01a,
  author	= {Su, M. -C. and Liu, I. -C.},
  title		= {Application of the self-organizing feature map algorithm
		  in facial image morphing},
  journal	= {Neural Processing Letters},
  year		= {2001},
  volume	= {14},
  number	= {1},
  month		= {August },
  pages		= {35--47},
  organization	= {Dept. of Comp. Sci. and Info. Eng., National Central
		  University},
  publisher	= {},
  address	= {},
  abstract	= {A new facial image morphing algorithm based on the Kohonen
		  self-organizing feature map (SOM) algorithm is proposed to
		  generate a smooth 2D transformation that reflects anchor
		  point correspondences. Using only a 2D face image and a
		  small number of anchor points, we show that the proposed
		  morphing algorithm provides a powerful mechanism for
		  processing facial expressions.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  su01b,
  author	= {Su, B. and Jeng, S. -K.},
  title		= {Multi-timbre chord classification using wavelet transform
		  and self-organized map neural networks},
  booktitle	= {ICASSP, IEEE International Conference on Acoustics, Speech
		  and Signal Processing---Proceedings},
  year		= {2001},
  editor	= {},
  volume	= {5},
  pages		= {3377--3380},
  organization	= {Graduate Inst. of Communication Eng., Department of
		  Electrical Engineering, National Taiwan University},
  publisher	= {},
  address	= {},
  abstract	= {This paper presents a new method for musical chord
		  recognition based on a model of human perception. We
		  classify the chords directly from the sound without the
		  information of timbres and notes. A wavelet-based transform
		  as well as a self-organized map (SOM) neural network is
		  adopted to imitate human ears and cerebra, respectively.
		  The resultant system can classify chords very well even in
		  a noisy environment.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  su01d,
  author	= {Mu-Chun Su and Chee-Yuen Tew and Hsin-Hua Chen},
  title		= {Musical symbol recognition using {SOM}-based fuzzy
		  systems},
  booktitle	= {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS
		  International Conference. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {4},
  pages		= {2150--3},
  abstract	= {A large number of research activities have been undertaken
		  to investigate optical music recognition (OMR). OMR
		  involves identifying musical symbols on a scanned sheet of
		  music and transforming them into a computer readable
		  format. We propose an efficient method based on SOM-based
		  fuzzy systems to recognize musical symbols. A database
		  consisting of 9 kinds of musical symbols were used to test
		  the performance of the SOM-based fuzzy systems.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from internet}
}

@Article{	  su01c,
  author	= {Su, Mu-Chun and Chang, Hsiao-Te},
  title		= {New model of self-organizing neural networks and its
		  application in data projection},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2001},
  volume	= {12},
  number	= {1},
  month		= {Jan},
  pages		= {153--158},
  organization	= {Natl Central Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {In this paper a new model of self-organizing neural
		  networks is proposed. An algorithm called `double
		  self-organizing feature map' (DSOM) algorithm is developed
		  to train the novel model, By the DSOM algorithm the network
		  will adaptively adjust its network structure during the
		  learning phase so as to make neurons responding to similar
		  stimulus have similar weight vectors and spatially move
		  nearer to each other at the same time. The final network
		  structure allows us to visualize high-dimensional data as a
		  two-dimensional scatter plot. The resulting representations
		  allow a straightforward analysis of the inherent structure
		  of clusters within the input data. One high-dimensional
		  data set is used to test the effectiveness of the proposed
		  neural networks.},
  dbinsdate	= {2002/1}
}

@Article{	  su02a,
  author	= {Su, Mu-Chun and Chou, Chien-Hsing and Chang, Hsiao-Te},
  title		= {A healing mechanism to improve the topological preserving
		  property of feature maps},
  journal	= {IEICE Transactions on Information and Systems},
  year		= {2002},
  volume	= {E85-D},
  number	= {4},
  month		= {April },
  pages		= {735--743},
  organization	= {Dept. of Computer Science, National Central University},
  publisher	= {Inst. of Electronics, Info. and Communications Eng. of
		  Japan},
  address	= {},
  abstract	= {Recently, feature maps have been applied to various
		  problem domains. The success of some of these applications
		  critically depends on whether feature maps are
		  topologically ordered. Several different approaches have
		  been proposed to improve the conventional self-organizing
		  feature map (SOM) algorithm. However, these approaches do
		  not guarantee that a topologically-ordered feature map can
		  be formed at the end of a simulation. Therefore, the
		  trial-and-error procedure still dominates the procedure of
		  forming feature maps. In this paper, we propose a healing
		  mechanism to repair feature maps that are not well
		  topologically ordered. The healed map is then further
		  finetuned by the conventional SOM algorithm with a small
		  learning rate and a small-sized neighborhood set so as to
		  improve the accuracy of the map. Two data sets were tested
		  to illustrate the performance of the proposed method.},
  dbinsdate	= {2002/1}
}

@Article{	  su02b,
  author	= {Su, C. T. and Yang, T. and Ke, C. M.},
  title		= {A neural-network approach for semiconductor wafer
		  post-sawing inspection},
  journal	= {IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING},
  year		= {2002},
  volume	= {15},
  number	= {2},
  month		= {MAY},
  pages		= {260--266},
  abstract	= {Semiconductor wafer post-sawing requires full inspection
		  to assure defect-free outgoing dies. A defect problem is
		  usually identified through visual judgment by the aid of a
		  scanning electron microscope. By this means, potential
		  misjudgment may be introduced into the inspection process
		  due to human fatigue. In addition, the full inspection
		  process can incur significant personnel costs. This
		  research proposed a neural-network approach for
		  semiconductor wafer post-sawing inspection. Three types of
		  neural networks: backpropagation, radial basis function
		  network, and learning vector quantization, were proposed
		  and tested. The inspection time by the proposed approach
		  was less than one second per die, which is efficient enough
		  for a practical application purpose. The pros and cons for
		  the proposed methodology in comparison with two other
		  inspection methods, visual inspection and feature
		  extraction inspection, are discussed. Empirical results
		  showed promise for the proposed approach to solve
		  real-world applications. Finally, we proposed a
		  neural-network-based automatic inspection system framework
		  as future research opportunities.},
  dbinsdate	= {2002/1}
}

@InCollection{	  su97a,
  author	= {Ching-Tzong Su and Guor-Rurng Lii and Hong-Rong Hwung},
  title		= {A neuro-fuzzy method for tracking control},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Industrial Technology (ICIT'96)},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {J. Bigun and G. Chollet and G. Borgefors},
  address	= {Berlin, Germany},
  pages		= {682--6},
  dbinsdate	= {oldtimer}
}

@Article{	  su97b,
  author	= {Su, Mu Chun and DeClaris, Nicholas and Liu, Ta Kang},
  title		= {Application of neural networks in cluster analysis},
  journal	= {Proceedings of IEEE International Conference on Systems,
		  Man, and Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1997},
  number	= {},
  volume	= {1},
  pages		= {1--6},
  abstract	= {How to efficiently specify the `correct' number of
		  clusters from a given multidimensional data set is one of
		  the most fundamental and unsolved problems in cluster
		  analysis. In this paper, we propose a method for
		  automatically discovering the number of clusters and
		  estimating the locations of the centroids of the resulting
		  clusters. This method is base on the interpretation of a
		  self-organizing feature map (SOFM) formed by the given data
		  set. The other difficult problem in cluster analysis is how
		  to choose an appropriate metric for measuring the
		  similarity between a pattern and a cluster centroid. The
		  performance of clustering algorithms greatly depends on the
		  chosen measure of similarity. Clustering algorithms
		  utilizing the Euclidean metric view patterns as a
		  collection of hyperspherical-shaped swarms. Actually,
		  genetic structures of real data sets often exhibit
		  hyperellipsoidal-shaped clusters. In the second part of
		  this paper we present a method of training a single-layer
		  neural network composed of quadratic neurons to cluster
		  data into hyperellipsoidal- and/or hyperspherical-shaped
		  swarms. Two data sets are utilized to illustrate the
		  proposed methods.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  su98a,
  author	= {Mu-Chun Su and Hsiao-Te Chang},
  title		= {Genetic-algorithms-based approach to
		  \mbox{self-organizing} feature map and its application in
		  cluster analysis},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks, Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {735--40},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  su99a,
  author	= {Mu Chun Su and I Chen Liu},
  title		= {Facial image morphing by \mbox{self-organizing} feature
		  maps},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1969--72},
  abstract	= {We propose a new facial image morphing algorithm based on
		  the Kohonen self-organizing feature map (SOM) algorithm to
		  generate a smooth 2D transformation that reflects anchor
		  point correspondences. Using only a 2D face image and a
		  small number of anchor points, we show that the proposed
		  morphing algorithm provides a powerful mechanism for
		  processing facial expressions.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  su99b,
  author	= {Mu Chun Su and Ta Kang Liu and Hsiao Te Chang},
  title		= {An efficient initialization scheme for the
		  \mbox{self-organizing} feature map algorithm},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1906--10},
  abstract	= {It is often reported in the technique literature that the
		  success of the self-organizing feature map formation is
		  critically dependent on the initial weights and the
		  selection of main parameters of the algorithm, namely, the
		  learning-rate parameter and the neighborhood function. In
		  this paper, we propose an efficient initialization scheme
		  to construct an initial map. We then use the
		  self-organizing feature map algorithm to make small
		  subsequent adjustments so as to improve the accuracy of the
		  initial map. Two data sets are tested to illustrate the
		  performance of the proposed method.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  su99c,
  author	= {Mu-Chun Su and Hisao-Te Chang},
  title		= {Self-organizing neural networks for data projection},
  booktitle	= {5th International Computer Science Conference ICSC'99.
		  Proceedings (Lecture Notes in Computer Science Vol. 1749).
		  Springer-Verlag, Berlin, Germany},
  year		= {1999},
  volume	= {},
  pages		= {206--15},
  abstract	= {In this paper we present a nonlinear projection method for
		  visualizing high-dimensional data as a two-dimensional
		  scatter plot. The method is based on a new model of
		  self-organizing neural networks. An algorithm called
		  "double self-organizing feature map" (DSOM) algorithm is
		  developed to train the novel model. By the DSOM algorithm
		  the network will adaptively adjust its architecture during
		  the learning phase so as to make neurons responding to
		  similar stimulus be clustered together. Then the
		  architecture of the network is graphically displayed to
		  show the underlying structure of the data. Two data sets
		  are used to test the effectiveness of the proposed neural
		  network.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  suchy99a,
  author	= {Suchy, J. and Majlath, J.},
  title		= {Application of \mbox{self-organizing} map to
		  transformation of coordinates in robotics},
  booktitle	= {IMEKO---XV. World Congress. Measurement to Improve the
		  Quality of Life in the 2st Century---Measurement Helps to
		  Coordinate Nature with Human Activities---Vol. X. TEG-17.
		  ISMCR'99 Topical Workshop on Virtual Reality and Advanced
		  Human-Robot Systems. IMEKO, Budapest, Hungary},
  year		= {1999},
  volume	= {},
  pages		= {277--82},
  abstract	= {Deals with solving the problem of forward and inverse
		  kinematics tasks of serious kinematics chains in robotics
		  with artificial neural networks. In difference to some
		  other authors, it is tried here to solve these problems
		  using 2D and 3D Kohonen self-organizing maps (SOM). The
		  main idea is to utilize the topology preserving property of
		  this kind of neural network. The results are illustrated
		  with the example of a two-degrees-of freedom robot and a
		  three-degrees-of-freedom robot. To avoid some inherent
		  problems caused by classical SOM, the so-called extended
		  version of SOM is utilized which is based on reinforcement
		  learning. To make the number of neurons in SOM not too high
		  it is attempted to make use of convenient approximations.
		  The described results enable us to plan the trajectory in
		  the workspace of the robot in a very simple way.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  suetake99a,
  author	= {Suetake, N. and Nakamura, Y. and Yamakawa, T.},
  title		= {Maximum entropy {ICA} constrained by individual entropy
		  maximization employing \mbox{self-organizing} maps},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1038--42},
  abstract	= {We propose new method of the independent component
		  analysis (ICA), which doesn't require to know the classes
		  of the distribution of the sources beforehand in contrast
		  with conventional methods and achieves separation of
		  sources with higher precision than the conventional
		  methods. The proposed method employs the self-organizing
		  maps (SOM) to the Bell-Sejnowski's method (1995) for the
		  purpose of making use of the ability of SOM to approximate
		  the probability density. SOM grasps the probability density
		  of the input signals by nature. It can also track the
		  changes of the probability density of the input signal
		  adaptively. In this paper, the effectiveness and validity
		  of the proposed method are verified by applying it to the
		  separation of linearly mixed sounds and linearly mixed
		  pictures by the computer simulations.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  suewatanakul93a,
  author	= {W. Suewatanakul and D. M. Himmelblau},
  title		= {Comparison of artificial neural networks and traditional
		  classifiers via the two-spiral problem},
  booktitle	= {Proc. Third Workshop on Neural Networks:
		  Academic/Industrial/NASA/Defense WNN92},
  year		= {1993},
  pages		= {275--282},
  organization	= {Auburn Univ. ; Center Commersial Dev. Space Power and Adv.
		  Electron. ; NASA},
  publisher	= {Soc. Comput. Simulation},
  address	= {San Diego, CA},
  abstract	= {For years pattern recognition techniques have been
		  successfully exploited in engineering to solve
		  discrimination and classification problems. In this work,
		  various neural network algorithms have been applied as
		  pattern classifiers and the results of each classifier were
		  compared with traditional methods for a difficult
		  2-dimensional problem so that the results could be graphed
		  and visualized. The test problem is the problem of
		  distinguishing between two spirals (Lang and Witbrock,
		  1988). The neural network classifiers involved include (1)
		  a multilayer feedforward network with standard
		  backpropagation, (2) a multilayer feedforward network with
		  quickprop, (3) a network architecture with full connections
		  between all succeeding layers proposed by Lang and Witbrock
		  (1988), and (4) learning vector quantization (LVQ). For the
		  traditional classifiers, both Bayesian and k-nearest
		  neighbor classifiers were employed. The results showed that
		  the LVQ classifier was considered to be the best classifier
		  in the sense of processing time and performance.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  suganthan01a,
  author	= {P. N. Suganthan},
  title		= {{SHAPESOM}},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {110--7},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  suganthan01b,
  author	= {Suganthan, P. N.},
  title		= {Pattern classification using multiple hierarchical
		  overlapped self-organising maps},
  journal	= {Pattern Recognition},
  year		= {2001},
  volume	= {34},
  number	= {11},
  month		= {November },
  pages		= {2173--2179},
  organization	= {Sch. of Elec. and Electronic Eng., Nanyang Technological
		  University},
  publisher	= {},
  address	= {},
  abstract	= {In this paper, we describe techniques for designing
		  high-performance pattern classification systems using
		  multiple hierarchical overlapped self-organising maps
		  (HOSOM) (Suganthan, Proceedings of the International Joint
		  Conference on Neural Networks, WCCI'98, Alaska, 1998). The
		  HOSOM model has one first level SOM and several partially
		  overlapping second-level SOMs. With this overlap, every
		  training and test sample is classified by multiple
		  second-level SOMs. Hence, the final classification decision
		  can be made by combining these multiple classification
		  decisions to obtain a better performance. In this paper, we
		  use multiple HOSOMs and each HOSOM is trained on a distinct
		  input feature set extracted from the same data set. Since
		  one HOSOM yields multiple classifications, these multiple
		  HOSOMs generate a large number of classification decisions.
		  To combine the individual classifications, we make use of
		  the global winner as well as a winner for every class. Our
		  experiments yielded a high recognition rate of 99.25% on
		  NIST 19 numeral database. },
  dbinsdate	= {2002/1}
}

@InCollection{	  suganthan97a,
  author	= {P. N. Suganthan},
  title		= {Structure Adaptive Multilayer {SOM} with Partial
		  Supervision for Numeral Recognition},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {1235--1238},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  suganthan98a,
  author	= {Suganthan, P. N.},
  title		= {Hierarchical self organising maps},
  booktitle	= {Proceedings of the Ninth Australian Conference on Neural
		  Networks (ACNN'98). Univ. Queensland, Brisbane,
		  Qld.,Australia},
  year		= {1998},
  volume	= {},
  pages		= {255--9},
  abstract	= {We present a hybrid learning algorithm, structure
		  adaptation techniques, and hierarchical and overlapped
		  structure, for the standard self-organising maps (SOM) to
		  obtain an extremely powerful labelled pattern
		  classification system. The learning algorithm consists of
		  the standard unsupervised SOM learning of synaptic weights
		  as well as a supervised learning of weights. The
		  supervision stage is used to guide the structure adaptation
		  process, to fine tune the weights and to obtain a network
		  with good generalisation performance by avoiding
		  over-training. In fact classifiers based on
		  self-organising/unsupervised neural networks commonly
		  suffer from over-training. As higher layer SOMs overlap,
		  the final classification is made by fusing the
		  classifications of individual overlapped SOMs. We obtained
		  the best results ever reported for any SOM-based numerals
		  classification system.},
  dbinsdate	= {oldtimer}
}

@Article{	  suganthan99a,
  author	= {Suganthan, P. N.},
  title		= {Hierarchical overlapped SOM's for pattern classification},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1999},
  number	= {1},
  volume	= {10},
  pages		= {193--196},
  abstract	= {We develop a multilayer overlapped self-organizing maps
		  (SOM's) with limited structure adaptation capabilities, and
		  associated learning scheme for labeled pattern
		  classification applications. The learning algorithm
		  consists of the standard unsupervised SOM learning of
		  synaptic weights as well as the supervised learning vector
		  quantization (LVQ) 2 learning. As higher layer SOM's
		  overlap, the final classification is made by fusing the
		  classifications of top-level overlapped SOM's. We obtained
		  the best results ever reported for any SOM-based numerals
		  classification system.},
  dbinsdate	= {oldtimer}
}

@Article{	  suh96a,
  author	= {Suk-Hwan Suh and Yang-Soo Shin},
  title		= {Neural network modeling for tool path planning of the
		  rough cut in complex pocket milling},
  journal	= {Journal of Manufacturing Systems},
  year		= {1996},
  volume	= {15},
  number	= {5},
  pages		= {295--304},
  dbinsdate	= {oldtimer}
}

@Article{	  sui00a,
  author	= {Sui Qing Mei and Wang Zheng Xin},
  title		= {Identification of dynamical nonlinear systems using
		  improved self-organization neural network. I},
  journal	= {Electric-Machines-and-Control},
  year		= {2000},
  volume	= {4},
  pages		= {168--70},
  abstract	= {In this paper a new learning algorithm is presented for
		  dynamical nonlinear system identification based on the
		  self-organization feature maps proposed by Kohonen is
		  presented. In the algorithm, the learning of multiple local
		  models instead of the global model is considered. Weights
		  of computing output layers were updated along with weights
		  of the neurons within a neighborhood in a similar way.
		  Simulation results show the improved algorithm has rapid
		  convergent speed and high fitting precision.},
  dbinsdate	= {2002/1}
}

@Article{	  sukhaswami96a,
  author	= {Sukhaswami, M. B. and Pujari, A. K. },
  title		= {Restoration of geometrically aberrated images using a
		  \mbox{self-organising} neural network},
  journal	= {Pattern Recognition Letters},
  year		= {1996},
  volume	= {17},
  number	= {1},
  pages		= {1--10},
  dbinsdate	= {oldtimer}
}

@Article{	  suksmono00a,
  author	= {Suksmono, Andriyan Bayu and Hirose, Akira},
  title		= {Adaptive complex-amplitude texture classifier that deals
		  with both height and reflectance for interferometric {SAR}
		  images},
  journal	= {IEICE Transactions on Electronics},
  year		= {2000},
  volume	= {E83-C},
  number	= {12},
  month		= {Dec},
  pages		= {1912--1916},
  organization	= {Univ of Tokyo},
  publisher	= {IEICE of Japan},
  address	= {Tokyo},
  abstract	= {We propose an adaptive complex-amplitude texture
		  classifier that takes into consideration height as well as
		  reflection statistics of interferometric synthetic aperture
		  radar (SAR) images. The classifier utilizes the phase
		  information to segment the images. The system consists of a
		  two-stage preprocessor and a complex-valued SOFM. The
		  preprocessor extracts a complex-valued feature vectors
		  corresponding to height and reflectance statistics of
		  blocks in the image. The following SOFM generates a set of
		  templates (references) adaptively and classifies a block
		  into one of the classes represented by the templates.
		  Experiment demonstrates that the system segments an
		  interferometric SAR image successfully into a lake, a
		  mountain, and so on. The performance is better than that of
		  a conventional system dealing only with the amplitude
		  information.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  suksmono98a,
  author	= {Suksmono, A. B. and Karsa, K. and Tjondronegoro, S. and
		  Soegijoko, S.},
  title		= {Low bit rate image coding based on vector transformation
		  with neural network approach},
  booktitle	= {IEEE. APCCAS 1998. 1998 IEEE Asia-Pacific Conference on
		  Circuits and Systems. Microelectronics and Integrating
		  Systems. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  volume	= {},
  pages		= {603--6},
  abstract	= {Vector transformation is a new method in unifying vector
		  quantization (VQ) and transform coding. So far, the
		  codebook generation that has been applied in this coding is
		  the LBG algorithm. With the development of neural networks,
		  especially Self Organizing Feature Maps (SOFM), there are
		  some advantages that can be used to improve a system's
		  performance. In this paper, we explore the application of
		  the SOFM algorithm to generate the Vector Transform Coding
		  (VTC) codebook and compare the result with some coding
		  rates using the LBG algorithm.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  suksmono98b,
  author	= {Suksmono, A. B. and Sastrokusumo, U. and Kondo, K.},
  title		= {Adaptive image coding based on vector quantization using
		  {SOFM}-{NN} algorithm},
  booktitle	= {IEEE. APCCAS 1998. 1998 IEEE Asia-Pacific Conference on
		  Circuits and Systems. Microelectronics and Integrating
		  Systems. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1998},
  volume	= {},
  pages		= {443--6},
  abstract	= {Vector quantization (VQ) has been applied widely in image
		  coding. The codebook of the VQ system can be generated by a
		  clustering algorithms such as K-Means and LBG algorithms or
		  a feature mapping algorithm such as SOFM-NN. In this paper
		  we present a performance comparison between the LBG and
		  SOFM algorithms in term of PSNR for several bit rates and
		  propose a simple adaptive mechanism for the SOFM codebook.
		  The result leads to a conclusion that LBG and SOFM are
		  comparable, but SOFM has greater advantage in providing
		  simple adaptation mechanisms.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  suksmono99a,
  author	= {Suksmono, A. B. and Sastrokusumo, U. and Suryana, J. and
		  Priyanto B. E.},
  title		= {Application of image coding system based on vector
		  quantization using {SOFM}-{NN} algorithm for X-ray images},
  booktitle	= {1999 IEEE International Symposium on Intelligent Signal
		  Processing and Communication Systems. Signal Processing and
		  Communications Beyond 2000. King Mongkuts Inst. Technol,
		  Bangkok, Thailand},
  year		= {1999},
  volume	= {},
  pages		= {613--16},
  abstract	= {We present our result on the application of low bit rate
		  X-ray image coding based on the SOFM-NN algorithm. It is
		  expected that this kind of coding scheme could be applied
		  in building a low speed telemedicine network in the future,
		  which is one of the POST-PARTNERS programs.},
  dbinsdate	= {2002/1}
}

@Article{	  sulaiman95a,
  author	= {Sulaiman, M. N. and Evans, D. J. },
  title		= {Using a general-purpose neural network simulation
		  tool---{NEUCOMP}---for character recognition problems},
  journal	= {Journal of Microcomputer Applications},
  year		= {1995},
  volume	= {18},
  number	= {1},
  pages		= {65--81},
  month		= {Jan},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sum94a,
  author	= {John Sum and Lai-Wan Chan},
  title		= {Fuzzy Self-Organizing Map: Mechanism and Convergence},
  pages		= {1674--1679},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {modification, hybrid},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sum94b,
  author	= {John Sum and Lai-Wan Chan},
  title		= {Convergence of One-Dimensional {S}elf-{O}rganizing {M}ap},
  booktitle	= {Proc. Int. Symp. on Speech, Image Processing and Neural
		  Networks},
  year		= {1994},
  volume	= {I},
  pages		= {81--84},
  organization	= {{IEEE} Hong Kong Chapt. of Signal Processing},
  address	= {Hong Kong},
  annote	= {analysis, one-dimensional map, convergence},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sum94c,
  author	= {John Sum and Lai-Wan Chan},
  title		= {Fuzzy {S}elf-{O}rganizing {M}ap},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {732--737},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {modification, fuzzy},
  dbinsdate	= {oldtimer}
}

@Article{	  sum97a,
  author	= {John Sum and Chi-sing Leung and Lai-wan Chan and Lei Xu},
  title		= {Yet Another Algorithm which can Generate Topography Map},
  journal	= {IEEE Transactions on Neural Networks},
  year		= 1997,
  volume	= 8,
  pages		= {1204--1207},
  dbinsdate	= {oldtimer}
}

@Article{	  sumathi00a,
  author	= {Sumathi, S. and Sivanandam, S. N. and Jagadeeswari},
  title		= {Design of soft computing models for data mining
		  applications},
  journal	= {Indian-Journal-of-Engineering-and-Materials-Sciences},
  year		= {2000},
  volume	= {7},
  pages		= {107--21},
  abstract	= {Data mining, also known as data or knowledge discovery, is
		  the process of analyzing data from different perspectives
		  and summarizing it into useful information. Pattern
		  classification is one particular category of data mining,
		  which enables the discovery of knowledge from very large
		  databases (VLDB). Artificial neural networks are used to
		  mine the database which has better noise immunity and less
		  training time. A self-organizing neural network
		  architecture called predictive ART or ARTMAP is introduced
		  that is capable of fast stable learning, hypothesis testing
		  in response to an arbitrary stream of input patterns. A
		  generalization of binary ARTMAP is the fuzzy ARTMAP, which
		  learns to classify input by a pattern of fuzzy membership
		  values between 0 and 1, indicating the extent to which each
		  feature is present. Generalization of fuzzy ARTMAP is the
		  Cascade ARTMAP which has pre-existing symbolic rules that
		  are used to initialize the network before learning so that
		  the network efficiency is increased. Another
		  self-organizing algorithm is proposed using the Kohonen
		  architecture which also requires less time and high
		  prediction accuracy compared to BPN. The performance
		  evaluation of all three networks namely, Cascade ARTMAP,
		  Fuzzy ARTMAP and Kohonen have been done and compared with
		  conventional methods. Simulation is carried out using the
		  medical databases taken from the UCI repository of machine
		  learning databases.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  sun00a,
  author	= {Sun, Y. and Chan, K. L. and Krishnan, S. M. and Dutt, D.
		  N.},
  title		= {Unsupervised classification of {ECG} beats using a {MLVQ}
		  neural network},
  booktitle	= {Annual International Conference of the IEEE Engineering in
		  Medicine and Biology---Proceedings},
  year		= {2000},
  editor	= {Enderle, J.D},
  volume	= {2},
  pages		= {1387--1390},
  organization	= {BMERC, School of EEE, Nanyang Technological University},
  publisher	= {},
  address	= {},
  abstract	= {In this study, a modified Learning Vector Quantization
		  (MLVQ) neural network is employed to develop an
		  unsupervised ECG beat classifier. In order to improve the
		  performance of the classifier for application to ECG
		  signal, three modifications are made on the original LVQ:
		  finding the clustering numbers in an unsupervised way by
		  appending two counters, decreasing the inaccuracy caused by
		  the imprecise input features of classifier using multiple
		  assignment of datum, and finding the global optimal
		  classification of input data using double objective
		  functions. This unsupervised classifier is tested with
		  selected ECG time series and experimental results show that
		  the proposed technique offers a great potential in the
		  unsupervised classification of ECG beats.},
  dbinsdate	= {2002/1}
}

@Article{	  sun00b,
  author	= {Sun, Y.},
  title		= {On quantization error of self-organizing map network},
  journal	= {NEUROCOMPUTING},
  year		= {2000},
  volume	= {34},
  month		= {SEP},
  pages		= {169--193},
  abstract	= {In this paper, we analyze how neighborhood size and number
		  of weights in the self-organizing map (SOM) effect
		  quantization error. A sequence of i.i.d. one-dimensional
		  random variable with uniform distribution is considered as
		  input of the SOM. First obtained is the linear equation
		  that an equilibrium state of the SOM satisfies with any
		  neighborhood size and number of weights. Then it is shown
		  that the SOM converges to the unique minimum point of
		  quantization error if and only if the neighborhood size is
		  one, the smallest. If the neighborhood size increases with
		  the increasing number of weights at the same ratio, the
		  asymptotic quantization error does not converge to zero and
		  the asymptotic distribution of weights differs from the
		  distribution of input samples. This suggests that in order
		  to achieve a small quantization error and good
		  approximation of input distribution, a small neighborhood
		  size must be used. Weight distributions in numerical
		  evaluation confirm the result. },
  dbinsdate	= {2002/1}
}

@InCollection{	  sun96a,
  author	= {Yi Sun},
  title		= {On reconstruction error of {K}ohonen
		  \mbox{self-organizing} mapping},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {190--5},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sun99a,
  author	= {Haoying Sun and Kaveh, M. and Tewfik, A.},
  title		= {Self-organizing map neural network for transient signal
		  classification in mechanical diagnostics},
  booktitle	= {Proceedings of the IEEE-EURASIP Workshop on Nonlinear
		  Signal and Image Processing (NSIP'99). Bogazici Univ,
		  Instanbul, Turkey},
  year		= {1999},
  volume	= {2},
  pages		= {539--43},
  abstract	= {Acoustic emissions (AE), generated by the formation and
		  growth of micro-cracks in metal components, provide us with
		  a promising mechanical fault detection technique in
		  monitoring complex-shaped components in helicopters and
		  aircraft. A major challenge for an AE-based fault detection
		  algorithm is to distinguish crack related AE signals from
		  other interfering transient signals, such as fretting
		  related AE signals and electromagnetic transients. In this
		  paper, we presents a classifier, which makes its decision
		  based on the features extracted from joint time-frequency
		  distribution data by a self-organizing map (SOM) neural
		  network. In-flight data are used to test the performance of
		  this classification system, with promising results.},
  dbinsdate	= {2002/1},
  merjanote     = {last name checked from Internet}
}

@InProceedings{	  surakka95a,
  author	= {Surakka, M. and Heikkonen, J. },
  title		= {Road direction detection based on {G}abor filters and
		  neural networks},
  booktitle	= {Intelligent Autonomous Vehicles 1995. Postprint Volume
		  from the 2nd IFAC Conference},
  year		= {1995},
  editor	= {Halme, A. and Koskinen, K. },
  pages		= {283--8},
  organization	= {Machine Intelligence Div. , Mech. Eng. Lab. , Ibaraki,
		  Japan},
  publisher	= {Pergamon},
  address	= {Oxford, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  surmann92a,
  author	= {Surmann, H. and Moller, B. and Goser, K. },
  title		= {A distributed \mbox{self-organizing} fuzzy rule-based
		  system},
  booktitle	= {Fifth International Conference. Neural Networks and their
		  Applications. NEURO NIMES 92},
  year		= {1992},
  pages		= {187--94},
  organization	= {Dept. of Electr. Eng. , Dortmund Univ. , Germany},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sussner00a,
  author	= {Peter Sussner},
  title		= {Fixed Points of Autoassociative Morphological Memories},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Institute of Mathematics, Statistics, and Scientic
		  Computation, State University of Campinas},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  sussner95a,
  author	= {M. S{\"{u}}ssner and M. Budil and Th. Binder and G.
		  Porental},
  title		= {Segmentation and Edge-Detection of Echocardiograms using
		  Artificial Neuronal Networks},
  booktitle	= {Proc. EANN'95, Engineering Applications of Artificial
		  Neural Networks},
  year		= {1995},
  pages		= {461--464},
  organization	= {Finnish Artificial Intelligence Society},
  dbinsdate	= {oldtimer}
}

@Article{	  sutton94a,
  author	= {Granger G. {Sutton~III} and James A. Reggia and Steven L.
		  Armentrout and C. Lynne D'Autrechy},
  title		= {Cortical Map Reorganization as a Competitive Process},
  journal	= {Neural Computation},
  year		= {1994},
  volume	= {6},
  number	= {1},
  pages		= {1--13},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  suzuki99a,
  author	= {Suzuki, D. and Hammami, O.},
  title		= {SOM on multi-{FPGA} {ISA} board-hardware aspects},
  booktitle	= {ICECS'99. Proceedings of ICECS '99. 6th IEEE International
		  Conference on Electronics, Circuits and Systems.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1401--5},
  abstract	= {This paper describes the hardware design of a multi-FPGA
		  hardware implementation of a 16 neurons Self-Organizing Map
		  (SOM) artificial neural network. The SOM designed includes
		  16 neurons controlled in a SIMD execution mode. The
		  application targeted is 3D to 2D projection. The clock
		  frequency of the hardware design is 11.386 MHz and it has
		  been implemented on 5 Xilinx FPGA chips mounted on a
		  plug-an-play PC ISA board. The resulting hardware
		  outperforms under some conditions several software
		  simulations implementations running on various PC
		  hardware.},
  dbinsdate	= {oldtimer}
}

@Article{	  swindale92a,
  author	= {N. V. Swindale},
  title		= {Elastic Nets, Travelling Salesmen and Cortical Maps},
  journal	= {Current Biology},
  volume	= 2,
  number	= 8,
  year		= 1992,
  pages		= {429--431},
  dbinsdate	= {oldtimer}
}

@Article{	  swindale96a,
  author	= {N. V. Swindale},
  title		= {Visual cortex: Looking into a {K}lein bottle},
  journal	= {Current Biology},
  year		= {1996},
  volume	= {6},
  number	= {7},
  pages		= {776--779},
  dbinsdate	= {oldtimer}
}

@Article{	  swindale96b,
  author	= {N. V. Swindale},
  title		= {The development of topography in the visual cortex: a
		  review of models},
  journal	= {Network: Computation in Neural Systems},
  year		= {1996},
  volume	= {7},
  pages		= {161--247},
  dbinsdate	= {oldtimer}
}

@Article{	  swindale98a,
  author	= {N. V. Swindale},
  title		= {Cortical organization: Modules, polymaps and mosaics},
  journal	= {Current Biology},
  year		= {1998},
  volume	= {8},
  pages		= {R270--R273},
  dbinsdate	= {oldtimer}
}

@Article{	  swindale98b,
  author	= {N. V. Swindale and H. U. Bauer},
  title		= {Application of {K}ohonen Self Organizing Feature Map
		  Algorithm to Cortical Maps of Orientation and Direction
		  Preference},
  journal	= {Proceedings of the Royal Society of London, Series B:
		  Biological Sciences},
  volume	= {265},
  pages		= {827--838},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  swiniarski01a,
  author	= {Swiniarski, R. W.},
  title		= {Rough sets methods in feature reduction and
		  classification},
  journal	= {International-Journal-of-Applied-Mathematics-and-Computer-Science}
		  ,
  year		= {2001},
  volume	= {11},
  pages		= {565--82},
  abstract	= {The paper presents an application of rough sets and
		  statistical methods to feature reduction and pattern
		  recognition. The presented description of rough sets theory
		  emphasizes the role of rough sets reducts in feature
		  selection and data reduction in pattern recognition. The
		  overview of methods of feature selection emphasizes feature
		  selection criteria, including rough set-based methods. The
		  paper also contains a description of the algorithm for
		  feature selection and reduction based on the rough sets
		  method proposed jointly with principal component analysis.
		  Finally, the paper presents numerical results of face
		  recognition experiments using the learning vector
		  quantization neural network, with feature selection based
		  on the proposed principal components analysis and rough
		  sets methods.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  syed92a,
  author	= {Syed, A. and ElMaraghy, H. A. and Chagneux, N. },
  title		= {Real-time monitoring and diagnosing of robotic assembly
		  with \mbox{self-organizing} neural maps},
  booktitle	= {Real-Time Systems Symposium},
  year		= {1992},
  pages		= {271--4},
  organization	= {Flexible Manuf. Centre, McMaster Univ. , Hamilton, Ont. ,
		  Canada},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  syed93a,
  author	= {Syed, A. and ElMaraghy, H. A. and Chagneux, N. },
  title		= {Real-time monitoring and diagnosing of robotic assembly
		  with \mbox{self-organizing} neural maps},
  booktitle	= {Proceedings IEEE International Conference on Robotics and
		  Automation},
  year		= {1993},
  volume	= {2},
  pages		= {188--95},
  organization	= {Flexible Manuf. Centre, McMaster Univ. , Hamilton, Ont. ,
		  Canada},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  sygnowski96a,
  author	= {W. Sygnowski and B. Macukow},
  title		= {Counter-propagation neural network for image compression},
  journal	= {Optical Engineering},
  year		= {1996},
  volume	= {35},
  number	= {8},
  pages		= {2214--17},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  syrjasuo99a,
  author	= {Syrj\"asuo, M. T. and Pulkkinen, T. I.},
  title		= {Determining the skeletons of the auroras},
  booktitle	= {Proceedings 10th International Conference on Image
		  Analysis and Processing},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {1063--6},
  abstract	= {The auroral emissions observed in the high-latitude
		  regions encircling the magnetic poles are a key element in
		  studying plasma physical processes in the near-Earth space,
		  the magnetosphere. The Finnish Meteorological Institute
		  operates five digital all-sky cameras, which routinely
		  monitor the auroral emissions in Northern Finland, Sweden,
		  and Svalbard; each camera records an image of the full sky
		  at 20 second intervals. In this paper, we develop a method
		  that allows us to examine such a large data set by
		  classifying the images through determining the shape
		  skeletons of the auroral forms in each auroral image. Shape
		  skeletons are a commonly used representation of object
		  shapes in machine vision applications. Once determined,
		  shape skeletons have the advantage that they can also be
		  used to represent noisy or unevenly distributed data. Here
		  we apply a skeletonising algorithm to determine the
		  skeletons of auroras in a noisy environment. The algorithm
		  is based on a batch mode self-organising map. The results
		  can be further improved by implementing understanding of
		  the auroral physics to the algorithm.},
  dbinsdate	= {oldtimer}
}

@Article{	  szabo00a,
  author	= {Szabo, Raisa R.},
  title		= {Study to evaluate the rough set theory and the learning
		  vector quantization neural network paradigm using
		  {B}ayesian probability theory},
  journal	= {International Journal of Smart Engineering System Design},
  year		= {2000},
  number	= {3},
  volume	= {2},
  pages		= {201--227},
  abstract	= {A study was conducted to evaluate the rough set theory and
		  the LVQ neural network approaches in terms of their
		  classification measures for handling uncertainty in data,
		  and their reasoning abilities for classifying new patterns.
		  It was found that in general, the decisions produced by
		  rough sets and LVQ paradigms for the same classification
		  problem are similar. In English 52 Refs.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  szczepaniak00a,
  author	= {Piotr S. Szczepaniak and Paulo J. Lisboa and Emmanuel C.
		  Ifeachor},
  title		= {Artificial Neural Networks in Medicine---Survey of
		  Applications},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Institute of Computer Science,Technical University of
		  Lodz; Systems Research Institute, Polish Academy of
		  Sciences; Liverpool John Moores University; School of
		  Electronics, Communication and Electrical Engineering,
		  University of Plymouth},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  szepesvari93a,
  author	= {Csaba Szepesv{\'{a}}ri and Andr{\'{a}}s L{\H{o}}rincz},
  title		= {Topology Learning Solved by Extended Objects: A Neural
		  Network Model},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {678},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  szepesvari93b,
  author	= {Csaba Szepesv{\'{a}}ri and L{\'{a}}szl{\'{o}} Bal{\'{a}}zs
		  and Andr{\'{a}}s L{\H{o}}rincz},
  title		= {Topology learning solved by extended objects: a neural
		  network model},
  journal	= {Neural Computation},
  year		= {1994},
  volume	= {6},
  pages		= {441--458},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  szepesvari93c,
  author	= {Csaba Szepesv{\'{a}}ri and Andr{\'{a}}s L{\H{o}}rincz},
  title		= {Topology Learning Solved by Extended Objects: A Neural
		  Network Model},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {497--500},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  szepesvari94a,
  author	= {Cs. Szepesv{\'{a}}ri and T. Fomin and A. L{\"{o}}rincz},
  title		= {Self-Organizing Neurocontrol},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1261--1264},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, control, modification},
  dbinsdate	= {oldtimer}
}

@Article{	  szepesvari96a,
  author	= {Szepesvari, Csaba and Lorincz, Andras},
  title		= {Approximate geometry representations and sensory fusion},
  journal	= {Neurocomputing},
  year		= {1996},
  number	= {2},
  volume	= {12},
  pages		= {267--287},
  abstract	= {Information from the external world goes through various
		  transformations. The learning of the original neighbourhood
		  relations of the world using only the transformed
		  information is examined in detail. An approximate
		  representation consists of a finite number of discretizing
		  points and connections between neighbouring points. The
		  goal here is to develop the theory of self-organizing
		  approximate representations. Such a self-organizing system
		  may be considered as a generalization of the Kohonen
		  topographical map that we now equip with self-organizing
		  neighbouring connections. For illustrative purposes an
		  example is presented for sensory fusion: the geometry of
		  the 3D world is learned using the outputs of two cameras.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tabarabaee94a,
  author	= {Tabarabaee, V. and Azimisadjadi, B. and Zahirazami, S. B.
		  and Lucas, C. },
  title		= {Isolated word recognition using a hybrid neural network},
  booktitle	= {ICASSP-94. 1994 IEEE International Conference on
		  Acoustics, Speech and Signal Processing},
  year		= {1994},
  volume	= {2},
  pages		= {II/649--52},
  organization	= {Electron. Res. Center, Sharif Univ. of Technol. , Tehran,
		  Iran},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tada00a,
  author	= {K. Tada and K. Obu-Cann},
  title		= {{SOM} Application to Qualitative Information Analysis},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {180--7},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@InProceedings{	  tadj93a,
  author	= {Chakib Tadj and Franck Poirier},
  title		= {Improved {DVQ} Algorithm for Speech Recognition: A New
		  Adaptative Learning Rule With Neurons Annihilation},
  booktitle	= {Proc. EUROSPEECH-93, 3rd European Conf. on Speech,
		  Communication and Technology},
  year		= {1993},
  volume	= {II},
  pages		= {1009--1012},
  publisher	= {ESCA},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tadj95a,
  author	= {Tadj, C. and Poirier, F. },
  title		= {Keyword spotting using supervised/unsupervised competitive
		  learning},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  year		= {1995},
  volume	= {1},
  pages		= {301--4},
  organization	= {Signal Dept. , Telecom Paris, France},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {In this paper, we present a novel hybrid keyword spotting
		  system that combines supervised and unsupervised
		  competitive learning algorithms. The first stage is a SOFM
		  (Self-Organizing Feature Maps) module which is specifically
		  designed for discriminating between keywords (KWs) and
		  non-keywords (NKWs). The second stage is a FDVQ (Fuzzy
		  Dynamic Vector Quantization) module which consists of
		  discriminating between KWs detected by the first stage
		  processing. The results show an improvement of about 9% on
		  the accuracy of the system comparing to our standard one.},
  dbinsdate	= {oldtimer}
}

@Article{	  tafeit99a,
  author	= {Tafeit, E. and Reibnegger, G.},
  title		= {Artificial neural networks in laboratory medicine and
		  medical outcome prediction},
  journal	= {CLINICAL CHEMISTRY AND LABORATORY MEDICINE},
  year		= {1999},
  volume	= {37},
  number	= {9},
  month		= {SEP},
  pages		= {845--853},
  abstract	= {Since the early nineties the number of scientific papers
		  reporting on artificial neural network (ANN) applications
		  in medicine has been quickly increasing. In the present
		  paper, we describe in some detail the architecture of
		  network types used most frequently in ANN applications in
		  the broad field of laboratory medicine and clinical
		  chemistry, present a technique-structured review about the
		  recent ANN applications in the field, and give information
		  about the improvements of available ANN software packages.
		  ANN applications are divided into two main classes:
		  supervised and unsupervised methods. Most of the described
		  supervised applications belong to the fields of medical
		  diagnosis (n=7) and outcome prediction (n=9). Laboratory
		  and clinical data are presented to multilayer feed- forward
		  ANNs which are trained by the back propagation algorithm.
		  Results are often better than those of traditional
		  techniques such as linear discriminant analysis,
		  classification and regression trees (CART), Cox regression
		  analysis, logistic regression, clinical judgement or expert
		  systems. Unsupervised ANN applications provide the ability
		  of reducing the dimensionality of a dataset.
		  Low-dimensional plots can be generated and visually
		  understood and compared. Results are very similar to that
		  of cluster analysis and factor analysis. The ability of
		  Kohonen's self-organizing maps to generate 2D maps of
		  molecule surface properties was successfully applied in
		  drug design.},
  dbinsdate	= {2002/1}
}

@InCollection{	  tahani96a,
  author	= {H. Tahani and B. Plummer and N. S. Hemamalini},
  title		= {A new data reduction algorithm for pattern
		  classification},
  booktitle	= {1996 IEEE International Conference on Acoustics, Speech,
		  and Signal Processing Conference Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {6},
  address	= {New York, NY, USA},
  pages		= {3446--9},
  dbinsdate	= {oldtimer}
}

@Article{	  tai00a,
  author	= {Tai, Wen-Pin and Liou, Cheng-Yuan},
  title		= {Image representation by self-organizing conformal
		  network},
  journal	= {Visual Computer},
  year		= {2000},
  volume	= {16},
  number	= {2},
  month		= {},
  pages		= {91--105},
  organization	= {Chinese Culture Univ},
  publisher	= {Springer-Verlag GmbH \& Company KG},
  address	= {Berlin},
  abstract	= {Conformal mappings are incorporated into the
		  self-organization model to represent images harmonically.
		  This network is used to partition an image into
		  quadrilateral regions, where each region contains similar
		  features. We then map each region to a corresponding square
		  region to unify information representation and facilitate
		  computations. This mapping is constructed to preserve
		  spatial information while complying with the conformal
		  property of the network. An approximated image in each
		  square region provides us with an effective representation
		  of the image in both modeling and compression applications.
		  This approach has been particularly developed for large
		  continues images.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  tai95a,
  author	= {Wen-Pin Tai},
  title		= {A Batch Training Network for Self-Organization},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {33--37},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  taibi92a,
  author	= {Taibi, G. and Vassallo, G. and Sorbello, F. },
  title		= {Self organizing maps for medical diagnosis},
  booktitle	= {Neural Nets Wirn Vietri 92---Proceedings of the 4th
		  Italian Workshop on Neural Nets},
  year		= {1992},
  editor	= {Caianiello, E. R. },
  organization	= {Centro per la Ricerca Elettronica in Silicia, Palermo,
		  Italy},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  takacs94a,
  author	= {Takacs, B. and Wechsler, H. },
  title		= {Locating facial features using {SOFM}},
  booktitle	= {Proceedings of the 12th IAPR International Conference on
		  Pattern Recognition},
  year		= {1994},
  volume	= {2},
  pages		= {55--60},
  organization	= {Inst. for Comput. Sci. , George Mason Univ. , Fairfax, VA,
		  USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  takacs96a,
  author	= {B. Takacs and H. Wechsler},
  title		= {Visual filters for face recognition},
  booktitle	= {Proceedings of the Second International Conference on
		  Automatic Face and Gesture Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  address	= {Los Alamitos, CA, USA},
  pages		= {218--23},
  abstract	= {We describe a general approach for the multiscale
		  representation, detection, and recognition of object
		  primitives as it applies to face recognition tasks. The
		  approach is based on radially non-uniform sampling
		  strategy, and a local light adaptation mechanism for
		  low-level image representation. Early processing involves
		  feature encoding and classification using visual filter
		  banks implemented via self-organizing feature maps (SOFM).
		  Optimal filters are constructed by means of an iterative,
		  cross-validation-like data reduction algorithm. The derived
		  visual filter representation is applicable to both (i)
		  facial landmark detection, and (ii) face identification.
		  Experimental results on a data set of over 200 images prove
		  the feasibility of our approach.},
  dbinsdate	= {oldtimer}
}

@Article{	  takacs97a,
  author	= {B. Takacs and H. Wechsler},
  title		= {Detection of faces and facial landmarks using iconic
		  filter banks},
  journal	= {Pattern Recognition},
  year		= {1997},
  volume	= {30},
  number	= {10},
  pages		= {1623--36},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  takahashi91a,
  author	= {M. Takahashi and H. Hashimukai and H. Ando},
  title		= {2-dimensional color sensor with combined neural network},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {II},
  pages		= {932},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  takahashi93a,
  author	= {Masanobu Takahashi and Kazuo Kyuma and Etsuo Funada},
  title		= {10000 Cell Placement Optimization usin a Self-Organizing
		  Map},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2417--2420},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {A new approach for the cell placement problem using a
		  self-organizing map is proposed. This method requires a
		  memory size of only O(N) to solve an N cell problem. Large
		  scale problems can be solved on a workstation in a
		  reasonable computation time. Simulation results show better
		  performance than two conventional methods; the neural
		  method using a feedback type neural network and the
		  heuristic method called the relaxation method. The true
		  optimum solution for the placement of 10,000 cells is
		  found. An application for the design of printed circuit
		  boards is also demonstrated.},
  dbinsdate	= {oldtimer}
}

@Article{	  takatsuka01a,
  author	= {Takatsuka, M. and Jarvis, R. A.},
  title		= {Encoding 3D structural information using multiple self-
		  organizing feature maps},
  journal	= {IMAGE AND VISION COMPUTING},
  year		= {2001},
  volume	= {19},
  number	= {3},
  month		= {FEB},
  pages		= {99--118},
  abstract	= {This paper describes a system which encodes a free-form
		  three- dimensional (3D) object using Artificial Neural
		  Networks. The types of surface shapes which the system is
		  able to handle include not only pre-defined surfaces such
		  as simple piecewise quadric surfaces but also more complex
		  free-form surfaces. The system utilizes two Self-Organizing
		  Maps to encode surface parts and their geometrical
		  relationships. Authors demonstrated the use of this
		  encoding technique on "simple" 3D free-form object
		  recognition systems [M. Takatsuka, R.A. Jarvis,
		  Hierarchical neural networks for learning 3D objects from
		  range images, Journal of Electronic Imaging 7 (1) (1998)
		  16--28]. This paper discusses the design and mechanism of
		  the Multiple SOFMs for encoding 3D information in greater
		  detail including an application to face ("complex" 3D
		  free-form object) recognition. },
  dbinsdate	= {2002/1}
}

@InCollection{	  takatsuka95a,
  author	= {M. Takatsuka and R. A. Jarvis},
  title		= {Range image segmentation for {{3D}} object recognition
		  using hybrid neural networks},
  booktitle	= {Eighth Australian Joint Conference on Artificial
		  Intelligence},
  publisher	= {World Scientific},
  year		= {1995},
  editor	= {X. Yao},
  address	= {Singapore},
  pages		= {235--42},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  takeda93a,
  author	= {Takeda, T. and Tanaka, A. and Tanno, K. },
  title		= {Parallel computing algorithm of neural networks on an
		  eight-neighbor processor array},
  booktitle	= {Twelfth Annual International Phoenix Conference on
		  Computers and Communications},
  year		= {1993},
  pages		= {559--64},
  organization	= {Dept. of Electr. \& Inf. Eng. , Yamagata Univ. , Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tallam00a,
  author	= {Tallam, Rangarajan M. and Habetler, Thomas G. and Harley,
		  Ronald G. and Gritter, David J. and Burton, Bruce H.},
  title		= {Neural network based on-line stator winding turn fault
		  detection for induction motors},
  booktitle	= {Conference Record---IAS Annual Meeting (IEEE Industry
		  Applications Society)},
  year		= {2000},
  editor	= {},
  volume	= {1},
  pages		= {375--380},
  organization	= {Georgia Inst of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A novel on-line neural network based diagnostic scheme,
		  for induction machine stator winding turn fault detection,
		  is presented. The scheme consists of a feed-forward neural
		  network combined with a Self-Organizing Feature Map (SOFM)
		  to visually display the operating condition of the machine
		  on a two-dimensional grid. The operating point moves to a
		  specific region on the map as a fault starts developing and
		  can be used to alert the motor protection system to an
		  incipient fault. This is a useful tool for commercial
		  condition monitoring systems. Experimental results are
		  provided, with data obtained from a specially wound test
		  motor, to illustrate the robustness of the proposed turn
		  fault detection scheme. The new method is not sensitive to
		  unbalanced supply voltages or asymmetries in the machine
		  and instrumentation.},
  dbinsdate	= {2002/1}
}

@Article{	  talumassawatdi01a,
  author	= {Talumassawatdi, R. and Lursinsap, C.},
  title		= {Fault immunization concept for self-organizing mapping
		  neural networks},
  journal	= {International-Journal-of-Uncertainty,-Fuzziness-and-Knowledge-Based-Systems}
		  ,
  year		= {2001},
  volume	= {9},
  pages		= {781--90},
  abstract	= {The self-organizing map (SOM) neural network has been
		  widely used in pattern classification, vector quantization
		  and image compression. We consider the problem of
		  strengthening the reliability of a SOM neural network by
		  the technique of fault immunization of the synaptic links
		  of each neuron which is similar to the concept of
		  biological immunization. Instead of assuming the stuck-at-0
		  and stuck-at-1 as in those studies, we consider a general
		  case of stuck-at-a, where a is a real value. The only
		  assumption that we consider is only one neuron can be
		  faulty at any time. There is no restriction on the number
		  of faulty links of the neuron. Let w/sub i,j/ be the weight
		  of synaptic link j of neuron i obtained after the
		  winner-take-all classification. Weight w/sub i,j/ is
		  immunized by adding a constant in /sub i,j/, either
		  positive or negative, to w/sub i,j/. A neuron reaches its
		  maximum fault immunization if the value of w/sub i,j/ + in
		  /sub i,j/ can be either increased or decreased as much as
		  possible without creating any misclassification. Thus, the
		  fault immunization problem is formulated as an optimization
		  problem on finding the value of each in /sub i,j/. A
		  technique to find the value of w/sub i,j/ + in /sub i,j/ is
		  proposed and its application to enhance the transmission
		  reliability in image compression is introduced.},
  dbinsdate	= {2002/1}
}

@Article{	  tamaru93a,
  author	= {Tamaru, Y. and Mori, H. and Tsuzuki, S. },
  title		= {Monitoring power system dynamic stability with a {K}ohonen
		  neural net},
  journal	= {Electrical Engineering in Japan},
  year		= {1993},
  volume	= {113},
  number	= {6},
  pages		= {71--80},
  month		= {Oct},
  dbinsdate	= {oldtimer}
}

@Article{	  tamayo99a,
  author	= {Pablo Tamayo and Donna Slonim and Jill Mesirov and Qing
		  Zhu and Sutisak Kitareewan and Ethan Dmitrovsky and Eric S.
		  Lander and Todd R. Golub},
  title		= {Interpreting patterns of gene expression with
		  \mbox{self-organizing} maps: Methods and application to
		  hematopoietic differentiation},
  journal	= {Proceedings of the National Academy of Science, USA},
  year		= 1999,
  volume	= 96,
  number	= 6,
  pages		= {2907--2912},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tambouratzis01a,
  author	= {G. Tambouratzis and N. Hairetakis and S. Markantonatou and
		  G. carayannis},
  title		= {Evaluating {SOM}-based models on text classification tasks
		  for the Greek language},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {267--74},
  year		= {2001},
  editor	= {Nigel Allison and Hujun Yin and Lesley Allison and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  tambouratzis02a,
  author	= {Tambouratzis, G. and Papakonstantinou, G. and
		  Stamatelopoulos, S. and Zakopoulos, N. and Moulopoulos,
		  S.},
  title		= {Analyzing the 24-hour blood pressure and heart-rate
		  variability with self-organizing feature maps},
  journal	= {International Journal of Intelligent Systems},
  year		= {2002},
  volume	= {17},
  number	= {1},
  month		= {January },
  pages		= {63--76},
  organization	= {Inst. for Language and Speech Proc.},
  publisher	= {},
  address	= {},
  abstract	= {In this article, the self-organizing map (SOM) is employed
		  to analyze data describing the 24-hour blood pressure and
		  heart-rate variability of human subjects. The number of
		  observations varies widely over different subjects, and
		  therefore a direct statistical analysis of the data is not
		  feasible without extensive pre-processing and interpolation
		  for normalization purposes. The SOM network operates
		  directly on the data set, without any pre-processing,
		  determines several important data set characteristics, and
		  allows their visualization on a two-dimensional plot. The
		  SOM results are very similar to those obtained using
		  classic statistical methods, indicating the effectiveness
		  of the SOM method in accurately extracting the main
		  characteristics from the data set and displaying them in a
		  readily understandable manner. In this article, the
		  relation is studied between the representation of each
		  subject on the SOM, and his blood pressure and pulse-rate
		  measurements. Finally, some indications are included
		  regarding how the SOM can be used by the medical community
		  to assist in d diagnosis tasks.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  tambouratzis93a,
  author	= {G. Tambouratzis and T. J. Stonham},
  title		= {Optimal Topology-Preservation Using Self-Organising
		  Logical Neural Networks},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {76--79},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tambouratzis93b,
  author	= {G. Tambouratzis and D. Patel and T. J. Stonham},
  title		= {Image Segmentation Using a Self-Organising Logical Neural
		  Networks},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {903--906},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  tambouratzis93c,
  author	= {G. Tambouratzis and T. J. Stonham},
  title		= {Evaluating the toplogy-preservation capabilities of a
		  \mbox{self-organising} logical neural network},
  journal	= {Pattern Recognition Letters},
  year		= {1993},
  volume	= {14},
  pages		= {927--934},
  dbinsdate	= {oldtimer}
}

@Article{	  tambouratzis93d,
  author	= {G. Tambouratzis},
  title		= {Comparison of supervised and unsupervised
		  discriminator-based logic neural networks},
  journal	= {Electronics Letters},
  year		= {1993},
  volume	= {30},
  number	= {3},
  pages		= {248--249},
  dbinsdate	= {oldtimer}
}

@Article{	  tambouratzis94a,
  author	= {G. Tambouratzis},
  title		= {Optimising the clustering performance of a
		  \mbox{self-organising} logic neural network with
		  topology-preserving capabilities},
  journal	= {Pattern Recognition Letters},
  year		= {1994},
  volume	= {15},
  pages		= {1019--1028},
  dbinsdate	= {oldtimer}
}

@Article{	  tambouratzis94b,
  author	= {G. Tambouratzis and D. Tambouratzis},
  title		= {Self-organization in complex pattern spaces using a logic
		  neural network},
  journal	= {Network: Computation in Neural Systems},
  year		= {1994},
  volume	= {5},
  pages		= {599--617},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tamminen00a,
  author	= {Tamminen, Satu and Pirttikangas, Susanna and Roning,
		  Juha},
  title		= {Self-organizing maps in adaptive health monitoring},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {4},
  pages		= {259--264},
  organization	= {Univ of Oulu},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A method for health monitoring is considered. Measured
		  physical signals have been dynamically classified to low-,
		  middle- or high-levels and self-organizing map (SOM) has
		  been utilized to combine the information. The data were
		  collected during the spring 1996 and consist of over eight
		  weeks of physical measurements and diaries recorded in a
		  home environment by four test subjects. The research shows
		  that this method can be used to monitor the system of human
		  being. The system finds some daily structures as well as
		  differences between weekdays and weekend. The physical
		  activities have much stronger effect on the signals than
		  mental stress states, which show no clear clustering on
		  maps.},
  dbinsdate	= {2002/1}
}

@Article{	  tamura91a,
  author	= {H. Tamura and T. Teraoka and I. Hatono and K. Yamagata},
  title		= {A method of solving traveling salesman problems using a
		  neural network-introducing the inhibitory signal into
		  {K}ohonen's \mbox{self-organizing} feature maps},
  journal	= {Trans. Inst. of Systems, Control and Information
		  Engineers},
  year		= {1991},
  volume	= {4},
  number	= {1},
  pages		= {57--59},
  month		= {January},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tan98a,
  author	= {Tan, R. S. and Narasimhan, V. Lakshmi},
  title		= {Mapping finite element grids onto parallel multicomputers
		  using a \mbox{self-organizing} map},
  booktitle	= {IEE Proceedings: Computers and Digital Techniques},
  year		= {1998},
  volume	= {145},
  number	= {3},
  pages		= {211--214},
  abstract	= {LSOM (load-balancing self-organizing map), a neural
		  network based on Kohonen's self-organizing map, is proposed
		  for the problem of mapping finite element method (FEM)
		  grids to distributed memory parallel computers with mesh
		  interconnection networks. The rough global ordering
		  produced by LSOM is combined with the local refinement
		  Kernighan-Lin algorithm (LSOM-KL) to obtain the solution.
		  LSOM-KL achieved a load imbalance of less than 0.1% and a
		  low number of hops, comparable to results obtained with
		  commonly used recursive bisection methods.},
  dbinsdate	= {oldtimer}
}

@Article{	  tanaka90a,
  author	= {S. Tanaka},
  title		= {Experience-dependent self-organization of biological
		  neural networks},
  journal	= {NEC Res. and Development},
  year		= {1990},
  volume	= {98},
  pages		= {1--14},
  dbinsdate	= {oldtimer}
}

@Article{	  tanaka92a,
  author	= {T. Tanaka and M. Saito},
  title		= {Quantitative properties of {K}ohonen's
		  \mbox{self-organizing} maps as adaptive vector quantizers},
  journal	= {Trans. Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1992},
  volume	= {J75D-II},
  number	= {6},
  pages		= {1085--1092},
  month		= {June},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@Article{	  tanaka93a,
  author	= {Tanaka, T. and Saito, M. },
  title		= {Quantitative properties of {K}ohonen's
		  \mbox{self-organizing} maps as adaptive vector quantizers},
  journal	= {Systems and Computers in Japan},
  year		= {1993},
  volume	= {24},
  number	= {5},
  pages		= {83--92},
  dbinsdate	= {oldtimer}
}

@Article{	  tanaka94a,
  author	= {Tanaka, T. },
  title		= {On evaluation of reference vector density for
		  \mbox{self-organizing} feature map},
  journal	= {IEICE Transactions on Information and Systems},
  year		= {1994},
  volume	= {E77-D},
  number	= {4},
  pages		= {402--8},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@TechReport{	  tanaka95a,
  author	= {Shin-Ichi Tanaka and Kikuo Fujimura and Heizo Tokutaka and
		  Satoru Kishida},
  title		= {A classifier using the {K}ohonen's Self-Organizing Feature
		  Maps---applied to the system where the overlapped data are
		  removed},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1995},
  number	= {NC94--140},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@TechReport{	  tanaka95b,
  author	= {Shin-Ichi Tanaka and Kikuo Fujimura and Heizo Tokutaka and
		  Satoru Kishida},
  title		= {The optimization of {TSP} using {SOM} method of many
		  cities, for example 532 cities in {USA}},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1995},
  number	= {NC95--70},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tanaka95c,
  author	= {Tanaka, M. and Sakawa, M. and Shiromaru, I. and Matsumoto,
		  T. },
  title		= {Application of {K}ohonen's \mbox{self-organizing} network
		  to the diagnosis system for rotating machinery},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {5},
  pages		= {4039--44},
  organization	= {Fac. of Eng. , Hiroshima Univ. , Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tanaka95d,
  author	= {Tanaka, M. and Watanabe, H. and Furukawa, Y. and Tanino,
		  T. },
  title		= {GA-based decision support system for multicriteria
		  optimization},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {2},
  pages		= {1556--61},
  organization	= {Dept. of Inf. Technol. , Okayama Univ. , Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tanaka95e,
  author	= {M. Tanaka},
  title		= {Nonlinear system identification by the combination of
		  \mbox{self-organizing} feature map and radial basis
		  function network},
  booktitle	= {Proceedings of the Third European Control Conference. ECC
		  95},
  publisher	= {Eur. Union Control Assoc},
  year		= {1995},
  volume	= {2},
  editor	= {A. Isidori and S. Bittanti and E. Mosca and A. {De Luca}
		  and M. D. {Di Benedetto} and G. Oriolo},
  address	= {Rome, Italy},
  pages		= {1580--5},
  dbinsdate	= {oldtimer}
}

@Article{	  tanaka96a,
  author	= {Tanaka, M. and Furukawa, Y. and Tanino, T. },
  title		= {Clustering by using self organizing map},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1996},
  volume	= {J79D-II},
  number	= {2},
  pages		= {301--4},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tanaka96b,
  author	= {M. Tanaka and Y. Furukawa and T. Tanino},
  title		= {Weight tuning and pattern classification by self
		  organizing map using genetic algorithm},
  booktitle	= {Proceedings of 1996 IEEE International Conference on
		  Evolutionary Computation (ICEC'96)},
  publisher	= {IEEE},
  year		= {1996},
  address	= {New York, NY, USA},
  pages		= {602--5},
  dbinsdate	= {oldtimer}
}

@Article{	  tanaka96c,
  author	= {M. Tanaka and H. Sakawa and I. Shiromaru and T.
		  Matsumoto},
  title		= {A fault detection method by {K}ohonen's
		  \mbox{self-organizing} map and backpropagation network
		  using normal condition data},
  journal	= {Bulletin of the Faculty of Engineering, Hiroshima
		  University},
  year		= {1996},
  volume	= {45},
  number	= {1},
  pages		= {21--7},
  dbinsdate	= {oldtimer}
}

@Article{	  taner01a,
  author	= {Taner, M. T. and Berge, T. and Walls, J. D. and Smith, M.
		  and Taylor, G. and Dumas, D. and Carr, M.B.},
  title		= {Well log calibration of Kohonen-classified seismic
		  attributes using bayesian logic},
  journal	= {Journal of Petroleum Geology},
  year		= {2001},
  volume	= {24},
  number	= {4},
  month		= {October },
  pages		= {405--416},
  organization	= {Rock Solid Images},
  publisher	= {},
  address	= {},
  abstract	= {We present a new method for calibrating a classified 3D
		  seismic volume. The classification process employs a
		  Kohonen self-organizing map, a type of unsupervised
		  artificial neural network; the subsequent calibration is
		  performed using one or more suites of well logs. Kohonen
		  self-organizing maps and other unsupervised clustering
		  methods generate classes of data based on the
		  identification of various discriminating features. These
		  methods seek an organization in a dataset and form
		  relational organized clusters. However, these clusters may
		  or may not have any physical analogues in the real world.
		  In order to relate them to the real world, we must develop
		  a calibration method that not only defines the relationship
		  between the clusters and real physical properties, but also
		  provides an estimate of the validity of these
		  relationships. With the development of this relationship,
		  the whole dataset can then be calibrated. The clustering
		  step reduces the multi-dimensional data into logically
		  smaller groups. Each original data point defined by
		  multiple attributes is reduced to a one- or two-dimensional
		  relational group. This establishes some logical clustering
		  and reduces the complexity of the classification problem.
		  Furthermore, calibration should be more successful since it
		  will have to consider less variability in the data. In this
		  paper, we present a simple calibration method that employs
		  Bayesian logic to provide the relationship between cluster
		  centres and the real world. The output will give the most
		  probable calibration between each self-organized map node
		  and wellbore-measured parameters such as lithology,
		  porosity and fluid saturation. The second part of the
		  output comprises the calibration probability. The method is
		  described in detail, and a case study is briefly presented
		  using data acquired in the Orange River Basin, South
		  Africa. The method shows promise as an alternative to
		  current techniques for integrating seismic and log data
		  during reservoir characterization.},
  dbinsdate	= {2002/1}
}

@InCollection{	  tang96a,
  author	= {H. Tang and O. Simula},
  title		= {The adaptive resource assignment and optimal utilization
		  of multi-service {SCP}},
  booktitle	= {4th International Conference on Intelligence in Networks,
		  ICIN 96 Proceedings},
  publisher	= {ADERA},
  year		= {1996},
  address	= {Pessac, France},
  pages		= {235--40},
  dbinsdate	= {oldtimer}
}

@Book{		  tang96b,
  author	= {Tang, X.},
  title		= {Transform Texture Classification. Doctoral thesis.},
  year		= {1996},
  abstract	= {This thesis addresses the three major components of a
		  texture classification system: texture image transform,
		  feature extraction/selection, and classification. For the
		  first component, a unique investigation of texture
		  analysis, drawing on an extensive survey of existing
		  approaches, defines the interrelations among 11 types of
		  texture analysis methods. A novel unification of the
		  different methods defines a framework of transformation and
		  representation in which three major classes of transform
		  matrices capture texture information of increasing
		  coherence length: the spatial domain method
		  (co-occurrence), the micro-structural method (run-length),
		  and the frequency multichannel method (Fourier spectrum).
		  For the second system component, we apply the
		  {K}arhunen-{L}oeve Transform (KLT) directly to the
		  transform matrix to extract a vector of dominant features,
		  optimally preserving texture information in the matrix.
		  This approach is made possible by the introduction of a
		  novel Multi-level Dominant Eigenvector Estimation (MDEE)
		  algorithm, which reduces the computational complexity of
		  the standard KLT by several orders of magnitude.
		  Experimental results of applying the new algorithm to the
		  three transform matrix classes show a strong increase in
		  performance. Using the same MDEE algorithm, the three
		  extracted feature vectors are then combined into a more
		  complete description of texture images. The same approach
		  is also used for a study of object recognition, where the
		  combined vector also include granulometric,
		  object-boundary, and moment-invariant features. The
		  plankton object recognition experiments use a Learning
		  Vector Quantization (LVQ) neural-net classifier to achieve
		  superior performance on the highly non-uniform plankton
		  database. By introducing a new parallel LVQ learning
		  scheme, the speed of network training is dramatically
		  increased.},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  tang98a,
  author	= {Haitao Tang},
  title		= {Applying Adaptive Techniques and Operations Research
		  Methods to the Resource Management in Telecommunications},
  school	= {Helsinki University of Technology},
  year		= 1998,
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tani93a,
  author	= {Jun Tani and Naohiro Fukumura},
  title		= {Learning Goal-Directed Navigation as Attractor Dynamics
		  for a Sensory Motor System (An Experiment by the Mobile
		  Robot {YAMABICO})},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1747--1752},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  tani97a,
  author	= {Jun Tani and Naohiro Fukumura},
  title		= {Self-Organizing Internal Representation in Learning of
		  Navigation: A Physical Experiment by the Mobile Robot
		  {YAMABICO}},
  journal	= {Neural Networks},
  year		= 1997,
  volume	= 10,
  pages		= {153--159},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tanomaru95a,
  author	= {J. Tanomaru and A. Inubushi},
  title		= {A Simple Coding Scheme for Neural Recognition of Binary
		  Visual Patterns},
  volume	= {V},
  pages		= {2432--2437},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {In this paper, a very compact coding scheme for binary
		  visual patterns bused on a novel approach dubbed shadow
		  codes is proposed, and the applicability of the method to
		  invariant recognition of handwritten patterns by neural
		  networks is investigated. In the best configuration so far,
		  the input pattern is surrounded by a rectangular frame with
		  orientation given by the pattern's principal axes of
		  inertia, and then a shadow vector is obtained by projecting
		  the pixels of the pattern into bars of the frame. After
		  normalization, the resulting vector is fed into a
		  rotation-invariant network, whose output is used for
		  classification by a neural network. For a task involving
		  the recognition of handwritten digits, experimental results
		  with three neural network approaches, namely,
		  self-organizing map, learning vector quantization and
		  multilayer perceptron, show that though very compact, the
		  proposed scheme is effective for translation, rotation, and
		  scaling invariant recognition of simple binary patterns.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tanomaru95b,
  author	= {Tanomaru, J. and Inubushi, A. },
  title		= {A compact representation of binary patterns for invariant
		  recognition},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {2},
  pages		= {1550--5},
  organization	= {Tokushima Univ. , Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tanomaru95c,
  author	= {Tanomaru, J. and Inubushi, A. and Ogura, K. },
  title		= {Neural network system for invariant recognition of
		  handwritten digits},
  booktitle	= {Proceedings of the ISCA International Conference, Fourth
		  Golden West Conference on Intelligent Systems},
  year		= {1995},
  editor	= {Louis, S. },
  pages		= {214--18},
  organization	= {Fac. of Eng. , Tokushima Univ. , Japan},
  publisher	= {Int. Soc. Comput. \& Their Appl. -ISCA},
  address	= {Raleigh, NC, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  tao92a,
  author	= {Shen Tao and Gan Junren and Yao Linsheng},
  title		= {A neural network approach to cell placement},
  journal	= {Acta Electronica Sinica},
  year		= {1992},
  volume	= {20},
  number	= {10},
  pages		= {100--5},
  month		= {Oct},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  taraglio90a,
  author	= {S. Taraglio},
  title		= {Boltzmann versus {K}ohonen networks, what is best for
		  character recognition?},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  volume	= {I},
  pages		= {103},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  taraglio91a,
  author	= {S. Taraglio and S. Moronesi and A. Sargeni and G. B. Meo},
  title		= {A {K}ohonen network for the recognition of underwater
		  structures},
  booktitle	= {Fourth Italian Workshop. Parallel Architectures and Neural
		  Networks},
  year		= {1991},
  editor	= {E. R. Caianiello},
  pages		= {378--382},
  organization	= {Univ. Salerno; Inst. Italiano di Studi Filosofici},
  publisher	= {World Scientific},
  address	= {Singapore},
  x		= {A study of Kohonen networks in a visual recognizing task
		  is presented. },
  dbinsdate	= {oldtimer}
}

@MastersThesis{	  tarr88a,
  author	= {G. L. Tarr},
  title		= {Dynamic Analysis of Feedforward Neural Networks Using
		  Simulated and Measured Data},
  school	= {Air Force Inst. of Tech. },
  year		= {1988},
  address	= {Wright-Patterson AFB, OH},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@Article{	  tarr92a,
  author	= {Tarr, G. and Priddy, K. and Rogers, S. },
  title		= {NeuralGraphics: a general purpose environment for neural
		  network simulation},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 2},
  pages		= {1047--56},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  tavan90a,
  author	= {P. Tavan and H. Grubm{\"{u}}ller and H. K{\"{u}}hnel},
  title		= {Self-organization of associative memory and pattern
		  classification: recurrent signal processing on topological
		  feature maps},
  journal	= {Biol. Cyb. },
  year		= {1990},
  volume	= {64},
  number	= {2},
  pages		= {95--105},
  annote	= {Self-organization of auto-associative memory and pattern
		  classification, Recurrent dynamics of signal processing,
		  cluster analysis},
  dbinsdate	= {oldtimer}
}

@Article{	  tay01a,
  author	= {Tay, F. E. H. and Li Juan Cao},
  title		= {Improved financial time series forecasting by combining
		  support vector machines with self-organizing feature map},
  journal	= {Intelligent-Data-Analysis},
  year		= {2001},
  volume	= {5},
  pages		= {339--54},
  abstract	= {A two-stage neural network architecture constructed by
		  combining support vector machines (SVMs) with a
		  self-organizing feature map (SOM) is proposed for financial
		  time series forecasting. In the first stage, an SOM is used
		  as a clustering algorithm to partition the whole input
		  space into several disjoint regions. A tree-structured
		  architecture is adopted in the partitioning to avoid the
		  problem of pre-determining the number of partitioned
		  regions. Then, in the second stage, multiple SVMs, also
		  called SVM experts, that best fit each partitioned region
		  are constructed by finding the most appropriate kernel
		  function and the optimal learning parameters of SVMs. The
		  Santa Fe exchange rate and five real futures contracts are
		  used in the experiment. It is shown that the proposed
		  method achieves both significantly higher prediction
		  performance and a faster convergence speed in comparison
		  with a single SVM model.},
  dbinsdate	= {2002/1}
}

@Article{	  tay94a,
  author	= {Tay, L. P. and Evans, D. J. },
  title		= {Fast learning artificial neural network ({FLANN II}) using
		  the nearest neighbour recall},
  journal	= {Neural, Parallel \& Scientific Computations},
  year		= {1994},
  volume	= {2},
  number	= {1},
  pages		= {17--27},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Proceedings{	  taylor89,
  title		= {New Developments in Neural Computing. {P}roc. Meeting on
		  Neural Computing},
  year		= {1989},
  editor	= {J. G. Taylor and C. L. T. Mannion},
  publisher	= {Adam Hilger},
  address	= {Bristol, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  taylor99a,
  author	= {Taylor, O. and Tait, J. and MacIntyre J.},
  title		= {Improved classification for a data fusing Kohonen self
		  organizing map using a dynamic thresholding technique},
  booktitle	= {IJCAI-99. Proceedings of the Sixteenth International Joint
		  Conference on Artificial Intelligence. Morgan Kaufmann
		  Publishers, San Francisco, CA, USA},
  year		= {1999},
  volume	= {2},
  pages		= {828--32},
  abstract	= {The use of linear data fusion is a fast developing area in
		  the field of military information and combat systems.
		  However, the use of data fusion in conventional application
		  areas is not as wide spread. To date linear data fusion has
		  been used only in applications in which substantial
		  knowledge of both the problem domain and the sensor devices
		  in use are available. However, in applications such as
		  condition monitoring the problem domain can be very
		  complex, with little or no knowledge about the interactions
		  between measured parameters. This paper describes the use
		  of nonlinear self-learning or self-organising systems as a
		  tool for data fusion, since these systems can learn complex
		  interrelationships between a number of parameters, and use
		  this information as a tool for improved classification.},
  dbinsdate	= {2002/1}
}

@Article{	  tebbe95a,
  author	= {Tebbe, D. L. and Billhartz, T. J. and Doner, J. R. and
		  Kraft, T. T. },
  title		= {Signal processing and neural network simulator},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2492},
  number	= {pt. 1},
  pages		= {42--50},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tellechea01a,
  author	= {Tellechea, M. and Grana, M.},
  title		= {On the application of competitive neural networks for
		  unsupervised analysis of hyperspectral remote sensing
		  images},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Serpico, S. B.},
  volume	= {4170},
  pages		= {65--72},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {We study the application of Competitive Neural Networks
		  (CNN) to the Unsupervised analysis of Remote Sensing
		  Hyperspectral images. CNN are applied as clustering
		  algorithms at the pixel level. We propose their use for the
		  extraction of endmembers and evaluate them through the
		  error induced by the compression/decompression with the CNN
		  in the supervised classification of the images. We show
		  results with the Self Organizing Map and Neural Gas applied
		  to a well known case study.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  teng00a,
  author	= {Teng Qizhi and He Xiaohai and Jiang li and Deng Zhouyu and
		  Wu Xiaoqiang and Tao Deyuan},
  title		= {Color image segmentation algorithm based on neural
		  networks},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.4224},
  year		= {2000},
  volume	= {4224},
  pages		= {109--13},
  abstract	= {This paper presents a color image segmentation method with
		  "Self-Organizing Feature Map" and "General Learning Vector
		  Quantity" which, in the uniform color space, divides color
		  into clusters based on the least sum of squares criterion
		  At the first step of this method, "SOFM" is employed to
		  make a preliminary classification on the original image,
		  and then "GLVQ" is used to segment it. Both of their
		  advantages can be taken to fully improve the precision and
		  velocity of color image segmentation.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  teng90a,
  author	= {Chungte Teng},
  title		= {A \mbox{self-organizing} {ANN}-implemented model for
		  invariant image understanding},
  booktitle	= {Proc. Second IASTED International Symposium. Expert
		  Systems and Neural Networks},
  year		= {1990},
  editor	= {M. H. Hamza},
  pages		= {35--39},
  organization	= {IASTED},
  publisher	= {Acta Press},
  address	= {Anaheim, CA},
  dbinsdate	= {oldtimer}
}

@Article{	  teng91a,
  author	= {Chungte Teng and P. A. Ligomenides},
  title		= {An {ANN}-implemented robust vision model},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1991},
  volume	= {1382},
  pages		= {74--86},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {A conference paper in journal},
  abstract	= {A robust vision model has been developed and implemented
		  with a self-organizing/unsupervised artificial neural
		  network (ANN) classifier-KART, which is a novel hybrid
		  model of a modified Kohonen's feature map and the
		  Carpenter/Grossberg's ART architecture. The six moment
		  invariants have been mapped onto a 7-dimensional unit
		  hypersphere and have been applied to the KART classifier.
		  In this paper the KART model with be presented. The
		  non-adaptive neural implementations on the image processing
		  and the moment invariant feature extraction will be
		  discussed. In addition, the simulation results that
		  illustrate the capabilities of this model will also be
		  provided.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tenhagen00a,
  author	= {Andreas Tenhagen and Ulrich Sprekelmeyer and Wolfram-M.
		  Lippe},
  title		= {Analysis of a Fuzzified Self-Organizing Map's Output
		  Behaviour},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {251--56},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@InProceedings{	  tenhagen01a,
  author	= {Tenhagen, A. and Sprekelmeyer, U. and Lippe, W. M.},
  title		= {On the combination of fuzzy logic and Kohonen nets},
  booktitle	= {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS
		  International Conference. IEEE, Piscataway, NJ, USA},
  year		= {2001},
  volume	= {4},
  pages		= {2144--9},
  abstract	= {Several ways of combining concepts of fuzzy set theory
		  with connectionist methods are known. We focus on the use
		  of fuzzy numbers in neural networks. Our goal is to create
		  a fully fuzzified self-organizing-map, which receives fuzzy
		  numbers as inputs and computes its output employing fuzzy
		  weights. We want to extend results about goodness
		  prediction, that exist for fuzzified multilayer perceptrons
		  (MLP). The main problem is the determination of the winning
		  neuron by the exclusive use of special, monotonic fuzzy
		  operations, which guarantee a certain goodness of the
		  input/output behaviour. A selection function is introduced,
		  solving this problem. Further on we formulate a fuzzified
		  version of the standard learning rule, that can be applied
		  on the fuzzified Kohonen neurons.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  teoh01a,
  author	= {Teoh, C. C. and Mansor, S. B. and Mispan, M. R. and
		  Mohamed Shariff, A.R. and Ahmad, N.},
  title		= {Extraction of infrastructure details from fused image},
  booktitle	= {International Geoscience and Remote Sensing Symposium
		  (IGARSS)},
  year		= {2001},
  editor	= {},
  volume	= {3},
  pages		= {1490--1492},
  organization	= {GISAT, Faculty of Engineering, University Putra Malaysia},
  publisher	= {Institute of Electrical and Electronics Engineers Inc.},
  address	= {},
  abstract	= {This paper demonstrates a method of extracting features
		  included edge features from a data fusion image. The
		  intensity-hue-saturation (IHS) transformation is used for
		  producing the data fusion image. This is achieved by
		  merging the SPOT panchromatic 10-m resolution image with
		  the Landsat TM 30-m resolution multispectral channel image.
		  The resultant imagery has a high resolution and spectral
		  chracteristics that enhance its visualization. The image
		  was then subjected to the processes of thresholding,
		  Gaussian filtering using a low pass filter and the
		  Iterative Self-Organizing Data Analysis (ISODATA)
		  unsupervised classification to derive the feature classes.
		  Ground truthing of this classification showed an overall
		  accuracy of 89%. To perform the edge features extraction,
		  an edge detection operator (Sobel edge filter) is applied
		  to the classification image and produces a gradient image.
		  This gradient image is threshold to produce a binary
		  digital edge map.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  teranishi00a,
  author	= {Teranishi, Masaru and Omatu, Sigeru and Kosaka,
		  Toshihisa},
  title		= {Classification of bill fatigue levels by feature-selected
		  acoustic energy pattern using competitive neural network},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {249--252},
  organization	= {Nara Natl Coll of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {This paper proposes a new method to classify bills into
		  different fatigue levels. Feature-selected acoustic energy
		  patterns obtained from an acoustic signal generated by a
		  bill passing through a banking machine are used for
		  classification. The feature-selected acoustic energy
		  patterns are fed to a competitive neural network with the
		  Learning Vector Quantization (LVQ) algorithm, and
		  classified the bill into three fatigue levels. Furthermore,
		  the selection of features in an acoustic energy pattern is
		  performed to improve classification performance. We
		  introduce a Genetic Algorithm to obtain the optimal feature
		  selection. The experimental results show that the proposed
		  method is useful for classification of fatigue levels of
		  bills, and the classification performances are improved by
		  selecting feature with Genetic Algorithm.},
  dbinsdate	= {2002/1}
}

@Article{	  terashima96a,
  author	= {M. Terashima and F. Shiratani and K. Yamamoto},
  title		= {Unsupervised cluster segmentation method using data
		  density histogram on \mbox{self-organizing} feature map},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1996},
  volume	= {J79D-II},
  number	= {7},
  pages		= {1280--90},
  dbinsdate	= {oldtimer}
}

@Article{	  terashima96b,
  author	= {M. Terashima and F. Shiratani and T. Hashimoto and K.
		  Yamamoto},
  title		= {A normalization method of input data that conserves the
		  norm information for competitive learning neural network
		  using inner product},
  journal	= {Optical Review},
  year		= {1996},
  volume	= {3},
  number	= {6A},
  pages		= {414--17},
  annote	= {International Topical Meeting on Optical Computing---OC 96
		  Conf. Date: 21--25 April 1996 Conf. Loc: Sendai, Japan
		  Conf. Sponsor: Japan Soc. Appl. Phys. ; Int. Comm. Opt. ;
		  IEICE of Japan},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  terekhoff95a,
  author	= {Terekhoff, S. A. },
  title		= {Experimental data analysis by neural nonparametric
		  methods},
  booktitle	= {Second International Symposium on Neuroinformatics and
		  Neurocomputers},
  year		= {1995},
  pages		= {337--45},
  organization	= {Federal Nucl. Center, All Russian Inst. of Tech. Phys. ,
		  Snezhinsk, Russia},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  terekhoff97a,
  author	= {S. A. Terekhoff},
  title		= {Direct, inverse, and combined problems in complex
		  engineered system modeling by artificial neural networks},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3077},
  pages		= {652--9},
  note		= {(Applications and Science of Artificial Neural Networks
		  III Conf. Date: 21--24 April 1997 Conf. Loc: Orlando, FL,
		  USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tereshko01a,
  author	= {Tereshko, V.},
  title		= {Topology-preserving elastic nets},
  booktitle	= {Connectionist Models of Neurons, Learning Processes, and
		  Artificial Intelligence. 6th International Work-Conference
		  on Artificial and Natural Neural Networks, IWANN 2001.
		  Proceedings, Part I (Lecture Notes in Computer Science Vol.
		  2084). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {554--60},
  abstract	= {We have developed a topology-preserving elastic net which
		  combines both lateral and synaptic interactions to obtain
		  topologically ordered representations (receptive fields) of
		  an external stimulus. Existing neural models that preserve
		  the topology by utilizing lateral interactions, such as the
		  Kohonen map and Goodhill's mapping (1992, 1993), and by
		  utilizing synaptic interactions, such as cortical mapping
		  and elastic net, appear as limiting cases of this model.},
  dbinsdate	= {2002/1}
}

@Article{	  terra01a,
  author	= {Terra, M. H. and Tinos, R.},
  title		= {Fault detection and isolation in robotic manipulators via
		  neural networks: A comparison among three architectures for
		  residual analysis},
  journal	= {Journal of Robotic Systems},
  year		= {2001},
  volume	= {18},
  number	= {7},
  month		= {July },
  pages		= {357--374},
  organization	= {Electrical Engineering Department, University of Sao
		  Paulo},
  publisher	= {},
  address	= {},
  abstract	= {In this article we discuss artificial neural
		  networks-based fault detection and isolation (FDI)
		  applications for robotic manipulators. The artificial
		  neural networks (ANNs) are used for both residual
		  generation and residual analysis. A multilayer perceptron
		  (MLP) is employed to reproduce the dynamics of the robotic
		  manipulator. Its outputs are compared with actual position
		  and velocity measurements, generating the so-called
		  residual vector. The residuals, when properly analyzed,
		  provides an indication of the status of the robot (normal
		  or faulty operation). Three ANNs architectures are employed
		  in the residual analysis. The first is a radial basis
		  function network (RBFN) which uses the residuals of
		  position and velocity to perform fault identification. The
		  second is again an RBFN, except that it uses only the
		  velocity residuals. The third is an MLP which also performs
		  fault identification utilizing only the velocity residuals.
		  The MLP is trained with the classical back-propagation
		  algorithm and the RBFN is trained with a Kohonen
		  self-organizing map (KSOM). We validate the concepts
		  discussed in a thorough simulation study of a Puma 560 and
		  with experimental results with a 3-joint planar
		  manipulator.},
  dbinsdate	= {2002/1}
}

@Article{	  tetko01a,
  author	= {Tetko, I. V. and Kovalishyn, V. V. and Livingstone, D.
		  J.},
  title		= {Volume learning algorithm artificial neural networks for
		  3D {QSAR} studies},
  journal	= {JOURNAL OF MEDICINAL CHEMISTRY},
  year		= {2001},
  volume	= {44},
  number	= {15},
  month		= {JUL 19},
  pages		= {2411--2420},
  abstract	= {The current study introduces a new method, the volume
		  learning algorithm (VLA), for the investigation of
		  three-dimensional quantitative structure-activity
		  relationships (QSAR) of chemical compounds. This method
		  incorporates the advantages of comparative molecular Geld
		  analysis (CoMFA) and artificial neural network approaches.
		  VLA is a combination of supervised and unsupervised neural
		  networks applied to solve the same problem. The supervised
		  algorithm is a feed-forward neural network trained with a
		  back-propagation algorithm while the unsupervised network
		  is a self-organizing map of Kohonen. The use of both of
		  these algorithms makes it possible to cluster the input
		  CoMFA field variables and to use only a small number of the
		  most relevant parameters to correlate spatial properties of
		  the molecules with their activity. The statistical
		  coefficients calculated by the proposed algorithm for
		  cannabimimetic aminoalkyl indoles were comparable to, or
		  improved, in comparison to the original study using the
		  partial least squares algorithm. The results of the
		  algorithm can be visualized and easily interpreted. Thus,
		  VLA is a new convenient tool for three-dimensional QSAR
		  studies.},
  dbinsdate	= {2002/1}
}

@InCollection{	  teucci97a,
  author	= {M. C. Teucci and G. Braccini and C. Carpeggiani and C.
		  Marchesi},
  title		= {An application of \mbox{self-organising} maps for a
		  knowledge base for use in cardiac domain},
  booktitle	= {Computers in Cardiology 1997},
  publisher	= {IEEE},
  year		= {1997},
  address	= {New York, NY, USA},
  pages		= {569--72},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  teucci98a,
  author	= {Teucci, C. M. and Marchesi, C. and Carpeggiani, C.},
  title		= {Comparing symbolic to connectionist knowledge
		  representation in the design of a patient record for
		  cardiology},
  booktitle	= {Proceedings on the Third International Conference on
		  Neural Networks and Expert Systems in Medicine Healthcare},
  publisher	= {World Scientific Publishing},
  address	= {Singapore},
  year		= {1998},
  volume	= {},
  pages		= {271--8},
  abstract	= {Computer-assisted clinical documentation requires explicit
		  expression of the syntactic and semantic relationships
		  among clinical data. This paper focuses on the performance
		  comparison between symbolic and connectionist techniques
		  for carrying out the frame-based knowledge representation
		  of concepts which are typical of a patient record for
		  cardiology. The symbolic approach integrates the lexical
		  analysis of a clinical report with the UMLS (Unified
		  Medical Language System) controlled vocabulary of medical
		  terms. The connectionist approach consists of a
		  self-organising map that tends to group the words of a text
		  according to the semantic categorizations of the natural
		  language. Both techniques have been evaluated on 60
		  diagnostic reports concerning patients admitted to the
		  Department of Cardiology of the Istituto di Fisiologia
		  Clinica in Pisa, Italy. The symbolic approach showed
		  typical hit-rate values ranging between 95% and 100%. The
		  connectionist approach revealed some limitations as far as
		  the field of application was concerned, since in our case
		  the hit rate values they showed were lower than those of
		  the symbolic approach; moreover, they were difficult to
		  assess because of a dependence on text redundancy. In
		  conclusion, only the symbolic approach has shown a
		  reliability that is adequate for application in a medical
		  domain. Still, their advantages should be balanced with
		  their typical limitations, such as cumbersome procedures
		  for building the knowledge base and their dependence on the
		  language of the documents.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  textor92a,
  author	= {Textor, W. and Wessel, S. and Hoffgen, K. -U. },
  title		= {Learning fuzzy rules from artificial neural nets},
  booktitle	= {CompEuro 1992 Proceedings. Computer Systems and Software
		  Engineering},
  year		= {1992},
  editor	= {Dewilde, P. and Vandewalle, J. },
  pages		= {121--6},
  organization	= {Lehrstuhl Inf. II, Dortmund Univ. , Germany},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  thang00a,
  author	= {Thang, K. F. and Aggarwal, R. K. and Esp, D. G. and
		  McGrail, A.J.},
  title		= {Statistical and neural network analysis of dissolved gases
		  in power transformers},
  booktitle	= {IEE Conference Publication},
  year		= {2000},
  editor	= {},
  volume	= {},
  pages		= {324--329},
  organization	= {Univ of Bath},
  publisher	= {IEE},
  address	= {Stevenage},
  abstract	= {The onset of electrical discharges or thermal stresses in
		  mineral oil or cellulose insulation of a power transformer
		  can cause the degradation of these materials with the
		  formation of various dissolved gases. These dissolved gases
		  can be extracted and identified with the application of gas
		  chromatography. The overall process, from oil sampling to
		  gas identification, is known as dissolved gas analysis
		  (DGA). In this paper, a comparison of conventional DGA
		  interpretation schemes is briefly presented. Moreover, some
		  new artificial intelligence (AI) techniques for transformer
		  incipient fault diagnosis based on DGA data, are also
		  discussed. The second part of the paper reports on the
		  initial work performed for the proposed new approach. This
		  includes simple statistical analysis on DGA records and is
		  followed by high-level data-mining (DM) using
		  self-organizing map (SOM) algorithm. The inherent data
		  `structure' revealed from the latter part of the analysis
		  could hypothetically be associated with certain transformer
		  faults, either electrical, thermal or cellulose
		  decomposition. The proposed approach could provide a viable
		  alternative for transformer incipient fault diagnosis and
		  condition-monitoring applications.},
  dbinsdate	= {2002/1}
}

@InCollection{	  thangavelu97a,
  author	= {A. V. Thangavelu and H. P. Moyer and M. Ghanevati and A.
		  S. Daryoush and R. Gutierrez},
  title		= {Push-pull frequency converter for mobile communication},
  booktitle	= {1997 IEEE MTT-S International Microwave Symposium Digest},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {2},
  editor	= {G. A. Koepf},
  address	= {New York, NY, USA},
  pages		= {661--4},
  dbinsdate	= {oldtimer}
}

@Article{	  theodoropoulos00a,
  author	= {Theodoropoulos, G. and Loumos, V. and Anagnostopoulos, C.
		  and Kayafas, E. and Martinez Gonzales, B.},
  title		= {Digital image analysis and neural network based system for
		  identification of third-stage parasitic strongyle larvae
		  from domestic animals},
  journal	= {Computer Methods and Programs in Biomedicine},
  year		= {2000},
  number	= {2},
  volume	= {62},
  pages		= {69--76},
  abstract	= {A competitive learning vector quantization artificial
		  neural network (ANN) was trained to identify third-stage
		  parasitic strongyle larvae from domestic animals on the
		  basis of quantitative data obtained from processed digital
		  images of larvae. For this reason, various quantitative
		  features obtained from processed digital images of larvae
		  were tested as to whether they are variant or invariant to
		  the shape taken by the motile larvae during image
		  recording. A total of 255 images of 57 individual larvae in
		  various shapes belonging to five genera were recorded.
		  Following image processing, 16 features were measured, of
		  which seven were selected as invariant to larva shape. By
		  trial and error, two of those features, `area' and
		  `perimeter', along with the quantitative features used in
		  conventional identification, `overall body length', `width'
		  and `extension of sheath' (tip of larva to tip of sheath),
		  were used as an effective training data set for the ANN.
		  This ANN coupled with an image analysis facility and a
		  knowledge relational database became the basis for
		  developing a computer-based larva identification system
		  whose overall identification performance was 91.9%. The
		  advantages of this system are its speed and objectivity.
		  The objectivity of the system is based on the fact that it
		  is not subject to inter- and intra-observer variability
		  arising from the user's profile of competency in
		  interpreting subjective and non-quantifiable descriptions.
		  The limitations of the system are that it cannot handle raw
		  images but only data extracted from images, its performance
		  depends on the reliability of the input vectors used as
		  training data for the ANN, and its use is restricted only
		  to well-equipped laboratories due to its requirement for
		  expensive instrumentation.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  thian99a,
  author	= {Teh Swee Thian and Teoh Wan Yen},
  title		= {Illumination-sensitive failure mechanism---a case study on
		  transient {I}/sub cc/ failure},
  booktitle	= {Proceedings of the 1999 7th International Symposium on the
		  Physical and Failure Analysis of Integrated Circuits.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {},
  pages		= {73--6},
  abstract	= {Illumination-sensitive failure in integrated circuits (IC)
		  has generated considerable interest in the failure analysis
		  community, and powerful techniques such as OBIC and LIVA
		  have emerged (Cole et al, 1995; Wills et al, 1990).
		  However, these techniques required the use of a new
		  generation of SOM-based equipment which may be inaccessible
		  to some failure analysis laboratories. This paper presents
		  an alternative to the SOM approach to the study of a
		  illumination-sensitive and transient I/sub cc/ failure. By
		  using emission microscopy, FIB isolation, and mechanical
		  probing, coupled with an in-depth circuit analysis, the
		  effort leads to a full understanding of the failure
		  mechanism.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  thiran92a,
  author	= {Patrick Thiran and Martin Hasler},
  title		= {R\'{e}seau de {K}ohonen avec Poids Synaptiques
		  Quantifi\'{e}s},
  year		= 1992,
  booktitle	= {Proc. Workshop `Aspects Theoriques des Reseaux de
		  Neurones'},
  editor	= {M. Cottrell and M. Chaleyat-Maurel},
  publisher	= {Universit\'e Paris~{I}},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  thiran92b,
  author	= {Patrick Thiran and Martin Hasler},
  title		= {Quantization Effects in {K}ohonen Networks},
  year		= 1992,
  booktitle	= {Proc. workshop `Aspects Theoriques des Reseaux de
		  Neurones'},
  editor	= {M. Cottrell and M. Chaleyat-Maurel},
  publisher	= {Universit\'e Paris~{I}},
  address	= {Paris, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  thiran93a,
  author	= {Patrick Thiran},
  title		= {Self-organization on a {K}ohonen network with quantized
		  weights and an arbitrary \mbox{\mbox{one-dimensional}}
		  stimuli distribution},
  year		= {1993},
  booktitle	= {Proc. ESANN'95, European Symposium on Artificial Neural
		  Networks},
  editor	= {Michel Verleysen},
  publisher	= {D Facto},
  pages		= {203--208},
  address	= {Brussels, Belgium},
  dbinsdate	= {oldtimer}
}

@Article{	  thiran94a,
  author	= {Thiran, P. and Hasler, M. },
  title		= {Self-organization of a \mbox{\mbox{one-dimensional}}
		  {K}ohonen network with quantized weights and inputs},
  journal	= {Neural Networks},
  year		= {1994},
  volume	= {7},
  number	= {9},
  pages		= {1427--39},
  dbinsdate	= {oldtimer}
}

@Article{	  thiran94b,
  author	= {Thiran, P. and Peiris, V. and Heim, P. and Hochet, B. },
  title		= {Quantization effects in digitally behaving circuit
		  implementations of {K}ohonen networks},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1994},
  volume	= {5},
  number	= {3},
  pages		= {450--8},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  thiran94c,
  author	= {Thiran, J. -P. and Macq, B. and Mairesse, J. },
  title		= {Morphological classification of cancerous cells},
  booktitle	= {Proceedings ICIP-94},
  year		= {1994},
  volume	= {3},
  pages		= {706--10},
  organization	= {Lab. de Telecommun. et Teledetection, Univ. Catholique de
		  Louvain, Belgium},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  thiran95a,
  author	= {Thiran, P. and Hasler, M. },
  title		= {Study of the {K}ohonen network with a discrete state
		  space},
  journal	= {Mathematics and Computers in Simulation},
  year		= {1995},
  volume	= {38},
  number	= {1--3},
  pages		= {189--97},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Book{		  thiran97a,
  author	= {Patrick Thiran},
  title		= {Dynamics and Self-organization of Locally Coupled Neural
		  Networks},
  publisher	= {Presses Polytechniques et Universitaires Romandes},
  year		= 1997,
  address	= {Lausanne, Switzerland},
  dbinsdate	= {oldtimer}
}

@InCollection{	  thiran98a,
  author	= {P. Thiran},
  title		= {Self-organization in cellular neural networks: a
		  comparison with {K}ohonen's \mbox{self-organizing} maps},
  booktitle	= {1998 Fifth IEEE International Workshop on Cellular Neural
		  Networks and their Applications. Proceedings},
  publisher	= {IEEE},
  year		= {1998},
  editor	= {V. Tavsanoglu},
  address	= {New York, NY, USA},
  pages		= {68--73},
  dbinsdate	= {oldtimer}
}

@InCollection{	  thiran99a,
  author	= {P. Thiran},
  title		= {{K}ohonen Self-Organizing Map with quantized weights},
  booktitle	= {Kohonen Maps},
  pages		= {145--156},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: Kohonen SOM, {M}arkov chain, quantisation
		  effects, digital implementation},
  dbinsdate	= {oldtimer}
}

@InCollection{	  thissen95a,
  author	= {P. Thissen and M. Verleysen and J. -D. Legat and J.
		  Madrenas and J. Dominguez},
  title		= {A {VLSI} system for neural {B}ayesian and {LVQ}
		  classification},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {696--703},
  dbinsdate	= {oldtimer}
}

@Article{	  thomas01a,
  author	= {Thomas, E. and {Van Hulle}, M. M. and Vogels, R.},
  title		= {Encoding of categories by noncategory-specific neurons in
		  the inferior temporal cortex},
  journal	= {JOURNAL OF COGNITIVE NEUROSCIENCE},
  year		= {2001},
  volume	= {13},
  number	= {2},
  month		= {MAR},
  pages		= {190--200},
  abstract	= {In order to understand how the brain codes natural
		  categories, e.g., trees and fish, recordings were made in
		  the anterior part of the macaque inferior temporal (IT)
		  cortex while the animal was performing a tree/nontree
		  categorization task. Most single cells responded to
		  exemplars of more than one category while other neurons
		  responded only to a restricted set of exemplars of a given
		  category. Since it is still not known which type of cells
		  contribute and what is the nature of the code used for
		  categorization in IT, we have performed an analysis on
		  single- cell data. A Kohonen self-organizing map (SOM),
		  which uses an unsupervised (competitive) learning
		  algorithm, was used to study the single cell responses to
		  tree and nontree images. Results from the Kohonen SOM
		  indicated that the collected neuronal data consisting of
		  spike counts was sufficient to account for a good level of
		  categorization success (approximately 83%) when
		  categorizing a group of 200 trees and nontrees. Contrary to
		  intuition, the results of the investigation suggest that
		  the population of category-specific neurons (neurons that
		  respond only to trees or only to nontrees) was unimportant
		  to the categorization. Instead, a large majority of the
		  neurons that were most important to the categorization was
		  found to belong to a class of more broadly tuned cells,
		  namely, cells that responded to both categories but that
		  favored one category over the other by seven or more
		  images. A simple algebraic operation (without the Kohonen
		  SOM) between the above-mentioned noncategory-specific
		  neurons confirmed the contribution of these neurons to
		  categorization. Thus, the modeling results suggest (1) that
		  broadly tuned neurons are critical for categorization, and
		  (2) that only one additional layer of processing is
		  required to extract the categories from a population of IT
		  neurons.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  thompson99a,
  author	= {Thompson, B. T.},
  title		= {Application of fuzzy logic to feature extraction from
		  images of agricultural material},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.3812},
  year		= {1999},
  volume	= {3812},
  pages		= {241--50},
  abstract	= {In the application of agricultural materials, specifically
		  tobacco leaves, the amount of natural variability in color
		  and texture creates new challenges to image feature
		  extraction. The goal is to measure the amount of thick stem
		  pieces in an image of tobacco leaves. By backlighting the
		  leaf, the stems appear dark on a lighter background. The
		  difference in lightness of leaf versus darkness of stem is
		  dependent on the orientation of the leaf and the amount of
		  folding. From this, any image thresholding approach must be
		  adaptive. Another factor that allows one to identify the
		  stem from the leaf is shape. The stem is long and narrow,
		  while dark folded leaf is larger and more oblate. These
		  criteria under the image collection limitations create a
		  good application for fuzzy logic. Several generalized
		  classification algorithms, such as fuzzy c-means and fuzzy
		  learning vector quantization, are evaluated and compared.
		  In addition, fuzzy thresholding based on image shape and
		  compactness are applied to this application.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  thouard90a,
  author	= {J. P. Thouard and P. Depalle and X. Rodet},
  title		= {Pitch classification of musical notes using {K}ohonen's
		  \mbox{self-organizing} feature map},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  volume	= {I},
  pages		= {196},
  organization	= {Thomsom; SUN; British Computer Society ; et al},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  thuillard96a,
  author	= {M. Thuillard},
  title		= {The development of algorithms for a smoke detector with
		  neuro-fuzzy logic},
  journal	= {Fuzzy Sets and Systems},
  year		= {1996},
  volume	= {77},
  number	= {2},
  pages		= {117--24},
  dbinsdate	= {oldtimer}
}

@InCollection{	  thursby94a,
  author	= {M. H. Thursby and L. V. Fausett and H. Kwon},
  title		= {Rotation invariant classification of chromosomes using
		  {LVQ} and {ARTMAP}},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {385--90},
  dbinsdate	= {oldtimer}
}

@Article{	  thyagarajan90a,
  author	= {K. S. Thyagarajan and A. Eghbalmoghadam},
  title		= {Design of a vector quantizer using a neural network},
  journal	= {Archiv f{\"{u}}r Elektronik und {\"{U}}bertragungstechnik},
  year		= {1990},
  volume	= {44},
  number	= {6},
  pages		= {439--444},
  month		= {November-December},
  note		= {(in English)},
  x		= {. . . A self-organizing neural network, as proposed by
		  Kohonen (1984), is treated as a vector quantizer and . . .
		  },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  thyagarajan94a,
  author	= {Thyagarajan, K. S. and Erickson, D. },
  title		= {Variable rate self organizing neural networks for video
		  compression},
  booktitle	= {Conference Record of the Twenty-Eighth Asilomar Conference
		  on Signals, Systems and Computers},
  year		= {1994},
  editor	= {Singh, A. },
  volume	= {1},
  pages		= {244--8},
  organization	= {Dept. of Electr. \& Comput. Eng. , San Diego State Univ. ,
		  CA, USA},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  thyssen93a,
  author	= {Jes Thyssen and Steffen Duus Hansen},
  title		= {Using Neural Networks for Vector Quatization in Low Rate
		  Speech Coders},
  booktitle	= {Proc. ICASSP-93, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1993},
  volume	= {II},
  pages		= {431--434},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tian96a,
  author	= {Bin Tian and M. R. Azimi-Sadjadi and M. A. Shaikh and T.
		  Vonder-Haar},
  title		= {An {FFT}-based algorithm for computation of {G}abor
		  transform with its application to cloud
		  detection/classification},
  booktitle	= {IGARSS '96. 1996 International Geoscience and Remote
		  Sensing Symposium. Remote Sensing for a Sustainable
		  Future},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  editor	= {T. I. Stein},
  address	= {New York, NY, USA},
  pages		= {1108--10},
  dbinsdate	= {oldtimer}
}

@Article{	  tian97a,
  author	= {Tian, Bin and Azimi Sadjadi, Mahmood R. and Haar, Thomas
		  H. Vonder and Reinke, Donald},
  title		= {Neural network-based cloud classification on satellite
		  imagery using textural features},
  journal	= {IEEE International Conference on Image Processing},
  year		= {1997},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  number	= {},
  volume	= {3},
  pages		= {209--212},
  abstract	= {Automatic cloud classification of satellite imagery can be
		  of great help to meteorological studies. A neural
		  network-based cloud classification system is developed and
		  introduced in this paper. Several image transformation
		  schemes such as Wavelet Transform (WT) and Singular Value
		  Decomposition (SVD) are used to extract the salient
		  textural feature of the data and is compared them with
		  those of the well-known Gray-leve Co-occurrence Matrix
		  (GLCM) approach. Two different neural network paradigms
		  namely probability neural network (PNN) and unsupervised
		  Kohonen self-organized feature map (SOM) are chosen and
		  examined in this paper. The performance of the proposed
		  cloud classification system is benchmarked on the
		  Geostationary Operational Environmental Satellite (GOES) 8
		  data set and promising results have been achieved.},
  dbinsdate	= {oldtimer}
}

@Article{	  tian99a,
  author	= {Tian, Bin and Shaikh, Mukhtiar A. and Azimi Sadjadi,
		  Mahmood R. and Vonder Haar, Thomas H. and Reinke, Donald
		  L.},
  title		= {Study of cloud classification with neural networks using
		  spectral and textural features},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1999},
  number	= {1},
  volume	= {10},
  pages		= {138--151},
  abstract	= {The problem of cloud data classification from satellite
		  imagery using neural networks is considered in this paper.
		  Several image transformations such as singular value
		  decomposition (SVD) and wavelet packet (WP) were used to
		  extract the salient spectral and textural features
		  attributed to satellite cloud data in both visible and
		  infrared (IR) channels. In addition, the well-known
		  gray-level cooccurrence matrix (GLCM) method and spectral
		  features were examined for the sake of comparison. Two
		  different neural-network paradigms namely probability
		  neural network (PNN) and unsupervised Kohonen
		  self-organized feature map (SOM) were examined and their
		  performance were also benchmarked on the geostationary
		  operational environmental satellite (GOES) 8 data.
		  Additionally, a postprocessing scheme was developed which
		  utilizes the contextual information in the satellite images
		  to improve the final classification accuracy. Overall, the
		  performance of the PNN when used in conjunction with these
		  feature extraction and postprocessing schemes showed the
		  potential of this neural-network-based cloud classification
		  system.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tianren92a,
  author	= {Yao Tianren and Wang Dayou},
  title		= {On the use of cluster structure of \mbox{self-organizing}
		  feature mapping nets to fast-search in {VQ} of speech},
  booktitle	= {ICCT '92. Proceedings of 1992 International Conference on
		  Communication Technology},
  year		= {1992},
  volume	= {2},
  pages		= {34. 04/1--5},
  organization	= {Huazhong Univ. of Sci. \& Technol. , Wuhan, China},
  publisher	= {Int. Acad. Publishers},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@Article{	  tillmann00a,
  author	= {Barbara Tillmann and Jamshed J. Bharucha and Emmanuel
		  Bigand},
  title		= {Implicit Learning of Tonality: A Self-Organizing
		  Approach},
  journal	= {Psychological Review},
  year		= {2000},
  key		= {},
  volume	= {107},
  number	= {4},
  pages		= {885--913},
  month		= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InCollection{	  tin95a,
  author	= {S. Tin and I. Erkmen},
  title		= {Short-term load forecasting using unsupervised/supervised
		  cascaded artificial neural networks},
  booktitle	= {Stockholm Power Tech International Symposium on Electric
		  Power Engineering},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {564--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tino01a,
  author	= {Tino, P. and Nabney, I. and Sun, Y.},
  title		= {Using directional curvatures to visualize folding patterns
		  of the {GTM} projection manifolds},
  booktitle	= {Artificial Neural Networks---ICANN 2001. International
		  Conference. Proceedings (Lecture Notes in Computer Science
		  Vol.2130). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {421--8},
  abstract	= {In data visualization, characterizing local geometric
		  properties of nonlinear projection manifolds provides the
		  user with valuable additional information that can
		  influence further steps in the data analysis. We take
		  advantage of the smooth characteristic of the generalized
		  topographic mapping (GTM) projection manifold and
		  analytically calculate its local directional curvatures.
		  Curvature plots are useful for detecting regions where
		  geometry is distorted, for changing the amount of
		  regularization in nonlinear projection manifolds, and for
		  choosing regions of interest when constructing detailed
		  lower-level visualization plots.},
  dbinsdate	= {2002/1}
}

@Article{	  tino02a,
  author	= {Tino, P. and Nabney, I.},
  title		= {Hierarchical {GTM}: constructing localized nonlinear
		  projection manifolds in a principled way},
  journal	= {IEEE-Transactions-on-Pattern-Analysis-and-Machine-Intelligence}
		  ,
  year		= {2002},
  volume	= {24},
  pages		= {639--56},
  abstract	= {It has been argued that a single two-dimensional
		  visualization plot may not be sufficient to capture all of
		  the interesting aspects of complex data sets and,
		  therefore, a hierarchical visualization system is
		  desirable. In this paper, we extend an existing locally
		  linear hierarchical visualization system PhiVis in several
		  directions: 1) We allow for nonlinear projection manifolds.
		  The basic building block is the Generative Topographic
		  Mapping (GTM). 2) We introduce a general formulation of
		  hierarchical probabilistic models consisting of local
		  probabilistic models organized in a hierarchical tree.
		  General training equations are derived, regardless of the
		  position of the model in the tree. 3) Using tools from
		  differential geometry, we derive expressions for local
		  directional curvatures of the projection manifold. Like
		  PhiVis, our system is statistically principled and is built
		  interactively in a top-down fashion using the EM algorithm.
		  It enables the user to interactively highlight those data
		  in the ancestor visualization plots which are captured by a
		  child model. We also incorporate into our system a
		  hierarchical, locally selective representation of
		  magnification factors and directional curvatures of the
		  projection manifolds. Such information is important for
		  further refinement of the hierarchical visualization plot,
		  as well as for controlling the amount of regularization
		  imposed on the local models. We demonstrate the principle
		  of the approach on a toy data set and apply our system to
		  two more complex 12- and 18-dimensional data sets.},
  dbinsdate	= {2002/1}
}

@Article{	  tino95a,
  author	= {Tino, P. and Sajda, J. },
  title		= {Learning and extracting initial mealy automata with a
		  modular neural network model},
  journal	= {Neural Computation},
  year		= {1995},
  volume	= {7},
  number	= {4},
  pages		= {822--44},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@Article{	  tinos01a,
  author	= {Tinos, R. and Terra, M. H.},
  title		= {Fault detection and isolation in robotic manipulators
		  using a multilayer perceptron and a {RBF} network trained
		  by the Kohonen's Self-Organizing Map},
  journal	= {Controle and Automacao},
  year		= {2001},
  volume	= {12},
  number	= {1},
  month		= {Janaury/April },
  pages		= {11--18},
  organization	= {Departamento de Engenharia Eletrica, EESC/USP},
  publisher	= {},
  address	= {},
  abstract	= {In this work, Artificial Neural Networks are employed in a
		  Fault Detection and Isolation scheme for robotic
		  manipulators. Two networks are utilized: a Multilayer
		  Perceptron is employed to reproduce the manipulator
		  dynamical behavior, generating a residual vector that is
		  classified by a Radial Basis Function Network, giving the
		  fault isolation. Two methods are utilized to choose the
		  radial unit centers in this network. The first method,
		  Forward Selection, employs Subset Selection to choose the
		  radial units from the training patterns. The second employs
		  the Kohonen's Self-Organizing Map to fix the radial unit
		  centers in more interesting positions. Simulations
		  employing a two link manipulator and the Puma 560
		  manipulator indicate that the second method gives a smaller
		  generalization error.},
  dbinsdate	= {2002/1}
}

@InCollection{	  tinos98a,
  author	= {R. Tinos and M. H. Terra},
  title		= {Fault detection and isolation in robotic manipulators and
		  the radial basis function network trained by the
		  {K}ohonen's \mbox{self-organizing} map},
  booktitle	= {Proceedings 5th Brazilian Symposium on Neural Networks},
  publisher	= {IEEE Computer Society},
  year		= {1998},
  editor	= {A. {de Padua Braga} and T. B. Ludermir},
  address	= {Los Alamitos, CA, USA},
  pages		= {85--90},
  dbinsdate	= {oldtimer}
}

@Article{	  tipping98a,
  author	= {Tipping, Michael E. and Lowe, David},
  title		= {Shadow targets: a novel algorithm for topographic
		  projections by radial basis functions},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {19},
  pages		= {211--222},
  abstract	= {The archetypal neural network topographic paradigm,
		  Kohonen's self-organising map, has proven highly effective
		  in many applications but nevertheless has significant
		  disadvantages which can limit its utility. Alternative
		  feed-forward neural network approaches, including a model
		  called 'NeuroScale', have recently been developed based on
		  explicit distance-preservation criteria. Excellent
		  generalisation properties have been observed for such
		  models, and recent analysis indicates that such behaviour
		  is relatively insensitive to model complexity. As such, it
		  is important that the training of such networks is
		  performed efficiently, as computation of error and
		  gradients scales in the order of the square of the number
		  of patterns to be mapped. We therefore detail and
		  demonstrate a novel training algorithm for NeuroScale which
		  outperforms present approaches.},
  dbinsdate	= {oldtimer}
}

@Article{	  tirri91a,
  author	= {H. Tirri},
  title		= {Implementing expert system rule conditions by neural
		  networks},
  journal	= {New Generation Computing},
  year		= {1991},
  volume	= {10},
  number	= {1},
  pages		= {55--71},
  x		= {. . . He also discusses the use of self organizing Kohonen
		  networks as a means to determine those attributes
		  (properties) of data that reflect meaningful statistical
		  relationships in the expert system input space. },
  dbinsdate	= {oldtimer}
}

@InCollection{	  tirri95a,
  author	= {H. Tirri and S. Mallenius},
  title		= {Optimizing the hard address distribution for sparse
		  distributed memories},
  booktitle	= {1995 IEEE International Conference on Neural Networks
		  Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {4},
  address	= {New York, NY, USA},
  pages		= {1966--70},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tissainayagam97a,
  author	= {D. Tissainayagam and D. Everitt and M. Palaniswami},
  title		= {Mosaic Learning: A new Algorithm for Self Organizing
		  Neural Networks to Learn Dynamic Channel Assignment
		  Schemes},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {910--913},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  toborg94a,
  author	= {Scott T. Toborg},
  title		= {Performance comparison of neural networks for undersea
		  mine detection},
  booktitle	= {Proc. SPIE---The International Society for Optical
		  Engineering, Volume 2243 Applications of Artificial Neural
		  Networks V},
  year		= {1994},
  editor	= {Steven K. Rogers amd Dennis W. Ruck},
  pages		= {200--211},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {application, image processing, classification},
  dbinsdate	= {oldtimer}
}

@Article{	  todeschini99a,
  author	= {R. Todeschini and D. Galvagni and J. L. Vilchez and M.
		  Delolmo and N. Navas},
  title		= {{K}ohonen Artificial Neural Networks as a Tool for
		  Wavelength Selection in Multicomponent Spectrofluorometric
		  {Pls} Modeling Application to Phenol, {O} Cresol, {M}
		  Cresol and {P} Cresol Mixtures},
  journal	= {TrAC---Trends in Analytical Chemistry},
  volume	= {18},
  pages		= {93--98},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  togawa91a,
  author	= {F. Togawa and T. Ueda and T. Aramaki and A. Tanaka},
  title		= {Receptive field neural network with shift tolerant
		  capability for {K}anji character recognition},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1991},
  volume	= {II},
  pages		= {1490--1499},
  organization	= {IEEE; Int. Neural Networks Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  togneri90a,
  author	= {R. Togneri and M. D. Alder and Y. Attikiouzel},
  title		= {Parameterisation of the speech space using the
		  \mbox{self-organising} neural network},
  booktitle	= {Proc. AI'90, 4th Australian Joint Conf. on Artificial
		  Intelligence},
  year		= {1990},
  editor	= {C. P. Tsang},
  pages		= {274--283},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  togneri90b,
  author	= {Roberto Togneri and Michael Alder and Yianni Attikiouzel},
  title		= {Speech Processing Using Artificial Neural Networks},
  booktitle	= {Proc. Third Australian International Conference on Speech
		  Science and Technology},
  year		= {1990},
  pages		= {304--309},
  address	= {Melbourne, Australia},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  togneri91a,
  author	= {R. Togneri and Y. Attikiouzel},
  title		= {Parallel Implementation of the {K}ohonen Algorithm on
		  Transputer},
  booktitle	= {Proc. IJCNN-91, International Joint Conference on Neural
		  Networks, Singapore},
  year		= {1991},
  volume	= {II},
  pages		= {1717--1722},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  togneri92a,
  author	= {Roberto Togneri and Michael Alder and Yianni Attikiouzel},
  title		= {Dimension and structuure of the speech space},
  journal	= {IEE Proceedings-I},
  year		= {1992},
  volume	= {139},
  number	= {2},
  pages		= {123--127},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  togneri92b,
  author	= {R. Togneri and D. Farrokhi and Y. Zhang and Y.
		  Attikiouzel},
  title		= {A Comparison of the {LBG}, {LVQ}, {MLP}, {SOM} and {GMM}
		  Algorithms for Vector Quantization and Clustering
		  Analysis},
  booktitle	= {Proc. Fourth Australian International Conference on Speech
		  Science and Technology},
  year		= {1992},
  pages		= {173--177},
  address	= {Brisbane, Australia},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  togneri92c,
  author	= {Roberto Togneri and Yaxin Zhang and Christopher J. S.
		  deSilva and Yianni Attikiouzel},
  title		= {A Comparison of the {LVQ} and {EM} Algorithms for Vector
		  Quantization},
  booktitle	= {Proc. Third Int. Symp. on Signal Processing and its
		  Applications},
  year		= {1992},
  volume	= {II},
  pages		= {384--387},
  dbinsdate	= {oldtimer}
}

@InCollection{	  toivanen98a,
  author	= {Pekka J. Toivanen and J. Ansam{\"a}ki and S.
		  Lepp{\"a}j{\"a}rvi and J. Parkkinen},
  title		= {Edge Detection of Multispectral Images Using the 1-{D}
		  Self-Organizing Map},
  booktitle	= {Proceedings of ICANN98, the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer},
  year		= 1998,
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= 2,
  address	= {London},
  pages		= {737--742},
  abstract	= {In this paper, a new method for edge detection in
		  multispectral images is presented. It is based on the use
		  of the self-organizing map (SOM) and a conventional edge
		  detector. The method presented in this paper orders the
		  vectors of the original image in such a way that vectors
		  that are near each other according to some similarity
		  criterion should have scalar ordering values near each
		  other. This is achieved using the 1D self-organizing map.
		  After ordering, the original vector image reduces to a
		  gray-value image, and conventional edge detectors can be
		  applied. In this paper, the Laplace and Canny edge
		  detectors are used. It is shown, that using the SOM in
		  ordering the vectors of the original spectral image it is
		  possible to find also those edges that the R-ordering based
		  methods miss.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  toivanen99a,
  author	= {Toivanen, P. J. and Ansam\"aki, J. and Lepp\"aj\"arvi, S.
		  and Parkkinen, J. P. S.},
  title		= {Multispectral edge detection using the 2-dimensional
		  Self-Organizing Map},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3647},
  pages		= {103--110},
  abstract	= {In this paper, a new method for edge detection in
		  multispectral images is presented. It is based on the use
		  of the Self-Organizing Map (SOM), Peano scan and a
		  conventional edge detector. The method presented in this
		  paper orders the vectors of the original image in such a
		  way that vectors that are near each other according to some
		  similarity criterium have scalar ordering values near each
		  other. This is achieved using a 2-dimensional
		  Self-Organizing Map and the Peano scan. After ordering, the
		  original vector image reduces to a gray-value image, and a
		  conventional edge detector can be applied. In this paper,
		  the Laplace and the Canny edge detectors are used. It is
		  shown, that using the proposed methods it is possible to
		  find the same relevant edges that R-ordering based methods
		  find. Furthermore, it is also possible to find edges in
		  images which consist of metameric colors, i.e. images in
		  which every pixel vector maps into the same location in RGB
		  space. This is not possible using conventional edge
		  detectors which use an RGB image as input. Finally, the new
		  method is tested with a real-world airplane image, giving
		  results comparable with R-ordering based methods.},
  dbinsdate	= {oldtimer}
}

@Article{	  toiviainen95a,
  author	= {Toiviainen, P. and Kaipainen, M. and Louhivuori, J. },
  title		= {Musical timbre: similarity ratings correlate with
		  computational feature space distances},
  journal	= {Journal of New Music Research},
  year		= {1995},
  volume	= {24},
  number	= {3},
  pages		= {282--98},
  month		= {Sept},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tokunaga92a,
  author	= {M. Tokunaga and K. Kohno and Y. Hashizume and K. Hamatani
		  and M. Watanabe and K. Nakamura and Y. Ageishi},
  title		= {Learning Mechanism and an Application of {FFS}-Network
		  Reasoning System},
  booktitle	= {Proc. 2nd International Conference on Fuzzy Logic and
		  Neural Networks, Iizuka, Japan},
  year		= {1992},
  pages		= {123--126},
  dbinsdate	= {oldtimer}
}

@TechReport{	  tokutaka95a,
  author	= {Heizo Tokutaka and Akito Tanaka and Kikuo Fujimura and
		  Takanori Koukami and Satoru Kishida and Hidemi Hase},
  title		= {Solving traveling salesman problem using the {K}ohonen's
		  {SOM} method with the renewal function of the lateral
		  inhibitory interaction},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1995},
  number	= {NC94--79},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tokutaka97a,
  author	= {Heizo Tokutaka},
  title		= {Condensed review of {SOM} and {LVQ} research in Japan},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {322--329},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tokutaka97b,
  author	= {Heizo Tokutaka and Kikuo Fujimura and Kazuyuki Iwamoto and
		  Satoru Kishida and Kazuhiro Yoshihara},
  title		= {Applications of Self-Organizing Maps to a Chemical
		  Analysis},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {1318--1321},
  dbinsdate	= {oldtimer}
}

@Article{	  tokutaka97c,
  author	= {H. Tokutaka},
  title		= {Application of Self-Organizing Maps {(SOM)} to the Data of
		  Chemical Analysis and the Surroundings of {SOM} (in
		  Japanese)},
  journal	= {Journal of Surface Analysis},
  year		= {1997},
  volume	= {3},
  pages		= {545--557},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tokutaka98a,
  author	= {H. Tokutaka and K. Yoshihara and K. Fujimura and K.
		  Iwamoto and T. Watanabe and S. Kishida},
  title		= {Applications of \mbox{self-organizing} maps ({SOM}) to the
		  composition determination of chemical products},
  booktitle	= {1998 IEEE International Joint Conference on Neural
		  Networks Proceedings. IEEE World Congress on Computational
		  Intelligence},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {301--5},
  dbinsdate	= {oldtimer}
}

@Article{	  tokutaka98b,
  author	= {H. Tokutaka and K. Yoshihara and K. Iwamoto and K.
		  Fujimura and T. Watanabe and K. Kishida},
  title		= {Application of Self-Organizing Maps to Chemical Analysis},
  journal	= {Journal of the Vacuum Society of Japan},
  year		= {1998},
  volume	= {41},
  note		= {(in Japanese)},
  number	= {3},
  dbinsdate	= {oldtimer}
}

@Article{	  tokutaka98c,
  author	= {Tokutaka, H. and Fujimura, K. and Iwamoto, K. and
		  Obu-Cann, K. and Kishida, S. and Yoshihara, K.},
  title		= {Applications of \mbox{self-organising} maps to a chemical
		  analysis},
  journal	= {Australian Journal of Intelligent Information Processing
		  Systems},
  year		= {1998},
  volume	= {5},
  pages		= {181--6},
  abstract	= {Self-Organising Map (SOM) method which was developed by T.
		  Kohonen (1995) has been applied to some problems of
		  chemical analysis using AES, XPS, and XRD (X-ray
		  Diffraction) data. Using a 2-dimensional SOM, the items
		  that are described qualitatively by linguistic expressions
		  can be explained more quantitatively by the position of the
		  spectral data on the SOM together with a grey level
		  expression. Furthermore, the composition of an unknown
		  sample can be determined very precisely by the SOM that has
		  been constructed using the spectra from samples of known
		  composition. Thus SOM is a very important tool for data
		  mining in chemical analysis.},
  dbinsdate	= {oldtimer}
}

@Book{		  tokutaka99a,
  author	= {H. Tokutaka and M. Tanaka and A. Kubono and W. Shiraki and
		  T. Miyoshi and K. Fujimura},
  title		= {Visual Explorations in Finance with Self-Organizing Maps
		  (Translated into Japanese)},
  publisher	= {Springer-Verlag Tokyo},
  year		= {1999},
  note		= {Authors of English version: Guido Deboeck and Teuvo
		  Kohonen},
  dbinsdate	= {oldtimer}
}

@Article{	  tokutaka99b,
  author	= {H. Tokutaka and K. Yoshihara and K. Fujimura and K.
		  Iwamoto and K. Obu-Cann},
  title		= {Application of Self-Organizing Maps ({SOM}) to Auger
		  Electron Spectroscopy ({{AES}})},
  journal	= {Surface and Interface Analysis},
  year		= {1999},
  volume	= {27},
  number	= {8},
  pages		= {783--788},
  abstract	= {The self-organizing map (SOM) method that was developed by
		  Kohonen (1995) has been examined preliminarily by applying
		  some problems of chemical analysis using AES data, leading
		  to promising results: using a two-dimensional SOM, the
		  items that are described qualitatively by linguistic
		  expressions can be explained more quantitatively by the
		  position of the spectral data on the SOM, together with a
		  grey level expression. The composition of an unknown sample
		  also can be determined precisely by a SOM that has been
		  constructed using spectra from samples of known
		  composition.},
  dbinsdate	= {oldtimer}
}

@Article{	  tokutaka99c,
  author	= {H. Tokutaka and K. Yoshihara and K. Fujimura and Kazuyuki
		  Iwamoto and K. Obu-Cann and T. Watanabe and S. Kishida},
  title		= {Application of Self-Organizing Maps ({SOM}) to Chemical
		  Data Analysis},
  journal	= {Journal of Surface Analysis},
  year		= {1999},
  volume	= {5},
  number	= {1},
  pages		= {102--105},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tokutaka99d,
  author	= {H. Tokutaka and K. Fujimura},
  title		= {{SOM}-{TSP}: An approach to optimize surface component
		  mounting on a printed circuit board},
  booktitle	= {Kohonen Maps},
  pages		= {219--230},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: Self-Organising Maps, Travelling Salesman
		  Problem, SOM-TSP, Optimisation, Neural Network},
  dbinsdate	= {oldtimer}
}

@Article{	  tokutaka99e,
  author	= {H. Tokutaka and K. Yoshihara and K. Fujimura and K.
		  Obu-Cann and K. Iwamoto},
  title		= {Application of Self-Organizing Maps ({SOM}) to Chemical
		  Analysis},
  journal	= {Applied Surface Science},
  year		= {1999},
  volume	= {144--145},
  pages		= {59--63},
  dbinsdate	= {oldtimer}
}

@Book{		  tokutaka99f,
  author	= {H. Tokutaka and S. Kishida and K. Fujimura},
  title		= {Application of Self-Organizing Maps---2 Dimensional
		  Visualization of Multi-Dimensional Informations},
  publisher	= {Kaibundo Publishing Co. Ltd.},
  note		= {(in Japanese)},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@Article{	  tokutaka99g,
  author	= {H. Tokutaka and K. Fujimura and K. Yoshihara},
  title		= {Application of Self-Organizing Maps ({SOM}) to the
		  Round-Robin {C}o{N}i Alloy Spectra Data: By that, Is It
		  Possible to See the Characteristics of the Measurement
		  Instruments?},
  note		= {(in Japanese)},
  journal	= {Journal of the Surface Science Society of Japan},
  year		= {1999},
  volume	= {20},
  number	= {3},
  pages		= {52--59},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tolat89a,
  author	= {V. V. Tolat and A. M. Peterson},
  title		= {A Self-Organizing Neural Network for Classifying
		  Sequences},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  pages		= {561--568 },
  dbinsdate	= {oldtimer}
}

@Article{	  tolat90a,
  author	= {V. V. Tolat},
  title		= {An analysis of {K}ohonen's \mbox{self-organizing} maps
		  using a system of energy functions},
  journal	= {Biol. Cyb. },
  year		= {1990},
  volume	= {64},
  number	= {2},
  pages		= {155--164},
  dbinsdate	= {oldtimer}
}

@Article{	  tolba00a,
  author	= {Tolba, A. S.},
  title		= {A parameter-based combined classifier for invariant face
		  recognition},
  journal	= {CYBERNETICS AND SYSTEMS},
  year		= {2000},
  volume	= {31},
  number	= {8},
  month		= {DEC},
  pages		= {837--849},
  abstract	= {A system for invariant face recognition is presented. A
		  combined classifier uses the generalization capabilities of
		  learning vector quantization (LVQ) neural networks to build
		  a representative model of a face from a variety of training
		  patterns with different poses, details, and facial
		  expressions. The combined generalization error of the
		  classifier is found to be lower than that of each
		  individual classifier. The system is tested on an in-house
		  built database and is capable of recognizing a face in
		  about 1 second. The system performance compares favorably
		  with the state-of-the-art systems. While the recognition
		  rates of the individual classifiers ranged from 94% to 96%,
		  a correct recognition rate of 100% is achieved by the
		  combined classifier at 0% rejection.},
  dbinsdate	= {2002/1}
}

@Article{	  tolba00b,
  author	= {Tolba, A. S. and Abu Rezq, A. N.},
  title		= {Combined classifiers for invariant face recognition},
  journal	= {Pattern-Analysis-and-Applications},
  year		= {2000},
  volume	= {3},
  pages		= {289--302},
  abstract	= {This paper presents a system for invariant face
		  recognition. A combined classifier uses the generalisation
		  capabilities of both learning vector quantisation (LVQ) and
		  radial basis function (RBF) neural networks to build a
		  representative model of a face from a variety of training
		  patterns with different poses, details and facial
		  expressions. The combined generalisation error of the
		  classifier is found to be lower than that of each
		  individual classifier. A new face synthesis method is
		  implemented for reducing the false acceptance rate and
		  enhancing the rejection capability of the classifier. The
		  system is capable of recognising a face in less than one
		  second. The ORL database is used for testing the combined
		  classifier. Comparisons with several other systems show
		  that our system compares favourably with the
		  state-of-the-art systems. In the case of the ORL database,
		  a correct recognition rate of 99.5% at 0.5% rejection rate
		  is achieved.},
  dbinsdate	= {2002/1}
}

@Article{	  tolba01a,
  author	= {Tolba, A. S.},
  title		= {Invariant gender identification},
  journal	= {Digital Signal Processing: A Review Journal},
  year		= {2001},
  volume	= {11},
  number	= {3},
  month		= {July },
  pages		= {222--240},
  organization	= {Department of Physics, Kuwait University},
  publisher	= {},
  address	= {},
  abstract	= {In this paper, we address the problem of gender
		  identification using different neural network classifiers:
		  a learning vector quantization (LVQ) network and a radial
		  basis function (RBF) network. Our results indicate that it
		  is more favorable to use either the LVQ network or the RBF
		  network than any feature-based methods. We present results
		  showing identification of gender with a hit rate of 100% in
		  the case of a LVQ network and 98.04% in the case of an RBF
		  network. When hair information was excluded, the best LVQ
		  classifier resulted in 95.1% correct identification. We
		  show that while the two models are nearly accurate, the RBF
		  model learns the task considerably faster than the LVQ
		  model. These results are favorable compared with
		  eigen-decomposition-based techniques. The effect of head
		  covers (e.g., a scarf) used by both men and women on system
		  performance is studied.},
  dbinsdate	= {2002/1}
}

@Article{	  tolba97a,
  author	= {A. S. Tolba and A. N. Abu-Rezeq},
  title		= {Self-organizing feature map for automated visual
		  inspection of textile products},
  journal	= {Computers in Industry},
  year		= {1997},
  number	= {3},
  volume	= {32},
  pages		= {319--333},
  abstract	= {Automated visual inspection plays a vital role in the
		  production process in many industries such as textile,
		  wood, steel, glass, foil, paper and rubber. This paper
		  presents a new technique for feature extraction based on
		  the autocorrelation function and applies a self-organizing
		  feature map (SOFM) to the automatic detection and
		  classification of textile defects. Feature vectors are
		  first extracted from the one dimensional autocorrelation
		  function (ACF). The extracted feature set has the advantage
		  of being immune to both the continuous variations of the
		  illumination intensity and noise as a result of the noise
		  rejection property of the ACF. The topology preserving
		  feature mapping algorithm clusters the input feature
		  vectors of a training set onto the network structure. The
		  performance of the proposed technique is tested with a
		  number of real textile samples over a restricted width. The
		  results of applying a SOFM to defect classification are
		  reported and they reveal the practical advantages of
		  unsupervised systems.},
  dbinsdate	= {oldtimer}
}

@Article{	  tolba99a,
  author	= {Tolba, A. S.},
  title		= {GloveSignature: a virtual-reality-based system for dynamic
		  signature verification},
  journal	= {Digital Signal Processing},
  year		= {1999},
  volume	= {9},
  pages		= {241--66},
  abstract	= {A survey of the principal schemes in the literature
		  suggested that a new way of addressing the problem of
		  signature recognition be formulated in order to find a
		  satisfactory solution for eliminating random forgeries. A
		  fundamental problem in the field of off-line signature
		  recognition is the lack of a pertinent shape representation
		  or shape factor. This paper introduces a novel idea for a
		  dynamic signature recognition system. An initial attempt is
		  presented to demonstrate the data glove as an effective
		  high-bandwidth data entry device for signature recognition.
		  GloveSignature is a virtual-reality-based environment to
		  support the signing process. The proposed approach retains
		  the power to discriminate against forgeries. This paper
		  extends the use of instrumented data gloves, gloves
		  equipped with sensors for detecting finger bend and hand
		  position and orientation for recognizing hand signatures.
		  Several researchers have already explored the use of gloves
		  in other application areas but using the gloves for the
		  recognition of hand signatures has never been reported. An
		  attempt is made in this research to explore the feasibility
		  of using the 5th Glove in on-line signature recognition.
		  Two hundred signatures were collected from 20 subjects, and
		  features were extracted. We demonstrate the effectiveness
		  of a hybrid technique, which is based on both the most
		  discriminating eigenfeatures and the self-organizing maps
		  (SOFM) for signature recognition.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tolba99b,
  author	= {Tolba, A. S. and Abu-Rezq, A. N.},
  title		= {Combined classifiers for invariant face recognition},
  booktitle	= {Proceedings 1999 International Conference on Information
		  Intelligence and Systems},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {350--9},
  abstract	= {We present a system for invariant face recognition. A
		  combined classifier uses the generalization capabilities of
		  both learning vector quantization (LVQ) and radial basis
		  function (RBF) neural networks to build a representative
		  model of a face from a variety of training patterns with
		  different poses, details and facial expressions. The
		  combined generalization error of the classifier is found to
		  be lower than that of each individual classifier. A new
		  face synthesis method is implemented for reducing the false
		  acceptance rate and enhancing the rejection capability of
		  the classifier. The system is capable of recognizing a face
		  in less than one second. The system is tested on the
		  well-known ORL database. The system performance compares
		  favorably with the state-of-the-art systems. In the case of
		  the ORL database, a correct recognition rate of 99.5% at
		  0.5% rejection rate is achieved. This rate compares
		  favorably with the rates achieved by other systems on the
		  same database. The volumetric frequency domain
		  representation resulted in a rate of 92.5% while the
		  combination of a convolutional neural network and
		  self-organizing map resulted in 96.2% for the same number
		  of training faces (five) per person in a database
		  representing 40 people.},
  dbinsdate	= {oldtimer}
}

@Article{	  tolba99c,
  author	= {Tolba, A. S. and Abu-Rezq, A. N. and {Al Mazeedi}, M.},
  title		= {Adaptive texture classification with {H}artley transform
		  and application to visual inspection},
  journal	= {Microcomputer-Applications},
  year		= {1999},
  volume	= {18},
  pages		= {71--6},
  abstract	= {We describe a vision system designed for automatic
		  classification and inspection of textures based on both the
		  fast 2-D Hartley transform and the learning vector
		  quantization (LVQ) neural networks. We show that a minimal
		  feature set results in effective texture discrimination.
		  Hartley transform-based feature extraction results in
		  real-time performance. An LVQ neural network maps each
		  feature vector on a single neuron that represents the
		  texture class. The system has been tested on a number of
		  synthetic and real textures. An example of defect detection
		  and classification using data on woven aluminum wire webs
		  is presented. We report a 95% accuracy in classification
		  attained using the minimum feature set for classifying a
		  set of 6 Brodatz textures.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tolba99d,
  author	= {Tolba, A. S. and Ashraf, M. and Abu Rezq, A. N.},
  title		= {Eyes-strip extraction for detection of a human face},
  booktitle	= {ICM'99. Proceedings. Eleventh International Conference on
		  Microelectronics. Kuwait Univ, Safat, Kuwait},
  year		= {1999},
  volume	= {},
  pages		= {109--12},
  abstract	= {Face detection in a natural scene is an essential step in
		  automated human face recognition. The eyes-strip of a human
		  face plays the most important part in the detection
		  process, since this area is not affected by hairstyle.
		  Geometric techniques for feature extraction have the
		  problems of sensitivity to lighting conditions and facial
		  expressions. The weakness of the geometric techniques could
		  be easily avoided using neural networks. In this paper, a
		  learning vector quantization (LVQ) neural network is
		  trained to detect the eyes-strip in skin-like areas only.
		  First, a modified color system is used to extract skin-like
		  areas from a natural scene. The fuzzy C-means algorithm is
		  then used for thresholding the hue weighted chroma image.
		  The skin-like area builds the basis of further search for
		  human facial features. A LVQ network is trained on
		  different eyes-strips and other areas of both facial and
		  nonfacial images.},
  dbinsdate	= {2002/1}
}

@PhDThesis{	  tomberg92a,
  author	= {Jouni Tomberg},
  title		= {Integrated Circuit Implementations of Artificial Neural
		  Networks},
  school	= {Tampere University of Technology},
  year		= {1992},
  address	= {Tampere, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tomberg92b,
  author	= {Jouni Tomberg and Kimmo Kaski},
  title		= {{VLSI} Architecture of the Self-Organizing Neural Network
		  using Synchronous Pulse-Density Modulation Technique},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1431--1434},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  tomescu97a,
  author	= {Tomescu, Bogdan and VanLandingham, H. F.},
  title		= {Neuro-fuzzy multi-model control using Sugeno inference and
		  {K}ohonen tuning in parameter space},
  journal	= {Proceedings of IEEE International Conference on Systems,
		  Man, and Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1997},
  number	= {},
  volume	= {2},
  pages		= {1028--1032},
  abstract	= {A multimodel adaptive control scheme used Kohonen neural
		  structures and Sugeno fuzzy inference in adapting and
		  switching the control action of the multimodel bank,
		  respectively. The structure can be easily used in a
		  parametrized gain scheduling framework, practical to
		  engineering situations such as complex power electronics
		  systems. A simple example of a tracking filter (radar) has
		  been simulated with good results.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tomsich00a,
  author	= {Tomsich, P. and Rauber, A. and Merkl, D.},
  title		= {par{SOM}: Using parallelism to overcome memory latency in
		  self- organizing neural networks},
  booktitle	= {HIGH PERFORMANCE COMPUTING AND NETWORKING, PROCEEDINGS},
  year		= {2000},
  pages		= {136--145},
  abstract	= {The self-organizing map is a prominent unsupervised neural
		  network model which lends itself to the analysis of high-
		  dimensional input data. However, the high execution times
		  required to train the map put a limit to its application in
		  many high-performance data analysis application domains,
		  where either very large datasets are encountered and/or
		  interactive response times are required. In this paper we
		  present the parSOM, a software-laced parallel
		  implementation of the self- organizing map, winch is
		  particularly optimized for the analysis of high-dimensional
		  input data. This model scales well in a parallel execution
		  environment, and, ly coping with memory latencies, a better
		  than linear speed-up can be achieved using a simple,
		  asymmetric model of parallelization. We demonstrate the
		  benefits of the proposed implementation in the field of
		  text classification, which due to the high dimensionalities
		  of the data spaces encountered, forms a prominent
		  application domain for high-performance computing.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  tomsich00b,
  author	= {Tomsich, P. and Rauber, A. and Merkl, D.},
  title		= {Optimizing the par{SOM} neural network implementation for
		  data mining with distributed memory systems and cluster
		  computing},
  booktitle	= {Proceedings 11th International Workshop on Database and
		  Expert Systems Applications. IEEE Comput. Soc, Los
		  Alamitos, CA, USA},
  year		= {2000},
  volume	= {},
  pages		= {661--5},
  abstract	= {The self-organizing map is a prominent unsupervised neural
		  network model which lends itself to the analysis of
		  high-dimensional input data and data mining applications.
		  However, the high execution times required to train the map
		  limit its application in many high-performance data
		  analysis application domains. We discuss the /sub par/SOM
		  implementation, a software-based parallel implementation of
		  the self-organizing map, and its optimization for the
		  analysis of high-dimensional input data using distributed
		  memory systems and clusters. The original /sub par/SOM
		  algorithm scales very well in a parallel execution
		  environment with low communication latencies and exploits
		  parallelism to cope with memory latencies. However it
		  suffers from poor scalability on distributed memory
		  computers. We present optimizations to further decouple the
		  subprocesses, simplify the communication model and improve
		  the portability of the system.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  torkkola00a,
  author	= {Torkkola, K. and Gardner, R. M. and Kaysser Kranich, T.
		  and Ma, C.},
  title		= {Mining gene expression data: clustering with
		  self-organizing maps},
  booktitle	= {Proceedings of the Fourth International Conference on the
		  Practical Application of Knowledge Discovery and Data
		  Mining. Practical Application Company, Blackpool, UK},
  year		= {2000},
  volume	= {},
  pages		= {63--72},
  abstract	= {Demonstrates the efficiency in applying self-organizing
		  maps (SOM) to gene expression data from Stanford yeast DNA
		  microarray database in order to very rapidly find gene
		  families with similar expression patterns using no other
		  source of original information except the fluorescence
		  ratios (R/G) at each timepoint. SOM not only facilitated
		  quickly selecting the gene families identified in previous
		  work, but it enables identifying additional genes and
		  additional families. Furthermore, additional insight into
		  the primary pattern variations that discriminate between
		  the families became explicit.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  torkkola01a,
  author	= {Torkkola, K. and Gardner, R. M. and Kaysser-Kranich, T.
		  and Ma, C.},
  title		= {Self-organizing maps in mining gene expression data},
  booktitle	= {Information Sciences},
  year		= {2001},
  editor	= {Gattiker, J. R. and Wang, J. T. L. and Wang, P. P.},
  volume	= {139},
  pages		= {79--96},
  organization	= {Motorola Labs, MD ML28},
  publisher	= {},
  address	= {},
  abstract	= {Modern DNA microarray technology provides means of
		  measuring gene expression patterns of the whole genome of
		  simple organisms at once. Exploratory analysis of these
		  large-scale expression datasets is becoming vital to
		  extracting functional information from the measurements. We
		  demonstrate how self-organizing maps (SOM) can be applied
		  to exploratory analysis of gene expression data from a
		  yeast DNA microarray database in order to very rapidly find
		  gene families with similar expression patterns. SOM not
		  only enabled quickly selecting the gene families identified
		  in previous work, but it facilitated identifying additional
		  genes with similar expression patterns. Identifying new
		  families of genes also appears to be possible as
		  demonstrated by additional clusters of genes discovered
		  from the data. Moreover, further insight into the primary
		  pattern variations that discriminate between the families
		  became explicit. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  torkkola88a,
  author	= {Kari Torkkola},
  title		= {Automatic Alignment of Speech with Phonetic Transcriptions
		  in Real Time},
  booktitle	= {Proc. ICASSP-88, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1988},
  pages		= {611--614},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  torkkola91a,
  author	= {K. Torkkola and M. Kokkonen},
  title		= {Using the topology-preserving properties of {SOFM}s in
		  speech recognition},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {I},
  pages		= {261--264},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  torkkola91b,
  author	= {Kari Torkkola and Jari Kangas and Pekka Utela and Sami
		  Kaski and Mikko Kokkonen and Mikko Kurimo and Teuvo
		  Kohonen},
  title		= {Status Report of the {F}innish Phonetic Typewriter
		  Project},
  booktitle	= {Artificial Neural Networks},
  booktitles	= {Proc. ICANN},
  year		= {1991},
  volume	= {I},
  pages		= {771--776},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  annote	= {LVQ forms codebooks for HMM:s. },
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  torkkola91c,
  author	= {Kari Torkkola},
  title		= {Short-Time Feature Vector Based Phonemic Speech
		  Recognition with the Aid of Local Context},
  school	= {Helsinki University of Technology},
  schoolf	= {Teknillinen korkeakoulu},
  year		= {1991},
  address	= {Espoo, Finland},
  addressf	= {Otaniemi},
  dbinsdate	= {oldtimer}
}

@InCollection{	  torkkola91d,
  author	= {Kari Torkkola},
  title		= {A Combination of Neural Network and Low Level
		  {AI}-Techniques to Transcribe Speech into Phonemes},
  booktitle	= {COGNITIVA-90},
  editor	= {T. Kohonen and F. Fogelman-Souli{\'{e}}},
  year		= {1991},
  pages		= {405--416},
  publisher	= {Elsevier},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  torkkola91e,
  author	= {Kari Torkkola and Mikko Kokkonen and Mikko Kurimo and
		  Pekka Utela},
  title		= {Improving Short-Time Speech Frame Recognition Results by
		  Using Context},
  booktitle	= {Proc. Eurospeech'91, 2nd European Conference on Speech
		  Communication and Technology},
  year		= {1991},
  pages		= {793--796},
  volume	= {2},
  address	= {Genova, Italy},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  torkkola93a,
  author	= {Kari Torkkola},
  title		= {{LVQ}-Based Codebooks in Phonemic Speech Recognition},
  booktitle	= {Proc. of NATO ASI workshop on new advances and trends in
		  speech recognition and coding},
  year		= {1993},
  publisher	= {Springer-Verlag},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  torkkola93b,
  author	= {Torkkola, K. },
  title		= {An efficient way to learn {E}nglish grapheme-to-phoneme
		  rules automatically},
  booktitle	= {ICASSP-93. 1993 IEEE International Conference on
		  Acoustics, Speech, and Signal Processing},
  year		= {1993},
  volume	= {2},
  pages		= {199--202},
  organization	= {Inst. Dalle Molle D'Intelligence Artificielle Perceptive,
		  Martigny, Switzerland},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  torkkola94a,
  author	= {Kari Torkkola},
  title		= {New ways to use {LVQ}-codebooks together with hidden
		  {M}arkov models},
  booktitle	= {Proc. ICASSP-94, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1994},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {April},
  pages		= {401--404},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  torkkola94b,
  author	= {Kari Torkkola},
  title		= {{LVQ} as a feature transformation for {HMM}s},
  booktitle	= {Proc. NNSP'94, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1994},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {299--308},
  annote	= {application, speech recognition},
  dbinsdate	= {oldtimer}
}

@InCollection{	  torkkola95a,
  author	= {K. Torkkola and T. Kohonen},
  title		= {Speech Recognition: A Hybrid Approach},
  booktitle	= {The Handbook of Brain Theory and Neural Networks},
  editor	= {M. A. Arbib},
  year		= {1995},
  publisher	= {The MIT Press},
  address	= {Cambridge, Massachusetts},
  pages		= {907--910},
  dbinsdate	= {oldtimer}
}

@InCollection{	  torkkola97a,
  author	= {Kari Torkkola},
  title		= {{WarpNet}: \mbox{self-organizing} time warping},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {169--174},
  dbinsdate	= {oldtimer}
}

@Article{	  torma94a,
  author	= {Torma, M. },
  title		= {{K}ohonen \mbox{self-organizing} feature map and its use
		  in clustering},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1994},
  volume	= {2357},
  number	= {pt. 2},
  pages		= {830--5},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  toronen99,
  author	= {Petri T{\"o}r{\"o}nen and Mikko Kolehmainen and Garry Wong
		  and Eero Castr{\'e}n},
  title		= {Analysis of gene expression data using self-organizing
		  maps},
  journal	= {FEBS Letters},
  year		= 1999,
  volume	= 451,
  pages		= {142--146},
  dbinsdate	= {2002/1}
}

@Article{	  toshiji01a,
  author	= {Toshiji, K. and Genyo, U.},
  title		= {Fault location identification in power transformer impulse
		  test using Kohonen's self-organizing map},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-C}
		  ,
  year		= {2001},
  volume	= {121},
  pages		= {1670--5},
  abstract	= {It is useful, for objectivity and effectiveness of the
		  power transformer impulse test, to computerize its
		  diagnosis procedure and fault location identification
		  process. It is possible to recognize measured current
		  patterns using Kohonen's self-organizing map which only
		  requires a learning process of the relationship between
		  fault locations and their transfer characteristics. The
		  transfer function method which computes transfer
		  characteristics via FFT (fast Fourier transform) is used
		  for preprocessing the fault characteristics. An example of
		  applying the simple mathematical model of a transformer for
		  results simulated using the developed Matlab program is
		  reported.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  toth93c,
  author	= {G{\'{a}}bor J. T{\'{o}}th and Andr{\'{a}}s L{\H{o}}rincz},
  title		= {Genetic Algorithm with Migration on Topology Conserving
		  Maps},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {III},
  pages		= {168--171},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  toth93d,
  author	= {G{\'{a}}bor J. T{\'{o}}th and Andr{\'{a}}s L{\H{o}}rincz},
  title		= {Genetic Algorithm with Migration on Topology Conserving
		  Maps},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {605--608},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  toth93e,
  author	= {G{\'{a}}bor J. T{\'{o}}th and Tam{\'{a}}s Szak{\'{a}}cs
		  and Andr{\'{a}}s L{\H{o}}rincz},
  title		= {Simulation of Pulsed Laser Material Processing Controlled
		  by an Extended Self-Organizing {K}ohonen Feature Map},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {861},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  toth93f,
  author	= {G. J. T{\'{o}}th and T. Szakacs and A. L{\"{o}}rincz},
  title		= {Simulation of pulsed laser material processing controlled
		  by an extended \mbox{self-organizing} {K}ohonen feature
		  map},
  journal	= {Materials Science \& Engineering B (Solid-State Materials
		  for Advanced Technology)},
  year		= {1993},
  volume	= {B18},
  number	= {3},
  pages		= {281--288},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  toth93g,
  author	= {G{\'{a}}bor J. T{\'{o}}th and Tam{\'{a}}s Szak{\'{a}}cs
		  and Andr{\'{a}}s L{\H{o}}rincz},
  title		= {Simulation of Pulsed Laser Material Processing Controlled
		  by an Extended {S}elf-{O}rganizing {K}ohonen {F}eature
		  {M}ap},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {III},
  pages		= {127--130},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  tourassi95a,
  author	= {Georgia D. Tourassi and Carey E. Floyd Jr},
  title		= {Lesion Size Quantification in SPECT Using an Artificial
		  Neural Network Classification Approach},
  journal	= {Computers and Biomedical Research},
  year		= {1995},
  volume	= {28},
  number	= {3},
  month		= {June},
  pages		= {257--270},
  abstract	= {An artificial neural network (ANN) has been developed to
		  determine the size of lesions detected in single photon
		  emission computed tomographic images. The network is the
		  Learning Vector Quantizer and is trained to perform size
		  quantification based on image neighborhoods extracted
		  around the lesions. The ANN is compared to the optimal,
		  Bayesian algorithm developed to perform the same task using
		  the unreconstructed, projection data. The performance of
		  the neural network is evaluated at two different noise
		  levels. The Bayesian algorithm provides the upper bound for
		  size quantification performance against which the ANN is
		  compared. In the ideal case where the Bayesian algorithm
		  has explicit knowledge of the underlying distributions, its
		  performance is superior to that of the neural network.
		  However, in the more realistic case where the distributions
		  need to be estimated from the same learning sample the ANN
		  was trained on, the two algorithms have comparable
		  performances. Copyright 1995, 1999 Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  touzet01a,
  author	= {Touzet, C. F. and Santos, J. M.},
  title		= {Q-learning and robotics},
  booktitle	= {Simulation in Industry '2001. 13th European Simulation
		  Symposium 2001. ESS'2001. SCS Eur. BVBA, Ghent, Belgium},
  year		= {2001},
  volume	= {},
  pages		= {685--8},
  abstract	= {Because it allows the synthesis of behaviors despite the
		  absence of a robot-world interaction model, Q-learning has
		  become the most used learning algorithm for autonomous
		  robotics in applications such as obstacle avoidance, wall
		  following, go-to-the-nest, etc. This is mostly due to
		  neural-based implementations such as multilayer perceptrons
		  trained with backpropagation, or self-organizing maps. Such
		  implementations provide an efficient generalization, i.e.,
		  fast learning, and designate the critic, the reinforcement
		  function definition, as the real issue. The paper discusses
		  Q-learning for robots and Q-Kohon self organising map.},
  dbinsdate	= {2002/1}
}

@Book{		  touzet92a,
  author	= {C. Touzet},
  title		= {Reseaux de neurones artificiels: introduction au
		  connexionnisme ({A}rtificial neural nets: introduction to
		  connectionism)},
  publisher	= {EC2},
  year		= {1992},
  address	= {Nanterre, France},
  note		= {(in French)},
  annote	= {Book covers the most representative neural net models and
		  their applications to strongly connected components of
		  interest to engineers in fields such as vision{,} signal
		  processing or decision support. {T}here is a chapter and an
		  appendix of self-organizing maps. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  touzet96a,
  author	= {Touzet, C. },
  title		= {Neural reinforcement learning for an obstacle avoidance
		  behavior},
  booktitle	= {IEE Colloquium on Self Learning Robots (Digest No.
		  1996/026)},
  year		= {1996},
  pages		= {6/1--3},
  organization	= {DIAM-IUSPIM, Domaine Univ. , France},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  touzet97a,
  author	= {C. Touzet and N. Giambiasi and S. Sehad},
  title		= {Neural reinforcement learning for behavior synthesis},
  booktitle	= {Symposium on Robotics and Cybernetics. CESA '96 IMACS
		  Multiconference. Computational Engineering in Systems
		  Applications},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {A. Hameurlain and A. M. Tjoa},
  address	= {Berlin, Germany},
  pages		= {734--9},
  dbinsdate	= {oldtimer}
}

@Article{	  touzet97b,
  author	= {C. F. Touzet},
  title		= {Neural reinforcement learning for behaviour synthesis},
  journal	= {Robotics and Autonomous Systems},
  year		= {1997},
  volume	= {22},
  number	= {3--4},
  pages		= {251--81},
  dbinsdate	= {oldtimer}
}

@Book{		  touzet98a,
  author	= {Touzet, C. and Santos, J. M.},
  title		= {Reinforcement function design and bias for efficient
		  learning in mobile robots.},
  year		= {1998},
  abstract	= {The main paradigm in sub-symbolic learning robot domain is
		  the reinforcement learning method. Various techniques have
		  been developed to deal with the memorization/generalization
		  problem, demonstrating the superior ability of artificial
		  neural network implementations. In this paper, the authors
		  address the issue of designing the reinforcement so as to
		  optimize the exploration part of the learning. They also
		  present and summarize works relative to the use of bias
		  intended to achieve the effective synthesis of the desired
		  behavior. Demonstrative experiments involving a
		  self-organizing map implementation of the Q-learning and
		  real mobile robots (Nomad 200 and Khepera) in a task of
		  obstacle avoidance behavior synthesis are described. 3
		  figs., 5 tabs.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  townsend94a,
  author	= {Neil W. Townsend and Mike J. Brownlow and Lionel
		  Tarassenko},
  title		= {Radial Basis Function Networks for Mobile Robot
		  Localisation},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {II},
  pages		= {9--14},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, position estimation},
  dbinsdate	= {oldtimer}
}

@InCollection{	  trautmann94a,
  author	= {T. Trautmann and T. Denceux},
  title		= {A constructive algorithm for {SOM} applied to water
		  quality monitoring},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks},
  volume	= {4},
  publisher	= {ASME},
  year		= {1994},
  editor	= {C. H. Dagli and B. R. Fernandez and J. Ghosh and R. T. S.
		  Kumara},
  address	= {New York, NY, USA},
  pages		= {17--22},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  trautmann95a,
  author	= {T. Trautmann and T. Den{\oe}ux},
  title		= {Comparison of dynamic feature map models for environmental
		  monitoring},
  volume	= {I},
  pages		= {73--78},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  treleaven89a,
  author	= {P. C. Treleaven},
  title		= {Neurocomputers},
  journal	= {Int. J. Neurocomputing},
  volume	= {1},
  year		= {1989},
  number	= {1},
  pages		= {4--31 },
  dbinsdate	= {oldtimer}
}

@Article{	  trumper92a,
  author	= {W. Trumper},
  title		= {A neural network as a self-learning controller},
  journal	= {Automatisierungstechnik},
  year		= {1992},
  volume	= {40},
  number	= {4},
  pages		= {142--147},
  month		= {April},
  note		= {(in German)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  truong90a,
  author	= {K. K. Truong and R. M. Mersereau},
  title		= {Structural image codebooks and the \mbox{self-organizing}
		  feature map algorithm},
  booktitle	= {Proc. ICASSP-90, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1990},
  volume	= {IV},
  pages		= {2289--2292},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  truong91a,
  author	= {K. K. Truong},
  title		= {Multilayer {K}ohonen image codebooks with a logarithmic
		  search complexity},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {IV},
  pages		= {2789--2792},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tryba88b,
  author	= {V. Tryba and K. M. Marks and U. R{\"u}ckert and K. Goser},
  title		= {Selbstorganisierende Karten als lernende klassifizierende
		  Speicher},
  year		= {1988},
  booktitle	= {Tagungsband der ITG-Fachtagung},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tryba89a,
  author	= {V. Tryba and S. Metzen and K. Goser},
  title		= {Designing basic integrated circuits by
		  \mbox{self-organizing} feature maps},
  booktitle	= {Neuro-N\^{i}mes '89. Int. Workshop on Neural Networks and
		  their Applications},
  year		= {1989},
  pages		= {225--235},
  organization	= {ARC; SEE},
  publisher	= {EC2},
  address	= {Nanterre, France},
  month		= {November},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tryba90a,
  author	= {V. Tryba and H. Speckmann and K. Goser},
  title		= {A Digital Harware-Implementation of Self-Organizing
		  Feature Map as a Neural Coprocessor to a Von-{N}eumann
		  Computer},
  booktitle	= {Proc. 1st Int. Workshop on Microelectronics for Neural
		  Networks},
  year		= {1990},
  pages		= {177--186},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tryba91a,
  author	= {V. Tryba and K. Goser},
  title		= {A modified algorithm for \mbox{self-organizing} maps based
		  on the {S}chrodinger equation},
  booktitle	= {Proc. of the 2nd International Conference on
		  Microelectronics for Neural Networks},
  year		= {1991},
  editor	= {U. Ramacher and U. Ruckert and J. A. Nossek},
  pages		= {83--93},
  publisher	= {Kyril {\&} Method Verlag},
  address	= {Munich, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tryba91b,
  author	= {Viktor Tryba and Karl Goser},
  title		= {{S}elf-{O}rganizing {F}eature {M}aps for Process Control
		  in Chemistry},
  booktitle	= {Artificial Neural Networks},
  booktitles	= {Proc. ICANN},
  year		= {1991},
  pages		= {847--852},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tryba91c,
  author	= {V. Tryba and K. Goser},
  title		= {A modified Algorithm for Self-Organizing Maps based on the
		  {S}chroedinger Equation},
  booktitle	= {Proc. IWANN, Int. Workshop on Artificial Neural Networks},
  year		= {1991},
  editor	= {A. Prieto},
  pages		= {33--47},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tryba93a,
  author	= {V. Tryba and K. Goser},
  title		= {Three algorithms for searching the minimum distance in
		  \mbox{self-organizing} maps},
  booktitle	= {Digest of ESANN'93},
  year		= {1993},
  editor	= {Michel Verleysen},
  pages		= {215--220},
  publisher	= {D facto conference services},
  address	= {Brussels, Belgium},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tsai91a,
  author	= {W. K. Tsai and Z. -P. Lo and H. -M. Lee and T. Liau and R.
		  Chien and R. Yang and A. Parlos},
  title		= {A novel \mbox{self-organizing} associative memory and its
		  application to nonlinear system identification},
  booktitle	= {Proc. IJCNN-91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {II},
  pages		= {1003},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tsai99a,
  author	= {Jia Horng Tsai and Jung Hua Wang},
  title		= {Using self-creating neural network for surface
		  reconstruction},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {886--90},
  abstract	= {Surface reconstruction is a very important step in surface
		  rendering of medical virtual reality. In addition to
		  conventional methods, many researchers have employed
		  growing cell structures (GCS) neural networks to implement
		  surface reconstruction. Due to its characteristic of
		  learning vector quantization (VQ) using GCS in surface
		  reconstruction could lead to some serious problems. To
		  solve these problems, we use a hybrid network that
		  incorporates GCS and BNN to perform surface reconstruction.
		  The method is adaptive, in the sense that the regions of
		  high curvature will be represented with more and smaller
		  polygons, and the rest with less and bigger polygons. The
		  excellent topological preserving capability of GCS allows
		  us to use the curvature of topological mapping to replace
		  the curvature of original input data. Simulation results
		  have shown that the proposed hybrid network can achieve
		  better reconstruction result than does the GCS network.},
  dbinsdate	= {oldtimer}
}

@Article{	  tsang94a,
  author	= {Tsang, K. and Wei, B. W. Y. },
  title		= {A {VLSI} architecture for a real-time code book generator
		  and encoder of a vector quantizer},
  journal	= {IEEE Transactions on Very Large Scale Integration [VLSI]
		  Systems},
  year		= {1994},
  volume	= {2},
  number	= {3},
  pages		= {360--4},
  month		= {Sept},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tsao92a,
  author	= {Eric Chen-Kuo Tsao and James C. Bezdek and Nikhil R. Pal},
  title		= {Image Segmentation Using Fuzzy {LVQ} Clustering Networks},
  booktitle	= {NAFIPS'92, NASA Conf. Publication 10112},
  year		= {1992},
  volume	= {I},
  pages		= {98--107},
  publisher	= {North American Fuzzy Information Processing Society},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tsao92b,
  author	= {Tsao, E. C. -K. and Wei-Chung Lin and Chin-Tu Chen and
		  Bezdek, J. C. and Pal, N. R. },
  title		= {A neural network system for medical image understanding},
  booktitle	= {Proceedings of the 5th Florida Artificial Intelligence
		  Research Symposium},
  year		= {1992},
  editor	= {Fisherman, M. B. },
  pages		= {24--8},
  organization	= {Div. of Comput. Sci. , Univ. of West Florida, Pensacola,
		  FL, USA},
  publisher	= {Florida AI Res. Soc},
  address	= {St. Petersburg, FL, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  tsao93a,
  author	= {E. C. -K. Tsao and Wei-Chung Lin and Chin-Tu Chen},
  title		= {Constraint satisfaction neural networks for image
		  recognition},
  journal	= {Pattern Recognition},
  year		= {1993},
  volume	= {26},
  number	= {4},
  pages		= {553--567},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tsao93b,
  author	= {Eric Chen-Kuo Tsao and Hong-Yuan Liao},
  title		= {Fuzzy {K}ohonen Clustering Networks for Reducing Search
		  Space in 3-{D} Object Recognition},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {249},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  tsao94a,
  author	= {Tsao, E. Chen-Kuo and Bezdek, J. C. and Pal, N. R. },
  title		= {Fuzzy {K}ohonen clustering networks},
  journal	= {Pattern Recognition},
  year		= {1994},
  volume	= {27},
  number	= {5},
  pages		= {757--64},
  month		= {May},
  dbinsdate	= {oldtimer}
}

@Article{	  tsay99a,
  author	= {Tsay, Mu King and Shyu, Keh Hwa and Chang, Pao Chung},
  title		= {Feature transformation with generalized learning vector
		  quantization for hand-written Chinese character
		  recognition},
  journal	= {IEICE Transactions on Information and Systems E82-D 3
		  1999},
  year		= {1999},
  number	= {},
  volume	= {},
  pages		= {687--692},
  abstract	= {In this paper, the generalized learning vector
		  quantization (GLVQ) algorithm is applied to design a
		  hand-written Chinese character recognition system. The
		  system proposed herein consists of two modules, feature
		  transformation and recognizer. The feature transformation
		  module is designed to extract discriminative features to
		  enhance the recognition performance. The initial feature
		  transformation matrix is obtained by using Fisher's linear
		  discriminant (FLD) function. A template matching with
		  minimum distance criterion recognizer is used and each
		  character is represented by one reference template. These
		  reference templates and the elements of the feature
		  transformation matrix are trained by using the generalized
		  learning vector quantization algorithm. In the experiments,
		  540100 (5401 × 100) hand-written Chinese character samples
		  are used to build the recognition system and the other
		  540100 (5401 × 100) samples are used to do the open test. A
		  good performance of 92.18% accuracy is achieved by proposed
		  system.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tsay99b,
  author	= {Tsay, T. I. J. and Jyh Yeuan Chen},
  title		= {Intelligent visual control of robot manipulators},
  booktitle	= {30th International Symposium on Robotics. Celebrating the
		  30th Anniversary toward the Next Millennium. Japan Robot
		  Assoc, Tokyo, Japan},
  year		= {1999},
  volume	= {},
  pages		= {445--52},
  abstract	= {Visual sensor integrated robotic systems have been
		  extensively studied in recent years. With the help of
		  visual sensors, a robot manipulator can deal properly or
		  flexibly with charges in its environment and execute
		  intelligent tasks. The work focuses mainly on positioning a
		  robot manipulator to the target and picking it up using
		  visual information from two CCD cameras. Herein, visual
		  sensor integrated robotic systems are composed of two CCD
		  cameras and one robot manipulator. In industrial
		  applications, a manipulator has difficulty in reaching the
		  target with the assistance of cameras due to the model
		  uncertainty of cameras. In this work we apply a
		  self-organizing map algorithm to robotic systems for the
		  end-effector positioning of robot manipulators. Using this
		  algorithm allows us to achieve the nonlinear relationship
		  between image data and position command after some learning
		  steps. After the training phase,the visual sensor
		  integrated system can position the end-effector accurately.
		  Finally, a visual sensor integrated robotic system is used
		  to experimentally verify the theoretical results. For
		  industrial applications, an industrial robot manipulator is
		  positioned to an object on the conveyor which is then
		  picked up by using the visual information from two
		  cameras.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  tschichold-gurman93a,
  author	= {Nadine Tschichold-G{\"{u}}rman and Vlad G. Dabija},
  title		= {Meaning-Based Handling of Don't Care Attributes in
		  Artificial Neural Networks},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {I},
  pages		= {281--286},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tse95a,
  author	= {Peter Tse and D. D. Wang and Derek Atherton},
  title		= {Improving Learning Vector Quantization Classifier in
		  Machine Fault Diagnosis by Adding Consistency},
  volume	= {II},
  pages		= {927--931},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tse95b,
  author	= {P. Tse and D. D. Wang and Jinwu Xu},
  title		= {Classification of image texture inherited with overlapped
		  features using learning vector quantization},
  booktitle	= {Proceedings of the Second International Conference on
		  Mechatronics and Machine Vision in Practice. M/sup 2/VIP
		  `95},
  publisher	= {City Univ. Hong Kong},
  year		= {1995},
  address	= {Hong Kong},
  pages		= {286--90},
  dbinsdate	= {oldtimer}
}

@Article{	  tse96a,
  author	= {P. Tse and D. D. Wang and D. Atherton},
  title		= {Harmony theory yields robust machine fault-diagnostic
		  systems based on learning vector quantization classifiers},
  journal	= {Engineering Applications of Artificial Intelligence},
  year		= {1996},
  volume	= {9},
  number	= {5},
  pages		= {487--98},
  dbinsdate	= {oldtimer}
}

@Article{	  tsopanoglou94a,
  author	= {Tsopanoglou, A. and Mourjopoulos, J. and Kokkinakis, G.},
  title		= {Adaptation of an isolated word speech recognition system
		  to continuous speech using multisection {LVQ} codebook
		  modification and prosodic parameter transformation},
  journal	= {Speech Communication},
  year		= {1994},
  number	= {1},
  volume	= {15},
  pages		= {1--20},
  abstract	= {An improved, phoneme-based IWSR system is described, which
		  employs a robust reference data extraction procedure and
		  achieves increased recognition accuracy. Furthermore, a
		  novel method for the adaptation of the IWSR-system to
		  continuous speech is presented. The IWSR system employs a
		  multisection codebook design technique and the LVQ
		  algorithm, which provide well-defined and accurate
		  codebooks, minimize the influence of the within-word
		  coarticulation and allow the use of time-sequence
		  information at the recognition stage. The adaptation method
		  is based on modifications of the system's reference data
		  codebook using a small amount of representative continuous
		  speech data and on linear transformations of the main
		  prosodic parameters (i.e. energy and duration). Extensive
		  testing under different conditions (speaker dependent
		  versus speaker independent reference data, single versus
		  multisection codebooks, adapted versus unadapted codebooks,
		  phoneme versus word recognition accuracy, etc.) has shown
		  the efficiency of the proposed methods.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tsuji93a,
  author	= {Tsuji, T. and Ito, K. and P. Morasso},
  title		= {Learning of robot arm impedance in operational space using
		  neural networks},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks (IJCNN'93---Nagoya 25--29 october '93)},
  volume	= {1},
  year		= {1993},
  pages		= {635--638},
  abstract	= {Impedance control is one of the most effective control
		  methods for the manipulators in contact with their
		  environments. The characteristic of force and motion
		  control, however, is influenced by a desired impedance of a
		  manipulator's end-effector, which must be designed
		  according to a given task and an environment. The present
		  paper proposes a new method to regulate the impedance of
		  the end-effector through learning of neural networks. The
		  method can regulate not only stiffness and viscosity but
		  also the inertia and virtual trajectory of the end-effector
		  and can realize a smooth transition from free to contact
		  movements by regulating the impedance parameters before a
		  contact.},
  dbinsdate	= {oldtimer}
}

@Article{	  tsuji95a,
  author	= {Tsuji, T. and Morasso, P. and Shigehashi, K. and M.
		  Kaneko},
  title		= {Motion Planning for Manipulators using Artificial
		  Potential Field Approach that can Adjust Convergence Time
		  of Generated Arm Trajectory},
  journal	= {Journal of the Robotics Society of Japan},
  year		= {1995},
  volume	= {13},
  number	= {3},
  pages		= {285--290.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tsuruta01a,
  author	= {Tsuruta, N. and Yoshiki, Y. and Tobely, T. E.},
  title		= {A randomized hypercolumn model and gesture recognition},
  booktitle	= {Connectionist Models of Neurons, Learning Processes, and
		  Artificial Intelligence. 6th International Work-Conference
		  on Artificial and Natural Neural Networks, IWANN 2001.
		  Proceedings, Part I (Lecture Notes in Computer Science Vol.
		  2084). Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {235--42},
  abstract	= {Gesture recognition is an appealing tool for a natural
		  interface with computers especially for physically impaired
		  persons. In the paper, it is proposed to use a hypercolumn
		  model (HCM), which is constructed by hierarchically piling
		  up self-organizing maps (SOM), as an image recognition
		  system for gesture recognition, since the HCM allows
		  alleviating many difficulties associated with gesture
		  recognition. It is, however, required for online systems to
		  reduce the recognition time to the range of normal video
		  camera rates. To achieve this, the randomized HCM (RHCM),
		  which is derived from the HCM by replacing SOM with
		  randomized SOM, is introduced. With the RHCM algorithm, the
		  recognition time is drastically reduced without accuracy
		  deterioration. Experimental results for recognizing hand
		  gestures using RHCM are presented.},
  dbinsdate	= {2002/1}
}

@Article{	  tsuruta98a,
  author	= {N. Tsuruta and R. -I. Taniguchi and M. Amamiya},
  title		= {Hypercolumn model: a combination model of hierarchical
		  \mbox{self-organizing} maps and neocognitron for image
		  recognition},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1998},
  volume	= {J81D-II},
  number	= {10},
  pages		= {2288--300},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tu01a,
  author	= {Tu, F. and Wen, F. and Willett, P. and Pattipati, K. and
		  Jordan, E. H.},
  title		= {Signal processing and neural network toolbox and its
		  application to failure diagnosis and prognosis},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Willett, P. K. and Kirubarajan, T.},
  volume	= {4389},
  pages		= {121--132},
  organization	= {Department of Electrical Engineering, Univ. of
		  Connecticut, U-2157},
  publisher	= {},
  address	= {},
  abstract	= {Many systems are comprised of components equipped with
		  self-testing capability; however, if the system is complex
		  involving feedback and the self-testing itself may
		  occasionally be faulty, tracing faults to a single or
		  multiple causes is difficult. Moreover, many sensors are
		  incapable of reliable decision-making on their own. In such
		  cases, a signal processing front-end that can match
		  inference needs will be very helpful. The work is concerned
		  with providing an object-oriented simulation environment
		  for signal processing and neural network-based fault
		  diagnosis and prognosis. In the toolbox, we implemented a
		  wide range of spectral and statistical manipulation methods
		  such as filters, harmonic analyzers, transient detectors,
		  and multi-resolution decomposition to extract features for
		  failure events from data collected by data sensors. Then we
		  evaluated multiple learning paradigms for general
		  classification, diagnosis and prognosis. The network models
		  evaluated include Restricted Coulomb Energy (RCE) Neural
		  Network, Learning Vector Quantization (LVQ), Decision Trees
		  (C4.5), Fuzzy Adaptive Resonance Theory (Fuzzy Artmap),
		  Linear Discriminant Rule (LDR), Quadratic Discriminant Rule
		  (QDR), Radial Basis Functions (RBF), Multiple Layer
		  Perceptrons (MLP) and Single Layer Perceptrons (SLP).
		  Validation techniques, such as N-fold cross-validation and
		  bootstrap techniques, are employed for evaluating the
		  robustness of network models. The trained networks are
		  evaluated for their performance using test data on the
		  basis of percent error rates obtained via cross-validation,
		  time efficiency, generalization ability to unseen faults.
		  Finally, the usage of neural networks for the prediction of
		  residual life of turbine blades with thermal barrier
		  coatings is described and the results are shown. The neural
		  network toolbox has also been applied to fault diagnosis in
		  mixed-signal circuits.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  tu94a,
  author	= {Tu, Yaqing and Huang, Shanglian and Cheng, Xiaoping},
  title		= {Two kinds of neural network algorithms suitable for
		  fiberoptic sensing array signal processing},
  booktitle	= {PRICAI-94. Proceedings of the 3rd Pacific Rim
		  International Conference on Artificial Intelligence},
  year		= {1994},
  volume	= {1},
  pages		= {528--34},
  organization	= {Dept. of Optoelectron. Instrum. , Chongqing Univ. ,
		  China},
  publisher	= {Int. Acad. Publishers},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@Article{	  tu95a,
  author	= {Tu, Yaqing and Liu, Weihua and Huang, Shanglian},
  title		= {A smart structure state monitoring system using {OFS}
		  array and {NN} processing},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2566},
  pages		= {63--71},
  annote	= {(Advanced Imaging Technologies and Commercial Applications
		  Conf. Date: 10--12 July 1995 Conf. Loc: San Diego, CA, USA
		  Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@Article{	  tu96a,
  author	= {Yaqing Tu and Shanglian Huang},
  title		= {Two kinds of neural network algorithms suitable for fiber
		  optic sensing array signal processing},
  journal	= {Optical Engineering},
  year		= {1996},
  volume	= {35},
  number	= {8},
  pages		= {2196--202},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tucker99a,
  author	= {Tucker, C. A.},
  title		= {Self-organizing maps for time series analysis of
		  electromyographic data},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3577--80},
  abstract	= {In the present work, time series analysis is used for
		  simultaneous analysis of multiple channels of data, and to
		  define complex inter- and intra-channel features of
		  electromyographic (EMG) data for pattern classification.
		  The ability to objectively quantify differences in complex
		  patterns of EMG data has potential value for clinical and
		  research applications. In this report, an unsupervised
		  clustering neurocomputational approach, self-organizing
		  maps (SOM), was applied to the problem of time series
		  analysis of EMG data to provide a means to objectively
		  quantify differences in muscle activity patterns related to
		  differences in the underlying movement task, ambulation at
		  different velocities and cadences on a treadmill. The SOM
		  technique provided a means to discern differences between
		  LEEMG data from the different ambulation tasks. In
		  addition, observation of the weight vector associated with
		  each SOM cluster in comparison to the ensemble averaged
		  LEEMG for each condition helped determine underlying
		  task-related changes in LEEMG patterns.},
  dbinsdate	= {oldtimer}
}

@Article{	  tuckova96a,
  author	= {J. Tuckova and P. Bores},
  title		= {Influence of the number of the features with the neural
		  network function},
  journal	= {Radioengineering},
  year		= {1996},
  volume	= {5},
  number	= {1},
  pages		= {15--18},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tulkki98a,
  author	= {A. Tulkki},
  title		= {Real Estate Investment Appraisal of Buildings using SOM},
  booktitle	= {Visual Explorations in Finance with Self-Organizing Maps},
  publisher	= {Springer},
  year		= {1998},
  editor	= {G. Deboeck and T. Kohonen},
  address	= {London},
  pages		= {128--140},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tumuluri96a,
  author	= {Chaitanya Tumuluri and Chilukuri K. Mohan and Alok N.
		  Choudfary},
  title		= {{GST} Networks: Learning Emergent Spatiotemporal
		  Correlations},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1652--1394},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tung94a,
  author	= {Shin-Lun Tung and Yau-Tarng Juang},
  title		= {Modifying the adjustable weights of \mbox{self-organizing}
		  feature maps},
  booktitle	= {1994 International Symposium on Artificial Neural
		  Networks. ISANN '94. Proceedings},
  year		= {1994},
  pages		= {435--9},
  organization	= {Dept. of Electr. Eng. , Nat. Central Univ. , Chung-Li,
		  Taiwan},
  publisher	= {Nat. Cheng Kung Univ},
  address	= {Tainan, Taiwan},
  dbinsdate	= {oldtimer}
}

@InCollection{	  tung96a,
  author	= {Shin-Lun Tung and Yau-Tarng Juang and L. -Y. Lee and
		  Mei-Fang Liu},
  title		= {On weight adjustment of \mbox{self-organizing} feature
		  maps},
  booktitle	= {1996 IEEE International Conference on Systems, Man and
		  Cybernetics. Information Intelligence and Systems},
  publisher	= {Springer-Verlag},
  year		= {1996},
  volume	= {1},
  editor	= {K. H. Hohne and R. Kikinis},
  address	= {Berlin, Germany},
  pages		= {747--51},
  dbinsdate	= {oldtimer}
}

@Article{	  turhan_sayan98a,
  author	= {{Turhan Sayan}, G. and Inan, S. and Ince, T. and
		  Leblebicioglu, K.},
  title		= {Applications of artificial neural networks and genetic
		  algorithms to electromagnetic target classification},
  journal	= {The Application of Information Technologies (Computer
		  Science) to Mission Systems},
  year		= {1998},
  publisher	= {NATA Research and Technology Organization},
  address	= {Neuilly-sur-Seine, France},
  volume	= {},
  pages		= {23},
  abstract	= {This paper presents two approaches for electromagnetic
		  target classification which utilize learning,
		  self-organizing and evolutionary algorithms for data
		  processing. The first approach to be discussed is based on
		  artificial neural networks where either a feedforward
		  network (a multi-layer perceptron) or a self-organizing map
		  can be used as the main building block of the target
		  classifier that must also contain a special signal
		  processing unit for feature selection and/or feature
		  enhancement. Based on the simulation results to be
		  summarized, a modified self-organizing map supported by a
		  Wigner distribution type two-dimensional signal processing
		  unit has been found to exhibit an excellent classification
		  performance. The second target classification approach
		  outlined in this paper describes an ultra-wide band
		  classifier based on the annihilation of natural resonances
		  of radar targets. Use of genetic algorithms are found to be
		  invaluable in the design of the target-specific filters
		  characterized by special time-limited signals.},
  dbinsdate	= {oldtimer}
}

@Article{	  turhan_sayan99a,
  author	= {{Turhan Sayan}, Gonul and Leblebicioglu, Kemal and Ince,
		  Turker},
  title		= {Electromagnetic target classification using time-frequency
		  analysis and neural networks},
  journal	= {Microwave and Optical Technology Letters},
  year		= {1999},
  number	= {1},
  volume	= {21},
  pages		= {63--69},
  abstract	= {This paper demonstrates the feasibility and advantages of
		  using a self-organizing map (SOM)-type neural network
		  classifier for electromagnetic target recognition. The
		  classifier is supported by a novel feature extraction unit
		  in which the Wigner distribution (WD), a time-frequency
		  representation, is utilized for the extraction of
		  natural-resonance-related energy feature vectors from
		  scattered fields. The proposed target classification
		  technique is tested for a set of canonical targets,
		  displaying an excellent performance in terms of both
		  real-time classification speed and accuracy, even in the
		  presence of noisy data.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  turhan_sayan99b,
  author	= {{Turhan Sayan}, G. and Ince, T.},
  title		= {Neural network techniques in electromagnetic target
		  classification: a comparison study},
  booktitle	= {IEEE Antennas and Propagation Society International
		  Symposium. 1999 Digest. Held in conjunction with: USNC/URSI
		  National Radio Science Meeting.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {2222--5},
  abstract	= {The performances of a self-organizing map classifier, a
		  multilayer perceptron classifier and a conventional
		  classifier, based on the well-known principal component
		  analysis technique, are compared in classifying a group of
		  model aircraft, according to their accuracy and their
		  real-time classification speed.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  turker94a,
  author	= {Turker, M. A. and Severcan, M. },
  title		= {Intraframe coding with {DCT-VQ} for image sequence
		  compression},
  booktitle	= {7th Mediterranean Electrotechnical Conference.
		  Proceedings},
  year		= {1994},
  editor	= {Yuksel, O. },
  volume	= {1},
  pages		= {238--41},
  organization	= {Ankara Electron. Res. \& Dev. Inst. . , Turkey},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  turner92b,
  author	= {M. Turner and J. Austin and N. Allinson and P. Thompson},
  title		= {A neural network approach to recognition of structural
		  aberrations in chromosomes},
  booktitle	= {Proc. British Machine Vision Association Conf. },
  year		= {1992},
  pages		= {257--265},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  turner92c,
  author	= {M. Turner and J. Austin and N. Allinson and P. Thomson},
  title		= {An Attempt to Recognize Structural Aberrations in
		  Chromsoms using a Neural Network System},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {799--802},
  publisher	= {North-Holland},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  turner93a,
  author	= {M. Turner and J. Austin and N. M. Allinson and P.
		  Thompson},
  title		= {Chromosome location and feature extraction using neural
		  networks},
  journal	= {Image and Vision Computing},
  year		= {1993},
  number	= {4},
  volume	= {11},
  pages		= {235--239},
  month		= {May},
  abstract	= {We present a technique for initial location of scattered
		  chromosomal objects within multi-resolution images of human
		  blood cells. Kohonen Self Organising Maps learn to extract
		  salient image features in the vicinity of located objects.
		  Feature extraction is to form the first stage in a neural
		  network system applied to the problem of recognizing
		  structural aberrations in chromosomes.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  turner94a,
  author	= {M. Turner and J. Austin and N. M. Allinson and P.
		  Thomson},
  title		= {Chromsom Feature Extraction and Feature Grouping
		  incorporating {K}ohonen's {SOM}},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1087--1090},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, feature extraction, image processing},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tuv93a,
  author	= {Tuv, E. and Loizou, G. },
  title		= {HyperStore: a persistent object store for next-generation
		  applications},
  booktitle	= {ADC '94. Proceedings of the 5th Australasian Database
		  Conference},
  year		= {1993},
  editor	= {Sacks-Davis, R. },
  pages		= {213--26},
  organization	= {Dept. of Comput. Sci. , London Univ. , UK},
  publisher	= {Global Publications Services},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  tuya93a,
  author	= {Javier Tuya and Efren Arias and Luciano Sanchez and Jose
		  A. Corrales},
  title		= {Combination of Self-Organizing Maps and Multilayer
		  Perceptrons for Speaker Independent Isolated Word
		  Recognition},
  booktitle	= {Proc. IWANN'93, Int. Workshop on Neural Networks, Sitges,
		  Spain},
  year		= {1993},
  editor	= {J. Mira, J. Cabestany, A. Prieto},
  pages		= {550--555},
  publisher	= {Springer},
  address	= {Berlin},
  dbinsdate	= {oldtimer}
}

@Article{	  tyystjarvi99a,
  author	= {Tyystj\"arvi, E. and Koski, A. and Ker\"anen, M. and
		  Nevalainen, O.},
  title		= {The Kautsky curve is a built-in barcode},
  journal	= {Biophysical-Journal},
  year		= {1999},
  volume	= {77},
  pages		= {1159--67},
  abstract	= {We identify objects from their visually observable
		  morphological features. Automatic methods for identifying
		  living objects are often needed in new technology, and
		  these methods try to utilize shapes. When it comes to
		  identifying plant species automatically, machine vision is
		  difficult to implement because the shapes of different
		  plants overlap and vary greatly because of different
		  viewing angles in field conditions. In the present study we
		  show that chlorophyll a fluorescence, emitted by plant
		  leaves, carries information that can be used for the
		  identification of plant species. Transient changes in
		  fluorescence intensity when a light is turned on were
		  parameterized and then subjected to a variety of pattern
		  recognition procedures. A self-organizing map constructed
		  from the fluorescence signals was found to group the
		  signals according to the phylogenetic origins of the
		  plants. We then used three different methods of pattern
		  recognition, of which the Bayesian minimum distance
		  classifier is a parametric technique, whereas the
		  multilayer perceptron neural network and k-nearest neighbor
		  techniques are nonparametric. Of these techniques, the
		  neural network turned out to be the most powerful one for
		  identifying individual species or groups of species from
		  their fluorescence transients. The excellent recognition
		  accuracy, generally over 95%, allows us to speculate that
		  the method can be further developed into an application in
		  precision agriculture as a means of automatically
		  identifying plant species in the field.},
  dbinsdate	= {oldtimer}
}

@Article{	  tzovaras98a,
  author	= {Tzovaras, Dimitrios and Strintzis, Michael G.},
  title		= {Use of nonlinear principal component analysis and vector
		  quantization for image coding},
  journal	= {IEEE Transactions on Image Processing},
  year		= {1998},
  number	= {8},
  volume	= {7},
  pages		= {1218--1223},
  abstract	= {In the present work, the nonlinear principal component
		  analysis (NLPCA) method is combined with vector
		  quantization for the coding of images. The NLPCA is
		  realized using the backpropagation neural network (NN),
		  while vector quantization is performed using the learning
		  vector quantizer (LVQ) NN. The effects of quantization in
		  the quality of the reconstructed images are then
		  compensated by using a novel codebook vector optimization
		  procedure.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  uchino00a,
  author	= {Eiji Uchino and Masaki Kawamura and Kazuhiro Nagata},
  title		= {Dynamic Pruning Process of {SOM} by Using the Updated
		  Weight Information},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {245--250},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@InProceedings{	  uchino98a,
  author	= {Uchino, E. and Nakashima, S. and Yamakawa, T.},
  title		= {Signal classification by modified {LVQ} and fuzzy template
		  matching with special reference to gas/water pipe
		  discrimination},
  booktitle	= {Proceedings of the 5th International Conference on Soft
		  Computing and Information/Intelligent Systems.
		  Methodologies for the Conception, Design and Application of
		  Soft Computing},
  publisher	= {World Scientific},
  address	= {Singapore},
  year		= {1998},
  volume	= {2},
  pages		= {720--3},
  abstract	= {This paper proposes a signal classification method by
		  using the modified LVQ (learning vector quantization) and
		  fuzzy template matching. In this method, the feature of the
		  signal is captured efficiently by the modified LVQ, and it
		  is reflected on the weight vectors of the units on the
		  competing layers. The fuzzy templates are then constructed
		  with the use of these weight vectors. The classification of
		  the signal is performed according to the matching grade of
		  each signal to this fuzzy template. The present method is
		  actually applied to the discrimination problem between gas
		  and water pipes. The results are very promising for
		  practical use.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  uchino99a,
  author	= {Eiji Uchino and Shigeru Nakashima and Takeshi Yamakawa and
		  Yasuhiro Toyoda},
  title		= {Pattern Classification by using Self-Organized Fuzzy
		  Templates and Its Application},
  booktitle	= {15th Fuzzy System Symposium (Osaka June 2--5, 1999)},
  year		= {1999},
  pages		= {607--608},
  note		= {in Japanese},
  abstract	= {This paper proposes a signal classification method by
		  using the modified LVQ (learning vector quantization) and
		  fuzzy template matching. In this method, the feature of
		  pattern is captured efficiently by the modified LVQ, and it
		  is reflected on the weight vectors on the competing layers.
		  The fuzzy templates are then constructed with the use of
		  these weight vectors. The classification of pattern is
		  performed according to the matching grade of each pattern
		  to this fuzzy template. The present method is applied to
		  the actual classification problem of signal patterns. The
		  results are promising for practical use.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ueda93a,
  author	= {Naonori Ueda and Ryohei Nakano},
  title		= {A Competitive {\&} Selective Learning Method for Designing
		  Optimal Vector Quantizers},
  booktitle	= {Proc. of {IEEE} International Conference on Neural
		  Networks, San Francisco},
  year		= {1993},
  volume	= {III},
  pages		= {1444--1450},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  ueda94a,
  author	= {Naonori Ueda and Ryohei Nakano},
  title		= {A New Competitive Learning Approach Based on an
		  Equidistortion Principle for Designing Optimal Vector
		  Quantizers},
  journal	= {Neural Networks},
  year		= {1994},
  volume	= {7},
  number	= {8},
  pages		= {1211--1227},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch00a,
  author	= {A. Ultsch},
  title		= {The Neuro-Data-Mine},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Philipps-University of Marburg, Department of Computer
		  Science},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@TechReport{	  ultsch89a,
  author	= {A. Ultsch and H. P. Siemon},
  title		= {Exploratory Data Analysis: Using {{K}ohonen} Networks on
		  Transputers},
  institution	= {Univ. of Dortmund},
  number	= {329},
  month		= {December},
  address	= {Dortmund, Germany},
  year		= {1989 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch90a,
  author	= {A. Ultsch and H. P. Siemon},
  title		= {{K}ohonen's Self Organizing Feature Maps for Exploratory
		  Data Analysis},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  pages		= {305--308},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  month		= {},
  annote	= {Based on a toy data set, it is claimed that the SOM can't
		  form accuracte clusters and a new method, U-matrix is
		  proposed. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch91a,
  author	= {Alfred Ultsch and G{\"{u}}nter Halmans},
  title		= {Data normalization with \mbox{self-organizing} maps},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {403--406},
  abstract	= {The processing of empirical data brings forth the need to
		  transform observed distributions to a normal distribution
		  before they can be processed or analyzed furthermore. The
		  selection of a fitting transformation is typically an
		  expert task including trial and error. This paper presents
		  a method to find a suitable transformation using a
		  self-organizing feature map. The feature map's learning
		  algorithm was suitably modified in order to predict the
		  parameter for a transformation. First results show that the
		  map is able to recall the training set almost exactly and,
		  furthermore, that the model is able to generalize.
		  Experiments with unknown distributions exhibit promising
		  error rates.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch91b,
  author	= {A. Ultsch and G. Halmans and R. Mantyk},
  title		= {{CONKAT}: A Connectionist Knowledge Acquisition Tool},
  booktitle	= {Proc. Twenty-Fourth Annual Hawaii International Conference
		  on System Sciences},
  year		= {1991},
  volume	= {I},
  editor	= {Milutinovic, Veljko and Shriver, Bruce D. },
  pages		= {507--513},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {Uses SOM. },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch91c,
  author	= {A. Ultsch and R. Hannuschka and U. Hartmann M. Mandischer
		  and V. Weber},
  title		= {Optimizing Logical Proofs with Connectionist Networks},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {585--590},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch92b,
  author	= {Alfred Ultsch},
  title		= {Knowledge Acquisition with Self-Organizing Neural
		  Networks},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {735--738},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch93a,
  author	= {Alfred Ultsch},
  title		= {Self Organized Feature Maps for Monitoring and Knowledge
		  Aquisition of a Chemical Process},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {864--867},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch93b,
  author	= {Alfred Ultsch},
  title		= {Knowledge Extraction from Self-Organizing Neural
		  Networks},
  booktitle	= {Information and Classification},
  year		= {1993},
  editor	= {O. Opitz and B. Lausen and R. Klar},
  pages		= {301--306},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch93c,
  author	= {Alfred Ultsch},
  title		= {Self-Organizing Neural Networks for Visualization and
		  Classification},
  booktitle	= {Information and Classification},
  year		= {1993},
  editor	= {O. Opitz and B. Lausen and R. Klar},
  pages		= {307--313},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch94a,
  author	= {Ultsch, A. and Guimaraes, G. and Weber, V. },
  title		= {Self organizing feature maps for logical unification},
  booktitle	= {Moving Towards Expert Systems Globally in the 21st
		  Century},
  year		= {1994},
  editor	= {Liebowitz, J. },
  pages		= {1288--94},
  organization	= {Dept. of Math. , Marburg Univ. , Germany},
  publisher	= {Cognizant Commun. Corp},
  address	= {Elmsford, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ultsch95a,
  author	= {Alfred Ultsch and Dieter Korus},
  title		= {Integration of Neural Networks with Knowledge-Based
		  Systems},
  volume	= {IV},
  pages		= {1828--1833},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ultsch96a,
  author	= {A. Ultsch and G. Guimaraes and W. Schmid},
  title		= {Classification and prediction of hail using
		  \mbox{self-organizing} neural networks},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1622--7},
  abstract	= {The occurrence of severe hailstorms in Switzerland is a
		  relatively frequent event. Therefore reliable predictions
		  of hail would be extremely important for the protection of
		  human lives and property. Quite different developments of
		  hail types with a different duration time can be observed.
		  The main problem predicting hail lies in the need of a
		  prediction after a short observation period. That means,
		  that after typically 5 minutes, an estimate should be given
		  on what will happen 30 minutes in the future of a hailstorm
		  cell observed by the maximal radar reflectivity. Using
		  Self-organizing Feature Maps (SOM) for data analysis in its
		  original form leads to a misspecification of the
		  prediction. We developed an extended version of the SOM
		  such that a prediction of hail becomes possible. The main
		  idea of our approach is first to use an extended SOM for
		  the classification of a 'typical' hail development. After
		  the classification a prediction using the extended vector
		  can be made. We compared our results to classical
		  approaches, as used in the meteorology, and to a regression
		  model. For an increasing prediction time we achieved an
		  significant improvement of the prediction error in
		  comparison to the classical and statistical models.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  ultsch99a,
  author	= {A. Ultsch},
  title		= {Data mining and knowledge discovery with emergent
		  Selg-Organizing Feature Maps for multivariate time series},
  booktitle	= {Kohonen Maps},
  pages		= {33--46},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  um00a,
  author	= {Ig-Tae Um and Jong-Hei Ra and Moon-Hyun Kim},
  title		= {Comparison of clustering methods for {MLP}-based speaker
		  verification},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {2},
  pages		= {475--8},
  abstract	= {This paper compares two clustering methods: SOM, and a
		  graph-based clustering technique , for text-independent
		  speaker verification. The focus of comparison is given to
		  the distribution characteristics of representative frames
		  for each cluster, to the use of processing time of
		  clustering and MLP learning, and to verification
		  performance. Simulation results show that the graph-based
		  technique produces better verification performance than
		  SOM. Other statistics are collected to explain significant
		  difference in MLP learning time with each clustering
		  method. This experiment suggests that there is a best match
		  between a classifier and a clustering method for a given
		  application.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from internet}
}

@Article{	  umano96a,
  author	= {M. Umano and S. Fukunaka and I. Hatono and H. Tamura},
  title		= {Extraction of fuzzy rules using fuzzy neural networks with
		  forgetting},
  journal	= {Transactions of the Society of Instrument and Control
		  Engineers},
  year		= {1996},
  volume	= {32},
  number	= {3},
  pages		= {409--32},
  dbinsdate	= {oldtimer}
}

@InCollection{	  umano97a,
  author	= {M. Umano and S. Fukunaka and I. Hatono and H. Tamura},
  title		= {Acquisition of fuzzy rules using fuzzy neural networks
		  with forgetting},
  booktitle	= {1997 IEEE International Conference on Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  volume	= {4},
  editor	= {J. Mira and R. Moreno-Diaz and J. Cabestany},
  address	= {Berlin, Germany},
  pages		= {2369--73},
  dbinsdate	= {oldtimer}
}

@Article{	  umezaki01a,
  author	= {Umezaki, T. and Hirano, T. and Sato, Y.},
  title		= {Automatic synthesis of 3D facial expression},
  journal	= {Transactions-of-the-Institute-of-Electrical-Engineers-of-Japan,-Part-C}
		  ,
  year		= {2001},
  volume	= {121},
  pages		= {417--22},
  abstract	= {We explain the technique to produce the face expression
		  change using the basic picture such as the expressionless
		  face and the smiling expression produced automatically by
		  the interpolation ability of the Kohonen neural networks
		  (KNN), also known as competition networks. The 3D shape and
		  color information of the facial surface which is obtained
		  by the range finder are used for the KNN's learning. Next,
		  as the method of searching the optimal path of the face
		  expression change, the DP matching is used. It uses the
		  weight which was generated by the learning of KNN for the
		  distance calculation of the DP matching. Finally, we
		  examine the difference between the face expression taken by
		  the photograph and the face expression produced by the
		  KNN.},
  dbinsdate	= {2002/1}
}

@Book{		  unknown96b,
  author	= {},
  title		= {Neural Networks: Producing Dependable Systems. Conference
		  Proceedings. Held in Solihull (England) on November 2,
		  1995.},
  year		= {1996},
  abstract	= {Neural networks have received a lot of interest in recent
		  times, and tremendous commercial advantages have been
		  demonstrated for neural network computer systems, creating
		  pressure for their early adoption into all spheres of
		  measurement, monitoring and control. The papers in these
		  proceedings look at the problem of producing dependable
		  neural network computing systems from both theoretical and
		  practical angles. The different approaches to producing and
		  demonstrating dependable systems are discussed, and case
		  studies illustrate the state-of-the-art and draw out
		  lessons that can be applied from one area to another. The
		  papers under 'A question of dependability' covered; and
		  overview of research in certification of neural networks,
		  and dependable software and inductive programming. 'Case
		  studies: Decision support systems' looked at; neural
		  network alarm monitoring in intensive care ward, and from
		  laboratory to casualty: neural networks in the management
		  of chast pain. 'Validation for principles' to practice'
		  studied; neural networks-a principled perspective, and
		  safety critical software and validation of neural networks.
		  'Methods and process control' dealt with; dependable neural
		  network modelling and model-based control, using the
		  neurofuzzy approach to building dependable neural networks
		  systems, and using neural networks for real-world adaptive
		  control. 'Data dependability' reviewed; using the Kohonen
		  self-organizing map for novel data handling in adaptive
		  learning, and robust rejection of data in neural networks.
		  Poster paper covers; medium and short term, electric load
		  forecasting using artificial neural networks, and
		  interpretation and validation of the multi-layer perceptron
		  network on a case by case basis. A delegates list and
		  exhibitor details are also included.},
  dbinsdate	= {oldtimer}
}

@Book{		  unknown97a,
  author	= {},
  title		= {WSOM'97: Workshop on Self-Organizing Maps. Held in Espoo,
		  Finland on June 4--6, 1997. Proceedings.},
  year		= {1997},
  abstract	= {Partial Table of Contents: Self-organizing maps:
		  state-of-the-art; Learning with the parameterized
		  self-organizing map; Self-organizing maps of symbol strings
		  with application to speech recognition; SOM based density
		  function approximation for mixture density HMMs; Modeling
		  and data analysis of multisensor systems with the
		  self-organizing map: application to the electronic nose;
		  Neural detection of QAM signal with strongly nonlinear
		  receiver; Self-organization and segmentation with laterally
		  connected maps of spiking neurons; and Local subspace
		  classifier and local supspace SOM.},
  dbinsdate	= {oldtimer}
}

@Book{		  unknown97b,
  author	= {},
  title		= {Self-Organizing Feature Maps. (Latest Citations from the
		  INSPEC Database). Published Search.},
  year		= {1997},
  abstract	= {The bibliography contains citations concerning
		  implementation and assessment of Kohonen's self-organizing
		  feature map, a vector quantization technique for a wide
		  variety of signal processing applications. Self-organizing
		  algorithms, digital and parallel implementation, pattern
		  classification and recognition, and neural networks are
		  discussed. References also review applications in chemical
		  process control, financial data processing, video signal
		  processing, medical computing, bioacoustics, and brain
		  modeling. (Contains 50--250 citations and includes a
		  subject term index and title list.) (Copyright NERAC, Inc.
		  1995)},
  dbinsdate	= {oldtimer}
}

@Article{	  unknown99a,
  author	= {},
  title		= {Process Unknowns Cant Hide from {K}ohonen},
  journal	= {Solid State Technology},
  year		= {1999},
  volume	= {42},
  number	= {6},
  pages		= {28},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  unlu90a,
  author	= {Didem Unlu and Ugur Halici},
  title		= {User Identification through Neural Networks},
  booktitle	= {Artificial Intelligence Application \& Neural Networks
		  (AINN'90)},
  year		= {1990},
  pages		= {152--155},
  organization	= {The Int. Association of Science and Technology for
		  Development},
  publisher	= {ACTA Press},
  address	= {Anaheim, CA},
  annote	= {application, user identification, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  unlu90b,
  author	= {Didem Unlu and Ugur Halici},
  title		= {Neural Network Applications in User Identification},
  booktitle	= {Proc. of the Fifth Int. Symposium on Computer and
		  Information Sciences},
  year		= {1990},
  editor	= {A. Emre Harmanci and Erol Gelenbe},
  pages		= {1051--1060},
  annote	= {application, user identification, comparison},
  dbinsdate	= {oldtimer}
}

@Article{	  unneberg01a,
  author	= {Unneberg, P. and Merelo, J. J. and Chacon, P. and Moran,
		  F.},
  title		= {{SOMCD}: Method for evaluating protein secondary structure
		  from {UV} circular dichroism spectra},
  journal	= {PROTEINS-STRUCTURE FUNCTION AND GENETICS},
  year		= {2001},
  volume	= {42},
  number	= {4},
  month		= {MAR 1},
  pages		= {460--470},
  abstract	= {This article presents SOMCD, an improved method for the
		  evaluation of protein secondary structure from circular
		  dichroism spectra, based on Kohonen's self-organizing maps
		  (SOM). Protein circular dichroism (CD) spectra are used to
		  train a SOM, which arranges the spectra on a
		  two-dimensional map. Location in the map reflects the
		  secondary structure composition of a protein. With SOMCD,
		  the prediction of p-turn has been included. The number of
		  spectra in the training set has been increased, and it now
		  includes 39 protein spectra and 6 reference spectra.
		  Finally, SOM parameters have been chosen to minimize
		  distortion and make the network produce clusters with known
		  properties. Estimation results show improvements compared
		  with the previous version, K2D, which, in addition,
		  estimated only three secondary structure components; the
		  accuracy of the method is more uniform over the different
		  secondary structures. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  urushibata00a,
  author	= {Kiyohiro Urushibata and Toru Hoshida and Takamasa Suzumura
		  and Matashige Oyabu},
  title		= {Application of Self-Organizing Maps to Cluster of X-ray
		  Photoelectron Spectra of Organic Polymers},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {305--10},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@InProceedings{	  utela92a,
  author	= {Pekka Utela and Samuel Kaski and Kari Torkkola},
  title		= {Using phoneme group specific {LVQ}-codebooks with {HMM}s},
  booktitle	= {Proc. ICSLP'92 International Conference on Spoken Language
		  Processing (ICSLP 92). Banff, Alberta, Canada, October
		  12--16},
  year		= {1992},
  pages		= {551--554},
  publisher	= {Personal Publishing Ltd. },
  address	= {Edmonton, Canada},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  utela92b,
  author	= {Pekka Utela and Jari Kangas and Lea Leinonen},
  title		= {Self-Organizing Map in Acoustic Analysis and On-line
		  Visual Imaging of Voice and Articulation},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {791--794},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  utela92c,
  author	= {Pekka Utela and Kari Torkkola and Lea Leinonen and Jari
		  Kangas and Samuel Kaski and Teuvo Kohonen},
  title		= {Speech Recognition and Analysis},
  booktitle	= {Proc. SteP'92, Fifth Finnish Artificial Intelligence Conf.
		  , New Directions in Artificial Intelligence},
  year		= {1992},
  pages		= {178--182},
  volume	= {II},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  month		= {},
  annote	= {LVQ forms codebooks for HMM:s. },
  dbinsdate	= {oldtimer}
}

@Article{	  utsugi00a,
  author	= {Utsugi, Akio},
  title		= {Bayesian sampling and ensemble learning in generative
		  topographic mapping},
  journal	= {Neural Processing Letters},
  year		= {2000},
  volume	= {12},
  number	= {3},
  month		= {Dec},
  pages		= {277--290},
  organization	= {Natl Inst of Bioscience and Human-Technology},
  publisher	= {Kluwer Academic Publishers},
  address	= {Dordrecht},
  abstract	= {Generative topographic mapping (GTM) is a statistical
		  model to extract a hidden smooth manifold from data, like
		  the self-organizing map (SOM). Although a deterministic
		  search algorithm for the hyperparameters regulating the
		  smoothness of the manifold has been proposed previously, it
		  is based on approximations that are valid only on abundant
		  data. Thus, it often fails to obtain suitable estimates on
		  small data. In this paper, to improve the hyperparameter
		  search in GTM, we construct a Gibbs sampler on the model,
		  which generates random sample series following the
		  posteriors on the hyperparameters. Reliable estimates are
		  obtained from the samples. In addition, we obtain another
		  deterministic algorithm using the ensemble learning. From
		  the result of an experimental comparison of these
		  algorithms, an efficient method for reliable estimation in
		  GTM is suggested.},
  dbinsdate	= {2002/1}
}

@Article{	  utsugi94a,
  author	= {Utsugi, A. },
  title		= {Lateral interaction in {B}ayesian \mbox{self-organizing}
		  maps},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers},
  year		= {1994},
  volume	= {J77D-II},
  number	= {7},
  pages		= {1329--36},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@Article{	  utsugi96a,
  author	= {A. Utsugi},
  title		= {Topology selection for \mbox{self-organizing} maps},
  journal	= {Network: Computation in Neural Systems},
  year		= {1996},
  volume	= {7},
  number	= {4},
  pages		= {727--40},
  dbinsdate	= {oldtimer}
}

@Article{	  utsugi97a,
  author	= {Akio Utsugi},
  title		= {Hyperparameter Selection for Self-Organizing Maps},
  journal	= {Neural Computation},
  volume	= 9,
  number	= 3,
  year		= 1997,
  pages		= {623--635},
  dbinsdate	= {oldtimer}
}

@Article{	  utsugi98a,
  author	= {Akio Utsugi},
  title		= {Density Estimation by Mixture Models with Smoothing
		  Priors},
  journal	= {Neural Computation},
  year		= 1998,
  volume	= 10,
  pages		= {2115--2135},
  dbinsdate	= {oldtimer}
}

@Article{	  vahey02a,
  author	= {Vahey, M. T. and Nau, M. E. and Jagodzinski, L. L. and
		  Yalley-Ogunro, J. and Taubman, M. and Michael, N. L. and
		  Lewis, M. G.},
  title		= {Impact of viral infection on the gene expression profiles
		  of proliferating normal human peripheral blood mononuclear
		  cells infected with {HIV} type 1 {RF}},
  journal	= {AIDS RESEARCH AND HUMAN RETROVIRUSES},
  year		= {2002},
  volume	= {18},
  number	= {3},
  month		= {FEB},
  pages		= {179--192},
  abstract	= {Exploiting the power of high-density gene arrays, the
		  simultaneous expression analysis of 5600 cellular genes was
		  executed on proliferating peripheral blood mononuclear
		  cells (PBMCs) from three normal human donors that were
		  infected in vitro with the T cell tropic laboratory strain
		  of HIV-1, RF. Profiles of expressed genes were assessed at
		  1, 12, 24, 48, and 72 hr postinfection and compared with
		  those of matched uninfected PBMCs. Viral infection resulted
		  in an overall increase in the number of genes expressed
		  with peaks of expression at 1, 12, and 48 hr postinfection.
		  Functional clustering of genes whose expression level in
		  infected PBMCs varied by 2-fold or greater from levels in
		  the controls indicated that cellular activation markers,
		  proteins associated with immune cell function and with
		  transcription and translation, exhibited increased
		  expression subsequent to viral infection. Gene families
		  exhibiting a decline in gene expression were confined to
		  the 72 hr time point and included genes associated with
		  catabolism and a subset of genes involved with cell
		  signaling and synthetic pathways. Self-organizing map (SOM)
		  cluster analysis identified temporal patterns of
		  coordinated gene expression in infected PBMCs including
		  genes associated with the immune response, the
		  cytoskeleton, and ribosomal subunit structural proteins
		  required for protein synthesis.},
  dbinsdate	= {2002/1}
}

@Article{	  vailaya00a,
  author	= {Vailaya, Aditya and Jain, Anil},
  title		= {Detecting sky and vegetation in outdoor images},
  journal	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  volume	= {3972},
  number	= {},
  month		= {},
  pages		= {411--420},
  organization	= {Michigan State Univ},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {Developing semantic indices into large image databases is
		  a challenging and important problem in content-based image
		  retrieval. We address the problem of detecting objects in
		  an image based on color and texture features. Specifically,
		  we consider the following two problems of detecting sky and
		  vegetation in outdoor images. An image is divided into
		  16\times{}16 sub-blocks and color, texture, and position
		  features are extracted from every sub-block. We demonstrate
		  how a small set of codebook vectors, extracted from a
		  learning vector quantizer, can be used to estimate the
		  class-conditional densities of the low-level observed
		  features needed for the Bayesian methodology. The sky and
		  vegetation detectors have been trained on over 400 color
		  images from the Corel database. We achieve classification
		  accuracies of over 94% for both the classifiers on the
		  training data. We are currently extending our evaluation to
		  a larger database of 1,700 images.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  vailaya00b,
  author	= {Vailaya, A. and Jain, A.},
  title		= {Reject option for {VQ}-based Bayesian classification},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {2},
  pages		= {48--51},
  abstract	= {We have developed a reject option for VQ-based supervised
		  Bayesian classification to improve classification accuracy
		  by sieving out patterns that are classified with a low
		  confidence value. A small codebook extracted from a
		  learning vector quantizer (LVQ) is used to estimate the
		  class-conditional densities of the feature vector. We adapt
		  the two commonly used rejection criteria, outlier rejection
		  and ambiguity rejection, for the VQ-based Bayesian
		  classifiers. Using three high-level image classification
		  problems, we demonstrate how local rejection criteria can
		  improve the error vs. reject characteristics of our
		  classifier over a global rejection method.},
  dbinsdate	= {2002/1}
}

@Article{	  valentin01a,
  author	= {Valentin, N. and Denoeux, T.},
  title		= {A neural network-based software sensor for coagulation
		  control in a water treatment plant},
  journal	= {Intelligent-Data-Analysis},
  year		= {2001},
  volume	= {5},
  pages		= {23--39},
  abstract	= {This paper reports on the application of artificial neural
		  network techniques to coagulation control in drinking water
		  treatment plants. The coagulation process involves many
		  complex physical and chemical phenomena which are difficult
		  to model using traditional methods. The amount of coagulant
		  ensuring optimal treatment efficiency has been shown
		  experimentally to be non-linearly correlated to raw water
		  characteristics such as turbidity, conductivity, pH,
		  temperature, etc. The software sensor developed is a hybrid
		  system including a self-organising map (SOM) for sensor
		  data validation and missing data reconstruction, and a
		  multilayer perceptron (MLP) for modelling the coagulation
		  process. A key feature of the system is its ability to take
		  into account various sources of uncertainty, such as
		  atypical input data, measurement errors and limited
		  information content of the training set. Experimental
		  results with real data are presented.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  valentin99a,
  author	= {Valentin, N. and Denoeux, T. and Fotoohi, F.},
  title		= {An hybrid neural network based system for optimization of
		  coagulant dosing in a water treatment plant},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3380--5},
  abstract	= {Artificial neural network techniques are applied to the
		  control of coagulant dosing in a drinking water treatment
		  plant. Coagulant dosing rate is nonlinearly correlated to
		  raw water parameters such as turbidity, conductivity, pH,
		  temperature, etc. An important requirement of the
		  application is robustness of the system against erroneous
		  sensor measurements or unusual water characteristics. The
		  hybrid system developed includes raw data validation and
		  reconstruction based on the Kohonen self-organizing feature
		  map, and prediction of coagulant dosage using multilayer
		  perceptrons. A key feature of the system is its ability to
		  take into account various sources of uncertainty, such as a
		  typical input data, measurement errors and limited
		  information content of the training set. Experimental
		  results with real data are presented.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  valentin99b,
  author	= {Valentin, N. and Denoeux, T. and Fotoohi, F.},
  title		= {Modelling of coagulant dosage in a water treatment plant},
  booktitle	= {Engineering Applications of Neural Networks. Proceedings
		  of the 5th International Conference on Engineering
		  Applications of Neural Networks (EANN'99)},
  publisher	= {Wydawnictwo Adam Marszalek},
  address	= {Torun, Poland},
  year		= {1999},
  volume	= {},
  pages		= {165--70},
  abstract	= {Artificial neural network (ANN) techniques are applied to
		  the control of coagulant dosing in a drinking water
		  treatment plant. Coagulant dosing rate is nonlinearly
		  correlated to raw water parameters such as turbidity,
		  conductivity, pH, temperature, etc. An important
		  requirement of the application is robustness of the system
		  against erroneous sensor measurements or unusual water
		  characteristics. The hybrid system developed includes raw
		  data validation and reconstruction based on a Kohonen
		  self-organizing feature map, and prediction of coagulant
		  dosage using multilayer perceptrons. A key feature of the
		  system is its ability to take into account various sources
		  of uncertainty, such as atypical input data, measurement
		  errors and limited information content of the training set.
		  Experimental results with real data are presented.},
  dbinsdate	= {oldtimer}
}

@Article{	  valerand00a,
  author	= {Valerand, S. and Maldague, X.},
  title		= {Defect characterization in pulsed thermography: A
		  statistical method compared with Kohonen and Perceptron
		  neural networks},
  journal	= {NDT and E International},
  year		= {2000},
  volume	= {33},
  number	= {5},
  month		= {},
  pages		= {307--315},
  organization	= {Universite Laval},
  publisher	= {Elsevier Science Ltd},
  address	= {},
  abstract	= {Pulsed thermography is a popular NDE technique. In this
		  paper, a novel statistical processing method is proposed
		  and compared in term of defect detection and
		  characterization with two common neural network
		  architectures (Perceptron and Kohonen). Interest is on
		  characterization of aluminum corrosion. The statistical
		  method and neural network architectures use temperature,
		  phase and amplitude data with phase and amplitude data
		  coming from the so-called pulsed phase thermography
		  approach. The statistical method reveals interesting
		  performance over tested neural networks, especially in the
		  `interference technique,' a combined `two-step' approach:
		  detection with phase and characterization with amplitude.
		  Theory is discussed and examples of results are
		  presented.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  valkealahti92a,
  author	= {Kimmo Valkealahti and Ari Visa and Olli Simula},
  title		= {Applications of Texture Segmentation Based on
		  Self-Organizing Feature Maps},
  booktitle	= {Proc. Fifth Finnish Artificial Intelligence Conf.
		  (SteP-92): New Directions in Artificial Intelligence},
  year		= {1992},
  volume	= {2},
  pages		= {189--193},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  valkealahti93a,
  author	= {K. Valkealahti and J. Iivarinen and A. Visa and O.
		  Simula},
  title		= {An Operational Cloud Classifier Based on a Self-Organized
		  Texture Map},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1993},
  number	= {A19},
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  valkealahti95a,
  author	= {K. Valkealahti and A. Visa},
  title		= {Simulated Annealing in Feature Weighting for
		  Classification with Learning Vector Quantization},
  booktitle	= {Proc. 9th Scandinavian Conference on Image Analysis},
  year		= {1995},
  volume	= {2},
  pages		= {965--971},
  dbinsdate	= {oldtimer}
}

@InCollection{	  valkealahti96a,
  author	= {K. Valkealahti and E. Oja},
  title		= {Optimal texture feature selection for the co-occurrence
		  map},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {245--50},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  valkealahti97a,
  author	= {Valkealahti, K.},
  title		= {Texture classification with single- and double-resolution
		  co-occurrence maps},
  booktitle	= {Neural Networks in Engineering Systems. Proceedings of the
		  1997 International Conference on Engineering Applications
		  of Neural Networks},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1997},
  volume	= {1},
  pages		= {63--6},
  abstract	= {Multidimensional co-occurrence histograms, reduced by the
		  tree-structured self-organizing map, were applied to
		  texture classification. Two different transforms of gray
		  levels were used in the construction of feature vectors.
		  Single-resolution features were obtained with the discrete
		  cosine transform, and double-resolution features with the
		  discrete wavelet frame transform. The feature vectors were
		  quantized using the reference vectors of a co-occurrence
		  map, a tree-structured codebook, trained with the
		  self-organizing map algorithm. In such a tree-structured
		  codebook, the quantization error introduced by fast but
		  suboptimal hierarchical search is reduced by the
		  topological ordering of the reference vectors. Textures
		  were modeled by histograms of the best-matching reference
		  vectors. Textures were classified by matching histograms
		  using the 3-nearest-neighbor rule and the log-likelihood
		  measure of agreement. The method was compared with channel
		  histograms of the same features. The single- and
		  double-resolution features performed equally well with both
		  types of histograms. The reduced multidimensional
		  histograms provided significantly higher classification
		  accuracies than the channel histograms.},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  valkealahti98a,
  author	= {Kimmo Valkealahti},
  title		= {Analysis of Stochastic Textures with Reduced
		  Multidimensional Histograms},
  school	= {Helsinki University of Technology},
  year		= 1998,
  address	= {Espoo, Finland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  van_berg99a,
  author	= {{van den Berg}, J. and Schuemie, M.},
  title		= {Information retrieval systems using an associative
		  conceptual space},
  booktitle	= {7th European Symposium on Artificial Neural Networks.
		  ESANN'99. Proceedings},
  publisher	= {D-Facto},
  address	= {Brussels, Belgium},
  year		= {1999},
  volume	= {},
  pages		= {351--6},
  abstract	= {An Al-based retrieval system inspired by the
		  WEBSOM-algorithm is proposed. Contrary to the WEBSOM, we
		  introduce a system using only the index of every document.
		  The knowledge extraction process results into a so-called
		  associative conceptual space where the words as found in
		  the documents are organised using a Hebbian-type of
		  (un)learning. Next, `concepts' (i.e. word-clusters) are
		  identified using the SOM-algorithm. Thereupon, each
		  document is characterised by comparing the concepts found
		  in it to those present in the concept space. Applying the
		  characterisations, all documents can be clustered such that
		  semantically similar documents lie close together on a
		  self-organising map.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  van_de_wouwer96a,
  author	= {G. {Van de Wouwer} and P. Scheunders and D. {Van Dyck} and
		  M. {de Bodt} and F. Wuyts and P. H. {Van de Heyning}},
  title		= {Wavelet-{FI {LVQ} } classifier for speech analysis},
  booktitle	= {Proceedings of the 13th International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  volume	= {4},
  address	= {Los Alamitos, CA, USA},
  pages		= {214--18},
  dbinsdate	= {oldtimer}
}

@InCollection{	  van_de_wouwer96b,
  author	= {G. {Van de Wouwer} and P. Scheunders and D. {van Dyck} and
		  M. {de Bodt} and F. Wuyts and P. H. {Van de Heyning}},
  title		= {Voice classification by wavelet transform and fuzzy
		  interpreted {LVQ} networks},
  booktitle	= {IIA'96/SOCO'96. International ICSC Symposia on Intelligent
		  Industrial Automation and Soft Computing},
  publisher	= {Int. Comput. Sci. Conventions},
  year		= {1996},
  editor	= {P. G. Anderson and K. Warwick},
  address	= {Millet, Alta. , Canada},
  pages		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  van_den_bout89a,
  author	= {D. E. {Van den Bout} and T. K. {Miller III}},
  title		= {{T}{I}n{M}{A}{N}{N}: the integer {M}arkovian artificial
		  neural network},
  booktitle	= {Proc. IJCNN'89, International Joint Conference on Neural
		  Networks},
  year		= {1989},
  volume	= {II},
  pages		= {205--211},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  van_den_bout90a,
  author	= {D. E. {Van den Bout} and W. Snyder and T. K. {Miller
		  III}},
  title		= {Rapid prototyping for neural networks},
  booktitle	= {Advanced Neural Computers},
  year		= {1990},
  editor	= {R. Eckmiller},
  pages		= {219--226},
  organization	= {Air Force Office Sci. Res. ; Defence Adv. Res. Agency; et
		  al},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  van_den_bout95a,
  author	= {D. E. {Van den Bout} and T. K. {Miller III}},
  title		= {{TInMANN}: the integer {M}arkovian artificial neural
		  network for performing competitive and {K}ohonen learning},
  journal	= {Journal of Parallel and Distributed Computing},
  year		= {1995},
  volume	= {25},
  number	= {2},
  pages		= {107--14},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  van_der_herik88a,
  author	= {H. J. {van der Herik} and J. C. Scholtes and C. R. J.
		  Verhoest},
  title		= {The Design of a Parallel Knowledge-based Optical Character
		  Recognition System},
  booktitle	= {Proc. European Simulation Multiconference},
  year		= {1988},
  pages		= {350--358},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  van_der_smagt94a,
  author	= {P. {van der Smagt} and F. Groen and F. {van het
		  Groenewoud}},
  title		= {The locally linear Nested Network for robot manipulation},
  pages		= {2787--2792},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {control application, comparison},
  dbinsdate	= {oldtimer}
}

@InCollection{	  van_der_smagt95a,
  author	= {P. {van der Smagt} and F. Groen},
  title		= {Approximation with neural networks: between local and
		  global approximation},
  booktitle	= {1995 IEEE International Conference on Neural Networks
		  Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1060--4},
  dbinsdate	= {oldtimer}
}

@InCollection{	  van_deventer95a,
  author	= {J. S. J. {van Deventer} and C. Aldrich and D. W. Moolman},
  title		= {The tracking of changes in chemical processes using
		  computer vision and \mbox{self-organizing} maps},
  booktitle	= {1995 IEEE International Conference on Neural Networks
		  Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {6},
  address	= {New York, NY, USA},
  pages		= {3068--73},
  abstract	= {Frequently, chemical processes are poorly understood owing
		  to their complexity, so that phenomenological models are
		  rarely reliable predictors of their behaviour.
		  Consequently, processes such as the recovery of minerals by
		  froth flotation, the selective leaching of minerals, and
		  the recovery of dissolved chemical species by adsorption
		  onto activated, are mostly controlled in an empirical way
		  by using rules of thumb. In addition, these processes
		  involve so many independent and dependent variables that
		  the plant operator finds it difficult to visualise or even
		  observe a change in process conditions. In froth flotation
		  the operator is supposed to visually observe process
		  changes from the appearance of the froth, which is an
		  unreasonable demand under industrial conditions. An on-line
		  computer vision system based on a textural analysis of the
		  froth phase has been developed in South Africa and has been
		  in operation on an industrial flotation plant since the end
		  of 1994. This system determines textural parameters
		  on-line, and tracks the changes in process conditions via a
		  Self-Organizing Map (SOM) incorporating a Kohonen layer.
		  This monitoring system warns the operator about
		  fluctuations in reagent addition, and gives an idea of the
		  type of froth encountered. In a further example, changes in
		  the mineralogical characteristics of gold ores are
		  represented on an SOM map, based on the diagnostic leaching
		  behaviour of such ores.},
  dbinsdate	= {oldtimer}
}

@Article{	  van_deventer96a,
  author	= {J. S. J. {van Deventer} and D. W. Moolman and C. Aldrich},
  title		= {Visualisation of plant disturbances using
		  \mbox{self-organising} maps},
  journal	= {Computers \& Chemical Engineering},
  year		= {1996},
  volume	= {20},
  number	= {pt. B, suppl. is},
  pages		= {S1095--100},
  note		= {(European Symposium on Computer Aided Process Engineering
		  -6. ESCAPE-6 Conf. Date: 26--29 May 1996 Conf. Loc: Rhodes,
		  Greece)},
  abstract	= {Ill-defined processes such as the froth flotation of
		  minerals are mostly controlled in an empirical way by using
		  rules of thumb. These processes involve so many variables
		  that the plant operator finds it difficult to visualise or
		  even observe a change in process conditions. In froth
		  flotation the operator is supposed to visually observe
		  process changes from the appearance of the froth, which is
		  an unreasonable demand under industrial conditions. An
		  on-line computer vision system based on a textural analysis
		  of the froth phase has been developed in South Africa and
		  has been in operation on two industrial plants since early
		  1995. Textural parameters are determined on-line, and
		  disturbances in process conditions, such as a change in
		  reagent addition or froth depth, are visualised via a
		  Self-Organizing Map (SOM) neural net.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  van_gils93a,
  author	= {M. J. {van Gils} and P. J. M. Cluitsman},
  title		= {Assessing the Latence of Peak Pa in Auditory Evoked
		  Potential using Neural Networks},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {1015},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Book{		  van_hulle00a,
  author	= {Marc M. {van Hulle}},
  title		= {Faithful Representations and Topographic Maps From
		  Distortion- to Information-based Self-organization},
  publisher	= {J. Wiley \& Sons, Inc.},
  year		= {2000},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle00b,
  author	= {{Van Hulle}, Marc M.},
  title		= {Monitoring the formation of kernel-based topographic
		  maps},
  journal	= {Neural Networks for Signal Processing---Proceedings of the
		  IEEE Workshop},
  year		= {2000},
  volume	= {1},
  number	= {},
  month		= {},
  pages		= {241--250},
  organization	= {Katholieke Universiteit Leuven},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Topographic maps have recently attracted the attention of
		  the data mining community since they can be used for
		  representing and visualizing multidimensional data. For
		  applications like these, it is crucial that the maps are
		  free of topological defects. We introduce a new algorithm
		  for monitoring the degree of topology preservation of
		  kernel-based maps during learning. The algorithm is applied
		  to a synthetic example in this article, and a large,
		  real-world example in our companion article.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  van_hulle01a,
  author	= {M. M. {Van Hulle}},
  title		= {Towards an information-theoretic approach to kernel-based
		  topographic map formation},
  booktitle	= {Advances in Self-Organising Maps},
  crossref	= {},
  key		= {},
  pages		= {1--6},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {Springer},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  van_hulle93a,
  author	= {Marc M. {Van Hulle} and Dominique Martinez},
  title		= {On an Unsupervised Learning Rule for Scalar Quantization
		  following the Maximum Entropy Principle},
  journal	= {Neural Computation},
  year		= {1993},
  volume	= {5},
  number	= {6},
  pages		= {939--953},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  van_hulle95a,
  author	= {Marc M. {Van Hulle}},
  title		= {Globally-Ordered Topology-Preserving Maps Achieved with a
		  Learning Rule Performing Local Weight Updates Only},
  booktitle	= {Proc. NNSP'95, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1995},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {95--104},
  annote	= {SOM modification},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle96a,
  author	= {{van Hulle}, Marc M},
  title		= {Topographic map formation by maximizing unconditional
		  entropy: a plausible strategy for 'on-line' unsupervised
		  competitive learning and nonparametric density estimation},
  journal	= {IEEE Transactions on Neural Networks },
  year		= {1996},
  number	= {5},
  volume	= {7},
  pages		= {1299--1305},
  abstract	= {An unsupervised competitive learning rule, called the
		  vectorial boundary adaptation rule (VBAR), is introduced
		  for topographic map formation. Since VBAR is aimed at
		  producing an equiprobable quantization of the input space,
		  it yields a nonparametric model of the input probability
		  density function. Furthermore, since equiprobable
		  quantization is equivalent to unconditional entropy
		  maximization, we will argue that this is a plausible
		  strategy for maximizing mutual information (Shannon
		  information rate) in the case of 'on-line' learning. We
		  will use mutual information as a tool for comparing the
		  performance of our rule with Kohonen's self-organizing
		  (feature) map algorithm.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  van_hulle96b,
  author	= {M. M. {Van Hulle}},
  title		= {Nonparametric density estimation and regression achieved
		  with a learning rule for equiprobabilistic topographic map
		  formation},
  booktitle	= {Neural Networks for Signal Processing VI. Proceedings of
		  the 1996 IEEE Signal Processing Society Workshop},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {S. Usui and Y. Tohkura and S. Katagiri and E. Wilson},
  address	= {New York, NY, USA},
  pages		= {33--41},
  abstract	= {An 'on-line' learning rule, called the Vectorial Boundary
		  Adaptation Rule (VBAR), is proposed for topographic map
		  formation. Since VBAR is aimed at achieving an
		  equiprobabilistic quantization of the input space, the
		  weight density at convergence will the proportional to the
		  input density. In this way, the converged map yields a
		  nonparametric model of the input density. We will use an
		  information-theoretic measure (mutual information) to
		  assess and compare the performance of VBAR with Kohonen's
		  SOM algorithm. Finally, we will show that topographic map
		  formation with VBAR in 'batch' mode is equivalent to
		  statistical kernel smoothing (nonparametric regression).},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle96c,
  author	= {M. M. {Van Hulle}},
  title		= {Combining topographic map formation with projection
		  pursuit learning for nonparametric regression analysis},
  journal	= {Neural Processing Letters},
  year		= {1996},
  volume	= {4},
  number	= {2},
  pages		= {97--105},
  abstract	= {The original Self-Organizing Map (SOM) algorithm is known
		  to perform poorly on regression problems due to the
		  occurrence of nonfunctional mappings. Recently, we have
		  introduced an unsupervised learning rule, called the
		  Maximum Entropy learning Rule (MER), which performs
		  topographic map formation without using a neighborhood
		  function. In the present paper, MER is extended with a
		  neighborhood function and applied to nonparametric
		  projection pursuit regression. The extended rule, called
		  eMER, alleviates the occurrence of nonfunctional mappings.
		  The performance of our regression procedure is quantified
		  and compared to other neural network-based parametric and
		  nonparametric regression procedures.},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle97a,
  author	= {M. M. {van Hulle}},
  title		= {The formation of topographic maps that maximize the
		  average mutual information of the output responses to
		  noiseless input signals},
  journal	= {Neural Computation},
  year		= {1997},
  volume	= {9},
  number	= {3},
  pages		= {595--606},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle97b,
  author	= {M. M. {van Hulle}},
  title		= {Topology-preserving map formation achieved with a purely
		  local unsupervised competitive learning rule},
  journal	= {Neural Networks},
  year		= {1997},
  volume	= {10},
  number	= {3},
  pages		= {431--446},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle97c,
  author	= {M. M. {van Hulle}},
  title		= {Nonparametric density estimation and regression achieved
		  with topographic maps maximizing the information-theoretic
		  entropy of their outputs},
  journal	= {Biological Cybernetics},
  year		= {1997},
  volume	= {77},
  pages		= {49--61},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle97d,
  author	= {M. M. {van Hulle}},
  title		= {Nonparametric regression analysis achieved with
		  topographic maps developed in combination with projection
		  pursuit learning: An application to density estimation and
		  adaptive filtering of grey scale images},
  journal	= {IEEE Transactions on Signal Processing, Special issue on
		  Neural Network Applications to Signal Processing},
  year		= {1997},
  volume	= {45},
  number	= {11},
  pages		= {2663--2672},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle98a,
  author	= {M. M. {van Hulle}},
  title		= {Nonparametric Regression Modeling with Equiprobable
		  Topographic Maps and Projection Pursuit Learning with
		  Application to {PET} Image Processing},
  journal	= {Journal of VLSI Signal Processing Systems for Signal,
		  Image, and Video Technology. Special Issue on Applications
		  of Neural Networks in Biomedical Image Processing},
  year		= {1998},
  volume	= {18},
  pages		= {275--285},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle98b,
  author	= {M. M. {van Hulle}},
  title		= {Kernel-based equiprobabilistic topographic map formation},
  journal	= {Neural Computation},
  year		= {1998},
  volume	= {10},
  number	= {7},
  pages		= {1847--1871},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  van_hulle98c,
  author	= {M. M. {van Hulle}},
  title		= {Clustering with kernel-based equiprobabilistic topographic
		  maps},
  booktitle	= {Proceedings of the IEEE Workshop on Neural Networks for
		  Signal Processing},
  year		= {1998},
  address	= {Cambridge, UK},
  pages		= {204--213},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  van_hulle98d,
  author	= {M. M. {van Hulle}},
  title		= {Faithful representations with topographic maps},
  booktitle	= {Proceedings of Neural Networks---Networks for Signal
		  Processing},
  year		= {1998},
  address	= {Cambridge, UK},
  pages		= {204--213},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle99a,
  author	= {{van Hulle}, M. M.},
  title		= {Faithful representations with topographic maps},
  journal	= {Neural Networks},
  year		= {1999},
  number	= {6},
  volume	= {12},
  pages		= {803--823},
  abstract	= {Topographic map algorithms that are aimed at building
		  `faithful representations' also yield maps that transfer
		  the maximum amount of information available about the
		  distribution from which they receive input. The weight
		  density (magnification factor) of these maps is
		  proportional to the input density, or the neurons of these
		  maps have an equal probability to be active
		  (equiprobabilistic map). As MSE minimization is not
		  compatible with equiprobabilistic map formation in general,
		  a number of heuristics have been devised in order to
		  compensate for this discrepancy in competitive learning
		  schemes, e.g. by adding a `conscience' to the neurons'
		  firing behavior. However, rather than minimizing a modified
		  MSE criterion, we introduce a new unsupervised competitive
		  learning rule, called the kernel-based Maximum Entropy
		  learning Rule (kMER), for topographic map formation, that
		  optimizes an information-theoretic criterion directly. To
		  each neuron a radially symmetric kernel is associated, with
		  a given center and radius, and the two are updated in such
		  a way that the (unconditional) information-theoretic
		  entropy of the neurons' outputs is maximized. We review a
		  number of competitive learning rules for building
		  equiprobabilistic maps. As benchmark tests for the
		  faithfulness of the representations, we consider two types
		  of distributions and compare the performances of these
		  rules and kMER, for batch and incremental learning. As a
		  first example application, we consider non-parametric
		  density estimation where the maps are used for generating
		  `pilot' estimates in kernel-based density estimation. The
		  second application we envisage for kMER is `on-line'
		  adaptive filtering of speech signals, using Gabor functions
		  as wavelet filters. The topographic feature maps that are
		  developed in this way differ in several respects from those
		  obtained with Kohonen's Adaptive-Subspace SOM algorithm.},
  dbinsdate	= {oldtimer}
}

@Article{	  van_hulle99b,
  author	= {M. M. {van Hulle}},
  title		= {Density-based clustering with topographic maps},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1999},
  volume	= {10},
  number	= {1},
  pages		= {204--207},
  dbinsdate	= {oldtimer}
}

@Article{	  van_laerhoven00a,
  author	= {{Van Laerhoven}, Kristof and Cakmakci, Ozan},
  title		= {What shall we teach our pants?},
  journal	= {International Symposium on Wearable Computers, Digest of
		  Papers},
  year		= {2000},
  volume	= {},
  number	= {},
  month		= {},
  pages		= {77--83},
  organization	= {Starlab Research},
  publisher	= {IEEE Comp Soc},
  address	= {Los Alamitos, CA},
  abstract	= {If a wearable device can register what the wearer is
		  currently doing, it can anticipate and adjust its behavior
		  to avoid redundant interaction with the user. However, the
		  relevance and properties of the activities that should be
		  recognized depend on both the application and the user.
		  This requires an adaptive recognition of the activities
		  where the user, instead of the designer, can teach the
		  device what he/she is doing. As a case study we connected a
		  pair of pants with accelerometers to a laptop to interpret
		  the raw sensor data. Using a combination of machine
		  learning techniques such as Kohonen maps and probabilistic
		  models, we build a system that is able to learn activities
		  while requiring minimal user attention.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  van_laerhoven01a,
  author	= {{Van Laerhoven}, K.},
  title		= {Combining the Self-Organizing Map and k-means clustering
		  for on-line classification of sensor data},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {464--469},
  abstract	= {Many devices, like mobile phones, use contextual profiles
		  like "in the car" or "in a meeting" to quickly switch
		  between behaviors. Achieving automatic context detection,
		  usually by analysis of small hardware sensors, is a
		  fundamental problem in human-computer interaction. However,
		  mapping the sensor data to a context is a difficult problem
		  involving near real-time classification and training of
		  patterns out of noisy sensor signals. This paper proposes
		  an adaptive approach that uses a Kohanen Self-Organizing
		  Map, augmented with on-line k-means clustering for
		  classification of the incoming sensor data. Over-writing of
		  prototypes on the map, especially during the untangling
		  phase of the Self-Organizing Map, is avoided by a refined
		  k-means clustering of labeled input vectors.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  van_osdol95a,
  author	= {William W. {van Osdol} and Timothy G. Myers and Kenneth D.
		  Paull and Kurt W. Kohn and John N. Weinstein},
  title		= {The {K}ohonen {S}elf-{O}rganizing {M}ap Applied to In
		  Vitro Screening Data for Chemotherapeutic Agents},
  volume	= {II},
  pages		= {762--766},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@Article{	  van_riet91a,
  author	= {R. W. M. {Van Riet} and P. C. Duives},
  title		= {Artificial neural networks: an introduction},
  journal	= {Informatie},
  year		= {1991},
  volume	= {33},
  number	= {6},
  pages		= {368--375},
  month		= {June},
  note		= {(in Dutch)},
  x		= {An account is given, starting from . . . models suggested
		  by Kohonen and Hopfield. . . . },
  dbinsdate	= {oldtimer}
}

@TechReport{	  van_velzen92a,
  author	= {G. A. {van Velzen}},
  title		= {Instabilities in {K}ohonen's Self-Organizing Feature Map},
  institution	= {Utrecht Biophysics Res. Institute},
  year		= {1992},
  number	= {UBI-T-92. MF-077},
  address	= {Utrecht, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  van_velzen94a,
  author	= {{van Velzen}, G. A. },
  title		= {Instabilities in {K}ohonen's \mbox{self-organizing}
		  feature map},
  journal	= {Journal of Physics A [Mathematical and General]},
  year		= {1994},
  volume	= {27},
  number	= {5},
  pages		= {1665--81},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  vanbiesen98a,
  author	= {Vanbiesen,W. and Sieben,G. and Lameire,N. and Vanholder,R.
		  },
  title		= {Application of {K}ohonen Neural Networks for the Non
		  Morphological Distinction Between Glomerular and Tubular
		  Renal Disease},
  journal	= {Nephrol Dialysis Transplant },
  year		= {1998},
  pages		= {59--66},
  volume	= {13},
  dbinsdate	= {oldtimer}
}

@Article{	  vanderheyden00a,
  author	= {Vanderheyden, Y. and Vankeerberghen, P. and Novic, M. and
		  Zupan, J. and Massart, D.},
  title		= {The Application of {K}ohonen Neural Networks to Diagnose
		  Calibration Problems in Atomic-Absorption Spectrometry},
  journal	= {Talanta},
  year		= {2000},
  volume	= {51},
  number	= {3},
  pages		= {455--466},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vapola94a,
  author	= {Mauri Vapola and Olli Simula and Teuvo Kohonen and Pekka
		  Meril{\"{a}}inen},
  title		= {Representation and Identification of Fault Conditions of
		  an Anaesthesia System by Means of the {S}elf-{O}rganizing
		  {M}ap},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {350--353},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, monitoring},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vapola94b,
  author	= {Mauri Vapola and Olli Simula and Teuvo Kohonen and Pekka
		  Meril{\"{a}}inen},
  title		= {Monitoring of an Anaesthesia System Using Self-Organizing
		  Maps},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conf. Proc. of Finnish Artificial Intelligence Society},
  pages		= {55--58},
  booktitle	= {Proc. Conf. on Artificial Intelligence Res. in Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {application, monitoring, visualization},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  varfis92a,
  author	= {A. Varfis and C. Versino},
  title		= {Selecting Reliable {K}ohonen Maps for Data Analysis},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1583--1586},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  varfis92b,
  author	= {A. Varfis and C. Versino},
  title		= {Clustering of Socio-Economic Data with {K}ohonen Maps},
  journal	= {Neural Network World},
  year		= {1992},
  volume	= {2},
  number	= {6},
  pages		= {813--834},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  varfis93a,
  author	= {A. Varfis and C. Versino},
  title		= {An Intuitive Characterization for the Reference Vectors of
		  a {K}ohonen Map},
  year		= {1993},
  booktitle	= {Proc. ESANN'93, European Symposium on Artificial Neural
		  Networks},
  editor	= {Michel Verleysen},
  publisher	= {D Facto},
  pages		= {229--234},
  address	= {Brussels, Belgium},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  varfis93b,
  author	= {Aristide Varfis},
  title		= {On the use of two traditional statistical techniques to
		  improve the readibility of {{K}ohonen Maps}},
  booktitle	= {Proc. of NATO ASI workshop on Statistics and Neural
		  Networks},
  year		= {1993},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  varjani95a,
  author	= {Varjani, A. Y. and Doulai, P. },
  title		= {Neural network versus time series methods for short-term
		  load forecasting},
  booktitle	= {IPEC '95. Proceedings of the International Power
		  Engineering Conference},
  year		= {1995},
  volume	= {2},
  pages		= {672--7},
  organization	= {Wollongong Univ. , NSW, Australia},
  publisher	= {Nanyang Technol. Univ},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  varsta00a,
  author	= {Varsta, M. and Heikkonen, J. and Lampinen, J.},
  title		= {Analytical comparison of the temporal Kohonen map and the
		  recurrent self organizing map},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {273--80},
  abstract	= {The basic SOM is indifferent to the ordering of the input
		  patterns. Real data, however, is often sequential in nature
		  thus the context of a pattern may significantly influence
		  its correct interpretation. One simple SOM model that takes
		  the context of a pattern into account is the temporal
		  Kohonen map (TKM), which was modified into the recurrent
		  self organizing map (RSOM). We show analytically and with
		  experiments that the RSOM is a significant improvement over
		  the TKM because the RSOM model allows simple derivation of
		  a consistent update rule.},
  dbinsdate	= {2002/1}
}

@Article{	  varsta01a,
  author	= {Varsta, M. and Heikkonen, J. and Lampinen, J. and Millan,
		  J. D. R.},
  title		= {Temporal Kohonen map and the recurrent self-organizing
		  map: Analytical and experimental comparison},
  journal	= {Neural Processing Letters},
  year		= {2001},
  volume	= {13},
  number	= {3},
  month		= {June },
  pages		= {237--251},
  organization	= {Lab. of Computational Engineering, Helsinki University of
		  Technology},
  publisher	= {},
  address	= {},
  abstract	= {This paper compares two Self-Organizing Map (SOM) based
		  models for temporal sequence processing (TSP) both
		  analytically and experimentally. These models, Temporal
		  Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM),
		  incorporate leaky integrator memory to preserve the
		  temporal context of the input signals. The learning and the
		  convergence properties of the TKM and RSOM are studied and
		  we show analytically that the RSOM is a significant
		  improvement over the TKM, because the RSOM allows simple
		  derivation of a consistent learning rule. The results of
		  the analysis are demonstrated with experiments.},
  dbinsdate	= {2002/1}
}

@InCollection{	  varsta96a,
  author	= {M. Varsta and P. Koikkalainen},
  title		= {Surface modeling and robot path generation using
		  self-organization},
  booktitle	= {Proceedings of the 13th International Conference on
		  Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  year		= {1996},
  volume	= {4},
  address	= {Los Alamitos, CA, USA},
  pages		= {30--4},
  dbinsdate	= {oldtimer}
}

@InCollection{	  varsta97a,
  author	= {M. Varsta and J. {del R. Millan} and J. Heikkonen},
  title		= {A recurrent \mbox{self-organizing} map for temporal
		  sequence processing},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {421--6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  varsta97b,
  author	= {Markus Varsta and Jukka Heikkonen and Jose {del R.
		  Millan}},
  title		= {Context learning with the self organizing map},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {197--202},
  abstract	= {In this paper, a Recurrent Self-Organizing Map (RSOM)
		  algorithm is proposed for temporal sequence processing. The
		  experimental results in the paper demonstrate that the RSOM
		  is able to learn and distinguish temporal sequences, and
		  that the RSOM algorithm can be utilized, for instance, in
		  electroencephalogram (EEG) based epileptic activity
		  detection.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  varsta97c,
  author	= {Varsta, M. and Heikkonen, J. and {Del R. Millan}, J.},
  title		= {Epileptic activity detection in {EEG} with neural
		  networks},
  booktitle	= {Neural Networks in Engineering Systems. Proceedings of the
		  1997 International Conference on Engineering Applications
		  of Neural Networks},
  publisher	= {Systems Engineering Association, Turku, Finland},
  year		= {1997},
  volume	= {1},
  pages		= {179--86},
  abstract	= {The electroencephalogram (EEG) is an important clinical
		  tool for diagnosing, monitoring, and managing neurological
		  disorders related to epilepsy. Neural networks provide
		  intriguing possibilities for the analysis of the EEG. We
		  propose a neural network-based system to detect epileptic
		  activity. The system comprises three main components:
		  feature extraction, feature quantization and
		  classification. Key components in the proposed approach are
		  the self organizing maps (SOM) used to quantize feature
		  vectors and the multilayer perceptron (MLP) network used to
		  classify the quantized vectors. We performed tests with
		  three sets of features: Fourier spectral energy features,
		  wavelet energy features, and Haralick's co-occurrence
		  features. Over 96% of the epileptic activity was correctly
		  identified with wavelet and Fourier features and with
		  Haralick features the detection rate was in excess of 99%.
		  Though roughly 95% of the normal activity was also
		  correctly identified room for improvement still exists.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  varsta98a,
  author	= {Varsta, M. and Heikkonen, J. and Lampinen, J. and {del R.
		  Millan}, J.},
  title		= {On the Convergence Properties of the Temporal {K}ohonen
		  Map and the Recurrent Self-Organizing Map},
  booktitle	= {Proceedings of ICANN98, the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer},
  year		= 1998,
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= 2,
  address	= {London},
  pages		= {687--692},
  abstract	= {Compares two self-organizing map (SOM) based approaches
		  for temporal sequence processing: The recurrent
		  self-organizing map (RSOM) and temporal Kohonen map (TKM).
		  The convergence properties of these algorithms are studied,
		  and their difference in learning is emphasized both
		  theoretically and with simulations. The results show that
		  RSOM is superior over TKM.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vassilas95a,
  author	= {Vassilas, N. and Thiran, P. and Ienne, P. },
  title		= {How to modify {K}ohonen`s \mbox{self-organising} feature
		  maps for an efficient digital parallel implementation},
  booktitle	= {Fourth International Conference on `Artificial Neural
		  Networks`},
  year		= {1995},
  pages		= {86--91},
  organization	= {Nat. Res. Center Demokritos, Greece},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  vassilas96a,
  author	= {Nikolaos Vassilas and Patrick Thiran},
  title		= {On Modifications of {K}ohonen's Feature Map Algorithm for
		  an Efficient Parallel Implementation},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1390--1394},
  dbinsdate	= {oldtimer}
}

@Article{	  vassilas96b,
  author	= {Vassilas, Nikolaos and Thiran, Patrick and Ienne, Paolo},
  title		= {Modifications of {K}ohonen's feature map algorithm for an
		  efficient parallel implementation},
  journal	= {IEEE International Conference on Neural Networks},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1996},
  number	= {},
  volume	= {2},
  pages		= {932--937},
  abstract	= {Two new variants of Kohonen's self-organizing feature maps
		  based on batch processing are presented in this work. The
		  purpose is to make available a finer grain of parallelism
		  to be used in massively parallel systems. Ordering and
		  convergence to asymptotic values for 1-D maps and 1-D
		  continuous input and weight spaces are proved for both
		  variants. Simulations on uniform 2-D data using 1-D and 2-D
		  maps as well as simulations on speech 12-D data using 2-D
		  maps are also presented to back the theoretical results.},
  dbinsdate	= {oldtimer}
}

@Article{	  vassilas97a,
  author	= {Vassilas, N. and Charou, E. and Varoufakis, S.},
  title		= {Fast and efficient land-cover classification of
		  multispectral remote sensing data using artificial neural
		  network techniques},
  journal	= {International Conference on Digital Signal Processing.
		  DSP},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1997},
  number	= {},
  volume	= {2},
  pages		= {995--998},
  abstract	= {A time and memory efficient methodology for supervised and
		  unsupervised land-over classification of multi-spectral
		  remote sensing (MRS) data based on artificial neural
		  network (ANN) techniques is presented. The proposed
		  methodology first performs a vector quantization (VQ) using
		  the self-organizing maps (SOM) algorithm to compress the
		  MRS data followed by either efficient clustering and
		  automatic classification or, when training sets are
		  available, by a forced reduction of the training set sized
		  induced by vector quantization resulting to a faster
		  training of the supervised ANN algorithms.},
  dbinsdate	= {oldtimer}
}

@Article{	  vassilas98a,
  author	= {Vassilas, Nikolaos},
  title		= {Theoretical analysis of the batch variant of the
		  \mbox{self-organizing} feature map algorithm for 1-{D}
		  networks mapping a continuous 1-{D} input space},
  journal	= {International Journal of Computer Mathematics},
  year		= {1998},
  number	= {1},
  volume	= {67},
  pages		= {77--103},
  abstract	= {This work investigates the batch variant of Kohonen's
		  self-organizing feature map (SOFM) algorithm both
		  analytically and with simulations. In this algorithm, the
		  winning neurons as well as the weight updates are computed
		  in batch mode (epoch mode). It is shown that for 1-D maps
		  and 1-D continuous input and weight spaces the strictly
		  increasing or decreasing weight configurations form
		  absorbing classes provided certain conditions on the
		  parameters are satisfied. Ordering of the maps, convergence
		  in distribution and asymptotic convergence are also proved
		  analytically. Finally, simulations and comparisons with the
		  original Kohonen algorithm on 1-D and 2-D maps are provided
		  and are found to be in complete agreement with the
		  theoretical results.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  veelenturf92a,
  author	= {L. P. J. Veelenturf},
  title		= {Representation of Spoken Words in a Self-Organizing Neural
		  Net},
  booktitle	= {Twente Workshop on Language Technology 3: {C}onnectionism
		  and Natural Language Processing},
  year		= {1992},
  editor	= {Marc F. J. Drossaers, Anton Nijholt},
  pages		= {1--4},
  publisher	= {Department of Computer Science, University of Twente},
  address	= {Enschede, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  velay93a,
  author	= {J. L. Velay and J. C. Gilhodes and B. Ans and Y. Coiton},
  title		= {A Neural Network Model for Motor Shapes Learning and
		  Programming},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {51--54},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vellido00a,
  author	= {Vellido, A. and Lisboa, P. J. G. and Meehan, K.},
  title		= {Segmenting the e-commerce market using the Generative
		  Topographic Mapping},
  booktitle	= {MICAI 2000: ADVANCES IN ARTIFICIAL INTELLIGENCE,
		  PROCEEDINGS},
  year		= {2000},
  pages		= {470--481},
  abstract	= {The neural network-based Generative Topographic Mapping
		  (GTM) (Bishop et al. 1998a, 1998b) is a statistically sound
		  alternative to the well-known Self Organizing Map (Kohonen
		  1982, 1995). In this paper we propose the GTM as a
		  principled model for cluster-based market segmentation and
		  data visualization. It has the capability to define, using
		  Bayes' theorem, a posterior probability of cluster/segment
		  membership for each individual in the data sample. This, in
		  turn, enables the GTM to be used to perform segmentation to
		  different levels of detail or granularity, encompassing
		  aggregate segmentation and one-to-one micro-segmentation.
		  The definition of that posterior probability also makes the
		  GTM a tool for fuzzy clustering/segmentation. The
		  capabilities of the model are illustrated by a segmentation
		  case study using real-world data of Internet users opinions
		  on business-to-consumer electronic commerce.},
  dbinsdate	= {2002/1}
}

@Article{	  vellido00b,
  author	= {Vellido, A. and Lisboa, P. J. G. and Meehan, K.},
  title		= {The generative topographic mapping as a principal model
		  for data visualization and market segmentation: an
		  electronic commerce case study},
  journal	= {International-Journal-of-Computers,-Systems-and-Signals},
  year		= {2000},
  volume	= {1},
  pages		= {119--38},
  abstract	= {The process of extracting knowledge from data involves the
		  discovery of patterns of interest which may be implicit,
		  for instance, in specific clusters of data points. In the
		  context of Internet retailing, finding clusters of typical
		  consumer types is among the most important uses of data
		  mining techniques. Cluster-based market segmentation
		  models, grounded on surveys of customer opinion, can give
		  the online retailer a competitive edge, forming the basis
		  for effective targeting and enabling the redirection of
		  made-to-measure content towards the customer. The
		  generative topographic mapping (GTM) is proposed as a
		  statistically principled technique for cluster-based market
		  segmentation. In this nonlinear latent variable model, a
		  posterior probability of cluster membership can be defined
		  for each individual, providing a robust framework for the
		  visualization of high dimensional data and the segmentation
		  to different levels of granularity. The advantages of the
		  GTM over the well-known self-organizing map (SOM), to which
		  it is an alternative, are described and this new model is
		  applied in a business-to-consumer e-commerce case study. In
		  addition, an entropy-based measure is defined to quantify
		  the information content of the GTM unsupervised maps about
		  an externally imposed class label.},
  dbinsdate	= {2002/1}
}

@Article{	  vellido99a,
  author	= {Vellido, A. and Lisboa, P. J. G. and Meehan, K.},
  title		= {Segmentation of the on-line shopping market using neural
		  networks},
  journal	= {Expert Systems with Applications},
  year		= {1999},
  volume	= {17},
  pages		= {303--14},
  abstract	= {The characterization and analysis of on-line customers'
		  needs and expectations, regarding the Internet as a new
		  marketing channel, is considered a prerequisite to the
		  realization of the expected growth of the consumer-oriented
		  electronic commerce market. The aim of the present study is
		  twofold: to carry out an exploratory segmentation of this
		  market that can throw some light upon its structure, and to
		  characterize the on-line shopping adoption process. The
		  self-organizing map (SOM), an unsupervised neural network
		  model devised by Kohonen (Kohonen, T. (1982).
		  Self-organized formation of topologically correct feature
		  maps. Biological Cybernetics, 43(1), 59--69; Kohonen, T.,
		  (1995). Self-organizing maps. Berlin: Springer) will be
		  used as part of a tandem approach to segmentation, which
		  involves the factor analysis of the observable variables in
		  the data to be analyzed, prior to clustering. The SOM is
		  shown to be a powerful data visualization tool, able to
		  assist the data analysis, providing supervised methods with
		  useful explanatory capabilities. It is also applied, in a
		  completely unsupervised mode, to discover the clusters or
		  segments that naturally occur in the data. The SOM is
		  proposed as a flexible clustering model able to accommodate
		  both finer segmentation and normative segmentation
		  approaches. Within the latter, a cluster partition is
		  proposed and analysed, and high-level customer profiles, of
		  potential interest to on-line marketers, are derived and
		  described in marketing terms.},
  dbinsdate	= {oldtimer}
}

@InBook{	  venkatasubramanian95a,
  author	= {V. Venkatasubramanian and R. Rengaswamy},
  title		= {Neural Networks for Chemical Engineers},
  chapter	= {27, Clustering and statistical techniques in neural
		  networks},
  publisher	= {Elsevier},
  year		= {1995},
  volume	= {6},
  series	= {Computer-Aided Chemical Engineering},
  address	= {Amsterdam},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  venna01a,
  author	= {Venna, J. and Kaski, S.},
  title		= {Neighborhood preservation in nonlinear projection methods:
		  An experimental study},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {485--491},
  abstract	= {Several measures have been proposed for comparing
		  nonlinear projection methods but so far no comparisons have
		  taken into account one of their most important properties,
		  the trustworthiness of the resulting neighborhood or
		  proximity relationships. One of the main uses of nonlinear
		  mapping methods is to visualize multivariate data, and in
		  such visualizations it is crucial that the visualized
		  proximities can be trusted upon: If two data samples are
		  close to each other on the display they should be close-by
		  in the original space as well. A local measure of
		  trustworthiness is proposed and it is shown for three data
		  sets that neighborhood relationships visualized by the
		  Self-Organizing Map and its variant, the Generative
		  Topographic Mapping, are more trustworthy than
		  visualizations produced by traditional multidimensional
		  scaling-based nonlinear projection methods.},
  dbinsdate	= {2002/1}
}

@Article{	  venugopal94a,
  author	= {Venugopal, V. and Narendran, T. T. },
  title		= {Machine-cell formation through neural network models},
  journal	= {International Journal of Production Research},
  year		= {1994},
  volume	= {32},
  number	= {9},
  pages		= {2105--16},
  month		= {Sept},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vercauteren90a,
  author	= {L. Vercauteren and R. A. Vingerhoeds and L. Boullart},
  title		= {Intelligent dimensional data-reduction by a topological
		  map (the interpretation and use of an insurance database)},
  booktitle	= {Parallel Processing in Neural Systems and Computers},
  year		= {1990},
  editor	= {R. Eckmiller and G. Hartmann and G. Hauske},
  pages		= {503--507},
  organization	= {Robert Bosch; IBM; Philips; Siemens; et al},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vercauteren90b,
  author	= {L. Vercauteren and G. Sieben and M. Praet},
  title		= {The Classification of Brain Tumours by a Topological Map},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conference},
  year		= {1990},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  pages		= {387--391 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vercelli94a,
  author	= {G. Vercelli},
  title		= {{NAVNEX}: an Hybrid System which learns Navigation
		  Situations from {SOM}},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1307--1310},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, path planning, robot},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vercelli98a,
  author	= {Vercelli, G. and Morasso, P.},
  title		= {Recognition and classification of path features with
		  \mbox{self-organizing} maps during reactive navigation},
  booktitle	= {Proceedings. 1998 IEEE/RSJ International Conference on
		  Intelligent Robots and Systems. Innovations in Theory,
		  Practice and Applications},
  year		= {1998},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  volume	= {3},
  pages		= {1437--42},
  abstract	= {The paper focuses on recognition and classification of
		  path features during navigation of a mobile robot. The
		  extracted features play the role of relevant navigation
		  situations as (in a corridor), (at a turning point), (in a
		  narrow passage). The method is an incremental learning and
		  classification technique, based on a self-organizing neural
		  model. Two different self-organizing networks are used to
		  encode occupancy bitmaps generated from sonar patterns in
		  terms of obstacles boundaries and free paths, and heuristic
		  procedures are applied to these growing networks to add and
		  prune units, to determine topological correctness between
		  units, to distinguish and categorize features.},
  dbinsdate	= {oldtimer}
}

@Article{	  verikas00a,
  author	= {Verikas, A. and Malmqvist, K. and Bacauskiene, M. and
		  Bergman, L.},
  title		= {Monitoring the de-inking process through neural
		  network-based colour image analysis},
  journal	= {NEURAL COMPUTING \& APPLICATIONS},
  year		= {2000},
  volume	= {9},
  number	= {2},
  pages		= {142--151},
  abstract	= {This paper presents an approach to determining the colours
		  of specks in an image of a pulp being recycled. The task is
		  solved through colour classification by an artificial
		  neural network. The network is trained using fuzzy
		  possibilistic target values. The number of colour classes
		  found in the images is determined through the
		  self-organising process in the two-dimensional
		  self-organising map. The experiments performed have shown
		  that the colour classification results correspond well with
		  human perception of the colours of the specks.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  verikas93a,
  author	= {Verikas, A. and Malmqvist, K. and Bergman, L. and Nilsson,
		  K. },
  title		= {Color classification by neural network},
  booktitle	= {Sixth International Conference. Neural Networks and their
		  Industrial and Cognitive Applications. NEURO-NIMES 93
		  Conference Proceedings and Exhibition Catalog},
  year		= {1993},
  pages		= {329--38},
  organization	= {Halmstad Univ. , Sweden},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  verikas94a,
  author	= {A. Verikas and K. Malmqvist and M. Bachauskene and L.
		  Bergman and K. Nilsson},
  title		= {{HIERARCHCAL} Neural Network for {COLOR} Classification},
  pages		= {2938--2941},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {pattern recognition, hybrid},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  verleysen93a,
  author	= {Michel Verleysen and Philippe Thissen and Jean-Didier
		  Legat},
  title		= {An Improvement on {LVQ} Algorithms to Create Classes of
		  Patterns},
  booktitle	= {New Trends in Neural Computation, Lecture Notes in
		  Computer Science No. 686},
  publisher	= {Springer},
  year		= {1993},
  editor	= {J. Mira and J. Cabestany and A. Prieto},
  pages		= {340--345},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  verleysen93b,
  author	= {Michel Verleysen and Philippe Thissen and Jean-Didier
		  Legat},
  title		= {Optimal decision surfaces in {LVQ1} classification of
		  patterns},
  year		= {1993},
  booktitle	= {Proc. ESANN'95, European Symposium on Artificial Neural
		  Networks},
  editor	= {Michel Verleysen},
  publisher	= {D Facto},
  pages		= {209--214},
  address	= {Brussels, Belgium},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  verona93a,
  author	= {F. Bini Verona and F. E. Lauria and M. Sette and S.
		  Visco},
  title		= {A {B}oolean Net Trainable as a Computing Robot Control},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {II},
  pages		= {1861--1864},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  versino95a,
  author	= {C. Versino and L. M. Gambardella},
  title		= {Learning the visuomotor coordination of a mobile robot by
		  using the invertible {K}ohonen map},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {1084--91},
  dbinsdate	= {oldtimer}
}

@InCollection{	  versino96a,
  author	= {C. Versino and L. M. Gambardella},
  title		= {Learning fine motion in robotics: experiments with the
		  hierarchical extended {K}ohonen map},
  booktitle	= {Progress in Neural Information Processing. Proceedings of
		  the International Conference on Neural Information
		  Processing},
  publisher	= {Springer-Verlag},
  year		= {1996},
  volume	= {2},
  editor	= {S. -I. Amari and L. Xu and L. -W. Chan and I. King and K.
		  -S. Leung},
  address	= {Singapore},
  pages		= {921--5},
  dbinsdate	= {oldtimer}
}

@InCollection{	  versino96b,
  author	= {C. Versino and L. M. Gambardella},
  title		= {Learning fine motion by using the hierarchical extended
		  {K}ohonen map},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {221--6},
  dbinsdate	= {oldtimer}
}

@Article{	  vesanto00a,
  author	= {Vesanto, Juha and Alhoniemi, Esa},
  title		= {Clustering of the self-organizing map},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2000},
  volume	= {11},
  number	= {3},
  month		= {May},
  pages		= {586--600},
  organization	= {Helsinki Univ of Technology},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The self-organizing map (SOM) is an excellent tool in
		  exploratory phase of data mining. It projects input space
		  on prototypes of a low-dimensional regular grid that can be
		  effectively utilized to visualize and explore properties of
		  the data. When the number of SOM units is large, to
		  facilitate quantitative analysis of the map and the data,
		  similar units need to be grouped, i.e., clustered. In this
		  paper, different approaches to clustering of the SOM are
		  considered. In particular, the use of hierarchical
		  agglomerative clustering and partitive clustering using
		  k-means are investigated. The two-stage procedure-first
		  using SOM to produce the prototypes that are then clustered
		  in the second stage-is found to perform well when compared
		  with direct clustering of the data and to reduce the
		  computation time.},
  dbinsdate	= {2002/1}
}

@InCollection{	  vesanto97a,
  author	= {Juha Vesanto},
  title		= {Using the {SOM} and local models in time-series
		  prediction},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {209--214},
  dbinsdate	= {oldtimer}
}

@Article{	  vesanto97b,
  author	= {Vesanto, J. and Vasara, P. and Helminen, R. R. and Simula,
		  O.},
  title		= {Integrating environmental, technological and financial
		  data in forest industry analysis},
  journal	= {Neural Networks: Best Practice in Europe},
  year		= {1997},
  publisher	= {World Scientific},
  address	= {Singapore},
  volume	= {},
  pages		= {153--6},
  abstract	= {The self-organizing map (SOM) is a powerful neural network
		  method for the analysis and visualisation of high
		  dimensional data. The SOM algorithm is applied to the
		  analysis of the technology of world paper and pulp
		  industry. It is seen that the method can be used on
		  environmental, technological and financial data to produce
		  a comprehensive view of the industry as a whole.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  vesanto98a,
  author	= {J. Vesanto and J. Himberg and M. Siponen and O. Simula},
  title		= {Enhancing {SOM} Based Data Visualization},
  booktitle	= {Proceedings of the 5th International Conference on Soft
		  Computing and Information/Intelligent Systems.
		  Methodologies for the Conception, Design and Application of
		  Soft Computing.},
  publisher	= {World Scientific},
  address	= {Singapore},
  year		= {1998},
  volume	= {1},
  pages		= {64--7},
  abstract	= {The self-organizing map (SOM) is an effective data
		  exploration tool. One of the reasons for this is that it is
		  conceptually very simple and its visualization is easy. We
		  propose new ways to enhance the visualization capabilities
		  of the SOM in three areas: clustering, correlation hunting
		  and novelty detection. These enhancements are illustrated
		  by various examples using real-world data.},
  dbinsdate	= {oldtimer}
}

@Article{	  vesanto99a,
  author	= {Vesanto, J.},
  title		= {SOM-based data visualization methods},
  journal	= {Intelligent Data Analysis},
  year		= {1999},
  volume	= {3},
  pages		= {111--26},
  abstract	= {The self-organizing map (SOM) is an efficient tool for
		  visualization of multidimensional numerical data. In this
		  paper, an overview and categorization of both old and new
		  methods for the visualization of SOM is presented. The
		  purpose is to give an idea of what kind of information can
		  be acquired from different presentations and how the SOM
		  can best be utilized in exploratory data visualization.
		  Most of the presented methods can also be applied in the
		  more general case of first making a vector quantization
		  (e.g. K-means) and then a vector projection (e.g. Sammon's
		  mapping).},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vesanto99b,
  author	= {Vesanto, J. and Ahola, J.},
  title		= {Hunting for Correlations in Data Using the Self-Organizing
		  Map},
  booktitle	= {Proc. of International ICSC Congress on Computational
		  Intelligence Methods and Applications (CIMA'99), Rochester,
		  New York, USA, June 22--25},
  pages		= {279--285},
  year		= {1999},
  publisher	= {ICSC Academic Press},
  abstract	= {The self-organizing map (SOM) is an efficient tool for
		  visualization of multidimensional numerical data. One of
		  the tasks it is used for is correlation hunting. In this
		  paper we present a simple method to enhance correlation
		  hunting in the case of a large number of variables.
		  Different variations of the method-component plane
		  reorganization-are evaluated on a complex test data. The
		  purpose is to somewhat validate the use of SOM in
		  correlation hunting and to evaluate the strengths and
		  weaknesses of different reorganization procedures. A case
		  with a real world data is also presented to show the
		  usefulness of the method.},
  dbinsdate	= {oldtimer}
}

@Article{	  vesanto99c,
  author	= {Vesanto, J. and Alhoniemi, E. and Himberg, J. and
		  Kiviluoto, K. and Parviainen, J.},
  title		= {Self-Organizing Map for Data Mining in MATLAB: The SOM
		  Toolbox.},
  journal	= {Simulation News Europe},
  year		= {1999},
  number	= {25},
  pages		= {54},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vesanto99d,
  author	= {Vesanto, J. and Himberg, J. and Alhoniemi, E. and
		  Parhankangas, J.},
  title		= {Self-Organizing Map in Matlab: the SOM Toolbox.},
  booktitle	= {Proc. of Matlab DSP Conference 1999, Espoo, Finland,
		  November 16--17},
  pages		= {35--40},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  viademonte00a,
  author	= {Viademonte, Sergio and Burstein, Frada and Beckenkamp,
		  Fabio G.},
  title		= {Empirical study of distribution based on voyager: a
		  performance analysis},
  booktitle	= {Proceedings of the Hawaii International Conference on
		  System Sciences},
  year		= {2000},
  editor	= {},
  volume	= {},
  pages		= {37},
  organization	= {Monash Univ},
  publisher	= {IEEE},
  address	= {Los Alamitos, CA},
  abstract	= {The paper describes the model, implementation and
		  experimental evaluation of a distributed Kohonen Neural
		  Network application (Kohonen Application). The aim of this
		  research is to empirically verify the suitability and the
		  performance of a distributed application based on mobile
		  objects and, in perspective, intelligent agents. This
		  research is aims to provide distribution features in
		  decision support systems. The experiment was based in the
		  Java-ABC project. The Java-ABC project is concerned with
		  flexible software architecture for decision support
		  systems, which rely on artificial neural network (ANN)
		  technology. Three parameters: used CPU, used memory (RAM)
		  and time consumed by each Kohonen Application, were taken
		  as evaluation measures in this experiment. Three hardware
		  environments were used: two PCs (one local and one remote)
		  under Windows NT with different RAM capacity and a SUN
		  Ultra under SunOS 5.6. This paper presents the comparison
		  of performance measures from our experimental studies and
		  the analysis of the results. In conclusion, the paper
		  presents the implications of these results for the area of
		  distributed intelligent decision support and future
		  directions of this work.},
  dbinsdate	= {2002/1}
}

@InCollection{	  vieira97a,
  author	= {Karina Vieira and Bogdan Wilamowski and Robert Kubichek},
  title		= {Speaker Identification Based on a Modified {K}ohonen
		  Network},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {IV},
  pages		= {2103--2106},
  dbinsdate	= {oldtimer}
}

@InCollection{	  vignoli96a,
  author	= {F. Vignoli and S. Curinga and F. Lavagetto},
  title		= {A neural clustering algorithm for estimating visible
		  articulatory trajectory},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {863--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vilain99a,
  author	= {Vilain, Joseph and Giron, Alain and Brahmi, Djamel and
		  Deschavanne, Patrick and Fertil, Bernard},
  title		= {Application of curvilinear component analysis to chaos
		  game representation images of genome},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3647},
  pages		= {111--119},
  abstract	= {Curvilinear component analysis (CCA) is performed by an
		  original self-organized neural network, which provides a
		  convenient approach for dimension reduction and data
		  exploration. It consists in a non-linear, preserving
		  distances projection of a set of quantizing vectors
		  describing the input space. The CCA technique is applied to
		  the analysis of CGR (Chaos Game Representation) fractal
		  images of DNA sequences from different species. The CGR
		  method produces images where pixels represent frequency of
		  small sequences of bases revealing nested patterns in DNA
		  sequences. Comparisons of the results obtained using CCA,
		  PCA (principal component analysis) and Kohonen's SOMs are
		  carried out using several hundred of CGR images. CCA
		  provides a good topology-preserving mapping of images, in
		  contrast with PCA, the residual error on distances between
		  images after projection being found much smaller whatever
		  the dimensionality of the output space. Kohonen's SOMs
		  offers attractive results, which unfortunately can be
		  sometimes impeded by their too strongly dependence on
		  predefined constraints about neighborhood between output
		  neurons. All 3 methods achieve interesting grouping of
		  images, often in relation with phylogenetic characteristics
		  of species. The results obtained with CCA and SOM make up a
		  good basis for further phylogenetic classification.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villman00a,
  author	= {T. Villman and B. Badel and D. K\"{a}mpf and M. Geyer},
  title		= {Monitoring of Physiological Parameters of Patients and
		  Therapists During Psychotherapy Sessions using
		  Self-Organizing Maps},
  booktitle	= {Artificial Neural Networks in Medicine and Biology,
		  Prodeedings of the ANNIMAB-1 COnference, Göteborg, Sweden,
		  13--16 May 2000},
  pages		= {221--226},
  year		= {2000},
  editor	= {H. Malmgren and M. Boga and L. Niklasson},
  abstract	= {In the present contribution the authors show the
		  applications of SOMs for visualization of physiological
		  parameters of patients and therapists on psycho-therapy
		  sessions. Thereby, using the topology preserving prperty of
		  SOMs a color presentation can be generated allowing an easy
		  asessment of the underlying parameter change which can be
		  interpreted by the therapists. To achieve the topology
		  preserving map a growing extension of the SOM was used
		  together with a magnification control strategy which
		  maximizes the mutual information of the network.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  villman99a,
  author	= {T. Villmann},
  title		= {Topology Preservation in Self-Organizing Maps},
  booktitle	= {Kohonen Maps},
  pages		= {279--292},
  publisher	= {Elsevier},
  year		= {1999},
  editor	= {Oja, E. and Kaski, S.},
  address	= {Amsterdam},
  annote	= {Keywords: self-organising map, topology preservation,
		  growing self-organizing map},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann00a,
  author	= {Villmann, Th. and and Mer\'{e}nyi, E.},
  title		= {Extensions and Modifications of the SOM and its
		  Application in Satellite Remote Sensoring Processing},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {Klinik un Poliklinik für Psychotherapie und
		  Psycosomatische Medizin, Universität Leipzig; Lunar and
		  Planetory Laboratory, University of Arizona},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann00b,
  author	= {Villmann, T. and Hermann, W. and Geyer, M.},
  title		= {Data mining and knowledge discovery in medical
		  applications using self-organizing maps},
  booktitle	= {Medical Data Analysis. First International Symposium,
		  ISMDA 2000. Proceedings (Lecture Notes in Computer Science
		  Vol.1933). Springer-Verlag, Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {138--51},
  abstract	= {In this paper, the authors discuss the application of
		  self-organizing maps (SOMs) to data mining and knowledge
		  discovery in medicine. Thereby, the usually assumed (but
		  not verified) topology preservation is the main focus.
		  Extensions of the usual SOM are offered to obtain correct
		  results. The authors give examples of applications, such as
		  visualization and clustering.},
  dbinsdate	= {2002/1}
}

@Article{	  villmann00c,
  author	= {Villmann, T. and Hermann, W. and Geyer, M.},
  title		= {Variants of self-organizing maps for data mining and data
		  visualization in medicine},
  journal	= {Neural-Network-World},
  year		= {2000},
  volume	= {10},
  pages		= {751--62},
  abstract	= {In this paper the authors show the application of
		  self-organizing maps (SOMs) for visualization of data in
		  medical area. For this purpose extensions of the usual SOM
		  are discussed to obtain an adequate visualization result
		  for easy medical expert assessment.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  villmann00d,
  author	= {Villmann, Th. and Haupt, R. and Hering, K.},
  title		= {Parallel evolutionary algorithms with {SOM}-like migration
		  and its application to {VLSI}-design},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {167--172},
  organization	= {Universitaet Leipzig},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {We introduce a multiple subpopulation approach for
		  parallel evolutionary algorithms the migration scheme of
		  which follows a SOM-like dynamics. We successfully apply
		  this approach to clustering in VLSI-design. The advantages
		  of the approach are shown which consist in a reduced
		  communication overhead between the subpopulations
		  preserving a non-vanishing information flow.},
  dbinsdate	= {2002/1}
}

@InBook{	  villmann02a,
  author	= {Thomas Villmann and Erzs{\'e}bet Mer{\'e}nyi},
  editor	= {Udo Seiffert and Lakhmi C. Jain},
  title		= {Self-Organizing Neural Networks---Recent Advances and
		  Applications},
  chapter	= {Extensions and Modifications of the Kohonen-SOM and
		  Applications in Remote Sensing Image Analysis},
  publisher	= {Physica-Verlag Heidelberg},
  year		= {2002},
  key		= {},
  volume	= {78},
  number	= {},
  series	= {Studies in Fuzziness and Soft Computing},
  type		= {},
  address	= {},
  edition	= {},
  month		= {},
  pages		= {121--40},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  villmann94a,
  author	= {Th. Villmann and R. Der and Th. Martinetz},
  title		= {A Novel Approach to Measure the Topology Preservation of
		  Feature Maps},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {298--301},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {analysis, topological preservation},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann94b,
  author	= {Th. Villmann and R. Der and Th. Martinetz},
  title		= {A New Quantitative Measure of Topology Preservation in
		  {K}ohonen's Feature Maps},
  pages		= {645--648},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {analysis, topology measure},
  dbinsdate	= {oldtimer}
}

@InCollection{	  villmann97a,
  author	= {Thomas Villmann and H. -U. Bauer and Th. Villmann},
  title		= {The {GSOM}-algorithm for growing hypercubical output
		  spaces in \mbox{self-organizing} maps},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {286--291},
  dbinsdate	= {oldtimer}
}

@Article{	  villmann97b,
  author	= {Villmann, Thomas and Der, Ralf and Herrmann, Michael and
		  Martinetz, Thomas M},
  title		= {Topology preservation in \mbox{self-organizing} feature
		  maps: exact definition and measurement},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  number	= {2},
  volume	= {8},
  pages		= {256--266},
  abstract	= {The neighborhood preservation of self-organizing feature
		  maps like the Kohonen map is an important property which is
		  exploited in many applications. However, if a dimensional
		  conflict arises this property is lost. Various qualitative
		  and quantitative approaches are known for measuring the
		  degree of topology preservation. They are based on using
		  the locations of the synaptic weight vectors. These
		  approaches, however, may fail in case of nonlinear data
		  manifolds. To overcome this problem, in this paper we
		  present an approach which uses what we call the induced
		  receptive fields for determining the degree of topology
		  preservation. We first introduce a precise definition of
		  topology preservation and then propose a tool for measuring
		  it, the topographic function. The topographic function
		  vanishes if and only if the map is topology preserving. We
		  demonstrate the power of this tool for various examples of
		  data manifolds.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann97c,
  author	= {Th. Villmann and H. -U. Bauer and M. Herrmann},
  editor	= {Horst-Michael Gro{\ss}},
  year		= 1997,
  title		= {{Neuronale Merkmalskarten und Topologieerhaltung}},
  booktitle	= {Proceedings of Selbstorganisation Von Adaptivem Verfahren
		  (SOAVE'97) Ilmenau},
  pages		= {119--126},
  publisher	= {Fortschrittsberichte des VDI},
  address	= {VDI-Verlag D\"usseldorf},
  dbinsdate	= {oldtimer}
}

@Article{	  villmann98a,
  author	= {Th. Villmann and H. -U. Bauer},
  title		= {Applications of the growing \mbox{self-organizing} map},
  journal	= {Neurocomputing},
  year		= {1998},
  number	= {1},
  volume	= {21},
  pages		= {91--100},
  abstract	= {The growing self-organizing map (GSOM), an extension of
		  Kohonen's self-organizing map algorithm, adapts not only
		  the position of the map weight vectors in the input space,
		  but also the topology of the map output space grid. This
		  additional feature allows for an unsupervised generation of
		  dimension-reducing projections with optimal neighborhood
		  preservation, even if the effective dimensionality of the
		  input data set is not known. In three case studies
		  involving real-world data sets we show that the GSOM is
		  able to reproducably generate projections with a very good
		  degree of neighborhood preservation. For one of the data
		  sets, an experimentally obtained time series from a
		  nonlinear system, the correct dimensionality d super(A)
		  approximately 3 of the underlying attractor is known from
		  other methods; here the GSOM leads to maps without space
		  grids which are also three dimensional.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann98b,
  author	= {Th. Villmann and A. K{\"o}rner and C. Albani},
  year		= 1998,
  title		= {Evolutionary Algorithms with Self-Organizing Population
		  Dynamic for Clustering of Categories in Psychotherapy
		  Research Using Large Clinical Data Sets},
  booktitle	= {Proceedings of International ICSC/IFAC Symposium on Neural
		  Computation (NC'98)},
  pages		= {130--136},
  publisher	= {International Computer Science Conventions Academic
		  Press},
  address	= {Wien, Austria},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann98c,
  author	= {Th. Villmann and M. Herrmann},
  year		= 1998,
  title		= {Magnification Control in Neural Maps},
  booktitle	= {Proc. of European Symposium on Artificial Neural Networks
		  (ESANN'98)},
  pages		= {191--196},
  publisher	= {D facto publications},
  address	= {Brussels, Belgium},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann98d,
  author	= {Th. Villmann},
  editor	= {K. Lieven},
  year		= 1998,
  title		= {Estimation of Topography in Self-Organizing Maps and Data
		  Driven Growing of Suitable Network Structures},
  booktitle	= {Proceedings of European Congress on Intelligent Techniques
		  and Soft Computing (EUFIT'98)},
  volume	= {1},
  pages		= {235--244},
  publisher	= {ELITE Foundation},
  address	= {Aachen, Germany},
  abstract	= {Concerns the problem of neighbourhood preservation in
		  self-organising neural map representation. 2 solutions are
		  presented. One is to perform self-organisation on several
		  maps with different output space topology, and to select
		  that with optimal preservation. The other is to use an
		  advanced learning scheme which not only adapts the weight
		  vectors of the neurons, but also the topology of the output
		  space itself.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann98e,
  author	= {Th. Villmann and A. K{\"o}rner and C. Albani},
  editor	= {E. C. Ifeachor and A. Sperduti and A. Starita},
  year		= 1998,
  title		= {Evolutionary Algorithms with Migration Scheme Inspired by
		  Neural Dynamic and its Application to Reformulation of
		  Categories in Psychotherapy Research},
  booktitle	= {Neural Networks and Expert Systems in Medicine and
		  Healthcare},
  pages		= {313--321},
  publisher	= {World Scientific},
  address	= {Singapore},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann98f,
  author	= {Th. Villmann and A. Hessel and G. Pl{\"o}ttner},
  editor	= {P. P. Wang and G. M. Georgiou},
  year		= 1998,
  title		= {The Growing {SOM} for Estimation of the Intrinsic
		  Dimension and Range of Psychotherapy Process Data},
  booktitle	= {Proceedings of the 3rd International Conference on
		  Computational Intelligence and Neuroscience},
  volume	= {2},
  pages		= {72--75},
  publisher	= {Association for Intelligent Machinery, Inc.},
  address	= {Duke University and Research Triangle Park, Durham, North
		  Carolina (USA)},
  abstract	= {In psychotherapy research the success of a therapy often
		  is judged by measuring characteristics of the personality
		  by psychological self-reported questionnaires. These
		  methods suggest the use of 5 or 6 psychological dimensions
		  of personality. However, we apply several special empirical
		  tests which allow a better differentiation. Yet, they
		  should comprise the whole spectrum of the personality and,
		  hence, the relevant (may be transformed) parameters should
		  match the respective psychological dimension. In this
		  investigation we use neural maps, especially the new
		  growing self-organizing map (GSOM), to extract the
		  intrinsic dimension, based on a patient database. We show
		  that the nonlinear methods of neural maps lead to a
		  verification of the expected assumption of which respective
		  dimensions are comprised. Moreover the presented
		  GSOM-approach allows an estimation of the range of the
		  psychological dimensions which coincide with theoretical
		  results.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann99a,
  author	= {Thomas Villmann and Ulrike Rietz and Aike Hessel and
		  G{\"u}nter Pl{\"o}ttner},
  editor	= {G. Krell and B. Michaelis and D. Nauck and R. Kruse},
  year		= 1999,
  title		= {Estimation of the Intrinsic Dimension of Psychotherapy
		  Process Data---a Comparing Study Including the {GSOM}},
  booktitle	= {Proceedings of International Workshop 'Neuronale Netze in
		  der Anwendung', Magdeburg},
  pages		= {7--16},
  publisher	= {University Magdeburg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann99b,
  author	= {Th. Villmann},
  year		= 1999,
  title		= {Benefits and Limits of the Self-Organizing Map and its
		  Variants in the Area of Satellite Remote Sensoring
		  Processing},
  booktitle	= {Proc. of European Symposium on Artificial Neural Networks
		  (ESANN'99)},
  pages		= {111--116},
  publisher	= {D facto publications},
  address	= {Brussels, Belgium},
  abstract	= {We consider advantages and limits of the self-organizing
		  maps (SOM) in the area of satellite remote sensing
		  processing. We concentrate on the topology preservation
		  property as well as the magnification control. We
		  demonstrate the effectiveness of SOM using LANDSAT-TM
		  satellite images as examples.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  villmann99c,
  author	= {Villmann, T. and Hessel, A.},
  title		= {Analyzing psychotherapy process time series using neural
		  maps},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  publisher	= {IEE},
  address	= {London, UK},
  year		= {1999},
  volume	= {2},
  pages		= {767--72},
  abstract	= {In psychotherapy research the success of a therapy often
		  is judged by measuring characteristics of the personality
		  by psychological self-reported questionnaires. These
		  methods suggest to use 5 or 6 psychological dimensions of
		  personality. In this study we use neural maps, especially
		  the new growing self-organizing map (GSOM), to extract the
		  intrinsic dimension, based on a patient database. We show
		  that the nonlinear methods of neural maps leads to a
		  verification of the expected assumption of that respective
		  dimensions are comprised. Moreover, the presented
		  GSOM-approach allows an estimation of the range of the
		  psychological dimensions which coincide with theoretical
		  results. We solve the question whether the database of
		  psychotherapy process data allows a prediction of the
		  relevant parameters of the process, which describes the
		  therapy.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vincent95a,
  author	= {Daniel Vincent and John McCardle and Raymond Stroud},
  title		= {Classification of Metal Transfer Mode Using Neural
		  Networks},
  volume	= {I},
  pages		= {522--525},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  vinson02a,
  author	= {Vinson, D. P. and Vigliocco, G.},
  title		= {A semantic analysis of grammatical class impairments:
		  semantic representations of object nouns, action nouns and
		  action verbs},
  journal	= {JOURNAL OF NEUROLINGUISTICS},
  year		= {2002},
  volume	= {15},
  number	= {3--5},
  month		= {MAY-SEP},
  pages		= {317--351},
  abstract	= {The grammatical class distinction between nouns and verbs
		  is largely parallel to (be semantic distinction between
		  objects and actions; in this paper, we explore the extent
		  to which grammatical class effects (noun- or verb-specific
		  naming deficits) can be explained by lexical semantic
		  factors alone. In order to do so, we investigate
		  lexical-semantic clustering properties, not only of nouns
		  depicting objects and verbs depicting actions, but also of
		  nouns depicting actions, which should exhibit some patterns
		  of similarity to other nouns if grammatical class emerges
		  on the basis of lexical semantics. We collected
		  speaker-generated features and used self-organizing map, to
		  model lexical-semantic similarity among words. We simulated
		  lesions on the resulting map, finding patterns of
		  object-noun/action-verb naming impairments consistent with
		  those reported in the literature. Importantly, we found
		  that action-nouns exhibited no tendency to be more similar
		  to object-nouns than their corresponding action-verbs, a
		  finding inconsistent with a semantic account of grammatical
		  class.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  vintan99a,
  author	= {Vintan, L. N. and Iridon, M.},
  title		= {Towards a high performance neural branch predictor},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {868--73},
  abstract	= {The main aim of this short paper is to propose a new
		  branch prediction approach called by us "neural branch
		  prediction". We developed a first neural predictor model
		  based on a simple neural learning algorithm, known as
		  learning vector quantization algorithm. Based on a trace
		  driven simulation method we investigated the influences of
		  the learning step, training processes, etc. Also we
		  compared the neural predictor with a powerful classical
		  predictor and we establish that they result in close
		  performances. Therefore, we conclude that in the near
		  future it might be necessary to model and simulate other
		  more powerful neural adaptive predictors, based on more
		  efficient neural networks architectures, in order to obtain
		  better prediction accuracies compared with the previous
		  known schemes.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vinz94a,
  author	= {Bradley L. Vinz},
  title		= {An Interpolated Counterpropagation Approach for
		  Determining Target Spacegraft Attitude},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {686--691},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, image analysis, control system},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  viredaz93a,
  author	= {Marc A. Viredaz},
  title		= {{MANTRA I}: An {SIMD} Processor Array for Neural
		  Computation},
  booktitle	= {Proc. of Euro-ARCH'93, Munich},
  year		= {1993},
  editor	= {Peter Paul Spies},
  pages		= {99--110},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa00a,
  author	= {Visa, Ari and Toivonen, Jarmo and Ruokonen, Piia and
		  Vanharanta, Hannu and Back, Barbro},
  title		= {Knowledge discovery from text documents based on paragraph
		  maps},
  booktitle	= {Proceedings of the Hawaii International Conference on
		  System Sciences Jan 4-Jan 7 2000},
  year		= {2000},
  volume	= {},
  pages		= {38},
  abstract	= {A new technology to organize documents is presented. The
		  technology is based on a hierarchy of Self-Organizing Maps
		  (SOM) and on smart encoding of words. Using this
		  technology, unknown documents can be categorized without
		  reading them.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa00b,
  author	= {Visa, Ari and Toivonen, Jarmo and Back, Barbro and
		  Vanharanta, Hannu},
  title		= {Toward text understanding---classification of text
		  documents by word map},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {4057},
  pages		= {299--305},
  organization	= {Lappeenranta Univ of Technology},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  abstract	= {In many fields, for example in business, engineering, and
		  law there is interest in the search and the classification
		  of text documents in large databases. To information
		  retrieval purposes there exist methods. They are mainly
		  based on keywords. In cases where keywords are lacking the
		  information retrieval is problematic. One approach is to
		  use the whole text document as a search key. Neural
		  networks offer an adaptive tool for this purpose. This
		  paper suggests a new adaptive approach to the problem of
		  clustering and search in large text document databases. The
		  approach is a multilevel one based on word, sentence, and
		  paragraph level maps. Here only the word map level is
		  reported. The reported approach is based on smart encoding,
		  on Self-Organizing Maps, and on document histograms. The
		  results are very promising.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  visa01a,
  author	= {Visa, A. and Toivonen, J. and Vanharanta, H. and Back,
		  B.},
  title		= {Prototype matching---Finding meaning in the books of the
		  Bible},
  booktitle	= {Proceedings of the Hawaii International Conference on
		  System Sciences},
  year		= {2001},
  editor	= {},
  volume	= {},
  pages		= {72},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {It is common that text documents are characterised and
		  classified by keywords and that the authors use to give and
		  name these text characteristics. Visa et al. have, however,
		  developed a new methodology based on prototype matching.
		  The prototype is an interesting document or a part of an
		  extracted, interesting text. This prototype is matched with
		  the existing document database or the monitored document
		  flow. Our claim is that the new methodology is capable of
		  extracting meaning automatically from the contents of the
		  document. To verify this hypothesis a test was designed
		  with the Bible. Two different translations, one in English
		  and another in Finnish, were selected as test text
		  material. Verification tests that included the search of
		  the ten nearest books to every book of the Bible were
		  performed with a designed prototype version of the software
		  application. The interesting test results are reported in
		  this paper. The new methodology is based on a hierarchy of
		  Self-Organizing Maps (SOM) and on a smart encoding of
		  words. The words of a text document are encoded. The
		  encoded words are represented as word vectors. The word
		  vectors are clustered by the SOM and this process creates a
		  word map. The words of a text document are replaced with
		  the addresses on the word map. Now the document consists of
		  a sequence of addresses. These addresses contain
		  information of word order. The document is considered
		  sentence by sentence. These sentence vectors are clustered
		  by SOM. This process creates a sentence map. Now the
		  sentences of the text document are replaced with addresses
		  on the sentence map. After that the document consists of a
		  sequence of addresses. These addresses contain information
		  of different types of sentences. The document is then
		  considered paragraph by paragraph. The paragraphs are
		  considered as context vectors and clustered by SOM. The
		  created map is called a context map. The paragraphs are
		  replaced with the addresses on the context map. The
		  document consists finally of a sequence of addresses on the
		  context map. The more detailed description of the
		  methodology can be found in several proceedings. The test
		  hypothesis was that the words, the word order in the
		  sentences and the order of sentences in paragraphs can
		  grasp higher level of information than ordinary word based
		  searches. Two tests were designed. It was important to find
		  a text that is well translated at least into two languages.
		  The Bible was selected. Each book of 66 books in the Bible
		  was selected as a prototype both in English and in Finnish
		  versions. A window of ten closest books was considered. The
		  window size ten was selected to guarantee a statistical
		  significance. In the first test the number of books in the
		  window that matched with other books in the Old Testament,
		  respectively in the New Testament, was counted for each
		  book. In the second test the same books within the window
		  in English and in Finnish versions were considered. The
		  results from these tests are statistically significant. The
		  methodology is capable of understanding the contents of the
		  document at least on a certain level.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  visa90a,
  author	= {Ari Visa},
  title		= {Texture Boundary Detection Based on {LVQ} Method},
  booktitle	= {Proc. 5th European Signal Processing Conf. },
  year		= {1990},
  editor	= {L. Torres and E. Masgrau and M. A. Lagunes},
  pages		= {991--994},
  publisher	= {Elsevier},
  address	= {Amsterdam, Netherlands},
  abstract	= {This paper concerns images containing stochastic textures.
		  A new image segmentation method is shortly described. It's
		  behaviour is studied at region boundaries. The power of the
		  method is demonstrated on realistic stochastic textures.
		  The suggested method is based on multiresolution
		  representation of co-occurrence matrices, feature maps and
		  Learning Vector Quantization (LVQ). The edge detection is
		  achieved by region recognition. Each region is assumed to
		  consist of unique texture type. The reported results are
		  promising.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa90b,
  author	= {Ari Visa},
  title		= {Identification of Stochastic Textures with Multiresolution
		  Features and \mbox{Self-organizing} Maps},
  booktitle	= {Proc. 10ICPR, International Conference on Pattern
		  Recognition},
  year		= {1990},
  pages		= {518--522},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa90c,
  author	= {Ari Visa},
  title		= {A Texture Classifier Based on Neural Network Principles},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, San Diego},
  year		= {1990},
  volume	= {I},
  pages		= {491--496},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa90d,
  author	= {Ari Visa},
  title		= {Stability Study of {L}earning {V}ector {Q}uantization},
  booktitle	= {Proc. INNC'90, Int. Neural Network Conf. },
  year		= {1990},
  pages		= {729--732},
  publisher	= {Kluwer},
  address	= {Dordrecht, Netherlands},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@PhDThesis{	  visa90e,
  author	= {Ari Visa},
  title		= {Texture Classification and Segmentation Based on Neural
		  Network Methods},
  school	= {Helsinki University of Technology},
  schoolf	= {Teknillinen korkeakoulu},
  year		= {1990},
  address	= {Espoo, Finland},
  addressf	= {Otaniemi},
  dbinsdate	= {oldtimer}
}

@TechReport{	  visa90f,
  author	= {Ari Visa},
  title		= {Comparison Between Classical and Neural Networks Methods
		  in Texture Recognition},
  institution	= {Helsinki University of Technology, Laboratory of Computer
		  and Information Science},
  year		= {1990},
  type		= {Report},
  number	= {A13},
  address	= {Espoo, Finland},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa90g,
  author	= {A. Visa and A. Langinmaa and U. Lindquist},
  title		= {Comparison of Stochastic Textures},
  booktitle	= {Proc. TAPPI, Int. Printing and Graphic Arts Conf. },
  year		= {1990},
  pages		= {91--97},
  publisher	= {Canadian Pulp and Paper Assoc. },
  address	= {Montreal, Canada},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa91a,
  author	= {Ari Visa},
  title		= {Neural Networks on Characterisation of Paper Properties},
  booktitle	= {Proc. European Res. Symp. 'Image Analysis for Pulp and
		  Paper Res. and Production'},
  year		= {1991},
  pages		= {},
  address	= {Center Technique du Papier, Grenoble, France},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  visa91b,
  author	= {Ari Visa},
  title		= {Texture Classification Based on Neural Networks},
  journal	= {Graphic Arts in Finland},
  year		= {1991},
  volume	= {20},
  number	= {3},
  pages		= {7--12},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa91c,
  author	= {Ari Visa and Anu Langinmaa},
  title		= {A Texture Based Approach to Evaluate Solid Print Quality},
  booktitle	= {Proc. IARIGAI},
  year		= {1992},
  editor	= {W. H. Banks},
  pages		= {},
  publisher	= {Pentech Press},
  address	= {London, UK},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa91d,
  author	= {Ari Visa and Kimmo Valkealahti and Olli Simula},
  title		= {Cloud Detection Based on Texture Segmentation by Neural
		  Network Methods},
  booktitle	= {Proc. IJCNN-91, International Joint Conference on Neural
		  Networks, Singapore},
  year		= {1991},
  pages		= {1001--1006},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa91e,
  author	= {Ari Visa},
  title		= {Texture Classification and Neural Networks Methods},
  booktitle	= {Proc. Applications of Artificial Neural Networks II, SPIE
		  Vol. 1469},
  year		= {1991},
  pages		= {820--831},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa92a,
  author	= {Ari Visa},
  title		= {Industrial Applications of Artificial Neural Networks in
		  {F}inland},
  booktitle	= {Proc. DECUS Finland ry. Spring Meeting},
  year		= {1992},
  pages		= {323--332},
  publisher	= {DEC Users' Society},
  address	= {Helsinki, Finland},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InCollection{	  visa92b,
  author	= {Ari Visa},
  title		= {Automatic Feature Selection by Self-Organization},
  booktitle	= {Artificial Neural Networks 2},
  publisher	= {Elsevier},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  chapter	= {},
  pages		= {803---807},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa92c,
  author	= {Ari Visa},
  title		= {Unsupervised Image Segmentation Based on a Self-Organizing
		  Feature Map and a Texture Measure,},
  booktitle	= {Proc. 11ICPR, International Conference on Pattern
		  Recognition},
  year		= {1992},
  pages		= {101--104},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa92d,
  author	= {Ari Visa},
  title		= {Topological Feature Map and Automatic Feature Selection},
  booktitle	= {Proc. of SPIE Aerospace Sensing, Vol. 1709 Science of
		  Neural Networks},
  year		= {1992},
  pages		= {642--649},
  publisher	= {SPIE},
  address	= {Bellingham, USA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa94a,
  author	= {Ari Visa},
  title		= {Texture Segmentation Based on Neural Networks},
  pages		= {145--148},
  booktitle	= {Proc. 3rd International Conference on Fuzzy Logic, Neural
		  Nets and Soft Computing},
  year		= {1994},
  publisher	= {Fuzzy Logic Systems Institute},
  address	= {Iizuka, Japan},
  annote	= {application, texture analysis, image analysis},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa94b,
  author	= {Ari Visa and Kimmo Valkealahti and Jukka Iivarinen and
		  Olli Simula},
  title		= {Experiences from operational cloud classifier based on
		  {S}elf-{O}rganizing {M}ap},
  booktitle	= {Proc. SPIE---The International Society for Optical
		  Engineering, Applications of Artificial Neural Networks V},
  year		= {1994},
  volume	= {2243},
  editor	= {Steven K. Rogers and Dennis W. Ruck},
  pages		= {484--495},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  annote	= {application, image processing, classification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  visa95a,
  author	= {A. Visa and J. Iivarinen and K. Valkealahti and O.
		  Simula},
  title		= {Neural Network Based Cloud Classifier},
  booktitle	= {Proc. International Conference on Artificial Neural
		  Networks (ICANN'95), Industrial Session 14 (Remote
		  Sensing)},
  year		= {1995},
  dbinsdate	= {oldtimer}
}

@InCollection{	  visala97a,
  author	= {A. Visala and H. Pitkanen and A. Halme},
  title		= {Wiener type {SOM}-and {MLP}-classifiers for recognition of
		  dynamic modes},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {1071--6},
  dbinsdate	= {oldtimer}
}

@Article{	  vishwanathan00a,
  author	= {Vishwanathan, S. V. N. and Murty, M. Narasimha},
  title		= {Kohonen's {SOM} with cache},
  journal	= {Pattern Recognition},
  year		= {2000},
  volume	= {33},
  number	= {11},
  month		= {Nov},
  pages		= {1927--1929},
  organization	= {Indian Inst of Science},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {A sub-optimal algorithm has been proposed for the training
		  of the self-organizing map (SOM). By limiting the search
		  space for nearest neighbor the algorithm reduces the
		  computational effort involved. One possible direction is to
		  use a cache which stores the k-nearest neighbors of a
		  sample in the cache.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  vishwanathan01a,
  author	= {Vishwanathan, N. and {Wunsch II}, D. C.},
  title		= {{ART}/{SOFM}: A hybrid approach to the {TSP}},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2001},
  editor	= {},
  volume	= {4},
  pages		= {2554--2557},
  organization	= {Appl. Computational Intell. Lab., Dept. of Elec. and Comp.
		  Engineering, University of Missouri---Rolla},
  publisher	= {},
  address	= {},
  abstract	= {We present a new method of solving large scale Traveling
		  Salesman Problem (TSP) instances using a combination of
		  Adaptive Resonance Theory (ART) and Self Organizing Feature
		  Maps (SOFM). We divide our algorithm into three phases.
		  Phase one uses ART to form clusters of cities. Phase two
		  uses a novel modification of the traditional SOFM algorithm
		  to solve a slight variant of the TSP in each cluster of
		  cities. Phase three uses another version of the SOFM to
		  link all the clusters. The experimental results show that
		  our algorithm finds approximate solutions which are about
		  13% longer than those reported by the chained Lin Kernighan
		  method for problem sizes of 14,000 cities.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  viswanathan99a,
  author	= {Viswanathan, S. and Ersoy, I. and Bunyak, F. and Dagli,
		  C.},
  title		= {Evolving neural networks applied to predator-evader
		  problem},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {2394--7},
  abstract	= {The creation of strategies to meet abstract goals is an
		  important behavior exhibited by natural organisms. A
		  situation requiring the development of such strategies is
		  the predator-evader problem. To study this problem, Khepera
		  robots are chosen as the competing agents. Using computer
		  simulations the evolution of the adaptive behavior is
		  studied in a predator-evader interaction. A bilaterally
		  symmetrical multilayer perceptron neural network
		  architecture with evolvable weights is used to model the
		  "brains" of the agents. Evolutionary programming is
		  employed to evolve the predator for developing adaptive
		  strategies to meet its goals. To study the effect of
		  learning on evolution a self-organizing map (SOM) is added
		  to the architecture, it is trained continuously and all the
		  predators can access its weights. The results of these two
		  different approaches are compared.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vittoz89a,
  author	= {E. Vittoz and P. Heim and X. Arreguit and F. Krummenacher
		  and E. Sorouchyari},
  title		= {Analog {VLSI} Implementation of a {{K}ohonen} Map},
  booktitle	= {Proc. Journ{\'e}es d'{\'E}lectronique 1989, Artificical
		  Neural Networks, Lausanne, Switzerland, October 10--12},
  year		= {1989},
  pages		= {291--301},
  publisher	= {Presses Polytechniques Romandes},
  address	= {Lausanne, Switzerland},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vleugels93a,
  author	= {Jules M. Vleugels and Joost N. Kok and Mark H. Overmars},
  title		= {A Self-Organizing Neural Network for Robot Motion
		  Planning},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {281--284},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  voegtlin00a,
  author	= {Voegtlin, Thomas},
  title		= {Context quantization and Contextual Self-Organizing Maps},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {6},
  pages		= {20--25},
  organization	= {Inst des Sciences Cognitives},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Vector quantization consists in finding a discrete
		  approximation of a continuous input. One of the most
		  popular neural algorithms related to vector quantization is
		  the, so called, Kohonen map. In this paper we generalize
		  vector quantization to temporal data, introducing context
		  quantization. We propose a recurrent network inspired by
		  the Kohonen map, the Contextual Self-Organizing Map, that
		  develops near-optimal representations of context. We
		  demonstrate quantitatively that this algorithm shows better
		  performance than the other neural methods proposed so
		  far.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  voegtlin01a,
  author	= {T. Voegtlin and P. F. Dominey},
  title		= {Recursive self-organising maps},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {210--5},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  vogt93a,
  author	= {Michael Vogt},
  title		= {Combination of Radial Basis Function Neural Networks with
		  Optimized Learning Vector Quantization},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1841--1846},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InCollection{	  voitovetsky97a,
  author	= {I. Voitovetsky and H. Guterman and A. Cohen},
  title		= {Unsupervised speaker classification using
		  \mbox{self-organizing} maps ({SOM})},
  booktitle	= {Neural Networks for Signal Processing VII. Proceedings of
		  the 1997 IEEE Signal Processing Society Workshop},
  publisher	= {IEEE},
  year		= {1997},
  editor	= {J. Principe and L. Gile and N. Morgan and E. Wilson},
  address	= {New York, NY, USA},
  pages		= {578--87},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vollmer99a,
  author	= {Vollmer, U. and Strey, A.},
  title		= {Experimental study on the precision requirements of {RBF},
		  {RPROP} and {BPTT} training},
  booktitle	= {ICANN99. Ninth International Conference on Artificial
		  Neural Networks (IEE Conf. Publ. No.470)},
  publisher	= {IEE},
  address	= {London, UK},
  year		= {1999},
  volume	= {1},
  pages		= {239--44},
  abstract	= {Most neurocomputer architectures support only fixed point
		  arithmetic which allows a higher degree of VLSI integration
		  but limits the range and precision of all variables. Up to
		  now the effect of this limitation on neural network
		  training algorithms has been studied only for standard
		  models like SOM or BP. This paper presents the results of
		  an experimental study in which the precision requirements
		  of three other learning algorithms (RBF, RPROP and BPTT) on
		  exemplary task have been investigated. While the RBF and
		  BPTT key variables required more than 16 bit for training
		  to solve the selected problems, the RPROP algorithm showed
		  good results with far less than 16 bit.},
  dbinsdate	= {oldtimer}
}

@Article{	  volmer97a,
  author	= {R. Volmer and F. B. Lehrbass},
  title		= {{K}ohonen's \mbox{self-organizing} maps and the future of
		  the {DAX}},
  journal	= {Wirtschaftsinformatik},
  year		= {1997},
  volume	= {39},
  number	= {4},
  pages		= {339--44},
  dbinsdate	= {oldtimer}
}

@Article{	  vonk96a,
  author	= {E. Vonk and L. P. J. Veelenturf and L. C. Jain},
  title		= {Neural networks: implementations and applications},
  journal	= {IEEE Aerospace and Electronics Systems Magazine},
  year		= {1996},
  volume	= {11},
  number	= {7},
  pages		= {11--16},
  dbinsdate	= {oldtimer}
}

@InCollection{	  voukydis95a,
  author	= {P. C. Voukydis},
  title		= {A neural network system for detection of life-threatening
		  arrhythmias, based on {K}ohonen networks},
  booktitle	= {Computers in Cardiology 1995},
  publisher	= {IEEE},
  year		= {1995},
  address	= {New York, NY, USA},
  pages		= {165--7},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vrieze95a,
  author	= {Vrieze, O. J. },
  title		= {{K}ohonen network},
  booktitle	= {Artificial Neural Networks. An Introduction to ANN Theory
		  and Practice},
  year		= {1995},
  editor	= {Braspenning, P. J. and Thuijsman, F. and Weijters, A. J.
		  M. M. },
  pages		= {83--100},
  organization	= {Dept. of Math. , Limburg Univ. , Maastricht, Netherlands},
  publisher	= {Springer},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  vuori02a,
  author	= {Vuori, Vuokko and Laaksonen, Jorma and Kangas, Jari},
  title		= {Influence of erroneous learning samples on adaptation in
		  on-line handwriting recognition},
  journal	= {Pattern Recognition},
  year		= {2002},
  volume	= {35},
  number	= {4},
  month		= {April },
  pages		= {915--925},
  organization	= {Lab. of Computer and Info. Science, Helsinki University of
		  Technology},
  publisher	= {},
  address	= {},
  abstract	= {We have considered problems involved in the
		  self-supervised learning process of an on-line handwriting
		  recognition system. Our system is able to recognize
		  isolated characters by comparing them to prototype
		  characters with a method based on the Dynamic Time Warping
		  algorithm. The recognition system is adapted by adding new
		  prototypes, inactivating confusing or erroneous ones, and
		  reshaping existing prototypes with a method based on the
		  Learning Vector Quantization. We have analyzed the sources
		  of erroneous learning samples and studied the influence of
		  such samples on the performance of the recognizer via
		  simulations. In these simulations, two adaptation
		  strategies combined with four methods for inactivating
		  prototypes were applied. The results of the simulations
		  showed that the adaptation strategies are able to improve
		  the system's recognition rate and the prototype
		  inactivation methods do reduce the harmful effects of
		  erroneous learning samples. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  vuori95a,
  author	= {Jarkko Vuori and Teuvo Kohonen},
  title		= {Fast {DSP} Implementation of High-Dimensional Vector
		  Classifiers},
  volume	= {IV},
  pages		= {2019--2022},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vuori99a,
  author	= {Vuori, V. and Laaksonen, J. and Oja, E. and Kangas, J.},
  title		= {On-line adaptation in recognition of handwritten
		  alphanumeric characters},
  booktitle	= {Proceedings of the Fifth International Conference on
		  Document Analysis and Recognition. ICDAR '99},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {792--5},
  abstract	= {We have developed an adaptive online recognizer that is
		  suitable for recognizing isolated alphanumeric characters.
		  It is based on the k nearest neighbor rule. Various
		  dissimilarity measures, all based on dynamic time warping
		  (DTW), have been studied. The main focus of this work is on
		  online adaptation. The adaptation is performed by modifying
		  the prototype set of the classifier according to its
		  recognition performance and the user's writing style. These
		  adaptations include: (1) adding new prototypes, (2)
		  inactivating confusing prototypes, and (3) reshaping
		  existing prototypes. The reshaping algorithm is based on
		  learning vector quantization (LVQ). The writers are allowed
		  to use their own natural style of writing, and the
		  adaptation is carried out during normal use in a
		  self-supervised fashion and thus remains otherwise
		  unnoticed by the user.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vuorimaa94a,
  author	= {Petri Vuorimaa},
  title		= {A Model Based Neuro-Fuzzy Controller},
  editor	= {Christer Carlsson and Timo J{\"{a}}rvi and Tapio Reponen},
  number	= {12},
  series	= {Conf. Proc. of Finnish Artificial Intelligence Society},
  pages		= {177--183},
  booktitle	= {Proc. Conf. on Artificial Intelligence Res. in Finland},
  year		= {1994},
  publisher	= {Finnish Artificial Intelligence Society},
  address	= {Helsinki, Finland},
  annote	= {application, control, modification},
  dbinsdate	= {oldtimer}
}

@Article{	  vuorimaa94b,
  author	= {Vuorimaa, Petri},
  title		= {Fuzzy \mbox{self-organizing} map},
  journal	= {Fuzzy Sets and Systems},
  year		= {1994},
  number	= {2},
  volume	= {66},
  pages		= {223--231},
  month		= {Sept},
  annote	= {A conference paper in journal},
  abstract	= {A version of Kohonen's Self-Organizing Map, called Fuzzy
		  Self-Organizing Map, replaces the neurons of the original
		  by fuzzy rules, composed of fuzzy sets. It performs a
		  mapping from a n-dimensional input space to one-dimensional
		  output space. The learning capability of the new version
		  enables it to model a continuous valued function to an
		  arbitrary accuracy. Simulation results of a two-dimensional
		  sinc function show good accuracy and fast convergence.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vuorimaa94c,
  author	= {Vuorimaa, P. },
  title		= {Use of the Fuzzy Self-Organizing Map in pattern
		  recognition},
  booktitle	= {Proceedings of the Third IEEE Conference on Fuzzy Systems.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {2},
  pages		= {798--801},
  organization	= {Signal Process. Lab. , Tampere Univ. of Technol. ,
		  Finland},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {Kohonen's Self-Organizing Map is one of the best-known
		  neural network models. In our previous work, we have
		  developed a fuzzy version of the model called: Fuzzy
		  Self-Organizing Map. The new version is similar to the
		  fuzzy logic controllers, and thus it is easy to use and
		  computationally efficient. On the other hand, since the
		  Fuzzy Self-Organizing Map is derived from the original
		  model, the Kohonen's learning laws can be used to tune the
		  system. In this paper, we show how the Fuzzy
		  Self-Organizing Map can be used in pattern recognition. For
		  this purpose, we introduce a new multiple input, multiple
		  output version of the Fuzzy Self-Organizing Map.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  vuorimaa94d,
  author	= {Petri Vuorimaa},
  title		= {Use of a Default Rule in Fuzzy Self-Organizing Map},
  booktitle	= {Advances in Fuzzy Theory and Technology},
  publisher	= {Duke University},
  year		= 1994,
  editor	= {Paul P. Wang},
  address	= {Durham, North Carolina},
  pages		= {219--232},
  dbinsdate	= {oldtimer}
}

@Article{	  vuorimaa95a,
  author	= {Vuorimaa, P. and Jukarainen, T. and Karpanoja, E. },
  title		= {A neuro-fuzzy system for chemical agent detection},
  journal	= {IEEE Transactions on Fuzzy Systems},
  year		= {1995},
  volume	= {3},
  number	= {04},
  pages		= {415--24},
  month		= {Nov},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  vuurpijl94a,
  author	= {Vuurpijl, L. and Schouten, T. and Vytopil, J. },
  title		= {A scalable performance prediction method for parallel
		  neural network simulations},
  booktitle	= {High-Performance Computing and Networking. International
		  Conference and Exhibition Proceedings. Vol. 1:
		  Applications},
  year		= {1994},
  editor	= {Gentzsch, W. and Harms, U. },
  pages		= {396--401},
  organization	= {Nijmegen Univ. , Netherlands},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  vuurpijl95a,
  author	= {Vuurpijl, L. and Schouten, T. and Vytopil, J. },
  title		= {Performance prediction of large {MIMD} systems for
		  parallel neural network simulations},
  journal	= {Future Generation Computer Systems},
  year		= {1995},
  volume	= {11},
  number	= {2},
  pages		= {221--32},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wacquant94a,
  author	= {Wacquant, S. and Joublin, F. and Debrie, R. },
  title		= {GALIEN: a simulation environment for modular neural
		  networks},
  booktitle	= {Proceedings of the 1994 Summer Computer Simulation
		  Conference. Twenty-Sixth Annual Summer Computer Simulation
		  Conference},
  year		= {1994},
  editor	= {Pace, D. K. and Fayek, A. -M. },
  pages		= {211--16},
  organization	= {LCIA, Inst. Nat. des Sci. Appliques, Rouen, France},
  publisher	= {SCS},
  address	= {San Diego, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wagatsuma99a,
  author	= {Wagatsuma, H. and Yamaguchi, Y.},
  title		= {A neural network model \mbox{self-organizing} a cognitive
		  map using theta phase precession},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {199--204},
  abstract	= {The cognitive map is thought to consist of place cells in
		  the hippocampus. We hypothesize that "theta phase
		  precession", a characteristic relation between the firing
		  of the place cell and local field theta rhythm, contributes
		  to the generation of the cognitive map as a network of
		  place cells. We propose a network model of the hippocampus
		  based on the hypothesis. In our model, the entorhinal
		  cortex (EC) receives sensory-inputs of local views. The
		  dynamic pattern of theta phase precession is generated by
		  mutual synchronization of neural oscillators in the EC. In
		  CA3, neural units receive inputs from the EC and the
		  cognitive map is self-organized in the recurrent neural
		  network. In the computer experiments, inheritance of theta
		  phase precession in CA3 results in a kind of phase wave
		  propagating along a 2-D plane. The 2-D plane is define as
		  the desired array of place cells in accordance with their
		  place fields. The wave propagation deriving from theta
		  phase precession globally controls the synaptic
		  modification among place cells so that their network is
		  self-organized as the cognitive map.},
  dbinsdate	= {oldtimer}
}

@Article{	  waizumi00a,
  author	= {Waizumi, Yuji and Kato, Nei and Saruta, Kazuki and Nemoto,
		  Yoshiaki},
  title		= {High speed and high accuracy rough classification for
		  handwritten characters using Hierarchical Learning Vector
		  Quantization},
  journal	= {IEICE Transactions on Information and Systems},
  year		= {2000},
  volume	= {E83-D},
  number	= {6},
  month		= {},
  pages		= {1282--1290},
  organization	= {Tohoku Univ},
  publisher	= {IEICE of Japan},
  address	= {Tokyo},
  abstract	= {We propose a rough classification system using
		  Hierarchical Learning Vector Quantization (HLVQ) for large
		  scale classification problems which involve many
		  categories. HLVQ of proposed system divides categories
		  hierarchically in the feature space, makes a tree and
		  multiplies the nodes down the hierarchy. The feature space
		  is divided by a few codebook vectors in each layer. The
		  adjacent feature spaces overlap at the borders. HLVQ
		  classification is both speedy and accurate due to the
		  hierarchical architecture and the overlapping technique. In
		  a classification experiment using ETL9B, the largest
		  database of handwritten characters in Japan, (it contains a
		  total of 607,200 samples from 3036 categories) the speed
		  and accuracy of classification by HLVQ was found to be
		  higher than that by Self-Organizing feature Map (SOM) and
		  Learning Vector Quantization methods. We demonstrate that
		  the classification rate of the proposed system which uses
		  multi-codebook vectors for each category under HLVQ can
		  achieve higher speed and accuracy than that of systems
		  which use average vectors.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  waldemark95a,
  author	= {Joakim Waldemark and Per-Ola Dovner and Jan Karlsson},
  title		= {Hybrid Neural Network Pattern Recognition System for
		  Satellite Measurements},
  volume	= {I},
  pages		= {195--199},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Book{		  waldemark95b,
  author	= {Waldemark, J. and Dovner, P.O. and Karlsson, J.},
  title		= {Neural network detection system for lower-hybrid cavities
		  in electron plasma density measured by the FREJA
		  satellite.},
  year		= {1995},
  abstract	= {This paper presents a lower-hybrid cavity detection
		  system, CDS, for measurements of electron plasma density on
		  the FREJA satellite wave experiment. The system can reduce
		  the amount of data to be analyzed by as much as 96% and
		  still retain more than 85% of the desired information. The
		  CDS is a combination of a hybrid neural network, HNN and
		  expert rules. The HNN is a Self Organizing Map, SOM,
		  combined with a feed forward back propagation neural net,
		  BP. The CDS can be controlled by the user to operate with
		  various degrees of sensitivity. Maximum detection
		  capability is as high as 95% with data reduction lowered to
		  85%. 10 refs. (Atomindex citation 26:072522)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  waldemark96a,
  author	= {Waldemark, J.},
  title		= {An automated procedure for cluster analysis of
		  multivariate satellite data},
  booktitle	= {Solving Engineering Problems with Neural Networks.
		  Proceedings of the International Conference on Engineering
		  Applications of Neural Networks (EANN'96). Syst. Eng.
		  Assoc, Turku, Finland},
  year		= {1996},
  volume	= {1},
  pages		= {237--40},
  abstract	= {A study of the applicability of available cluster analysis
		  methods on multivariate satellite data is made. Traditional
		  computer science methods like principal component analysis
		  (PCA) and K-means as well as neural network methods such as
		  self organizing maps (SOM) and adaptive resonance theory
		  (ART) have been studied. Special focus is made on the
		  usefulness of these cluster analysis methods, that is if
		  results generated by these methods have any relevance to,
		  and can be of help to, the physical analysis of
		  multivariate data. A combined SOM adaptive dynamic K-means
		  procedure suitable for automated cluster analysis is
		  presented. This new method is capable of achieving useful
		  categorisation of multivariate satellite data, that is
		  established categories that are similar to those identified
		  by physicist.},
  dbinsdate	= {oldtimer}
}

@Article{	  waldemark97a,
  author	= {J. Waldemark},
  title		= {An automated procedure for cluster analysis of
		  multivariate satellite data},
  journal	= {International Journal of Neural Systems},
  year		= {1997},
  volume	= {8},
  number	= {1},
  pages		= {3--15},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  waldron94a,
  author	= {Manjula B. Waldron and Soowon Kim},
  title		= {Increasing Manual Sign Recognition Vocabulary through
		  Relabelling},
  pages		= {2885--2889},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {pattern recognition},
  dbinsdate	= {oldtimer}
}

@Article{	  waldron95a,
  author	= {Waldron, M. B. and Soowon Kim},
  title		= {Isolated {ASL} sign recognition system for deaf persons},
  journal	= {IEEE Transactions on Rehabilitation Engineering},
  year		= {1995},
  volume	= {3},
  number	= {3},
  pages		= {261--71},
  month		= {Sept},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  walker93a,
  author	= {Ashley Walker and John Hallam and David Willshaw},
  title		= {Bee-havior in a Mobile Robot: The Construction of a
		  Self-Organized Cognitive Map and its Use in Robot
		  Navigation within a Complex, Natural Environment},
  booktitle	= {Proc. ICNN'93, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1451--1456},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {In this work, we model a mobile robotic control system on
		  the spatial memory and navigatory behaviours attributed to
		  foraging honey bees in an effort to exploit some of the
		  robustness and efficiency these insects are known to enjoy.
		  Our robot uses a self-organizing feature-mapping neural
		  network to construct a topographically ordered map from the
		  ultra-sound range images collected while exploring the
		  environment. This map is then annotated with metric
		  positional information from a dead reckoning system. The
		  resulting cognitive map can be used by the robot to
		  localize in the world and to plan safe and efficient routes
		  through the environment. This system has been thoroughly
		  tested in simulation and is currently being implemented on
		  the robot.},
  dbinsdate	= {oldtimer}
}

@Article{	  walker94a,
  author	= {Walker, N. P. and Eglen, S. J. and Lawrence, B. A. },
  title		= {Image compression using neural networks},
  journal	= {GEC Journal of Research Incorporating the Marconi Review
		  and the Plessey Research Review},
  year		= {1994},
  volume	= {11},
  number	= {2},
  pages		= {66--75},
  dbinsdate	= {oldtimer}
}

@Article{	  walker96a,
  author	= {C. G. H. Walker},
  title		= {Analysis of Multispectral Microscope Images Using Neural
		  Networks},
  journal	= {Surface and Interface Analysis},
  year		= {1996},
  volume	= {24},
  pages		= {173--180},
  dbinsdate	= {oldtimer}
}

@Article{	  walker99a,
  author	= {Walker, Andrew J.~ and Cross, Simon S.~ and Harrison,
		  Robert F.~},
  title		= {Visualization of biomedical datasets by use of growing
		  cell structure networks: a novel diagnostic classification
		  technique},
  journal	= {The Lancet},
  year		= {1999},
  volume	= {354},
  pages		= {1518--1521},
  month		= {October},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  walley00a,
  author	= {Walley, W. J. and Martin, R. W. and O'Connor MA},
  title		= {Self-organising maps for the classification and diagnosis
		  of river quality from biological and environmental data},
  booktitle	= {Environmental Software Systems. Environmental Information
		  and Decision Support. IFIP TC5 WG5.11. 3rd International
		  Symposium. Kluwer Academic Publishers, Norwell, MA, USA},
  year		= {2000},
  volume	= {},
  pages		= {27--41},
  abstract	= {The paper addresses the problem of how to classify and
		  diagnose the state of health of a river from the
		  composition of its biological community. It is claimed that
		  experts use two complex mental processes when interpreting
		  such data, knowledge-based reasoning and pattern
		  recognition. It is argued that existing classification
		  methods are inadequate and that the application of advanced
		  computer-based techniques is vital to the realisation of
		  the full potential of biological monitoring. The paper then
		  concentrates on a pattern recognition approach and
		  demonstrates how self organising maps (SOM), a type of
		  unsupervised-learning neural network, can be used to
		  classify and diagnose river quality. A brief introduction
		  is given to the theory of SOMs and the interpretation of
		  their output, as expressed in feature maps and class
		  templates. SOMs are developed using two different methods
		  of accounting for the confounding effects of environmental
		  factors, and their relative performances are compared. Some
		  improvements to the SOM architecture and functionality that
		  are currently being implemented are briefly described,
		  together with plans to use information theory for the
		  assessment of performance. Finally, it is concluded that
		  the methods of classification/diagnosis described in the
		  paper have considerable potential not only in river quality
		  monitoring, but also in other environmental fields.},
  dbinsdate	= {2002/1}
}

@Article{	  walley01a,
  author	= {Walley, W. J. and O'Connor, M. A.},
  title		= {Unsupervised pattern recognition for the interpretation of
		  ecological data},
  journal	= {ECOLOGICAL MODELLING},
  year		= {2001},
  volume	= {146},
  number	= {1--3},
  month		= {DEC 1},
  pages		= {219--230},
  abstract	= {The paper describes a novel pattern recognition system
		  (MIR- max) that was developed to facilitate the
		  construction of a river pollution diagnostic system for the
		  British Environment Agency. MIR-max is a non-neural
		  self-organising map based on information theory, which,
		  unlike Kohonen's Self-organised map (SOM), separates the
		  processes of clustering and ordering. It first clusters the
		  input samples into a pre-defined number of classes by
		  maximising the mutual information between the samples and
		  the classes. The classes are then ordered in a two-
		  dimensional output space by maximising the correlation
		  coefficient (r) between the Euclidean distances separating
		  the classes in data space and their corresponding distances
		  in output space. This produces a map of the classes which
		  when labelled can be used for the classification/diagnosis
		  of new samples. A novel feature of MIR-max is that it
		  permits the disaggregation of the classes in the output
		  map, thus permitting exceptional classes to separate from
		  their neighbours. MIR-max is designed specifically for use
		  with ordinal data, but can also be used for interval-valued
		  data. Its application in the ecological field is
		  demonstrated via two examples based on data from the 1995
		  river quality survey of England and Wales, In the first
		  example, MIR-max is used to classify biological samples
		  into 100 river quality classes for each of five site types.
		  These classifiers are then tested against two corresponding
		  neural network classifiers, and are shown to provide better
		  performance. In the second example, MIR-max is used to
		  classify combined biological and environmental (i.e.
		  physical characteristics of the site) data directly into
		  500 quality classes. The way in which this pattern
		  classifier has been used to produce a river pollution
		  diagnostic system is then explained. The advantages of the
		  system over traditional river quality assessment systems,
		  like RIVPACS, are outlined. It is concluded that MIR-max
		  has considerable potential for use in the visualisation and
		  interpretation of multivariate ecological data. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  walter00a,
  author	= {J\"{o}rg Walter and Claudia N\"{o}lker and Helge Ritter},
  title		= {The {PSOM} Algorithm and Applications},
  booktitle	= {Proceeding of the ICSC Symposia on Neural Computation
		  (NC'2000) May 23-26, 2000 in Berlin, Germany},
  editor	= {H. Bothe and R. Rojas},
  year		= {2000},
  organization	= {University of Bielefeld, Department of Computer Science},
  publisher	= {ICSC Academic Press},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  walter00b,
  author	= {Walter, Joerg and Arnrich, Bert and Scheering, Christian},
  title		= {Learning fine positioning of a robot manipulator based on
		  Gabor wavelets},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {137--142},
  organization	= {Univ of Bielefeld},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {A system for learning the pre-grasp positioning task for a
		  robot manipulator is presented. The images delivered from a
		  gripper mounted camera are analyzed using Gabor filters
		  which resemble the spatial response profiles of receptive
		  fields found in visual cortex neurons. Using a quite small
		  feature set, the system demonstrated efficiency with
		  respect to speed and accuracy, as well as robustness
		  against changing light conditions. Furthermore, we compare
		  it to two other approaches, aiming at the same goal: an
		  appearance-based PCA fuzzy control and a PSOM based
		  Hough-Transform system.},
  dbinsdate	= {2002/1}
}

@InCollection{	  walter90a,
  author	= {J\"org Walter and Helge Ritter and Klaus Schulten},
  title		= {Non-Linear Prediction with Self-Organizing Maps},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, San Diego},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  volume	= {1},
  year		= 1990,
  pages		= {589--594},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  walter91a,
  author	= {J{\"{o}}rg A. Walter and Thomas M. Martinetz and Klaus J.
		  Schulten},
  title		= {Industrial Robot Learns Visuo-Motor Coordination by Means
		  of 'Neural Gas' Network},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {I},
  pages		= {357--364},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  walter93a,
  author	= {Walter, J. A. and Schulten, K. I. },
  title		= {Implementation of \mbox{self-organizing} neural networks
		  for visuo-motor control of an industrial robot},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1993},
  volume	= {4},
  number	= {1},
  pages		= {86--96},
  month		= {Jan},
  abstract	= {We report on the implementation of two neural network
		  algorithms for visuo-motor control of an industrial robot
		  (Puma 562). The first algorithm uses a vector quantization
		  technique, the 'neural-gas' network, together with an error
		  correction scheme based on a Widrow-Hoff-type learning
		  rule. The second algorithm employs an extended
		  self-organizing feature map algorithm. Based on visual
		  information provided by two cameras, the robot learns to
		  position its end effector without an external teacher.
		  Within only 3000 training steps, the robot---camera system
		  is capable of reducing the positioning error of the robot's
		  end effector to approximately 0.1% of the linear dimension
		  of the work space. By employing adaptive feedback the robot
		  succeeds in compensating not only slow calibration drifts,
		  but also sudden changes in its geometry. Hardware aspects
		  of the robot---camera system are discussed.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  walter95a,
  author	= {J{\"{o}}rg Walter and Helge Ritter},
  title		= {Local {PSOM}s and {C}hebyshev {PSOM}s Improving the
		  Parametrised Self-Organizing Maps},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {I},
  pages		= {95--102},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InCollection{	  walter96a,
  author	= {J. Walter and H. Ritter},
  title		= {Investment learning with hierarchical {PSOM}s},
  booktitle	= {Advances in Neural Information Processing 8. Proceedings
		  of the 1995 Conference},
  publisher	= {MIT Press},
  year		= {1996},
  editor	= {D. S. Touretzky and M. C. Mozer and M. E. Hasselmo},
  address	= {Cambridge, MA, USA},
  pages		= {570--6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  walter96b,
  author	= {J. Walter and H. Ritter},
  title		= {Associative completion and investment learning using
		  {PSOM}s},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {157--64},
  accessvia	= {http://www. techfak. uni-bielefeld. de/~walter/},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wan00a,
  author	= {Wan, W. J. and Fraser, D.},
  title		= {A Multiple Self-Organizing Map scheme for remote sensing
		  classification},
  booktitle	= {MULTIPLE CLASSIFIER SYSTEMS},
  year		= {2000},
  pages		= {300--309},
  abstract	= {This paper presents a multiple classifier scheme, known as
		  Multiple Self-Organizing Maps (MSOM), for remote sensing
		  classification problems. Based on the Kohonen SOM, multiple
		  maps are fused, in either unsupervised, supervised or
		  hybrid manners, so as to explore discrimination information
		  from the data itself. The MSOM has the capability to
		  extract and represent high-order statistics of high
		  dimensional data from disparate sources in a nonparametric,
		  vector-quantization fashion. The computation cost is linear
		  in relation to the dimensionality and the operation
		  complexity is simple and equivalent to a minimum-distance
		  classifier. Thus, MSOM is very suitable for remote sensing
		  applications under various data and design-sample
		  conditions. We also demonstrate that the MSOM can be used
		  for hyperspectral data clustering and joint spatio-
		  temporal classification.},
  dbinsdate	= {2002/1}
}

@InCollection{	  wan93a,
  author	= {W. Wan and D. Fraser},
  title		= {A \mbox{self-organising} neural network for contextual
		  analysis of spatial patterns of multisource data},
  booktitle	= {Conference Proceedings DICTA-93 Digital Image Computing:
		  Techniques and Applications},
  publisher	= {Australian Pattern Recognition Soc},
  year		= {1993},
  volume	= {1},
  editor	= {K. K. Fung and A. Ginige},
  address	= {Broadway, NSW, Australia},
  pages		= {71--8},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wan93b,
  author	= {Weijian Wan and Donald Fraser},
  title		= {{M2dSOMAP}: {C}lustering and Classification of Remotely
		  Sensed Imagery by Combining Multible {K}ohonen
		  Self-Organizing Maps and Associative Memory},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2464--2467},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wan94a,
  author	= {Weijian Wan and Donald Fraser},
  title		= {Multiple {K}ohonen {S}elf-{O}rganising {M}aps: Supervised
		  and Unsupervised Formation, with Application to Remotely
		  Sensed Imagery Analysis},
  editor	= {A. C. Tsoi and T. Downs},
  pages		= {17--20},
  booktitle	= {Proc. of 5th Australian Conf. on Neural Networks},
  publisher	= {University of Queensland},
  address	= {St. Lucia, Australia},
  year		= {1994},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wan94b,
  author	= {Weijian Wan and Donald Fraser},
  title		= {A Self-Organising Neural Network Framework for
		  Unsupervised and Supervised Classification},
  booktitle	= {Proc. 7th Australasian Remote Sensing Conference,
		  Melborne, Australia},
  year		= {1994},
  pages		= {423--430},
  publisher	= {Remote Sensing and Photogrammetry Association Australia,
		  Ltd},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wan94c,
  author	= {Weijian Wan and Donald Fraser},
  title		= {A Self-Organising Neural Network Framework for Multisource
		  Data and Contextual Analysis},
  booktitle	= {Proc. 7th Australasian Remote Sensing Conference,
		  Melborne, Australia},
  year		= {1994},
  pages		= {145--150},
  publisher	= {Remote Sensing and Photogrammetry Association Australia,
		  Ltd},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wan94d,
  author	= {Weijian Wan and Donald Fraser},
  title		= {A Self-Organising Neural Network Framework for High
		  Dimensional Data Analysis},
  booktitle	= {Proc. 7th Australasian Remote Sensing Conference,
		  Melborne, Australia},
  year		= {1994},
  pages		= {151--156},
  publisher	= {Remote Sensing and Photogrammetry Association Australia,
		  Ltd},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wan94e,
  author	= {Weijian Wan and Fraser, D. },
  title		= {A \mbox{self-organizing} map model for spatial and
		  temporal contextual classification},
  booktitle	= {IGARSS '94. International Geoscience and Remote Sensing
		  Symposium. Surface and Atmospheric Remote Sensing:
		  Technologies, Data Analysis and Interpretation},
  year		= {1994},
  volume	= {4},
  pages		= {1867--9},
  organization	= {Dept. of Electr. Eng, New South Wales Univ. , Canberra,
		  ACT, Australia},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wan96a,
  author	= {W. Wan and D. Fraser},
  title		= {Spatial and temporal classification with multiple
		  \mbox{self-organising} maps},
  booktitle	= {Proceedings of the 3rd Conference on Image and signal
		  processing for remote sensing III},
  volume	= {2955},
  year		= {1996},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  pages		= {307--314},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wan96b,
  author	= {H. B. Wan and Y. H. Song and A. T. Johns},
  title		= {Identification of voltage weak buses/areas using neural
		  network based classifier},
  booktitle	= {MELECON '96. 8th Mediterranean Electrotechnical
		  Conference. Industrial Applications in Power Systems,
		  Computer Science and Telecommunications. Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  editor	= {M. {de Sario} and B. Maione and P. Pugliese and M.
		  Savino},
  address	= {New York, NY, USA},
  pages		= {1482--5},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wan97a,
  author	= {Weijian Wan and D. Fraser},
  title		= {An {MSOM} framework for multi-source fusion and spatio-
		  temporal classification},
  booktitle	= {IGARSS'97. 1997 International Geoscience and Remote
		  Sensing Symposium. Remote Sensing---A Scientific Vision for
		  Sustainable Development},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {4},
  editor	= {T. I. Stein},
  address	= {New York, NY, USA},
  pages		= {1657--9},
  dbinsdate	= {oldtimer}
}

@Article{	  wan99a,
  author	= {Weijian Wan and Fraser, D.},
  title		= {Multisource data fusion with multiple
		  \mbox{self-organizing} maps},
  journal	= {IEEE Transactions on Geoscience and Remote Sensing},
  year		= {1999},
  volume	= {37},
  pages		= {1344--9},
  abstract	= {This paper presents a self-organizing neural network
		  approach, known as multiple self-organizing maps (MSOMs),
		  to multisource data fusion and compound classification. The
		  authors use the Kohonen SOM as a building block to set up a
		  design framework for a range of classifiers. They
		  demonstrate that the MSOM is suitable for multisource
		  fusion, where the issues of high dimensionality, complex
		  characteristics and disparity, and joint exploration of
		  spatiality and temporality of mixed data can be adequately
		  addressed. Experiments with a bitemporal data set show the
		  effectiveness of their approach.},
  dbinsdate	= {oldtimer}
}

@Article{	  wang00a,
  author	= {Wang, J. -H. and Peng, C. -Y. and Rau, J. -D.},
  title		= {Harmonic neural networks for on-line learning vector
		  quantization},
  journal	= {IEE Proceedings: Vision, Image and Signal Processing},
  year		= {2000},
  volume	= {147},
  number	= {5},
  month		= {Oct},
  pages		= {485--492},
  organization	= {Natl Taiwan Ocean Univ},
  publisher	= {IEE},
  address	= {Stevenage},
  abstract	= {A self-creating harmonic neural network (HNN) trained with
		  a competitive algorithm effective for on-line learning
		  vector quantization is presented. It is shown that by
		  employing dual resource counters to record the activity of
		  each node during the training process, the equi-error and
		  equi-probable criteria can be harmonized. Training in HNNs
		  is smooth and incremental, and it not only achieves the
		  biologically plausible on-line learning property, but it
		  can also avoid the stability-plasticity dilemma, the
		  dead-node problem, and the deficiency of the local minimum.
		  Characterizing HNNs reveals the great controllability of
		  HNNs in favouring one criterion over the other, when faced
		  with a must-choose situation between equi-error and
		  equi-probable. Comparison studies on learning vector
		  quantization involving stationary and non-stationary,
		  structured and non-structured inputs demonstrate that the
		  HNN outperforms other competitive networks in terms of
		  quantization error, learning speed and codeword search
		  efficiency.},
  dbinsdate	= {2002/1}
}

@Article{	  wang00b,
  author	= {Wang, Jin and Yu, Song-Yu and Zhang, Wen-Jun},
  title		= {Design of optimal codebook using evolutionary strategy},
  journal	= {Journal-of-China-Institute-of-Communications},
  year		= {2000},
  volume	= {21},
  pages		= {60--4},
  abstract	= {The evolutionary strategic competitive learning algorithm
		  is presented. It introduces the evolutionary strategy into
		  vector quantization. After the conventional competitive
		  learning algorithm (CL) has been used to decrease the
		  expected distortion, the evolutionary strategy is utilized
		  to modulate the subdistortion of each region determined by
		  corresponding codebook in order to improve the expected
		  distortion. The results show that the algorithm is superior
		  compared with other conventional codebook design algorithm,
		  it can modulate the subdistortion of each region and
		  achieve the global optimality.},
  dbinsdate	= {2002/1},
  merjanote     = {last name assumed according to small capitals in names (yu, jun)}
}

@Article{	  wang00c,
  author	= {Wang, Lei and Qi, Feihu},
  title		= {Adaptive {FKCN} method for image segmentation},
  journal	= {Tien Tzu Hsueh Pao/Acta Electronica Sinica},
  year		= {2000},
  volume	= {28},
  number	= {2},
  month		= {Feb},
  pages		= {4--6},
  organization	= {},
  publisher	= {},
  address	= {},
  abstract	= {Fuzzy Kohonen clustering network (FKCN) is a kind of
		  self-organizing fuzzy neural network. It shows great
		  superiority in processing the ambiguity and uncertainty of
		  image for its integration of the fuzzy c-means (FCM)
		  conception into the learning mechanism of Kohonen network.
		  But there are many defects, for example, the number of
		  network nodes can't be determined automatically, the speed
		  of network convergence is very slow, and the computation
		  cost is too large, when using FKCN to segment images. In
		  order to overcome these defects, an adaptive FKCN model is
		  presented, which can determine the network structure
		  automatically according to the gray level distribution
		  character of the image. By using the new fuzzy
		  intensification operator and implementing a sample space
		  transition in the network learning procedure, the network
		  convergence speed is greatly improved and the segmentation
		  result is also improved.},
  dbinsdate	= {2002/1}
}

@Article{	  wang00d,
  author	= {Wang, J. H. and Rau, J. D. and Peng, C. Y.},
  title		= {Toward optimizing a self-creating neural network},
  journal	= {IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-
		  CYBERNETICS},
  year		= {2000},
  volume	= {30},
  number	= {4},
  month		= {AUG},
  pages		= {586--593},
  abstract	= {This paper optimizes the performance of the GCS model [1]
		  in learning topology and vector quantization. Each node in
		  GCS is attached with a resource counter. During the
		  competitive learning process, the counter of the
		  best-matching node is increased by a defined resource
		  measure after each input presentation, and then all
		  resource counters are decayed by a factor or. We show that
		  the summation of all resource counters conserves. This
		  conservation principle provides useful clues for exploring
		  important characteristics of GCS, which in turn provide an
		  insight into how the GCS can be optimized. In the context
		  of information entropy, we show that performance of GCS in
		  learning topology and vector quantization can be optimized
		  by using alpha = 0 incorporated with a threshold-free node-
		  removal scheme, regardless of input data being stationary
		  or nonstationary. The meaning of optimization is twofold:
		  1) for learning topology, the information entropy is
		  maximized in terms of equiprobable criterion and 2) for
		  Learning vector quantization, the mse is minimized in terms
		  of equi-error criterion.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  wang00e,
  author	= {Wang Xiaodan and Jin Hua and Zhao Rongchun},
  title		= {Texture segmentation method based on incomplete tree
		  structured wavelet transform and fuzzy Kohonen clustering
		  network},
  booktitle	= {Proceedings of the 3rd World Congress on Intelligent
		  Control and Automation. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {4},
  pages		= {2684--7},
  abstract	= {A new texture segmentation method based on incomplete tree
		  structured wavelet transform and fuzzy Kohonen clustering
		  network (FKCN) is proposed in this paper. It consists of
		  three main steps: 1) feature extraction using incomplete
		  tree structured wavelet transform; 2) feature coarse
		  classification using fuzzy Kohonen clustering network which
		  involves initializing the weight vector of the network
		  according to the density function of input patterns and
		  training the network using the reduced set of feature
		  vectors to get the coarse segmentation result; 3) the
		  refinement of the coarse segmentation result. Texture
		  segmentation experiments give the effectiveness of this
		  method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  wang00f,
  author	= {Wang, Aimin and Shen, Lansun and Zhao, Zhongxu},
  title		= {Color tongue image segmentation using Fuzzy Kohonen
		  Networks and Genetic Algorithm},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {3962},
  pages		= {182--190},
  organization	= {Beijing Polytechnic Univ},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  abstract	= {A Tongue Imaging and Analysis System (TIAS) is being
		  developed to acquire digital color tongue images, and to
		  automatically classify and quantify the tongue
		  characteristics for traditional Chinese medical
		  examinations. An important processing step is to segment
		  the tongue pixels into two categories, the tongue body (no
		  coating) and the coating. In this paper, we present a
		  two-stage clustering algorithm that combines Fuzzy Kohonen
		  Clustering Networks (FKCN) and Genetic Algorithm (GA) for
		  the segmentation, of which the major concern is to increase
		  the interclass distance and at the same time decrease the
		  intraclass distance. Experimental results confirm the
		  effectiveness of this algorithm.},
  dbinsdate	= {2002/1}
}
 
@Article{	  wang00g,
  author	= {Wang, Jung-Hua and Peng, Chung-Yun},
  title		= {Novel self-creating neural network for learning vector
		  quantization},
  journal	= {Neural Processing Letters},
  year		= {2000},
  volume	= {11},
  number	= {2},
  month		= {},
  pages		= {139--151},
  organization	= {Natl Taiwan Ocean Univ},
  publisher	= {Kluwer Academic Publishers},
  address	= {Dordrecht},
  abstract	= {This paper presents a novel self-creating neural network
		  scheme which employs two resource counters to record
		  network learning activity. The proposed scheme not only
		  achieves the biologically plausible learning property, but
		  it also harmonizes equi-error and equi-probable criteria.
		  The training process is smooth and incremental: it not only
		  avoids the stability-and-plasticity dilemma, but also
		  overcomes the dead-node problem and the deficiency of local
		  minimum. Comparison studies on learning vector quantization
		  involving stationary and non-stationary, structured and
		  non-structured inputs demonstrate that the proposed scheme
		  outperforms other competitive networks in terms of
		  quantization error, learning speed, and codeword search
		  efficiency.},
  dbinsdate	= {2002/1}
}

@Article{	  wang01a,
  author	= {Wang, X. -Z. and Yoshizawa, M. and Tanaka, A. and Abe, K.
		  and Yambe, T. and Nitta, S.-I. and Chinzei, T. and Abe, Y.
		  and Imachi, K.},
  title		= {Automatic monitoring system for artificial hearts using
		  self organizing map},
  journal	= {ASAIO Journal},
  year		= {2001},
  volume	= {47},
  number	= {6},
  month		= {},
  pages		= {686--691},
  organization	= {Div. on Advanced Info. Technology, Information Synergy
		  Center, Tohoku University},
  publisher	= {},
  address	= {},
  abstract	= {This study presents an automatic monitoring system for
		  artificial hearts. The self organizing map (SOM) was
		  applied to monitoring and analysis of an aortic pressure
		  (AoP) signal measured from an adult goat equipped with a
		  total artificial heart. In the proposed system, two
		  different SOMs were used to detect and classify
		  abnormalities in the measured AoP signal. In the first
		  stage, an ordinary SOM, taught with only normal AoP data,
		  was used for detection of abnormalities on the basis of the
		  quantization error in the real-time monitoring task. In the
		  second stage, a supervised SOM was used for classification
		  of abnormalities. The supervised SOM can be regarded as an
		  ordinary SOM with an extra class vector for solving the
		  classification problem. The class vector is assigned to
		  every node in the second SOM as an output weight learned
		  according to Kohonen's learning rule. The effectiveness of
		  detection and classification of abnormalities using these
		  two SOMs was confirmed.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  wang01b,
  author	= {Wang peng and Sun guangmin and Zhang xinming},
  title		= {Handwritten character recognition based on hybrid neural
		  networks},
  booktitle	= {Proceedings-of-the-SPIE
		  --The-International-Society-for-Optical-Engineering.
		  vol.4555},
  year		= {2001},
  volume	= {4555},
  pages		= {65--70},
  abstract	= {A hybrid neural network system for the recognition of
		  handwritten character using SOFM, BP and fuzzy network is
		  presented. The horizontal and vertical projection of
		  preprocessed character and 4-directional edge projection
		  are used as feature vectors. In order to improve the
		  recognition effect, the global affine transformation
		  algorithm is applied. Using the hybrid neural network
		  system, the recognition rate is improved compared with the
		  BP neural network.},
  dbinsdate	= {2002/1}
}

@Article{	  wang01c,
  author	= {Wang, X. and Lin, H. and Lu, J. and Yahagi, T.},
  title		= {Detection of nonlinearly distorted M-ary {QAM} signals
		  using self-organizing map},
  journal	= {IEICE Transactions on Fundamentals of Electronics,
		  Communications and Computer Sciences},
  year		= {2001},
  volume	= {E84-A},
  number	= {8},
  month		= {August },
  pages		= {1969--1976},
  organization	= {Grad. Sch. of Science and Technology, Chiba University},
  publisher	= {},
  address	= {},
  abstract	= {Detection of nonlinearly distorted signals is an essential
		  problem in telecommunications. Recently, neural network
		  combined conventional equalizer has been used to improve
		  the performance especially in compensating for nonlinear
		  distortions. In this paper, the self-organizing map (SOM)
		  combined with the conventional symbol-by-symbol detector is
		  used as an adaptive detector after the output of the
		  decision feedback equalizer (DFE), which updates the
		  decision levels to follow up the nonlinear distortions. In
		  the proposed scheme, we use the box distance to define the
		  neighborhood of the winning neuron of the SOM algorithm.
		  The error performance has been investigated in both 16 QAM
		  and 64 QAM systems with nonlinear distortions. Simulation
		  results have shown that the system performance is
		  remarkably improved by using SOM detector compared with the
		  conventional DFE scheme.},
  dbinsdate	= {2002/1}
}

@Article{	  wang01d,
  author	= {Wang, Xiao Dan and Jin, Hua and Zhao, Rong Chun and 
                  Wu, Chong Ming},
  title		= {Texture segmentation method based on incomplete
		  tree-structured wavelet transform and fuzzy clustering
		  network},
  journal	= {Mini-Micro-Systems},
  year		= {2001},
  volume	= {22},
  pages		= {325--8},
  abstract	= {A new type of incomplete tree-structured wavelet transform
		  used for texture feature extraction is proposed. A new
		  texture segmentation method consistent with the human
		  visual process and based on the incomplete tree-structured
		  wavelet transform and the fuzzy clustering network (FKCN)
		  is also proposed. It consists of three main steps: 1)
		  feature extraction using the incomplete tree-structured
		  wavelet transform; 2) coarse feature classification using a
		  fuzzy Kohonen clustering network (before the training
		  process, the weight vector of the network is initialized
		  according to the density function of input patterns, then
		  network is trained using the reduced set of feature vectors
		  and get the coarse segmentation results); and 3) refining
		  the coarse segmentation results. After the refinement of
		  the coarse segmentation results, the final segmentation
		  result is obtained. Texture segmentation experiments show
		  the effectiveness of this method.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name assumed, similarly to other papers in 
                   Mini-Micro-Systems}
}

@Article{	  wang01e,
  author	= {Wang, X. D. and Zhao, R. C.},
  title		= {New texture segmentation method based on visual perception
		  and {IFKCN}},
  journal	= {Xibei Gongye Daxue Xuebao/Journal of Northwestern
		  Polytechnical University},
  year		= {2001},
  volume	= {19},
  number	= {1},
  month		= {February },
  pages		= {31--34},
  organization	= {Northwestern Polytech. Univ.},
  publisher	= {},
  address	= {},
  abstract	= {Based on visual perception and improved fuzzy Kohonen
		  clustering network (IFKCN), a new texture segmentation
		  method was proposed, which was composed of 2D orthogonal
		  polar separable orientational filter to extract feature
		  images, quadrifurcate tree of the feature images, feature
		  clustering using IFKCN and edge determination. Simulation
		  results show the effectiveness of the method.},
  dbinsdate	= {2002/1}
}

@Article{	  wang01f,
  author	= {Wang, Y. and Zhu, Y. -S. and Thakor, N. V. and Xu, Y.
		  -H.},
  title		= {A short-time multifractal approach for arrhythmia
		  detection based on fuzzy neural network},
  journal	= {IEEE Transactions on Biomedical Engineering},
  year		= {2001},
  volume	= {48},
  number	= {9},
  month		= {},
  pages		= {989--995},
  organization	= {Department of Biomedical Engineering, Shanghai Jiao Tong
		  University},
  publisher	= {},
  address	= {},
  abstract	= {We have proposed the notion of short-time multifractality
		  and used it to develop a novel approach for arrhythmia
		  detection. Cardiac rhythms are characterized by short-time
		  generalized dimensions (STGDs), and different kinds of
		  arrhythmias are discriminated using a neural network. To
		  advance the accuracy of classification, a new fuzzy Kohonen
		  network, which overcomes the shortcomings of the classical
		  algorithm, is presented. In our paper, the potential of our
		  method for clinical uses and real-time detection was
		  examined using 180 electrocardiogram records [60 atrial
		  fibrillation, 60 ventricular fibrillation, and 60
		  ventricular tachycardial. The proposed algorithm has
		  achieved high accuracy (more than 97%) and is
		  computationally fast in detection.},
  dbinsdate	= {2002/1}
}

@Article{	  wang01g,
  author	= {Wang, H. C. and Badger, J. and Kearney, P. and Li, M.},
  title		= {Analysis of codon usage patterns of bacterial genomes
		  using the self-organizing map},
  journal	= {MOLECULAR BIOLOGY AND EVOLUTION},
  year		= {2001},
  volume	= {18},
  number	= {5},
  month		= {MAY},
  pages		= {792--800},
  abstract	= {Codon usage varies both between organisms and between
		  different genes in the same organism. This observation has
		  been used as a basis for earlier work in identifying highly
		  expressed and horizontally transferred genes in Escherichia
		  coli. In this work, we applied Kohonen's self-organizing
		  map to analysis of the codon usage pattern of the
		  Escherichia coli, Aquifex aeolicus, Archaeoglobus fulgidus,
		  Haemophilus influenzae Rd., Methanococcus jannaschii,
		  Methanobacterium thermoautotrophicum, and Pyrococcus
		  horikoshii genomes for evidence of highly expressed genes
		  and horizontally transferred genes. All of the analyzed
		  genomes had a clear category of horizontally transferred
		  genes, and their apparent percentages ranged from 7.7% to
		  21.4%. The apparent percentage of highly expressed genes
		  ranges from 0% to 11.8%. A clustering of average codon
		  usage of main gene categories of the seven genomes showed
		  an interesting mixing of gene classes in four
		  thermophilic/hyperthermophilic organisms, A. aeolicus, A.
		  fulgidus, M. thermoautotrophicum, and P. horikoshii, which
		  suggests possible origins of their horizon tally
		  transferred genes as well as the need for adaptation to a
		  specific environment. Further classification of the three
		  gene categories in E. coli and H. influenzae according to
		  gene function revealed that genes involved in communication
		  (such as regulation and cell process) and structure (cell
		  structure and structural proteins) are more likely to be
		  horizontally transferred than are genes involved in
		  information (transcription, translation, and related
		  processes) and in some groups of energy (such as energy
		  metabolism and carbon compound catabolism).},
  dbinsdate	= {2002/1}
}

@Article{	  wang01i,
  author	= {Wang, X. Z. and Yoshizawa, M. and Tanaka, A. and Abe, K.
		  and Yambe, T. and Nitta, S.},
  title		= {Automatic detection and classification of abnormalities
		  for artificial hearts using a hierarchical self-organizing
		  map},
  journal	= {ARTIFICIAL ORGANS},
  year		= {2001},
  volume	= {25},
  number	= {2},
  month		= {FEB},
  pages		= {150--153},
  abstract	= {A hierarchical self-organizing map (SOM) has been
		  developed for automatic detection and classification of
		  abnormalities for artificial hearts. The hierarchical SOM
		  has been applied to the monitoring and analysis of an
		  aortic pressure (AoP) signal measured from an adult goat
		  equipped with a total artificial heart. The architecture of
		  the network actually consists of 2 different SOMs. The
		  first SOM clusters the AoP beat patterns in an unsupervised
		  way. Afterward, the outputs of the first SOM combined with
		  the original time-domain features of beat-to-beat data are
		  fed to the second SOM for final classification. Each input
		  vector of the second SOM is associated with a class vector.
		  This class vector is assigned to every node in the second
		  map as an output weight and learned according to Kohonen's
		  learning rule. Some experimental results revealed that a
		  certain abnormality caused by breakage of sensors could be
		  identified and detected correctly and that the change in
		  the state of the circulatory system could be recognized and
		  predicted to some extent.},
  dbinsdate	= {2002/1}
}

@Article{	  wang02j,
  author	= {Wang, Shouhong},
  title		= {Nonlinear pattern hypothesis generation for data mining},
  journal	= {Data and Knowledge Engineering},
  year		= {2002},
  volume	= {40},
  number	= {3},
  month		= {March },
  pages		= {273--283},
  organization	= {Dept. of Mktg./Bus. Info. Systems, Charlton College of
		  Business, Univ. of Massachusetts Dartmouth},
  publisher	= {},
  address	= {},
  abstract	= {This paper reports on conceptual development in
		  applications of neural networks to data mining and
		  knowledge discovery. Hypothesis generation is one of the
		  significant differences of data mining from statistical
		  analyses. Nonlinear pattern hypothesis generation is a
		  major task of data mining and knowledge discovery. Yet, few
		  methods of nonlinear pattern hypothesis generation are
		  available. This paper proposes a model of data mining to
		  support nonlinear pattern hypothesis generation. This model
		  is an integration of linear regression analysis model,
		  Kohonen's self-organizing maps, the algorithm for convex
		  polytopes, and back-propagation neural networks. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  wang91a,
  author	= {Jhing-Fa Wang and Chung-Hsien Wu and Chaug-Ching Haung and
		  Jau-Yien Lee},
  title		= {Integrating neural nets and one-stage dynamic programming
		  for speaker independent continuous {M}andarin digit
		  recognition},
  booktitle	= {Proc. ICASSP-91, International Conference on Acoustics,
		  Speech and Signal Processing},
  year		= {1991},
  volume	= {I},
  pages		= {69--72},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang91b,
  author	= {Wang, Xinwen and Zou, Lihe and He, Zhenya},
  title		= {A neural network approach to vector quantization codebook
		  generation},
  booktitle	= {Proc. China 1991 International Conference on Circuits and
		  Systems},
  year		= {1991},
  volume	= {II},
  pages		= {523--525},
  organization	= {IEEE; Shenzhen Univ. , China; CIE Circuits \& Syst. Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang92a,
  author	= {W. Wang and G. Zhang and D. Cai and F. Wan},
  title		= {Image data compression using hybrid neural network},
  booktitle	= {Proc. Second IASTED International Conference. Computer
		  Applications in Industry},
  year		= {1992},
  editor	= {H. T. Dorrah},
  volume	= {I},
  pages		= {197--200},
  organization	= {IASTED},
  publisher	= {ACTA Press},
  address	= {Zurich, Switzerland},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang93a,
  author	= {Zhicheng Wang and John V. Hanson},
  title		= {Cauchy {L}earning {V}ector {Q}uantization},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {IV},
  pages		= {605--608},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang93b,
  author	= {Lance Zhicheng Wang},
  title		= {Winning-Weighted Competitive Learning: {A} Generalization
		  of {K}ohonen Learning},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2452--2455},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang93c,
  author	= {Zhicheng Wang},
  title		= {Non-greedy adaptive vector quantizers},
  booktitle	= {New Trends in Neural Computation. International Workshop
		  on Artificial Neural Networks. IWANN '93 Proceedings},
  year		= {1993},
  editor	= {Mira, J. and Cabestany, J. and Prieto, A. },
  pages		= {346--50},
  organization	= {Dept. of Electr. \& Comput. Eng. , Waterloo Univ. , Ont. ,
		  Canada},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang93d,
  author	= {Wang, Z. and Hanson, J. V. },
  title		= {Competitive learning and winning-weighted competition for
		  optimal vector quantizer design},
  booktitle	= {Neural Networks for Processing III Proceedings of the 1993
		  IEEE-SP Workshop},
  year		= {1993},
  editor	= {Kamm, C. A. and Kuhn, G. M. and Yoon, B. and Chellappa, R.
		  and Kung, S. Y. },
  pages		= {50--9},
  organization	= {Dept. of Electr. \& Comput. Eng. , Waterloo Univ. , Ont. ,
		  Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wang93e,
  author	= {Lifeng Wang and H. D. Cheng and D. H. Cooley},
  title		= {Training a neural network into a {T}uring machine},
  booktitle	= {Sixth International Conference on Parallel and Distributed
		  Computing Systems},
  publisher	= {Int. Soc. Comput. \& Their Appl. -ISCA},
  year		= {1993},
  editor	= {A. Kumar and K. Kamel},
  address	= {Raleigh, NC, USA},
  pages		= {399--404},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang94a,
  author	= {Zheng-Zhi Wang and De-Wen Hu and Qi-Ying Xiao},
  title		= {Adaptive Self-Organizing Neural Network Method for
		  Tracking Problems of Nonlinear Dynamic Systems},
  pages		= {2793--2796},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {control application, modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang95a,
  author	= {Jun Wang and Ce Zhu and Chenwu Wu and Zhenya He},
  title		= {Neural network approaches to fast and low rate vector
		  quantization},
  booktitle	= {1995 IEEE Symposium on Circuits and Systems},
  year		= {1995},
  volume	= {1},
  pages		= {486--9},
  organization	= {Dept. of Radio Eng. , Southeast Univ. , Nanjing, China},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  wang95b,
  author	= {Wang, Z. and Guerriero, A. and {De Sario}, M. },
  title		= {Comparison of several approaches for the segmentation of
		  texture images},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2424},
  pages		= {580--91},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  wang95c,
  author	= {Wang, Z. and Barraco, I. and Ravazzotti, M. and Ravera, F.
		  and {De Sanctis}, S. },
  title		= {Fuzzy neural network for the analysis of partially
		  occluded objects},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2424},
  pages		= {567--78},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  wang95d,
  author	= {Wang, Z. and Guerriero, A. and {De Sario}, M. and Losito,
		  S. },
  title		= {Unsupervised/supervised hybrid networks for identification
		  of {TSS}-1 satellite},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2620},
  pages		= {209--16},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wang96a,
  author	= {Yue Wang and T. Adah and M. T. Freedman and S. K. Mun},
  title		= {{MR} brain image analysis by distribution learning and
		  relaxation labeling},
  booktitle	= {Proceedings of the 1996 Fifteenth Southern Biomedical
		  Engineering Conference},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {P. K. Bajpai},
  address	= {New York, NY, USA},
  pages		= {133--6},
  dbinsdate	= {oldtimer}
}

@Article{	  wang96b,
  author	= {Zhiling Wang and A. Guerriero and M. {De Sario}},
  title		= {Comparison of several approaches for the segmentation of
		  texture images},
  journal	= {Pattern Recognition Letters},
  year		= {1996},
  volume	= {17},
  number	= {5},
  pages		= {509--21},
  dbinsdate	= {oldtimer}
}

@Article{	  wang96c,
  author	= {W. Wang and X. Li and D. Lu},
  title		= {Structural codebook design and address-dependent vector
		  quantization},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2847},
  pages		= {637--44},
  annote	= {Applications of Digital Image Processing XIX Conf. Date:
		  7--9 Aug. 1996 Conf. Loc: Denver, CO, USA Conf. Sponsor:
		  SPIE},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wang96d,
  author	= {Yue Wang and T. Adali},
  title		= {Efficient learning of standard finite normal mixtures for
		  image quantification},
  booktitle	= {1996 IEEE International Conference on Acoustics, Speech,
		  and Signal Processing Conference Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {6},
  address	= {New York, NY, USA},
  pages		= {3422--5},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wang96e,
  author	= {D. D. Wang and Jinwu Xu},
  title		= {Fault detection based on evolving {LVQ} neural networks},
  booktitle	= {1996 IEEE International Conference on Systems, Man and
		  Cybernetics. Information Intelligence and Systems},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {255--60},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wang96f,
  author	= {Wei Wang and Xing Li and Dajin Lu},
  title		= {Selectively tree-structured vector quantizer using
		  {K}ohonen neural network},
  booktitle	= {ICSP '96. 1996 3rd International Conference on Signal
		  Processing Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  editor	= {B. Yuan and X. Tang},
  address	= {New York, NY, USA},
  pages		= {1504--7},
  dbinsdate	= {oldtimer}
}

@Article{	  wang97a,
  author	= {Yue Wang and Chi-Ming Lau and T. Adali and M. T. Freedman
		  and Seong K. Mun},
  title		= {Quantification of {MR} brain image sequence by adaptive
		  structure probabilistic \mbox{self-organizing} mixture},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3034},
  number	= {pt. 1--2},
  pages		= {150--64},
  note		= {(Medical Imaging 1997: Image Processing Conf. Date: 25--28
		  Feb. 1997 Conf. Loc: Newport Beach, CA, USA Conf. Sponsor:
		  SPIE)},
  dbinsdate	= {oldtimer}
}

@Article{	  wang97b,
  author	= {Jung-Hua Wang and Chih-Ping Hsiao},
  title		= {Representation-burden conservation network applied to
		  learning VQ (NPL270)},
  journal	= {Neural Processing Letters},
  year		= {1997},
  volume	= {5},
  number	= {3},
  pages		= {209--17},
  dbinsdate	= {oldtimer}
}

@Article{	  wang97c,
  author	= {W. Wang and Y. He and X. Li and D. Lu},
  title		= {Image coding using address-dependent vector quantization
		  based on {K}ohonen neural network},
  journal	= {Chinese Journal of Electronics},
  year		= {1997},
  volume	= {6},
  number	= {4},
  pages		= {73--6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wang97d,
  author	= {Dali Wang and A. Zilouchian},
  title		= {Solutions of kinematics of robot manipulators using a
		  {K}ohonen \mbox{self-organizing} neural network},
  booktitle	= {Proceedings of the 1997 IEEE International Symposium on
		  Intelligent Control},
  publisher	= {IEEE},
  year		= {1997},
  editor	= {K. Ciliz and Y. Istefanopulos},
  address	= {New York, NY, USA},
  pages		= {251--5},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang99a,
  author	= {Jung Hua Wang and Chih Ping Hsiao},
  title		= {On disparity matching in stereo vision via a neural
		  network framework},
  booktitle	= {Proceedings of the National Science Council, Republic of
		  China, Part A (Physical Science and Engineering)},
  year		= {1999},
  volume	= {23},
  number	= {5},
  pages		= {665--77},
  abstract	= {This paper presents a neural framework for dealing with
		  the problem of disparity matching in stereo vision. Two
		  different types of neural networks are used in this
		  framework: one is called the vitality conservation (VC)
		  network for learning clustering, and the other is the
		  back-propagation (BP) network for learning disparity
		  matching. The VC network utilizes a vitality conservation
		  principle to facilitate self-development in network
		  growing. The training process of VC is smooth and
		  incremental; it not only achieves the biologically
		  plausible learning property, but also facilitates
		  systematic derivations for training parameters. Using the
		  [intensity, variation, orientation, x,y]-of each pixel (or
		  a block) as the training vector, the VC network dismembers
		  the input image into several clusters, and results can be
		  used by the BP network to achieve accurate matching. Unlike
		  the conventional k-means and self-organizing feature map
		  (SOFM), VC is a self-creating network; the number of
		  clusters is self-organizing and need not be pre-specified.
		  The BP network, using differential features as input
		  training data, can learn the functional relationship
		  between differential features and the matching degree.
		  After training, the BP network is first used to generate an
		  initial disparity (range) map. With the clustering results
		  and the initial map, a matching algorithm that incorporates
		  the BP network is then applied to recursively refine the
		  map in a cluster-by-cluster manner. In the matching
		  process, useful constraints, such as a epipolar line,
		  ordering, geometry and continuity, are employed to reduce
		  the occurrence of mismatching. The matching process
		  continues until all clusters are matched. Empirical results
		  indicate that the proposed framework is very promising for
		  applications in stereo vision.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang99b,
  author	= {Sheng De Wang and Chih Jen Lee},
  title		= {Fingerprint recognition using directional micropattern
		  histograms and {LVQ} networks},
  booktitle	= {Proceedings 1999 International Conference on Information
		  Intelligence and Systems},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {300--3},
  abstract	= {The paper is about designing a fingerprint recognition
		  system that makes use of the directional micropattern
		  histograms of a fingerprint image for local ridge
		  orientation calculation, core point detection, and feature
		  extraction. An improved learning vector quantization
		  network is also proposed to avoid the unfairness of the
		  winning rate and to determine a proper number of hidden
		  units. Experimental results show that the recognition rate
		  of the proposed method is 99.62% for a small-scale
		  fingerprint database.},
  dbinsdate	= {oldtimer}
}

@Article{	  wang99c,
  author	= {Y. Wang and G. Rong},
  title		= {A Self Organizing Neural Network Based Fuzzy System},
  journal	= {Fuzzy Sets and Systems},
  volume	= {103},
  pages		= {1--11},
  year		= {1999},
  dbinsdate	= {oldtimer}
}

@Article{	  wang99d,
  author	= {Jung Hua Wang and Wei Der Sun},
  title		= {Online learning vector quantization: a harmonic
		  competition approach based on conservation network},
  journal	= {IEEE Transactions on Systems, Man and Cybernetics, Part B
		  (Cybernetics)},
  year		= {1999},
  volume	= {29},
  pages		= {642--53},
  abstract	= {This paper presents a self-creating neural network in
		  which a conservation principle is incorporated with the
		  competitive learning algorithm to harmonize equi-probable
		  and equi-distortion criteria. Each node is associated with
		  a measure of vitality which is updated after each input
		  presentation. The total amount of vitality in the network
		  at any time is 1, hence the name conservation. Competitive
		  learning based on a vitality conservation principle is
		  near-optimum, in the sense that problem of trapping in a
		  local minimum is alleviated by adding perturbations to the
		  learning rate during node generation processes. Combined
		  with a procedure that redistributes the learning rate
		  variables after generation and removal of nodes, the
		  competitive conservation strategy provides a novel approach
		  to the problem of harmonizing equi-error and equi-probable
		  criteria. The training process is smooth and incremental,
		  it not only achieves the biologically plausible learning
		  property, but also facilitates systematic derivations for
		  training parameters. Comparison studies on learning vector
		  quantization involving stationary and nonstationary,
		  structured and nonstructured inputs demonstrate that the
		  proposed network outperforms other competitive networks in
		  terms of quantization error, learning speed, and codeword
		  search efficiency.},
  dbinsdate	= {oldtimer}
}

@Article{	  wang99e,
  author	= {Wang, Jung Hua and Peng, Chung Yun},
  title		= {Competitive neural network scheme for learning vector
		  quantization},
  journal	= {Electronics Letters},
  year		= {1999},
  number	= {9},
  volume	= {35},
  pages		= {725--726},
  abstract	= {A novel self-development neural network scheme, which
		  employs two resource counters to record node activity, is
		  presented. The proposed network not only harmonizes
		  equi-error and equi-probable criteria, but it also avoids
		  the stability-and-plasticity dilemma. Simulation results
		  show that the new scheme displays superior performance (in
		  terms of measured MSE, MAE, and training speed) over other
		  neural network models.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wang99f,
  author	= {Wang, Xian Zheng and Yoshizawa, Makoto and Tanaka, Akira
		  and Abe, Ken ichi and Takeda, Hiroshi and Yambe, Tomoyuki
		  and Nitta, Shin ichi and Imachi, Kou},
  title		= {Automatic monitoring system for artificial hearts using
		  \mbox{self-organizing} map},
  booktitle	= {Annual International Conference of the IEEE Engineering in
		  Medicine and Biology---Proceedings 2 (Oct 13-Oct 16 1999)},
  year		= {1999},
  volume	= {},
  pages		= {756},
  abstract	= {The overall goal of our research is an automatic,
		  real-time and on-line monitoring system of artificial
		  hearts. In this task, it is very important to automatically
		  detect and classify abnormalities of the artificial heart
		  control system and the recipient's circulatory system. The
		  self-organizing map was applied to the pattern recognition
		  of aortic pressure (AOP) which is considered to mostly
		  represent the state of the circulatory system. The AOP
		  signal data were fed to a Self-Organizing Map (SOM) beat by
		  beat. During the unsupervised learning process the SOM
		  units organize in such a way that similar AOP beat patterns
		  were represented in particular areas of the SOM. The map
		  location areas of the AOP signals in the different states
		  of the circulatory system were also different. The results
		  of visual examination revealed that the states of
		  circulatory system were distinguished well by the map. It
		  is expected that a map can be trained off-line with a large
		  database and then used for on-line monitoring and analysis
		  for artificial hearts.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wann93a,
  author	= {Chin-Der Wann and Stelios C. A. Thomopoulos},
  title		= {Clustering with Self-Organizing Neural Networks},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {545--548},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wann93b,
  author	= {Chin-Der Wann and Stelios C. A. Thomopoulos},
  title		= {Comparative Study of Self-Organizing Neural Network
		  Models},
  booktitle	= {Proc. of the World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {549--552},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wann93c,
  author	= {Wann, C. -D. and Thomopoulos, S. C. A. },
  title		= {Comparative study of \mbox{self-organizing} neural
		  networks},
  booktitle	= {New Trends in Neural Computation. International Workshop
		  on Artificial Neural Networks. IWANN '93 Proceedings},
  year		= {1993},
  editor	= {Mira, J. and Cabestany, J. and Prieto, A. },
  pages		= {316--21},
  organization	= {Dept. of Electr. \& Comput. Eng. , Pennsylvania State
		  Univ. , University Park, PA, USA},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@Article{	  wann97a,
  author	= {C. -D. Wann and S. C. A. Thomopoulos},
  title		= {Application of \mbox{self-organizing} neural networks to
		  multiradar data fusion},
  journal	= {Optical Engineering},
  year		= {1997},
  volume	= {36},
  number	= {3},
  pages		= {799--813},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  warwick94a,
  author	= {Warwick, K. },
  title		= {Neural network applications-- {SOM} case studies},
  booktitle	= {Adaptive Computing and Information Processing},
  year		= {1994},
  volume	= {2},
  pages		= {663--76},
  organization	= {Dept. of Cybern. , Reading Univ. , UK},
  publisher	= {Unicom Seminars},
  address	= {Uxbridge, UK},
  dbinsdate	= {oldtimer}
}

@InCollection{	  warwick96a,
  author	= {K. Warwick},
  title		= {System identification using neural networks},
  booktitle	= {Identification in Engineering Systems. Proceedings of the
		  Conference},
  publisher	= {Univ. Wales Swansea},
  year		= {1996},
  editor	= {M. I. Friswell and J. E. Mottershead},
  address	= {Swansea, UK},
  pages		= {689--701},
  dbinsdate	= {oldtimer}
}

@Article{	  wasaki93a,
  author	= {H. Wasaki and Y. Horio and S. Nakamura},
  title		= {A modified {H}ebbian algorithm for analog {VLSI} neural
		  network implementation},
  journal	= {Trans. Inst. of Electronics, Information and Communication
		  Engineers A},
  year		= {1993},
  volume	= {J76-A},
  number	= {3},
  pages		= {348--356},
  month		= {March},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@Article{	  watanabe90a,
  author	= {K. Watanabe and S. G. Tzafestas},
  title		= {Learning algorithms for neural networks with the {K}alman
		  filters},
  journal	= {J. Intelligent and Robotic Systems: Theory and
		  Applications},
  year		= {1990},
  volume	= {3},
  number	= {4},
  pages		= {305--319},
  dbinsdate	= {oldtimer}
}

@InCollection{	  watanabe95a,
  author	= {H. Watanabe and T. Yamaguchi and S. Katagiri},
  title		= {Discriminative metric design for pattern recognition},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {3439--42},
  dbinsdate	= {oldtimer}
}

@Article{	  watanabe98a,
  author	= {T. Watanabe and K. Kishida and K. Ishihara and Y. Yamauchi
		  and H. Tokutaka},
  title		= {Application of Neural Networks to Quantitative Chemical
		  Analysis},
  journal	= {Journal of the Surface Science Society of Japan},
  year		= {1998},
  volume	= {19},
  number	= {2},
  note		= {(in Japanese)},
  pages		= {36--43},
  dbinsdate	= {oldtimer}
}

@Article{	  watanabe98b,
  author	= {T. Watanabe and S. Kishida and K. Ishihara and T. Kawai
		  and T. Tokutaka and S. Fukushima},
  title		= {Application of Neural Networks to Chemical Analysis (in
		  Japanese)},
  journal	= {Journal of the Surface Science Society of Japan},
  year		= {1998},
  volume	= {19},
  number	= {6},
  pages		= {46--54},
  dbinsdate	= {oldtimer}
}

@InCollection{	  watkins98a,
  author	= {D. Watkins},
  title		= {Discovering geographical clusters in a {{{{U.S.}}}}
		  telecommunications company call detail records using
		  {K}ohonen self organising maps},
  booktitle	= {PADD98. Proceedings of the Second International Conference
		  on the Practical Application of Knowledge Discovery and
		  Data Mining},
  publisher	= {Practical Application Co. Ltd},
  year		= {1998},
  address	= {Blackpool, UK},
  pages		= {67--73},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  watanabe98c,
  author	= {T. Watanabe and S. Kishida and T. Kawai and K. Ishihara
		  and H. Tokutaka and S. Fukushima},
  title		= {Smoothing of Chemical Analysis Data by Neural Networks},
  booktitle	= {Proceedings of the Fifth International Conference on
		  Neural Information Processing},
  year		= {1998},
  address	= {Kitakyushu, Japan},
  pages		= {707--709},
  dbinsdate	= {oldtimer}
}

@Article{	  webb00a,
  author	= {Webb, Andrew R.},
  title		= {Gamma mixture models for target recognition},
  journal	= {Pattern Recognition},
  year		= {2000},
  volume	= {33},
  number	= {12},
  month		= {Dec},
  pages		= {2045--2054},
  organization	= {Defence Evaluation and Research Agency},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {This paper considers a mixture model approach to automatic
		  target recognition using high-resolution radar
		  measurements. The mixture model approach is motivated from
		  several perspectives including requirements that the target
		  classifier is robust to uncertainty in amplitude scaling,
		  rotation and translation of the target. Estimation of the
		  model parameters is achieved using the
		  expectation-maximization (EM) algorithm. Gamma mixtures are
		  introduced and the re-estimation equations derived. The
		  models are applied to the classification of high-resolution
		  radar range profiles of ships and results compared with a
		  previously published self-organizing map approach.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  weber93a,
  author	= {Weber, V. },
  title		= {Unification in Prolog by connectionist models},
  booktitle	= {Proceedings of the Fourth Australian Conference on Neural
		  Networks (ACNN'93)},
  year		= {1993},
  editor	= {Leong, P. and Jabri, M. },
  pages		= {5--8supl. },
  organization	= {Syst. Dept. of Comput. Sci. , Hamburg Univ. , Germany},
  publisher	= {Sydney Univ. Electr. Eng},
  address	= {Sydney, NSW, Australia},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  weber93b,
  author	= {Weber, V. },
  title		= {Connectionist Unifying Prolog},
  booktitle	= {Artificial Neural Nets and Genetic Algorithms. Proceedings
		  of the International Conference},
  year		= {1993},
  editor	= {Albrecht, R. F. and Reeves, C. R. and Steele, N. C. },
  pages		= {213--20},
  organization	= {Dept. of Comput. Sci. , Hamburg Univ. , Germany},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@TechReport{	  wedel96a,
  author	= {Jan Wedel and Daniel Polani},
  title		= {Critic-based Learning of Actions with
		  \mbox{Self-Organizing} Feature Maps},
  institution	= {{\language1 Institut f\"ur Informatik,
		  Johannes~Gutenberg-Universit\"at Mainz}},
  year		= {1996},
  type		= {Technical Report},
  month		= {October},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wehenkel95a,
  author	= {L. Wehenkel},
  title		= {A statistical approach to the identification of electrical
		  regions in power systems},
  booktitle	= {Stockholm Power Tech International Symposium on Electric
		  Power Engineering},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {530--5},
  dbinsdate	= {oldtimer}
}

@Article{	  wei01b,
  author	= {Wei, Hui},
  title		= {A {SOM} network model for feature extraction by hyper
		  columns architecture of primary visual cortex},
  journal	= {Journal-of-Zhejiang-University},
  year		= {2001},
  volume	= {35},
  pages		= {258--63},
  abstract	= {The primary visual cortex consists of six layers of neural
		  cells. Apart from discovering that there are three types of
		  cells in the visual cortex, the cells, specifically the
		  simple and complex cells, are arranged in a very orderly
		  way. They are organized in narrow columns that cut across
		  the six layers of the cortex, each column constituting
		  cells with the same line orientation. In the language of
		  pattern recognition, they works as feature extraction
		  units. The architecture and mechanism of natural biological
		  system, which processing visual image, are taken as a model
		  for the development of artificial electrical system. The
		  self-organizing mapping algorithm are used to train the
		  network for the acquisition of sensitivity to special
		  linear feature. All these research are significant for
		  knowledge representation, pattern recognition and computer
		  vision, which is based on cognitive neuropsychology.},
  dbinsdate	= {2002/1},
  merjanote     = {last name assumed based on similarity to other chinese journals}
}

@InProceedings{	  wei93a,
  author	= {Hsien-Chung Wei and Yung-Ching Chang and Jia-Shang Wang},
  title		= {A {K}ohonen-based structured codebook design for image
		  compression},
  booktitle	= {Proceedings TENCON '93. 1993 IEEE Region 10 Conference on
		  'Computer, Communication, Control and Power Engineering'},
  year		= {1993},
  editor	= {Yuan Baozong},
  volume	= {3},
  pages		= {426--9},
  organization	= {Inst. of Comput. Sci. , Nat. Tsing Hua Univ. , Hsinchu,
		  Taiwan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  wei93b,
  author	= {Hsien-Chung Wei and Yung-Ching Chang and Jia-Shung Wang},
  title		= {A {K}ohonen-based structured codebook design for image
		  compression},
  journal	= {Journal of Information Science and Engineering},
  year		= {1993},
  volume	= {9},
  number	= {3},
  pages		= {431--43},
  month		= {Sept},
  dbinsdate	= {oldtimer}
}

@Article{	  wei95a,
  author	= {Wang Wei and Cai Dejun and Wan Faguan},
  title		= {The study of correlation vector quantization for image
		  coding},
  journal	= {Acta Electronica Sinica},
  year		= {1995},
  volume	= {23},
  number	= {4},
  pages		= {30--4},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@Article{	  wei97a,
  author	= {Zhang Wei},
  title		= {The inverse kinematics for the orientation of a robot arm
		  based on neural network},
  journal	= {Journal of Nanjing University of Aeronautics \&
		  Astronautics},
  year		= {1997},
  volume	= {29},
  number	= {1},
  pages		= {46--50},
  dbinsdate	= {oldtimer}
}

@Article{	  wei97b,
  author	= {Zhang Wei and Ding Qiuling},
  title		= {Inverse kinematics for a 6 DOF manipulator based on neural
		  network},
  journal	= {Transactions of Nanjing University of Aeronautics \&
		  Astronautics},
  year		= {1997},
  volume	= {14},
  number	= {1},
  pages		= {73--6},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  weida00a,
  author	= {Weida Zhou and Li Zhang and Licheng Jiao},
  title		= {Association rules mining based on Kohonen network},
  booktitle	= {16th World Computer Congress 2000. Proceedings of
		  Conference on Intelligent Information Processing.
		  Publishing House of Electron. Ind, Beijing, China},
  year		= {2000},
  volume	= {},
  pages		= {87--90},
  abstract	= {We present a new method based on a self-organized Kohonen
		  network with an evolutionary algorithm to mine association
		  rules. A detailed analysis of and an introduction to the
		  principle, functions, merits and shortcomings of the method
		  are presented.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  weierich94a,
  author	= {Peter Weierich and Michael {von Rosenberg}},
  title		= {Unsupervised Detection of Driving States with Hierarchical
		  {S}elf-{O}rganizing {M}aps},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {I},
  pages		= {246--249},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, monitoring, state detection, time series},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  weierich94b,
  author	= {Peter Weierich and Michael {von Rosenberg}},
  title		= {The Use of Formal Measures for the Training of
		  Hierarchical {K}ohonen Maps},
  pages		= {612--615},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {classification, analysis},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  weigang99a,
  author	= {Weigang, Li and {da Silva}, Nilton Correia},
  title		= {Implementation of parallel \mbox{self-organizing} map to
		  the classification of image},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {1999},
  volume	= {3722},
  pages		= {284--292},
  abstract	= {A study of Parallel Self-Organizing Map (Parallel-SOM) is
		  proposed to modify Self-Organizing Map for parallel
		  computing environments. In this model, the conventional
		  repeated learning procedure is modified to learn just once.
		  The once learning manner is more similar to human learning
		  and memorizing activities. During training, every
		  connection between neurons of input/output layers is
		  considered as an independent processor. In this way, all
		  elements of every matrix are calculated simultaneously.
		  This synchronization feature improves the weight updating
		  sequence significantly. In this paper, the detail sequence
		  of Parallel-SOM is demonstrated through the classification
		  of coin for deeply understanding the properties of the
		  proposed model. In conventional computing environment (one
		  processor), Parallel-SOM can be implemented without the
		  once learning and parallel weight updating features. As an
		  application, its implementation for the classification of
		  the meteorological radar images is also shown.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  weijian00a,
  author	= {Weijian Wan and Fraser, D.},
  title		= {A multiple self-organizing map scheme for remote sensing
		  classification},
  booktitle	= {Multiple Classifier Systems. First International Workshop,
		  MCS 2000. Proceedings (Lecture Notes in Computer Science
		  Vol.1857). Springer-Verlag, Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {300--9},
  abstract	= {This paper presents a multiple classifier scheme, known as
		  multiple self-organizing maps (MSOM), for remote sensing
		  classification problems. Based on the Kohonen SOM, multiple
		  maps are fused, in either unsupervised, supervised or
		  hybrid manners, so as to explore discrimination information
		  from the data itself. The MSOM has the capability to
		  extract and represent high-order statistics of high
		  dimensional data from disparate sources in a nonparametric,
		  vector-quantization fashion. The computation cost is linear
		  in relation to the dimensionality and the operation
		  complexity is simple and equivalent to a minimum-distance
		  classifier. Thus, MSOM is very suitable for remote sensing
		  applications under various data and design-sample
		  conditions. We also demonstrate that the MSOM can be used
		  for hyperspectral data clustering and joint spatio-temporal
		  classification.},
  dbinsdate	= {2002/1}
}

@Article{	  weijters95a,
  author	= {Weijters, A. J. M. M. },
  title		= {The {BP-SOM} architecture and learning rule},
  journal	= {Neural Processing Letters},
  year		= {1995},
  volume	= {2},
  number	= {6},
  pages		= {13--16},
  dbinsdate	= {oldtimer}
}

@InCollection{	  weijters96a,
  author	= {A. Weijters and A. {Van den Bosch} and E. Postma and H. J.
		  {van den Herik}},
  title		= {Avoiding overfitting in {BP-SOM}},
  booktitle	= {Proceedings of BENELEARN-96},
  year		= 1996,
  editor	= {H. J. {van den Herik} and A. Weijters},
  address	= {Maastricht},
  pages		= {157--166},
  dbinsdate	= {oldtimer}
}

@InCollection{	  weijters96b,
  author	= {A. J. M. M. Weijters},
  title		= {{BP-SOM}: A Profitable Cooperation},
  booktitle	= {Proceedings of NAIC-96, the Eight Dutch Conference on
		  Artificial Intelligence},
  year		= 1996,
  editor	= {J. -J. Ch. Meyer and L. C. {van der Gaag}},
  pages		= {381--391},
  dbinsdate	= {oldtimer}
}

@Article{	  weijters97a,
  author	= {A. Weijters and A. {van den Bosch} and H. J. {van den
		  Herik}},
  title		= {Behavioral Aspects of Combining Backpropagation Learning
		  and Self-Organizing Maps},
  journal	= {Connection Science},
  year		= 1997,
  volume	= 9,
  pages		= {235--251},
  dbinsdate	= {oldtimer}
}

@InCollection{	  weijters97b,
  author	= {A. Weijters and A. {Van den Bosch} and H. J. {Van den
		  Herik}},
  title		= {Intelligible neural networks with {BP-SOM}},
  booktitle	= {Proceedings of NAIC-97, the Ninth Dutch Conference on
		  Artificial Intelligence},
  year		= 1997,
  pages		= {27--36},
  dbinsdate	= {oldtimer}
}

@InCollection{	  weijters97c,
  author	= {Ton Weijters and H. Jaap {van den Herik} and Antal {van
		  den Bosch} and Eric Postma},
  title		= {Avoiding Overfitting with {BP-SOM}},
  booktitle	= {Proceedings of IJCAI-97, the Fifteenth International Joint
		  Conference on Artificial Intelligence},
  publisher	= {Morgan Kaufmann},
  year		= 1997,
  address	= {San Francisco},
  pages		= {1140--1145},
  dbinsdate	= {oldtimer}
}

@InCollection{	  weijters98a,
  author	= {T. Weijters and A. Van den Bosch},
  title		= {Interpretable neural networks with {BP}-{SOM}},
  booktitle	= {Tasks and Methods in Applied Artificial Intelligence.
		  Proceedings of 11th International Conference on Industrial
		  and Engineering Applications of Artificial Intelligence and
		  Expert Systems IEA-98-AIE},
  publisher	= {Springer-Verlag},
  year		= {1998},
  volume	= {2},
  editor	= {J. Mira and A. Pasqual {del Pobil} and M. Ali},
  address	= {Berlin, Germany},
  pages		= {564--73},
  dbinsdate	= {oldtimer}
}

@InCollection{	  weijters98b,
  author	= {A. Weijters and A. {Van den Bosch} and H. J. {Van den
		  Herik}},
  title		= {Intelligible Neural Networks with {BP-SOM}},
  booktitle	= {Proceedings of ECML-98},
  year		= {1998},
  note		= {Accepted for publication},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  weinstein95a,
  author	= {John N. Weinstein and Timothy G. Myers and Y. Kan and
		  Kenneth D. Paull and D. W. Zaharevitz and Kurt W. Kohn
		  William W. {van Osdol}},
  title		= {An 'Information-Intensive' Strategy for Drug Discovery at
		  the National Cancer Institute: The Role of Neural
		  Networks},
  volume	= {II},
  pages		= {750--753},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wells94a,
  author	= {P. D. Wells and P. C. J. Hill},
  title		= {An adaptive layered network approach to antenna
		  beamforming and bearing estimation},
  booktitle	= {Extended Synopses of the Third UK/Australian International
		  Symposium on DSP for Communication Systems},
  publisher	= {Lancaster Univ},
  year		= {1994},
  address	= {Lancaster, UK},
  pages		= {21--3},
  dbinsdate	= {oldtimer}
}



@InProceedings{	  wen00a,
  author	= {Wen, Fang and Willett, Peter and Deb, Somnath},
  title		= {Condition monitoring for helicopter data},
  booktitle	= {Proceedings of the IEEE International Conference on
		  Systems, Man and Cybernetics},
  year		= {2000},
  editor	= {},
  volume	= {1},
  pages		= {224--229},
  organization	= {Univ of Connecticut},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {In this paper the classical `Westland' set of empirical
		  accelerometer helicopter data is analyzed with the aim of
		  condition monitoring for diagnostic purposes. The goal is
		  to determine features for failure events from these data,
		  via a proprietary signal processing toolbox, and to weigh
		  these according to a variety of classification algorithms.
		  As regards signal processing, it appears that the
		  autoregressive (AR) coefficients from a simple linear model
		  encapsulate a great deal of information in a relatively few
		  measurements; it has also been found that augmentation of
		  these by harmonic and other parameters can improve
		  classification significantly. As regards classification,
		  several techniques have been explored, among these
		  restricted Coulomb energy (RCE) networks, learning vector
		  quantization (LVQ), Gaussian mixture classifiers and
		  decision trees. A problem with these approaches, and in
		  common with many classification paradigms, is that
		  augmentation of the feature dimension can degrade
		  classification ability. Thus, we also introduce the
		  Bayesian data reduction algorithm (BDRA), which imposes a
		  Dirichlet prior on training data and is thus able to
		  quantify probability of error in an exact manner, such that
		  features may be discarded or coarsened appropriately.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  wen01a,
  author	= {Wen, F. and Willett, P. and Deb, S.},
  title		= {Analysis of the westland data set},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2001},
  editor	= {Willett, P. K. and Kirubarajan, T.},
  volume	= {4389},
  pages		= {204--215},
  organization	= {Elec. and Computer Eng. Department, U-2157, University of
		  Connecticut},
  publisher	= {},
  address	= {},
  abstract	= {The "Westland" set of empirical accelerometer helicopter
		  data with seeded and labeled faults is analyzed with the
		  aim of condition monitoring. The autoregressive (AR)
		  coefficients from a simple linear model encapsulate a great
		  deal of information in a relatively few measurements; and
		  it has also been found that augmentation of these by
		  harmonic and other parameters can improve classification
		  significantly. Several techniques have been explored, among
		  these restricted Coulomb energy (RCE) networks, learning
		  vector quantization (LVQ), Gaussian mixture classifiers and
		  decision trees. A problem with these approaches, and in
		  common with many classification paradigms, is that
		  augmentation of the feature dimension can degrade
		  classification ability. Thus, we also introduce the
		  Bayesian data reduction algorithm (BDRA), which imposes a
		  Dirichlet prior on training data and is thus able to
		  quantify probability of error in an exact manner, such that
		  features may be discarded or coarsened appropriately.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  wen92a,
  author	= {Fushuan Wen and Zhenxiang Han},
  title		= {Combined use of {K}ohonen's model and {BP} model for the
		  calculation of energy losses in distribution systems},
  booktitle	= {Third Biennial Symp. on Industrial Electric Power
		  Applications},
  year		= {1992},
  pages		= {268--277},
  organization	= {Soc. Electr. Power Res. Implementation; IEEE; ISA; LES},
  publisher	= {Louisiana Tech. Univ},
  address	= {Ruston, LA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wen93a,
  author	= {W. X. Wen and V. Pang and A. Jennings},
  title		= {Self-Generating vs. Self-Organizing, What's Different},
  booktitle	= {Proc. ICNN'39, International Conference on Neural
		  Networks},
  year		= {1993},
  volume	= {III},
  pages		= {1469--1473},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  wenwei98a,
  author	= {Wenwei Yu and Yokoi, H. and Nishikawa, D.},
  title		= {Adaptive electromyographic ({EMG}) prosthetic hand control
		  using reinforcement learning},
  journal	= {Intelligent Autonomous Systems. IAS-5. IOS Press,
		  Amsterdam, Netherlands; 1998; xiii+799 pp.p.266--71},
  year		= {1998},
  volume	= {},
  pages		= {266--71},
  abstract	= {There existed researches on EMG signal classification and
		  recognition depending on a desired-output-indicating
		  time-varying vector signal, i.e., using the supervised
		  learning method. In the case of prosthetic arm/hand
		  control, it is difficult or even impossible to supply this
		  kind of desired output signal. In this paper, we apply an
		  AHC based reinforcement learning method to learn to
		  classify the series of EMG signals. Two network structures
		  are involved in the learning scheme: the first one is for
		  the internal model of the environmental reinforcement, and
		  the second one is for the classified different action
		  patterns. The learning of these two networks are all based
		  on the temporal difference theory. Additionally, an input
		  EMG pattern is generalized by a feature map network. A
		  output stochastic exploration unit and gradient estimator
		  are proposed for credit discrimination. The classification
		  of the real EMG pattern is preliminarily accomplished.},
  dbinsdate	= {2002/1}
}

@MastersThesis{	  werowitz91a,
  author	= {E. B. Werkowitz},
  title		= {Computer Simulation of {B}raitenberg Vehicles},
  school	= {Air Force Inst. of Tech. , School of Engineering},
  year		= {1991},
  address	= {Wright-Patterson AFB, OH, USA},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@Article{	  wesolowski01a,
  author	= {Wesolowski, M. and Suchacz, B.},
  title		= {Classification of rapeseed and soybean oils by use of
		  unsupervised pattern-recognition methods and neural
		  networks},
  journal	= {FRESENIUS JOURNAL OF ANALYTICAL CHEMISTRY},
  year		= {2001},
  volume	= {371},
  number	= {3},
  month		= {OCT},
  pages		= {323--330},
  abstract	= {Unsupervised pattern-recognition methods and Kohonen
		  neural networks have been applied to the classification of
		  rapeseed and soybean oil samples according to their type
		  and quality by use of chemical and physical properties
		  (density, refractive index, saponification value, and
		  iodine and acid numbers) and thermal properties (thermal
		  decomposition temperatures) as variables. A multilayer
		  feed-forward (MLF) neural network (NN) has been used to
		  select the most important variables for accurate
		  classification of edible oils. To accomplish this task
		  different neural networks architectures trained by back
		  propagation of error method, using chemical, physical, and
		  thermal properties as inputs, were employed. The network
		  with the best performance and the smallest root mean
		  squared (RMS) error was chosen. The results of MLF network
		  sensitivity analysis enabled the identification of key
		  properties, which were again used as variables in principal
		  components analysis (PCA), cluster analysis (CA), and in
		  Kohonen self-organizing feature maps (SOFM) to prove their
		  reliability.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  wessels00a,
  author	= {Wessels, T. and Omlin, C. W.},
  title		= {Hybrid system for signature verification},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {5},
  pages		= {509--514},
  organization	= {Univ of Stellenbosch},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Biometric authentication has become a popular research
		  topic due to its wide applicability including the
		  prevention of fraud in financial transactions. Handwritten
		  signature verification, in contrast with other biometric
		  based authentication methods such as fingerprint and
		  retinal scanning, has the advantage that it is already
		  widely used to endorse financial transactions. However,
		  very little verification on these signatures is done today
		  in practical scenarios. This paper reports on our ongoing
		  research on automatic, on-line, handwritten signature
		  verification. The hybrid system consists of a Kohonen
		  self-organizing map which find cluster centers in the
		  training data and Hidden Markov Models which are trained to
		  model the dynamics of signatures. Our initial results are
		  very promising: The system achieves a 0% false rejection
		  rate and a 13% false acceptance rate.},
  dbinsdate	= {2002/1}
}

@Article{	  west00a,
  author	= {West, David and West, Vivian},
  title		= {Improving diagnostic accuracy using a hierarchical neural
		  network to model decision subtasks},
  journal	= {International Journal of Medical Informatics },
  year		= {2000},
  number	= {1},
  volume	= {57},
  pages		= {41--55},
  abstract	= {A number of quantitative models including linear
		  discriminant analysis, logistic regression, k nearest
		  neighbor, kernel density, recursive partitioning, and
		  neural networks are being used in medical diagnostic
		  support systems to assist human decision-makers in disease
		  diagnosis. This research investigates the decision accuracy
		  of neural network models for the differential diagnosis of
		  six erythamatous-squamous diseases. Conditions where a
		  hierarchical neural network model can increase diagnostic
		  accuracy by partitioning the decision domain into subtasks
		  that are easier to learn are specifically addressed.
		  Self-organizing maps (SOM) are used to portray the 34
		  feature variables in a two dimensional plot that maintains
		  topological ordering. The SOM identifies five inconsistent
		  cases that are likely sources of error for the quantitative
		  decision models; the lower bound for the diagnostic
		  decision error based on five errors is 0.0140. The
		  traditional application of the quantitative models cited
		  above results in diagnostic error levels substantially
		  greater than this target level. A two-stage hierarchical
		  neural network is designed by combining a multilayer
		  perceptron first stage and a mixture-of-experts second
		  stage. The second stage mixture-of-experts neural network
		  learns a subtask of the diagnostic decision, the
		  discrimination between seborrheic dermatitis and pityriasis
		  rosea. The diagnostic accuracy of the two stage neural
		  network approaches the target performance established from
		  the SOM with an error rate of 0.0159.},
  dbinsdate	= {oldtimer}
}

@Article{	  west00b,
  author	= {West, D. and West, V.},
  title		= {Model selection for a medical diagnostic decision support
		  system: a breast cancer detection case},
  journal	= {ARTIFICIAL INTELLIGENCE IN MEDICINE},
  year		= {2000},
  volume	= {20},
  number	= {3},
  month		= {NOV},
  pages		= {183--204},
  abstract	= {There are a number of different quantitative models that
		  can be used in a medical diagnostic decision support system
		  (MDSS) including parametric methods (linear discriminant
		  analysis or logistic regression), non-parametric models (K
		  nearest neighbor, or kernel density) and several neural
		  network models. The complexity of the diagnostic task is
		  thought to be one of the prime determinants of model
		  selection. Unfortunately, there is no theory available to
		  guide model selection. Practitioners are left to either
		  choose a favorite model or to test a small subset using
		  cross validation methods. This paper illustrates the use of
		  a self-organizing map (SOM) to guide model selection for a
		  breast cancer MDSS. The topological ordering properties of
		  the SOM are used to define targets for an ideal accuracy
		  level similar to a Bayes optimal level. These targets can
		  then be used in model selection, variable reduction,
		  parameter determination, and to assess the adequacy of the
		  clinical measurement system. These ideas are applied to a
		  successful model selection for a real-world breast cancer
		  database. Diagnostic accuracy results are reported for
		  individual models, for ensembles of neural networks, and
		  for stacked predictors. },
  dbinsdate	= {2002/1}
}

@Article{	  west00c,
  author	= {West, David},
  title		= {Neural network credit scoring models},
  journal	= {Computers and Operations Research},
  year		= {2000},
  volume	= {27},
  number	= {11},
  month		= {},
  pages		= {1131--1152},
  organization	= {East Carolina Univ},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {This paper investigates the credit scoring accuracy of
		  five neural network models: multilayer perception,
		  mixture-of-experts, radial basis function, learning vector
		  quantization, and fuzzy adaptive resonance. The neural
		  network credit scoring models are tested using 10-fold
		  crossvalidation with two real world data sets. Results are
		  benchmarked against more traditional methods under
		  consideration for commercial applications including linear
		  discriminant analysis, logistic regression, k nearest
		  neighbor, kernel density estimation, and decision trees.
		  Results demonstrate that the multilayer perceptron may not
		  be the most accurate neural network model, and that both
		  the mixture-of-experts and radial basis function neural
		  network models should be considered for credit scoring
		  applications. Logistic regression is found to be the most
		  accurate of the traditional methods.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  west98a,
  author	= {West, D.},
  title		= {An investigation of the bias/variance dilemma for neural
		  network classification models},
  booktitle	= {Joint Conference on Intelligent Systems 1999 (JCIS'98)},
  publisher	= {Association for Intelligent Machinery, Inc},
  year		= {1998},
  volume	= {4},
  pages		= {48--51},
  abstract	= {The purpose of this research is to investigate the
		  bias/variance error tradeoff for the following five neural
		  network classification models: (1) multilayer perceptron
		  (MLP), (2) mixture of experts network (MOE), (3) radial
		  basis function network (RBF), (4) learning vector
		  quantization network (LVQ), and (5) fuzzy adaptive
		  resonance network (FAR). Bias/variance error comparisons
		  are based on a collection of fifteen diverse real-world
		  data sets. For each data set, the neural network results
		  are compared to the nonparametric k nearest neighbor
		  method. The end result of this research is guidance for the
		  practitioner in selecting an appropriate neural
		  architecture for a given data set.},
  dbinsdate	= {oldtimer}
}

@Article{	  whittaker95a,
  author	= {Whittaker, A. D. and Cook, D. F. },
  title		= {Counterpropagation neural network for modelling a
		  continuous correlated process},
  journal	= {International Journal of Production Research},
  year		= {1995},
  volume	= {33},
  number	= {7},
  pages		= {1901--10},
  month		= {July},
  dbinsdate	= {oldtimer}
}

@Article{	  whittington90a,
  author	= {G. Whittington and T. Spracklen},
  title		= {The application of a neural network model to sensor data
		  fusion},
  journal	= {Proc. SPIE---The International Society for Optical
		  Engineering},
  year		= {1990},
  volume	= {1294},
  pages		= {276--283},
  annote	= {Conf. paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  whittington91a,
  author	= {G. Whittington and C. T. Spracklen},
  title		= {Visualisation of artificial neural networks to assist in
		  application development},
  booktitle	= {IEE Colloquium on 'Neural Networks: Design Techniques and
		  Tools' (Digest No. 037)},
  year		= {1991},
  pages		= {6/1--4},
  organization	= {IEE},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  whittington92a,
  author	= {G. Whittington and C. T. Spracklen},
  title		= {Automated Radar Behaviour Analysis using Hierarchical
		  Neural Network Architecures},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {II},
  pages		= {1559--1564},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@Article{	  whittington92b,
  author	= {Whittington, G. and Spracklen, T. },
  title		= {Applying visualisation techniques to the development of
		  real-world artificial neural networks applications},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1992},
  volume	= {1709},
  number	= {pt. 2},
  pages		= {1024--33},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  whittington94a,
  author	= {G. Whittington and C. T. Spracklen},
  title		= {An Efficient Multiprocessor Mapping Algorithm for the
		  {K}ohonen Feature Map and its Derivative Models},
  pages		= {17--21},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {implementation, multiprocessor},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wichert93a,
  author	= {Andreas Wichert},
  title		= {{MTC}n-Nets},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {IV},
  pages		= {59--62},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  wiegerinck94a,
  author	= {Wiegerinck, W. and Heskes, T. },
  title		= {On-line learning with time-correlated patterns},
  journal	= {Europhysics Letters},
  year		= {1994},
  volume	= {28},
  number	= {6},
  pages		= {451--5},
  month		= {Nov},
  dbinsdate	= {oldtimer}
}

@Article{	  wiemer00a,
  author	= {Wiemer, J. and Burwick, T. and Seelen, W.},
  title		= {Self-Organized Maps for Visual Feature Representation
		  Based on Natural Binocular stimuli},
  journal	= {Biological Cybernetics},
  year		= {2000},
  volume	= {82},
  number	= {2},
  pages		= {97--110},
  month		= {February},
  dbinsdate	= {oldtimer}
}

@Article{	  wienke94a,
  author	= {Dietrich Wienke and Philip K. Hopke},
  title		= {Visual Neural Mapping Technique for Locating Fine Airborne
		  Particles Sources},
  journal	= {Environ. Sci. Technol. },
  year		= {1994},
  volume	= {28},
  number	= {6},
  pages		= {1015--1022},
  dbinsdate	= {oldtimer}
}

@Article{	  wienke94b,
  author	= {Dietrich Wienke and Ning Gao and Philip K. Hopke},
  title		= {Multiple Site Receptor Modeling with a Minimal Spanning
		  Tree Combined with a Neural Network},
  journal	= {Environ. Sci. Technol. },
  year		= {1994},
  volume	= {28},
  number	= {6},
  pages		= {1022--1030},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wilde93a,
  author	= {Wilde, S. A. and Curtis, K. M. },
  title		= {A transputer based \mbox{self-organizing} neural network
		  for speech synthesis parameter arbitration},
  booktitle	= {Transputer Applications and Systems '93. Proceedings of
		  the 1993 World Transputer Congress},
  year		= {1993},
  editor	= {Grebe, R. and Hektor, J. and Hilton, S. C. and Jane, M. R.
		  and Welch, P. H. },
  pages		= {1242--53},
  organization	= {Dept. of Electr. \& Electron. Eng. , Nottingham Univ. ,
		  UK},
  publisher	= {IOS Press},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@Article{	  wilfert01a,
  author	= {Wilfert, H. H. and Voigtlander, K. and Erlich, I.},
  title		= {Dynamic coherency identification of generators using self-
		  organising feature maps},
  journal	= {CONTROL ENGINEERING PRACTICE},
  year		= {2001},
  volume	= {9},
  number	= {7},
  month		= {JUL},
  pages		= {769--775},
  abstract	= {An important step in constructing dynamic equivalents of
		  large power systems is the coherency identification and
		  grouping of generators. Self-organising feature maps can do
		  this task, if the attribute vectors, which characterise the
		  features of the generator dynamics inside the network are
		  well chosen. It is shown in the paper that the principal
		  components of the correlation matrix of the simulated time
		  responses of the generators after faults are especially
		  suitable to form the attribute vectors. The results are
		  compared with the use of right eigenvectors and
		  participation factors of the linearised system matrix as
		  attribute vectors. },
  dbinsdate	= {2002/1}
}

@InCollection{	  wilinski97a,
  author	= {P. Wilinski and B. Solaiman and A. Hillion and W.
		  Czarnecki},
  title		= {A multiresolution hybrid neuro-{M}arkovian image modeling
		  and segmentation},
  booktitle	= {Proceedings. International Conference on Image
		  Processing},
  publisher	= {Academic Press},
  year		= {1997},
  volume	= {3},
  editor	= {O. Omidvar and P. van der Smagt},
  address	= {San Diego, CA, USA},
  pages		= {951--4},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wilinski99a,
  author	= {Wilinski, P.},
  title		= {Neuro-{M}arkovian modeling and segmentation of remotely
		  sensed images},
  booktitle	= {International Symposium on Pattern Recognition `In
		  Memoriam Pierre Devijver'. Royal Mil. Acad, Brussels,
		  Belgium},
  year		= {1999},
  volume	= {},
  pages		= {72--7},
  abstract	= {The paper presents a textured image model. This model is
		  described by means of a neuro-{M}arkovian hybrid approach
		  using a Kohonen map and a hidden {M}arkov model (HMM). Each
		  state of the HMM is associated to one resolution level in
		  the image. The change of the state corresponds to the
		  change of image analysis resolution level. The HMM
		  observation space is composed of clusters which are
		  estimated using a Kohonen map. The second role of the
		  Kohonen algorithm is to achieve a segmentation that is done
		  in parallel with the one proceeded by a Viterbi algorithm.
		  This model is applied to describe remote-sensing images.
		  The segmentation obtained exploits the advantages of each
		  of separate approaches.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  willett94a,
  author	= {D. Willett and C. Busch and F. Siebert},
  title		= {Fast Image Analysis using {{K}ohonen} Maps},
  booktitle	= {Proc. NNSP'94, IEEE Workshop on Neural Networks for Signal
		  Processing},
  year		= {1994},
  month		= {September},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  pages		= {461--470},
  annote	= {application, image analysis},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  williams00a,
  author	= {Williams, M. D. and Lake, S.},
  title		= {Artificial neural network classification of {UK} hospitals
		  using National Health Service indicators},
  booktitle	= {Proceedings of the International Conference on Artificial
		  Intelligence. IC-AI'2000. CSREA Press, Athens, GA, USA},
  year		= {2000},
  volume	= {2},
  pages		= {967--73},
  abstract	= {This paper presents an investigation into the use of
		  artificial neural networks for the analysis of health
		  information. The Kohonen self-organising map technique was
		  used to group two data sets which were produced using the
		  UK National Health Service indicators: the first set
		  contained indicators reflecting measures of composition and
		  efficiency; the second set contained indicators reflecting
		  measures of composition, efficiency and quality. Results
		  obtained (which were subsequently confirmed by the use of
		  statistical tests) differed significantly from those
		  obtained using traditional hospital grouping techniques,
		  suggesting both that the neural network approach to
		  classifying hospitals is valid and useful, and that
		  existing perceptions of membership of UK hospital groups
		  may be challenged.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  williams93a,
  author	= {Williams, P. and Duller, A. W. G. },
  title		= {Identification of lighting flicker sources using a neural
		  network},
  booktitle	= {Techniques and Applications of Neural Networks},
  year		= {1993},
  editor	= {Taylor, M. and Lisboa, P. },
  pages		= {183--97},
  organization	= {Sch. of Electron. Eng. \& Comput. Syst. , Univ. Coll. of
		  North Wales, Bangor, UK},
  publisher	= {Ellis Horwood},
  address	= {Hemel Hempstead, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  williamson01a,
  author	= {Williamson, J. R.},
  title		= {Self-organization of topographic mixture networks using
		  attentional feedback},
  journal	= {Neural-Computation},
  year		= {2001},
  volume	= {13},
  pages		= {563--93},
  abstract	= {This article proposes a neural network model of supervised
		  learning that employs biologically motivated constraints of
		  using local, on-line, constructive learning. The model
		  possesses two novel learning mechanisms. The first is a
		  network for learning topographic mixtures. The network's
		  internal category nodes are the mixture components, which
		  learn to encode smooth distributions in the input space by
		  taking advantage of topography in the input feature maps.
		  The second mechanism is an attentional biasing feedback
		  circuit. When the network makes an incorrect output
		  prediction, this feedback circuit modulates the learning
		  rates of the category nodes, by amounts based on the
		  sharpness of their tuning, in order to improve the
		  network's prediction accuracy. The network is evaluated on
		  several standard classification benchmarks and shown to
		  perform well in comparison to other classifiers.},
  dbinsdate	= {2002/1}
}

@Book{		  wilppu99a,
  author	= {Eva Wilppu},
  editor	= {},
  title		= {Controlling Distribution with Neural Networks},
  publisher	= {Turku School of Economics and Business Administration},
  year		= {1999},
  series	= {A-11},
  dbinsdate	= {oldtimer}
}

@Article{	  wilson93a,
  author	= {E. Wilson and G. Anspach},
  title		= {Neural Networks for Sign Language Translation},
  journal	= {Proc. of SPIE},
  year		= 1993,
  pages		= {589--599},
  publisher	= {SPIE},
  address	= {Bellingham, WA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wilson93b,
  author	= {Elizabeth Wilson and Gretel Anspach and Raytheon Company},
  title		= {Applying Neural Network Developments to Sigma Language
		  Translation},
  booktitle	= {Neural Networks for Signal Processing 3---Proceedings of
		  the 1993 IEEE Workshop},
  year		= {1993},
  editor	= {Kamm, C. A. and Kung, S. Y. and Yoon, B. and Chellappa, R.
		  and Kung, S. Y. },
  pages		= {301--310},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, New Jersey, USA},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wilson93c,
  author	= {C. L. Wilson},
  title		= {Evaluation of Character Recognition Systems},
  booktitle	= {Neural Networks for Signal Processing 3---Proceedings of
		  the 1993 IEEE Workshop},
  year		= {1993},
  editor	= {Kamm, C. A. and Kung, S. Y. and Yoon, B. and Chellappa, R.
		  and Kung, S. Y. },
  pages		= {485--496},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, New Jersey, USA},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wilson94a,
  author	= {C. L. Wilson},
  title		= {Self-Organizing Neural Network System for Trading Common
		  Stocks},
  pages		= {3651--3654},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, pattern recognition},
  dbinsdate	= {oldtimer}
}

@Article{	  wing01a,
  author	= {Wing Kai Leung},
  title		= {Solving application problems involving large real type
		  data sets by single layered backpropagation networks},
  journal	= {Neural-Network-World},
  year		= {2001},
  volume	= {11},
  pages		= {249--57},
  abstract	= {It is generally accepted that most benchmark problems
		  known today can be solved by artificial neural networks
		  with one single hidden layer. Networks with more than one
		  hidden layer normally slow down learning dramatically.
		  Furthermore, generalisation to new input patterns is
		  generally better in small networks. However, most benchmark
		  problems only involve a small training data set which is
		  normally discrete (such as binary values 0 and 1) in
		  nature. The ability of single hidden layer supervised
		  networks to solve problems with large and continuous type
		  of data (e.g. most engineering problems) is virtually
		  unknown. A fast learning method for solving continuous type
		  problems has been proposed by Evans et al. (1995). However,
		  the method is based on the Kohonen competitive, and ART
		  unsupervised network models. In addition, almost every
		  benchmark problem has the training set containing all
		  possible input patterns, so there is no study of the
		  generalisation behaviour of the network. This study
		  attempts to show that single hidden layer supervised
		  networks can be used to solve large and continuous type
		  problems within measurable algorithmic complexities.},
  dbinsdate	= {2002/1}
}

@InCollection{	  winkler97a,
  author	= {S. Winkler and P. Wunsch and G. Hirzinger},
  title		= {A feature map approach to pose estimation based on
		  quaternions},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {949--54},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wirth96a,
  author	= {G. Wirth and C. F. Ball and D. A. Mlynski},
  title		= {Fuzzy classification algorithms for analysis of polymer
		  spectra},
  booktitle	= {Proceedings of the Fifth IEEE International Conference on
		  Fuzzy Systems. FUZZ-IEEE '96},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  address	= {New York, NY, USA},
  pages		= {1339--43},
  dbinsdate	= {oldtimer}
}

@Article{	  wismueller98a,
  author	= {A. Wismueller and F. Vietze and D. R. Dersch and G. L.
		  Leinsinger and T. Pfluger and K. Hahn},
  title		= {Automatic Segmentation and Volumetry of Multispectral
		  {MRI} Data Sets of the Human Brain by Self Organizing
		  Neural Networks},
  journal	= {Radiology},
  volume	= {209P},
  pages		= {1156--1156},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wismuller00a,
  author	= {Wismuller, A. and Dersch, D. R. and Lipinski, B. and Hahn,
		  K. and Auer D.},
  title		= {Hierarchical clustering of functional {MRI} time-series by
		  deterministic annealing},
  booktitle	= {Medical Data Analysis. First International Symposium,
		  ISMDA 2000. Proceedings (Lecture Notes in Computer Science
		  Vol.1933). Springer-Verlag, Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {49--54},
  abstract	= {Presents a neural network approach to the hierarchical
		  unsupervised clustering of functional magnetic resonance
		  imaging (fMRI) time sequences of the human brain by
		  self-organized fuzzy minimal free energy vector
		  quantization (VQ). In contrast to conventional model-based
		  fMRI data analysis techniques, this deterministic annealing
		  procedure does not imply presumptive knowledge of expected
		  stimulus-response patterns, and may thus be applied to fMRI
		  experiments in which the time course of the stimulus is
		  unknown, like in spontaneously occurring events, e.g.
		  hallucinations, epileptic fits or sleep. Moreover, as
		  minimal free energy VQ represents a hierarchical data
		  analysis strategy implying repetitive cluster splitting, it
		  can provide a natural approach to the subclassification
		  task of activated brain regions on different scales of
		  resolution with respect to fine-grained differences in
		  pixel dynamics.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  wismuller00b,
  author	= {Wismuller, A. and Vietze, F. and Dersch, D. R. and Hahn,
		  K. and Ritter H.},
  title		= {A neural network approach to adaptive pattern analysis-the
		  deformable feature map},
  booktitle	= {8th European Symposium on Artificial Neural Networks.
		  ESANN"2000. Proceedings. D-Facto, Brussels, Belgium},
  year		= {2000},
  volume	= {},
  pages		= {189--94},
  abstract	= {We present an algorithm that provides adaptive plasticity
		  in function approximation problems: the deformable
		  (feature) map (DM) algorithm. The DM approach reduces a
		  class of similar function approximation problems to the
		  explicit supervised one-shot training of a single data set.
		  This is followed by a subsequent, appropriate similarity
		  transformation which is based on a self-organized
		  deformation of the underlying multidimensional probability
		  distributions. After discussing the theory of the DM
		  algorithm, we use a computer simulation to visualize its
		  effects on a two-dimensional toy example. Finally, we
		  present results of its application to the real-world
		  problem of fully automatic voxel-based multispectral image
		  segmentation, employing magnetic resonance data sets of the
		  human brain.},
  dbinsdate	= {2002/1}
}

@InCollection{	  wismuller98a,
  author	= {Axel Wismuller and Frank Vietze and Dominik R. Dersch and
		  Klaus Hahn and Helge Ritter},
  title		= {The Deformable Feature Map---Adaptive Plasticity for
		  Function Approximation},
  booktitle	= {Proceedings of ICANN98, the 8th International Conference
		  on Artificial Neural Networks},
  publisher	= {Springer},
  year		= 1998,
  editor	= {L. Niklasson and M. Bod{\'e}n and T. Ziemke},
  volume	= 1,
  address	= {London},
  pages		= {123--128},
  dbinsdate	= {oldtimer}
}

@InCollection{	  witkosski97a,
  author	= {U. Witkosski and S. Ruping and U. Ruckert and F. Schutte
		  and S. Beineke and H. Grotstollen},
  title		= {System identification using selforganizing feature maps},
  booktitle	= {Fifth International Conference on Artificial Neural
		  Networks},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {D. B. Leake and E. Plaza},
  address	= {Berlin, Germany},
  pages		= {100--5},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wittenburg92a,
  author	= {Peter Wittenburg and Uli H. Frauenfelder},
  title		= {Modeling the Human Mental Lexicon with Self-Organizing
		  Feature Maps},
  booktitle	= {Twente Workshop on Language Technology 3: {C}onnectionism
		  and Natural Language Processing},
  year		= {1992},
  editor	= {Marc F. J. Drossaers and Anton Nijholt},
  pages		= {5--15},
  publisher	= {Department of Computer Science, University of Twente},
  address	= {Enschede, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wlitsukura00a,
  author	= {Wlitsukura, Y. and Fukumi, M. and Akamatsu, N.},
  title		= {A design of genetic fog occurrence forecasting system by
		  using {LVQ} network},
  booktitle	= {SMC 2000 Conference Proceedings. 2000 IEEE International
		  Conference on Systems, Man and Cybernetics. `Cybernetics
		  Evolving to Systems, Humans, Organizations, and their
		  Complex Interactions'. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {3895},
  pages		= {3678--81},
  abstract	= {A transportation development in recent years is quite
		  remarkable. However, poor visibility often cause an
		  accident. Therefore, it is very important to forecast a fog
		  occurrence. In this paper, we propose a scheme to forecast
		  a fog occurrence by using the Learning Vector Quantization
		  (LVQ) and a Genetic Algorithm (GA). This scheme forecasts
		  the fog occurrence by the weather data which are provided
		  from the Japan Meteorological Agency. First, the provided
		  data formation are shown. Next, the prediction scheme is
		  described in detail. In this method, input attributes for a
		  LVQ network are selected by real-coded GA to improve
		  forecast accuracy. Furthermore, a partial selection
		  processing in the real-coded GA improves its convergence
		  properties. Finally, in order to show the effectiveness of
		  the proposed prediction scheme, computer simulations are
		  performed.},
  dbinsdate	= {2002/1}
}

@InCollection{	  wolf97a,
  author	= {F. Wolf and T. Geisel},
  title		= {Must pinwheels move during visual development?},
  booktitle	= {Artificial Neural Networks---ICANN '97. 7th International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {W. Gerstner and A. Germond and M. Hasler and J. -D.
		  Nicoud},
  address	= {Berlin, Germany},
  pages		= {195--200},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wolfer94a,
  author	= {James Wolfer and James Roberg{\'{e}} and Thom Grace},
  title		= {Robust Multispectral Road Classification in {L}andsat
		  Thematic Mapper Imagery},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {I},
  pages		= {260--268},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, image processing, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wolfer95a,
  author	= {James Wolfer and James Roberg{\'{e}} and Thom Grace},
  title		= {Learning Vector Quantization vs Multilayered Perceptrons
		  for Classifing {L}andsat Thematic Mapper Imagery},
  volume	= {I},
  pages		= {157--165},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@Article{	  wolkenstein97a,
  author	= {M. Wolkenstein and H. Hutter and C. Mittermayr and W.
		  Schiesser},
  title		= {Classification of {SIMS} Images Using a {K}ohonen
		  Network},
  journal	= {Analytical Chemistry},
  year		= {1997},
  volume	= {69},
  number	= {4},
  pages		= {777--782},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wolters96a,
  author	= {M. Wolters},
  title		= {A dual route neural net approach to grapheme-to-phoneme
		  conversion},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {233--8},
  dbinsdate	= {oldtimer}
}

@Article{	  wong00a,
  author	= {Wong, Kok Wai and Gedeon, Tamas},
  title		= {Modular signal processing model for permeability
		  prediction in petroleum reservoir},
  journal	= {Neural Networks for Signal Processing---Proceedings of the
		  IEEE Workshop},
  year		= {2000},
  volume	= {2},
  number	= {},
  month		= {},
  pages		= {906--915},
  organization	= {Murdoch Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {The use of Artificial Neural Network (ANN) especially
		  Backpropagation Neural Network (BPNN) has been a promising
		  tool for well log analysis in predicting permeability.
		  However, due to the range of permeability data, it is
		  normally converted using logarithmic transform before being
		  used for data analysis by the BPNN. This has an impact on
		  the accuracy of the permeability prediction. This paper
		  suggests a model for improving the permeability prediction.
		  It first divides the whole sample space of the permeability
		  values according to their logarithmic region, and then
		  generates individual BPNNs for each logarithmic region. In
		  this initial study, Learning Vector Quantization (LVQ) is
		  used for this Purpose for separating the data. After that,
		  each region is then handled by each BPNN. This method not
		  only preserves the resolution of the permeability, but at
		  the same time, increase the prediction accuracy. The
		  contributions of this paper are to identify the problems in
		  the signal processing of permeability prediction, and
		  exploit new direction of improving permeability prediction
		  using well logs.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  wong95a,
  author	= {Wong, T. and Gargour, C. S. and Batani, N. },
  title		= {Fuzzy learning vector quantization generation of
		  codebooks},
  booktitle	= {1995 Canadian Conference on Electrical and Computer
		  Engineering},
  year		= {1995},
  editor	= {Gagnon, F. },
  volume	= {2},
  pages		= {1180--3},
  organization	= {Ecole de Technol. Superieure, Quebec Univ. , Montreal,
		  Que. , Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {A modification of the learning vector quantization (LVQ)
		  method used for the generation of codebooks is presented.
		  It retains the simplicity of the LVQ method while
		  eliminating the non uniform spatial distribution of the
		  prototype vectors which could result from an inadequate
		  choice of the prototype vectors. The method is based upon
		  the segmentation of the input vector space in fuzzy
		  partitions. A fuzzy objective function is defined. An
		  algorithm for its minimization is presented. Simulations
		  results are given.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wong97a,
  author	= {P. M. Wong and K. W. Wong and C. C. Fung and T. D.
		  Gedeon},
  title		= {A neural-fuzzy technique for interpolating spatial data
		  via the use of learning curve},
  booktitle	= {Biological and Artificial Computation: From Neuroscience
		  to Technology. International Work Conference on Artificial
		  and Natural Neural Networks, IWANN'97. Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1997},
  editor	= {J. Mira and R. Moreno-Diaz and J. Cabestany},
  address	= {Berlin, Germany},
  pages		= {323--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wong97b,
  author	= {Kok Wai Wong and Chun Che Fung and H. Eren},
  title		= {A study of the use of \mbox{self-organising} map for
		  splitting training and validation sets for backpropagation
		  neural network},
  booktitle	= {1996 IEEE TENCON Digital Signal Processing Applications
		  Proceedings},
  publisher	= {Inst. Ind. Eng},
  year		= {1997},
  volume	= {1},
  editor	= {G. L. Curry and B. Bidanda and S. Jagdale},
  address	= {Norcross, GA, USA},
  pages		= {157--62},
  dbinsdate	= {oldtimer}
}

@Article{	  woodland90a,
  author	= {P. C. Woodland and S. G. Smyth},
  title		= {An experimental comparison of connectionist and
		  conventional classification systems on natural data},
  journal	= {Speech Communication},
  year		= {1990},
  volume	= {9},
  number	= {1},
  pages		= {73--82},
  dbinsdate	= {oldtimer}
}

@Article{	  wriggers98a,
  author	= {W. Wriggers and R. A. Milligan and K. Schulten and J. A.
		  McCammon},
  title		= {Self Organizing Neural Networks Bridge the Biomolecular
		  Resolution Gap},
  journal	= {Journal of Molecular Biology},
  volume	= {284},
  pages		= {1247--1254},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  wu00a,
  author	= {Wu, D. and Linders, J.},
  title		= {Comparison of three different methods to select feature
		  for discriminating forest cover types using {SAR} imagery},
  journal	= {International Journal of Remote Sensing},
  year		= {2000},
  volume	= {21},
  number	= {10},
  month		= {},
  pages		= {2089--2099*},
  organization	= {Technology Service Div},
  publisher	= {Taylor \& Francis Ltd},
  address	= {London},
  abstract	= {Three methods (fuzzy partition method, stepwise regression
		  analysis and principal component analysis) were used to
		  select meaningful texture features for discriminating
		  forest cover types. The initial texture set was extracted
		  from the wavelet sub-images. Feature selection was based on
		  all texture features of four sub-images combined.
		  Recognition of forest cover types was accomplished by the
		  neural network of learning vector quantization. The
		  performance of these techniques was evaluated using a case
		  study area at Whitecourt, Alberta, Canada. The selection
		  procedure seemed to be adequate to extract meaningful
		  texture features to help discriminate forest cover types,
		  because the classification accuracy of the selected feature
		  sets was improved. In addition, the optimization process
		  can be considered as an efficient one, since the number of
		  features was reduced to about 24.5--66.8% of the total 208
		  features using the three selection methods.},
  dbinsdate	= {2002/1}
}

@Article{	  wu00b,
  author	= {Wu, C. S. and Polte, T. and Rehfeldt, D.},
  title		= {Gas metal arc welding process monitoring and quality
		  evaluation using neural networks},
  journal	= {SCIENCE AND TECHNOLOGY OF WELDING AND JOINING},
  year		= {2000},
  volume	= {5},
  number	= {5},
  pages		= {324--328},
  abstract	= {To ensure product quality, it is essential to ensure
		  process quality. Thus, early monitoring and detection of
		  process disturbances in welding production lines are of
		  great significance. The present paper introduces a neural
		  network system for process monitoring and quality
		  evaluation in gas metal arc welding. The system is based
		  only on the measured and statistically processed data for
		  welding voltage and short circuiting time. It is a
		  self-organising feature map Kohonen network which can
		  automatically recognise and classify process disturbances
		  occurring during welding.},
  dbinsdate	= {2002/1}
}

@Article{	  wu02a,
  author	= {Wu, Haiqiao and Liu, Yi and Ding, Yunliang and Zhang,
		  Xiangwei},
  title		= {Application study of {SOM} artificial neural net in
		  airliner fault diagnosis},
  journal	= {Journal-of-Nanjing-University-of-Aeronautics-\&-Astronautics}
		  ,
  year		= {2002},
  volume	= {34},
  pages		= {31--4},
  abstract	= {It is unavoidable for modern airliner to malfunction in
		  its routine use, and fault diagnosis is a very important
		  guarantee for its flight safety. The general fault
		  diagnosis method of airliner is to combine the
		  probabilistically statistical method with expertise. It
		  shows that the trained Kohonen's self-organizing map (SOM)
		  artificial neural net reflects the probability density of
		  the input samples through its output without need of the
		  knowledge of the prior probability of input samples, and it
		  can be applied in function approximation, too. The SOM is
		  used to calculate the probability of the airliner fault
		  occurrence, and associating the probability with the
		  expertise. The technical feasibility of the method is
		  proved by actual fault diagnosis.},
  dbinsdate	= {2002/1},
  merjanote     = {last name guessed, haiqiao sounds more like first name etc.}
}

@Article{	  wu02b,
  author	= {Wu, Chuansong and Polte, T. and Rehfeldt, D.},
  title		= {Kohonen network system for process monitoring in gas metal
		  arc welding},
  journal	= {Chinese-Journal-of-Mechanical-Engineering},
  year		= {2002},
  volume	= {38},
  pages		= {131--4},
  abstract	= {The electric parameters in gas metal arc welding process
		  are measured in real time. A Kohonen artificial neural
		  network system is developed. Based directly on the
		  probability density distribution of welding voltage and the
		  class frequency distribution of short-circuiting time, the
		  system can automatically recognize the various disturbances
		  during the welding process.},
  dbinsdate	= {2002/1},
  merjanote     = {last name assumed from european last names, and Wu more last 
                   name sounding}
}

@Article{	  wu01a,
  author	= {Wu, Q. and Iyengar, S. S. and Zhu, M.},
  title		= {Web image retrieval using self-organizing feature map},
  journal	= {Journal of the American Society for Information Science
		  and Technology},
  year		= {2001},
  volume	= {52},
  number	= {10},
  month		= {August },
  pages		= {868--875},
  organization	= {Department of Computer Science, Louisiana State
		  University},
  publisher	= {},
  address	= {},
  abstract	= {The explosive growth of digital image collections on the
		  Web sites is calling for an efficient and intelligent
		  method of browsing, searching, and retrieving images. In
		  this article, an artificial neural network (ANN)-based
		  approach is proposed to explore a promising solution to the
		  Web image retrieval (IR). Compared with other image
		  retrieval methods, this new approach has the following
		  characteristics. First of all, the Content-Based features
		  have been combined with Text-Based features to improve
		  retrieval performance. Instead of solely relying on
		  low-level visual features and high-level concepts, we also
		  take the textual features into consideration, which are
		  automatically extracted from image names, alternative
		  names, page titles, surrounding texts, URLs, etc. Secondly,
		  the Kohonen neural network model is introduced and led into
		  the image retrieval process. Due to its self-organizing
		  property, the cognitive knowledge is learned, accumulated,
		  and solidified during the unsupervised training process.
		  The architecture is presented to illustrate the main
		  conceptual components and mechanism of the proposed image
		  retrieval system. To demonstrate the superiority of the new
		  IR system over other IR systems, the retrieval result of a
		  test example is also given in the article.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  wu89a,
  author	= {F. H. Wu and K. Ganesan},
  title		= {Comparative study of algorithms for {VQ} design using
		  conventional and neural-net based approaches},
  booktitle	= {Proc. ICASSP-89 International Conference on Acoustics,
		  Speech and Signal Processing, Glasgow, Scotland},
  year		= {1989},
  pages		= {751--754},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wu90a,
  author	= {F. H. Wu and K. Ganesan},
  title		= {Comparative study of algorithms for {VQ} design using
		  conventional and neural-net based approaches},
  booktitle	= {Proc. Ninth Annual Int. Phoenix Conf. on Computers and
		  Communications},
  year		= {1990},
  pages		= {263--267},
  organization	= {IEEE},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wu90b,
  author	= {Lizhong Wu and Frank Fallside},
  title		= {The optimal gain sequence for fastest learning in
		  connectionist vector quantiser design},
  booktitle	= {Proc. International Conference on Spoken Language
		  Processing},
  year		= {1990},
  pages		= {1029--1032},
  publisher	= {Acoustical Society of Japan},
  address	= {Tokyo, Japan},
  dbinsdate	= {oldtimer}
}

@Article{	  wu91a,
  author	= {C. -H. Wu and J. -F. Wang and C. -C. Huang and J. -Y.
		  Lee},
  title		= {Speaker-independent recognition of isolated words using
		  concatenated neural networks},
  journal	= {Int. J. Pattern Recognition and Artificial Intelligence},
  year		= {1991},
  volume	= {5},
  number	= {5},
  pages		= {693--714},
  month		= {December},
  x		= {The speech recognizer proposed is obtained by
		  concatenating a Bayesian neural network and a Hopfield
		  time-alignment network. . . . A proposed splitting learning
		  vector quantization (LVQ) algorithm derived from the LBG
		  clustering algorithm and the Kohonen LVQ algorithm is first
		  used to train the Bayesian network. The LVQ2 algorithm is
		  subsequently adopted as a final refinement step. },
  abstract	= {A speaker-independent isolated word recognizer is
		  proposed. It is obtained by concatenating a Bayesian neural
		  network and a Hopfield time-alignment network. In this
		  system, the Bayesian network outputs the a posteriori
		  probability for each speech frame, and the Hopfield network
		  is then concatenated for time warping. Comparisons with
		  K-means and DTW algorithms show that the integration of the
		  splitting LVQ and LVQ2 algorithms makes this system well
		  suited to speaker-independent isolated word recognition. A
		  cookbook approach for the determination of parameters in
		  the Hopfield time-alignment network is also described.},
  dbinsdate	= {oldtimer}
}

@Article{	  wu91b,
  author	= {C. -H. Wu and R. E. Hodges and C. J. Wang},
  title		= {Parallelizing the \mbox{self-organizing} feature map on
		  multiprocessor systems},
  journal	= {Parallel Computing},
  year		= {1991},
  volume	= {17},
  number	= {6--7},
  pages		= {821--832},
  month		= {September},
  dbinsdate	= {oldtimer}
}

@Article{	  wu91c,
  author	= {Lizhong Wu and Frank Fallside},
  title		= {On the design of connectionst vector quantizer},
  journal	= {Computer Speech and Language},
  year		= {1991},
  volume	= {5},
  pages		= {207--229},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wu91d,
  author	= {J. -M. Wu and J. -Y. Lee and Y. -C. Tu and C. -Y. Liou},
  title		= {Diagnoses for machine vibrations based on
		  self-organization neural network},
  booktitle	= {Proc. IECON '91, International Conference on Industrial
		  Electronics, Control and Instrumentation},
  year		= {1991},
  volume	= {II},
  pages		= {1506--1510},
  organization	= {IEEE; Soc. Instrum. {\&} Control Eng. Japan},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wu91e,
  author	= {P. Wu and K. Warwick},
  title		= {Dynamic coupling weights in a neural network system},
  booktitle	= {Proc. ICANN'91, International Conference on Artificial
		  Neural Networks},
  year		= {1991},
  pages		= {350--353},
  organization	= {IEE},
  publisher	= {IEE},
  address	= {London, UK},
  x		= {A number of applications of Neural Network Systems (NNS)
		  for speech signal processing have been developed, Kohonen
		  (1988), . . . The authors introduce new improvements in
		  that the dynamic coupling weight concept is provided by
		  means of a switch structure. },
  dbinsdate	= {oldtimer}
}

@Article{	  wu92a,
  author	= {P. Wu and K. Warwick and M. Koska},
  title		= {Neural network feature maps for {C}hinese phonemes},
  journal	= {Neurocomputing},
  year		= {1992},
  volume	= {4},
  number	= {1--2},
  pages		= {109--112},
  abstract	= {It has been shown through a number of experiments that
		  neural networks can be used for a phonetic typewriter.
		  Algorithms can be looked on as producing self-organizing
		  feature maps which correspond to phonemes. In the Chinese
		  language the utterance of a Chinese character consists of a
		  very simple string of Chinese phonemes. With this as a
		  starting point, a neural network feature map for Chinese
		  phonemes can be built up. In this paper, feature map
		  structures for Chinese phonemes are discussed and tested.
		  This research on a Chinese phonetic feature map is
		  important both for Chinese speech recognition and for
		  building a Chinese phonetic typewriter.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wu92b,
  author	= {Wu, Yan Yan and Huangfu, Kan and Zhou, Liangzhu and Wan
		  Jian Wei},
  title		= {The detection theory of \mbox{self-organizing} feature map
		  and its application},
  booktitle	= {Proc. NAECON 1992, National Aerospace and Electronics
		  Conference},
  year		= {1992},
  volume	= {I},
  pages		= {108--112},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  wu94a,
  author	= {Jing Wu and Hong Yan and Chalmers, A. },
  title		= {Handwritten digit recognition using two-layer
		  \mbox{self-organizing} maps},
  journal	= {International Journal of Neural Systems},
  year		= {1994},
  volume	= {5},
  number	= {4},
  pages		= {357--62},
  month		= {Dec},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wu95a,
  author	= {Jing Wu and Hong Yan},
  title		= {Combined {SOM} and {LVQ} Based Classifiers for Handwritten
		  Digit Recognition},
  volume	= {VI},
  pages		= {3074--3077},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {This paper presents a two-layer self-organizing neural
		  network based technique for handwritten digit recognition.
		  In this method, two classifiers are built with different
		  sets of features using self-organizing map (SOM) and
		  learning vector quantization (LVQ) based algorithms. The
		  two classifiers are then combined to make the final
		  decision. For over 10,000 digit samples which are not used
		  for training extracted from the NIST database, the two
		  classifiers can correctly recognize 97.11% and 97.16% of
		  the digits respectively and the combined classifier has a
		  recognition rate of 98.88%.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wu96a,
  author	= {Chung-Yu Wu and Ron-Yi Liu and I-Chang Jou and Famm-Jiang
		  Shyh Jye},
  title		= {The {CMOS} design of robust neural chip with the on-chip
		  learning capability},
  booktitle	= {1996 IEEE International Symposium on Circuits and Systems.
		  Circuits and Systems Connecting the World, ISCAS 96},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {426--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wu96b,
  author	= {Duanpei Wu and J. N. Gowdy},
  title		= {K-subspaces and time-delay autoassociators for phoneme
		  recognition},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {4},
  address	= {New York, NY, USA},
  pages		= {1871--6},
  abstract	= {This paper presents a new approach using Time-Delay
		  AutoAssociators (TDAA) to perform phoneme recognition. The
		  time-delay autoassociator combines the time-delay design
		  for phoneme recognition and the technique of MLP
		  autoassociators. Each time-delay autoassociator is
		  constructed and trained to model one and only one phoneme
		  using data belonging to that phoneme category. This
		  non-classification training procedure provides a method
		  with high recognition performance to avoid the drawback
		  encountered in most conventional speech recognition neural
		  networks that the network output values do not represent
		  candidate likelihoods. The approach with the proposed
		  architecture, K-subspaces with linear time-delay
		  autoassociators, in which each phoneme is modelled by K
		  linear TDAAs, has yielded a high recognition performance
		  compared to that of a TDNN and a Shift-Tolerant LVQ trained
		  by classification learning procedures, over the three
		  difficult phonemes 'B', 'D' and 'G'. It has also been
		  observed that the nonlinear time-delay autoassociators
		  could perform better than linear ones.},
  dbinsdate	= {oldtimer}
}

@Article{	  wu96c,
  author	= {W. Wu and B. Walczak and D. L. Massart and S. Heuerding
		  and F. Erni and I. R. Last and K. A. Preddle},
  title		= {Artificial neural networks in classification of {NIR}
		  spectral data: design of the training set},
  journal	= {Chemometrics and Intelligent Laboratory Systems},
  year		= {1996},
  volume	= {33},
  number	= {1},
  pages		= {35--46},
  dbinsdate	= {oldtimer}
}

@InCollection{	  wu96d,
  author	= {Duanpei Wu and J. N. Gowdy},
  title		= {Shift-tolerant K-subspaces for phoneme recognition},
  booktitle	= {1996 IEEE International Conference on Acoustics, Speech,
		  and Signal Processing Conference Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {6},
  address	= {New York, NY, USA},
  pages		= {3378--81},
  dbinsdate	= {oldtimer}
}

@Article{	  wu97a,
  author	= {C. Wu and Hsi-Lien Chen and Sheng-Chih Chen},
  title		= {Counter-propagation neural networks for molecular sequence
		  classification: supervised {LVQ} and dynamic node
		  allocation},
  journal	= {Applied Intelligence: The International Journal of
		  Artificial Intelligence, Neural Networks, and Complex
		  Problem-Solving Technologies},
  year		= {1997},
  volume	= {7},
  number	= {1},
  pages		= {27--38},
  dbinsdate	= {oldtimer}
}

@Article{	  wu99a,
  author	= {Wu, Ying and Yan, Pingfan},
  title		= {A study on structural adapting \mbox{self-organizing}
		  neural network},
  journal	= {Acta Electronica Sinica},
  year		= {1999},
  volume	= {27},
  pages		= {55--8},
  abstract	= {A new structural adapting self-organizing network model
		  SASONN, which can be thought as an extension of Kohonen's
		  SOFM, is presented in this paper. We proved that, if each
		  neuron in the network have equal winning frequency in
		  stationary status, the density of neuron is proportional of
		  the p.d.f of sample set instead of its 2/3 power. The
		  essence of evolutionary computing is introduced to network
		  optimization. We treat each neuron as individual of the
		  evolutionary population and construct rules of neuron
		  growing and pruning. Some defects in SOFM, such as
		  incorrect mapping, neuron under use and boundary effect can
		  be overoomed in SASONN.},
  dbinsdate	= {oldtimer}
}

@Article{	  wuertz99a,
  author	= {Wuertz, Rolf P. and Konen, Wolfgang and Behrmann, Kay
		  Ole},
  title		= {On the performance of neuronal matching algorithms},
  journal	= {Neural Networks},
  year		= {1999},
  number	= {1},
  volume	= {12},
  pages		= {127--134},
  abstract	= {For a solution of the visual correspondence problem we
		  have modified the Self Organizing Map (SOM) to map image
		  planes onto another in a neighborhood- and
		  feature-preserving way. We have investigated the
		  convergence speed of this SOM and Dynamic Link Matching
		  (DLM) on a benchmark problem for the solution of which both
		  algorithms are good candidates. We show that even after
		  careful parameter adjustment the SOM needs a large number
		  of simple update steps and DLM a small number of
		  complicated ones. The results are consistent with an
		  exponential vs. polynomial scaling behavior with increased
		  pattern size. Finally, we present and motivate a rule for
		  adjusting the parameters of DLM for all problem sizes,
		  which we could not find for SOM.},
  dbinsdate	= {oldtimer}
}

@Article{	  wunstell01a,
  author	= {Wunstell, M. and Polani, D. and Uthmann, T. and Perl, J.},
  title		= {Behavior classification with self-organizing maps},
  journal	= {RoboCup 2000: Robot Soccer World Cup IV (Lecture Notes in
		  Artificial Intelligence Vol.2019). Springer-Verlag, Berlin,
		  Germany; 2001; xvii+658 pp.p.108--18},
  year		= {2001},
  volume	= {},
  pages		= {108--18},
  abstract	= {We describe a method that applies self-organizing maps to
		  direct clustering of spatio-temporal data, We use the
		  method to evaluate the behavior of RoboCup players. By
		  training the self-organizing map with player data we have
		  the possibility to identify various clusters representing
		  typical agent behavior patterns. Thus we can draw certain
		  conclusions about their tactical behavior, using purely
		  motion data, i.e. logfile information. In addition, we
		  examine the player-ball interaction that gives information
		  about the players' technical capabilities.},
  dbinsdate	= {2002/1}
}

@InCollection{	  wurtz96a,
  author	= {R. P. Wurtz and W. Konen and K. -O. Behrmann},
  title		= {How fast can neuronal algorithms match patterns?},
  booktitle	= {Artificial Neural Networks---ICANN 96. 1996 International
		  Conference Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1996},
  editor	= {C. {von der Malsburg} and W. {von Seelen} and J. C.
		  Vorbruggen and B. Sendhoff},
  address	= {Berlin, Germany},
  pages		= {145--50},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  wyler93a,
  author	= {Kuno Wyler},
  title		= {Self-Organizing Process Mapping in a Multiprocessor
		  System},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {II},
  pages		= {562--566},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  xiang-yun98a,
  author	= {Ye Xiang-Yun and Qi Fei-Hu and Jiang Jun},
  title		= {Adaptive image segmentation based on selective
		  multiresolutional {K}ohonen neural network},
  journal	= {Journal of Infrared and Millimeter Waves},
  year		= {1998},
  volume	= {17},
  number	= {1},
  pages		= {48--53},
  dbinsdate	= {oldtimer}
}

@Article{	  xiangyang95a,
  author	= {Xue Xiangyang and Fan Changxin},
  title		= {Study on {SOFM}-based image vector quantization},
  journal	= {Acta Electronica Sinica},
  year		= {1995},
  volume	= {23},
  number	= {4},
  pages		= {24--9},
  month		= {April},
  dbinsdate	= {oldtimer}
}

@InCollection{	  xiao98a,
  author	= {Rongrui Xiao and V. Chandrasekar and H. Liu and E.
		  Gorgucci},
  title		= {Detection of rain/no rain condition on ground from radar
		  data using a {K}ohonen neural network},
  booktitle	= {IGARSS '98. Sensing and Managing the Environment. 1998
		  IEEE International Geoscience and Remote Sensing. Symposium
		  Proceedings},
  publisher	= {IEEE},
  year		= {1998},
  volume	= {1},
  editor	= {T. I. Stein},
  address	= {New York, NY, USA},
  pages		= {159--61},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  xiaochuan00a,
  author	= {Xiaochuan Luo and Chanan Singh and Qing Zhao},
  title		= {Loss-of-load probability calculation using learning vector
		  quantization},
  booktitle	= {PowerCon 2000. 2000 International Conference on Power
		  System Technology. Proceedings. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {3},
  pages		= {1707--12},
  abstract	= {This paper proposes a new method employing learning vector
		  quantization (LVQ) and Monte Carlo simulation to calculate
		  the loss-of-load probability (LOLP) of power systems. LVQ
		  is a type of classification method whose goal is to use
		  data samples to position the codebook vector in such a way
		  that the nearest neighbor classification method will result
		  in the maximum classification accuracy. The proposed method
		  greatly reduces the computing burden of the loss-of-load
		  probability calculation compared to Monte Carlo simulation
		  only. A case study of the IEEE RTS system is presented,
		  demonstrating the efficiency of this approach.},
  dbinsdate	= {2002/1}
}

@Article{	  xiaoqiu01a,
  author	= {Xiaoqiu Wang and Hua Lin and Jianming Lu and Yahagi, T.},
  title		= {Detection of nonlinearly distorted m-ary {QAM} signals
		  using self-organizing map},
  journal	= {IEICE-Transactions-on-Fundamentals-of-Electronics,-Communications-and-Computer-Sciences}
		  ,
  year		= {2001},
  volume	= {},
  pages		= {1969--76},
  abstract	= {Detection of nonlinearly distorted signals is an essential
		  problem in telecommunications. Previously, a neural network
		  combined with a conventional equalizer has been used to
		  improve the performance especially in compensating for
		  nonlinear distortions. In the paper, a self-organizing map
		  (SOM) combined with a conventional symbol-by-symbol
		  detector is used as an adaptive detector after the output
		  of the decision feedback equalizer (DFE), which updates the
		  decision levels to follow up the nonlinear distortions. In
		  the proposed scheme, we use the box distance to define the
		  neighborhood of the winning neuron of the SOM algorithm.
		  The error performance has been investigated in both 16 QAM
		  and 64 QAM systems with nonlinear distortions. Simulation
		  results have shown that the system performance is
		  remarkably improved by using SOM detector compared with the
		  conventional DFE scheme.},
  dbinsdate	= {2002/1}
}

@Article{	  xiaowei02a,
  author	= {XiaoWei Zhao and Chen TianLun},
  title		= {Type of self-organized criticality model based on neural
		  networks},
  journal	= {Physical-Review-E-(Statistical,-Nonlinear,-and-Soft-Matter-Physics)}
		  ,
  year		= {2002},
  volume	= {65},
  pages		= {026114},
  abstract	= {Based on the standard self-organizing map neural network
		  model, we introduce a kind of coupled map lattice system to
		  investigate self-organized criticality (SOC) in the
		  activity of model neural populations. Our system is
		  simulated by a more detailed integrate-and-fire mechanism
		  and a kind of local perturbation driving rule; it can
		  display SOC behavior in a certain range of system
		  parameters, even with period boundary condition. More
		  importantly, when the influence of synaptic plasticity is
		  adequately considered, we can find that our system's
		  learning process plays a promotive role in the emergence of
		  SOC behavior.},
  dbinsdate	= {2002/1}
}

@Article{	  xie00a,
  author	= {Xie, Tao and Chen, Huowang and Zhang, Yulin},
  title		= {Optimal statistical clustering for high dimensional fault
		  sample using evolution strategies},
  journal	= {Tuijin Jishu/Journal of Propulsion Technology},
  year		= {2000},
  volume	= {21},
  number	= {5},
  month		= {Oct},
  pages		= {34--37, 52},
  organization	= {Natl Univ of Defense Technology},
  publisher	= {JPT},
  address	= {},
  abstract	= {A clustering algorithm based on Evolution Strategies was
		  proposed to make analysis on high dimensional data of
		  liquid rocket engine propulsion system. In order to prevent
		  the solution population from premature convergence, the
		  dynamic fitness adaptation technique and all-sharing
		  function were introduced. An adaptive regulation scheme for
		  evolution control parameters was specially presented for
		  clustering analysis of high dimensional data. A local
		  clustering deadlock can also be overcome by the
		  deadlock-check and cluster-recombination and collapse
		  strategies. This algorithm was used in the optimal
		  clustering analysis for the 560 data samples of 14 sorts of
		  common faults, each of 68 dimensions, which were simulated
		  for a liquid rocket engine. In addition, comparison with
		  fuzzy Kohonen clustering networks (FKCN) has also been
		  made, based on IRIS data. The simulation results show that
		  the evolution strategies based on clustering algorithm is
		  superior to other non-evolutionary clustering algorithms,
		  particularly when the data is of high dimensions.},
  dbinsdate	= {2002/1}
}

@Article{	  xie95a,
  author	= {Weixin Xie and Wenhua Li and Xinbo Gao},
  title		= {Fuzzy {K}ohonen clustering neural network trained by
		  genetic algorithm and fuzzy competition learning},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2620},
  pages		= {493--8},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  xu01a,
  author	= {L. Xu},
  title		= {An overview on unsupervised learning from data mining
		  perspective},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {181--209},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  xu01b,
  author	= {Xu, Yong and Chen En-hong and Wang, Xu-fa},
  title		= {Neural network based Web user behavior cluster analysis},
  journal	= {Mini-Micro-Systems},
  year		= {2001},
  volume	= {22},
  pages		= {699--702},
  abstract	= {This paper provides a neural network based method for Web
		  user's behavior analysis. First, the log file of WWW server
		  is analyzed to create session vectors, from which frequent
		  item sets are discovered. Next, based on these frequent
		  item sets, the interesting session vectors are extracted
		  and then normalized to generate pattern vectors. Finally,
		  we adopt Kohonen's self-organising feature map model to
		  cluster these vectors to create user behavior patterns. The
		  experimental results and comparative analysis demonstrate
		  that our method is effective for Web user's behavior
		  analysis.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name assumed, similarly to other papers in 
                   Mini-Micro-Systems}
}

@TechReport{	  xu89a,
  author	= {Lei Xu and Erkki Oja},
  title		= {Extended \mbox{self-organizing} map for curve detection},
  institution	= { Department of Information Technology, Lappeenranta,
		  Finland},
  year		= {1989},
  type		= {Res. Report},
  number	= {16},
  address	= {},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  xu90a,
  author	= {Lei Xu},
  title		= {Adding learning expectation into the learning procedure of
		  \mbox{self-organizing} maps},
  journal	= {Int. J. Neural Systems},
  volume	= {1},
  year		= {1990},
  number	= {3},
  publisher	= {World Scientific Publishing Company},
  pages		= {269--283 },
  dbinsdate	= {oldtimer}
}

@InProceedings{	  xu90b,
  author	= {Lei Xu and Erkki Oja},
  title		= {Adding top-down expectation into the learning procedure of
		  \mbox{self-organizing} maps},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {I},
  pages		= {735--738},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  xu90c,
  author	= {L. Xu and E. Oja},
  title		= {Vector pair correspondence by a simplified
		  counter-propagation model: a twin topographic map},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {II},
  pages		= {531--534},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  xu90d,
  author	= {L. Xu and E. Oja and P. Kultanen},
  title		= {A new curve detection method: {R}andomized {H}ough
		  {T}ransform {(RHT)}. },
  journal	= {Pattern Recognition Letters},
  year		= {1990},
  volume	= {11},
  number	= {},
  pages		= {331--338},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@TechReport{	  xu90e,
  author	= {L. Xu and E. Oja and P. Kultanen},
  title		= {Randomized {H}ough Transform {(RHT)}: Theoretical analysis
		  and extensions},
  institution	= {Lappeenranta University of Technology, Department of
		  Information Technology, Lappeenranta, Finland},
  year		= {1990},
  type		= {Res. Report},
  number	= {18},
  address	= {},
  month		= {},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  xu92a,
  author	= {L. Xu and E. Oja},
  title		= {Further developments on {RHT}: Basic mechanisms,
		  algorithms, and computational complexities},
  booktitle	= {Proc. 11ICPR, International Conference on Pattern
		  Recognition},
  year		= {1992},
  pages		= {125---128},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  xu92b,
  author	= {L. Xu and A. Krzyzak and E. Oja},
  title		= {Unsupervised and supervised classifications by rival
		  penalized competitive learning},
  booktitle	= {Proc. 11ICPR, International Conference on Pattern
		  Recognition},
  year		= {1992},
  pages		= {496---499},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  xu93a,
  author	= {L. Xu and E. Oja},
  title		= {Randomized {H}ough Transform {(RHT)}: basic mechanisms,
		  algorithms, and complexities},
  journal	= {Computer Vision, Graphics, and Image Processing: Image
		  Understanding},
  year		= {1993},
  volume	= {57},
  number	= {},
  pages		= {131---154},
  annotate	= {},
  dbinsdate	= {oldtimer}
}

@Article{	  xu93b,
  author	= {Lei Xu and Adam Krzy{\'{z}}ak and Erkki Oja},
  title		= {Rival Penalized Competitive Learning for Clustering
		  Analysis, {RBF} Net, and Curve Detection},
  journal	= {{IEEE} Trans. on Neural Networks},
  year		= {1993},
  volume	= {4},
  number	= {4},
  pages		= {636--649},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  xu94a,
  author	= {Lei Xu},
  title		= {Multisets Modeling Learning: An Unified Theory for
		  Supervised and Unsupervised Learning},
  pages		= {315--320},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {analysis, unification, theory},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  xu95a,
  author	= {Lei Xu},
  title		= {A Unified Learning Framework: Multisets Modeling
		  Learning},
  volume	= {I},
  pages		= {35--42},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  xu95b,
  author	= {Xu, M. and Kuh, A. },
  title		= {Unsupervised learning applied to image coding},
  booktitle	= {1995 IEEE Symposium on Circuits and Systems},
  year		= {1995},
  volume	= {3},
  pages		= {1632--5},
  organization	= {Dept. of Electr. Eng. , Hawaii Univ. , Honolulu, HI, USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  xu97a,
  author	= {Shuxiang Xu and Ming Zhang},
  title		= {A {SOM} network group for {DOMM} financial prediction
		  system},
  booktitle	= {1997 IEEE International Conference on Intelligent
		  Processing Systems},
  publisher	= {IEEE},
  year		= {1997},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {482--4},
  dbinsdate	= {oldtimer}
}

@Article{	  xu99a,
  author	= {Xu, Z. M. and Ivanusic, J. J. and Bourke, D. W. and
		  Butler, E. G. and Horne, M. K.},
  title		= {Automatic detection of bursts in spike trains recorded
		  from the thalamus of a monkey performing wrist movements},
  journal	= {Journal of Neuroscience Methods},
  year		= {1999},
  volume	= {91},
  pages		= {123--33},
  abstract	= {In a previous paper (P.R. Churchward et al., ibid., vol.
		  76, p. 203--10, 1997), the authors showed that a simple
		  back propagation neural network could reliably model visual
		  inspection by human observers in detecting the point of
		  change of neuronal discharge patterns. The data for that
		  study was deliberately chosen so that the point of change
		  was readily detected and there would be high concordance
		  between human observers. The authors wished to extend this
		  investigation by comparing a variety of automatic analysis
		  methods on more complex data sets. Two automatic analysis
		  methods are discussed here. The knowledge based spike train
		  analysis (KBSTA) was designed to emulate the detection of
		  bursts by human observers. The self-organizing feature map
		  (SOFM) spike train analysis determined a burst by
		  classifying the patterns of neuronal discharge. Neuronal
		  discharge was recorded from the motor thalamus and nucleus
		  ventralis posterior lateralis caudalis (VPLc) of a monkey
		  performing consecutive trials of skilled wrist movements.
		  Recordings were made from 36 neurons whose discharge
		  patterns were related to wrist movement. Three hundred and
		  sixty trials performed during the recording of these 36
		  neurons were chosen at random and used to compare the 3
		  methods, KBSTA, SOFM, and visual inspection. The main
		  results of this study show that for the 360 trials the 3
		  detection methods have very similar results in detecting
		  the onset and offset of neuronal bursts. The SOFM method is
		  not the best first approach for detecting a burst, but it
		  does provides independent evidence to support the KBSTA and
		  visual inspection methods. In conclusion the authors
		  propose the KBSTA method as a practical, automatic
		  technique to identify bursts of neuronal discharge.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  xuan95a,
  author	= {Jianhua Xuan and T{\"{u}}lay Adali},
  title		= {Learning Tree-Structured Vector Quantization for Image
		  Compression},
  volume	= {I},
  pages		= {756--759},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@Article{	  xue00a,
  author	= {Xue Jian qiao and Li Qi Bin and Zhao Yong Beng},
  title		= {Automatic classification of stellar spectra using {SOFM}
		  method},
  journal	= {Acta-Astrophysica-Sinica},
  year		= {2000},
  volume	= {20},
  pages		= {437--50},
  abstract	= {In this paper, an automatic classification method of
		  stellar spectra using the self-organization feature mapping
		  (SOFM) method is given. The SOFM is an unsupervised
		  learning algorithm of an artificial neural network (ANN).
		  It allows the data to be organised onto a feature graph
		  while conserving most of the topological features of the
		  original data space. We use this method to classify stellar
		  spectra automatically. The result is very similar to the
		  Harvard sequence with an accuracy comparable to that
		  obtained by human experts. The SOFM should be a useful
		  method for on-line classification of stellar spectrum
		  samples of very large size. The SOFM can be executed
		  automatically, so it can be applied to process large
		  numbers of target spectra.},
  dbinsdate	= {2002/1}
}

@Article{	  xue01a,
  author	= {Xue, J. Q. and Li, Q. B. and Zhao, Y. H.},
  title		= {Automatic classification of stellar spectra using the
		  {SOFM} method},
  journal	= {CHINESE ASTRONOMY AND ASTROPHYSICS},
  year		= {2001},
  volume	= {25},
  number	= {1},
  month		= {JAN-MAR},
  pages		= {120--131},
  abstract	= {In this paper, an automatic classification method of
		  stellar spectra using the Self-Organization Feature Mapping
		  (SOFM) method is given. The SOFM is an unsupervised
		  learning algorithm of Artificial Neural Network (ANN). It
		  allows the data organized onto a feature graph while
		  conserving most of the topological characters of the
		  original data space. We used this method to classify
		  stellar spectra automatically. The result is very similar
		  to the Harvard sequence with an accuracy comparable to that
		  obtained by human experts. The SOFM should be a useful
		  method for on-line classification of stellar spectrum
		  samples of very large size. The SOFM can be executed
		  automatically so it call be applied to process large
		  numbers of target spectra.},
  dbinsdate	= {2002/1}
}

@Article{	  xuemin97a,
  author	= {Wang Xuemin and Cheng Junshi and Tie Jincheng and Chen
		  Jiapin},
  title		= {An identification algorithm for dynamic walking gait of
		  quadruped walking robot},
  journal	= {Journal of Shanghai Jiaotong University},
  year		= {1997},
  volume	= {31},
  number	= {3},
  pages		= {17--19, 23},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yacoub01a,
  author	= {Yacoub, M. and Badran, F. and Thiria, S.},
  title		= {A topological hierarchical clustering: Application to
		  ocean color classification},
  booktitle	= {ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS},
  year		= {2001},
  pages		= {492--499},
  abstract	= {We propose a new criteria to cluster the referent vectors
		  of the self-organizing map. This criteria contains two
		  terms which take into account two different errors
		  simultaneously: the square error of the entire clustering
		  and the topological structure given by the Self Organizing
		  Map. A parameter T allows to control the corresponding
		  influence of these two terms. The efficiency of this
		  criteria is illustrated on the problem of top of the
		  atmosphere spectra of satellite ocean color measurements.},
  dbinsdate	= {2002/1}
}

@Article{	  yair92a,
  author	= {E. Yair and K. Zeger and A. Gersho},
  title		= {Competitive learning and soft competition for vector
		  quantizer design},
  journal	= {IEEE Trans. on Signal Processing},
  year		= {1992},
  volume	= {40},
  number	= {2},
  pages		= {294--309},
  month		= {February},
  x		= {The authors provide a convergence analysis for the Kohonen
		  learning algorithm (KLA) with respect to vector quantizer
		  (VQ) optimality criteria and introduce . . . By
		  incorporating the principles of the stochastic approach
		  into the KLA, a deterministic VQ design algorithm, the soft
		  competition scheme (SCS), is introduced. },
  dbinsdate	= {oldtimer}
}

@InCollection{	  yamada96a,
  author	= {S. Yamada and M. Murota},
  title		= {Applying \mbox{self-organizing} networks to recognizing
		  rooms with behavior sequences of a mobile robot},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {3},
  address	= {New York, NY, USA},
  pages		= {1790--4},
  abstract	= {In this paper, we describe the application of a
		  self-organizing network to the robot which learns to
		  recognize rooms (enclosures) using behavior sequences. In
		  robotics research, most studies on recognizing environments
		  have tried to build the precise geometric map with high
		  sensitive sensors. However many natural agents like animals
		  recognize the environments with low sensitive sensors, and
		  a geometric map may not be necessary. Thus we attempt to
		  build a mobile robot using a self-organizing network to
		  recognize the enclosures, in which it acts, with low
		  sensitive and local sensors. The mobile robot is
		  behavior-based and does wall-following in an enclosure.
		  Then the sequences of behaviors executed in each enclosure
		  are obtained. The sequences are transformed into real-value
		  vectors, and inputted to the Kohonen's self-organizing
		  network. Unsupervised-learning is done and a mobile robot
		  becomes able to distinguish and identify enclosures. We
		  fully implemented the system using a real mobile robot and
		  made experiments for evaluating the ability. Consequently
		  we found out the recognition of enclosures was done well
		  and our method was robust against small obstacles in an
		  enclosure.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yamada99a,
  author	= {Yamada, T. and Hattori, M. and Morisawa, M. and Ito, H.},
  title		= {Sequential learning for associative memory using {K}ohonen
		  feature map},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {3},
  pages		= {1920--3},
  abstract	= {We propose a sequential learning algorithm for an
		  associative memory based on Kohonen feature map. In order
		  to store new information without retraining weights on
		  previously learned information, weights fixed neurons and
		  weights semi-fixed neurons are used in the proposed
		  algorithm. Owing to the semi-fixed neurons, the associative
		  memory becomes structurally robust. Moreover, it has the
		  following features: 1) it is robust for noisy inputs; 2) it
		  has high storage capacity; and 3) it casts deal with
		  one-to-many associations.},
  dbinsdate	= {oldtimer}
}

@Article{	  yamagishi94a,
  author	= {Yamagishi, K. },
  title		= {Spontaneous symmetry breaking and the formation of
		  columnar structures in the primary visual cortex},
  journal	= {Network: Computation in Neural Systems},
  year		= {1994},
  volume	= {5},
  number	= {1},
  pages		= {61--73},
  month		= {Feb},
  dbinsdate	= {oldtimer}
}

@Article{	  yamaguchi91a,
  author	= {T. Yamaguchi and T. Takagi and M. Tanabe},
  title		= {An intelligent sensor architecture with fuzzy associative
		  memory system},
  journal	= {Trans. Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1991},
  volume	= {J74C-II},
  number	= {5},
  pages		= {289--299},
  month		= {May},
  note		= {(in Japanese)},
  x		= {Engl. kielinen artikkeli on jo mukana. },
  dbinsdate	= {oldtimer}
}

@Article{	  yamaguchi91b,
  author	= {T. Yamaguchi and M. Tanabe and J. Murakami and K. Goto},
  title		= {An adaptive control with fuzzy associative memory system},
  journal	= {Trans. Inst. of Electrical Engineers of Japan, Part C},
  year		= {1991},
  volume	= {111-C},
  number	= {1},
  pages		= {40--46},
  month		= {January},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yamaguchi91c,
  author	= {T. Yamaguchi and M. Tanabe and T. Takagi},
  title		= {Fuzzy associative memory applications to control},
  booktitle	= {Artificial Neural Networks},
  year		= {1991},
  editor	= {T. Kohonen and K. M{\"{a}}kisara and O. Simula and J.
		  Kangas},
  volume	= {II},
  pages		= {1249--1252},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yamaguchi91d,
  author	= {T. Yamaguchi and M. Tanabe and K. Kuriyama and T. Mita},
  title		= {Fuzzy adaptive control with an associative memory system},
  booktitle	= {International Conference on Control '91},
  year		= {1991},
  volume	= {II},
  pages		= {944--949},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  yamaguchi92a,
  author	= {T. Yamaguchi and T. Takagi and M. Tanabe},
  title		= {An intelligent sensor architecture with fuzzy associative
		  memory system},
  journal	= {Electronics and Communications in Japan, Part 2
		  [Electronics]},
  year		= {1992},
  volume	= {75},
  number	= {3},
  pages		= {52--64},
  month		= {March},
  abstract	= {In realizing the intelligent sensor which flexibly
		  transforms input information into the information required
		  by the user employing such knowledge processing as fuzzy
		  inference, the following points are especially important:
		  (1) the representation of the knowledge as well as the
		  acquisition of the knowledge should be easy; and (2) the
		  inference should be executed with a high speed. This paper
		  proposes a sensor construction which satisfies both of the
		  forementioned requirements. The intelligent sensor proposed
		  in this paper employs the if-then type knowledge
		  representation as the fuzzy rule with a high affinity to
		  human thought. The sensor is composed of three independent
		  neural nets: the input (if) part, the output (then part),
		  and the input/output relation (if-then) part. In the input
		  part, the learning vector quantization (LVQ) network
		  evaluates the features of the input waveform, and the
		  result is mapped directly on the concept represented in the
		  condition part of the fuzzy rule. LVQ executes
		  self-learning without a supervisor based on the features of
		  the input waveform and automatically generates the
		  membership function needed in the condition part of the
		  fuzzy rule. This simplifies the knowledge acquisition
		  process. In the input/output relation part, the fuzzy
		  inference is executed with a high speed by the parallel
		  processing of the associative memory network. Thus, the
		  intelligent sensor based on the fuzzy associative memory
		  can improve the forementioned two points (1) and (2).
		  Furthermore, to demonstrate the high versatility of the
		  proposed construction, this paper considers the problem in
		  which two entirely different sample problems are realized
		  by the same construction. The realization example is shown
		  by a simulation.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yamakawa00a,
  author	= {Takeshi Yamakawa and Keiichi Horio and Ryosuke Kubota},
  title		= {Modified Adaptive Subspace Self-Organizing Map and its
		  Application to Speaker Classification},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {215--20},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yamakawa01a,
  author	= {T. Yamakawa and K. Horio and R. Kubota},
  title		= {A {SOM} association network},
  booktitle	= {Advances in Self-Organising Maps},
  crossref	= {},
  key		= {},
  pages		= {15--20},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {Springer},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yamakawa01b,
  author	= {T. Yamakawa and K. Horio and Y. Oosako and T. Miki},
  title		= {A new interpolation algorithm employing a self organising
		  map},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {118--23},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yamakawa01c,
  author	= {T. Yamakawa and K. Horio and S. Izumi and T. Miki},
  title		= {A New method of {H}ough transform by using {SOM} with
		  input vector transformation},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {167--72},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yamakawa97a,
  author	= {Takeshi Yamakawa and Megumi Nakamura and Eiji Uchino},
  title		= {An Adaptive Self Organization Network and It's Application
		  to a Distributed Temperature Control System},
  booktitle	= {13th Fuzzy Symposium (Toyama, June 4--6, 1997)},
  year		= {1997},
  pages		= {673--674},
  note		= {in Japanese},
  abstract	= {The temperature control of a wide open area is currently
		  carried out locally by PID control method without any
		  information exchange. If the heat sources and the loads are
		  distributed complicatedly and change dynamically, it is
		  very difficult to get the desired temperature distribution
		  by using the conventional PID method. Here we propose an
		  adaptive self-organization network, an improvement over the
		  Kohonen's self-organization network, which is applicable to
		  this kind of complicated control problem. The application
		  results to the experimental model are also given.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yamakawa98a,
  author	= {Takeshi Yamakawa and Megumi Nakamura and Eiji Uchino},
  title		= {An Adaptive Self Organization Network and It's Application
		  to a Distributed Control System},
  booktitle	= {VJFUZZY'98: Vietnam-Japan Bilateral Symposium on Fuzzy
		  Systems and Applications, Halong Bay, Vietnam, 30th
		  September---2nd October, 1998},
  editor	= {Nguyen Hoang Phuong and Ario Ohsato},
  year		= {1998},
  pages		= {33--38},
  keywords	= {multiple-input vectors, self-organization with stimulative
		  inputs, relaxatios unless inputs applied, adaptive
		  self-organising network, temperature control},
  abstract	= {The temperature control of a wide open area is currently
		  carried out locally by parrallel PID control methods
		  without any information exchange among the controllers. If
		  the heat sources and the loads are distributed
		  complicatedly and change dynamically, it is very difficult
		  to get the desired temperature distribution by using the
		  conventional PID method. Here we propose an adaptive
		  self-organization network, an improvement over the
		  Kohonen's self-organization network, which is applicable to
		  this kind of complicated control problem. The application
		  results to the experimental model are also given.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yamakawa99a,
  author	= {Takeshi Yamakawa and Yuu Nakamura and Noriaki Suetake and
		  Tsutimu Miki},
  title		= {Independent Component Analysis Based on Marginal Entropy
		  Maximization Employing Self-Organizing Maps},
  booktitle	= {15th Fuzzy System Symposium (Osaka, June 2--5, 1999)},
  year		= {1999},
  pages		= {323--326},
  note		= {in Japanese},
  abstract	= {Blind Signal Separation (BSS) recently draws much
		  attention from the field of the signal prosessing. BSS is a
		  problem to separate mutually statistically independent
		  source signals only by using mixed signals, under the
		  condition that the properties of the source signals and the
		  way they are mixed are both unknown. A. J. Bell and T. J.
		  Sejnowski proposed a method based on Information Theory in
		  1995. The calculation accomplished in this method is less
		  than that in the methods based on statistics. However it
		  doesn't always work correctly if the assumption on the
		  probability distribution of the unknown sources is not
		  valid. In thsi paper, we propose a method which needs no
		  assumption on the distribution of the sources, by making
		  use of the ability of the Self-Organizing Map to
		  approximate the probability density of the sources.},
  dbinsdate	= {2002/1}
}

@Article{	  yamakawa99b,
  author	= {Takeshi Yamakawa and Keiichi Horio},
  title		= {Self-Organising Relationship ({SOR}) Network},
  journal	= {IEICE Trans. Fundamentals},
  year		= {1999},
  volume	= {E82-A},
  number	= {8},
  month		= {August},
  pages		= {1674--77},
  abstract	= {In this letter, the novel mapping network named
		  self-organizing relationship network, which can approximate
		  the desired I/O relationship by employing the modified
		  Kohonen's learning law, is proposed. In the modified
		  Kohonen's learning law, the weight vectors are updated to
		  be attracted to or repulsed from the input vector.},
  keywords	= {self-organizing, I/O relationship, evaluation, attraction,
		  repulsion},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yamakawa99c,
  author	= {Takeshi Yamakawa and Keiichi Horio},
  title		= {A Nonlinear Control System Established by Self-Organizing
		  Relationship ({SOR}) Network},
  booktitle	= {Mathematical Modelling of Nonlinear Systems},
  editor	= {J. C: Misra and S. B. Sinha},
  volume	= {1},
  year		= {1999},
  pages		= {23--5},
  abstract	= {In this letter, the novel mapping network named
		  self-organizing relationship network, which can approximate
		  the desired I/O relationship by employing the modified
		  Kohonen's learning law, is proposed. In the modified
		  Kohonen's learning law, the weight vectors are updated to
		  be attracted to or repulsed from the input vector.},
  keywords	= {self-organizing, I/O relationship, evaluation, attraction,
		  repulsion},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yamamoto01a,
  author	= {Yamamoto, T.},
  title		= {Vector quantization for image compression using circular
		  structured self-organization feature map},
  booktitle	= {IEEE International Conference on Image Processing},
  year		= {2001},
  editor	= {},
  volume	= {2},
  pages		= {443--446},
  organization	= {Univ. of Maryland at College Park, Dept. of Electrical and
		  Comp. Eng.},
  publisher	= {},
  address	= {},
  abstract	= {We propose a stable and robust vector quantization coding
		  scheme for image compression known as circular self
		  organization feature map (CSOM) by introducing circular
		  structure to a basic codebook. This structure enables self
		  organization feature map (SOM) method to converge faster,
		  and to learn input vectors more efficiently. The results
		  suggest that CSOM gains approximately 30% speedup in
		  computation time and 0.3dB in the PSNR compared to the
		  conventional SOM algorithm. In addition, robustness for
		  initial state of a codebook is achieved by CSOM.},
  dbinsdate	= {2002/1}
}

@TechReport{	  yamane93a,
  author	= {Ko Yamane and Kikuo Fuzimura and Hideo Tokimatu and Heizo
		  Tokutaka and Satoru Kisida},
  title		= {Classified of handwritten numeric-character using the
		  Self-Organizing Feature Maps},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1993},
  number	= {NC93--25},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@TechReport{	  yamane94a,
  author	= {Ko Yamane and Kikuo Fujimura and Heizo Tokutaka and Satoru
		  Kishida},
  title		= {The recurrent {K}ohonen's network for the recognition
		  system of on-line hand-writing numeric character},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1994},
  number	= {NC93--86},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@TechReport{	  yamane94b,
  author	= {Ko Yamane and Heizo Tokutaka and Kikuo Fujimura and Satoru
		  Kishida},
  title		= {Application of distance network to the problem classifying
		  the clusters},
  institution	= {The Inst. of Electronics, Information and Communication
		  Engineers},
  year		= {1994},
  number	= {NC94--36},
  address	= {Tottori University, Koyama, Japan},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yamauchi99a,
  author	= {Yamauchi, K. and Takeichi, M. and Ishii, N.},
  title		= {Restoration of images via \mbox{self-organizing} feature
		  map},
  booktitle	= {IEEE SMC'99 Conference Proceedings. 1999 IEEE
		  International Conference on Systems, Man, and
		  Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {942--7},
  abstract	= {S. Geman and D. Geman (1984) presented a basic statistical
		  method for image restoration. In the method, the system
		  searches an image X which makes a posterior probability
		  p(X|Y) maximum, where Y is a noisy image given as an input.
		  Using a Bayesian method, the posterior probability is
		  rewritten as log p(X|Y) varies as log p(X)+log p(Y|X),
		  where p(X) and p(Y|X) are prior probability and likelihood
		  of the image, respectively. The prior probability p(X) is
		  usually represented by a heuristic function. S. Geman and
		  D. Geman defined p(X) using the estimators to detect
		  smoothness and edge. The prior probability greatly affects
		  to the performance of the system so that it should be
		  optimized to fit a class of images, which users want to
		  restore. However, it is hard to optimize the estimator by
		  hand. In this paper, we show a self-organizing feature map
		  (SOM) proposed by Kohonen (1982) which approximately
		  represents the prior probability of local features of
		  images via learning. Therefore, the system can tune the
		  estimator only by seeing original clean images. In the
		  experiment section, we show that the new system using the
		  SOM can restore actual images well.},
  dbinsdate	= {oldtimer}
}

@Article{	  yamauchi99b,
  author	= {Yamauchi, T. and Hasegawa, T. and Kuroda, H. and Kondoh,
		  K. and Takagi, H. and Ohta, K. and Okazaki, S. and
		  Fukushima, T.},
  title		= {Japanese full-address recognition algorithm for carrier
		  sequence {OCR} sorter},
  journal	= {NEC Research and Development},
  year		= {1999},
  volume	= {40},
  pages		= {142--7},
  abstract	= {Introduces a knowledge-based algorithm for Japanese
		  full-address recognition. In the algorithm, precise
		  individual character recognition is most important. An
		  algorithm for non-linear normalization and non-linear
		  matching for handwritten Kanji recognition is applied as
		  the individual character recognition method, and
		  generalized learning vector quantization is applied as the
		  character dictionary training algorithm. For efficient and
		  flexible knowledge-based processing, the character tag
		  method is also introduced. Finally, to minimize the error
		  rate, a control method considering the balanced
		  segmentation capability, the performance of the individual
		  character recognition and the validity of the address
		  knowledge is shown.},
  dbinsdate	= {oldtimer}
}

@Article{	  yan01a,
  author	= {Yan, X. and Chen, D. and Chen, Y. and Hu, S.},
  title		= {{SOM} integrated with {CCA} for the feature map and
		  classification of complex chemical patterns},
  journal	= {Computers and Chemistry},
  year		= {2001},
  volume	= {25},
  number	= {6},
  month		= {November },
  pages		= {597--605},
  organization	= {Department of Chemical Engineering, Zhejiang University},
  publisher	= {},
  address	= {},
  abstract	= {Considering that the two-dimensional (2D) feature map of
		  the high-dimensional chemical patterns can more concisely
		  and efficiently represent the pattern characteristic, a new
		  procedure integrating self-organizing map (SOM) networks
		  with correlative component analysis (CCA) is proposed.
		  Firstly, CCA was used to identify the most important
		  classification characteristics (CCs) from the original
		  high-dimensional chemical pattern information. Then, the
		  SOM maps the first several CCs, which include the most
		  useful information for pattern classification, onto a 2D
		  plane, on which the pattern classification feature is
		  concisely represented. To improve the learning efficiency
		  of SOM networks, two new algorithms for dynamically
		  adjusting the learning rate and the range of neighborhood
		  around the winning unit were further worked out. Besides, a
		  convenient method for detecting the topologic nature of SOM
		  results was proposed. Finally, a typical example of mapping
		  two classes natural spearmint essence was employed to
		  verify the effectiveness of the new approach. The
		  feature-topology-preserving (FTP) map obtained can well
		  represent the classification of original patterns and is
		  much better than what obtained by SOM alone.},
  dbinsdate	= {2002/1}
}

@InCollection{	  yanez-suarez97a,
  author	= {O. Y{\'a}{\~n}ez-Su{\'a}rez and M. R. Azimi-Sadjadi},
  title		= {Entropy-Driven Structural Adaptation in Sample-Space
		  Self-Organizing Feature Maps for Pattern Classification},
  booktitle	= {Proceedings of ICNN'97, International Conference on Neural
		  Networks},
  publisher	= {IEEE Service Center},
  year		= 1997,
  address	= {Piscataway, NJ},
  volume	= {I},
  pages		= {287--291},
  dbinsdate	= {oldtimer}
}

@Article{	  yanez-suarez99a,
  author	= {Yanez-Suarez, Oscar and Azimi Sadjadi, Mahmood R.},
  title		= {Unsupervised clustering in Hough space for identification
		  of partially occluded objects},
  journal	= {IEEE Transactions on Pattern Analysis and Machine
		  Intelligence},
  year		= {1999},
  number	= {9},
  volume	= {21},
  pages		= {946--950},
  abstract	= {An automated approach for template-free identification of
		  partially occluded objects is presented. The contour of
		  each relevant object in the analyzed scene is modeled with
		  an approximating polygon whose edges are then projected
		  into the Hough space. A structurally adaptive
		  self-organizing map neural network generates clusters of
		  collinear and/or parallel edges, which are used as the
		  basis for identifying the partially occluded objects within
		  each polygonal approximation. Results on a number of cases
		  under different conditions are provided.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yang00a,
  author	= {Yang, Hsin-Chang and Lee, Chung-Hong},
  title		= {Automatic category generation for text documents by
		  self-organizing maps},
  booktitle	= {Proceedings of the International Joint Conference on
		  Neural Networks},
  year		= {2000},
  editor	= {},
  volume	= {3},
  pages		= {581--586},
  organization	= {Chang Jung Univ},
  publisher	= {IEEE},
  address	= {Piscataway, NJ},
  abstract	= {Recently knowledge discovery and data mining in
		  unstructured or semi-structured texts has been attracted
		  lots of attention from both commercial and research fields.
		  The task is not easy to tackle due to the unstructured
		  nature of ordinary text documents. Text data mining
		  approaches has been proposed to resolve such difficulties.
		  One important task for text data mining is automatic text
		  categorization, which assigns a text document to some
		  predefined category according to their correlations.
		  Traditionally these categories as well as the correlations
		  among them are determined by human experts. In this paper,
		  we devised a novel approach to automatically generate
		  categories. The self-organizing map model is used to
		  generate two maps, namely the word cluster map and the
		  document cluster map, in which a neuron represents a
		  cluster of words and documents respectively. Our approach
		  is to analyze the document cluster map to find centroids of
		  some super-clusters. We also devised a method to select the
		  category term from the word cluster map. The hierarchical
		  structure of categories may be generated by recursively
		  applying the same method. Text categorization is the
		  natural consequence of such automatic category generation
		  process.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yang00b,
  author	= {Hsin-Chang Yang and Chung-Hong Lee},
  title		= {Automatic category structure generation and categorization
		  of Chinese text documents},
  booktitle	= {Principles of Data Mining and Knowledge Discovery. 4th
		  European Conference, PKDD 2000. Proceedings (Lecture Notes
		  in Artificial Intelligence Vol.1910). Springer-Verlag,
		  Berlin, Germany},
  year		= {2000},
  volume	= {},
  pages		= {673--8},
  abstract	= {Recently knowledge discovery and data mining in
		  unstructured or semi-structured texts (text mining) has
		  attracted lots of attention from both commercial and
		  research fields. One aspect of text mining is automatic
		  text categorization, which assigns a text document to some
		  predefined category according to the correlation between
		  the document and the category. Traditionally, the
		  categories are arranged in hierarchical manner to achieve
		  effective searching and indexing, as well as easy
		  comprehension for humans. The determination of categories
		  and their hierarchical structures were most done by human
		  experts. The authors developed an approach to automatically
		  generate categories and reveal the hierarchical structure
		  among them. We also used the generated structure to
		  categorize text documents. The document collection is
		  trained by a self-organizing map to form two feature maps.
		  We then analyzed the two maps to obtain the categories and
		  the structure among them. Although the corpus contains
		  documents written in Chinese, the proposed approach can be
		  applied to documents written in any language and such
		  documents can be transformed into a list of separated
		  terms.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from internet}
}

@InProceedings{	  yang01a,
  author	= {Z. R. Yang},
  title		= {Analysing health iequalities using {SOM}},
  booktitle	= {Advances in Self-Organising Maps},
  crossref	= {},
  key		= {},
  pages		= {47--53},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  volume	= {},
  number	= {},
  series	= {},
  address	= {},
  month		= {},
  organization	= {},
  publisher	= {Springer},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  yang01b,
  author	= {Yang Wang and Yi Sheng Zhu and Thakor, N. V. and Yu Hong
		  Xu},
  title		= {A short-time multifractal approach for arrhythmia
		  detection based on fuzzy neural network},
  journal	= {IEEE-Transactions-on-Biomedical-Engineering},
  year		= {2001},
  volume	= {48},
  pages		= {989--95},
  abstract	= {The authors have proposed the notion of short-time
		  multifractality and used it to develop a novel approach for
		  arrhythmia detection. Cardiac rhythms are characterized by
		  short-time generalized dimensions (STGDs), and different
		  kinds of arrhythmias are discriminated using a neural
		  network. To advance the accuracy of classification, a new
		  fuzzy Kohonen network, which overcomes the shortcomings of
		  the classical algorithm, is presented. In the authors'
		  paper, the potential of their method for clinical uses and
		  real-time detection was examined using 180
		  electrocardiogram records [60 atrial fibrillation, 60
		  ventricular fibrillation, and 60 ventricular tachycardia].
		  The proposed algorithm has achieved high accuracy (more
		  than 97%) and is computationally fast in detection.},
  dbinsdate	= {2002/1}
}

@Article{	  yang01c,
  author	= {Yang, Cheng-Hong},
  title		= {Morse code recognition using learning vector quantization
		  for persons with physical disabilities},
  journal	= {IEICE Transactions on Fundamentals of Electronics,
		  Communications and Computer Sciences},
  year		= {2001},
  volume	= {E84-A},
  number	= {1},
  month		= {Jan},
  pages		= {356--362},
  organization	= {Natl Kaohsiung Univ of Applied Sciences},
  publisher	= {IEICE of Japan},
  address	= {Tokyo},
  abstract	= {For physically disabled persons, the conventional computer
		  keyboard is insufficient as a useable communication device.
		  In this paper, Morse code is selected as a communication
		  adaptive device for persons with impaired hand
		  co-ordination and dexterity. Morse code is composed of a
		  series of dots, dashes, and space intervals. Each element
		  is transmitted by sending a signal for a defined length of
		  time. Maintaining a stable typing rate by the disabled is
		  difficult. To solve this problem, a suitable adaptive
		  automatic recognition method, which combines a variable
		  degree variable step size LMS algorithm with a learning
		  vector quantization method, was applied to this problem in
		  the present study. The method presented here is divided
		  into five stages: space recognition, tone recognition,
		  learning process, adaptive processing, and character
		  recognition. Statistical analyses demonstrated that the
		  proposed method elicited a better recognition rate in
		  comparison to alternative methods in the literature.},
  dbinsdate	= {2002/1}
}

@Article{	  yang01d,
  author	= {Yang, Ming-Hsuan and Kriegman, David and Ahuja, Narendra},
  title		= {Face detection using multimodal density models},
  journal	= {Computer Vision and Image Understanding},
  year		= {2001},
  volume	= {84},
  number	= {2},
  month		= {November },
  pages		= {264--284},
  organization	= {Department of Computer Science, Beckman Institute,
		  University of IL at Urbana-Champaign},
  publisher	= {Academic Press Inc.},
  address	= {},
  abstract	= {We present two methods using multimodal density models for
		  face detection in gray-level images. One generative method
		  uses a mixture of factor analyzers to concurrently perform
		  clustering and, within each cluster, perform local
		  dimensionality reduction. The parameters of the mixture
		  model are estimated using the EM algorithm. A face is
		  detected if the probability of an input sample is above a
		  predefined threshold. The other discriminative method uses
		  Kohonen's self-organizing map for clustering, Fisher's
		  linear discriminant to find an optimal projection for
		  pattern classification, and a Gaussian distribution to
		  model the class-conditional density function of the
		  projected samples for each class. The parameters of the
		  class-conditional density functions are maximum likelihood
		  estimates, and the decision rule is also based on maximum
		  likelihood. A wide range of face images including ones in
		  different poses, with different expressions and under
		  different lighting conditions, is used as the training set
		  to capture variations of the human face. Our methods have
		  been tested on three data sets with a total of 225 images
		  containing 871 faces. Experimental results on the first two
		  data sets show that our generative and discriminative
		  methods perform as well as the best methods in the
		  literature, yet have fewer false detections. Meanwhile,
		  both methods are able to detect faces of nonfrontal views
		  and under more extreme lighting in the third data set. },
  dbinsdate	= {2002/1}
}

@InProceedings{	  yang01e,
  author	= {Yang, H. -C.},
  title		= {Shaped-based image retrieval by spatial topology
		  distances},
  booktitle	= {Proceedings of the ACM International Multimedia Conference
		  and Exhibition},
  year		= {2001},
  editor	= {},
  volume	= {},
  pages		= {38--41},
  organization	= {Department of Information Management, Chang Jung
		  University},
  publisher	= {},
  address	= {},
  abstract	= {Shape-based image retrieval is achieved by measuring the
		  spatial topology distance between the input image and each
		  template image in the database. The contour of an object in
		  the input image is regularly sampled as feature points
		  which are matched to those of a template image by a pseudo
		  elastic matching process. The elastic matching is achieved
		  by performing the self-organizing map algorithm on the
		  network which is constructed by distributing neurons to
		  feature locations of the template image. The spatial
		  topology distance is measured by the degree of distortion
		  of the template pattern before and after elastic matching.
		  We tested the method on the Columbia Object Image Library
		  database. Preliminary experiments suggested promising
		  result by our approach.},
  dbinsdate	= {2002/1}
}

@Article{	  yang01g,
  author	= {Yang, H. -T. and Liao, C. -C. and Chou, J. -H.},
  title		= {Fuzzy learning vector quantization networks for power
		  transformer condition assessment},
  journal	= {IEEE Transactions on Dielectrics and Electrical
		  Insulation},
  year		= {2001},
  volume	= {8},
  number	= {1},
  month		= {February 2001},
  pages		= {143--149},
  organization	= {Department of Electrical Engineering, Chung Yuan Christian
		  University},
  publisher	= {},
  address	= {},
  abstract	= {To improve the assessment capability of power
		  transformers, this paper proposes a new intelligent
		  decision support system based on fuzzy learning vector
		  quantization (FLVQ) networks. In constructing the system, a
		  fuzzy-based classifier is designed to divide the historical
		  data for dissolved gas analysis (DGA) into various
		  categories with different levels of gas attributes. For
		  each category of gas attributes, a learning vector
		  quantization (LVQ) network is trained to be responsible for
		  the classification of the potential faults due to
		  insulation deterioration. The assessment approach has been
		  tested on the DGA data from Taiwan Power Company (TPC) and
		  compared with the previous fuzzy diagnosis system and the
		  existing multi-layered back-propagation based artificial
		  neural networks (BPANN) methods. Remarkable classification
		  accuracy and far less training efforts of the proposed
		  approach are achieved in this paper.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yang01h,
  author	= {Hsin-Chang Yang and Chung-Hong Lee},
  title		= {Automatic hypertext construction through a text mining
		  approach by self-organizing maps},
  booktitle	= {Advances in Knowledge Discovery and Data Mining. 5th
		  Pacific-Asia Conference, PAKDD 2001. Proceedings (Lecture
		  Notes in Artificial Intelligence Vol.2035).
		  Springer-Verlag, Berlin, Germany},
  year		= {2001},
  volume	= {},
  pages		= {108--13},
  abstract	= {In this work we developed a new automatic hypertext
		  construction method based on a proposed text mining
		  approach. Our method applies the self-organizing map
		  algorithm to cluster some flat text documents in a training
		  corpus and generate two maps. We then use these maps to
		  identify the sources and destinations of some important
		  hyperlinks within these training documents. The constructed
		  hyperlinks are then inserted into the training documents to
		  translate them into hypertext form. Such translated
		  documents form the new corpus. Incoming documents can also
		  be translated into hypertext form and added to the corpus
		  through the same approach. Our method had been tested on a
		  set of flat text documents collecting from several newswire
		  sites. Although we only used Chinese text documents, our
		  approach can be applied to any document that can be
		  transformed to a set of indexed terms.},
  dbinsdate	= {2002/1},
  merjanote     = {Last name checked from internet}
}

@InProceedings{	  yang02a,
  author	= {Yang, C. C. and Hsinchun Chen and Hong, K.},
  title		= {Internet browsing: visualizing category map by fisheye and
		  fractal views},
  booktitle	= {Proceedings International Conference on Information
		  Technology: Coding and Computing. IEEE Comput. Soc, Los
		  Alamitos, CA, USA},
  year		= {2002},
  volume	= {},
  pages		= {34--9},
  abstract	= {A category map developed based on Kohonen's
		  self-organizing map has been proven to be a promising
		  browsing tool for solving the information overload problem
		  of the World Wide Web. The SOM algorithm automatically
		  compresses and transforms a complex information space into
		  a two-dimensional graphical representation. Such graphical
		  representation provides a user-friendly interface for users
		  to explore the automatically generated mental model.
		  However, as the amount of information increases, the size
		  of the category map is expected to increase accordingly in
		  order to accommodate the important concepts in the
		  information space, which increases the visual load of the
		  category map. In this paper, we propose the fisheye views
		  and fractal views to support the visualization of category
		  map. Fisheye views are developed based on the distortion
		  approach while fractal views are developed based on the
		  information reduction approach. We have developed a
		  prototype system and conducted a user evaluation to
		  investigate the performance of fisheye views and fractal
		  views. The results show that both fisheye views and fractal
		  views significantly increase the effectiveness of
		  visualizing the category map. In addition, fractal views
		  are significantly better than fisheye views.},
  dbinsdate	= {2002/1}
}

@Article{	  yang92a,
  author	= {Hua Yang and T. S. Dillon},
  title		= {Convergence of Self-Organizing Neural Algorithms},
  journal	= {Neural Networks},
  year		= {1992},
  volume	= {5},
  number	= {3},
  pages		= {485--493},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yang92b,
  author	= {Hua Yang and Palaniswami, M. },
  title		= {On the issue of neighborhood in \mbox{self-organising}
		  maps},
  booktitle	= {Applied Computing: Technological Challenges of the 1990's.
		  Proceedings of the 1992 ACM/SIGAPP Symposium on Applied
		  Computing},
  year		= {1992},
  editor	= {Berghel, H. and Deaton, E. and Hedrick, G. and Roach, D.
		  and Wainwright, R. },
  pages		= {412--16},
  organization	= {La Trobe Univ. , Bundoora, Vic. , Australia},
  publisher	= {ACM},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  yang97a,
  author	= {B. Yang and M. C. Carotta and G. Faglia and M. Ferroni and
		  V. Guidi and G. Martinelli and P. Nelli and G.
		  Sberveglieri},
  title		= {Implementation of sensor arrays with neural networks},
  booktitle	= {Conference Proceedings. Vol. 54. SAA '96 National Meeting
		  on Sensors for Advanced Applications},
  publisher	= {Italian Phys. Soc},
  year		= {1997},
  editor	= {G. Sberveglieri and E. Tondello},
  address	= {Bologna, Italy},
  pages		= {175--9},
  dbinsdate	= {oldtimer}
}

@Article{	  yang98a,
  author	= {H. S. Yang and J. D. Jegla and P. R. Griffiths},
  title		= {Classification and Recognition of Compounds in Low
		  Resolution Open Path {FT} {IR} Spectrometry by {K}ohonen
		  Self Organizing Maps},
  journal	= {Fresenius' Journal of Analytical Chemistry},
  volume	= {362},
  pages		= {25--33},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  yang98b,
  author	= {H. T. Yang and Y. C. Huang},
  title		= {Intelligent Decision Support for Diagnosis of Incipient
		  Transformer Faults Using Self Organizing Polynomial
		  Networks},
  journal	= {IEEE Transactions on Power Systems},
  volume	= {13},
  pages		= {946--952},
  year		= {1998},
  dbinsdate	= {oldtimer}
}

@Article{	  yang99a,
  author	= {Yang, H.~S. and Lewis, I.~R. and Griffiths, P.~R.},
  title		= {Raman-Spectrometry and Neural Networks for the
		  Classification of Wood Types---2---{K}ohonen
		  Self-Organizing Map},
  journal	= {Spectrochimica Acta Part A: Molecular and Biomolecular
		  Spectroscopy},
  year		= {1999},
  volume	= {55},
  number	= {14},
  pages		= {2783--2791},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yang99b,
  author	= {Yang, C. C. and Chen, H. and Hong, K. K.},
  title		= {Visualization tools for self-organizing maps},
  booktitle	= {Digital 99 Libraries. Fourth ACM Conference on Digital
		  Libraries. ACM, New York, NY, USA},
  year		= {1999},
  volume	= {},
  pages		= {258--9},
  abstract	= {Various statistical and pattern recognition techniques,
		  such as concept spaces and category maps in the Digital
		  Library of Illinois (DLI) project, have been explored to
		  solve the semantic interoperability problem in DLI-1. The
		  self-organizing category map has been identified as a
		  powerful tool for information summarization. However,
		  visualizing a large-scale self-organizing map in a
		  restricted window size is difficult, while, for smaller
		  regions, displaying labels is infeasible. In this paper,
		  two visualization tools, the fisheye view and the fractal
		  view, are presented. They assist users to visualize a
		  large-scale self-organizing map both geographically and
		  semantically.},
  dbinsdate	= {2002/1}
}

@Article{	  yao00a,
  author	= {Yao, K. C. and Mignotte, M. and Collet, C. and Galerne, P.
		  and Burel, G.},
  title		= {Unsupervised segmentation using a self-organizing map and
		  a noise model estimation in sonar imagery},
  journal	= {Pattern Recognition},
  year		= {2000},
  volume	= {33},
  number	= {9},
  month		= {},
  pages		= {1575--1584},
  organization	= {Lab GTS (Groupe de Traitement du Signal)},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {This work deals with unsupervised sonar image
		  segmentation. We present a new estimation and segmentation
		  procedure on images provided by a high-resolution sonar.
		  The sonar image is segmented into two kinds of regions:
		  shadow (corresponding to a lack of acoustic reverberation
		  behind each object lying on the seabed) and reverberation
		  (due to the reflection of acoustic wave on the seabed and
		  on the objects). The unsupervised contextual method we
		  propose is defined as a two-step process. Firstly, the
		  iterative conditional estimation is used for the estimation
		  step in order to estimate the noise model parameters and to
		  accurately obtain the proportion of each class in the
		  maximum likelihood sense. Then, the learning of a Kohonen
		  self-organizing map (SOM) is performed directly on the
		  input image to approximate the discriminating functions,
		  i.e. the contextual distribution function of the grey
		  levels. Secondly, the previously estimated proportion, the
		  contextual information and the Kohonen SOM, after learning,
		  are then used in the segmentation step in order to classify
		  each pixel on the input image. This technique has been
		  successfully applied to real sonar images, and is
		  compatible with an automatic processing of massive amounts
		  of data.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yasumatsu99a,
  author	= {Yasumatsu, K. and Takeda, Y. and Murakoshi, H. and
		  Funakubo, N.},
  title		= {Distributed processing system by History for load
		  balancing},
  booktitle	= {Control in Natural Disasters (CND'98) Proceedings volume
		  from the IFAC Workshop. Elsevier Sci, Kidlington, UK},
  year		= {1999},
  volume	= {},
  pages		= {},
  abstract	= {A method based on History, a database, is proposed for
		  balancing the computing load over a distributed processing
		  system. Communication costs are considered. History is a
		  database of load states learned using a self-organization
		  feature map. Capsuled objects of a class described in an
		  object-oriented programming language are distributed. The
		  remote class controller (RCC) and remote class (RC) are
		  generated from the source class. RC are sent to the servers
		  suggested by History, and, by linking RCC, a main program
		  can communicate with RC. As a result, the load was balanced
		  and the communication cost in execution became low.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yasunaga00a,
  author	= {Moritoshi Yasunaga and Keiji Moki and Jung. H. Kim and
		  Ikuo Yoshihara},
  title		= {A Bus-based Neuro-{CO}mputer for High Speed {SOM}
		  Calculation and Its Fault Tolerance against Defective
		  Circuits},
  booktitle	= {6 th International COnference on Soft Computing,
		  IIZUKA2000, Iizuka, Fukuoka, Japan, October 1--4, 2000},
  pages		= {264--271},
  year		= {2000},
  dbinsdate	= {2002/1}
}

@Article{	  yasunaga92a,
  author	= {M. Yasunaga and M. Asai and K. Shibata and M. Yamada},
  title		= {Self-organization capability for eliminating defective
		  neurons in neural network {LSI}s},
  journal	= {Trans. of the Inst. of Electronics, Information and
		  Communication Engineers},
  year		= {1992},
  volume	= {J75D-I},
  number	= {11},
  pages		= {1099--1108},
  month		= {November},
  note		= {(in Japanese)},
  dbinsdate	= {oldtimer}
}

@Article{	  yasunaga95a,
  author	= {Yasunaga, M. },
  title		= {Fault tolerance of the \mbox{self-organizing} maps
		  implemented by wafer scale integration},
  journal	= {Transactions of the Institute of Electronics, Information
		  and Communication Engineers D-I},
  year		= {1995},
  volume	= {J78D-I},
  number	= {12},
  pages		= {960--71},
  dbinsdate	= {oldtimer}
}

@InCollection{	  yasunaga96a,
  author	= {M. Yasunaga and I. Hachiya and M. Keiji},
  title		= {Fault-tolerance evaluation of {SOM} \mbox{self-organizing}
		  map) using a neuro-computer: {MY-NEUPOWER}},
  booktitle	= {Progress in Neural Information Processing. Proceedings of
		  the International Conference on Neural Information
		  Processing},
  publisher	= {Springer-Verlag},
  year		= {1996},
  volume	= {2},
  editor	= {S. -I. Amari and L. Xu and L. -W. Chan and I. King and K.
		  -S. Leung},
  address	= {Singapore},
  pages		= {1395--9},
  dbinsdate	= {oldtimer}
}

@InCollection{	  yasunaga96b,
  author	= {M. Yasunaga and I. Hachiya},
  title		= {{SOM} \mbox{self-organizing} map) implemented by wafer
		  scale integration-its \mbox{self-organizing} behavior under
		  defects},
  booktitle	= {Proceedings of the Eighth Annual IEEE International
		  Conference on Innovative Systems in Silicon},
  publisher	= {IEEE},
  year		= {1996},
  editor	= {S. Tewksbury and G. Chapman},
  address	= {New York, NY, USA},
  pages		= {323--9},
  dbinsdate	= {oldtimer}
}

@Article{	  yasunaga98a,
  author	= {M. Yasunaga and I. Hachiya and K. Moki and Jung Hwan Kim},
  title		= {Fault-tolerant \mbox{self-organizing} map implemented by
		  wafer-scale integration},
  journal	= {IEEE Transactions on Very Large Scale Integration (VLSI)
		  Systems},
  year		= {1998},
  volume	= {6},
  number	= {2},
  pages		= {257--65},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yasunaga99a,
  author	= {Yasunaga, M. and Tominaga, K. and Jung Hwan Kim},
  title		= {Parallel self-organization map using multiple stimuli},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1127--30},
  abstract	= {We propose a parallel SOM algorithm to speedup the
		  fundamental SOM calculation using parallel computer
		  environments. In the parallel SOM algorithm synaptic
		  weights are updated in parallel corresponding to multiple
		  stimuli (inputs). Parallelism in the proposed algorithm is
		  based on the analogy of the biological neural networks in
		  which neurons respond to the stimuli in parallel.
		  Performance is evaluated by implementing the newly
		  developed performance simulator in a personal
		  computer-cluster under the message passing interface
		  library (MPI) environment. A speedup ratio of about 5.0 is
		  achieved with 8 processors (personal computers) when the
		  width of the neighborhood function is less than 5% of the
		  total number of neurons in the SOM network.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yata98a,
  author	= {Y. Yata and H. Tokutaka and K. Fujimura},
  title		= {Learning of Simple Oscillating Wave Form by Adaptive
		  Subspace {SOM}},
  booktitle	= {Proceedings of the Fifth International Conference on
		  Neural Information Processing},
  year		= {1998},
  address	= {Kitakyushu, Japan},
  pages		= {1157--1159},
  dbinsdate	= {oldtimer}
}

@Article{	  ye00a,
  author	= {Ye, H. and Lo, B. W. N.},
  title		= {A visualised software library: Nested self-organising maps
		  for retrieving and browsing reusable software assets},
  journal	= {NEURAL COMPUTING \& APPLICATIONS},
  year		= {2000},
  volume	= {9},
  number	= {4},
  pages		= {266--279},
  abstract	= {This paper presents: an approach to self-structuring
		  software libraries. The authors developed a representation
		  scheme to construct a feature space over a collection of
		  software assets. The feature space is represented and
		  classified by a variety of the self-organising map, called
		  the Nested Software Self- Organising Map (NSSOM),
		  consisting of a top map and a set of sub-maps nested in the
		  top map. The clustering on the tap map provides general
		  improvements in retrieval recall while the lower-level
		  nested maps further elaborate the clusters into more
		  specific groups enhancing retrieval precision. The results
		  of preliminary evaluation showed that NSSOM is capable of
		  enhancing precision without sacrificing recall. In
		  addition, a user-friendly browsing facility has also been
		  developed which helps users predict the desired components
		  by providing an intelligible search space., The present
		  approach attempts to achieve an optimal combination of
		  efficiency, accuracy and user-friendliness, which is not
		  offered by the existing software retrieval systems.},
  dbinsdate	= {2002/1}
}

@Article{	  ye00b,
  author	= {Ye, H. L. and Lo, B. W. N.},
  title		= {Feature competitive algorithm for dimension reduction of
		  the self-organizing map input space},
  journal	= {APPLIED INTELLIGENCE},
  year		= {2000},
  volume	= {13},
  number	= {3},
  month		= {NOV},
  pages		= {215--230},
  abstract	= {The self-organizing map (SOM) can classify documents by
		  learning about their interrelationships from its input
		  data. The dimensionality of the SOM input data space based
		  on a document collection is generally high. As the
		  computational complexity of the SOM increases in proportion
		  to the dimension of its input space, high dimensionality
		  not only lowers the efficiency of the initial learning
		  process but also lowers the efficiencies of the subsequent
		  retrieval and the relearning process whenever the input
		  data is updated. A new method called feature competitive
		  algorithm (FCA) is proposed to overcome this problem. The
		  FCA can capture the most significant features that
		  characterize the underlying interrelationships of the
		  entities in the input space to form a dimensionally reduced
		  input space without excessively losing of essential
		  information about the interrelationships. The proposed
		  method was applied to a document collection, consisting of
		  97 UNIX command manual pages, to test its feasibility and
		  effectiveness. The test results are encouraging. Further
		  discussions on several crucial issues about the FCA are
		  also presented.},
  dbinsdate	= {2002/1}
}

@Article{	  ye01a,
  author	= {Ye, H. and Lo, B. W. N.},
  title		= {Towards a self-structuring software library},
  journal	= {IEE Proceedings: Software},
  year		= {2001},
  volume	= {148},
  number	= {2},
  month		= {April },
  pages		= {45--55},
  organization	= {Dept. of Comp. Sci. and Software, The University of
		  Newcastle},
  publisher	= {},
  address	= {},
  abstract	= {Software storage structuring and retrieval remain a major
		  challenge to the widespread adoption of software reuse. An
		  approach that can facilitate the automatic structuring of
		  software components libraries is presented here. Based on
		  the automatic indexing and the self-organising map (SOM)
		  technologies, key features associated with software
		  components can be identified and organised in a very simple
		  way that makes their distance relations geographically
		  explicit on the two-dimensional output layer of the SOM.
		  The proposed approach was applied to a collection of UNIX
		  commands to evaluate its retrieval effectiveness.
		  Preliminary results were encouraging and showed improvement
		  on both recall and precision, with substantial reduction in
		  the amount of human effort required in the process.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  ye94a,
  author	= {Ye, Lenian and Li, Zaigen and Dai, Feng},
  title		= {A self-tuning fuzzy controller},
  booktitle	= {PRICAI-94. Proceedings of the 3rd Pacific Rim
		  International Conference on Artificial Intelligence},
  year		= {1994},
  volume	= {2},
  pages		= {1083--5},
  organization	= {Dept. of Autom. , South China Univ. of Technol. ,
		  Guangzhou, China},
  publisher	= {Int. Acad. Publishers},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ye94b,
  author	= {Shiwei Ye and Zhongzhi Shi},
  title		= {Homotopy scheme and learning vector quantization},
  booktitle	= {PRICAI-94. Proceedings of the 3rd Pacific Rim
		  International Conference on Artificial Intelligence},
  year		= {1994},
  volume	= {1},
  pages		= {495--500},
  organization	= {Inst. of Comput. Technol. , Acad. Sinica, Beijing, China},
  publisher	= {Int. Acad. Publishers},
  address	= {Beijing, China},
  dbinsdate	= {oldtimer}
}

@Article{	  ye96a,
  author	= {X. Ye and Z. Li},
  title		= {Edge-preserving vector quantization using neural network},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1996},
  volume	= {2898},
  pages		= {210--16},
  note		= {(Electronic Imaging and Multimedia Systems Conf. Date:
		  4--5 Nov. 1996 Conf. Loc: Beijing, China Conf. Sponsor:
		  SPIE; China Opt. \& Optoelectron. Manuf. Assoc. ; Chinese
		  Opt. Soc)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ye98a,
  author	= {Ye, Xujun and Li, Zhineng},
  title		= {Hybrid neural networks for gray image recognition},
  booktitle	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1998},
  volume	= {3561},
  pages		= {7--13},
  abstract	= {A novel hybrid neural network model for gray level image
		  recognition is presented. By image segmentation based on
		  vector quantization which is carried out by Kohonen's self
		  organizing feature map neural networks, the gray level
		  image can be mapped into an Hopfield network, and each
		  neuron has several states. The performance of this model is
		  compared with that of the traditional model. It is
		  concluded that the new one not only has a smaller number of
		  neurons and interconnections, but also has better error
		  correction capabilities.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ye99a,
  author	= {Huilin Ye and Lo, B. W. N.},
  title		= {A self-classification scheme for software reuse},
  booktitle	= {Proceedings of the Seventeenth IASTED International
		  Conference. Applied Informatics},
  publisher	= {ACTA Press},
  address	= {Anaheim, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {358--61},
  abstract	= {A major concern of software reuse is the provision of
		  well-organized software repositories to facilitate
		  retrieval. This paper proposes a classification scheme that
		  integrates automatic indexing and self-organizing map
		  techniques to organize semantic relationships among
		  software components onto the output layer of an
		  unsupervised neural network. Reusable software artifacts
		  can then be retrieved effectively and efficiently with
		  minimal human intervention.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yeh95a,
  author	= {Jeffrey C. H. Yeh and Leonard G. C. Hamey and Tas Westcott
		  and Samuel K. Y. Sung},
  title		= {Colour Bake Inspection System Using Hybrid Artificial
		  Neural Networks},
  volume	= {I},
  pages		= {37--42},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yen90a,
  author	= {M. M. Yen and M. R. Blackburn and H. G. Nguyen},
  title		= {Feature maps based weight vectors for spatiotemporal
		  pattern recognition with neural nets},
  booktitle	= {Proc. IJCNN-90, International Joint Conference on Neural
		  Networks, Washington, DC},
  year		= {1990},
  volume	= {II},
  pages		= {149--155},
  organization	= {IEEE; Int. Neural Network Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@Article{	  yi00a,
  author	= {Yi Sun},
  title		= {On quantization error of self-organizing map network},
  journal	= {Neurocomputing},
  year		= {2000},
  volume	= {34},
  pages		= {169--93},
  abstract	= {We analyze how neighborhood size and number of weights in
		  the self-organizing map (SOM) effect quantization error. A
		  sequence of i.i.d. one-dimensional random variables with
		  uniform distribution is considered as input of the SOM.
		  First obtained is the linear equation that an equilibrium
		  state of the SOM satisfies with any neighborhood size and
		  number of weights. Then it is shown that the SOM converges
		  to the unique minimum point of quantization error if and
		  only if the neighborhood size is one, the smallest. If the
		  neighborhood size increases with the increasing number of
		  weights at the same ratio, the asymptotic quantization
		  error does not converge to zero and the asymptotic
		  distribution of weights differs from the distribution of
		  input samples. This suggests that in order to achieve a
		  small quantization error and good approximation of input
		  distribution, a small neighborhood size must be used.
		  Weight distributions in numerical evaluation confirm the
		  result.},
  dbinsdate	= {2002/1}
}

@InCollection{	  yiming96a,
  author	= {Pi Yiming and Liu Zemin},
  title		= {{K}ohonen neural network based admission control in {ATM}
		  telecommunication network},
  booktitle	= {ICCT'96. 1996 International Conference on Communication
		  Technology Proceedings},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {2},
  editor	= {C. A. O. Zhigang},
  address	= {New York, NY, USA},
  pages		= {905--8},
  dbinsdate	= {oldtimer}
}

@Article{	  yiming96b,
  author	= {Pi Yiming and Liu ZeMin},
  title		= {Call admission control by {K}ohonen neural network in
		  {ATM} network},
  journal	= {High Technology Letters},
  year		= {1996},
  volume	= {6},
  number	= {8},
  pages		= {11--14},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yin01a,
  author	= {H. Yin},
  title		= {Visualisation induced {SOM} (Vi{SOM})},
  booktitle	= {Advances in Self-Organising Maps},
  pages		= {81--88},
  year		= {2001},
  editor	= {Nigel Allinson and Hujun Yin and Lesley Allinson and Jon
		  Slack},
  publisher	= {Springer},
  dbinsdate	= {2002/1}
}

@Article{	  yin01b,
  author	= {Yin, H. and Allinson, N. M.},
  title		= {Self-organizing mixture networks for probability density
		  estimation},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2001},
  volume	= {12},
  number	= {2},
  month		= {March 2001},
  pages		= {405--411},
  organization	= {Department of Electrical Engineering, University of
		  Manchester, Institute of Science and Technology},
  publisher	= {},
  address	= {},
  abstract	= {A self-organizing mixture network (SOMN) is derived for
		  learning arbitrary density functions. The network minimizes
		  the Kullback-Leibler information metric by means of
		  stochastic approximation methods. The density functions are
		  modeled as mixtures of parametric distributions A mixture
		  needs not to be homogenous, i.e., it can have different
		  density profiles. The first layer of the network is similar
		  to Kohonen's self-organizing map (SOM), but with the
		  parameters of the component densities as the learning
		  weights. The winning mechanism is based on maximum
		  posterior probability, and updating of the weights is
		  limited to a small neighborhood around the winner. The
		  second layer accumulates the responses of these local
		  nodes, weighted by the learned mixing parameters. The
		  network possesses a simple structure and computational
		  form, yet yields fast and robust convergence. The network
		  has a generalization ability due to the relative entropy
		  criterion used. Applications to density profile estimation
		  and pattern classification are presented. The SOMN can also
		  provide an insight to the role of neighborhood function
		  used in the SOM.},
  dbinsdate	= {2002/1}
}

@Article{	  yin01c,
  author	= {Yin, H. and Allinson, N. M.},
  title		= {Bayesian self-organising map for Gaussian mixtures},
  journal	= {IEE Proceedings: Vision, Image and Signal Processing},
  year		= {2001},
  volume	= {148},
  number	= {4},
  month		= {August },
  pages		= {234--240},
  organization	= {Dept. of Elec. Eng. and Electronics, UMIST},
  publisher	= {},
  address	= {},
  abstract	= {A Bayesian self-organising map (BSOM) is proposed for
		  learning mixtures of Gaussian distributions. It is derived
		  naturally from minimising the Kullback-Leibler divergence
		  between the data density and the neural model. The inferred
		  posterior probabilities of the neurons replace the common
		  Euclidean distance winning rule and define explicitly the
		  neighbourhood function. Learning can be retained in a small
		  but fixed neighbourhood of the winner. The BSOM in turn
		  provides an insight into the role of neighbourhood
		  functions used in the common SOM. A formal comparison
		  between the BSOM and the expectation-maximisation (EM)
		  algorithm is also presented, together with experimental
		  results.},
  dbinsdate	= {2002/1}
}

@Article{	  yin02a,
  author	= {Yin, Hujun},
  title		= {Vi{SOM}-a novel method for multivariate data projection
		  and structure visualization},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {2002},
  volume	= {13},
  number	= {1},
  month		= {January },
  pages		= {237--243},
  organization	= {Dept. of Elec. Eng. and Electronics, Univ. Manchester
		  Inst. Sci. Technol.},
  publisher	= {Institute of Electrical and Electronics Engineers Inc.},
  address	= {},
  abstract	= {When used for visualization of high-dimensional data, the
		  self-organizing map (SOM) requires a coloring scheme such
		  as the U-matrix to mark the distances between neurons. Even
		  so, the structures of the data clusters may not be apparent
		  and their shapes are often distorted. In this paper, a
		  visualization-induced SOM (ViSOM) is proposed to overcome
		  these shortcomings. The algorithm constrains and
		  regularizes the inter-neuron distance with a parameter that
		  controls the resolution of the map. The mapping preserves
		  the inter-point distances of the input data on the map as
		  well as the topology. It produces a graded mesh in the data
		  space such that the distances between mapped data points on
		  the map resemble those in the original space, like in the
		  Sammon mapping. However, unlike the Sammon mapping, the
		  ViSOM can accommodate both training data and new arrivals
		  and is much simpler in computational complexity. Several
		  experimental results and comparisons with other methods are
		  presented.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yin91a,
  author	= {H. Yin and R. Lengelle and P. Gaillard},
  title		= {Inverse-step competitive learning},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  volume	= {I},
  pages		= {839--844},
  organization	= {IEEE; Int. Neural Networks Soc},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {The authors first review several variants of the
		  competitive learning rule: simple competitive learning, the
		  Kohonen self-organization map, and frequency-sensitive
		  competitive learning. They then propose a novel learning
		  rule based on competitive learning, called inverse-step
		  competitive learning (ISCL). The isolated points play a
		  more important role than the normal points in simple
		  competitive learning, because the learning step is
		  proportional to the distance between the input pattern and
		  the weights value. The basic idea of this learning rule is
		  to take a learning step which is a descending function of
		  this distance. The authors give the first results of the
		  ISCL rule and compare it to simple competitive learning.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yin93a,
  author	= {H. Yin and N. M. Allinson},
  title		= {On the distribution of feature space in self-organisng
		  mapping and convergence accelerating by a {K}alman filter},
  booktitle	= {New Trends in Neural Computation},
  year		= {1993},
  editor	= {J. Mira and J. Cabestany and A Prieto},
  pages		= {291--96},
  publisher	= {Springer},
  address	= {Berlin, Heidelberg},
  annote	= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yin93b,
  author	= {Yin, H. and Allinson, N. M. },
  title		= {Stochastic analysis and comparison of {K}ohonen {SOM} with
		  optimal filter},
  booktitle	= {Third International Conference on Artificial Neural
		  Networks},
  year		= {1993},
  pages		= {182--5},
  organization	= {York Univ. , UK},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yin94a,
  author	= {H. Yin and N. M. Allinson},
  title		= {Self-Organised Segmentation for Textured Images},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1149--1152},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, image segmentation},
  dbinsdate	= {oldtimer}
}

@Article{	  yin94b,
  author	= {Yin, H. and Allinson, N. M. },
  title		= {Unsupervised segmentation of textured images using a
		  hierarchical neural structure},
  journal	= {Electronics Letters},
  year		= {1994},
  volume	= {30},
  number	= {22},
  pages		= {1842--3},
  month		= {Oct},
  abstract	= {A hierarchical learning structure, combining a
		  randomly-placed local window, a self-organising map and a
		  local-voting scheme, has been developed for the
		  unsupervised segmentation of textured images, which are
		  modelled by {M}arkov random fields. The system learns to
		  progressively estimate model parameters, and hence classify
		  the various textured regions. A globally correct
		  segregation has consistently been obtained during extensive
		  experiments on both synthetic and natural textured
		  images.},
  dbinsdate	= {oldtimer}
}

@Article{	  yin95a,
  author	= {Hujun Yin and Nigel M. Allinson},
  title		= {On the Distribution and Convergence of Feature Space in
		  Self-Organizing Maps},
  journal	= {Neural Computation},
  year		= {1995},
  volume	= {7},
  number	= {6},
  pages		= {1178--1187},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yin95b,
  author	= {Hujun Yin and Nigel M. Allinson},
  title		= {Towards the Optimal {B}ayes Classifier Using an Extended
		  Self-Organising Map},
  booktitle	= {Proc. ICANN'95, International Conference on Artificial
		  Neural Networks},
  year		= {1995},
  editor	= {F. Fogelman-Souli{\'{e}} and P. Gallinari},
  volume	= {II},
  pages		= {45--49},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InCollection{	  yin96a,
  author	= {Hujun Yin and N. M. Allinson},
  title		= {An equidistortion principle constrained {SOM} for vector
		  quantisation},
  booktitle	= {Progress in Neural Information Processing. Proceedings of
		  the International Conference on Neural Information
		  Processing},
  publisher	= {Springer-Verlag},
  year		= {1996},
  volume	= {1},
  editor	= {S. -I. Amari and L. Xu and L. -W. Chan and I. King and K.
		  -S. Leung},
  address	= {Singapore},
  pages		= {80--3},
  dbinsdate	= {oldtimer}
}

@Article{	  yin97a,
  author	= {H. Yin and N. M. Allinson},
  title		= {{B}ayesian learning for \mbox{self-organising} maps},
  journal	= {Electronics Letters},
  year		= {1997},
  volume	= {33},
  number	= {4},
  pages		= {304--5},
  dbinsdate	= {oldtimer}
}

@InCollection{	  yin97b,
  author	= {Hujun Yin and Nigel M. Allinson},
  title		= {Comparison of a {B}ayesian {SOM} with the {EM} algorithm
		  for {G}aussian mixtures},
  booktitle	= {Proceedings of WSOM'97, Workshop on Self-Organizing Maps,
		  Espoo, Finland, June 4--6},
  publisher	= {Helsinki University of Technology, Neural Networks
		  Research Centre},
  year		= 1997,
  address	= {Espoo, Finland},
  pages		= {118--123},
  dbinsdate	= {oldtimer}
}

@Article{	  yin99a,
  author	= {Yin, H. and Allinson, N. M.},
  title		= {Interpolating \mbox{self-organizing} map (i{SOM})},
  journal	= {Electronics Letters},
  year		= {1999},
  number	= {19},
  volume	= {35},
  pages		= {1649--1650},
  abstract	= {A new learning algorithm is presented for enhancing the
		  scale or structure of an already trained self-organizing
		  map (SOM) without the need to re-use the original training
		  data. Alternative methods for the insertion of these
		  additional interpolating neurons, while still preserving
		  the learnt topology, are presented together with two
		  illustrative examples of the algorithm in operation.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  hujun99a,
  author	= {Hujun Yin and Allinson, N. M.},
  title		= {Averaging ensembles of \mbox{self-organising} mixture
		  networks for density estimation},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {2},
  pages		= {1456--60},
  abstract	= {The self-organising mixture network (SOMN) is a learning
		  algorithm for mixture densities, derived from minimising
		  the Kullback-Leibler information by means of stochastic
		  approximation methods. It has been shown the SOMN converges
		  faster than the EM-based algorithms and generalises better
		  as it is based on the expected likelihood rather than the
		  sample likelihood. The derived algorithm has similar
		  updating forms to the self-organising map (SOM), thus
		  reveals the mixture interpreter role of the neighbourhood
		  function used in the SOM. When the sample set is small,
		  overfitting problems often occur in most algorithms.
		  Further improvement can be achieved by averaging ensembles
		  of the SOMNs. The algorithms have been applied to both
		  experimental data and real-world problems. The results show
		  that smoothed mixtures with improved accuracy have been
		  obtained. Estimation variance has been reduced.},
  dbinsdate	= {oldtimer},
  merjanote     = {New label should be yin99b. Last name is Yin, as in 
                   other papers with Allinson}
}

@InProceedings{	  ying99a,
  author	= {Ying, Wu and Qiong, Liu and Huang, T. S.},
  title		= {Robust real-time human hand localization by
		  \mbox{self-organizing} color segmentation},
  booktitle	= {Proceedings International Workshop on Recognition,
		  Analysis, and Tracking of Faces and Gestures in Real-Time
		  Systems. In Conjunction with ICCV'99},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {161--6},
  abstract	= {This paper describes a robust tracking algorithm used to
		  localize a human hand in video sequences. The localization
		  system relies mainly on an automatic color-based
		  segmentation scheme combined with the motion cue. An
		  automatic self-organizing clustering algorithm, is proposed
		  to learn the color clusters unsupervisedly in the HSI space
		  without specifying the number of clusters in advance. The
		  schemes of growing, pruning and merging of 1-D
		  self-organizing map (SOM) are facilitated to find an
		  appropriate number of clusters in the forming stage of SOM.
		  The training and segmentation in our approach is fast
		  enough to make possible real-time applications. This
		  segmentation scheme is capable of tracking multiple objects
		  of different colors simultaneously. A motion cue is
		  employed to focus the attention of the tracking algorithm.
		  This approach is also applied to other tasks such as human
		  face tracking and color indexing. Our localization system
		  implemented on a SGI O2 R10000 workstation is reliable and
		  efficient at 20--30 Hz.},
  dbinsdate	= {oldtimer}
}

@Article{	  yiping94a,
  author	= {Guo Yiping and Forster, B. C. },
  title		= {Unsupervised classification of high spectral resolution
		  images using the {K}ohonen self-organization neural
		  network},
  journal	= {Journal of Infrared and Millimeter Waves},
  year		= {1994},
  volume	= {13},
  number	= {6},
  pages		= {409--17},
  month		= {Dec},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  ylakoski93a,
  author	= {I. Yl{\"{a}}koski and A. Visa},
  title		= {A Two-Stage Classifier for Wooden Boards},
  booktitle	= {Proc. 8SCIA, Scand. Conf. on Image Analysis},
  year		= {1993},
  volume	= {I},
  pages		= {637--641},
  publisher	= {NOBIM},
  address	= {Troms{\o}, Norway},
  dbinsdate	= {oldtimer}
}

@Article{	  ylakoski94a,
  author	= {Ylakoski, I. },
  title		= {Unsupervised classification of ultrasonic {NDT} data},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1994},
  volume	= {2345},
  pages		= {182--6},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  yli-rantala96a,
  author	= {E. Yli-Rantala and T. Ojala and P. Vuorimaa},
  title		= {Vector quantization of residual images using
		  \mbox{self-organizing} map},
  booktitle	= {ICNN 96. The 1996 IEEE International Conference on Neural
		  Networks},
  publisher	= {IEEE},
  year		= {1996},
  volume	= {1},
  address	= {New York, NY, USA},
  pages		= {464--7},
  abstract	= {Vector Quantization (VQ) is a signal compression technique
		  which can provide high compression rates. The
		  Self-Organizing Map (SOM) can be employed in the generation
		  of the VQ codebooks. Exploiting the ordering property of
		  the SOM, the encoding process can be considerably
		  accelerated by using two-level search. In this paper, we
		  concern the VQ of prediction error (residual) images in
		  image sequence coding. The results show that the codebooks
		  generated by the SOM and the widely-used LBG algorithm
		  archive almost the same performance, but the encoding
		  process can be realized in a more efficient way by
		  exploiting the ordering property of the SOM.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  yli-rantala96b,
  author	= {E. Yli-Rantala and T. Ojala and P. Vuorimaa},
  title		= {Vector quantization of residual images using
		  \mbox{self-organizing} map with sample weighted training},
  booktitle	= {Fourth European Congress on Intelligent Techniques and
		  Soft Computing Proceedings, EUFIT '96},
  publisher	= {Verlag Mainz},
  year		= {1996},
  volume	= {1},
  address	= {Aachen, Germany},
  pages		= {325--8},
  dbinsdate	= {oldtimer}
}

@Article{	  yonezu98a,
  author	= {H. Yonezu and K. Tsuji and D. Sudo and J. -K. Shin},
  title		= {\mbox{Self-organizing} network for feature-map formation:
		  analog integrated circuit robust to device and circuit
		  mismatch},
  journal	= {Computers \& Electrical Engineering},
  year		= {1998},
  volume	= {24},
  number	= {1--2},
  pages		= {63--73},
  dbinsdate	= {oldtimer}
}

@Article{	  yong97a,
  author	= {Hu Yong and Tan Zheng},
  title		= {Iterative fuzzy vector quantization and its neural net
		  algorithm},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1997},
  volume	= {3074},
  pages		= {292--8},
  note		= {(Visual Information Processing VI Conf. Date: 21--22 April
		  1997 Conf. Loc: Orlando, FL, USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yong99a,
  author	= {Yong, Liu and Bin, Zhao and Shaowei, Xia and Ming, Sheng
		  Zhao},
  title		= {A \mbox{self-organizing} network with fuzzy
		  hyperellipsoidal classifying and its application in
		  handwritten numeral recognition},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {4},
  pages		= {2859--62},
  abstract	= {This paper proposes a self-organizing network with the
		  fuzzy hyperellipsoid-classifier (FHECFN) and utilizes it to
		  recognize handwritten numerals. Based on the clustering
		  result of SOM, FHECFN divides the center that performs
		  worse taking the advantage of the fuzzy hyperellipsoidal
		  clustering algorithm. When reaching the satisfying
		  requirement, the network stops divining and then obtains
		  the suitable number of prototypes and the hyperellipsoidal
		  classifying result. With the supervised learning algorithm,
		  such as learning vector quantization, the network achieves
		  a better learning result and in the experiments of
		  recognizing the handwritten numerals, the network shows a
		  promising performance.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yoo94a,
  author	= {Jang-Hee Yoo and Byoung-Ho Kang and Jae-Woo Kim},
  title		= {A Clustering Analysis and Learning Rate for
		  Self-Organizing Feature Map},
  pages		= {79--80},
  booktitle	= {Proc. 3rd International Conference on Fuzzy Logic, Neural
		  Nets and Soft Computing},
  year		= {1994},
  publisher	= {Fuzzy Logic Systems Institute},
  address	= {Iizuka, Japan},
  annote	= {analysis},
  dbinsdate	= {oldtimer}
}

@InCollection{	  yoo97a,
  author	= {Jang-Hee Yoo and See-Young Oh},
  title		= {A Coloring Method of Gray-Level Image using Neural
		  Networks},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {1203--1206},
  dbinsdate	= {oldtimer}
}

@Article{	  yoon94a,
  author	= {Seok Hyun Yoon and Kwang Woo Chung and Kwang Seok Hong and
		  Byung Chul Park},
  title		= {Isolated word recognition using the {SOFM}-{HMM} and the
		  inertia},
  journal	= {Journal of the Korean Institute of Telematics and
		  Electronics},
  year		= {1994},
  volume	= {31B},
  number	= {6},
  pages		= {17--24},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@Article{	  yoon98a,
  author	= {Yong-Han Yoon and Young-Jae Jeon and Jae-Chul Kim},
  title		= {A {K}ohonen network for fault diagnosis of power
		  transformers using dissolved gas analysis},
  journal	= {Transactions of the Korean Institute of Electrical
		  Engineers},
  year		= {1998},
  volume	= {47},
  number	= {6},
  pages		= {741--5},
  dbinsdate	= {oldtimer}
}

@Article{	  yoshida94a,
  author	= {Yoshida, T. and Omatu, S. },
  title		= {Neural network approach to land cover mapping},
  journal	= {IEEE Transactions on Geoscience and Remote Sensing},
  year		= {1994},
  volume	= {32},
  number	= {5},
  pages		= {1103--9},
  month		= {Sept},
  dbinsdate	= {oldtimer}
}

@Article{	  yoshida95a,
  author	= {T. Yoshida and Y. Jyo and S. Omatu},
  title		= {Extraction of edge information by {K}ohonen's networks},
  journal	= {Bulletin of University of Osaka Prefecture, Series A},
  year		= {1995},
  volume	= {44},
  number	= {2},
  pages		= {103--9},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yoshida99a,
  author	= {Yoshida, Hisashi and Shinoi, Hideaki and Yana, Kazuo},
  title		= {Phonocardiogram classification using
		  \mbox{self-organizing} map with learning vector
		  quantization},
  booktitle	= {Annual International Conference of the IEEE Engineering in
		  Medicine and Biology---Proceedings},
  year		= {1999},
  volume	= {2},
  pages		= {929},
  abstract	= {This paper proposes a method for classifying the
		  phonocardiogram (PCG) into three target classes (Type I:
		  normal, Type II: innocent murmur, Type III: abnormal
		  murmur). The method detects the presence of systolic murmur
		  by the first self-organizing map with learning vector
		  quantization algorithm. The second self-organizing map
		  classifies the PCG into innocent and abnormal murmur. The
		  effectiveness of the method was confirmed by applying the
		  method to the data obtained from nation wide health
		  screening for elementary school children conducted in
		  Japan. The first stage of detecting the presence of
		  systolic murmur achieves correcting rate of 99.4%, and
		  correcting classification rates for innocent and abnormal
		  murmur was 96.9% and 94.9% respectively. Furthermore, the
		  proposed method not only has good performance of
		  classifying the target classes, but also it could have
		  ability to classify the systolic murmur into sub classes
		  such as ejection murmur or musical murmur, etc. because of
		  its structure of the system.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yoshihara91a,
  author	= {Takafumi Yoshihara and Toshiaki Wada},
  title		= {Optimization by extended {LVQ}},
  booktitle	= {Proc. IJCNN'91, International Joint Conference on Neural
		  Networks},
  year		= {1991},
  pages		= {407--414},
  abstract	= {We introduce a self-organizing neural network model based
		  on Kohonen's self-organizing feature maps for solving
		  combinatorial optimization problems better than other
		  neural network models. In our model, the best matching
		  neuron of the self-organizing feature maps is calculated
		  with an energy function. The performance of this model was
		  examined through two problems, the traveling salesman
		  problem and the n-queen problem. Simulations of the
		  traveling salesman problem have been carried out for 10 and
		  30 cities.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yoshimura00a,
  author	= {Yoshimura, H. and Etoh, M. and Kondo, K. and Yokoya, N.},
  title		= {Gray-scale character recognition by Gabor jets
		  projection},
  booktitle	= {Proceedings 15th International Conference on Pattern
		  Recognition. ICPR-2000. IEEE Comput. Soc, Los Alamitos, CA,
		  USA},
  year		= {2000},
  volume	= {2},
  pages		= {335--8},
  abstract	= {We propose a gray-scale character recognition method for
		  video indexing. It is robust against the problems of
		  binarization against a complex background and low
		  resolution. Unlike a traditional character recognition
		  scheme through image binarization, we directly extract
		  Gabor features (called Gabor jets) from video contents. The
		  use of the Gabor filters contributes to freeing a tricky
		  binarization process for cluttered images, and furthermore
		  provides localized directional edge features, which have
		  phase-shift invariance to edge positions. To form a feature
		  vector to be classified, we accumulate the extracted Gabor
		  features along projection lines in local regions, and then
		  categorize them with a standard LVQ classifier. The
		  projective accumulation provides robustness under character
		  deformation caused by variation of font types or imprecise
		  segmentation. We compare the proposed method by experiments
		  with a typical OCR method, for which correct binarization
		  is advantageously given. The proposed method shows similar
		  or superior performance to the other method in
		  understanding video captions.},
  dbinsdate	= {2002/1}
}

@InCollection{	  yoshimura96a,
  author	= {M. Yoshimura and S. Oe},
  title		= {Texture image segmentation by genetic algorithms},
  booktitle	= {Proceedings of 1996 IEEE International Conference on
		  Evolutionary Computation (ICEC'96)},
  publisher	= {IEEE},
  year		= {1996},
  address	= {New York, NY, USA},
  pages		= {125--30},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  you95a,
  author	= {Su-Jeong You and Chong-Ho Choi},
  title		= {{LVQ} with a Weighted Objective Function},
  volume	= {V},
  pages		= {2763--2768},
  booktitle	= {Proc. ICNN'95, IEEE International Conference on Neural
		  Networks},
  year		= {1995},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {In competitive learning neural network, pattern clustering
		  is one of the main research areas. Many competitive neural
		  networks are based on vector quantization. Depending on the
		  method of choosing the representative weight vectors,
		  competitive neural networks have a great variety of
		  algorithms. In this paper, an algorithm, a variety of GLVQ,
		  is proposed and is compared with other algorithms. It is
		  shown from simulation results that the proposed algorithm
		  gives better performance than other algorithms in
		  clustering.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  youssefi99a,
  author	= {Youssefi, M. and Faez, K.},
  title		= {Fabric handle prediction using neural networks},
  booktitle	= {Proceedings of the IEEE-EURASIP Workshop on Nonlinear
		  Signal and Image Processing (NSIP'99). Bogazici Univ,
		  Instanbul, Turkey},
  year		= {1999},
  volume	= {2},
  pages		= {731--2},
  abstract	= {Handle is an important property of fabrics. In this work
		  we tried to predict the handles of some worsted fabrics by
		  their physical properties using a backpropagation network.
		  Also an unsupervised Kohonen network was used for
		  clustering the fabrics. Physical properties of fabrics were
		  measured by universal test equipment and hand values of the
		  fabrics were determined by a panel of judges consisting of
		  textile experts. The results showed that the
		  backpropagation network could predict the hand values of
		  the untrained fabrics with average one degree of
		  difference. Also the Kohonen network could cluster the
		  fabrics well and near the clustering of experts. These
		  results show that these two kinds of neural network are
		  good tools for predicting hand values and clustering
		  fabrics.},
  dbinsdate	= {2002/1}
}

@InCollection{	  ypma97a,
  author	= {Alexander Ypma and Robert P. W. Duin},
  title		= {Novelty detection using Self-Organizing Maps},
  booktitle	= {Progress in Connectionist-Based Information Systems},
  publisher	= {Springer},
  year		= {1997},
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  pages		= {1322--1325},
  address	= {London},
  dbinsdate	= {oldtimer}
}

@Article{	  yu00a,
  author	= {Yu, I. K. and Kim, C. I. and Song, Y. H.},
  title		= {Industrial load forecasting using the Kohonen neural
		  network and the wavelet transform},
  journal	= {Proceedings of the Universities Power Engineering
		  Conference},
  year		= {2000},
  volume	= {},
  number	= {},
  month		= {},
  pages		= {107},
  organization	= {Department of Electrical Engineering, Changwon National
		  University},
  publisher	= {},
  address	= {},
  abstract	= {This paper presents Kohonen neural network and wavelet
		  transform analysis based technique for the industrial
		  hourly load forecasting for the purpose of peak demand
		  control. Firstly, one year of historical load data were
		  sorted and clustered into several groups using Kohonen
		  neural network and then wavelet transforms are adopted
		  using the biorthogonal mother wavelet in order to forecast
		  the peak load of one hour ahead. The 5-level decomposition
		  of the daily industrial load curve is implemented to
		  consider the weather sensitive component of loads
		  effectively. The wavelet coefficients associated with
		  certain frequency and time localization is adjusted using
		  the conventional multiple regression method and the
		  components are reconstructed to predict the final loads
		  through a five-scale synthesis technique. The outcome of
		  the study clearly indicates that the proposed composite
		  model of Kohonen neural network and wavelet transform
		  approach can be used as an attractive and effective means
		  for the industrial hourly peak load forecasting.},
  dbinsdate	= {2002/1}
}

@Article{	  yu01a,
  author	= {Yu, D. and Wang, J. and Wang, G.},
  title		= {Leak fault identification of rocket engine using
		  self-organizing feature map network},
  journal	= {Tuijin Jishu/Journal of Propulsion Technology},
  year		= {2001},
  volume	= {22},
  number	= {1},
  month		= {February 2001},
  pages		= {47--49},
  organization	= {School of Energy Science and Eng., Harbin Inst. of
		  Technology},
  publisher	= {},
  address	= {},
  abstract	= {Some kinds of leak fault were analyzed in liquid rocket
		  engine. The method of principle component analysis was used
		  to reduce the dimension of the original samples, which can
		  represent the leak. And then input the low-dimension
		  samples, the self-organizing feature map network can
		  identify the leak fault. The simulating results show that
		  the approach is feasible and effective.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yu90a,
  author	= {G. Yu and W. Russell and R. Schwartz and J. Makhoul},
  title		= {Discriminant analysis and supervised vector quantization
		  for continuous speech recognition},
  booktitle	= {ICASSP-90, International Conference on Acoustics, Speech
		  and Signal Processing},
  year		= {1990},
  volume	= {II},
  pages		= {685--688},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yu90b,
  author	= {F. T. S. Yu and T. Lu},
  title		= {Adaptive optical system for neural computing},
  booktitle	= {Proc. IEEE TENCON'90, 1990 IEEE Region 10 Conf. Computer
		  and Communication Systems},
  year		= {1990},
  volume	= {I},
  pages		= {59--62},
  organization	= {IEEE},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yu91a,
  author	= {Yu, Jilai and Guo, Zhizhong and Liu, Zhuo},
  title		= {A new fast method for supplying measures to avoid the high
		  voltage mode of electromagnetic voltage transformer},
  booktitle	= {Proc. First Int. Forum on Applications of Neural Networks
		  to Power Systems},
  year		= {1991},
  editor	= {M. A. El-Sharkawi and R. J. Marks II},
  pages		= {293--296},
  organization	= {IEEE; NSF; Pugent Power \& Light; EPRI},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yu93a,
  author	= {Yu, Wenxian and Lu, Jun and Wu, Jianhui and Guo, Guirong},
  title		= {Fuzzy sets-based neural network for pattern
		  understanding},
  booktitle	= {Proceedings TENCON '93. 1993 IEEE Region 10 Conference on
		  'Computer, Communication, Control and Power Engineering'},
  year		= {1993},
  editor	= {Yuan Baozong},
  volume	= {2},
  pages		= {834--40},
  organization	= {Dept. of Syst. \& Eng. , Nat. Univ. of Defense Technol. ,
		  Hunan, China},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  yu93b,
  author	= {Yu, J. S. and Dagli, C. H. },
  title		= {Using \mbox{self-organizing} maps adaptive resonance
		  theory ({ARTMAP}) for manufacturing feature recognition},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1993},
  volume	= {1959},
  pages		= {452--63},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InCollection{	  yu97a,
  author	= {Francis T. S. Yu},
  title		= {Optical Implementation of Artificial Neural Nets
		  ({ANN}s)},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {1},
  address	= {Singapore},
  pages		= {741--744},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  yu97b,
  author	= {Yu, Q. and Kashiwamura, T. and Shiratori, M. and Satoh,
		  K.},
  title		= {Reliability and structure optimization of {BGA} packages},
  booktitle	= {Advances in Electronic Packaging 1997. Proceedings of the
		  Pacific Rim/ASME International Intersociety Electronic and
		  Photonic Packaging Conference. INTERpack '97. ASME, New
		  York, NY, USA},
  year		= {1997},
  volume	= {2},
  pages		= {1761--5},
  abstract	= {The fatigue strength of BGA solder joints was studied by
		  an isothermal mechanical fatigue test method. It was found
		  that the fatigue life of BGA solder joints could be
		  estimated by Manson-Coffin's law as N=A Delta epsilon /sub
		  in//sup n/, where the equivalent inelastic strain range
		  Delta epsilon /sub in/ was used as the associated fracture
		  parameter. In order to conduct the fatigue life design
		  easily, a statistical optimization method (SOM), which was
		  based upon the concept of orthogonal table used in normal
		  design of experiments, was applied for developing the
		  estimation expression between the inelastic strain range
		  Delta epsilon /sub in/ and the design factors and thermal
		  cyclic conditions. By substituting the expression of the
		  Delta epsilon /sub in/,in Manson-Coffin's law, a direct
		  expression between the fatigue life N/sub 1/ and design
		  factors was obtained, and the dispersion or the reliability
		  of fatigue life could be easily evaluated by the
		  expression. Furthermore, a mathematical programming
		  (successive quadratic programming) was employed to solve
		  the optimization problem of the weight of solder joints,
		  where the expression of fatigue life was used as the
		  constraints of the problem. It was found that the SOM could
		  be used as a very practical tool to estimate the problem of
		  fatigue life design of BGA solder joints.},
  dbinsdate	= {oldtimer}
}

@Article{	  yuan01a,
  author	= {Yuan, Soe-Tsyr and Chang, Wei-Lun},
  title		= {Mixed-initiative synthesized learning approach for
		  web-based {CRM}},
  journal	= {Expert Systems with Applications},
  year		= {2001},
  volume	= {20},
  number	= {2},
  month		= {Feb},
  pages		= {187--200},
  organization	= {Fu-Jen Univ},
  publisher	= {Elsevier Science Ltd},
  address	= {Exeter},
  abstract	= {The issue of customer relationship management has emerged
		  rapidly. Customers have become one of the most important
		  considerations to new companies being built. Accordingly,
		  customer retention is a very important topic. In this
		  paper, we present a mixed-initiative synthesized learning
		  approach for better understanding of customers and the
		  provision of clues for improving customer relationships
		  based on different sources of web customer data. The
		  approach is a combination of hierarchical automatic
		  labeling SOM, decision tree, cross-class analysis, and
		  human tacit experience. The objective of this approach is
		  to hierarchically segment data sources into clusters,
		  automatically label the features of the clusters, discover
		  the characteristics of normal, defected and possibly
		  defected clusters of customers, and provide clues for
		  gaining customer retention.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  yuan99a,
  author	= {Yuan, C. and Niemann, H.},
  title		= {Object localization in {2D} images based on {K}ohonen's
		  self-organization feature maps},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3134--7},
  abstract	= {This paper presents a hybrid approach for neural object
		  localization and recognition in 2D grey level images. The
		  system combines an auto-associative network, two
		  self-organization feature maps (SOM), and a three layer
		  feedforward network trained with dynamic learning vector
		  quantization (DLVQ). By using a hidden layer smaller than
		  the input/output layers, the auto-associative network can
		  be expected to find efficient ways of encoding the
		  information contained in the input data set. Thus a
		  dimension reduction of the input image can be achieved. The
		  object localization scheme is then directly based on
		  features which are detected automatically using the
		  Kohonen's SOMs. After preprocessing images are split into
		  small blocks and input to two Kohonen maps. Through
		  training, the first map can detect the object area of the
		  input image, while the second map can detect the object
		  specific features. By integrating the features extracted
		  from the output of the two maps and the DLVQ methods, we
		  can locate different objects and estimate object pose
		  (translation, rotation within the image plane and scale
		  parameter).},
  dbinsdate	= {oldtimer}
}

@Article{	  yuanda96a,
  author	= {Cao Yuanda and Chen Yifeng},
  title		= {A hybrid neural network for spatio-temporal pattern
		  recognition},
  journal	= {Journal of Beijing Institute of Technology},
  year		= {1996},
  volume	= {5},
  number	= {1},
  pages		= {1--6},
  dbinsdate	= {oldtimer}
}

@Article{	  yudong95a,
  author	= {Cai Yudong and Xu Weije and Chen Nianyi},
  title		= {Discrimination of {D88} structure of inter-metallic
		  compounds by self-organization artificial neural network},
  journal	= {Acta Metallurgica Sinica},
  year		= {1995},
  volume	= {31},
  number	= {6},
  pages		= {B280--3},
  month		= {June},
  dbinsdate	= {oldtimer}
}

@Article{	  yuhua96a,
  author	= {Li Yuhua and Sun Ying and Zhang Yanxin},
  title		= {Study of optical pattern recognition of {3-D} multiple-
		  targets based on multi-encoding method},
  journal	= {Journal of Infrared and Millimeter Waves},
  year		= {1996},
  volume	= {15},
  number	= {4},
  pages		= {262--6},
  dbinsdate	= {oldtimer}
}

@Article{	  yunping93a,
  author	= {Chen Yunping and Guo Bin},
  title		= {Artificial neural network and its application in control
		  and system engineering. {III}},
  journal	= {Power System Technology},
  year		= {1993},
  volume	= {5},
  pages		= {57--61},
  month		= {Sept},
  dbinsdate	= {oldtimer}
}

@InCollection{	  zaharia96a,
  author	= {C. N. Zaharia and C. Barbu},
  title		= {On the use of neural networks for the diagnosis and
		  prognostic establishment in chronic hepatopathies},
  booktitle	= {Simulation in Industry. 8th European Simulation Symposium.
		  ESS'96},
  publisher	= {SCS},
  year		= {1996},
  volume	= {2},
  editor	= {A. G. Bruzzone and E. J. H. Kerckhoffs},
  address	= {Ghent, Belgium},
  pages		= {73--6},
  dbinsdate	= {oldtimer}
}

@Article{	  zait97a,
  author	= {M. Zait and H. Messatfa},
  title		= {A comparative study of clustering methods},
  journal	= {Future Generation Computer Systems},
  year		= {1997},
  volume	= {13},
  number	= {2--3},
  pages		= {149--59},
  dbinsdate	= {oldtimer}
}

@InCollection{	  zamani95e,
  author	= {M. S. Zamani and G. R. Hellestrand},
  title		= {A new neural network approach to the floorplanning of
		  hierarchical {VLSI} designs},
  booktitle	= {From Natural to Artificial Neural Computation.
		  International Workshop on Artificial Neural Networks.
		  Proceedings},
  publisher	= {Springer-Verlag},
  year		= {1995},
  editor	= {J. Mira and F. Sandoval},
  address	= {Berlin, Germany},
  pages		= {1128--34},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zamani99a,
  author	= {Zamani, M. S. and Mehdipur, F.},
  title		= {An efficient method for placement of {VLSI} designs with
		  {K}ohonen map},
  booktitle	= {IJCNN'99. International Joint Conference on Neural
		  Networks. Proceedings.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1999},
  volume	= {5},
  pages		= {3328--31},
  abstract	= {In this paper a Kohonen map-based algorithm for the
		  placement of gate arrays and standard cells is presented.
		  An abstract specification of the design is converted to a
		  set of appropriate input vectors using a mathematical
		  method, called "multidimensional scaling". These vectors
		  which have, in general, higher dimensionality are fed to
		  the self-organizing map at random in order to map them onto
		  a 2D plane of the regular chip. The mapping is done in such
		  a way that the cells with higher connectivity are placed
		  close to each other, hence minimizing total connection
		  length in the design. Two processes, called reassignment
		  and rearrangement, are employed to make the algorithm
		  applicable to the standard cell designs. In addition to the
		  small examples introduced in other papers, two standard
		  cell benchmarks were tried and better results were observed
		  for these large designs compared to other neural net-barred
		  approaches.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zamani99b,
  author	= {Zamani, M. S. and Mehdipur, F.},
  title		= {Using {K}ohonen map for the placement of regular {VLSI}
		  designs},
  booktitle	= {Proceedings Third International Conference on
		  Computational Intelligence and Multimedia Applications.
		  ICCIMA'99},
  publisher	= {IEEE Computer Society},
  address	= {Los Alamitos, CA, USA},
  year		= {1999},
  volume	= {},
  pages		= {65--9},
  abstract	= {The paper presents the formulation of a VLSI placement
		  problem for regular designs (gate arrays) using a Kohonen
		  self-organizing map. An abstract specification of the
		  design is converted to a set of appropriate input vectors
		  using a mathematical method, called "multidimensional
		  scaling". These vectors which have, in general, higher
		  dimensionality, are fed to the self-organizing map at
		  random in order to map them onto a 2-dimensional plane of
		  the regular chip. The mapping is done in such a way that
		  the cells with higher connectivity are placed close to each
		  other, hence minimizing total connection length in the
		  design. The results show improvement over other neural
		  network based approaches in terms of both efficiency and
		  the quality of results. The capability of our approach in
		  handling external ports as well as nonrectangular
		  (rectilinear) boundaries makes it appropriate for the
		  placement of hierarchical designs.},
  dbinsdate	= {oldtimer}
}

@TechReport{	  zandhuis92a,
  author	= {J. A. Zandhuis},
  title		= {Storing Sequential Data in Self-Organizing Feature Maps},
  institution	= {Max-Planck-Institut f{\"{u}}r Psycholinguistik},
  year		= {1992},
  type		= {Internal Report},
  number	= {MPI-NL-TG-4/92},
  address	= {Nijmegen, Netherlands},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zaremba00a,
  author	= {Zaremba, M. and Niemann, O. and St-Laurent, L. and
		  Richardson, D.},
  title		= {Integration of self-organizing maps with spatial indexing
		  for efficient processing of multi-dimensional data},
  booktitle	= {Proceedings of the ACM Workshop on Advances in Geographic
		  Information Systems},
  year		= {2000},
  editor	= {Li, K. and Makki, K. and Pissinou, N. and Ravada, S.},
  volume	= {},
  pages		= {77--82},
  organization	= {Departement d'informatique, Universite du Quebec},
  publisher	= {},
  address	= {},
  abstract	= {This paper investigates the integration of a class of
		  adaptive soft-computing techniques and architectures with
		  helical hyperspatial codes (HHCode)---indexing technology
		  developed at Canadian Hydrographic Services---and their use
		  in developing automated systems for processing of complex,
		  multi-dimensional geo-spatial information, mainly
		  multi-spectral satellite imagery, in a broader context of
		  knowledge extraction and representation. The soft-computing
		  methods investigated here involve fusion of techniques used
		  in self-organizing maps (SOM---a class of unsupervised
		  neural networks) and fuzzy logic. The topological
		  relationships between the features---automatically
		  extracted by SOM from multi-spectral images---are formed
		  into a neural network in a meaningful order. The ordered
		  features can later be interpreted and labeled according to
		  the specific requirements of the application. Two
		  SOM/HHCode integration architectures are proposed and
		  discussed in the paper: closely-coupled integration where
		  HHCode queries control the size of the neural network---in
		  other words, the generalization level; and an architecture
		  where HHCode is used to encode the results of clustering by
		  SOM as well as the topological ordering of heterogeneous
		  data encoded in the network. Results of tests performed on
		  multi-spectral 20-m SPOT satellite images are given.},
  dbinsdate	= {2002/1}
}

@MastersThesis{	  zavrel95a,
  author	= {Jakub Zavrel},
  title		= {Neural Information Retrieval---An Experimental Study of
		  Clustering and Browsing of Document Collections with Neural
		  Networks},
  school	= {University of Amsterdam, Amsterdam, Netherlands},
  year		= {1995},
  dbinsdate	= {oldtimer}
}

@Article{	  zavrel96a,
  author	= {J. Zavrel},
  title		= {Neural navigation interfaces for information retrieval:
		  are they more than an appealing idea?},
  journal	= {Artificial Intelligence Review},
  year		= {1996},
  volume	= {10},
  number	= {5--6},
  pages		= {477--504},
  dbinsdate	= {oldtimer}
}

@Article{	  zayas95a,
  author	= {I. Y. Zayas and O. K. Chung and M. Caley},
  title		= {Neural network classification and machine vision for bread
		  crumb grain evaluation},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2597},
  pages		= {292--308},
  annote	= {(Machine Vision Applications, Architectures, and Systems
		  Integration IV Conf. Date: 23--24 Oct. 1995 Conf. Loc:
		  Philadelphia, PA, USA Conf. Sponsor: SPIE)},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zell94a,
  author	= {Andreas Zell and Michael Schmalzl},
  title		= {Dynamic {LVQ}---A Fast Neural Net Learning Algorithm},
  booktitle	= {Proc. ICANN'94, International Conference on Artificial
		  Neural Networks},
  year		= {1994},
  editor	= {Maria Marinaro and Pietro G. Morasso},
  volume	= {II},
  pages		= {1095--1098},
  publisher	= {Springer},
  address	= {London, UK},
  annote	= {application, comparison},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zell94b,
  author	= {Andreas Zell and Harald Bayer and Henri Bauknecht},
  title		= {Similarity Analysis of Molecules with Self-Organizing
		  Surfaces---An Extension of the Self-Organizing Map},
  pages		= {719--724},
  booktitle	= {Proc. ICNN'94, International Conference on Neural
		  Networks},
  year		= {1994},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  annote	= {application, modification},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zell94c,
  author	= {Andreas Zell and Harald Bayer and Henri Bauknecht},
  title		= {{S}elf-{O}rganizing Surfaces and Volumes---An Extension of
		  the {S}elf-{O}rganizing {M}ap},
  booktitle	= {Proc. WCNN'94, World Congress on Neural Networks},
  year		= {1994},
  volume	= {IV},
  pages		= {269--274},
  organization	= {INNS},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {modification, variable topology},
  dbinsdate	= {oldtimer}
}

@InCollection{	  zeller95a,
  author	= {M. Zeller and K. R. Wallace and K. Schulten},
  title		= {Biological visuo-motor control of a pneumatic robot arm},
  booktitle	= {Intelligent Engineering Systems Through Artificial Neural
		  Networks. Vol. 5. Fuzzy Logic and Evolutionary Programming.
		  Proceedings of the Artificial Neural Networks in
		  Engineering (ANNIE'95)},
  publisher	= {ASME Press},
  year		= {1995},
  editor	= {C. H. Dagli and M. Akay and C. L. P. Chen and B. R.
		  Fernandez and J. Ghosh},
  address	= {New York, NY, USA},
  pages		= {645--50},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zemen98a,
  author	= {Zemen, T. and Clabian, M. and Pfutzner, H.},
  title		= {Classification of sleep apnea events by means of radial
		  basis function networks},
  booktitle	= {Proceedings of NC 1998. International ICSC/IFAC Symposium
		  on Neural Computation. ICSC Academic Press, Zurich,
		  Switzerland},
  year		= {1998},
  volume	= {},
  pages		= {351--7},
  abstract	= {Sleep apneas, cessations of breathing during sleep, which
		  lead to shortened life expectation (adults) and indicate
		  SIDS-risks (premature born babies) must be diagnosed
		  reliably. Therefore a monitoring system is established and
		  attempts are made for automatic scoring to reduce clinical
		  efforts. This paper describes the detection and
		  classification of sleep apnea events by means of radial
		  basis function networks (RBFN). A monitoring device, based
		  on electric field plethysmography, which detects
		  respiratory and cardiac activity is presented. In addition,
		  heart rate and blood oxygen saturation are recorded.
		  Pre-processing is performed by several feature extraction
		  algorithms including time dependent Fourier transform,
		  cepstrum transform, linear predictive coding, linear
		  predictive coding cepstrum (LPCC) and wavelet transform.
		  RBFNs are trained with learning vector quantization (LVQ)
		  and self organizing map (SOM). To achieve high
		  classification rates, optimization of number and spread of
		  neurons are performed. Best results with classification
		  rates of 64%+or-3.4% (adults) and 62.6%+or-6.8% (babies) is
		  obtained using LPCC and data reduction through principle
		  components analysis (PCA). The net consisted of 20 hidden
		  neurons and is trained with LVQ. All results are derived
		  using 10-fold cross validation.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  zerr94a,
  author	= {Zerr, B. and Maillard, E. and Gueriot, D. },
  title		= {Sea-floor classification by neural hybrid system},
  booktitle	= {OCEANS 94. Oceans Engineering for Today's Technology and
		  Tomorrow's Preservation. Proceedings},
  year		= {1994},
  volume	= {2},
  pages		= {II/239--43},
  organization	= {STSN/GESMA, Brest Naval, France},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zha95a,
  author	= {Hongbin Zha and Onitsuka, T. and Nagata, T. },
  title		= {Self-organization based visuo-motor coordination for a
		  real camera and manipulator system},
  booktitle	= {1995 IEEE International Conference on Systems, Man and
		  Cybernetics. Intelligent Systems for the 21st Century},
  year		= {1995},
  volume	= {4},
  pages		= {3322--7},
  organization	= {Dept. of Comput. Sci. \& Commun. Eng. , Kyushu Univ. ,
		  Fukuoka, Japan},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InCollection{	  zha96a,
  author	= {H. Zha and T. Onitsuka and T. Nagata},
  title		= {Visual-motor coordination in unstructured environments: a
		  self-organization approach},
  booktitle	= {Proceedings of the Twelfth International Conference on
		  CAD/CAM Robotics and Factories of the Future},
  publisher	= {Middlesex Univ. Press},
  year		= {1996},
  editor	= {R. Gill and C. S. Syan},
  address	= {London, UK},
  pages		= {471--7},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang00a,
  author	= {Bai-Ling Zhang and T. D. Gedeon},
  title		= {A General Hebbian Learning for Nonlinear Neuron with
		  Application to Laterally Interconnected Synergetically
		  Self-organizing Map},
  journal	= {Australian Journal of Intelligent Information Processing
		  Systems},
  year		= {2000},
  key		= {},
  volume	= {6},
  number	= {2},
  pages		= {105--9},
  month		= {},
  note		= {},
  annote	= {},
  dbinsdate	= {2002/1}
}

@Article{	  zhang00b,
  author	= {Zhang, H. X. and Zhang, R. S. and Liu, M. C. and Hu, Z.
		  and Fan, B. T.},
  title		= {Application of self-organizing maps to the visual
		  classification of the carcinogenicity of polycyclic
		  aromatic hydrocarbons},
  journal	= {CHINESE JOURNAL OF ANALYTICAL CHEMISTRY},
  year		= {2000},
  volume	= {28},
  number	= {11},
  month		= {NOV},
  pages		= {1336--1343},
  abstract	= {The self-organizing map (SOM) was used in the visual
		  classification of the carcinogenicity of 77 polycyclic
		  aromatic hydrocarbons (PAHs). They were classified based on
		  the following attributes: the total surface area (TSA) of
		  the molecule, the delocalization energy of the center
		  carbon atom in the metabolic active region (DeltaE(1)), the
		  delocalization energy of the center carbon atom in the
		  electrophilic active region (DeltaE(2)), and the number of
		  de-toxic regions in the molecule (N-d). The classification
		  of SOM in this field is very satisfactory.},
  dbinsdate	= {2002/1}
}

@Article{	  zhang00c,
  author	= {Zhang, Jihong and Li, Xia and Xie, Weixin},
  title		= {Stochastic competitive learning vector quantization
		  algorithm for image coding},
  journal	= {Tien Tzu Hsueh Pao/Acta Electronica Sinica},
  year		= {2000},
  volume	= {28},
  number	= {10},
  month		= {Oct},
  pages		= {23--26},
  organization	= {Shenzhen Univ},
  publisher	= {},
  address	= {},
  abstract	= {We analyze the principles of deterministic annealing
		  technique and competitive learning algorithm for image
		  coding, and present a new stochastic competitive learning
		  vector quantization algorithm for image coding. It combines
		  the procedure of competition with the minimization of cost
		  function. Simulated annealing is used in learning
		  procedure, and several new strategies are presented in the
		  vector quantization for image coding. The algorithm can
		  eliminate the effect of initial codebook selection on the
		  quality of clustering, is not trapped in local minimum, has
		  a good convergence rate, and can get the codebook with good
		  performance. Computer simulation results confirm the
		  effectiveness and robustness of the approach.},
  dbinsdate	= {2002/1}
}

@Article{	  zhang00d,
  author	= {Zhang, Zhaoli and Sun, Shenghe},
  title		= {Image data fusion algorithm based on the one-dimensional
		  self-organizing neural network},
  journal	= {Tien Tzu Hsueh Pao/Acta Electronica Sinica},
  year		= {2000},
  volume	= {28},
  number	= {9},
  month		= {Sep},
  pages		= {74--77},
  organization	= {Harbin Inst of Technology},
  publisher	= {},
  address	= {},
  abstract	= {Multisensor data fusion has played an important role in
		  image processing recently. Traditional image processing is
		  a typical two-dimensional signal processing. The image data
		  fusion belongs to this area as well. For some images from
		  the same scene with different kinds of noise, this paper
		  presents a new kind of image data fusion algorithm based on
		  the one-dimensional self-organizing feature map neural
		  network. This method uses equal gray density figure to
		  determine the number of clusters, and utilizes
		  one-dimensional pixel value to perform two-dimensional
		  image processing. Simulation results demonstrate the
		  effectiveness of the proposed image data fusion method.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  zhang00f,
  author	= {Zhang Rubo and Sun Yu and Wang Xingoe and Yang Guangmin
		  and Gu Guochang},
  title		= {Research on reinforcement learning of the intelligent
		  robot based on self-adaptive quantization},
  booktitle	= {Proceedings of the 3rd World Congress on Intelligent
		  Control and Automation. IEEE, Piscataway, NJ, USA},
  year		= {2000},
  volume	= {2},
  pages		= {1226--9},
  abstract	= {The concept of the reinforcement learning comes from
		  behavior psychology that takes behavior learning as trial
		  and error, by which the states of the environment are
		  mapped into corresponding actions. There is a question of
		  how can the behaviourism be used to learn the actions in
		  interaction with the environment in designing an
		  intelligent robot. In the paper, the actions that the robot
		  takes to avoid obstacles are taken as one class of
		  behaviors and the reinforcement learning is used to realize
		  behavior learning of obstacle avoidance. The quantization
		  of the state space is very important in improving the
		  robot's learning speed. The SOM neural network is adopted
		  to get self-adaptive quantization of the state space. The
		  self-organization characteristic of the SOM neural network
		  makes it possible to solve the adaptation problem and is
		  flexible in space quantization. The reinforcement learning
		  is used to settle the robot learning of collision avoidance
		  behavior based on quantization of the state space and
		  satisfying results are obtained.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  zhang00k,
  author	= {Zhang, Zhaoli and Sun, Shenghe},
  title		= {Image fusion based on the self-organizing feature map
		  neural networks},
  booktitle	= {Proceedings of SPIE---The International Society for
		  Optical Engineering},
  year		= {2000},
  editor	= {},
  volume	= {4052},
  pages		= {270--275},
  organization	= {Harbin Inst of Technology},
  publisher	= {Society of Photo-Optical Instrumentation Engineers},
  address	= {Bellingham, WA},
  abstract	= {This paper presents a new image data fusion scheme by
		  combining median filtering with self-organizing feature map
		  (SOFM) neural networks. The scheme consists of three steps:
		  (1) pre-processing of the images, where weighted median
		  filtering removes part of the noise components corrupting
		  the image, (2) pixel clustering for each image using
		  self-organizing feature map neural networks, and (3) fusion
		  of the images obtained in Step (2), which suppresses the
		  residual noise components and thus further improves the
		  image quality. It proves that such a three-step combination
		  offers an impressive effectiveness and performance
		  improvement, which is confirmed by simulations involving
		  three image sensors (each of which has a different noise
		  structure).},
  dbinsdate	= {2002/1}
}

@Article{	  zhang01a,
  author	= {Zhang, Gao and Yu, Song Yu and Wang, Jin},
  title		= {Design of image vector quantization using evolutionary
		  strategy},
  journal	= {Journal-of-Shanghai-Jiaotong-University},
  year		= {2001},
  volume	= {35},
  pages		= {205--8},
  abstract	= {This paper analyzes the self-organizing feature map (SOFM)
		  algorithm. Based on it, the evolution into vector
		  quantization was introduced. After the SOFM algorithm is
		  used to decrease the expected distortion, the evolutionary
		  strategy is utilized to modulate the subdistortion of each
		  region in order to improve the expected distortion. The
		  experimental results show that the algorithm can well
		  modulate the subdistortion of each region by overcoming the
		  local optimality.},
  dbinsdate	= {2002/1},
  merjanote     = {last name assumed similarily to other paper in same journal}
}

@Article{	  zhang01b,
  author	= {Zhang, Guo-Jiang and Qiu, Jia-Ju and Li, Ji-Hong},
  title		= {Outlier identification and justification based on neural
		  network},
  journal	= {Zhongguo Dianji Gongcheng Xuebao/Proceedings of the
		  Chinese Society of Electrical Engineering},
  year		= {2001},
  volume	= {21},
  number	= {8},
  month		= {August },
  pages		= {104--107+113},
  organization	= {Zhejiang Univ.},
  publisher	= {},
  address	= {},
  abstract	= {A new method is presented to identify outliers in load
		  data by fully utilizing the features of electrical load
		  curves. First, the day load curves are clustered by a
		  Kohonen neural network, and a typical load curve is thus
		  obtained for each cluster. Then a BP neural network is
		  trained with each typical load curve and some other curves
		  derived from it with some outliers included. Owing to its
		  generalization ability, the network can identify the
		  outliers in the curves included in the corresponding
		  cluster. At last, the outliers are adjusted with typical
		  curves. The off-line trained neural networks can be used to
		  identify the outliers on-line. Test results using actual
		  data are served for demonstrating the feasibility of the
		  proposed method.},
  dbinsdate	= {2002/1}
}

@Article{	  zhang01c,
  author	= {Zhang, Z. L. and Sun, S. H.},
  title		= {Image fusion based on the self-organizing feature map
		  neural networks},
  journal	= {CHINESE JOURNAL OF ELECTRONICS},
  year		= {2001},
  volume	= {10},
  number	= {1},
  month		= {JAN},
  pages		= {96--99},
  abstract	= {This paper presents a new image data fusion scheme based
		  on the self-organizing feature map (SOFM) neural networks.
		  The scheme consists of three steps: (1) pre-processing of
		  the images, where weighted median filtering removes part of
		  the noise components corrupting the image, (2) pixel
		  clustering for each image using two-dimensional
		  self-organizing feature map neural networks, and (3) fusion
		  of the images obtained in Step (2) utilizing fuzzy logic,
		  which suppresses the residual noise components and thus
		  further improves the image quality. It proves that such a
		  three-step combination offers an impressive effectiveness
		  and performance improvement, which is confirmed by
		  simulations involving three image sensors (each of which
		  has a different noise structure).},
  dbinsdate	= {2002/1}
}

@Article{	  zhang01d,
  author	= {Zhang, Z. -L. and Sun, S. -H. and Zheng, F. -C.},
  title		= {Image fusion based on median filters and {SOFM} neural
		  networks: A three-step scheme},
  journal	= {Signal Processing},
  year		= {2001},
  volume	= {81},
  number	= {6},
  month		= {June 2001},
  pages		= {1325--1330},
  organization	= {Department of Automatic Test, Harbin Institute of
		  Technology},
  publisher	= {},
  address	= {},
  abstract	= {This paper presents a new image data fusion scheme by
		  combining median filtering with self-organizing feature map
		  (SOFM) neural networks. The scheme consists of three steps:
		  (1) pre-processing of the images, where weighted median
		  filtering removes part of the noise components corrupting
		  the image, (2) pixel clustering for each image using
		  self-organizing feature map neural networks, and (3) fusion
		  of the images obtained in Step (2), which suppresses the
		  residual noise components and thus further improves the
		  image quality. It proves that such a three-step combination
		  offers an impressive effectiveness and performance
		  improvement, which is confirmed by simulations involving
		  three image sensors (each of which has a different noise
		  structure). },
  dbinsdate	= {2002/1}
}

@Article{	  zhang02a,
  author	= {Zhang, Zhi Hua and Zheng, Nan Ning and Zhang, Huai Feng and
		  Yu, Hai Xia},
  title		= {Entropy-constrained generalized learning vector
		  quantization neural network and soft competitive learning
		  algorithm},
  journal	= {Acta-Automatica-Sinica},
  year		= {2002},
  volume	= {28},
  pages		= {244--50},
  abstract	= {According to the generalized learning vector quantization
		  (GLVQ) network and the maximum-entropy principle, an
		  entropy-constrained generalized learning vector
		  quantization (ECGLVQ) neural network is proposed. A
		  learning algorithm of the network, a generalization of the
		  soft-competition scheme (SCS), is derived via the gradient
		  descent method. Because the loss factor and the
		  corresponding scaling function are defined as the same
		  fuzzy membership function, it can overcome the problems for
		  fuzzy algorithms of the GLVQ network. Many important
		  properties of the ECGLVQ network and its soft competitive
		  learning algorithm are given. Thereby, the rule for
		  choosing the Lagrangian multiplier is designed.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  zhang90a,
  author	= {Chen-Xiong Zhang and Dieter A. Mlynski},
  title		= {{VLSI}-placement with a neural network model},
  booktitle	= {Proc. Int. Symp. on Circuits and Systems, New Orleans,
		  Luisiana, May},
  year		= {1990},
  pages		= {475--478},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {},
  annote	= {Application},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang91a,
  author	= {Jun Zhang},
  journal	= {Neural Computation},
  volume	= 3,
  number	= {1},
  year		= {1991},
  pages		= {54--66},
  title		= {Dynamics and Formation of Self-Organizing Maps},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang91b,
  author	= {C. Zhang and D. A. Mlynski},
  title		= {Ein neuer {VLSI}-Plazierungsalgorithmus mit neuronalem
		  Lernmodell},
  journal	= {GME Fachbericht},
  volume	= {8},
  year		= {1991},
  pages		= {297--302},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang91c,
  author	= {C. Zhang and A. Vogt and D. A. Mlynski},
  title		= {Neuronale Plazierungsalgorithmen},
  journal	= {Elektronik},
  year		= {1991},
  volume	= {15},
  pages		= {68--72},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhang91d,
  author	= {Chen-Xiong Zhang and Andreas Vogt and Dieter A. Mlynski},
  title		= {Floorplan design using a hierarchical neural learning
		  algorithm},
  booktitle	= {Proc. Int. Symp. on Circuits and Systems, Singapore},
  year		= {1991},
  pages		= {2060--2063},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {},
  annote	= {Application},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhang91e,
  author	= {Chen-Xiong Zhang and Dieter A. Mlynski},
  title		= {Neural {SOM} totopical mapping for {VLSI} placement
		  optimization},
  booktitle	= {Proc. IJCNN-91, International Joint Conference on Neural
		  Networks, Singapore},
  year		= {1991},
  pages		= {863--868},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  month		= {},
  annote	= {Application},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhang92a,
  author	= {Zhang, B. and Grant, E. },
  title		= {Neural network based competitive learning for control},
  booktitle	= {Proceedings of the Fourth International Conference on
		  Tools with Artificial Intelligence, TAI '92},
  year		= {1992},
  pages		= {236--43},
  organization	= {Singapore Inst. for Stand. \& Ind. Res. , Singapore},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhang93a,
  author	= {Xuegong Zhang and Yanda Li},
  title		= {Self-Organizing Map as a New Method for Clustering and
		  Data Analysis},
  booktitle	= {Proc. IJCNN-93, International Joint Conference on Neural
		  Networks, Nagoya},
  year		= {1993},
  volume	= {III},
  pages		= {2448--2451},
  organization	= {{JNNS}},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  abstract	= {In this paper, we present a new application of
		  self-organizing map as a method of clustering and data
		  analysis. It is called SOM Analysis. It has some advantages
		  over the traditional clustering algorithms in that it suits
		  more general data distributions and needs less a priori
		  knowledge. And it can also be used as a general tool for
		  analyzing high-dimensional data relations. Some
		  experimental results are also given in this paper.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhang93b,
  author	= {Chen-Xiong Zhang},
  title		= {Optimal Traffic Routing Using {S}elf-{O}rganization
		  Principle},
  booktitle	= {Proc. Int. Workshop on Application of Neural Networks to
		  Telecommunications},
  year		= {1993},
  editor	= {Joshua Alspector and Rodney Goodman and Timothy X. Brown},
  pages		= {225--231},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  annote	= {application, optimization},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhang94a,
  author	= {Siyu Zhang and T. S. Sankar},
  title		= {Machine Condition Identification by {SOM} Algorithm},
  booktitle	= {Proc. IMACS Int. Symp. on Signal Processing, Robotics and
		  Neural Networks},
  year		= {1994},
  pages		= {183--186},
  publisher	= {IMACS},
  address	= {Lille, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhang94b,
  author	= {Siyu Zhang},
  title		= {Function estimation for multiple indices trend analysis
		  using \mbox{self-organizing} mapping},
  booktitle	= {ETFA '94. 1994 IEEE Symposium on Emerging Technologies and
		  Factory Automation. (SEIKEN Symposium). Novel Disciplines
		  for the Next Century Proceedings},
  year		= {1994},
  pages		= {160--5},
  organization	= {Dept. of Mech. Eng. , Concordia Univ. , Montreal, Que. ,
		  Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhang95a,
  author	= {Siyu Zhang and R. Ganesan and Yi Sun},
  title		= {A New Self-Organizing Mapping Algorithm for Regression
		  Problems},
  volume	= {I},
  pages		= {747--755},
  booktitle	= {Proc. WCNN'95, World Congress on Neural Networks},
  year		= {1995},
  organization	= {INNS},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhang95b,
  author	= {Jiajun Zhang and Ahmad, M. O. and Lynch, W. E. },
  title		= {Mean-gain-shape vector quantization using
		  counterpropagation networks},
  booktitle	= {1995 Canadian Conference on Electrical and Computer
		  Engineering},
  year		= {1995},
  editor	= {Gagnon, F. },
  volume	= {1},
  pages		= {563--6},
  organization	= {Dept. of Electr. \& Comput. Eng. , Concordia Univ. ,
		  Montreal, Que. , Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang95c,
  author	= {HongJiang Zhang and Yihong Gong and Low, C. Y. and
		  Smoliar, S. W. },
  title		= {Image retrieval based on color features: an evaluation
		  study},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2606},
  pages		= {212--20},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang95d,
  author	= {Yong Zhang and Kun Zhang and Zhijun Han},
  title		= {Detection of tool breakage in turning operations by using
		  neural network},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1995},
  volume	= {2620},
  pages		= {463--7},
  publisher	= {SPIE-Int. Soc. Opt. Eng},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhang95e,
  author	= {Zhang, Q. J. and Fang Wang and Nakhla, M. S. },
  title		= {A high-order temporal neural network for word
		  recognition},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  year		= {1995},
  volume	= {5},
  pages		= {3343--6},
  organization	= {Dept. of Electron. , Carleton Univ. , Ottawa, Ont. ,
		  Canada},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {An important yet challenging task for neural network based
		  speech recognizers is the effective processing of temporal
		  information in speech signals. A high-order fully recurrent
		  neural network is developed to effectively handle the
		  sequential nature of speech signals and to accommodate both
		  temporal and spectral variations. The proposed neural
		  network has 4 layers, namely, the input layer, self
		  organizing map, fully recurrent hidden layer and output
		  layer. The important characteristics of the hidden neurons
		  and the output neurons are their high-order processing
		  feature. A 2-stage unsupervised/supervised training method
		  is developed. The solution from unsupervised training
		  provides a good starting point for supervised training. The
		  proposed neural network and the training method are applied
		  to isolated word recognition using the TI20 data.},
  dbinsdate	= {oldtimer}
}

@InCollection{	  zhang95f,
  author	= {Siyu Zhang and R. Ganesan and T. S. Sankar},
  title		= {Self-organizing neural networks for automated machinery
		  monitoring systems},
  booktitle	= {Computers in Engineering---1995---and Proceedings of the
		  1995 Database Symposium. Presented at the 15th Annual
		  International Computers in Engineering Conference the 9th
		  Annual ASME Engineering Database Symposium},
  publisher	= {ASME},
  year		= {1995},
  editor	= {A. A. Busnaina and R. Rangan},
  address	= {New York, NY, USA},
  pages		= {1001--9},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang96a,
  author	= {S. Zhang and R. Ganesan and G. D. Xistris},
  title		= {Self-organising neural networks for automated machinery
		  monitoring systems},
  journal	= {Mechanical Systems and Signal Processing},
  year		= {1996},
  volume	= {10},
  number	= {5},
  pages		= {517--32},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang96b,
  author	= {Z. P. Zhang and H. F. Chen and S. W. Ye and J. W. Zhao},
  title		= {Comparison of the {BP} training algorithm and {LVQ} neural
		  networks for e, mu, pi identification},
  journal	= {Nuclear Instruments \& Methods in Physics Research,
		  Section A [Accelerators, Spectrometers, Detectors and
		  Associated Equipment]},
  year		= {1996},
  volume	= {379},
  number	= {2},
  pages		= {271--5},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang97a,
  author	= {Chen-Xiong Zhang and Dieter A. Mlynski},
  title		= {Mapping and Hierarchical Self-Organizing Neural Networks
		  for {VLSI} Placement},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  number	= {2},
  volume	= {8},
  pages		= {299--314},
  abstract	= {We have developed mapping and hierarchical self-organizing
		  neural networks for placement of very large scale
		  integrated (VLSI) circuits. In this paper, we introduce
		  MHSO and MHSO2 as two versions of mapping and hierarchical
		  self-organizing network (MHSO) algorithms. By using the
		  MHSO, each module in the placement wins the competition
		  with a probability density function that is defined
		  according to different design styles, e.g., the gate arrays
		  and standard cell circuits. The relation between a
		  placement carrier and movable modules is met by the
		  algorithm's ability to map an input space (somatosensory
		  source) into an output space where the circuit modules are
		  located. MHSO2 is designed for macro cell circuits. In this
		  algorithm, the shape and dimension of each module is
		  simultaneously considered together with the wire length by
		  a hierarchical order. In comparison with other conventional
		  placement approaches, the MHSO algorithms have shown their
		  distinct advantages. The results for benchmark circuits so
		  far obtained are quite comparable to simulated annealing
		  (SA), but the computation time is about eight-ten times
		  faster than with SA.},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang97b,
  author	= {Zhang, Ziping and Chen, Hongfang and Ye, Shuwei and Zhao,
		  Jiawei},
  title		= {Identification of e, mu , pi by neural network in BES},
  journal	= {High Energy Physics and Nuclear Physics},
  year		= {1997},
  volume	= {21},
  number	= {4},
  pages		= {297--303},
  dbinsdate	= {oldtimer}
}

@InCollection{	  zhang97c,
  author	= {Jing Zhang and Shunichiro Oe},
  title		= {Texture image segmentation method by usign pyramid linking
		  and \mbox{self-organizing} neural network},
  booktitle	= {Progress in Connectionsist-Based Information Systems.
		  Proceedings of the 1997 International Conference on Neural
		  Information Processing and Intelligent Information
		  Systems},
  publisher	= {Springer},
  year		= 1997,
  editor	= {Nikola Kasabov and Robert Kozma and Kitty Ko and Robert
		  O'Shea and George Coghill and Tom Gedeon},
  volume	= {2},
  address	= {Singapore},
  pages		= {1191--1194},
  dbinsdate	= {oldtimer}
}

@InCollection{	  zhang97d,
  author	= {Z. Zhang and S. Suthaharan},
  title		= {Neuro-fuzzy control and modeling in an adaptive
		  information visualization system},
  booktitle	= {Proceedings of the 1997 IEEE International Conference on
		  Control Applications},
  publisher	= {IEEE},
  year		= {1997},
  editor	= {T. I. Stein},
  address	= {New York, NY, USA},
  pages		= {91--6},
  dbinsdate	= {oldtimer}
}

@InCollection{	  zhang97e,
  author	= {Zhongwei Zhang and S. Suthaharan},
  title		= {Neural networks in design and implementation of a
		  neuro-fuzzy controller},
  booktitle	= {Proceedings of the Eighth Australian Conference on Neural
		  Networks (ACNN'97)},
  publisher	= {Telstra Res. Lab},
  year		= {1997},
  editor	= {M. Dale and A. Kowalczyk and R. Slaviero and J.
		  Szymanski},
  address	= {Clayton, Vic. , Australia},
  pages		= {124--8},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang99a,
  author	= {Zhang, Bailing and Fu, Minyue and Yan, Hong and Jabri,
		  Marwan A.},
  title		= {Handwritten digit recognition by adaptive-subspace
		  \mbox{self-organizing} map ({ASSOM})},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1999},
  number	= {4},
  volume	= {10},
  pages		= {939--945},
  abstract	= {The adaptive-subspace self-organizing map (ASSOM) proposed
		  by Kohonen is a recent development in self-organizing map
		  (SOM) computation. In this paper, we propose a method to
		  realize ASSOM using a neural learning algorithm in
		  nonlinear autoencoder networks. Our method has the
		  advantage of numerical stability. We have applied our ASSOM
		  model to build a modular classification system for
		  handwritten digit recognition. Ten ASSOM modules are used
		  to capture different features in the ten classes of digits.
		  When a test digit is presented to all the modules, each
		  module provides a reconstructed pattern and the system
		  outputs a class label by comparing the ten reconstruction
		  errors. Our experiments show promising results. For
		  relatively small size modules, the classification accuracy
		  reaches 99.3% on the training set and over 97% on the
		  testing set.},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang99b,
  author	= {Zhang, Zheliang and Lu, Weixue},
  title		= {{ECG} data compression using \mbox{self-organizing}
		  feature map},
  journal	= {Zhongguo Shengwu Yixue Gongcheng Xuebao/Chinese Journal of
		  Biomedical Engineering},
  year		= {1999},
  number	= {1},
  volume	= {18},
  pages		= {97--103},
  abstract	= {Vector quantization (VQ) is an important method for data
		  compression. Now vector quantization using Neural Network
		  methods received much attention in data compression
		  techniques, especially using Kohonen's selforganizing
		  feature map (SOFM) in the design of codebook, whose result
		  were super to the LBG algorithm. After describing the SOFM
		  algorithm and its change forms, a new SOFM algorithm was
		  presented and used to compress ECG data. Experiments showed
		  that this method is better than the traditional SOFM
		  algorithm in ECG data compression, and it could be used for
		  various ECG waveforms. The accuracy of reconstruction could
		  be improved if the number of codeword was increased.},
  dbinsdate	= {oldtimer}
}

@Article{	  zhang99c,
  author	= {Zhang, Jihong and Xie, Weixin},
  title		= {A fast fuzzy vector quantization algorithm for image
		  coding},
  journal	= {Acta Electronica Sinica},
  year		= {1999},
  volume	= {27},
  pages		= {106--8},
  abstract	= {In this paper, we design a new approach of the training
		  vector hypersphere shrinking and codebook learning formula,
		  and present a fast fuzzy vector quantization algorithm
		  (FFVQ) based on the learning vector quantization (LVQ) and
		  fuzzy vector quantization (FVQ) technique. It can reduce
		  the dependence of resulting codebook on the initial
		  codebook selection, is not trapped in local minimum and has
		  low operational complexity. Simulation results show that as
		  the image coding performance of FFVQ is compared with that
		  of FVQ, the training time is reduced more, and the PSNR
		  value is also improved.},
  dbinsdate	= {oldtimer}
}


@InProceedings{	  zhao00a,
  author	= {Jing Zhao and Kulkarni, A. D.},
  title		= {Market segmentation using self-organizing neural
		  networks},
  booktitle	= {Smart Engineering System Design: Neural Networks, Fuzzy
		  Logic, Evolutionary Programming, Data Mining, and Complex
		  Systems. Vol.10. Proceedings of the Artificial Neural
		  Networks in Engineering Conference (ANNIE 2000). ASME, New
		  York, NY, USA},
  year		= {2000},
  volume	= {},
  pages		= {929--34},
  abstract	= {A self-organizing neural network is used for a market
		  segmentation application. Two neural network models are
		  developed. They are: neural network model with Kohonen
		  learning, and a fuzzy-neural network model with Kohonen
		  learning. For the fuzzy-neural network model, we have used
		  two types of membership functions, the triangular and
		  Gaussian. The two models are first tested with a standard
		  data set. Then they are applied for the real data set-the
		  Boston house-price data from Harrison and Rubinfeld (1978).
		  The Boston house data set contains information such as the
		  house prices, quality of air, per capita income, and per
		  capita crime rate from the Boston area. Features in various
		  groups are compared and analyzed.},
  dbinsdate	= {2002/1},
  merjanote	= {Last name is Zhao, checked from internet}
}

@Article{	  zhao02a,
  author	= {Zhao, Xiaowei and Chen, Tianlun},
  title		= {Type of self-organized criticality model based on neural
		  networks},
  journal	= {Physical Review E. Statistical Physics, Plasmas, Fluids,
		  and Related Interdisciplinary Topics},
  year		= {2002},
  volume	= {65},
  number	= {2 II},
  month		= {February },
  pages		= {261141--261146},
  organization	= {Department of Physics, Nankai University},
  publisher	= {},
  address	= {},
  abstract	= {A kind of coupled map lattice system was introduced to
		  investigate self-organized criticality (SOC) in the
		  activity of model neural populations based on the standard
		  self-organizing map neural network model. The system was
		  simulated by a detailed integrate-and-fire mechanism and a
		  kind of local perturbation driving rule. It was found that
		  system's learning process played a promotive rule in the
		  emergence of SOC behavior when the influence of synaptic
		  plasticity was adequately considered.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  zhao91a,
  author	= {Z. Zhao and C. Rowden},
  title		= {Application of {K}ohonen \mbox{self-organising} feature
		  maps to smoothing parameters of hidden {M}arkov models for
		  speech recognition},
  booktitle	= {Second International Conference on Artificial Neural
		  Networks},
  year		= {1991},
  pages		= {175--179},
  organization	= {IEE},
  publisher	= {IEE},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  zhao92a,
  author	= {Z. Zhao and C. G. Rowden},
  title		= {Use of {K}ohonen \mbox{self-organising} feature maps for
		  {HMM} parameter smoothing in speech recognition},
  journal	= {IEE Proc. F [Radar and Signal Processing]},
  year		= {1992},
  volume	= {139},
  number	= {6},
  pages		= {385--390},
  month		= {December},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhao92b,
  author	= {Zuqiang Zhao},
  title		= {Integration of Neural Networks and Hidden {M}arkov Models
		  for Continuous Speech Recognition},
  booktitle	= {Artificial Neural Networks, 2},
  year		= {1992},
  editor	= {I. Aleksander and J. Taylor},
  volume	= {I},
  pages		= {779--782},
  publisher	= {North-Holland},
  address	= {Amsterdam, Netherlands},
  month		= {},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhao92c,
  author	= {Zuqiang Zhao},
  title		= {Weight distance display of {K}ohonen maps},
  booktitle	= {Fifth International Conference. Neural Networks and their
		  Applications. NEURO NIMES 92},
  year		= {1992},
  pages		= {611--20},
  organization	= {Dept. of Phys. \& Electron. Eng. , Keele Univ. , UK},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@Article{	  zhao94a,
  author	= {Zhao, Z. },
  title		= {Improvements to {K}ohonen \mbox{self-organising}
		  algorithm},
  journal	= {Electronics Letters},
  year		= {1994},
  volume	= {30},
  number	= {6},
  pages		= {502--3},
  month		= {March},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zheng00a,
  author	= {Nanning Zheng and Zhihua Zhang and Haibing Zheng and Shi
		  Gang},
  title		= {Deterministic annealing learning of the radial basis
		  function nets for improving the regression ability of {RBF}
		  networks},
  booktitle	= {Proceedings of the IEEE-INNS-ENNS International Joint
		  Conference on Neural Networks. IJCNN 2000. Neural
		  Computing: New Challenges and Perspectives for the New
		  Millennium. IEEE Comput. Soc, Los Alamitos, CA, USA},
  year		= {2000},
  volume	= {3},
  pages		= {601--7},
  abstract	= {The deterministic annealing method for training the center
		  vectors of RBF networks is proposed. The method is a
		  soft-competition scheme and derived from optimizing an
		  objective function using the gradient descent method. To
		  some extent it can overcome the problems that the learning
		  vector quantization algorithms with the winner-take-all
		  scheme and the heuristic procedure have. The emulation
		  experiment is given to validate the algorithm. The
		  experimental results show that, compared to the error
		  backpropagating algorithms of the multi-layer perception
		  and the RBF network, it not only enhances learning
		  precision and generalization ability, but also reduces
		  learning time as well.},
  dbinsdate	= {2002/1},
  merjanote     = {last name checked from internet}
}

@Article{	  zheng96a,
  author	= {Yi Zheng and J. F. Greenleaf},
  title		= {The effect of concave and convex weight adjustments on
		  \mbox{self-organizing} maps},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1996},
  volume	= {7},
  number	= {1},
  pages		= {87--96},
  dbinsdate	= {oldtimer}
}

@Article{	  zheng97a,
  author	= {Y. Zheng and J. F. Greenleaf and J. J. Gisvold},
  title		= {Reduction of breast biopsies with a modified
		  \mbox{self-organizing} map},
  journal	= {IEEE Transactions on Neural Networks},
  year		= {1997},
  volume	= {8},
  number	= {6},
  pages		= {1386--96},
  abstract	= {A modified self-organizing map with nonlinear weight
		  adjustments has been applied to reduce the number of breast
		  biopsies necessary for breast cancer diagnosis. Tissue
		  features representing texture information from digital
		  sonographic breast images were extracted from sonograms of
		  benign and malignant breast tumors. The resulting
		  hyperspace of data points was then used in a modified
		  self-organizing map that objectively segments population
		  distributions of lesions and accurately establishes benign
		  and malignant regions. These methods were applied to a
		  group of 102 problematic breast cases with sonographic
		  images, including 34 with malignant lesions. All lesions
		  were substantiated by excisional biopsy. The system can
		  isolate clusters of purely benign lesions from other
		  clusters containing both benign and malignant lesions. The
		  hybrid neural network defined a region in which about 60%
		  of the benign lesions were located exclusive of any
		  malignant lesions. Using a hybrid approach and
		  leave-one-out method of data evaluation, we estimate that
		  the number of biopsies in this group of women could be
		  decreased by 40--59% with high confidence and that no
		  malignancies were included in the nonbiopsied group. The
		  experimental results also suggest that the modified
		  self-organizing map provides more accurate population
		  distribution maps than conventional Kohonen maps.},
  dbinsdate	= {oldtimer}
}

@Article{	  zheng97b,
  author	= {Zheng, Xin Zhi and Ito, Koji},
  title		= {Self-organized learning and its implementation of robot
		  movements},
  journal	= {Proceedings of IEEE International Conference on Systems,
		  Man, and Cybernetics.},
  publisher	= {IEEE Service Center},
  address	= {Piscataway, NJ},
  year		= {1997},
  number	= {},
  volume	= {1},
  pages		= {281--286},
  abstract	= {The self-organizing map algorithm using artificial neural
		  network originally developed by Kohonen and extended and
		  modified later provides a distributed and autonomous
		  learning procedure in engineering modeling of human
		  sensory-motor mapping mechanism. Its extension and
		  adaptation to control problem of robot manipulator is
		  intensively discussed in past years. In this presentation
		  the application of the self-organizing map algorithm to the
		  generation of visuo-motor map is focused. Task-oriented
		  inverse kinematic solution to redundant manipulator is
		  formed and real-time implementation of the map on a
		  mechanical manipulator is performed.},
  dbinsdate	= {oldtimer}
}

@Article{	  zhengkai94a,
  author	= {Liu Zhengkai and Li Baoxin},
  title		= {An improvement on {K}ohonen's \mbox{self-organizing}
		  model},
  journal	= {Chinese Journal of Automation},
  year		= {1994},
  volume	= {6},
  number	= {3},
  pages		= {173--5},
  dbinsdate	= {oldtimer}
}

@Article{	  zhernakov01a,
  author	= {Zhernakov, S. V.},
  title		= {Active expert systems for complex diagnosis and control of
		  the hydromechanical devices of gas-turbine engines},
  journal	= {Avtomatizatsiya-i-Sovremennye-Tekhnologii. no.9; 2001;
		  p.20--4},
  year		= {2001},
  volume	= {},
  pages		= {20--4},
  abstract	= {A neural net (NN) approach was used for the diagnosis and
		  control of the parameters of gas-turbine engine's
		  hydromechanical devices, using direct-acting valve (DAV) as
		  an example. The hybrid neuronet ensemble consisting of a
		  radial-elemental network at the input, a perceptron as
		  intermediate layer, and a Kohonen network at the output was
		  considered the most suitable for recognising 3 the most
		  typical DAV failures, i.e., change of valve sensitivity,
		  throttle clogging, and seal failure. The advantage of
		  hybrid neuronet ensembles over common fully connected ones
		  is proved, as regards stability, the velocity of learning,
		  and the quality of failure recognition. Simulation results
		  demonstrated that though the teaching of a combined NN
		  takes more time than that of a common one, its efficiency
		  at solving the tasks of valve condition diagnosis and
		  prognosis is much higher than that of the common NN.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  zhi01a,
  author	= {Zhi Qiang Liu},
  title		= {Retrieving faces using adaptive subspace self-organising
		  map},
  booktitle	= {Proceedings of 2001 International Symposium on Intelligent
		  Multimedia, Video and Speech Processing. ISIMP 2001. IEEE,
		  Piscataway, NJ, USA},
  year		= {2001},
  volume	= {},
  pages		= {377--80},
  abstract	= {We present the adaptive manifold self-organising map
		  (AMSOM) for a face retrieval system. Our experimental
		  results show that it has an excellent potential for face
		  retrieval applications. As compared to the more traditional
		  sub-space self-organising map, the results in many cases
		  are better.},
  dbinsdate	= {2002/1}
}

@Article{	  zhou93a,
  author	= {Lijia Zhou and Franklin, S. },
  title		= {{ANN-TREE}: a hybrid method for pattern recognition},
  journal	= {Proceedings of the SPIE---The International Society for
		  Optical Engineering},
  year		= {1993},
  volume	= {1965},
  pages		= {358--63},
  annote	= {A conference paper in journal},
  dbinsdate	= {oldtimer}
}

@Article{	  zhou96a,
  author	= {R. W. Zhou and C. Quek},
  title		= {{POPFNN}: a pseudo outer-product based fuzzy neural
		  network},
  journal	= {Neural Networks},
  year		= {1996},
  volume	= {9},
  number	= {9},
  pages		= {1569--81},
  dbinsdate	= {oldtimer}
}

@Article{	  zhu00a,
  author	= {Zhu, Bin and Ramsey, Marshall and Chen, Hsinchun},
  title		= {Creating a large-scale content-based airphoto image
		  digital library},
  journal	= {IEEE Transactions on Image Processing},
  year		= {2000},
  number	= {1},
  volume	= {9},
  pages		= {163--167},
  abstract	= {This paper describes a content-based image retrieval
		  digital library that supports geographical image retrieval
		  over a testbed of 800 aerial photographs, each 25 megabytes
		  in size. In addition, this paper also introduces a
		  methodology to evaluate the performance of the algorithms
		  in the prototype system. The major contributions of this
		  paper are two. 1) We suggest an approach that incorporates
		  various image processing techniques including Gabor
		  filters, image enhancement, and image compression, as well
		  as information analysis technique such as self-organizing
		  map (SOM) into an effective large-scale geographical image
		  retrieval system. 2) We present two experiments that
		  evaluate the performance of the Gabor-filter-extracted
		  features along with the corresponding similarity measure
		  against that of human perception, addressing the lack of
		  studies in assessing the consistency between an image
		  representation algorithm or an image categorization},
  dbinsdate	= {oldtimer}
}

@Article{	  zhu00b,
  author	= {Zhu, Shao Hua and Wang, Fang},
  title		= {Application of {GA} based on fuzzy neural network to the
		  identification of dynamic systems},
  journal	= {Electric-Machines-and-Control},
  year		= {2000},
  volume	= {4},
  pages		= {171--4},
  abstract	= {A method for linguistic modeling based on fuzzy logic,
		  neural networks and genetic algorithms is introduced in
		  this paper, and a new hybrid learning algorithm is
		  proposed. Firstly, the initial membership functions of the
		  fuzzy neural network are found by using the
		  self-organization feature map algorithm; then the maximum
		  matching-factor algorithm is used to determine the fuzzy
		  rules; finally, a modified genetic algorithm is presented
		  and used for optimized tuning of the membership functions
		  obtained. A simulation example demonstrates the efficiency
		  of the proposed scheme.},
  dbinsdate	= {2002/1},
  merjanote     = {last name assumed similarily to other chinese papers (last name first)}
}

@InProceedings{	  zhu00c,
  author	= {Zhu, C. and Po, L. M and Hua, Y.},
  title		= {Optimised feature map finite-state vector quantisation for
		  image coding},
  booktitle	= {IEE-Proceedings-Vision,-Image-and-Signal-Processing.
		  vol.147, no.3},
  year		= {2000},
  volume	= {147},
  pages		= {266--70},
  abstract	= {An optimised feature map finite-state vector quantisation
		  (referred to as optimised FMFSVQ) is presented for image
		  coding. Based on the block-based gradient descent search
		  algorithm used for motion estimation in video coding, the
		  optimised FMFSVQ system finds a neighbourhood-based optimal
		  codevector for each input vector by extending the
		  associated state codebook stage by stage, thus rendering
		  each state quantiser a variable rate vector quantisation.
		  The optimised FMFSVQ system can be interpreted as a cascade
		  of a finite-state vector quantiser and classified vector
		  quantisers. Furthermore, an adaptive optimised FMFSVQ is
		  obtained. Experiments demonstrate the superior
		  rate-distortion performance of the adaptive optimised
		  FMFSVQ compared with the original adaptive FMFSVQ and the
		  memoryless vector quantisation.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  zhu93a,
  author	= {Ce Zhu and Lihua Li and Cuntai Guan and Zhenya He},
  title		= {A Study of {LVQ}-Based Architectures for Robust Speech
		  Recognition},
  booktitle	= {Proc. WCNN'93, World Congress on Neural Networks},
  year		= {1993},
  volume	= {IV},
  pages		= {177--180},
  organization	= {{INNS}},
  publisher	= {Lawrence Erlbaum},
  address	= {Hillsdale, NJ},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhu94a,
  author	= {Zhu, Bin and Zhu, Yisheng},
  title		= {Speaker classification based on combined neural network
		  and fuzzy decision},
  booktitle	= {Proceedings of the 16th Annual International Conference of
		  the IEEE Engineering in Medicine and Biology Society.
		  Engineering Advances: New Opportunities for Biomedical
		  Engineers},
  year		= {1994},
  editor	= {Sheppard, N. F. , Jr. and Eden, M. and Kantor, G. },
  volume	= {2},
  pages		= {1123},
  organization	= {Dept. of Electron. Eng. , Univ. of Sci. \& Technol. of
		  China, Hefei, China},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhu94b,
  author	= {Zhu, Daming and Ma, Shaohan and Qiu, Hongze},
  title		= {Analysis of the convergency of topology preserving neural
		  networks on learning},
  booktitle	= {Algorithms and Computation. 5th International Symposium,
		  ISAAC '94 Proceedings},
  year		= {1994},
  editor	= {Du, D. -Z. and Zhang, X. -S. },
  pages		= {128--36},
  organization	= {Dept. of Comput. Sci. , Shandong Univ. , China},
  publisher	= {Springer-Verlag},
  address	= {Berlin, Germany},
  dbinsdate	= {oldtimer}
}

@InCollection{	  zhu95c,
  author	= {Ce Zhu and Jun Wang and Taijun Wang},
  title		= {Analysis of learning vector quantization algorithms for
		  pattern classification},
  booktitle	= {1995 International Conference on Acoustics, Speech, and
		  Signal Processing. Conference Proceedings},
  publisher	= {IEEE},
  year		= {1995},
  volume	= {5},
  address	= {New York, NY, USA},
  pages		= {3471--4},
  abstract	= {Although the family of LVQ algorithms have been widely
		  used for pattern classification and have achieved a great
		  success, the rigirous theoretical studies on the
		  classification performance of LVQ algorithms have seldom
		  been made. In this paper, the asymptotical performance of
		  LVQ1, LVQ2 and LVQ2.1 algorithms have been studied
		  thoroughly, and three significant conclusions have been
		  achieved respectively. Furthermore, a simple modification
		  scheme to LVQ2 algorithm has been developed and analyzed on
		  the asymptotical performance, which can produce the optimal
		  or nearly-optimal classifier in the stable equilibrium
		  state for the classification problems with classes
		  overlapping.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zhu97a,
  author	= {Ce Zhu and Yingbo Hua and Lai Man Po},
  title		= {Optimized feature map finite-state vector quantization for
		  image coding},
  booktitle	= {WoSPA. Second Australian Workshop on Signal Processing
		  Applications'97. Proceedings. Queensland Univ. Technol,
		  Brisbane, Qld., Australia},
  year		= {1997},
  volume	= {},
  pages		= {55--8},
  abstract	= {Optimized feature map finite-state vector quantization
		  (OFMFSVQ) is developed in this paper by further exploiting
		  the topological ordering of the self-organizing feature map
		  (SOFM). It aims at finding a SOFM neighborhood-based
		  suboptimal codevector for each input vector with the
		  minimum bit rate. Based on the neighborhood-based gradient
		  searching algorithm, the OFMFSVQ system achieves a good
		  rate-distortion trade-off. Generating variable levels of
		  state codebooks in the process of coding input vectors,
		  this OFMFSVQ differs from the other ad hoc developed
		  variable rate FSVQs due to its distinctive feature that the
		  coding rates for different input vectors in a same state
		  may also be variable. An adaptive OFMFSVQ scheme is
		  obtained to achieve a better performance in image coding.
		  The comparative experiment demonstrates the superior
		  performance of OFMFSVQ.},
  dbinsdate	= {oldtimer}
}

@Article{	  zhuang01a,
  author	= {Zhuang, H. L. and Chiu, M. S.},
  title		= {An extended self-organizing map network for modeling and
		  control of pulse jet fabric filters},
  journal	= {JOURNAL OF THE AIR \& WASTE MANAGEMENT ASSOCIATION},
  year		= {2001},
  volume	= {51},
  number	= {7},
  month		= {JUL},
  pages		= {1035--1042},
  abstract	= {Pulse jet fabric filters (PJFFs) have become an attractive
		  option of particulate collection utilities, because they
		  can meet stringent particulate emission limits regardless
		  of variation in operating conditions. Despite their wide
		  applications, the present control algorithm for PJFFs can
		  best be described as rudimentary In this paper, a modeling
		  and control strategy based on the local model network (LMN)
		  is proposed. An extended self-organizing map (ESOM) network
		  is developed to construct the LMN model of the filtration
		  process using the filter's input-output data. Subsequently,
		  these ESOM local models are incorporated into the design of
		  local generalized predictive controllers (GPC), and the
		  proposed controller design is obtained as the weighted sum
		  of these local controllers. Simulation results show that
		  the proposed controller design yields a better performance
		  than both conventional GPC and proportional plus integral
		  (PI) controllers yield.},
  dbinsdate	= {2002/1}
}

@InProceedings{	  zhuang92a,
  author	= {X. Zhuang and Y. Huang},
  title		= {Optimal learning for {H}opfield associative memory},
  booktitle	= {Proc. 11th IAPR International Conference on Pattern
		  Recognition. Vol. II. Conf. B: Pattern Recognition
		  Methodology and Systems},
  year		= {1992},
  pages		= {397--400},
  organization	= {Int. Assoc. Pattern Recognition},
  publisher	= {IEEE Computer Society Press},
  address	= {Los Alamitos, CA},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zia94a,
  author	= {Zia, F. and Isik, C. },
  title		= {Neuro-fuzzy control using \mbox{self-organizing} neural
		  nets},
  booktitle	= {Proceedings of the Third IEEE Conference on Fuzzy Systems.
		  IEEE World Congress on Computational Intelligence},
  year		= {1994},
  volume	= {1},
  pages		= {70--5},
  organization	= {Dept. of Electr. \& Comput. Eng. , Syracuse Univ. , NY,
		  USA},
  publisher	= {IEEE},
  address	= {New York, NY, USA},
  abstract	= {This paper discusses a new approach to design a fuzzy
		  logic control system, based on the self-organizing map
		  (SOM) neural network. SOM is used to generate multivariate
		  fuzzy state space from system's input-output data through
		  unsupervised training. The trained SOM is then used as a
		  part of an inference mechanism for a fuzzy logic
		  controller. The proposed method is compared with other
		  fuzzy/NN approaches. Sample data from a chemical plant is
		  used to demonstrate the technique.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zimmer94a,
  author	= {Uwe R. Zimmer and Cornelia Fischer and Ewald {von
		  Puttkamer}},
  title		= {Navigation on Topologic Feature-Maps},
  pages		= {131--132},
  booktitle	= {Proc. 3rd International Conference on Fuzzy Logic, Neural
		  Nets and Soft Computing},
  year		= {1994},
  publisher	= {Fuzzy Logic Systems Institute},
  address	= {Iizuka, Japan},
  annote	= {application, navigation},
  dbinsdate	= {oldtimer}
}

@Article{	  zochowski99a,
  author	= {Zochowski, M. and Liebovitch, L.~S.},
  title		= {Self-Organizing Dynamics of Coupled Map Systems},
  journal	= {Physical Review},
  year		= {1999},
  volume	= {59},
  number	= {3},
  pages		= {2830--2837},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zrehen92a,
  author	= {Zrehen, S. and Blayo, F. },
  title		= {A geometric organization measure for {K}ohonen's map},
  booktitle	= {Fifth International Conference. Neural Networks and their
		  Applications. NEURO NIMES 92},
  year		= {1992},
  pages		= {603--10},
  organization	= {EPFL-DI/Lab. de Microinf. , Lausanne, Switzerland},
  publisher	= {EC2},
  address	= {Nanterre, France},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zrehen93a,
  author	= {St{\'{e}}phane Zrehen},
  title		= {Analyzing {K}ohonen Maps with Geometry},
  booktitle	= {Proc. ICANN'93, International Conference on Artificial
		  Neural Networks},
  year		= {1993},
  editor	= {Stan Gielen and Bert Kappen},
  pages		= {609--612},
  publisher	= {Springer},
  address	= {London, UK},
  dbinsdate	= {oldtimer}
}

@Article{	  zulkifli99a,
  author	= {Zulkifli, A. H. and Meeran, S.},
  title		= {Decomposition of interacting features using a {K}ohonen
		  \mbox{self-organizing} feature map neural network},
  journal	= {Engineering Applications of Artificial Intelligence},
  year		= {1999},
  number	= {1},
  volume	= {12},
  pages		= {59--78},
  abstract	= {A simple and robust system where interacting features are
		  decomposed into simple primitive features prior to
		  recognition is presented. The system starts by searching a
		  B-rep solid model, using a layering technique, for volumes
		  corresponding to interacting features. The interacting
		  features considered are of the type that has a uniform
		  thickness and a common bottom face, referred to as the
		  `to-be decomposed' type. The volume of an interacting
		  feature is then represented in a simple 2D framework as the
		  resultant area. The vertices of the resultant area are then
		  clustered using a Kohonen self-organizing feature map
		  neural network to generate maximal rectangular regions.},
  dbinsdate	= {oldtimer}
}

@InProceedings{	  zulkifli99b,
  author	= {Zulkifli, A. H. and Meeran, S.},
  title		= {Decomposition and recognition of non-orthogonal
		  interacting features using an {SOFM} neural network},
  booktitle	= {7th International Conference in Central Europe on Computer
		  Graphics, Visualization and Interactive Digital Media'99.
		  in co-operation with EUROGRAPHICS and IFIP WG 5.10.
		  WSCG'99. Conference Proceedings. Univ. West Bohemia, Plzen,
		  Czech Republic},
  year		= {1999},
  volume	= {1},
  pages		= {305--12},
  abstract	= {Feature recognition is a process by which the data
		  structure of a solid modeller is searched for geometric and
		  topological information that corresponds to a predefined
		  set of features. Recognition of interacting features has
		  been a difficult task in many existing feature-recognition
		  systems. The unique topological patterns of isolated
		  features change drastically when they interact. Hence many
		  surface-based methods encounter problems in accommodating
		  these changes in their generic feature definitions.
		  Recently, much effort has been concentrated on the
		  volumetric approach. However, many of these systems suffer
		  from a problem of combinatorial explosion as the
		  interaction between features becomes more complex. This
		  paper presents a simple and robust system, in which the
		  non-orthogonal interacting features are decomposed into
		  non-orthogonal regions using a Kohonen self-organizing
		  feature map (SOFM) neural network. The feature patterns in
		  these non-orthogonal regions are then used as input in a
		  multilayer feedforward neural network to recognize the
		  features. Self-organization, competitive learning and the
		  clustering of data are some of the SOFM's attributes,
		  exploited to deal with interacting features.},
  dbinsdate	= {oldtimer}
}

@Article{	  zulkifli99c,
  author	= {Zulkifli, A. H. and Meeran, S.},
  title		= {Recognizing interacting features using a {SOFM} neural
		  network},
  journal	= {Advanced Manufacturing Processes, Systems, and
		  Technologies (AMPST 99)},
  year		= {1999},
  publisher	= {Prof. Eng. Publishing},
  address	= {Bury St. Edmunds, UK},
  volume	= {},
  pages		= {267--76},
  abstract	= {Recognition of interacting features has been a difficult
		  task in many existing feature-recognition systems. The
		  unique topological patterns of isolated features change
		  drastically when they interact. Hence many surface-based
		  methods encounter problems in accommodating these changes
		  in their generic feature definitions. Much effort has been
		  concentrated on the volumetric approach. However, many of
		  these systems suffer from a problem of combinatorial
		  explosion as the interaction between features becomes more
		  complex. The paper presents a simple and robust system, in
		  which the interacting features are decomposed into
		  primitive regions using a Kohonen self-organizing feature
		  map (SOFM) neural network. The feature patterns in these
		  primitive regions are then used as input in a multilayer
		  feedforward neural network to recognize the features.
		  Self-organization, competitive learning and the clustering
		  of data are some of the SOFM's attributes, exploited in the
		  work to deal with interacting features.},
  dbinsdate	= {oldtimer}
}

@Article{	  zupan97a,
  author	= {Zupan,J. and Novic,M. and Ruisanchez,I. },
  title		= {{K}ohonen and Counterpropagation Artificial Neural
		  Networks in Analytical Chemistry},
  journal	= {Chemometrics and Intelligent Laboratory Systems},
  year		= {1997},
  pages		= {1--23},
  volume	= {38},
  dbinsdate	= {oldtimer}
}

@Article{	  zupan97b,
  author	= {Zupan, J. },
  title		= {Areas Where Error Backpropagation and {K}ohonen Networks
		  Touch},
  journal	= {Abstr. Pap. Amer. Chem. Soc. },
  year		= {1997},
  pages		= {27--29},
  volume	= {214},
  dbinsdate	= {oldtimer}
}

@Article{	  zuzan97a,
  author	= {H. Zuzan and J. A. Holbrook and P. T. Kim and G. Harauz},
  title		= {Coordinate-free \mbox{self-organising} feature maps
		  [biological macromolecules]},
  journal	= {Ultramicroscopy},
  year		= {1997},
  volume	= {68},
  number	= {3},
  pages		= {201--14},
  dbinsdate	= {oldtimer}
}
@Book{russell05amklcproceedings_bibuniq_14,
  editor =       "Ann Russell and Timo Honkela and Krista Lagus and Matti P{\"{o}}ll{\"{a}}",
  title =        "Proceedings of {AMKLC'05}, International Symposium on Adaptive Models of Knowledge, Language and Cognition",
  publisher =    "Helsinki University of Technology",
  year =         "2005",
  address =      "Espoo, Finland",
  month =        "June",
}

@Book{honkela05akrrproceedings_bibuniq_15,
  editor =       "Timo Honkela and Ville K{\"{o}}n{\"{o}}nen and Matti P{\"{o}}ll{\"{a}} and Olli Simula",
  title =        "Proceedings of {AKRR'05}, International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning",
  publisher =    "Helsinki University of Technology",
  year =         "2005",
  address =      "Espoo, Finland",
  month =        "June",
}

@Book{ovaska_bibuniq_4112,
  editor =       "Seppo Ovaska",
  title =        "Computationally Intelligent Hybrid Systems: the Fusion of Soft Computing and Hard Computing",
  publisher =    "Wiley",
  year =         "2004",
}

@Misc{webform_555_bibuniq_4184,
  author =       "{Merja Oja, Janne Nikkilä, Petri Törönen, Eero Castrén} and Samuel Kaski",
  editor =       "Pekka Ala-Siuru and Samuel Kaski",
  title =        "Learning metrics for visualizing gene functional similarities",
  howpublished = "{STeP} 2002 - Intelligence, the Art of Natural and Artificial. the 10th {F}innish Artificial Intelligence Conference, Oulu, Finland 15-17 Dec. 2002",
  pages =        "31--40",
  address =      "Oulu, Finland";
  note =         "Editors: Pekka Ala-Siuru and Samuel Kaski",
  year =         "2002",
}

@Misc{webform_649_bibuniq_4185,
  author =       " {Merja Oja, Janne Nikkilä, Petri Törönen, Garry Wong, Eero Castrén} and Samuel Kaski",
  title =        "Exploratory clustering of gene expression profiles of mutated yeast strains",
  howpublished = "Computational and Statistical Approaches To Genomics",
  pages =        "65--78",
  note =         "Editors: Wei Zhang and Ilya Shmulevich,",
  year =         "2002",
}

@Article{webform_744_bibuniq_4186,
  author =       " {Merja Oja, Samuel Kaski} and Teuvo Kohonen",
  title =        "Bibliography of Self-Organizing Map ({SOM}) Papers: 1998-2001 Addendum",
  journal =      "Neural Computing Surveys",
  volume =       "3",
  number =       "1",
  pages =        "1--156",
  year =         "2003",
}

@Article{rabow02a_bibuniq_485,
  author =       "A. A. Rabow and R. H. Shoemaker and E. A. Sausville and D. G. Covell",
  title =        "Mining the National Cancer Institute's tumor-screening database: Identification of compounds with similar cellular activities",
  journal =      "Journal of Medicinal Chemistry",
  year =         "2002",
  volume =       "45",
  number =       "4",
  month =        "February",
  pages =        "818--840",
}

@Article{ahmadi04a_bibuniq_4517,
  author =       "A. Ahmadi and S. Omatu and T. Fujinaka and T. Kosaka",
  title =        "Improvement of reliability in banknote classification using reject option and local {PCA}",
  journal =      "Information Sciences",
  year =         "2004",
  volume =       "168",
  number =       "1-4",
  month =        "December",
  pages =        "277--293",
  abstract =     "",
}

@Article{Ahmadi03a_bibuniq_1576,
  author =       "A. Ahmadi and S. Omatu and T. Kosaka",
  title =        "A reliable classification method for paper currency based on the non-linear {PCA}",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan",
  year =         "2003",
  volume =       "123-C",
  number =       "10",
  month =        "October",
  pages =        "1783--9",
  abstract =     "This paper addresses the reliability of neuro-classifiers for paper currency recognition. A local principal component analysis ({PCA}) method is applied to remove non-linear dependencies among variables and extract the main principal features of data. At first the data space is partitioned into regions by using a self organizing map ({SOM}) clustering and then the {PCA} is performed on each region. A learning vector quantization ({LVQ}) network is employed as the main classifier of the system. By defining a new algorithm for rating the reliability and using a set of test data, we estimate the reliability of the system. the experimental results taken from 1, 200 samples of US dollar bills show that the reliability is increased up to 100\% when the number of regions as well as number of codebook vectors in the {LVQ} classifier are taken properly.",
}

@Article{arab04a_bibuniq_164,
  author =       "A. Arab and S. Lek and A. Lounaci and Y. S. Park",
  title =        "Spatial and temporal patterns of benthic invertebrate communities in an intermittent river ({N}orth {A}frica)",
  journal =      "International Journal of Limnology",
  year =         "2004",
  volume =       "62",
  month =        "December",
  pages =        "267--292",
}

@Article{baraldi02a_bibuniq_469,
  author =       "A. Baraldi and E. Alpaydin",
  title =        "Constructive feedforward {ART} clustering networks - Part {II}",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2002",
  volume =       "13",
  number =       "3",
  month =        "May",
  pages =        "662--677",
}

@Article{jorgensen04a_bibuniq_195,
  author =       "A. C. Jorgensen and J. Rantanen and P. Luukkonen and S. Laine and J. Yliruusi",
  title =        "Visualization of a pharmaceutical unit operation: Wet granulation",
  journal =      "Analytical Chemistry",
  year =         "2004",
  volume =       "76",
  number =       "18",
  month =        "September",
  pages =        "5331--5338",
}

@InProceedings{ceguerra02a_bibuniq_4962,
  author =       "A. Ceguerra and I. Koprinska",
  title =        "Automatic fingerprint verification using neural networks",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "1281--1286",
  abstract =     "",
}

@InProceedings{Datta00a_bibuniq_3954,
  author =       "A. Datta and S. Pal",
  title =        "Computing convex-layers by a multi-layer self-organizing neural network",
  booktitle =    "Neural-Information-Processing. 11th International Conference, {ICONIP}-2004. Proceedings Lecture Notes in Computer Science",
  volume =       "3316",
  pages =        "647--52",
  year =         "2004",
}

@Article{de02a_bibuniq_471,
  author =       "A. De and N. Chatterjee",
  title =        "Recognition of impulse fault patterns in transformers using {K}ohonen's self-organizing feature map",
  journal =      "{IEEE} Transactions on Power Delivery",
  year =         "2002",
  volume =       "17",
  number =       "2",
  month =        "April",
  pages =        "489--494",
}

@Article{dragomir04a_bibuniq_134,
  author =       "A. Dragomir and S. Mavroudi and A. Bezerianos",
  title =        "{SOM}-based class discovery exploring the {ICA}-reduced features of microarray expression profiles",
  journal =      "Comparative and Functional Genomics",
  year =         "2004",
  volume =       "5",
  number =       "8",
  month =        "December",
  pages =        "596--616",
}

@Article{georgakis04a_bibuniq_257,
  author =       "A. Georgakis and C. Kotropoulos and A. Xafopoulos and I. Pitas",
  title =        "Marginal median {SOM} for document organization and retrieval",
  journal =      "Neural Networks",
  year =         "2004",
  volume =       "17",
  number =       "3",
  month =        "April",
  pages =        "365--377",
}

@InProceedings{georgakis02a_bibuniq_4959,
  author =       "A. Georgakis and C. Kotropoulos and I. Pitas",
  title =        "A {SOM} variant based on the Wilcoxon test for document organization and retrieval",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "993--998",
  abstract =     "",
}

@Article{givehchi04a_bibuniq_238,
  author =       "A. Givehchi and G. Schneider",
  title =        "Impact of descriptor vector scaling on the classification of drugs and nondrugs with artificial neural networks",
  journal =      "Journal of Molecular Modeling",
  year =         "2004",
  volume =       "10",
  number =       "3",
  month =        "June",
  pages =        "204--211",
}

@Article{gopalan03a_bibuniq_293,
  author =       "A. Gopalan and A. H. Titus",
  title =        "A new wide range euclidean distance circuit for neural network hardware implementations",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2003",
  volume =       "14",
  number =       "5",
  month =        "September",
  pages =        "1176--1186",
}

@InProceedings{gruzdz05a_bibuniq_92,
  author =       "A. Gruzdz and A. Ihnatowicz and D. Slezak",
  title =        "Interactive {SOM}-based gene grouping: An approach to gene expression data analysis",
  booktitle =    "Foundations of Intelligent Systems, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "673--683",
}

@Article{tan05b_bibuniq_99,
  author =       "A. H. Tan and H. Pan",
  title =        "Predictive neural networks for gene expression data analysis",
  journal =      "Neural Networks",
  year =         "2005",
  volume =       "18",
  number =       "3",
  month =        "April",
  pages =        "297--306",
}

@InProceedings{Hadid04a_bibuniq_1475,
  author =       "A. Hadid and M. Pietik\"{a}inen",
  title =        "Selecting models from videos for appearance-based face recognition",
  booktitle =    "Proceedings of the 17th International Conference on Pattern-Recognition",
  year =         "2004",
  volume =       "1",
  pages =        "304--8",
  abstract =     "In this paper, we propose an unsupervised approach to select representative face samples (models) from raw videos and build an appearance-based face recognition system. the approach is based on representing the face manifold in a low-dimensional space using the locally linear embedding (LLE) algorithm and then performing K-means clustering. We define the face models as the cluster centers. Our strategy is motivated by the efficiency of LLE to recover meaningful low-dimensional structures hidden in complex and high dimensional data such as face images. Two other well-known unsupervised learning algorithms (Isomap and SOM) are also considered. We compare and assess the efficiency of these different schemes on the CMU MoBo database which contains 96 face sequences of 24 subjects. the results clearly show significant performance enhancements over traditional methods such as the {PCA}-based one.",
}

@Article{hirose03a_bibuniq_276,
  author =       "A. Hirose and T. Nagashima",
  title =        "Predictive self-organiziiig map for vector quantization of migratory signals and its application to mobile communications",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2003",
  volume =       "14",
  number =       "6",
  month =        "November",
  pages =        "1532--1540",
}

@InProceedings{hirose02a_bibuniq_4948,
  author =       "A. Hirose and T. Nagashima",
  title =        "Predictive self-organizing map for vector quantization of migratory signals",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "884--889",
  abstract =     "",
}

@InProceedings{hunter02a_bibuniq_4958,
  author =       "A. Hunter and R. L. Kennedy",
  title =        "A Pareto Self-Organizing Map",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "987--992",
  abstract =     "",
}

@Article{beltzer03a_bibuniq_309,
  author =       "A. I. Beltzer and T. Sato",
  title =        "Neural classification of finite elements",
  journal =      "Computers \& Structures",
  year =         "2003",
  volume =       "81",
  number =       "24-25",
  month =        "September",
  pages =        "2331--2335",
}

@Article{richardson04a_bibuniq_259,
  author =       "A. J. Richardson and C. Risien and F. A. Shillington",
  title =        "Using self-organizing maps to identify patterns in satellite imagery",
  journal =      "Progress in Oceanography",
  year =         "2004",
  volume =       "34",
  number =       "2",
  month =        "April",
  pages =        "1089--1095",
}

@Article{nag05a_bibuniq_93,
  author =       "A. K. Nag and A. Mitra and S. Mitra",
  title =        "Multiple outlier detection in multivariate data using self-organizing maps title",
  journal =      "Computational Statistics",
  year =         "2005",
  volume =       "51",
  number =       "3",
  month =        "March",
  pages =        "251--259",
}

@Article{kitamoto02a_bibuniq_466,
  author =       "A. Kitamoto",
  title =        "Spatio-temporal data mining for typhoon image collection",
  journal =      "Journal of Intelligent Information Systems",
  year =         "2002",
  volume =       "19",
  number =       "1",
  month =        "July",
  pages =        "25--41",
}

@Article{kukovecz03a_bibuniq_295,
  author =       "A. Kukovecz and M. Smolik and S. N. Bokova and H. Kataura and Y. Achiba and H. Kuzmany",
  title =        "Diameter dependence of the fine structure of the Raman {G}-band of single wall carbon nanotubes revealed by a {K}ohonen self-organizing map",
  journal =      "Chemical Physics Letters",
  year =         "2003",
  volume =       "381",
  number =       "3-4",
  month =        "November 14",
  pages =        "434--440",
}

@InProceedings{laha05a_bibuniq_148,
  author =       "A. Laha",
  title =        "Detecting topology preserving feature subset with {SOM}",
  booktitle =    "Intelligent Information Technology, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "215--228",
}

@Article{lendasse02a_bibuniq_425,
  author =       "A. Lendasse and J. Lee and V. Wertz and M. Verleysen",
  title =        "Forecasting electricity consumption using nonlinear projection and self-organizing maps",
  journal =      "Neurocomputing",
  year =         "2002",
  volume =       "48",
  month =        "October",
  pages =        "299--311",
}

@Article{Lendasse00a_bibuniq_3967,
  author =       "A. Lendasse and P. Cardon and V. Wertz and E. de Bodt and M. Verleysen",
  title =        "Self-organizing feature maps for title classification of investment funds",
  journal =      "European-Journal of Economic and Social-Systems",
  volume =       "17",
  number =       "1-2",
  pages =        "183-95", 
  year =         "2004",
}

@InProceedings{lensu03a_bibuniq_364,
  author =       "A. Lensu and P. Koikkalainen",
  title =        "A parallel implementation of the tree-structured self-organizing map",
  booktitle =    "Applied Parallel Computing, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "389--403",
}

@InProceedings{lensu02b_bibuniq_4953,
  author =       "A. Lensu and P. Koikkalainen",
  title =        "Complexity selection of the self-organizing map",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "927--932",
  abstract =     "",
}

@Article{lin02b_bibuniq_456,
  author =       "A. Lin and M. Liljeholm and P. Ozdzynski and J. Beatty",
  title =        "Visualizing plastic change in a large model of somatosensory cortex using an adaptive coordinates algorithm",
  journal =      "Neurocomputing",
  year =         "2002",
  volume =       "44",
  month =        "June",
  pages =        "521--526",
}

@InProceedings{lansiluoto03n_bibuniq_4319,
  author =       "A. Länsiluoto and B. Back and H. Vanharanta and A. Visa",
  title =        "Comparison between macroeconomic financial and industry specific cycles with self-organizing maps",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Misc{webform_2545_bibuniq_4201,
  author =       "A. M. Esposito and S. Scarpetta and F. Giudicepietro and S. Masiello and L. Pugliese and A. Esposito",
  title =        "Nonlinear Exploratory Data Analysis Applied to Seismic Signals",
  howpublished = "In B. Apolloni, M. Marinaro, R. Tagliaferri (eds), Lecture Notes in Computer Science ({LNCS}),",
  pages =        "70--77",
  note =         "",
  year =         "2005",
}

@Article{meyer-baese04a_bibuniq_199,
  author =       "A. Meyer-Baese and A. Wismueller and O. Lange",
  title =        "Comparison of two exploratory data analysis methods for {fMRI}: Unsupervised clustering versus independent component analysis",
  journal =      "{IEEE} Transactions on Information Technology in Biomedicine",
  year =         "2004",
  volume =       "8",
  number =       "3",
  month =        "September",
  pages =        "387--398",
}

@InProceedings{nurnberger04a_bibuniq_217,
  author =       "A. Nurnberger",
  title =        "User adaptive categorization of document collections",
  booktitle =    "Adaptive Multimedia Retrieval, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "140--148",
}

@Article{ohtsuka05a_bibuniq_33,
  author =       "A. Ohtsuka and N. Kamiura and T. Isokawa and N. Matsui",
  title =        "Self-organizing map based on block learning",
  journal =      "Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences",
  year =         "2005",
  volume =       "E88A",
  number =       "11",
  month =        "November",
  pages =        "3151--3160",
}

@InProceedings{Ohtsuka04a_bibuniq_1359,
  author =       "A. Ohtsuka and N. Kamiura and T. Isokawa and N. Minamide and M. Okamoto and N. Koeda and N. Matsui",
  title =        "An application of self organizing map to detection of confused blood samples",
  booktitle =    "Proceeding of {SICE} Annual Conference in Sapporo",
  year =         "2004",
  pages =        "841--844",
  abstract =     "We propose a cost-aware method of detecting blood samples confused among different patients, using self organizing maps. the map consists of a cluster reacting to confused data and that reacting to non-confused data. It is completed so that the latter cluster can become larger than the former, and allows reducing drastically the number of samples wrongly judged to be retested.",
}

@InProceedings{Onea03a_bibuniq_1544,
  author =       "A. Onea and G. Collewet and C. Fernandez and C. Vertan and N. Richard and F. Mariette",
  title =        "Quality analysis of blue-veined cheeses by {MRI}: {A} preliminary study",
  booktitle =    "Proceedings of the {SPIE}",
  year =         "2003",
  volume =       "5132",
  pages =        "400--409",
  abstract =     "This paper describes a preliminary study aimed at improving the quality of soft-blue veined cheeses by the use of magnetic resonance images analysis. MRI measurements were performed on thirty-two samples from two different processing conditions and at three different stages from day 3 after the production to day 37. A segmentation algorithm based on a Self Organizing Map was used to segment the images into six classes. A cavity extraction was then performed. A principal component analysis was computed on variables corresponding to the cavities surface distribution. the results pointed out differences between the two types of cheeses, particularly for day 3 and day 37. This confirmed the interest to use MRI to analyze such products. Further investigations are planned for the analysis of other characteristics of the cheeses and other methods of segmentation.",
}

@InProceedings{Azcarraga02a_bibuniq_1644,
  author =       "A. P. Azcarraga and Tat Seng Chua and J. Tan",
  title =        "Retrieving news stories from a news integration archive",
  booktitle =    "Digital-Libraries: People, Knowledge, and Technology. 5th International Conference on Asian-Digital-Libraries, {ICADL}-2002",
  year =         "2002",
  volume =       "2555",
  pages =        "218--228",
  abstract =     "The distinctive features of the Bveritas online news integration archive are as follows: automatic clustering of related news documents into themes; organization of these news clusters in a theme map; extraction of meaningful labels for each news cluster; and generation of links to related news articles. Several ways of retrieving news stories form this Bveritas archive are described. the retrieval methods range from the usual query box and links to related stories, to an interactive world map that allows news retrieval by country, to an interactive theme map. Query and browsing are mediated by a Scatter/Gather interface that allows the user to select interesting clusters, out of which the subset of documents are gathered and re-clustered for the user to visually inspect. the user is then asked to select new interesting clusters. This alternating selection/clustering process continues until the user decides to view the individual news story titles.",
}

@Misc{webform_838_bibuniq_4187,
  author =       "A. P. Ijzerman {E. V. Samsonova, J. N. Kok}",
  title =        "Tree{SOM}: Cluster Analysis in the Self-Organizing Map",
  howpublished = "5th Workshop on Self-Organizing Maps ({WSOM})",
  pages =        "429--438",
  note =         "",
  year =         "2005",
}

@InProceedings{Paplinski00a_bibuniq_4045,
  author =       "A. P. Paplinski and L. Gustafsson",
  title =        "An attempt in modelling early intervention in autism using neural networks",
  booktitle =    "{IEEE} International Joint Conference on Neural Networks 29-34",
  pages =        "101--108",
  year =         "2004",
}

@Article{patnaik05a_bibuniq_43,
  author =       "A. Patnaik and D. Anagnostou and C. G. Christodoulou and J. C. Lyke",
  title =        "Neurocomputational analysis of a multiband reconfigurable planar antenna",
  journal =      "{IEEE} Transactions on Antennas and Propagation",
  year =         "2005",
  volume =       "53",
  number =       "11",
  month =        "November",
  pages =        "3453--3458",
}

@InProceedings{plebe02a_bibuniq_4951,
  author =       "A. Plebe",
  title =        "An effective traveling salesman problem solver based on self-organizing map",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "908--913",
  abstract =     "",
}

@InProceedings{Sharafat03a_bibuniq_1561,
  author =       "A. R. Sharafat and M. Rasti and A. Yazdian",
  title =        "Neural network based anomaly detection in computer networks: a novel training paradigm",
  booktitle =    "Computer-Applications in Industry and Engineering. Proceedings of the Isca-16th International Conference",
  year =         "2003",
  volume =       "",
  pages =        "50--53",
  abstract =     "We propose a new training paradigm in which we use the data representing abnormal behavior (in contrast to the conventional use of the data representing normal behavior) in computer networks to train a neural network based anomaly detection system in computer networks. We apply our proposed paradigm to an anomaly detection system that is constructed using a self organizing map ({SOM}) and a back propagation (BP) neural network. the new training paradigm in this system yields the same performance level or better as compared to other existing systems, but with about 50\% reduction in its computational complexity.",
}

@Article{rauber03b_bibuniq_367,
  author =       "A. Rauber and D. Merkl",
  title =        "Text mining in the Somlib Digital Library System: the representation of topics and genres",
  journal =      "Applied Intelligence",
  year =         "2003",
  volume =       "18",
  number =       "3",
  month =        "May-June",
  pages =        "271--293",
}

@Article{rauber02a_bibuniq_409,
  author =       "A. Rauber and D. Merkl and M. Dittenbach",
  title =        "The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2002",
  volume =       "13",
  number =       "6",
  month =        "November",
  pages =        "1331--1341",
}

@Article{rauber03a_bibuniq_298,
  author =       "A. Rauber and E. Pampalk and D. Merkl",
  title =        "The {SOM}-enhanced {J}uke{B}ox: Organization and visualization of music collections based on perceptual models",
  journal =      "Journal of new Music Research",
  year =         "2003",
  volume =       "32",
  number =       "2",
  month =        "June",
  pages =        "193--210",
}

@InProceedings{Rauber02a_bibuniq_1698,
  author =       "A. Rauber and E. Pampalk and D. Merkl",
  title =        "Content-based music indexing and organization",
  booktitle =    "Proceedings of {SIGIR}-2002, Twenty-Fifth Annual International {ACM} {SIGIR} Conference on Research and Development in Information-Retrieval",
  year =         "2002",
  volume =       "",
  pages =        "409--410",
  abstract =     "While electronic music archives are gaining popularity, access to and navigation within these archives is usually limited to text-based queries or manually predefined genre category browsing. We present a system that automatically organizes a music collection according to the perceived sound similarity resembling genres or styles of music. Audio signals are processed according to psychoacoustic models to obtain a time-invariant representation of its characteristics. Subsequent clustering provides an intuitive interface where similar pieces of music are grouped together on a map display.",
}

@InProceedings{saalbach02a_bibuniq_4950,
  author =       "A. Saalbach and G. Heidemann and H. Ritter",
  title =        "Parametrized {SOM}s for object recognition and pose estimation",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "902--907",
  abstract =     "",
}

@Article{sangole03a_bibuniq_291,
  author =       "A. Sangole and G. K. Knopf",
  title =        "Visualization of randomly ordered numeric data sets using spherical self-organizing feature maps",
  journal =      "Computers \& Graphics",
  year =         "2003",
  volume =       "27",
  number =       "6",
  month =        "December",
  pages =        "963--976",
}

@Article{Shibata66a_bibuniq_1763,
  author =       "A. Shibata and Y. Sakai",
  title =        "Budgetary transfer to local governments: equity, efficiency and political influence",
  journal =      "International-Journal of Knowledge-Based Intelligent Engineering Systems. Jan. 2002; 6(1): 23-30",
  year =         "2002 7214666",
  volume =       "",
  pages =        "",
  abstract =     "Mechanisms of budgetary resource allocations from the Japanese central government to local governments are analyzed in this paper utilizing statistical methods and a Self Organizing Map ({SOM}). All budgetary transfers to local governments are said to be redistributed on an equitable basis. As a result of the budgetary transfers to local governments that have been carried out for a long time on an equitable basis, we expect inefficient investment in public goods. We also suspect political influences. This paper tries to analyze the budgetary resource allocations from three points of view - equity, efficiency and political influences. Using a cross- sectional analysis of fiscal year 1991 and panel data analyses of data from the years 1977 through 1995, the following results were obtained. With the national data, we found that our results were as expected- namely, the budgetary resource allocations are made on an equitable basis, but inefficient public goods investment has taken place, and there is political influence. However, when data from 46 prefectures are clustered using SOM, and the two largest clusters are analyzed further statistically, the results differ. We also demonstrated that some variables have greater influences on the budgetary transfers than the others and that {SOM} is useful as a visual data mining method.",
}

@Article{sivaramakrishnan04a_bibuniq_196,
  author =       "A. Sivaramakrishnan and D. Graupe",
  title =        "Brain tumor demarcation by applying a Lamstar neural network to spectroscopy data",
  journal =      "Neurological Research",
  year =         "2004",
  volume =       "26",
  number =       "6",
  month =        "September",
  pages =        "613--621",
}

@Article{skupin05a_bibuniq_104,
  author =       "A. Skupin and R. Hagelman",
  title =        "Visualizing demographic trajectories with self-organizing maps",
  journal =      "Geoinformatica",
  year =         "2005",
  volume =       "9",
  number =       "2",
  month =        "June",
  pages =        "159--179",
}

@Article{vellido03a_bibuniq_334,
  author =       "A. Vellido and W. El-Deredy and P. J. G. Lisboa",
  title =        "Selective smoothing of the generative topographic mapping",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2003",
  volume =       "14",
  number =       "4",
  month =        "July",
  pages =        "847--852",
}

@Article{wallqvist05a_bibuniq_54,
  author =       "A. Wallqvist and R. Huang and D. G. Covell and A. V. Roschke and K. S. Gelhaus and I. R. Kirsch",
  title =        "Drugs aimed at targeting characteristic karyotypic phenotypes of cancer cells",
  journal =      "Molecular Cancer Therapeutics",
  year =         "2005",
  volume =       "4",
  number =       "10",
  month =        "October",
  pages =        "1559--1568",
}

@InProceedings{Wismuller04a_bibuniq_1503,
  author =       "A. Wismuller and A. Meyer Base and O. Lange and T. D. Otto and D. Auer",
  title =        "Data partitioning and independent component analysis techniques applied to {fMRI}",
  booktitle =    "Proceedings of the Spie-The International-Society for Optical-Engineering. 2004",
  year =         "2004",
  volume =       "",
  pages =        "",
  abstract =     "Exploratory data-driven methods such as data partitioning techniques and independent component analysis ({ICA}) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging ({fMRI}). in this paper, we present a comparison between data partitioning techniques and {ICA} in a systematic {fMRI} study. the comparative results were evaluated by (1) task-related activation maps and (2) associated time-courses. For the {fMRI} data, a comparative quantitative evaluation between the three clustering techniques, SOM, {"}neural gas{"} network, and fuzzy clustering based on deterministic annealing, and the three {ICA} methods, FastICA, Infomax and topographic {ICA} was performed. the {ICA} methods proved to extract features better than the clustering methods but are limited to the linear mixture assumption. the data partitioning techniques outperform {ICA} in terms of classification results but requires a longer processing time than the {ICA} methods.",
}

@Article{wismuller04a_bibuniq_247,
  author =       "A. Wismuller and A. Meyer-Base and O. Lange and D. Auer and M. F. Reiser and D. Sumners",
  title =        "Model-free functional {MRI} analysis based on unsupervised clustering",
  journal =      "Journal of Biomedical Informatics",
  year =         "2004",
  volume =       "37",
  number =       "1",
  month =        "February",
  pages =        "10--18",
}

@Article{yan03a_bibuniq_283,
  author =       "A. X. Yan and J. Gasteiger",
  title =        "Prediction of aqueous solubility of organic compounds by topological descriptors",
  journal =      "{QSAR} \& Combinatorial Science",
  year =         "2003",
  volume =       "22",
  number =       "8",
  month =        "November",
  pages =        "821--829",
}

@Article{yan03b_bibuniq_372,
  author =       "A. X. Yan and J. Gasteiger",
  title =        "Prediction of aqueous solubility of organic compounds based on a 3{D} structure representation",
  journal =      "Journal of Chemical Information and Computer Sciences",
  year =         "2003",
  volume =       "43",
  number =       "2",
  month =        "March-April",
  pages =        "429--434",
}

@InProceedings{zaim05a_bibuniq_81,
  author =       "A. Zaim",
  title =        "Automatic segmentation of the prostate from ultrasound data using feature-based self organizing map",
  booktitle =    "Image Analysis, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "2219--2240",
}

@Misc{webform_6057_bibuniq_4233,
  author =       "Aaron C. and Tadjeddine Y.",
  title =        "Description of the Group Dynamic of Funds’ Managers using {K}ohonen’s Map",
  howpublished = "European Symposium on Applied Neural Network ({ESANN04})",
  pages =        "",
  note =         "",
  year =         "2004",
}

@InProceedings{inspek606_bibuniq_809,
  author =       "Abdi A. M. and Szu H. H.",
  title =        "Independent component analysis ({ICA}) and self-organizing map ({SOM}) approach to multi detection system for network intruders",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering",
  volume =       "5102",
  pages =        "348--353",
  year =         "2003",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "With the growing rate of interconnection among computer systems, network security is becoming a real challenge. Intrusion detection system (IDS) is designed to protect the availability, confidentiality and integrity of critical network information systems. Today's approach to network intrusion detection involves the use of rule-based expert systems to identify an indication of known attack or anomalies. However, these techniques are less successful in identifying today's attacks. Hackers are perpetually inventing new and previously unanticipated techniques to compromise information infrastructure. This paper proposes a dynamic way of detecting network intruders on time serious data. the proposed approach consists of a two-step process. Firstly, obtaining an efficient multi-user detection method, employing the recently introduced complexity minimization approach as a generalization of a standard ICA. Secondly, we identified unsupervised learning neural network architecture based on {K}ohonen's self-organizing map for potential functional clustering. These two steps working together adaptively provide a pseudo-real time novelty detection attribute to supplement the current intrusion detection statistical methodology.",
}

@InProceedings{inspek197_bibuniq_917,
  author =       "Abe T. and Kanaya S. and Kinouchi M. and Kosaka Y. and Ikemura T.",
  editor =       "A. {Callaos, N. ; Horimoto, K. ; Chen, J. ; Kit-Sze-Chan}",
  title =        "A novel bioinformatics strategy for unveiling hidden characteristics in genome sequences and searching in silico for genetic signal sequences",
  booktitle =    "The 8th World Multi Conference on Systemics, Cybernetics and Informatics",
  pages =        "105--112",
  volume =       "7",
  year =         "2004",
  publisher =    "IIIS, Orlando, FL, USA",
  abstract =     "Novel bioinformatic tools are needed for comprehensive analyses of massive amounts of available genome {DNA} sequences. An unsupervised neural network algorithm, self-organizing map ({SOM}), is an effective tool for clustering and visualizing high-dimensional complex data on a single map. We generated SOMs for tri-, tetra-, and pentanucleotide frequencies in 300, 000 10-kb sequences from 13 eukaryotes for which almost complete genomic sequences are available (a total of 3 Gb). {SOM} recognized in most 10-kb sequences species-specific characteristics (key combinations of oligonucleotide frequencies), permitting species-specific classification of sequences without any information regarding the species. Because the classification power is very high, {SOM} is an efficient and powerful tool for extracting a wide range of genomic information. {SOM} constructed with oligonucleotide frequencies in 10-kb sequences from 2. 8 Gb of human sequences identified oligonucleotides occurring with frequencies characteristically biased from random occurrence predicted from the mononucleotide composition; 10-kb sequences rich in these oligonucleotides were self-organized on a map. Because these oligonucleotides often corresponded to genetic signals or their constituent elements, we propose an in silico method that should be useful for identification of genetic signal sequences in genomes for which large amounts of sequence data are available but additional experimental data are lacking.",
}

@InProceedings{inspek490_bibuniq_736,
  author =       "Abraham A. and Ramos V.",
  title =        "Web usage mining using artificial ant colony clustering and linear genetic programming",
  booktitle =    "2003 Congress on Evolutionary Computation {IEEE}",
  volume =       "2",
  pages =        "1384--1391",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. the study of ant colonies behavior and their self-organizing capabilities is of interest to knowledge retrieval/management and decision support systems sciences, because it provides models of distributed adaptive organization, which are useful to solve difficult optimization, classification, and distributed control problems, among others [Ramos, V. et al. (2002), (2000)]. in this paper, we propose an ant clustering algorithm to discover Web usage patterns (data clusters) and a linear genetic programming approach to analyze the visitor trends. Empirical results clearly show that ant colony clustering performs well when compared to a self-organizing map (for clustering Web usage patterns) even though the performance accuracy is not that efficient when compared to evolutionary-fuzzy clustering (i-miner) [Abraham, A. (2003)] approach.",
}

@InProceedings{inspek829_bibuniq_641,
  author =       "Aghbari Z. and Feng Y. Kun-Seok-Oh and Makinouchi A.",
  editor =       "P. {Zhou, X. ; Pu}",
  title =        "{SOM}-based {K}-nearest neighbors search in large image databases",
  booktitle =    "Visual and Multimedia Information Management. {IFIP TC2/WG2} Sixth Working Conference on Visual Database Systems. 2002: 51--65",
  pages =        "51--65",
  year =         "2002",
  publisher =    "Kluwer Academic Publishers, Norwell, MA, USA",
  abstract =     "We address the problem of K-nearest neighbors (KNN) search in large image databases. Our approach clusters the database of n points (i. e. images) using a self-organizing map algorithm. We then map each cluster into a point in one-dimensional distance space. From these mapped points, we construct a simple, compact and yet fast index structure, called array-index. Unlike most indexes of KNN algorithms that require storage space exponential in dimensions, the array-index requires storage space that is linear in the number of generated clusters. Due to the simplicity and compactness of the array-index, the experiments show that our method outperforms other well know methods.",
}

@Article{inspek10_bibuniq_1139,
  author =       "Agouris P. and Partsinevelos P. and Stefanidis A.",
  title =        "Reconstructing spatiotemporal trajectories from sparse data",
  journal =      "{ISPRS} Journal of Photogrammetry and Remote Sensing",
  volume =       "60",
  number =       "1",
  pages =        "3--16",
  year =         "2005",
  month =        "December",
  publisher =    "Elsevier",
  abstract =     "In motion imagery-based tracking applications, it is common to extract locations of moving objects without any knowledge about the identity of the objects they correspond to. the identification of individual spatiotemporal trajectories from such data sets is far from trivial when these trajectories intersect in space, time, or attributes. in this paper, we present a novel approach for the reconstruction of entangled spatiotemporal trajectories of moving objects captured in motion imagery data sets. We have developed ACENT (Attribute-aided Classification of Entangled Trajectories), a novel framework that comprises classification, clustering, and neural net processes to progressively reconstruct elongated trajectories using as input spatiotemporal coordinates of image patches and corresponding attribute values. ACENT proceeds by first forming brief fragments and then linking them and adding points to them. An initial classification allows us to form brief segments corresponding to distinct objects. These segments are then linked together through clustering to form longer trajectories. Back-propagation neural network classification and geometric/self-organizing map ({SOM}) analysis refine these trajectories by removing misclassified and redistributing unassigned points. Thus, ACENT integrates some established classification and clustering tools to devise a novel approach that can address the tracking challenges of busy environments. Furthermore, ACENT allows us to use spatiotemporal (ST) thresholds to cluster trajectories according to their spatial and temporal extent. in the paper, we present in detail our framework and experimental results that support the application potential of our approach. [All rights reserved Elsevier].",
}

@InProceedings{Ah00a_bibuniq_4017,
  author =       "Ah Hwee Tan",
  title =        "{FALCON}: a fusion architecture for learning, cognition, and navigation",
  booktitle =    "{IEEE} International Joint Conference on Neural Networks",
  volume =       "4",
  year =         "2004",
  address =      "Budapest",
  month =        "July",
  pages =        "3297--3302",
}

@InProceedings{inspek777_bibuniq_595,
  author =       "Ahmad A. M. and Mohamad F. Lee-Ing-Chen",
  title =        "Simulation of stable-adaptive control of robot arm using self-organizing neural network",
  booktitle =    "Proceedings of Student Conference on Research and Development {SCOReD2002}. Globalizing Research and Development in Electrical and Electronics Engineering",
  pages =        "162--164",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper, a simulation of neural network controller for a three links robot arm is presented. the network is based on modified {K}ohonen's self-organizing map. in the proposed model, recurrent network and modified {SOM} network are interconnected.",
}

@InProceedings{inspek479_bibuniq_731,
  author =       "Ahmadi A. and Omatu S. and Kosaka T.",
  editor =       "H. {Loncaric, S. ; Neri, A. ; Babic}",
  title =        "A {PCA} based method for improving the reliability of bank note classifier machines",
  booktitle =    "{ISPA} 2004 Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis {IEEE}",
  volume =       "1",
  pages =        "494--499",
  year =         "2003",
  publisher =    "Univ. of Zagreb, Zagreb, Croatia",
  abstract =     "This paper addresses the reliability of neuro-classifiers for bank note recognition. A local principal component analysis ({PCA}) method is applied to remove non-linear dependencies among variables and extract the main principal features of data. At first the data space is partitioned into regions by using a self-organizing map ({SOM}) clustering and then the {PCA} is performed in each region. A learning vector quantization ({LVQ}) network is employed as the main classifier of the system. By defining a new algorithm for rating the reliability and using a set of test data, we estimate the reliability of the system. the experimental results taken from 1, 200 samples of US dollar bills show that the reliability is increased up to 100\% when the number of regions as well as number of codebook vectors in the {LVQ} classifier is taken properly.",
}

@InProceedings{inspek615_bibuniq_816,
  author =       "Ahmadi A. and Omatu S. and Kosaka T.",
  title =        "A reliable method for recognition of paper currency by approach to local {PCA}",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks 2003 vol. 2",
  volume =       "4",
  pages =        "1258--1262",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper addresses the reliability of neuro-classifiers for paper currency recognition. A local principal component analysis ({PCA}) method is applied to remove non-linear dependencies among variables and extract the main principal features of data. At first the data space is partitioned into regions by using a self-organizing map ({SOM}) model and then the {PCA} is performed in each region. A learning vector quantization ({LVQ}) network is employed as the main classifier of the system. By defining a new algorithm for rating the reliability and using a set of test data, we estimate the reliability of the system. the experimental results taken from 1, 200 samples of US dollar bills show that the reliability is increased up to 100\% when the number of regions as well as the number of codebook vectors in the {LVQ} classifier is taken properly.",
}

@InProceedings{inspek627_bibuniq_827,
  author =       "Ahmadi A. and Omatu S. and Kosaka T.",
  title =        "A methodology to evaluate and improve reliability in paper currency neuro-classifiers",
  booktitle =    "Proceedings 2003 {IEEE} International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium vol. 3",
  volume =       "3",
  pages =        "1186--1189",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper the reliability of the paper currency classifiers is studied and a new method is proposed for improving the reliability based on the local principal components analysis ({PCA}). At first the data space is partitioned into regions by using self-organizing map ({SOM}) model and then the {PCA} is performed in each region. A learning vector quantization ({LVQ}) network is employed as the main classifier of the system. the reliability of classification is evaluated by using an algorithm, which employs a function of the winning class probability and second maximal probability. By using a set of test data, we estimate the overall reliability of the system. the experimental results taken fro 1, 200 samples of US dollar bills show that the reliability is increased up to 100\% when the number of regions as well as the number of codebook vectors in the {LVQ} classifier is taken properly.",
}

@InProceedings{inspek291_bibuniq_947,
  author =       "Ahmadi A. and Omatu S. and Kosaka T.",
  title =        "Improvement of the reliability of bank note classifier machines",
  booktitle =    "2004 {IEEE} International Joint Conference on Neural Networks",
  volume =       "2",
  pages =        "1313--1316",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper addresses the reliability of neuro-classifiers for bank note recognition. A local principal component analysis ({PCA}) method is applied to remove nonlinear dependencies among variables and extract the main principal features of data. At first the data space is partitioned into regions by using a self-organizing map ({SOM}) model and then the {PCA} is performed in each region. A learning vector quantization ({LVQ}) network is employed as the main classifier of the system. By defining a new algorithm for rating the reliability and using a set of test data, we estimate the reliability of the system. the experimental results taken from 1, 200 samples of US dollar bills show that the reliability is increased up to 100\% when the number of regions as well as the number of codebook vectors in the {LVQ} classifier are taken properly.",
}

@Article{inspek719_bibuniq_547,
  author =       "Ahmadi A. and Omatu S. and Yoshioka M.",
  title =        "Off-line Persian handwritten recognition using hidden {M}arkov models",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan",
  pages =        "2128--2134",
  volume =       "112C",
  number =       "12",
  year =         "2002",
  month =        "December",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "We present a system for recognition of Persian/Arabic handwritten scripts using hidden {M}arkov models (HMMs). the text is segmented to words at first and from words to characters by an appropriate algorithm using the strokes and contour of word image. Then the feature vectors are extracted from sequential vertical frames of characters. Next, a self-organizing map ({SOM}) is employed for clustering the features and reducing the size of inputs as well as smoothing the parameters of HMMs in classification phase. Finally, by using the {HMM} the characters are classified, and by concatenating the character HMMs, the word {HMM} is composed. the system is evaluated with five sorts of Persian handwritten data containing a number of 1, 025 words, and the mean correct classification rate is 97\% in word level.",
}

@InProceedings{inspek489_bibuniq_735,
  author =       "Akhmetshin A. M. and Akhmetshina L. G.",
  title =        "Sensitive analysis and segmentation of low-contrast images: knowledge discovery based on multiparameter topological resonance method",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering",
  pages =        "153--160",
  volume =       "5433",
  number =       "1",
  year =         "2003",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "A new method of low contrast images analysis and segmentation is outlined. the method provides high sensitivity to detection of visually invisible low contrast areas and simultaneous stability to influence local small brightness variations. the new method consists of the following four steps. 1) Forming a moving window of size (3*3). Analyzed image brightness pixels into the bounds of the window are considered as coefficients of some virtual nonrecursive digital filter. One is characterized by its spectral characteristic. It gives possibility for comparing to each pixel of analyzed image its virtual spectral characteristic. 2) Information features for further analysis use resonance points (magnitudes and frequencies) of virtual magnitude-frequency and group-delay characteristics (namely it is stipulated using the method designation as multiparameter topological resonance). 3) Visualization of new synthesized features and a separate analysis of the new images. 4) Multiparameter information fusion into one resulting image (if it is needed) on the basis of a self-organizing map method. On the basis of our experimental investigations, the importance virtual group-delay function (i. e. new information characteristic) for task detection and segmentation of low contrast fuzzy regions of analyzed images is established. Experiments were made with examples of real low contrast images: X-ray CT, digital mammograms, and geophysical field images. For all situations there were obtained very good practical results.",
}

@InProceedings{inspek848_bibuniq_656,
  author =       "Akhtar S. and Reilly R. G. and Dunnion J.",
  editor =       "A. Gelbukh",
  title =        "AutoMarkup: a tool for automatically marking up text documents",
  booktitle =    "Computational Linguistics and Intelligent Text Processing. Third International Conference, {CIC}Ling 2002. Proceedings Lecture Notes in Computer Science",
  pages =        "433--435",
  volume =       "2276",
  year =         "2002",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "We present a novel system that can automatically mark up text documents into XML. the system uses the self-organizing map ({SOM}) algorithm to organize marked documents on a map so that similar documents are placed on nearby locations. Then by using the inductive learning algorithm C5, it automatically generates and applies the markup rules from the nearest {SOM} neighbours of an unmarked document. the system is adaptive in nature and learns from errors in the automatically marked-up document to improve accuracy. the automatically marked-up documents are again arranged on the SOM.",
}

@InProceedings{inspek505_bibuniq_743,
  author =       "Akhtar S. and Reilly R. G. and Dunnion J.",
  editor =       "E. {Callaos, N. ; Lesso, W. ; Sanchez, B. ; Hansen}",
  title =        "Automating {XML} markup using machine learning techniques",
  booktitle =    "{SCI} 2003. 7th World Multiconference on Systemics, Cybernetics and Informatics Proceedings",
  pages =        "203--208",
  volume =       "6",
  year =         "2003",
  publisher =    "IIIS, Orlando, FL, USA",
  abstract =     "In this paper we present a system for automatically marking up text documents into XML. the system uses the techniques of the self-organizing map ({SOM}) algorithm in conjunction with an inductive learning algorithm, C5. 0. the {SOM} algorithm clusters the XML marked-up documents on a two-dimensional map such that documents having similar content are placed close to each other. the C5. 0 algorithm learns and applies markup rules derived from the nearest {SOM} neighbours of an unmarked document. the system is designed to be adaptive so that it learns from errors in order to improve the markup of resulting document. Experiments show that our system provides high accuracy and demonstrate that our approach is practical and feasible.",
}

@InProceedings{inspek512_bibuniq_746,
  author =       "Akhtar S. and Reilly R. G. and Dunnion J.",
  editor =       "E. B. {Arabnia, H. R. ; Kozerenko}",
  title =        "Applying machine learning techniques to automating {XML} markup",
  booktitle =    "Proceedings of the International Conference on Machine Learning; {M}odels, Technologies and Applications. {MLMTA}'03",
  pages =        "56--61",
  year =         "2003",
  publisher =    "CSREA Press, Las Vegas, NV, USA",
  abstract =     "In this paper we present a novel system which uses machine-learning techniques to automatically markup text documents into XML the system uses the {WEBSOM} method to arrange marked-up documents on a self-organizing map ({SOM}) such that semantically similar documents are placed close to each other. Then, by using the inductive learning algorithm, C5. 0, the system automatically extracts and applies markup rules from the nearest {SOM} neighbours of an unmarked document. the system is designed to be adaptive, so that once a document is marked-up, its behaviour is modified to improve accuracy. Our approach has been applied to a number of different document domains and the results indicate that the approach is feasible and practical.",
}

@InProceedings{hirose03_bibuniq_4308,
  author =       "Akira Hirose and Takahiro Hara",
  title =        "Complex-Valued Self-Organizing Map Dealing with Multi-Frequency Interferometric Data for Radar Imaging Systems",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{ohtsuka03_bibuniq_4305,
  author =       "Akitsugu Ohtsuka and Naotake Kamiura and Teijiro Isokawa and Nobuyuki Matsui",
  title =        "An Analysis of the Block-Matching-based Self-Organizing Map",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek183_bibuniq_915,
  author =       "Al-Shehabi S. and Lamirel J. C.",
  editor =       "J. M. {Callaos, N. ; Sanchez, M. ; Pineda}",
  title =        "Unsupervised neural networks of topographic and gas families for documentary data classification",
  booktitle =    "The 8th World Multi Conference on Systemics, Cybernetics and Informatics",
  pages =        "354--358",
  volume =       "16",
  year =         "2004",
  publisher =    "IIIS, Orlando, FL, USA",
  abstract =     "The unsupervised neural networks are excellent tools for the analysis of high-dimensional input data as in data mining applications. in this paper different methods of data clustering are considered as the self-organizing map ({SOM}), neural gas (NG) and growing neural gas (GNG). This paper demonstrates, in one side, the efficiency of a viewpoint-oriented-analysis as compared to a global analysis, and in the other side, it compare these three unsupervised neural clustering methods as classifier for documentary data. For that, two quality criteria are taken into account for quality evaluation. These criteria are used as well for highlighting the clustering methods internal operation.",
}

@Article{inspek441_bibuniq_1084,
  author =       "Alahakoon L. D.",
  title =        "Controlling the spread of dynamic self-organising maps",
  journal =      "Neural Computing \& Applications",
  pages =        "168--174",
  volume =       "13",
  number =       "2",
  year =         "2004",
  month =        "June",
  publisher =    "Springer-Verlag",
  abstract =     "The growing self-organising map (GSOM) has been proposed as an alternative neural network architecture based on the traditional self-organising map ({SOM}). the GSOM provides the user with the ability to control the spread of the map by defining a parameter called the spread factor (SF), which results in enhanced data mining and hierarchical clustering opportunities. When experimenting with the SOM, the grid size (number of rows and columns of nodes) can be changed until a suitable cluster distribution is achieved. in this paper we highlight the effect of the spread factor on the GSOM and contrast this effect with grid size change (increase and decrease) in the SOM. We also present experimental results in support of our claims regarding differences between GSOM and SOM.",
}

@InProceedings{ultsch03_bibuniq_4304,
  author =       "Alfred Ultsch",
  title =        "Maps for the Visualization of high-dimensional Data Spaces",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Misc{webform_3623_bibuniq_4211,
  author =       "Amarasiri R. and Alahakoon D.",
  title =        "Applying Dynamic Self Organizing Maps for Identifying Changes in Data Sequences",
  howpublished = "Proceedings of Hybrid Intelligent Systems 2003",
  pages =        "682--691",
  note =         "",
  year =         "2003",
}

@Misc{webform_3819_bibuniq_4213,
  author =       "Amarasiri R. and Alahakoon D.",
  title =        "Building a Cluster of Intelligent, Adaptive Web Sites",
  howpublished = "Special issue of the Journal Neural Computing and Applications, Neural Networks for Enhanced Intelligence",
  pages =        "149--156",
  volume =       "13",
  number =       "2
  note =         "",
  year =         "2004",
}

@Misc{webform_3525_bibuniq_4210,
  author =       "Amarasiri R. and Alahakoon D. and Smith K.",
  title =        "Applications of the Growing Self Organizing Map in High Dimensional Data",
  howpublished = "Proceedings of the International Information Technology Conference ({IITC}) 2004",
  pages =        "169--174",
  note =         "",
  year =         "2004",
}

@InProceedings{inspek268_bibuniq_932,
  author =       "Amarasiri R. and Alahakoon D. and Smith K. A.",
  editor =       "A. {Ishikawa, M. ; Hashimoto, S. ; Paprzycki, M. ; Barakova, E. ; Yoshida, K. ; Koppen, M. ; Corne, D. W. ; Abraham}",
  title =        "{HDGSOM}: a modified growing self-organizing map for high dimensional data clustering",
  booktitle =    "Fourth International Conference on Hybrid Intelligent Systems",
  pages =        "216--221",
  year =         "2004",
  publisher =    "{IEEE} Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "The growing self organizing map (GSOM) algorithm is a variant of the self organizing map ({SOM}). It has a dynamically growing structure that adapts to the natural structure of the data. It has been identified that the growing of the GSOM can get negatively affected when used with very large dimensional data such as those in text and {DNA} data sets. This paper addresses these issues and presents a modified version of the GSOM called the high dimensional GSOM (HDGSOM). the algorithm and experimental results showing the improved performance of the HDGSOM are also presented.",
}

@InProceedings{inspek98_bibuniq_1205,
  author =       "Amarasiri R. and Alahakoon D. and Smith K. and Premaratne M.",
  title =        "{HDGSOM}r: a high dimensional growing self-organizing map using randomness for efficient Web and text mining",
  booktitle =    "Proceedings. The 2005 {IEEE}/{WIC}/{ACM} International Conference on Web Intelligence",
  pages =        "215--221",
  year =         "2005",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Mining of text data from the Web has become a necessity in modern days due to the volumes of data available on the Web. While searching for information on the Web using search engines is popular, to analyze the content on large collections of Web pages, feature map techniques are still popular. One of the problems associated with processing large collections of text data from the Web using feature map techniques is the time taken to cluster them. This paper presents an algorithm based on a growing variant of the self organizing map called the HDGSOMr. This novel algorithm incorporates randomness into the self--organizing process to produce higher quality clusters within few epochs and utilizing smaller neighborhood sizes resulting in a significant reduction in overall processing time. Details of the HDGSOMr algorithm and results of processing large collections of text data proving the efficiency of the algorithm are also presented.",
}

@Misc{webform_3721_bibuniq_4212,
  author =       "Amarasiri R. and Wickramasinghe L. K. and Alahakoon D.",
  title =        "Enhanced Cluster Visualization Using the Data Skeleton Model",
  howpublished = "in Proceedings of Soft Computing and the Web ({ISCW})'03 at the third International Conference on Intelligent Systems Design and Application ({ISDA}) 2003",
  pages =        "239--548",
  note =         "",
  year =         "2003",
}

@InProceedings{konig03_bibuniq_4303,
  author =       "Andreas König and Thomas Michel",
  title =        "{DIPOL}-{SOM} - {A} Distance Preserving Enhancement of the Self-Organizing Map for Dimensionality Reduction and Multivariate Data Visualization",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{russell05amklc_bibuniq_6,
  author =       "Ann Russell and Timo Honkela",
  title =        "Analysis of Interprofessional Collaboration in an Online Learning Environment Using Self-Organizing Maps",
  booktitle =    "Proceedings of {AMKLC'05}, International Symposium on Adaptive Models of Knowledge, Language and Cognition",
  pages =        "52--57",
  year =         "2005",
}

@InProceedings{georgakis05akrr_a_bibuniq_2,
  author =       "Apostolos Georgakis and Haibo Li",
  title =        "A {SOM} Variant for Heavily Skewed Vectors",
  booktitle =    "Proceedings of {AKRR'05}, International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning",
  pages =        "41--48",
  year =         "2005",
}

@InProceedings{georgakis05akrr_b_bibuniq_3,
  author =       "Apostolos Georgakis and Haibo Li",
  title =        "An Ensemble of {SOM} Networks for Document Organization and Retrieval",
  booktitle =    "Proceedings of {AKRR'05}, International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning",
  pages =        "141--147",
  year =         "2005",
}

@Article{inspek700_bibuniq_886,
  author =       "Aras N. and Altinel I. K. and Oommen J.",
  title =        "A {K}ohonen-like decomposition method for the Euclidean traveling salesman problem-{KNIES}\_Dec{OMPOSE}",
  journal =      "{IEEE} Transactions on Neural Networks,
  volume =       "14",
  number =       "4",
  pages =        "869--890",
  month =        "July",
  year =         "2003",
  publisher =    "IEEE",
  abstract =     "In addition to the classical heuristic algorithms of operations research, there have also been several approaches based on artificial neural networks for solving the traveling salesman problem. Their efficiency, however, decreases as the problem size (number of cities) increases. A technique to reduce the complexity of a large-scale traveling salesman problem (TSP) instance is to decompose or partition it into smaller subproblems. We introduce an all-neural decomposition heuristic that is based on a recent self-organizing map called KNIES, which has been successfully implemented for solving both the Euclidean traveling salesman problem and the Euclidean Hamiltonian path problem. Our solution for the Euclidean TSP proceeds by solving the Euclidean HPP for the subproblems, and then patching these solutions together. No such all-neural solution has ever been reported.",
}

@InProceedings{inspek333_bibuniq_983,
  author =       "Arnonkijpanich B. and Chaikanha N. and Pathumnakul S. and Lursinsap C.",
  title =        "Proportional self-organizing map ({PSOM}) based on flexible capacity buffer for allocating sugar cane loading stations",
  booktitle =    "2004 {IEEE} International Conference on Systems, Man and Cybernetics",
  volume =       "7",
  pages =        "6206--6211",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper, the problem of locating sugar cane loading stations for the sugar cane industry in Thailand to reduce the expensive transportation cost is addressed. the suitable number of farmer clusters must be identified first. Then, a loading station is assigned to each cluster. the objective is to minimize the differences of farmer clusters in terms of the amount of supplied sugar cane and transporting distance. We develop an algorithm, which can simultaneously cluster sugar cane farmers and locate cluster's loading station. the proposed algorithm is named {"}proportional self-organizing map (PSOM){"}. Since the capacity of each loading station and transporting distance from farms to a station would be proportionally adapted.",
}

@InProceedings{inspek401_bibuniq_1047,
  author =       "Arnonkijpanich B. and Lursinsap C.",
  title =        "Adaptive second order self-organizing mapping for 2{D} pattern representation",
  booktitle =    "{IEEE} International Joint Conference on Neural Networks",
  volume =       "4",
  pages =        "775--780",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The problem of unsupervised classifying a set data and identifying the natural principal direction of each class at the same time is studied. A new adaptive unsupervised learning model called adaptive second order self-organizing map (ASOSOM) is proposed for this problem. ASOSOM combines the advantages of the self-organizing mapping with Karhunen-Loeve (KL) transformation. Instead of having one neuron representing each class, an additional neuron is introduced to cooperate with the class neuron for identifying the principal direction. Furthermore, a new performance measurement based on the co-variance between the natural principal direction and its perpendicular direction is introduced. This new model is applied to several applications and the obtained results are better than KL and MKL transformations.",
}

@InCollection{Klami02_bibuniq_1780,
  author =       "Arto Klami and Jaakko Peltonen and Samuel Kaski",
  title =        "Accurate self-organizing maps in learning metrics",
  editor =       "Pekka Ala-Siuru and Samuel Kaski",
  booktitle =    "{STeP} 2002-- Intelligence, the Art of Natural and Artificial. Proceedings of the 10th {F}innish Artificial Intelligence Conference",
  publisher =    "Finnish Artificial Intelligence Society",
  year =         "2002",
  address =      "Oulu, Finland",
  pages =        "41--49",
}

@Article{inspek470_bibuniq_1101,
  author =       "Asghar S. and Alahakoon D. and Hsu A.",
  title =        "Enhancing {OLAP} functionality using self-organizing neural networks",
  journal =      "Neural, Parallel \& Scientific Computations",
  volume =       "12",
  number =       "1",
  pages =        "1--20",
  month =        "March",
  year =         "2004",
  publisher =    "Dynamic Publishers",
  abstract =     "On-line analytical processing (OLAP) has become a popular management decision-making tool due to its user-friendly visualization abilities. With this popularity, user's expectation and demands from existing OLAP systems have increased. the described work extends the capabilities of OLAP with additional functionality by using neural network technology. in addition to the usual visualization capabilities, this new technique provides the user with the opportunity to analyse clusters in the data at different levels of abstraction. the technique used for enhancing OLAP functionality is a model called the Growing Self-Organizing Map (GSOM). the GSOM has been developed as a more flexible data mining friendly feature mapping method over the traditional Self-Organizing Map ({SOM}). One of the major innovations with the GSOM is the possibility of generating feature maps of different levels of data abstraction using a parameter called the spread factor. This spread factor has been used to develop a hierarchical cluster generation and analysis technique called the Dynamic {SOM} Tree. These hierarchical clusters facilitate the OLAP user to gain insight and obtain prior knowledge of the data set before performing OLAP operations. in addition, the hierarchical clusters from a Dynamic {SOM} Tree are used to provide the OLAP user with the ability to visualize and select data clusters at different levels of abstraction for further detailed analysis.",
}

@InProceedings{inspek160_bibuniq_1263,
  author =       "Astrov I. and Tatarly S. and Rustern E.",
  editor =       "O. I. {Shokin, Y. I. ; Potaturkin}",
  title =        "Processing of blurred image by the two-rate hybrid {K}ohonen neural network",
  booktitle =    "Proceedings of the Second {IASTED} International Multi Conference on Automation, Control and Information Technology",
  pages =        "30--35",
  year =         "2005",
  publisher =    "ACTA Press, Anaheim, CA, USA",
  abstract =     "This paper presents the two-rate hybrid {K}ohonen neural network (TRHKNN) for recognition and restoration of blurred images of complex structure. the illustrative examples - recognition and restoration for images of noised irregular figures - were carried out using the TRHKNNs. the received TRHKNNs have not only high speed of image processing, but also high speed of image restoration. These examples demonstrate that the proposed TRHKNNs are capable not only to distinguish the noisy input, but also to restore reference images. the simulation results with use the software package Simulink show the computing procedure and applicability of TRHKNNs for fast-acting image processing and analysis in real-time conditions.",
}

@InProceedings{inspek735_bibuniq_559,
  author =       "Atsalakis A. and Kroupis N. and Soudris D. and Papamarkos N.",
  title =        "A window-based color quantization technique and its embedded implementation",
  booktitle =    "Proceedings 2002 International Conference on Image Processing",
  volume =       "2",
  pages =        "365--368",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A new color quantization (CQ) technique and its VLSI implementation is introduced. It is based on image split into windows and uses {K}ohonen self organized neural network classifier (SONNC). Initially, the dominant colors of each window are extracted through the SONNC and then are used for the quantization of the colors of the entire image. the image split in windows offers reduction of the memory requirements and feasibility of suitable VLSI implementation of the most time consuming part of the technique. Applying a systematic design methodology into the developed CQ algorithm, an efficient system on chip based on the ARM processor, which achieves high speed processing and less energy consumption, is derived.",
}

@InProceedings{inspek709_bibuniq_541,
  author =       "Atsalakis A. and Papamarkos N. and Kroupis N. and Soudris D. and Thanailakis A.",
  editor =       "M. H. Hamza",
  title =        "A color quantization technique based on image decomposition and its hardware implementation",
  booktitle =    "Proceedings of the {IASTED} International Conference Signal Processing, Pattern Recognition, and Application",
  pages =        "348--353",
  year =         "2002",
  publisher =    "{ACTA} Press, Anaheim, CA, USA",
  abstract =     "A new color quantization (CQ) technique and its VLSI implementation are presented. It is based on an image spliting into windows and uses a {K}ohonen self organized neural network classifier (SONNC). Initially, the dominant colors of each window are extracted through the SONNC and then are used for the quantization of the colors of the entire image. the image split in to windows offers reduction of the memory requirements and feasibility of suitable VLSI implementation of the most time consuming part of the technique, i. e. the SONNC. Applying a systematic design methodology to the developed CQ algorithm, an efficient system on chip based on an ARM processor, which achieves high speed processing and less energy consumption, is derived.",
}

@Article{inspek386_bibuniq_1033,
  author =       "Atsalakis A. and Papamarkos N. and Kroupis N. and Soudris D. and Thanailakis A.",
  title =        "Colour quantisation technique based on image decomposition and its embedded system implementation",
  journal =      "{IEE} Proceedings Vision, Image and Signal Processing",
  pages =        "511--524",
  volume =       "151",
  number =       "6",
  year =         "2004",
  month =        "December",
  publisher =    "IEE",
  abstract =     "A new colour quantisation (CQ) technique and its corresponding embedded system realisation are introduced. the CQ technique is based on image split into sub-images and the use of {K}ohonen self-organised neural network classifiers (SONNC). Initially, the dominant colours of each sub-image are extracted through SONNCs and then are used for the quantisation of the colours of the entire image. the proposed CQ technique can use both colour components and spatial features, achieving better approximation of the final image to the spatial characteristics of the original one. in addition, for the estimation of the proper number of dominant image colours, a new algorithm based on the projection of the image colours into the first two principal components is proposed. the image split into sub-images offers reduction of the on-chip memory requirements and is suitable for embedded system (or system on chip) implementation of the most time-consuming part of the technique. Applying a systematic design methodology to the developed CQ algorithm, an efficient embedded architecture based on the ARM7 processor achieving high-speed processing and less energy consumption, is derived.",
}

@Article{SaalbachEtAl2005-IFB_bibuniq_4116,
  author =       "Axel Saalbach and J{\"o}rg Ontrup and Helge Ritter and Tim Wilhelm Nattkemper",
  title =        "Image Fusion based on Topographic Mappings using the Hyperbolic Space",
  journal =      "Information Visualization",
  pages =        "266--275",
  year =         "2005",
  volume =       "4",
  number =       "4",
}

@InProceedings{inspek675_bibuniq_874,
  author =       "Azuaje F. Haiying-Wang and Black N.",
  title =        "Biomedical pattern discovery and visualisation based on self-adaptive neural networks",
  booktitle =    "Conference Proceedings. 4th International {IEEE} {EMBS} Special Topic Conference on Information Technology Applications in Biomedicine",
  pages =        "306--309",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "With the rapid growth of the amount of biomolecular data, there is an increasing need to develop powerful techniques to enhance pattern discovery capabilities of bioscientists and machines. Based on a self-adaptive neural network, this paper presents a new approach to biomolecular pattern identification and visualisation. the method is applied to the classification of leukaemia samples, which are described by their expression profiles. the results indicate that this framework may significantly facilitate and improve pattern discovery and visualisation tasks, in comparison to traditional algorithms such as the {K}ohonen self-organising map.",
}

@InProceedings{Arnonkijpanich03a_bibuniq_1584,
  author =       "B. Arnonkijpanich and C. Lursinsap",
  title =        "Geometrical frame identification of 2-{D} structural objects by recursively bifurcating {SOM} and {KL} transformation",
  booktitle =    "SMC'03 Conference Proceedings. 2003 {IEEE} International Conference on Systems, Man and Cybernetics. Conference Theme System Security and Assurance",
  year =         "2003",
  volume =       "5",
  pages =        "4230--4235",
  abstract =     "Identifying the characteristics, structure, or features of a given set of data is the most essential step prior to the classification, recognition, or even understanding process. the identified features must preserve the nature of the data and the irrelevant features should be excluded after the extraction process. This study concerns the extraction of the actual 2-dimensional geometrical frame of each element from a set of given 2-dimensional structural objects. Our approach is based on self-organizing mapping and Karhunen-Loeve transformation. the number of clusters needs not be satisfied in advance as in the other approaches.",
}

@Article{barshan03a_bibuniq_386,
  author =       "B. Barshan and B. Ayrulu",
  title =        "Comparative analysis of different approaches to target differentiation and localization with sonar",
  journal =      "Pattern Recognition",
  year =         "2003",
  volume =       "36",
  number =       "5",
  month =        "May",
  pages =        "1213--1231",
}

@Article{biswas02a_bibuniq_445,
  author =       "B. Biswas and A. Konar",
  title =        "Speaker identification from voice using neural networks",
  journal =      "Journal of Scientific \& Industrial Research",
  year =         "2002",
  volume =       "61",
  number =       "8",
  month =        "August",
  pages =        "599--606",
}

@Article{brodaric04a_bibuniq_206,
  author =       "B. Brodaric and M. Gahegan and R. Harrap",
  title =        "The art and science of mapping: computing geological categories from field data",
  journal =      "Computers \& Geosciences",
  year =         "2004",
  volume =       "30",
  number =       "7",
  month =        "August",
  pages =        "719--740",
}

@Article{castellani03a_bibuniq_278,
  author =       "B. Castellani and J. Castellani and S. L. Spray",
  title =        "Grounded neural networking: Modeling complex quantitative data",
  journal =      "Symbolic Interaction",
  year =         "2003",
  volume =       "26",
  number =       "4",
  pages =        "577--589",
}

@InProceedings{Chakraborty02a_bibuniq_1754,
  author =       "B. Chakraborty",
  title =        "A neural network based seafloor classification using acoustic backscatter",
  booktitle =    "Advances in Soft-Computing-Afss-2002. 2002-Afss International Conference on Fuzzy-Systems. Proceedings Lecture Notes in Artificial Intelligence",
  year =         "2002",
  volume =       "2275",
  pages =        "245--250",
  abstract =     "This paper presents study results of the artificial neural network (ANN) architectures (self organizing map ({SOM}) and multi-layer perceptron (MLP)) using single beam echosounding data. the single beam echosounder, operable at 12 kHz, has been used for backscatter data acquisitions from three distinctly different seafloors from the Arabian Sea. With some preprocessing of the snapshots, the performance of the {SOM} network is observed to be quite good. For the unsupervised {SOM} network, only a single snapshot is used for the training, and a number of snapshots for subsequent testing of the network. Feature selection from ASCII data is an important component for a supervised MLP based network. Four selected features are used for training the the network. the test results of the MLP based network are also discussed.",
}

@Article{chakraborty03a_bibuniq_322,
  author =       "B. Chakraborty and E. Lourenco and V. Kodagali and J. Baracho",
  title =        "Application of artificial neural networks to segmentation and classification of topographic profiles of ridge-flank seafloor",
  journal =      "Current Science",
  year =         "2003",
  volume =       "85",
  number =       "3",
  month =        "August",
  pages =        "306--312",
}

@Article{chakraborty03b_bibuniq_373,
  author =       "B. Chakraborty and V. Kodagali and J. Baracho",
  title =        "Sea-floor classification using multibeam echo-sounding angular backscatter data: {A} real-time approach employing hybrid neural network architecture",
  journal =      "{IEEE} Journal of Oceanic Engineering",
  year =         "2003",
  volume =       "28",
  number =       "1",
  month =        "January",
  pages =        "121--128",
}

@Article{xu02a_bibuniq_437,
  author =       "B. G. Xu and S. Lin",
  title =        "Automatic color identification in printed fabrics by a neural-fuzzy system",
  journal =      "{AATCC} Review",
  year =         "2002",
  volume =       "2",
  number =       "9",
  month =        "September",
  pages =        "42--45",
}

@Article{hammer04a_bibuniq_253,
  author =       "B. Hammer and A. Micheli and A. Sperduti and M. Strickert",
  title =        "A general framework for unsupervised processing of structured data",
  journal =      "Neurocomputing",
  year =         "2004",
  volume =       "57",
  month =        "March",
  pages =        "3--35",
}

@InProceedings{hammer02b_bibuniq_4947,
  author =       "B. Hammer and A. Rechtien and M. Strickert and T. Villmann",
  title =        "Rule extraction from self-organizing networks",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "877--883",
  abstract =     "",
}

@InProceedings{hammer02a_bibuniq_4942,
  author =       "B. Hammer and M. Strickert and T. Villmann",
  title =        "Learning vector quantization for multimodal data",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "370--376",
  abstract =     "",
}

@Article{hasi04a_bibuniq_201,
  author =       "B. Hasi and J. W. Ma and Q. Q. Li and X. Z. Han and Z. L. Liu",
  title =        "Self-organizing feature map neural network classification of the Aster data based on wavelet fusion",
  journal =      "Science in China Series D-Earth Sciences",
  year =         "2004",
  volume =       "47",
  number =       "7",
  month =        "July",
  pages =        "651--658",
}

@Article{Zafar04a_bibuniq_1428,
  author =       "B. J. Zafar and V. Chandrasekar",
  title =        "{SOM} of space borne precipitation radar rain profiles on global scale",
  journal =      "Igarss-2004. 2004 {IEEE} International-Geoscience and Remote-Sensing {IEEE} vol. 2",
  year =         "2004",
  volume =       "2",
  pages =        "",
  abstract =     "The Precipitation Radar (PR) from the Tropical Rainfall Measuring Mission (TRMM) produces high resolution vertical profiles of precipitation. Extensive information about the type of storm is contained in its vertical structure. This paper develops classification methodology precipitation radar profiles using self organizing maps. Reflectivity observation of vertical rain profile on global scale obtained from TRMM PR is classified with Self-Organizing Maps. the methodology is demonstrated by computing a {SOM} for a month of TRMM radar data around the globe. A sample application of the methodology is provided to study the difference between east and west Pacific Ocean.",
}

@Article{mailachalam02a_bibuniq_404,
  author =       "B. Mailachalam and T. Srikanthan",
  title =        "Area-time issues in the {VLSI} implementation of self organizing map neural networks",
  journal =      "Microprocessors and Microsystems",
  year =         "2002",
  volume =       "26",
  number =       "9-10",
  month =        "December",
  pages =        "399--406",
}

@InProceedings{Kumar03a_bibuniq_1630,
  author =       "B. P. V. Kumar and P. Venkataram",
  title =        "Reliable multicast routing in mobile networks: a neural-network approach",
  booktitle =    "{IEE} Proceedings Communications",
  year =         "2003",
  volume =       "150",
  number =       "5",
  month =        "October",
  pages =        "377--384",
  abstract =     "For the sophisticated organisation of multicast communications in mobile networks, reliable and secure point-to-point, point-to-multipoint specific group communication is required. A reliable multicast tree is an efficient connectivity between the source node and the group members through dependable hosts. When a mobile host changes its access point, multicast routes must be updated. This poses several challenges to providing efficient multicast routing. A neural-network-based multicast routing algorithm is proposed for constructing a reliable multicast tree to connect multicast group participants. the mobile network is divided into clusters of nodes (mobile support stations) based on their adjacency relation, by considering a suitable neighbourhood distance. the centre cluster, whose nodes are almost equidistant from the group members, is computed to construct the shortest multicast tree that passes through the centre cluster and reliable routers among all the group members. A {K}ohonen self-organising-map neural network is used for clustering. Hopfield neural networks are used to construct a multicast tree which has a minimum number of links and passes through the nodes of the centre cluster. the tree is constructed as and when members move. This scheme should construct a reliable multicast tree and minimise recomputation time of the tree when the route is updated as mobile hosts change their access point. the computational power of the proposed algorithm is demonstrated by simulation. It is also tested for the mobility of participating mobile hosts. the proposed work facilitates a possible multicast routing algorithm for future high-speed mobile networks.",
}

@InProceedings{lee04b_bibuniq_4536,
  author =       "B. R. Lee and K. Park and H. C. Kang and H. Kim and C. Kim",
  title =        "Adaptive local binariation method for recognition of vehicle license plates",
  booktitle =    "Combinatorial Image Analysis, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "646--655",
  abstract =     "",
}

@Article{yang04c_bibuniq_264,
  author =       "B. S. Yang and T. Han and Y. S. Kim",
  title =        "Integration of {ART}-{K}ohonen neural network and case-based reasoning for intelligent fault diagnosis",
  journal =      "Expert Systems With Applications",
  year =         "2004",
  volume =       "26",
  number =       "3",
  month =        "April",
  pages =        "387--395",
}

@Article{yang05c_bibuniq_72,
  author =       "B. S. Yang and W. W. Hwang and M. H. Ko and S. J. Lee",
  title =        "Cavitation detection of butterfly valve using support vector machines",
  journal =      "Journal of Sound and Vibration",
  year =         "2005",
  volume =       "287",
  number =       "1-2",
  month =        "October",
  pages =        "25--43",
}

@InProceedings{taba05a_bibuniq_48,
  author =       "B. Taba and K. Boahen",
  title =        "Balancing guidance range and strength optimizes self-organization by silicon growth cones",
  booktitle =    "Artificial Neural Networks: Formal Models and Their Applications - {ICANN} 2005, Pt. 2, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "548--557",
}

@Article{yoon02a_bibuniq_439,
  author =       "B. U. Yoon and C. B. Yoon and Y. T. Park",
  title =        "On the development and application of a self-organizing feature map-based patent map",
  journal =      "R \& D Management",
  year =         "2002",
  volume =       "32",
  number =       "4",
  month =        "September",
  pages =        "291--300",
}

@Article{wyns04a_bibuniq_242,
  author =       "B. Wyns and L. Boullart and S. Sette and D. Baeten and I. Hoffman and F. De Keyser",
  title =        "Prediction of arthritis using a modified {K}ohonen mapping and case based reasoning",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2004",
  volume =       "17",
  number =       "2",
  month =        "March",
  pages =        "205--211",
}

@InProceedings{inspek42_bibuniq_1160,
  author =       "Baez-Monroy V. O. and O'Keefe S.",
  editor =       "S. {Duch, W. ; Kacprzyk, J. ; Oja, E. ; Zadrozny}",
  title =        "Principles of employing a self-organizing map as a frequent itemset miner",
  booktitle =    "Artificial Neural Networks:-Biological Inspirations {ICANN} 2005--15th International Conference. Proceedings, Part I Lecture Notes in Computer Science",
  pages =        "363--370",
  volume =       "3696",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This work proposes a theoretical guideline in the specific area of frequent itemset mining (FIM). It supports the hypothesis that the use of neural network technology for the problem of association rule mining (ARM) is feasible, especially for the task of generating frequent itemsets and its variants (e. g. maximal and closed). We define some characteristics which any neural network must have if we would want to employ it for the task of FIM. Principally, we interpret the results of experimenting with a self-organizing map ({SOM}) for this specific data mining technique.",
}

@InProceedings{inspek395_bibuniq_1041,
  author =       "Bajcar E. and Calvert D. and Thomason J.",
  title =        "Analysis of equine gaitprint and other gait characteristics using self-organizing maps ({SOM})",
  booktitle =    "2004 {IEEE} International Joint Conference on Neural Networks",
  pages =        "23--27",
  volume =       "4",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Detection and evaluation of lameness by visual assessment requires the examiner to consider several different and rapidly changing body movement patterns. When the patterns become too complex, tools like artificial neural networks (ANN) can be useful. ANNs can be gainfully applied to gait analysis to distinguish stride characteristics and to identify pathological gait. the self-organizing map ({SOM}) is trained to cluster strain measurement data collected from a single hoof of moving horses. An analysis of the characteristics of the data and the effects of testing different data elements is examined. the method was successful in differentiating certain stride characteristics such as shoeing, gait, speed, and direction of movement and produced a unique model for each horse's gait.",
}

@InProceedings{inspek538_bibuniq_504,
  author =       "Bakus J. and Hussin M. F. and Kamel M.",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "A {SOM}-based document clustering using phrases",
  booktitle =    "{ICONIP'02} Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        "2212--2216",
  volume =       "5",
  year =         "2002",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "Most of the existing techniques for document clustering rely on a {"}bag of words{"} document representation. Each word in the document is considered as a separate feature, ignoring the word order. We investigate the use of phrases rather than words as document features for the document clustering. We present a phrase grammar extraction technique, and use the extracted phrases as the features in a self-organizing map based document clustering algorithm. We present clustering results using the REUTERS corpus and show an improvement in clustering performance using both entropy and F-measure evaluation measures.",
}

@Article{inspek892_bibuniq_682,
  author =       "Baraldi A. and Alpaydin E.",
  title =        "Constructive feedforward {ART} clustering networks. {II}",
  journal =      "{IEEE} Transactions on Neural Networks",
  pages =        "662--677",
  year =         "2002",
  month =        "May",
  volume =       "13",
  number =       "3",
  publisher =    "IEEE",
  abstract =     "For pt. I see ibid., p. 645-61 (2002). Part I of this paper defines the class of constructive unsupervised on-line learning simplified adaptive resonance theory (SART) clustering networks. Proposed instances of class SART are the symmetric fuzzy ART (S-Fuzzy ART) and the Gaussian ART (GART) network. in Part II of our work, a third network belonging to class SART, termed fully self-organizing SART (FOSART), is presented and discussed. FOSART is a constructive, soft-to-hard competitive, topology-preserving, minimum-distance-to-means clustering algorithm capable of: 1) generating processing units and lateral connections on an example-driven basis and 2) removing processing units and lateral connections on a minibatch basis. FOSART is compared with Fuzzy ART, S-Fuzzy ART, GART and other well-known clustering techniques (e. g., neural gas and self-organizing map) in several unsupervised learning tasks, such as vector quantization, perceptual grouping and 3-D surface reconstruction. These experiments prove that when compared with other unsupervised learning networks, FOSART provides an interesting balance between easy user interaction, performance accuracy, efficiency, robustness, and flexibility.",
}

@InProceedings{inspek632_bibuniq_832,
  author =       "Barbalho J. M. and Costa J. A. F. and Neto A. D. D. and Netto M. L. A.",
  title =        "Hierarchical and dynamic {SOM} applied to image compression",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "753--758",
  volume =       "1",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A new hierarchal structure of the self-organizing map ({SOM}) with dynamic growth is presented and applied to codebook design in vector quantization (VQ) and image compression. the tree-structured approach for codebook design is motivated for reducing the high computational efforts in the training and image coding phases in traditional VQ algorithms. the DHSOM has the ability to self determine the structure of the network through heuristically rules, and its final structure reflects the variability of the data (image blocks). It is shown that training that training and coding times obtained with DHSOM algorithm are faster than conventional {SOM} and LBG algorithms, while the qualitative results are given.",
}

@InProceedings{inspek419_bibuniq_1063,
  author =       "Barsi A.",
  title =        "Generalization of topology preserving maps: a graph approach",
  booktitle =    "2004 {IEEE} International Joint Conference on Neural Networks",
  pages =        "809--813",
  volume =       "4",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The paper presents a novel algorithm, which is based on the self-organizing map ({SOM}) method. the combination of an undirected acyclic graph with the {K}ohonen learning rule results the efficient self-organizing neuron graph (SONG) algorithm. It has two modi: one is based on the adjacency information of the neuron graph, the other integrates an all-pair shortest path function, which permanently updates a generalized distance matrix. the newly developed SONG techniques were involved in pattern recognition tasks, where they proved their efficiency and flexibility.",
}

@InProceedings{inspek201_bibuniq_1297,
  author =       "Beixing-Deng {Minghu-Jiang, Chengqing-Zong}",
  editor =       "Z. {Wang, J. ; Liao, X. ; Yi}",
  title =        "Self-organizing map analysis of conceptual and semantic relations for noun",
  booktitle =    "Advances in Neural Networks {ISNN} 2005. Second International Symposium on Neural Networks. Proceedings, Part III Lecture Notes in Computer Science",
  pages =        "977--982",
  volume =       "3",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "In this paper, we analyzed self-organizing map of conceptual and semantic relations for noun, discussing the semantic distinction between conceptual nouns for natural language processing and syntax acquisition, summarizing the lexical meaning and a detailed description of semantic lexical tagging of nouns. Our result reflects the noun-attribute associations and focuses on the conceptual relationships. By using several features, map models provide an operational definition of the conceptual nouns distinction.",
}

@InProceedings{inspek323_bibuniq_973,
  author =       "Ben-Khalifa K. and Girau B. and Alexandre F. and Bedoui M. H.",
  editor =       "M. {Masmoudi, M. ; El-Masry, M. I. ; Abid}",
  title =        "Parallel {FPGA} implementation of self-organizing maps",
  booktitle =    "The 16th International Conference on Microelectronics",
  pages =        "709--712",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper presents an area-saving parallel implementation of a self-organizing map neural network ({SOM}) on FPGA. the purpose is to make available a finer grain of parallelism to be used in massively SIMD parallel {SOM} system architectures. We have handled a serial arithmetics (most significant bit first: MSBF and least significant bit first: LSBF), to process the different mathematical operations. Above all, our work has been oriented in such a way to get a light, easy to wear system for classification of vigilance states in humans from electroencephalographic (EEG) signals. the performances of our implementation in terms of area, speed and especially power consumption are highly satisfactory.",
}

@InProceedings{inspek95_bibuniq_1202,
  author =       "Berglund E. and Sitte J.",
  title =        "Sound source localisation through active audition",
  booktitle =    "2005 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems",
  pages =        "653--658",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper presents a novel method for enabling a robot to determine the direction to a sound source through interacting with its environment. the method uses a new neural network, the parameter-less self-organizing map algorithm, and reinforcement learning to achieve rapid and accurate response.",
}

@InProceedings{inspek320_bibuniq_971,
  author =       "Bing Shuan Qing and Dong Sun and Tian Shu Huang and Ge Li and Fu Xong Sun}",
  title =        "The fault tendency analysis of hydro-generator based on {WNN}",
  booktitle =    "Proceedings of 2004 International Conference on Machine Learning and Cybernetics {IEEE}",
  pages =        "3090--3094",
  volume =       "5",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In the research of fault diagnosis of the machinery, it is necessary to analyze the fault tendency of machinery. An approach is designed that predicts and controls the futural running status of machine in time by wavelet neural network. By this approach, feature analyzes the tendency of machine fault visually with histogram. Information is first extracted from original machinery signal by wavelet packet transform. Then, feature information is put into {K}ohonen self-organized mapping neural network to be clustered and form some different classification spaces of running status of machine. Finally, analyzing correlation between feature information during different running periods to predict the futural running status of machine. Experiment shows that the method works well in the fault diagnosis and tendency analysis of hydro-generator.",
}

@InProceedings{inspek753_bibuniq_575,
  author =       "E. Bingham and J. Kuusisto and K. Lagus",
  editor =       "K. {Beulieu, M. ; Baeza-Yates, R. ; Myaeng, S. H. ; J\"{a}rvelin}",
  title =        "{ICA} and {SOM} in text document analysis",
  booktitle =    "Proceedings of {SIGIR} 2002. Twenty Fifth Annual International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval",
  pages =        "361--362",
  year =         "2002",
  publisher =    "ACM, New York, NY, USA",
  abstract =     "In this study we show experimental results on using independent component analysis ({ICA}) and the self-organizing map ({SOM}) in document analysis. Our documents are segments of spoken dialogues carried out over the telephone in a customer service, transcribed into text. the task is to analyze the topics of the discussions, and to group the discussions into meaningful subsets. the quality of the grouping is studied by comparing to a manual topical classification of the documents.",
}

@InProceedings{Lessmann_ICANN2005_bibuniq_4118,
  author =       "Birgit Lessmann and Tim Wilhelm Nattkemper and Andreas Degenhard and Linda Pointon and Preminda Kessar and Michael Khazen and Martin O. Leach",
  title =        "{SOM}-based wavelet filtering for the exploration of medical images",
  booktitle =    "Proceedings of the 15th International Conference on Artificial Neural Networks",
  pages =        "671",
  month =        "September",
  year =         "2005",
  series =       "Lecture Notes in Computer Science",
  volume =       "3696",
}

@InProceedings{inspek191_bibuniq_1289,
  author =       "Bo-Yu {Qi-Wang, Jie-Zhu}",
  editor =       "Z. {Wang, J. ; Liao, X. ; Yi}",
  title =        "Combining classifiers in software quality prediction: a neural network approach",
  booktitle =    "Advances in Neural Networks {ISNN} 2005. Second International Symposium on Neural Networks. Proceedings, Part III Lecture Notes in Computer Science",
  pages =        "921--926",
  volume =       "3",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "Software quality prediction models seek to predict quality factors such as whether a component is fault prone or not. This can be treated as a kind of pattern recognition problem. in pattern recognition, there is a growing use of multiple classifier combinations with the goal to increase recognition performance. in this paper, we propose a neural network approach to combine multiple classifiers. the combination network consists of two neural networks: a {K}ohonen self-organization network and a multilayer perceptron network. the multilayer perceptron network is used as dynamic selection network (DSN) and {K}ohonen self-organization network is served as the final combiner. A case study illustrates our approach and provides the evidence that the combination network with DSN performs better than some other popular combining schemes and the DSN can efficiently improve the performance of the combination network.",
}

@InProceedings{inspek190_bibuniq_1288,
  author =       "Bo-Zhang {Minghu-Jiang, Huiying-Cai}",
  editor =       "Z. {Wang, J. ; Liao, X. ; Yi}",
  title =        "Self-organizing map analysis consistent with neuroimaging for {C}hinese noun, verb and class-ambiguous word",
  booktitle =    "Advances in Neural Networks {ISNN} 2005. Second International Symposium on Neural Networks. Proceedings, Part III Lecture Notes in Computer Science",
  pages =        "971--976",
  volume =       "3",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "In the paper we discussed the semantic distinction between {C}hinese noun, verb, and class-ambiguous word by using {SOM} (self-organizing map) neural networks. Comparing neuroimaging method with neural network method, our result shows neural network technique can be used to study lexical meaning, syntax relation and semantic description for the three kinds of words. After all, the response of human brain to {C}hinese lexical information is based mainly on conceptual and semantic attributes, seldom uses {C}hinese syntax and grammar features. Our experimental results are coincident with human brain's neuroimaging, our analysis helps to understand the role of feature description and relation of syntax and semantic features.",
}

@InProceedings{inspek534_bibuniq_501,
  author =       "Bojarczak P. and Osowski S.",
  title =        "Hybrid network application to fault detection in analog circuits",
  booktitle =    "25th Miedzynarodowa Konferenceja Z-Podstaw Elektrotechniki I-Teorii Obwodow. IC SPETO 25th International Conference on Fundamentals of Electrotechnics and Circuit Theory. {IC} {SPETO}",
  pages =        "533--536",
  volume =       "2",
  year =         "2002",
  publisher =    "National Education Ministry, Warsaw, Poland",
  abstract =     "The paper is concerned with the application of artificial neural network to the fault location in analog electrical circuits. the recognition of a fault is based on the measurements of terminal voltage and current of the network. the self-organizing {K}ohonen network is applied as the recognizing system and as the classifier. the numerical results of recognition of faulty elements in analog filter are presented and discussed in the paper.",
}

@InProceedings{inspek177_bibuniq_1277,
  author =       "Bonnel N. and Cotarmanac'h A. and Morin A.",
  editor =       "G. {Banissi, E. ; Sarfraz, M. ; Roberts, J. C. ; Loften, B. ; Ursyn, A. ; Burkhard, R. A. ; Lee, A. ; Andrienko}",
  title =        "Meaning metaphor for visualizing search results",
  booktitle =    "Proceedings. Ninth International Conference on Information Visualisation",
  pages =        "467--472",
  year =         "2005",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "While searching the Web, the user is often confronted by a great number of results, generally sorted by their rank. These results are then displayed as a succession of ordered lists. Facing the limits of this approach, we propose a prototype to explore new organizations and presentations of search results, as well as new types of interactions with the results in order to make their exploration more intuitive and efficient. the main topic of this paper is the processing of the results coming from an information retrieval system. Although the relevance depends on the result quality, the effectiveness of the result processing represents an alternative way to improve the relevance for the user. Given the current expectations, this processing is composed by an organization step and a visualization step. Then the proposed prototype organizes the results according to their meaning using a {K}ohonen self-organizing map, and also visualizes them in a 3{D} scene to increase the representation space. the 3{D} metaphor proposed here is a city.",
}

@Article{inspek782_bibuniq_600,
  author =       "Bortolan G. and Pedrycz W.",
  title =        "Fuzzy descriptive models: an interactive framework of information granulation [{ECG} data]",
  journal =      "{IEEE} Transactions on Fuzzy Systems",
  pages =        "743--755",
  volume =       "10",
  number =       "6",
  year =         "2002",
  month =        "December",
  publisher =    "IEEE",
  abstract =     "In this paper, we introduce and discuss an important class of endeavors of fuzzy modeling, such as fuzzy descriptive models. in a nutshell, the objective of fuzzy descriptive models is to provide with a sound, comprehensible, and relevant description of experimental data at a general level of relationships revealed there. the elements of such models called descriptors are inherently information granules as the notion of granularity goes hand in hand with the interpretability of the resulting constructs (information granules). This paper elaborates on the use of the language of fuzzy sets that are viewed as generic models of information granules. the development of the information granules is carried out in an interactive manner in which a designer can inspect a structure in a data set in a visual fashion. Such visualization is possible through a suitable visualization vehicle provided by self-organizing maps. the role of the designer is to choose from some already visualized regions of the self-organizing map characterized by a high level of data homogeneity. We provide a new algorithm of constructing membership functions of the information granules (fuzzy sets). in addition to some synthetic data, the study includes a comprehensive descriptive modeling of highly dimensional electrocardiogram data.",
}

@Article{inspek253_bibuniq_1338,
  author =       "Boschetti F.",
  title =        "Controlling and investigating cellular automaton behavior via interactive inversion and visualization of the search space",
  journal =      "New Generation Computing",
  pages =        "157--169",
  volume =       "23",
  number =       "2",
  year =         "2005",
  publisher =    "Ohmsha Ltd",
  abstract =     "An interactive genetic algorithm (IGA) provides a means to optimize the input parameters controlling the behavior of a cellular automaton (CA). the result is one or more combinations of parameters that allow the CA to reproduce geological patterns of fluid flow and chemical reactions in fractured media. Via the IGA, the user can provide subjective feedback on the quality of the CA results, which would otherwise be difficult to express numerically. A simple modification to the IGA ranking process, combined with a self-organizing map, enables the rapid on-line visualization of the high-dimensional parameter space, and consequent control over the inversion itself. the insights into the topology of the parameter space offer an understanding of which parameters control different CA behaviors.",
}

@InProceedings{inspek209_bibuniq_919,
  author =       "Boudor M. and Hellal A.",
  title =        "Large scale power system dynamic security assessment using the growing hierarchical self-organizing feature maps",
  booktitle =    "2004 {IEEE} International Conference on Industrial Technology",
  pages =        "370--375",
  volume =       "1",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper proposes a new methodology which combines supervised and unsupervised learning for evaluating power system dynamic security. Based on the concept of stability margin, pre-fault power system conditions are assigned to the output neurons on the two-dimensional grid with the growing hierarchical self-organizing map technique (GHSOM) via supervised ANNs which performs an estimation of post-fault power system state. the technique estimates the dynamic stability index that corresponds to the most critical value of synchronizing and damping torques of multimachine power systems. ANN-based pattern recognition is carried out with the growing hierarchical self-organizing feature mapping in order to provide an adaptive neural net architecture during its unsupervised training process. Numerical tests, carried out on a {IEEE} 9 bus power system are presented and discussed. the analysis using such method provides accurate results and improves the effectiveness of system security evaluation.",
}

@Article{inspek137_bibuniq_1240,
  author =       "Boudour M. and Hellal A.",
  title =        "Combined use of supervised and unsupervised learning for power system dynamic security mapping",
  journal =      "Engineering Applications of Artificial Intelligence",
  pages =        "673--683",
  volume =       "18",
  number =       "6",
  year =         "2005",
  month =        "September",
  publisher =    "Elsevier",
  abstract =     "This paper proposes a new methodology which combines supervised and unsupervised learning for evaluating power system dynamic security. Based on the concept of stability margin, pre-fault power system conditions are assigned to the output neurons on the two-dimensional grid with the growing hierarchical self-organizing map technique (GHSOM) via supervised artificial neural networks (ANNs) which perform an estimation of post-fault power system state. the technique estimates the dynamic stability index that corresponds to the most critical value of synchronizing and damping torques of multimachine power systems. ANN-based pattern recognition is carried out with the growing hierarchical self-organizing feature mapping in order to provide adaptive neural network architecture during its unsupervised training process. Numerical tests, carried out on a {IEEE} 9 bus power system are presented and discussed. the analysis using such method provides accurate results and improves the effectiveness of system security evaluation. [All rights reserved Elsevier].",
}

@InProceedings{inspek17_bibuniq_1145,
  author =       "Bougrain L. Mohammed-Attik and Alexandre F.",
  editor =       "S. {Duch, W. ; Kacprzyk, J. ; Oja, E. ; Zadrozny}",
  title =        "Self-organizing map initialization",
  booktitle =    "Artificial Neural Networks: Biological Inspirations {ICANN} 2005--15th International Conference. Proceedings, Part I Lecture Notes in Computer Science",
  pages =        "357--362",
  volume =       "3696",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "The solution obtained by self-organizing map ({SOM}) strongly depends on the initial cluster centers. However, all existing {SOM} initialization methods do not guarantee to obtain a better minimal solution. Generally, we can group these methods in two classes: random initialization and data analysis based initialization classes. This work proposes an improvement of linear projection initialization method. This method belongs to the second initialization class. Instead of using regular rectangular grid our method combines a linear projection technique with irregular rectangular grid. By this way the distribution of results produced by the linear projection technique is considered. the experiments confirm that the proposed method gives better solutions compared to its original version.",
}

@InProceedings{inspek511_bibuniq_745,
  author =       "Brabazon T. and McGonagle A.",
  editor =       "Y. {Arabnia, H. R. ; Joshua, R. ; Mun}",
  title =        "Self-organising maps: recognising patterns of corporate underperformance",
  booktitle =    "Proceedings of the International Conference on Artificial Intelligence {IC} {AI}'03",
  pages =        "931--937",
  year =         "2003",
  publisher =    "CSREA Press, Las Vegas, NV, USA",
  abstract =     "This study examines the potential of a form of neural network, a self-organising map, to predict corporate distress using information drawn from financial statements. Two models are developed, a linear discriminant analysis model and a self-organised map. A sample of 178 matched, publicly quoted, failed and nonfailed US firms, drawn from the period 1991 to 2000 are used to train and test the model. the best self-organised map correctly classified 96. 1 (95. 3)\% of the firms in the training set, one (two) year(s) prior to failure, and 86 (64)\% in the out of sample validation set. the LDA model correctly categorised 81. 3 (76. 6)\% and 78 (58)\% respectively. the preliminary results provide support for a hypothesis that corporate distress can be predicted, and that self-organised maps can be useful for this purpose.",
}

@InProceedings{inspek714_bibuniq_545,
  author =       "Buche D. and Guidati G. and Stoll P. and Koumoutsakos P.",
  editor =       "H-P. {Guervos, J. J. M. ; Adamidis, P. ; Beyer, H-G. ; Fernandez-Villacanas, J-L. ; Schwefel}",
  title =        "Self-organizing maps for Pareto optimization of airfoils",
  booktitle =    "Parallel Problem Solving from Nature {PPSN} {VII}. 7th International Conference. Proceedings Lecture Notes in Computer Science",
  pages =        "122--131",
  volume =       "2439",
  year =         "2002",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This work introduces a new recombination and a new mutation operator for an accelerated evolutionary algorithm in the context of Pareto optimization. Both operators are based on a self-organizing map, which is actively learning from the evolution in order to adapt the mutation step size and improve convergence speed. Standard selection operators can be used in conjunction with these operators. the new operators are applied to the Pareto optimization of an airfoil for minimizing the aerodynamic profile losses at the design operating point and maximizing the operating range. the profile performance is analyzed with a quasi 3{D} computational fluid dynamics (Q3D CFD) solver for the design condition and two off-design conditions (one positive and one negative incidence). the new concept is to define a free scaling factor, which is multiplied to the off-design incidences. the scaling factor is considered as an additional design variable and at the same time as an objective function for indexing the operating range, which has to be maximized. We show that 2 off-design incidences are sufficient for the Pareto optimization and that the computation of a complete loss polar is not necessary. in addition, this approach answers the question of how to set the incidence values by defining them as design variables of the optimization.",
}

@InProceedings{inspek375_bibuniq_1023,
  author =       "Byeong Man-Kim and Jong Wan Kim and Hee Jae Kim and Sin Jae Kang}",
  editor =       "W. K. {Zhang, C. ; Guesgen, H. W. ; Yeap}",
  title =        "Determination of usenet news groups by fuzzy inference and {K}ohonen network",
  booktitle =    "PRICAI 2004:-Trends in Artificial Intelligence. 8th Pacific Rim International Conference on Artificial Intelligence. Proceedings Lecture Notes in Artificial Intelligence",
  pages =        "654--663",
  volume =       "3157",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "In this work, we present a service determining user's preferred news groups among various ones. For this end, candidate terms from example documents of each news group are extracted and a number of representative keywords among them are chosen through fuzzy inference. They are then presented to {K}ohonen network for learning representative keywords of each news group. From the observation of training patterns, we could find the sparseness problem that lots of keywords in training patterns are empty. Thus, a method to train neural network through reduction of unnecessary dimensions by the statistical coefficient of determination is used in this paper. Experimental results show that the method is superior to the method using every input dimension in terms of cluster overlap defined by using within-cluster distance and between-clusters distance.",
}

@Article{delcarpiomunoz02a_bibuniq_440,
  author =       "C. A. Del Carpio-Munoz and E. Ichiishi and A. Yoshimori and T. Yoshikawa",
  title =        "Miax: {A} new paradigm for modeling biomacromolecular interactions and complex formation in condensed phases",
  journal =      "Proteins-Structure Function and Genetics",
  year =         "2002",
  volume =       "48",
  number =       "4",
  month =        "September",
  pages =        "696--732",
}

@Article{yang03e_bibuniq_402,
  author =       "C. C. Yang and H. Chen and K. Hong",
  title =        "Visualization of large category map for Internet browsing",
  journal =      "Decision Support Systems",
  year =         "2003",
  volume =       "35",
  number =       "1",
  month =        "April",
  pages =        "89--102",
}

@InProceedings{Yang02a_bibuniq_1677,
  author =       "C. C. Yang and Sai Ho Kwok and Milo Yip",
  title =        "Image browsing for feature-based products",
  booktitle =    "Proceedings of the Spie-The International-Society for Optical-Engineering. 2002",
  year =         "2002",
  volume =       "4925",
  pages =        "350--357",
  abstract =     "In the context of product search in information intermediary or infomediary, text-and navigation-based searching mechanisms such as keyword search are usually adopted. Google, WebSeer, and Alta Vista Photo Finder are some prominent examples. However, such search mechanisms are not efficient for feature-based products and the major problem is that the feature-based products are difficult to be described with textual expression. A potential candidate for the search of feature-based products is query-by-example (QBE). However, our study reveals that QBE is not an ideal searching method for feature-based products. This paper proposes an image browsing technique for the search of feature-based products in infomediary. the image browsing technique allows the users to access feature-based products through a two-dimensional map constructed with self organizing map ({SOM}) technique. the technique overcomes the problem of describing feature-based products. Simple view and pick operations can drive the user to the desired group of products. A task-based user evaluation was conducted to examine the usability of the proposed technique and the experimental results show that the proposed browsing technique is more practical and efficient compared with QBE.",
}

@Article{tsai04a_bibuniq_218,
  author =       "C. F. Tsai and C. W. Tsai and H. C. Wu and T. Yang",
  title =        "Acodf: a novel data clustering approach for data mining in large databases",
  journal =      "Journal of Systems and Software",
  year =         "2004",
  volume =       "73",
  number =       "1",
  month =        "September",
  pages =        "133--145",
}

@Article{garcia-osorio05a_bibuniq_37,
  author =       "C. Garcia-Osorio and C. Fyfe",
  title =        "The combined use of self-organizing maps and Andrews' Curves",
  journal =      "International Journal of Neural Systems",
  year =         "2005",
  volume =       "15",
  number =       "3",
  month =        "June",
  pages =        "197--206",
}

@Article{christodoulou03b_bibuniq_330,
  author =       "C. I. Christodoulou and C. S. Pattichis and M. Pantziaris and A. Nicolaides",
  title =        "Texture-based classification of atherosclerotic carotid plaques",
  journal =      "{IEEE} Transactions on Medical Imaging",
  year =         "2003",
  volume =       "22",
  number =       "7",
  month =        "July",
  pages =        "902--912",
}

@Article{christodoulou03a_bibuniq_294,
  author =       "C. I. Christodoulou and S. C. Michaelides and C. S. Pattichis",
  title =        "Multifeature texture analysis for the classification of clouds in satellite imagery",
  journal =      "{IEEE} Transactions on Geoscience and Remote Sensing",
  year =         "2003",
  volume =       "41",
  number =       "11",
  month =        "November",
  pages =        "2662--2668",
}

@InProceedings{Figueroa00a_bibuniq_4042,
  author =       "C. J. Figueroa and P. A. Estevez",
  title =        "A new visualization scheme for self-organizing neural networks",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks 757-62",
  year =         "2004",
}

@Article{Tan03a_bibuniq_1627,
  author =       "C. K. Tan and S. J. Wilcox and J. Ward and M. Lewitt",
  title =        "Monitoring near burner slag deposition with a hybrid neural network system",
  journal =      "Measurement-Science \& Technology",
  year =         "2003",
  volume =       "14",
  number =       "7",
  pages =        "1137--1145",
  month =        "July",
  abstract =     "This paper is concerned with the development of a system to detect and monitor slag growth in the near burner region in a pulverized-fuel (pf) fired combustion rig. These slag deposits are commonly known as `eyebrows' and can markedly affect the stability of the burner. the study thus involved a series of experiments with two different coals over a range of burner conditions using a 150 kW pf burner fitted with simulated eyebrows. These simulated eyebrows consisted of annular refractory inserts mounted immediately in front of the original burner quarl. Data obtained by monitoring the infra-red radiation and sound emitted by the flame were processed to yield time and frequency-domain features, which were then used to train and test a hybrid neural network. This hybrid `intelligent' system was based on self- organizing map and radial-basis-function neural networks. This system was able to classify different sized eyebrows with a success rate of at least 99. 5\%. Consequently, it is possible not only to detect the presence of an eyebrow by monitoring the flame, but also the network can provide an estimate of the size of the deposit, over a reasonably large range of conditions.",
}

@Article{Fernandez00a_bibuniq_4075,
  author =       "C. L. Fernandez and J. Torres Jimenez and M. A. Reyes Martinez and C. A. Coutino Gomez",
  title =        "Analysis of performance metrics from a database management system using {K}ohonen's self organizing maps",
  journal =      "{WSEAS} Transactions on Systems",
  volume =       "2",
  number =       "3",
  pages =        "629--634",
  month =        "July",
  year =         "2003",
}

@Article{chen03c_bibuniq_395,
  author =       "C. M. Chen and Y. H. Chou and K. C. Han and G. S. Hung and C. M. Tiu and H. J. Chiou and S. Y. Chiou",
  title =        "Breast lesions on sonograms: Computer-aided diagnosis with nearly setting-independent features and artificial neural networks",
  journal =      "Radiology",
  year =         "2003",
  volume =       "226",
  number =       "2",
  month =        "February",
  pages =        "504--514",
}

@Article{risien04a_bibuniq_260,
  author =       "C. M. Risien and C. J. C. Reason and F. A. Shillington and D. B. Chelton",
  title =        "Variability in satellite winds over the Benguela upwelling system during 1999-2000",
  journal =      "Journal of Geophysical Research-Oceans",
  year =         "2004",
  volume =       "109",
  number =       "C3",
  month =        "March",
  pages =        "Online",
}

@Article{yang03d_bibuniq_354,
  author =       "C. M. Yang and B. K. Wan and X. F. Gao",
  title =        "Data preprocessing in cluster analysis of gene expression",
  journal =      "Chinese Physics Letters",
  year =         "2003",
  volume =       "20",
  number =       "5",
  month =        "May",
  pages =        "774--777",
}

@InProceedings{mericli05a_bibuniq_108,
  author =       "C. Mericli and I. O. Tufanogullan and H. L. Akin",
  title =        "World modeling in disaster environments with constructive self-organizing maps for autonomous search and rescue robots",
  booktitle =    "Robocup 2004: Robot Soccer World {CUP} {VIII}, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "25--35",
}

@Article{nolker02a_bibuniq_451,
  author =       "C. Nolker and H. Ritter",
  title =        "Visual recognition of continuous hand postures",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2002",
  volume =       "13",
  number =       "4",
  month =        "July",
  pages =        "983--994",
}

@InProceedings{oh02a_bibuniq_399,
  author =       "C. Oh and S. G. Ritchie",
  title =        "Real-time inductive-signature-based level of service for signalized intersections",
  booktitle =    "Traffic Flow Theory and Highway Capacity 2002, Transportation Research Record",
  year =         "2002",
  pages =        "431--436",
}

@Article{ozkan05a_bibuniq_146,
  author =       "C. Ozkan and F. S. Erbek",
  title =        "Comparing feature extraction techniques for urban land-use classification",
  journal =      "International Journal of Remote Sensing",
  year =         "2005",
  volume =       "26",
  number =       "4",
  month =        "February",
  pages =        "747--757",
}

@InProceedings{panchev02a_bibuniq_4949,
  author =       "C. Panchev and S. Wermter and H. X. Chen",
  title =        "Spike-timing dependent competitive learning of integrate and fire neurons with active dendrites",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "896--901",
  abstract =     "",
}

@InProceedings{Pohl00a_bibuniq_4001,
  author =       "C. Pohl and M. Franzmeier and M. Porrmann and U. Ruckert",
  title =        "g{NBX} - reconfigurable hardware acceleration of self-organizing maps",
  booktitle =    "Proceedings. 2004 {IEEE} International Conference on Field-Programmable-Technology",
  year =         "2004",
  pages =        "97--104",
}

@Article{linder05a_bibuniq_68,
  author =       "C. R. Linder",
  title =        "Self-organization in a simple task of motor control based on spatial encoding",
  journal =      "Adaptive Behavior",
  year =         "2005",
  volume =       "219",
  number =       "A5",
  month =        "August",
  pages =        "383--394",
}

@InProceedings{moller-levet05a_bibuniq_77,
  author =       "C. S. Moller-Levet and H. J. Yin",
  title =        "Circular {SOM} for temporal characterisation of modelled gene expressions",
  booktitle =    "Intelligent Data Engineering and Automated Learning Ideal 2005, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "440--444",
}

@Article{rao04a_bibuniq_207,
  author =       "C. S. Rao and R. R. Srikant",
  title =        "Tool wear monitoring - an intelligent approach",
  journal =      "Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture",
  year =         "2004",
  volume =       "218",
  number =       "8",
  month =        "August",
  pages =        "905--912",
}

@Article{sun03a_bibuniq_377,
  author =       "C. Sun and S. G. Ritchie and S. Oh",
  title =        "Inductive classifying artificial network for vehicle type categorization",
  journal =      "Computer-Aided Civil and Infrastructure Engineering",
  year =         "2003",
  volume =       "18",
  number =       "3",
  month =        "May",
  pages =        "161--172",
}

@InProceedings{Thiebaut02a_bibuniq_1525,
  author =       "C. Thiebaut and M. Boer and S. Roques",
  title =        "Steps towards the development of an automatic classifier for astronomical sources",
  booktitle =    "Proceedings of the Spie-The International-Society for Optical-Engineering",
  year =         "2002",
  volume =       "",
  pages =        "Online",
  abstract =     "We present the progress we have made in implementing a new kind of automatic classifier for astronomical objects. the developed classifier will work both in the image and time domain and take into account the geometrical and temporal characteristics of the sources. We have first constructed a 2{D} classifier which is based on a Self Organizing Map. the developed network is able to learn through experience and to discriminate between astronomical objects such as stars, galaxies, saturated objects or blended objects. in order to recognize and classify variable objects, the method had to be improved. We present the next step of classification through our 3{D} (geometry - time) classifier. the temporal characteristics of the sources are obtained by different analysis of their light curves: time domain, frequency and time-frequency analysis. We add the geometrical and temporal characteristics to obtain a complete classification of the sources. We plan to use the difference image analysis to obtain block of images and analyze them directly through the classifier. Such a complete classification has not yet been realized in the astronomical domain. in general our method works better than other automatic methods and allows a more complete discrimination through astronomical sources.",
}

@Article{liou05a_bibuniq_95,
  author =       "C. Y. Liou and Y. T. Kuo",
  title =        "Conformal self-organizing map for a genus-zero manifold",
  journal =      "Visual Computer",
  year =         "2005",
  volume =       "21",
  number =       "5",
  month =        "June",
  pages =        "340--353",
}

@InProceedings{inspek153_bibuniq_1256,
  author =       "Caixin-Sun {Xiaoxing-Zhang, Haijun-Ren, Yuming-Liu, Qiyun-Cheng}",
  editor =       "Z. Y. {Li, X. ; Wang, S. ; Dong}",
  title =        "The dynamic character curve adjusting model of electric load based on data mining theory",
  booktitle =    "Advanced Data Mining and Applications. First International Conference, {ADMA2005}. Proceedings Lecture Notes in Artificial Intelligence",
  pages =        "626--633",
  volume =       "3584",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "There are a number of dirty data in the load database produced by SCADA system. Consequently, the data must be adjusted carefully and reasonably before being used for electric load forecasting or power system analysis. This paper proposes a dynamic and intelligent curve adjusting model based on data mining theory. Firstly the {K}ohonen neural network is meliorated according to fuzzy soft clustering arithmetic which can realize the collateral calculation of fuzzy c-means soft clustering arithmetic. the proposed dynamic algorithm can automatically find the new clustering center, namely, the character curve of data, according to the updating of swatch data. Combining an RBF neural network with this dynamic algorithm, the intelligent adjusting model is introduced to identify the dirty data. the rapidness and dynamic performance of model make it suitable for real-time calculation. Test results using actual data of Jiangbei power supply bureau in Chongqing demonstrate the effectiveness and feasibility of the model.",
}

@InProceedings{inspek199_bibuniq_1295,
  author =       "Calvert D. and Guan J.",
  title =        "Distributed artificial neural network architectures",
  booktitle =    "Proceedings of the 19th International Symposium on High Performance Computing Systems and Applications",
  pages =        "2--10",
  year =         "2005",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "The computational cost of training artificial neural network (ANN) algorithms limits the use of large systems capable of processing complex problems. Implementing ANNs on a parallel or distributed platform to improve performance is therefore desirable. This work illustrates a method to predict and evaluate the performance of distributed ANN algorithms by analyzing the performance of the comparatively simple mathematical operations, which are used to construct the ANN. the ANN algorithms are divided into simple components: matrix and vector multiplication, matrix processed through a function, competition in a matrix. These basic operational parts are examined individually and it is demonstrated that the computation processes of distributed neural networks can be derived from the composition of these basic operations. Three popular network architectures are examined: multi-layer perceptrons with back-propagation learning, self-organizing map, and radial basis functions network.",
}

@Article{inspek757_bibuniq_900,
  author =       "Cambio R. and Hendry D. C.",
  title =        "Low-power digital neuron for {SOM} implementations",
  journal =      "Electronics Letters",
  pages =        "448--450",
  volume =       "39",
  number =       "5",
  year =         "2003",
  month =        "March",
  publisher =    "IEE",
  abstract =     "A digital implementation of the self-organising map ({SOM}) is shown to have reduced power requirements through a strategy of increasing silicon area while reducing the number of clock cycles required to process each element of an input vector. Designs requiring two clock cycles, one clock cycle, and half clock cycle per element of the input vector have been constructed and analysed. the designs offer a reduction in power of a factor of 3 for an increase in silicon area of some 33\%.",
}

@InProceedings{inspek145_bibuniq_1248,
  author =       "Campbell A. and Berglund E. and Streit A.",
  editor =       "F. {Gallagher, M. ; Hogan, J. Maire}",
  title =        "Graphics hardware implementation of the parameter-less self-organizing map",
  booktitle =    "Intelligent Data Engineering and Automated Learning {IDEAL} 2005, 6th International Conference",
  pages =        "343--350",
  volume =       "3578",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This paper presents a highly parallel implementation of a new type of self-organizing map ({SOM}) using graphics hardware. the parameter-less {SOM} smoothly adapts to new data while preserving the mapping formed by previous data. It is therefore in principle highly suited for interactive use, however for large data sets the computational requirements are prohibitive. This paper presents an implementation on commodity graphics hardware which uses two forms of parallelism to significantly reduce this barrier. the performance is analyzed experimentally and algorithmically. An advantage to using graphics hardware is that visualization is essentially {"}free{"}, thus increasing its suitability for interactive exploration of large data sets.",
}

@InProceedings{inspek16_bibuniq_1144,
  author =       "Campoy P. and Vicente C. J.",
  editor =       "S. {Duch, W. ; Kacprzyk, J. ; Oja, E. ; Zadrozny}",
  title =        "Residual activity in the neurons allows {SOM}s to learn temporal order",
  booktitle =    "Artificial Neural Networks: Biological Inspirations {ICANN} 2005--15th International Conference",
  pages =        "379--384",
  volume =       "3696",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "A novel activity associated to the neurons of a SOM, called residual activity (RA), is defined in order to enlarge into the temporal domain the capabilities of a self-organizing map for clustering and classifying the input data when it offers a temporal relationship. This novel activity is based on the biological plausible idea of partially retaining the activity of the neurons for future stages, that increases their probability to become the winning neuron for future stimuli. the proposed paper also proposes two quantifiable parameters for evaluating the performances of algorithms that aim to exploit temporal relationship of the input data for classification. Special designed benchmarks with spatio-temporal relationship are presented in which the proposed new algorithm, called TESOM (acronym for time enhanced SOM), has demonstrated to improve the temporal index without decreasing the quantization error.",
}

@InProceedings{inspek257_bibuniq_929,
  author =       "Cao H. and Kot A. C.",
  title =        "Modified {K}ohonen learning network and application in {C}hinese character recognition",
  booktitle =    "{TENCON} Region 10 Conference",
  pages =        "136--139",
  volume =       "2",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Normal multilayer neural network is rarely used to solve pattern match problem of large scale without grouping classes and creating subnetworks. in this paper, a modified single-layer {K}ohonen learning network structure based on generalized learning vector quantization (G{LVQ}) theory is proposed. By cascading two of the proposed learning networks in handwritten {C}hinese character recognition, training, preclassification and final recognition processes are easily integrated. Experiments conducted with off-line handwritten samples show the efficiency of the network.",
}

@InProceedings{inspek535_bibuniq_502,
  author =       "Carlier F. and Nouvel F.",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "Kohonen neural networks for multi-user detection in {CDMA} systems",
  booktitle =    "{ICONIP}'02. Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        "2289--2293",
  volume =       "5",
  year =         "2002",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "Performance and implementation complexity issues restrict standard multi-user detection methods in the forthcoming high transmission rate system, based on code division multiple access. We propose self-organized neural networks to cope with this issue and suggest that an optimal multi-user detector can be implemented by using a {K}ohonen network.",
}

@InProceedings{inspek685_bibuniq_525,
  author =       "Carlier F. and Nouvel F.",
  title =        "Unsupervised neural networks for multi-user detection in {MC}-{CDMA} systems",
  booktitle =    "IEEE International Conference on Personal Wireless Communicatons {ICPWC}",
  pages =        "255--259",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Performance and implementation complexity issues restrict standard multi-user detection methods in the forthcoming high transmission rate systems based on code division multiple access. We propose self-organizing neural networks to cope with this issue and suggest that an optimal multi-user detector can be implemented by using a {K}ohonen network.",
}

@InProceedings{inspek70_bibuniq_1181,
  author =       "Carpinteiro O. A. S. and Leme R. C. and de-Souza A. C. Z. and Filho P. S. Q.",
  title =        "A hierarchical hybrid neural model with time integrators in long-term peak-load forecasting",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "2960--2965",
  volume =       "5",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A novel hierarchical hybrid neural model to the problem of long-term peak-load forecasting is proposed in this paper. the neural model is made up of two self-organizing map nets - one on top of the other -, and a single-layer perceptron. It has application into domains in which the context information given by former events plays a primary role. the model is compared to a multilayer perceptron. Both the hierarchical and the multilayer perceptron models are trained and assessed on load data extracted from a North-American electric utility. They are required to predict either once every week or once every month the electric peak-load during the next two years. the results are presented and evaluated in the paper.",
}

@Article{inspek835_bibuniq_647,
  author =       "Cartwright H. M.",
  title =        "Investigation of structure - biodegradability relationships in polychlorinated biphenyls using self-organising maps",
  journal =      "Neural Computing \& Applications",
  pages =        "30--36",
  volume =       "11",
  number =       "1",
  year =         "2002",
  publisher =    "Springer-Verlag",
  abstract =     "Polychlorinated biphenyls (PCBs) represent a significant, long-term environmental hazard. Persistent in the environment, accumulative in many species, and toxic, they are a serious pollutant at many industrial sites. the most common methods for environmental remediation - thermal or photochemical treatment - are often an unsatisfactory option if PCBs are present in chemically-contaminated sites, since these methods may convert PCBs into even more toxic chemicals such as dioxins. Microbial degradation offers a safer and more environmentally-friendly alternative, but susceptibility to microbial degradation varies widely among members of the PCB family, and the relationship of structure to degradation rates is poorly understood. This paper discusses the use of a self-organising map ({SOM}) to rationalise and predict degradation data for PCBs under the action of Aspergillus Niger. A {SOM} is shown to be able to predict biodegradability to within 25\% of the experimental values for three quarters of a set of 44 PCBs. It appears that prediction of the biodegradability of dichloro-PCBs may be more difficult than prediction for other types of PCB.",
}

@InProceedings{inspek107_bibuniq_1211,
  author =       "Cecotti H. and Belaid A.",
  title =        "Rejection strategy for convolutional neural network by adaptive topology applied to handwritten digits recognition",
  booktitle =    "Proceedings. Eighth International Conference on Document Analysis and Recognition",
  pages =        "765--769",
  volume =       "2",
  year =         "2005",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "In this paper, we propose a rejection strategy for convolutional neural network models. the purpose of this work is to adapt the network's topology injunction of the geometrical error. A self-organizing map is used to change the links between the layers leading to a geometric image transformation occurring directly inside the network. Instead of learning all the possible deformation of a pattern, ambiguous patterns are rejected and the network's topology is modified in function of their geometric errors thanks to a specialized self-organizing map. Our objective is to show how an adaptive topology, without a new learning, can improve the recognition of rejected patterns in the case of handwritten digits.",
}

@Article{inspek795_bibuniq_613,
  author =       "Chaiyaratana N. and Zalzala A. M. S.",
  title =        "Time-optimal path planning and control using neural networks and a genetic algorithm",
  journal =      "International Journal of Computational Intelligence and Applications",
  pages =        "153--172",
  volume =       "2",
  number =       "2",
  month =        "June",
  year =         "2002",
  publisher =    "Imperial Coll. Press",
  abstract =     "This paper presents the use of neural networks and a genetic algorithm in time-optimal control of a closed-loop 3-DOF robotic system. Extended {K}ohonen networks which contain an additional lattice of output neurons are used in conjunction with PID controllers in position control to minimize command tracking errors. the extended {K}ohonen networks are trained using reinforcement learning where the overall learning algorithm is derived from a self-organizing feature-mapping algorithm and a delta learning rule. the results indicate that the extended {K}ohonen network controller is more efficient than other techniques reported in early literature when the robot is operated under normal conditions. Subsequently, a multi-objective genetic algorithm (MOGA) is used to solve an optimization problem related to time-optimal control. This problem involves the selection of actuator torque limits and an end-effector path subject to time-optimality and tracking error constraints. Two chromosome coding schemes are explored in the investigation: Gray and integer-based coding schemes. the results suggest that the integer-based chromosome is more suitable at representing the decision variables. As a result of using both neural networks and a genetic algorithm in this application, an idea of a hybridization between a neural network and a genetic algorithm at the task level for use in a control system is also effectively demonstrated.",
}

@InProceedings{inspek547_bibuniq_765,
  author =       "Chang-Shui-Zhang {Shi-Feng-Weng, Fai-Wong}",
  title =        "Evolutionary mechanisms in self-organizing map",
  booktitle =    "Proceedings of the 2003 International Conference on Machine Learning and Cybernetics",
  volume =       "4",
  pages =        "2020--2024",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper a new model of self-organizing neural networks is proposed, which has realized the ability to project data from high dimensional space into low dimensional space, as well as to facilitate the visual inspection in the inherent topological structure of the projected data. in this model, a series of evolutionary working mechanisms of neurons have been introduced to overcome the weakness of conventional learning algorithm. the empirical evidences show the proposed algorithm is adaptive and robust. This work can be viewed as a development of classic {K}ohonen self-organizing feature map.",
}

@InProceedings{inspek432_bibuniq_1075,
  author =       "Changshui-Zhang {Shifeng-Weng, Fai-Wong}",
  editor =       "C. {Yin, F. ; Wang, J. ; Guo}",
  title =        "A new adaptive self-organizing map",
  booktitle =    "Advances in Neural Networks {ISNN} 2004. International Symposium on Neural Networks. Proceedings Lecture Notes in Comput. Sci.",
  pages =        "201--210",
  volume =       "1",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "The self-organizing map ({SOM}) method developed by {K}ohonen is a powerful tool for visualizing high-dimensional datasets. To improve the adaptability of SOM, this paper proposes a new model based on Kohenen SOM. the new model has integrated a series of evolutionary working mechanisms of neurons. the microcosmic analysis gives the reason that those introduced mechanisms can conquer the problems of instability in competitive learning. the empirical evidences show the performance of the proposed model.",
}

@Article{inspek413_bibuniq_1057,
  author =       "Chau R. and Chung-Hsing-Yeh",
  title =        "Filtering multilingual Web content using fuzzy logic and self-organizing maps",
  journal =      "Neural Computing \& Applications",
  pages =        "140--148",
  volume =       "13",
  number =       "2",
  year =         "2004",
  month =        "June",
  publisher =    "Springer-Verlag",
  abstract =     "Effective multilingual information filtering is required to alleviate users' burden of information overload resulting from the increasing flood of multilingual textual content available extensively over the World-Wide Web. This paper proposes a content-based self-organizing approach to multilingual information filtering using fuzzy logic and the self-organizing map. This approach screens and evaluates multilingual documents based on their semantic contents. Correlated multilingual documents are disseminated according to their corresponding themes or topics, thus enabling language-independent content-based information access efficiently and effectively. A Web-based multilingual online news-filtering system is developed to illustrate how the approach works.",
}

@InProceedings{inspek231_bibuniq_1324,
  author =       "Chau R. and Smith K. A. Chunghsing-Yeh",
  editor =       "Z. {Wang, J. ; Liao, X. ; Yi}",
  title =        "A neural network model for hierarchical multilingual text categorization",
  booktitle =    "Advances in Neural Networks {ISNN} 2005. Second International Symposium on Neural Networks. Proceedings, Part II Lecture Notes in Computer Science",
  pages =        "238--245",
  volume =       "2",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "Enabling navigation via a hierarchy of conceptually related multilingual documents constitutes the fundamental support to global knowledge discovery. This requirement of organizing multilingual document by concepts makes the goal of supporting global knowledge discovery a concept-based multilingual text categorization task. in this paper, intelligent methods for enabling concept-based hierarchical multilingual text categorization using neural networks are proposed. First, a universal concept space, encapsulating the semantic knowledge of the relationship between all multilingual terms and concepts, which is required by concept-based multilingual text categorization, is generated using a self-organizing map. Second, a set of concept-based multilingual document categories, which acts as the hierarchical backbone of a browseable multilingual document directory, are generated using a hierarchical clustering algorithm. Third, a concept-based multilingual text classifier is developed using a 3-layer feedforward neural network to facilitate the concept-based multilingual text categorization.",
}

@InProceedings{inspek215_bibuniq_1308,
  author =       "Chau R. and Yen C. H. and Smith K. A.",
  editor =       "C. J. K. {Gervasi, O. ; Gavrilova, M. L. ; Kumar, V. ; Lagana, A. ; Lee, H. P. ; Mun, Y. ; Taniar, D. ; Tan}",
  title =        "A personalized multilingual Web content miner: {PMW}eb{M}iner",
  booktitle =    "Computational Science and Its Applications ICCSA 2005. International Conference. Proceedings, Part II Lecture Notes in Computer Science",
  pages =        "956--965",
  volume =       "3481"
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This paper presents the development of a novel personal concept-based multilingual Web content mining system. Multilingual linguistic knowledge required by multilingual Web content mining is made available by encoding all multilingual concept-term relationships within a multilingual concept space using self-organising map. With this linguistic knowledge base, a personal space of interest is generated to reveal the conceptual content of a user's multiple topics of interest using the user's bookmark file. To personalise the multilingual Web content mining process, a concept-based Web crawler is developed to automatically gather multilingual Web documents that are relevant to the user's topics of interest. As such, user-oriented concept-focused knowledge discovery in the multilingual Web is facilitated.",
}

@InProceedings{inspek780_bibuniq_598,
  author =       "Chekima A. Lye-Wil-Liam and Dargham J. A. Liau-Chung-Fan",
  title =        "Iris recognition using self-organizing neural network",
  booktitle =    "Student Conference on Research and Development. SCOReD2002. Proceedings. Globalizing Research and Development in Electrical and Electronics Engineering",
  pages =        "169--172",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Among biometric systems for user verification, iris recognition systems represent a relatively new technology. Our system consists of two main parts: a localizing iris and iris pattern recognition. the raw image is captured using a digital camera. the iris is then extracted from the background after enhancement and noise elimination. Due to noise and the high degree of freedom in the iris pattern, only parts of the iris structure are selected for recognition. the selected iris structure is then reconstructed into a rectangle format. Using a trained self-organizing map neural network, iris patterns are recognized. the overall accuracy of our network is found to be about 83\%.",
}

@InProceedings{Chen02a_bibuniq_1683,
  author =       "Chen Lin and Lei Li",
  title =        "Research on content-based {C}hinese word retrieval",
  booktitle =    "Proceedings of International Conference on Machine-Learning and Cybernetics",
  year =         "2002",
  volume =       "3",
  pages =        "1144--1147",
  abstract =     "Chinese words are a combination of speech sound, outline and meaning. Our research on content-based {C}hinese word retrieval can help users to retrieve their desired results by using words that are similar to normal queries in speech sound, outline or meaning. It is based on the vector space model. We suggest methods to extract features and use a neural network to create the word indexing. the experimental results indicate the feature extraction methods are not only effective but also efficient.",
}

@Article{Chen00a_bibuniq_3946,
  author =       "Chen Min Rong and Deng Fei Qi",
  title =        "Approach of clustering based on self-organizing feature map",
  journal =      "Systems-Engineering and Electronics",
  volume =       "26",
  number =       "12",
  pages =        "1864--1866",
  month =        "December",
  year =         "2004",
}

@Article{Chen00b_bibuniq_3990,
  author =       "Chen Shan Xue",
  title =        "Vector quantization based on equidistortion self-organizing feature mapping algorithm",
  journal =      "Systems-Engineering and Electronics",
  year =         "2004",
  month =        "September",
  volume =       "26",
  number =       "9",
  pages =        "1189--1191",
}

@Article{Chen00c_bibuniq_4004,
  author =       "Chen Xue and Li Xiaowen and Ma Jianwen",
  title =        "Urban change detection based on self-organizing feature map neural network",
  journal =      "{IGARSS}-2004, {IEEE} International-Geoscience and Remote-Sensing",
  volume =       "5",
  pages =        "3428--3431",
  year =         "2004",
}

@Article{inspek661_bibuniq_860,
  author =       "Chen-Ming {Wang-Guang-jun, Chen-Hong, He-Zu-wei}",
  title =        "A method and its application of boiler operation pattern recognition",
  journal =      "Proceedings of the {CSEE}",
  pages =        "204--208",
  volume =       "23",
  number =       "6",
  month =        "June",
  year =         "2003",
  publisher =    "Chinese Soc. Electr. Eng",
  abstract =     "Pattern identification of energy loss in operation is one of the fundamental matters for diagnosing the economy of boiler operation. the aim is to propose an on-line identification method for the operation pattern of a boiler based on artificial intelligence theory. A study was made on the expression of operation pattern of boiler, the construction of eigenvector of operation pattern, the method of pattern identification and its application, etc. Some basic concepts were proposed such as the generalized eigenvector of operation pattern and redundant characteristic parameter. Furthermore, the pattern identification model of boiler operation based on self-organizing map ({SOM}) networks was established. the results show that this pattern identification model can effectively recognize the main energy loss in boiler operation. in addition to that, the impact of work condition variation on the effect of pattern identification can be avoided by constructing generalized eigenvector. Furthermore, the results showed that introduction of redundant characteristic parameter can obviously improve the ability of model to identify operation pattern. the validity of the method mentioned were proven by the case studies of pattern identification of energy loss about contamination on heating surface and air leak system in utility boiler.",
}

@Article{inspek804_bibuniq_620,
  author =       "Chen-Yunping Zhou-Wei",
  title =        "Self-organizing mapping ({SOM}) neural networks for power system transient stability assessment",
  journal =      "Automation of Electric Power Systems",
  pages =        "33--38",
  volume =       "26",
  number =       "15",
  month =        "August",
  year =         "2002",
  publisher =    "State Power Corp. of China",
  abstract =     "In this paper, several self-organizing mapping ({SOM}) neural networks are introduced. They are the common {K}ohonen network, its modified model which works in an {"}unsupervised grouping, supervised learning{"} way, the common {LVQ} (learning vector quantization) network, and the network model that combines {K}ohonen and {LVQ}. Their performance on transient stability assessment is compared and the second model is chosen in later study for its high reliability. This paper also compares the different ANN input attributes' abilities to represent the patterns in the real world, using the visual clustering maps on the output layers of self-organizing feature mapping (SOFM) neural networks. Based on these analyses, a novel online large power system contingency screening and rotor angle prediction approach is proposed and tested. All the simulation data comes from Central China Power Grid and the simulation results verify the effectiveness of the method.",
}

@InProceedings{Cheng00b_bibuniq_4037,
  author =       "Cheng Jian Lin and Huei Jen Chen and Chi Yung Lee",
  title =        "A self-organizing recurrent fuzzy {CMAC} model for dynamic system identification",
  booktitle =    "{IEEE} International Conference on Fuzzy-Systems",
  volume =       "2",
  pages =        "697--702",
  month =        "July",
  year =         "2004",
}

@InProceedings{inspek545_bibuniq_764,
  author =       "Cheung K. H. and Kong W. K. and You J. and Zhang D.",
  title =        "An integration of principal component analysis and self-organizing map for effective palmprint retrieval",
  booktitle =    "Computer Applications in Industry and Engineering. Proceedings of the {ISCA} 16th International Conference",
  pages =        "101--104",
  year =         "2003",
  publisher =    "ISCA, Cary, NC, USA",
  abstract =     "In this paper a novel retrieval method for effective search of palmprints based on principal component analysis ({PCA}) and self-organizing feature map ({SOM}) is presented. To reduce search space and speed up the query processing, an integration of {PCA} and {SOM} is proposed, where the global feature representation, coefficients obtained from {PCA}, is considered as inputs to SOM. the trained {SOM} can thus be used as a retrieval engine to identify similar palmprint images with respect to the query palmprint image for personal identification.",
}

@InProceedings{inspek120_bibuniq_1223,
  author =       "Chiba K. and Jeong S. and Obayashi S. and Morino H.",
  title =        "Data mining for multidisciplinary design space of regional-jet wing",
  booktitle =    "The 2005 {IEEE} Congress on Evolutionary Computation",
  pages =        "2333--2340",
  volume =       "3",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The data mining technique is an important facet of solving multi-objective optimization problem because it is one of the effective manners to discover the design knowledge in the multi-objective optimization problem which obtains large data. in the present study, two data mining techniques have been performed for a large-scale, real-world multidisciplinary design optimization (MDO) to provide knowledge regarding the design space. the MDO among aerodynamics, structures, and aeroelasticity of the regional-jet wing was carried out using high-fidelity evaluation models on adaptive range multi-objective genetic algorithm. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. All solutions evaluated during the evolution were analyzed for the influence of design variables using a self-organizing map ({SOM}) and a functional analysis of variance (ANovA) to extract key features of the design space. {SOM} and ANovA compensated with the respective disadvantages, and then the design knowledge could be obtained more clearly by the combination between them. Although the MDO results showed the inverted gull-wings as non-dominated solutions, one of the key features found by data mining was the non-gull wing geometry. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance compared with the original geometry designed in the conventional manner.",
}

@InProceedings{inspek164_bibuniq_1267,
  author =       "Chien-Sing-Lee Ching-Chieh-Kiu",
  title =        "Discovering ontological semantics for reuse and sharing of learning objects in a contextual learning environment",
  booktitle =    "Proceedings. 5th {IEEE} International Conference on Advanced Learning Technologies",
  pages =        "368--370",
  year =         "2005",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Ontologies enable learning objects sharing and reuse in a contextual learning environment and provide better search and navigation of learning objects. Ontologies add semantics to content components, which provide context to learning objects. This paper presents the use of Formal Concept Analysis (FCA) to structure knowledge ontologically and the self-organizing map ({SOM}) to reduce the problem size. To gain better visualization of the intrinsic relationship between ontological concepts, k-means clustering is applied on the clustered SOM.",
}

@Article{Chih00b_bibuniq_3902,
  author =       "Chih Ming Chen",
  title =        "Incremental personalized Web page mining utilizing self-organizing {HCMAC} neural network",
  journal =      "Web Intelligence and Agent-Systems",
  pages =        "21--38",
  volume =       "2",
  number =       "1",
  year =         "2004",
}

@InProceedings{inspek607_bibuniq_810,
  author =       "Chin-Shin-Wong {Han-Pang-Huang, Yi-Hung-Liu, Li-Wei-Liu}",
  title =        "{EMG} classification for prehensile postures using cascaded architecture of neural networks with self-organizing maps",
  booktitle =    "{IEEE} International Conference on Robotics and Automation",
  pages =        "1497--1502",
  volume =       "1",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Electromyograph (EMG) features have the properties of large variations and nonstationary issue in the classification of EMG is the classifier design. the major goal of this paper is to develop a classifier for the classification of eight kinds of prehensile postures to achieve high classification rate and reduce the online learning time. the cascaded architecture of neural networks with feature map (CANFM) is proposed to achieve the goal. the CANFM is composed of two kinds of neural networks: an unsupervised {K}ohonen's self-organizing map ({SOM}), and a supervised multi-layer feedforward neural network. Experimental results show that by extracting EMG features, forth-order autoregressive model (ARM) and histogram of EMG signals (IEMG), as inputs, the proposed CANFM can obtain and remain high classification rates compared with other classifiers, including k-nearest neighbor method (K-NN), fuzzy K-NN algorithm, and back-propagation neural network (BPNN) in several online testing.",
}

@Article{inspek163_bibuniq_1266,
  author =       "Ching-Chieh-Kiu Chien-Sing-Lee",
  title =        "A concept-based hybrid graphical-neural approach for ontological interoperability",
  journal =      "{WSEAS} Transactions on Information Science and Applications",
  pages =        "761--770",
  volume =       "2",
  number =       "6",
  month =        "June",
  year =         "2005",
  publisher =    "{WSEAS}",
  abstract =     "In an e-learning environment, the effectiveness of reusing and retrieving learning objects from different learning object repositories are often reduced due to the use of different ontological schemes in each learning object repository. This paper presents OntoShare, an ontology sharing architecture for learning object retrieval and reuse. the architecture aims to offer contextual and robust automated ontology mapping and merging through hybrid unsupervised clustering techniques comprising of formal concept analysis (FCA), self-organizing map ({SOM}) and K-means clustering incorporated with linguistic processing using WordNet. the external ontology is first formalized using FCA, followed by linguistic processing and {SOM} and K-means clustering, thence merging with the internal ontology. in the event of new concepts, they will be clustered and the merged ontology will be dynamically updated with the new concept. the merged ontology facilitates retrieval and sharing of learning objects from digital libraries or from learning object repositories such as ARIADNE, and EDUCAUSE. Experimental results can be extended to resources in data warehouses or databases.",
}

@InProceedings{inspek241_bibuniq_1330,
  author =       "Chongxun-Zheng {Hailong-Liu, Jue-Wang}",
  editor =       "Z. {Wang, J. ; Liao, X. ; Yi}",
  title =        "Using self-organizing map for mental tasks classification in brain-computer interface",
  booktitle =    "Advances in Neural Networks {ISNN} Second International Symposium on Neural Networks",
  pages =        "327--332",
  volume =       "2",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "One problem in brain-computer interface (BCI) is the requirement of online training of classifiers, since {EEG} patterns vary greatly at two separate time with long period. in this paper, the use of self-organizing map ({SOM}) as an adaptive classifier for mental tasks classification was proposed. As for SOM, there are two cases about the labeling of map units, which correspond to semi-supervised and unsupervised algorithm respectively. in one case, the map units are labeled according to the labels of training patterns. in the other case, the map structure information, e. g., the U-matrix is used to cluster map units. the ability of {SOM} to recognize mental task was analyzed for both cases. the organized {SOM} is tested on testing patterns. the averaged classification accuracy of 96. 2\% and 90. 8\% across 10 task pairs was obtained for both cases respectively. This result indicates the feasibility of online training of {SOM} for mental tasks classification.",
}

@Article{inspek588_bibuniq_796,
  author =       "Chow T. W. S. Sitao-Wu",
  title =        "Self organizing map based clustering using a local clustering validity index",
  journal =      "Neural Processing Letters",
  pages =        "253--271",
  volume =       "17",
  number =       "3",
  month =        "June",
  year =         "2003",
  publisher =    "Kluwer Academic Publishers",
  abstract =     "Classical clustering methods, such as partitioning and hierarchical clustering algorithms, often fail to deliver satisfactory results, given clusters of arbitrary shapes. Motivated by a clustering validity index based on inter-cluster and intra-cluster density, we propose that the clustering validity index be used not only globally to find optimal partitions of input data, but also locally to determine which two neighboring clusters are to be merged in a hierarchical clustering of Self-Organizing Map ({SOM}). A new two-level {SOM}-based clustering algorithm using the clustering validity index is also proposed. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data in a better way than classical clustering algorithms on an SOM.",
}

@InProceedings{inspek631_bibuniq_831,
  author =       "Chow T. W. S. Sitao-Wu",
  title =        "Support vector visualization and clustering using self-organizing map and vector one-class classification",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "803--808",
  volume =       "1",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper, a new algorithm of support vector visualization and clustering (SVVC) based on self-organizing map ({SOM}) and support vector one-class classification (SVOCC) is presented. Original SVOCC is to identify the support domain of input data. When it is used for clustering, the high computational complexity for identifying cluster gaps between any pair points makes it less likely to be used in large data sets. in addition, the identified clusters cannot be visually displayed in high dimensions larger than three. Self-organizing map ({SOM}) is a neural network approach, which can project high-dimensional data into usually 2-D grid while preserving topology of input data. By using the proposed SVVC algorithm, resulting map can visually display high-dimensional cluster shapes and corresponding clusters can be found. Outliers and cluster borders can be clearly identified on the map, which is better than other visualization and clustering methods on SOM. the computational complexity of SVVC is less than the method of directly clustering by SVOCC.",
}

@Article{inspek560_bibuniq_1133,
  author =       "Chow T. W. S. Sitao-Wu",
  title =        "Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density",
  journal =      "Pattern Recognition",
  pages =        "175--188",
  volume =       "37",
  number =       "2",
  year =         "2004",
  month =        "February",
  publisher =    "Elsevier",
  abstract =     "The self-organizing map ({SOM}) has been widely used in many industrial applications. Classical clustering methods based on the {SOM} often fail to deliver satisfactory results, specially when clusters have arbitrary shapes. in this paper, through some preprocessing techniques for filtering out noises and outliers, we propose a new two-level {SOM}-based clustering algorithm using a clustering validity index based on inter-cluster and intra-cluster density. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data better than the classical clustering algorithms based on the SOM, and find an optimal number of clusters.",
}

@InProceedings{Chuan00a_bibuniq_4005,
  author =       "Chuan Yu Chang and Jia Wei Chang and MuDer Jeng",
  title =        "Using a self-organizing neural network for wafer defect inspection",
  booktitle =    "{IEEE} International Conference on Systems, Man and Cybernetics",
  volume =       "5",
  pages =        "4312--4317",
  year =         "2004",
}

@InProceedings{Chun00a_bibuniq_4015,
  author =       "Chun Liang Lin and Horn Yong Jan and Thong Hsing Huang",
  title =        "Self-organizing {PID} control design based on {DNA} computing method",
  booktitle =    "Proceedings of the {IEEE} International Conference on Control-Applications",
  volume =       "1",
  pages =        "568--573",
  year =         "2004",
}

@InProceedings{inspek863_bibuniq_662,
  author =       "Chun-Wei-Tsai {Cheng-Fa-Tsai, Han-Chang-Wu}",
  editor =       "R. P. {Hsu, D. F. ; Ibarra, O. H. ; Saldana}",
  title =        "A new data clustering approach for data mining in large databases",
  booktitle =    "Proceedings International Symposium on Parallel Architectures, Algorithms and Networks",
  pages =        "315--320",
  year =         "2002",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Clustering is the unsupervised classification of patterns (data item, feature vectors, or observations) into groups (clusters). Clustering in data mining is very useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric-based similarity measure in order to partition the database such that data points in the same partition are more similar than points in different partitions. in this paper, we present a new data clustering method for data mining in large databases. Our simulation results show that the proposed novel clustering method performs better than a fast self-organizing map (FSOM) combined with the k-means approach (FSOM+k-means) and the genetic k-means algorithm (GKA). in addition, in all the cases we studied, our method produces much smaller errors than both the FSOM+k-means approach and GKA.",
}

@InProceedings{Chung04a_bibuniq_1363,
  author =       "Chung Hong Lee and Hsin Chang Yang",
  title =        "A text mining approach for measuring semantic relatedness using support vector machines",
  booktitle =    "The 8th World Multi Conference on Systemics, Cybernetics and Informatics",
  year =         "2004",
  volume =       "4",
  pages =        "320--323",
  abstract =     "Text mining has been gaining popularity in the knowledge discovery field, particularity with the increasing availability of digital documents from all around the world. This paper presents a hybrid approach of a text-mining technique for measuring semantic relatedness among texts. in this work we develop an approach applying a text categorization technique with support vector machines (SVM) method to supporting analysis of semantic relatedness among texts. We utilized our developed text mining algorithms and platforms, including text mining techniques based on self-organizing maps ({SOM}) and support vector machines (SVM) for performing clustering and classification of texts in several text collections. After that, we employ SVM methods to deal with analysis of semantic relatedness of the target documents of clustering and classification mining process, in order to find the semantic connections and relatedness among the mined texts. the research approach can also be applied to other general applications, such as re-categorizing texts according to the results of semantic relatedness measured by the developed algorithms.",
}

@InProceedings{inspek159_bibuniq_1262,
  author =       "Chung-Hong-Lee Hsin-Chang-Yang",
  title =        "Automatic metadata generation for Web pages using a text mining approach",
  booktitle =    "Proceedings. International Workshop on Challenges in Web Information Retrieval and Integration",
  pages =        "186--194",
  year =         "2005",
  publisher =    "IEEE Computer Society, Los Alamitos, CA, USA",
  abstract =     "The semantic Web has emerged to replace the World Wide Web (WWW or the Web) as the unique platform for information sharing. Applications such as e--commerce will be and could be plausible only if we can annotate the Web pages with their semantics. For newly developed semantic Web resources, such annotation can be done manually or by help of some authoring tools. However, it is not practical to semantically annotating existing Web pages due to the gigantic amount of them. To overcome this difficulty, we propose a machine learning approach to automatically generate semantic metadata for Web pages. the proposed automated process adopts the self-organizing map algorithm to cluster training Web pages and conducts a text mining process to discover some semantic descriptions about the Web pages. Preliminary experiments show that our method may generate semantically relevant metadata for the Web pages.",
}

@InProceedings{inspek651_bibuniq_851,
  author =       "Cielniak G. and Duckett T.",
  title =        "Person identification by mobile robots in indoor environments",
  booktitle =    "{ROSE'03} 1st {IEEE} International Workshop on Robotic Sensing 2003",
  pages =        "68",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper addresses the problem of identifying persons with a mobile robot. in the proposed system, people are first detected and then tracked with the robot's laser range-finder sensor, using an independent Kalman filter for each person. After segmentation, the rectangular region of the image containing the person is divided into regions corresponding to the person's head, torso and legs. Colour features are extracted from each region for input to a pattern recognition system. Five alternative classification methods were investigated, including experiments on a real robot and with a static camera system. the best identification performance was obtained with an ensemble of self-organizing maps (ESOM), where one self-organizing map is trained for each person in the robot's database. We also discuss how to incorporate the new method into a complete application of a robotic security guard.",
}

@Article{inspek614_bibuniq_815,
  author =       "Claussen J. C.",
  title =        "Winner-relaxing and winner-enhancing {K}ohonen maps: maximal mutual information from enhancing the winner",
  journal =      "Complexity",
  pages =        "15--22",
  volume =       "8",
  number =       "4",
  year =         "2003",
  publisher =    "Wiley",
  abstract =     "The magnification behavior of a generalized family of self-organizing feature maps, the winner-relaxing and winner-enhancing {K}ohonen algorithms is analyzed by the magnification law in the one-dimensional case, which can be obtained analytically. the winner-enhancing case allows to achieve a magnification exponent of one and therefore provides optimal mapping in the sense of information theory. A numerical verification of the magnification law is included, and the ordering behavior is analyzed. Compared to the original self-organizing map and some other approaches, the generalized winner enforcing algorithm requires minimal extra computations per learning step and is conveniently easy to implement.",
}

@Article{inspek570_bibuniq_781,
  author =       "Copsey K. and Webb A.",
  title =        "Bayesian gamma mixture model approach to radar target recognition",
  journal =      "{IEEE} Transactions on Aerospace and Electronic Systems",
  pages =        "1201--1217",
  volume =       "39",
  number =       "4",
  month =        "October",
  year =         "2003",
  publisher =    "IEEE",
  abstract =     "This paper develops a {B}ayesian gamma mixture model approach to automatic target recognition (ATR). the specific problem considered is the classification of radar range profiles (RRPs) of military ships. However, the approach developed is relevant to the generic discrimination problem. We model the radar returns (data measurements) from each target as a gamma mixture distribution. Several different motivations for the use of mixture models are put forward, with gamma components being chosen through a physical consideration of radar returns. {B}ayesian formalism is adopted and we obtain posterior distributions for the parameters of our mixture models. the distributions obtained are too complicated for direct analytical use in a classifier, so {M}arkov chain Monte Carlo (MCMC) techniques are used to provide samples from the distributions. the classification results on the ship data compare favorably with those obtained from two previously published techniques, namely a self-organizing map and a maximum likelihood gamma mixture model classifier.",
}

@InProceedings{inspek750_bibuniq_572,
  author =       "Correa R. F. and Ludermir T. B.",
  title =        "Automatic text categorization: case study",
  booktitle =    "Proceedings 7th Brazilian Symposium on Neural Networks",
  pages =        "150",
  year =         "2002",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Text categorization is a process of classifying documents with regard to a group of one or more existent categories according to themes or concepts present in their contents. the most common application of it is in information retrieval systems (IRS) to document indexing. A method to transform text categorization into a viable task is to use machine-learning algorithms to automate text classification, allowing it to be carried out fast, into concise manner and in broad range. the objective of this work is to present and compare the results of experiments on text categorization using artificial neural networks of multilayer perceptron and self-organizing map types, and traditional machine-learning algorithms used in this task: C4. 5 decision tree, PART decision rules and Naive {B}ayes classifier.",
}

@InProceedings{inspek751_bibuniq_573,
  author =       "Costa I. G. and de-Carvalho F. de A. T. and de-Souto M. C. P.",
  title =        "A symbolic approach to gene expression time series analysis",
  booktitle =    "Proceedings 7th Brazilian Symposium on Neural Networks",
  pages =        "25--30",
  year =         "2002",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "In the analysis of gene expression time series, emphasis has been given on the capture of shape similarity (or dissimilarity). A number of proximity functions have been proposed for this task. However, none of them will suitably measure shape similarity (or dissimilarity) with data containing multiple gene expression time series, unless special data handling is made. in this paper, a symbolic description of multiple gene expression time series, where each variable takes as a value a time series, in conjunction with a version of a proximity measure, is proposed. in this symbolic approach, the shape similarity of each time series is calculated independently, and aggregated at the end. Gene expression data from five distinct time series are presented to a symbolic dynamical clustering method and self-organising map algorithm. the quality of the results obtained is evaluated using gene annotation allowing a verification of this proposal's adequacy.",
}

@InProceedings{inspek458_bibuniq_726,
  author =       "Costea A. and Eklund T.",
  title =        "A two-level approach to making class predictions",
  booktitle =    "36th Hawaii International Conference on Systems Sciences",
  pages =        "CD-ROM",
  year =         "2003",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "In this paper we propose a new two-level methodology for assessing countries'/companies' economic/financial performance. the methodology is based on two major techniques of grouping data: cluster analysis and predictive classification models. First we use cluster analysis in terms of self-organizing maps to find possible clusters in data in terms of economic/financial performance. We then interpret the maps and define outcome values (classes) for each data row. Lastly we build classifiers using two different predictive models (multinomial logistic regression and decision trees) and compare the accuracy of these models. Our findings claim that the results of the two classification techniques are similar in terms of accuracy rate and class predictions. Furthermore, we focus our efforts on understanding the decision process corresponding to the two predictive models. Moreover, we claim that our methodology, if correctly implemented, extends the applicability of the self-organizing map for clustering of financial data, and thereby, for financial analysis.",
}

@InProceedings{inspek478_bibuniq_1108,
  author =       "Cretu A. M. and Petriu E. M. and Patry G. G.",
  editor =       "T. {Demidenko, S. ; Ottoboni, R. ; Petri, D. ; Piuri, V. ; Chong, D. ; Weng}",
  title =        "Neural network-based adaptive sampling of 3{D} object surface elastic properties",
  booktitle =    "Proceedings of the 21st {IEEE} Instrumentation and Measurement Technology Conference",
  volume =       "55",
  number =       "2",
  pages =        "483--492",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The paper discusses two self-organizing neural network (NN) architectures, the neural gas network and the {K}ohonen self-organizing map ({SOM}) for the adaptive sampling and the reduction of the dimensionality of the set of probing points in the measurement of the nonuniform elastic properties of 3{D} objects.",
}

@InProceedings{inspek817_bibuniq_629,
  author =       "Cuadrado A. A. and Diaz I. and Diez A. B. and Obeso F. and Gonzalez J. A.",
  title =        "Visual data mining and monitoring in steel processes",
  booktitle =    "Conference Record of the 2002 {IEEE} Industry Applications Conference. 37th {IAS} Annual Meeting",
  pages =        "493--500",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Steel processes are often of a complex nature and difficult to model. All information that we have at hand usually consists of more or less precise models of different parts of the process, some rules obtained on the basis of experience, and typically a great amount of high-dimensional data coming from numerous sensors and variables of process computers which convey a lot of information about the process state. We suggest in this paper the use of a continuous version of the self-organizing map ({SOM}) to project a high dimensional vector of process data on a 2{D} visualization space in which different process conditions are represented by different regions. Later, all sorts of information resulting from the fusion of knowledge obtained from data, mathematical models and fuzzy rules can be described in a graphical way in this visualization space.",
}

@InProceedings{inspek69_bibuniq_1180,
  author =       "Cuadros-Vargas E. and Romero R. A. F.",
  title =        "Introduction to the {SAM}-{S} {M}* and {MAM}-{S} {M}* families",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "2966--2970",
  volume =       "5",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper, two new families of constructive self-organizing maps (SOMs), SAM-SOM* and MAM-SOM*, are proposed. These families are specially useful for information retrieval from large databases, high-dimensional spaces and complex distance functions which usually consume a long time. They are generated by incorporating spatial access method (SAM) and metric access method (MAM) into {SOM} with the maximum insertion rate, i. e. the case when a new unit is created for each pattern presented to the network. in this specific case, the network presents interesting advantages and acquires new properties which are quite different of traditional SOM. in a constructive SOM, if new units are rarely inserted into network, the training algorithm would probably need a long time to converge. On the other hand, if new units are inserted frequently, the training algorithm would not have enough time to adapt these units to the data distribution. Besides, training time is increased because the search for the winning neuron is traditionally performed sequentially. the use of SAM and MAM combined with {SOM} open the possibility of training constructive {SOM} with as much units as existing patterns with less time and interesting advantages compared with both models: {K}ohonen network {SOM} and SAMSOM model (SOM using SAM). Advantages and drawbacks of these new families are also discussed. These new families are useful to improve both {SOM} and SAM techniques.",
}

@InProceedings{inspek529_bibuniq_757,
  author =       "Cupertino F. and de-Vanna E. and Forcella G. and Salvatore L. and Stasi S.",
  title =        "Detection of {IM} broken rotor bars using {MUSIC} pseudo-spectrum and pattern recognition",
  booktitle =    "IECON'03. 29th Annual Conference of the {IEEE} Industrial Electronics Society {IEEE} Vol. 3",
  pages =        "3224",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper presents some analysis techniques of the space-vector of voltages induced in the stator windings after supply disconnection, to detect broken rotor bars in squirrel cage induction machines. When the motor is disconnected from the supply no currents flow in the stator windings and the voltages measurable at its terminals are due to flux produced by rotor currents. When the rotor is healthy, the voltages measured at motor terminals are almost sinusoidal because of the symmetry of rotor windings. When there are broken rotor bars, the magneto-motive force (m. m. f. ) generated by rotor windings results distorted, and some particular harmonics, contained in the voltages induced in the stator windings, increase their amplitudes. the diagnostic technique is based on monitoring these voltage harmonics by analyzing the space vector of the voltages induced in the stator windings via short-time MUSIC (STMUSIC) time-frequency pseudo-representation. the obtained results have been used to train a {K}ohonen neural-network that is able to automatically classify data measured on healthy and faulty induction motors.",
}

@Article{inspek504_bibuniq_1119,
  author =       "Curry B. and Morgan P. H.",
  title =        "Evaluating {K}ohonen's learning rule: an approach through genetic algorithms",
  journal =      "European Journal of Operational Research",
  pages =        "191--205",
  volume =       "154",
  number =       "1",
  month =        "April",
  year =         "2004",
  publisher =    "Elsevier",
  abstract =     "This paper examines the technical foundations of the self-organising map ({SOM}). It compares {K}ohonen's heuristic-based training algorithm with direct optimisation of a locally-weighted distortion index, also used by Kohonen. Direct optimisation is achieved through a genetic algorithm (GA). Although GAs have been used before with the SOM, this has not been done in conjunction with the distortion index. Comparing heuristic-based training and direct optimisation for the {SOM} is analogous to comparing the backpropagation algorithm for feedforward networks with direct optimisation of RMS error. Our experiments reveal lower values of the distortion index with direct optimisation. As to whether the heuristic-based algorithm is able to provide an approximation to gradient descent, our results suggest the answer should be in the negative. Theorems for one-dimensional and for square maps indicate that different point densities will emerge for the two training approaches. Our findings are in accordance with these results.",
}

@InProceedings{inspek361_bibuniq_1011,
  author =       "D'Addabbo A. and Satalino G. and Pasquariello G. and Blonda P.",
  title =        "Three different unsupervised methods for change detection: an application",
  booktitle =    "{IGARSS} {IEEE} International Geoscience and Remote Sensing",
  volume =       "3",
  pages =        "1980--1983",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this work, unsupervised change detection techniques, based on three different way to compare images, are presented. Two Landsat T. M. registered and corrected multi-spectral images, acquired on the same geographical area on 18 May 1996 and 21 May 1997, have been used. in the first comparison technique, for each pair of corresponding pixels, the spectral change vector has been computed as the squared difference in the features vectors at the two times. in the second method, the difference image has been computed using, pixel by pixel, a chi square transformation. the third technique is based on the application of a Self-Organizing Map ({S. O. M.}) neural network to clusterize the two images before comparison. the three obtained difference images has been then analyzed by using a fully automatic thresholding method exploiting the expectation-maximization (EM) algorithm. the experimental results obtained for the three difference images are comparable, showing a reliable robustness of the unsupervised approach, and only few change are detected on the analyzed scene. Moreover, the experimental results have been compared with a change detection map computed by using a supervised technique, obtaining a good agreement between unsupervised and supervised results that confirms the reliability of the considered approach. the encouraging obtained results allow to use the so-computed percentage value of changes as probability of class transitions in input to a {B.}ayesian supervised change detection method, as presented in a companion paper by the same authors. in this framework, the unsupervised approach may be used to support supervised techniques, providing land cover transitions that can be used as guess values.",
}

@InProceedings{karras02a_bibuniq_4976,
  author =       "D. A. Karras and B. G. Mertzios",
  title =        "A robust meaning extraction methodology using supervised neural networks",
  booktitle =    "{AL} 2002: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence",
  volume =       "2557",
  year =         "2002",
  pages =        "498--510",
  abstract =     "",
}

@InProceedings{Coyle00a_bibuniq_3961,
  author =       "D. Coyle and G. Prasad and T. M. McGinnity",
  title =        "Extracting features for a brain-computer interface by self-organising fuzzy neural network-based time series prediction",
  booktitle =    "26th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society",
  pages =        "4371--4374",
  year =         "2004",
}

@Article{deng03a_bibuniq_368,
  author =       "D. Deng and N. Kasabov",
  title =        "On-line pattern analysis by evolving self-organizing maps",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "51",
  month =        "April",
  pages =        "87--103",
}

@Article{lamendola03a_bibuniq_356,
  author =       "D. E. Lamendola and Z. F. Duan and R. Z. Yusuf and M. V. Seiden",
  title =        "Molecular description of evolving paclitaxel resistance in the {S}kov-3 human ovarian carcinoma cell line",
  journal =      "Cancer Research",
  year =         "2003",
  volume =       "63",
  number =       "9",
  month =        "May",
  pages =        "2200--2205",
}

@Article{covell03a_bibuniq_376,
  author =       "D. G. Covell and A. Wallqvist and A. A. Rabow and N. Thanki",
  title =        "Molecular classification of cancer: Unsupervised self-organizing map analysis of gene expression microarray data",
  journal =      "Molecular Cancer Therapeutics",
  year =         "2003",
  volume =       "2",
  number =       "3",
  month =        "March",
  pages =        "317--332",
}

@InProceedings{goren-bar02a_bibuniq_477,
  author =       "D. Goren-Bar and T. Kuflik and D. Lev and P. Shoval",
  title =        "Automating personal categorization using artificial neural networks",
  booktitle =    "User Modeling 2001, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2002",
  pages =        "343--359",
}

@InProceedings{wang03c_bibuniq_394,
  author =       "D. H. Wang and T. Dillon and E. Chang",
  title =        "Trading off between misclassification, recognition and generalization in data mining with continuous features",
  booktitle =    "Developments in Applied Artificail Intelligence, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "21--36",
}

@Article{hanquet05a_bibuniq_80,
  author =       "D. Hanquet and M. Legalle and A. Compin and R. Cereghino",
  title =        "Assessment of an artificial intelligence technique in investigating habitat partitioning by coexisting benthic invertebrates in gravel-bed rivers",
  journal =      "River Research and Applications",
  year =         "2005",
  volume =       "21",
  number =       "6",
  month =        "July",
  pages =        "629--639",
}

@Article{lynn02a_bibuniq_421,
  author =       "D. J. Lynn and G. A. C. Singer and D. A. Hickey",
  title =        "Synonymous codon usage is subject to selection in thermophilic bacteria",
  journal =      "Nucleic Acids Research",
  year =         "2002",
  volume =       "30",
  number =       "19",
  month =        "October",
  pages =        "4272--4277",
}

@InProceedings{wang05a_bibuniq_34,
  author =       "D. L. Wang and G. Yu and Y. B. Bao and M. Zhang",
  title =        "An optimized {K}-Means algorithm of reducing cluster intra-dissimilarity for document clustering",
  booktitle =    "Advances in {WEB}-{AGE} Information Management, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "161--168",
}

@Article{Moshou00a_bibuniq_3965,
  author =       "D. Moshou and C. Bravo and J. West and S. Wahlen and A. McCartney and H. Ramon",
  title =        "Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks",
  journal =      "Computers and Electronics in Agriculture",
  volume =       "44",
  number =       "3",
  pages =        "173--188",
  september =    "September",
  year =         "2004",
}

@Article{moshou03a_bibuniq_313,
  author =       "D. Moshou and H. Ramon",
  title =        "Vibration control using self-organizing look-up tables",
  journal =      "Journal of Sound and Vibration",
  year =         "2003",
  volume =       "266",
  number =       "3",
  month =        "September",
  pages =        "601--612",
}

@Article{moshou04a_bibuniq_244,
  author =       "D. Moshou and K. Deprez and H. Ramon",
  title =        "Prediction of spreading processes using a supervised Self-Organizing Map",
  journal =      "Mathematics and Computers in Simulation",
  year =         "2004",
  volume =       "65",
  number =       "1-2",
  month =        "April",
  pages =        "77--85",
}

@Article{mukherjee02a_bibuniq_4983,
  author =       "D. Mukherjee and S. K. Mitra",
  title =        "Successive refinement lattice vector quantization",
  journal =      "{IEEE} Transactions on Image Processing",
  year =         "2002",
  volume =       "11",
  number =       "12",
  month =        "December",
  pages =        "1337--1348",
  abstract =     "",
}

@Article{vinson02a_bibuniq_482,
  author =       "D. P. Vinson and G. Vigliocco",
  title =        "A semantic analysis of grammatical class impairments: semantic representations of object nouns, action nouns and action verbs",
  journal =      "Journal of Neurolinguistics",
  year =         "2002",
  volume =       "15",
  number =       "3-5",
  month =        "May",
  pages =        "317--351",
}

@Article{pullwitt02a_bibuniq_420,
  author =       "D. Pullwitt",
  title =        "Integrating contextual information to enhance {SOM}-based text document clustering",
  journal =      "Neural Networks",
  year =         "2002",
  volume =       "15",
  number =       "8-9",
  month =        "October",
  pages =        "1099--1106",
}

@Article{maia03a_bibuniq_318,
  author =       "D. R. J. Maia and L. Balbinot and R. J. Poppi and M. A. De Paoli",
  title =        "Effect of conducting carbon black on the photostabilization of injection molded poly(propylene-co-ethylene) containing {TiO2}",
  journal =      "Polymer Degradation and Stability",
  year =         "2003",
  volume =       "82",
  number =       "1",
  month =        "October",
  pages =        "89--98",
}

@InProceedings{radke05a_bibuniq_158,
  author =       "D. Radke and U. Moller",
  title =        "Quantitative evaluation of established clustering methods for gene expression data",
  booktitle =    "Biological and Medical Data Analysis, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "75--92",
}

@Article{kim03c_bibuniq_352,
  author =       "D. S. Kim",
  title =        "Training ratio and comparison of trained vector quantizers",
  journal =      "{IEEE} Transactions on Signal Processing",
  year =         "2003",
  volume =       "51",
  number =       "6",
  month =        "June",
  pages =        "1632--1641",
}

@Article{theofilou03a_bibuniq_346,
  author =       "D. Theofilou and V. Steuber and E. De Schutter",
  title =        "Novelty detection in a {K}ohonen-like network with a long-term depression learning rule",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "52-4",
  month =        "June",
  pages =        "411--417",
}

@Article{kim05b_bibuniq_123,
  author =       "D. W. Kim and K. H. Lee and D. Lee",
  title =        "Detecting clusters of different geometrical shapes in microarray gene expression data",
  journal =      "Bioinformatics",
  year =         "2005",
  volume =       "21",
  number =       "9",
  month =        "May",
  pages =        "1927--1934",
}

@Article{nelson04a_bibuniq_163,
  author =       "D. W. Nelson and B. M. Bellander and R. M. MacCallum and J. Axelsson and M. Alm and M. Wallin and E. Weitzberg and A. Rudehill",
  title =        "Cerebral microdialysis of patients with severe traumatic brain injury exhibits highly individualistic patterns as visualized by cluster analysis with self-organizing maps",
  journal =      "Critical Care Medicine",
  year =         "2004",
  volume =       "32",
  number =       "12",
  month =        "December",
  pages =        "2428--2436",
}

@InProceedings{Wang00a_bibuniq_4080,
  author =       "D. Wang and C. Quek and G. S. Ng",
  title =        "{MS}-{TSK}fnn: novel {T}akagi-{S}ugeno-{K}ang fuzzy neural network using {ART} like clustering",
  booktitle =    "{IEEE} International Joint Conference on Neural Networks",
  volume =       "3",
  pages =        "2361- 2366",
  year =         "2004",
}

@InProceedings{Marques04a_bibuniq_1398,
  author =       "D. Z. Marques and K. A. de Almeida and A. M. de Deus and A. R. G. da Silva Paulo and W. da Silva Lima",
  title =        "A comparative analysis of neural and fuzzy cluster techniques applied to the characterization of electric load in substations",
  booktitle =    "{IEEE}/{PES} Transmision and Distribution Conference and-Exposition: {L}atin {A}merica",
  year =         "2004",
  volume =       "",
  pages =        "908--913",
  abstract =     "The hourly electric load behavior is necessary information to execute planning and operation activities of power systems. This information can be obtained through load characterization consisting of measurement and analysis of several load profiles aiming to find the typical profile that reflects consumer's behavior served by a certain utility or even the profiles of the consumers served by a specific substation. in this work, six techniques were used to perform clustering and classification of bus load profile in utilities substations: K-means, four variations of self organizing maps and fuzzy C-means. Many simulations with different parameters were used to improve clustering performance. Merit indexes of clustering performance (intracluster and intercluster) were used to compare these techniques. These indexes were not sufficient to guarantee satisfactory results but indicate reasonable performance. the combination of all techniques helps decision maker to understand results and increase the possibility to extract from data implicit information and potentially useful.",
}

@InProceedings{inspek283_bibuniq_943,
  author =       "Dag O. and Ucak C.",
  title =        "Fault classification for power distribution systems via a combined wavelet-neural approach",
  booktitle =    "International Conference on Power System Technology {POWERCON} {IEEE}",
  pages =        "1309--1314",
  volume =       "2",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper presents an integrated design of a fault classifier which uses a hybrid wavelet-artificial neural network (ANN) based approach. the data for the fault classifier is produced by PSCAD/EMTDC simulation program for 34. 5 kV Sagmalcilar-Maltepe distribution system in Istanbul, Turkey. It is aimed to design a classifier capable of recognizing ten classes of three-phase distribution system faults. A database of line currents and line-to-ground voltages is built up including system faults at different fault inception angles and fault locations. the characteristic information over six-channel of current and voltage samples is extracted by the wavelet multiresolution analysis technique. Then, an ANN-based tool was employed for classification task. the main idea of this approach is to solve the complex fault (three-phase short-circuit) classification problem under various system and fault conditions. A self-organizing map, with {K}ohonen's learning algorithm and type-one learning vector quantization technique is implemented into this study. the performance of the wavelet-neural fault classifier is presented and the results are analyzed in the paper. It is shown that the technique correctly recognizes and discriminates the fault types and faulted phases with a high degree of accuracy in the simulated model distribution system.",
}

@Article{inspek460_bibuniq_1094,
  author =       "Dahbur K. and Muscarello T.",
  title =        "Systematic identification of potential patterns for serial criminals",
  journal =      "International Journal of Computers and Their Applications",
  pages =        "41--59",
  volume =       "11",
  number =       "1",
  month =        "March",
  year =         "2004",
  publisher =    "ISCA",
  abstract =     "We describe an automated methodology that can systematically identify groups of records as potential patterns for serial criminals, with a good degree of accuracy. the type of crime that this research investigate is restricted to armed robbery. However, the methodology for identifying patterns in robbery records is in general applicable to other types of crimes, with little or no modification. Neural networks is the main tool for the classification of patterns because of their powerful capabilities at such tasks. We also investigate a methodology to process the data into the form needed for the neural networks to operate. Expert rules or heuristics are used to refine the outputs of the neural network.",
}

@InProceedings{nakatsuka03_bibuniq_4300,
  author =       "Daisuke Nakatsuka and Matashige Oyabu",
  title =        "Application of Spherical {SOM} in Clustering",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{inspek408_bibuniq_1054,
  author =       "Date A. and Kurata K.",
  title =        "Separation of position and direction information of robots by a product model of self-organizing map and neural gas",
  journal =      "Transactions of the Institute of Electronics, Information and Communication Engineers",
  volume =       "J87D II",
  number =       "7",
  month =        "July",
  pages =        "1529--1238",
  year =         "2004",
  publisher =    "Inst. Electron. Inf. \& Commun. Eng",
  abstract =     "The visual input to a robot in a room is a function of two factors, the position and direction of the robot. To separate two factors of information in the observed images, we have developed a computational model based on two learning algorithms, the {K}ohonen's self-organizing map ({SOM}) and the neural gas (GAS). Our model, a product of these two models, is SOM-like in one dimension of the unit array and GAS-like in the other dimensions. the robot is assumed to change only one of its position or direction at each learning step, and the learning rule is designed to take advantage of the information which of the two is changed. We demonstrate, by computer simulation, that position and direction are extracted separately in different dimensions of the unit array.",
}

@Article{inspek251_bibuniq_1336,
  author =       "Date A. and Kurata K.",
  title =        "A model of complex cell development by information separation",
  journal =      "Transactions of the Institute of Electronics, Information and Communication Engineers",
  pages =        "211--217",
  volume =       "J88D II",
  number =       "2",
  month =        "February",
  year =         "2005",
  publisher =    "Inst. Electron. Inf. \& Commun. Eng",
  abstract =     "Neurons in the primary visual cortex (V1) of primate are selective for location, orientation and spatial frequency. Among them, complex cells are characterized by their selectivity to orientation and spatial frequency while lacking sensitivity to position or phase tuning (dark/bright line center) within a restricted range. the development of the shift invariance property of complex cells has been successfully explained by the temporal trace learning which takes advantage of the temporal coherence of visual stimuli (P. Foldiak, neural computation, 3, 194, 1991). We have carried out mathematical modeling of complex cell development without temporal trace mechanism. the model network consists of three layers of E, S, and C layer which model excitatory cells in LGN or V1, and simple cells, and complex cells in V1 respectively. Neurons in the layer E have position selective, and neurons in the layer S are line detectors for a specific position. During the learning phase, the network is exposed to randomly located short oriented bars, and neurons in the layer C self-organize its selectivity to the inputs. the learning rules are Hebbian or {SOM} (self-organizing map) type between the layer S and C, and anti-Hebbian between the layer E and C by which neurons are forced to represent uncorrelated aspect of the inputs. We demonstrate that neurons in the layer C learns invariance to shift in input position. Our model explain complex cell development in terms of the principle of information separation.",
}

@Misc{webform_9661_bibuniq_4254,
  author =       "David Opolon and Fabien Moutarde",
  title =        "Fast semi-automatic segmentation algorithm for Self-Organizing Maps",
  howpublished = "Proceedings of 12th European Symposium on Artificial Neural Networks ({ESANN'2004})",
  pages =        "507--512",
  month =        "April",
  year =         "2004",
}

@InProceedings{inspek290_bibuniq_946,
  author =       "DeLooze L. L.",
  title =        "Classification of computer attacks using a self-organizing map",
  booktitle =    "Proceedings from the Fifth Annual {IEEE} {SMC} Information Assurance Workshop",
  pages =        "365--369",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "As computer technology evolves and the threat of computer crimes increases, the apprehension and preemption of such violations become more and more difficult and challenging. To date, it appears that completely preventing breaches of security is unrealistic. Therefore, we must try to detect and classify these intrusions as they occur so that immediate actions may be taken to repair the damage and prevent further harm. One attempt at classifying these intrusions is MITRE's Common Vulnerabilities and Exposures (CVE) list that provides a common name for all publicly known security weaknesses. the C. V. E. dictionary, however, is not taxonomy. the C. V. E. list is organized in simple numerical order by date of acceptance. Each entry in the dictionary includes a unique C. V. E. identification number, a text description of the vulnerability and any pertinent references. Creating a self-organizing map ({S. O. M.}) using the text description allows us to order attack profiles with common features in the same general area of the output space. Attacks in the general neighborhood of one another should be able to be mitigated by similar means. Plotting attacks on a {S. O. M.} also enables us to visually examine the placement of an attack relative to the four common classes of attacks (Denial of Service, Deception, Reconnaissance, and Unauthorized Access). Many attacks have features in common with more than one of these classes rather than corresponding directly to a single class. We have developed an effective technique to classify new attacks using a unique taxonomy, which breaks down threats into the four general categories, and the {S. O. M.} created by the baseline C. V. E. descriptions.",
}

@InProceedings{inspek392_bibuniq_1038,
  author =       "Debono C. J. and Buhagiar J. K.",
  title =        "Neural location detection in wireless networks",
  booktitle =    "Conference Proceedings. 7th European Conference on Wireless Technology",
  pages =        "133--136",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "An innovative solution towards the implementation of wireless location detection using a modified self-organizing map is presented. the network translates the measured cell data of a radio environment onto a neuron hierarchy that is immune to environmental variations. Simulation results demonstrate the network's ability to locate a mobile station within two meters in 94\% of the cases.",
}

@Article{inspek499_bibuniq_1117,
  author =       "Deng-Chang-hong {Tang-Bi-qiang, Chen-Yun-ping}",
  title =        "Application of compound neural network based genetic algorithm optimizing for power system transient stability assessment",
  journal =      "Proceedings of the {CSU} {EPSA}",
  pages =        "6--18",
  volume =       "16",
  number =       "1",
  year =         "2004",
  publisher =    "Editorial Board of the Proceedings of the CSU-EPSA",
  abstract =     "In this paper, a compound neural network optimized by genetic algorithm for transient stability assessment is proposed. the proposed neural network is composed of a {K}ohonen network and several radial-basis function (RBF) networks. the learning performance and the ability of classification of the compound neural network are largely enhanced by genetic algorithm. and the feasibility of the presented approach is validated by the simulation result of Central China Power Grid.",
}

@InProceedings{inspek442_bibuniq_1085,
  author =       "Depren M. O. and Topallar M. and Anarim E. and Ciliz K.",
  title =        "Network-based anomaly intrusion detection system using {SOM}s",
  booktitle =    "Proceedings of the {IEEE} 12th Signal Processing and Communications Applications Conference",
  pages =        "76--59",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Network-based anomaly intrusion detection systems using artificial neural networks are investigated. From knowledge of only normal traffic data, a mathematical model describing normal traffic is constructed and a test is conducted based on the deviations from the mathematical model. A self-organizing map ({SOM}) structure is used for constructing the mathematical model describing normal traffic and anomaly detection. the {SOM} structure preserves topological mappings between representations. A feature which is desired when classifying normal or intrusive behavior for network data, our hypothesis is that normal traffic representing normal behavior would be clustered around one or more cluster centers and any irregular traffic representing abnormal, and possibly suspicious, behavior would be clustered outside of the normal clustering or inside with high quantization error. the {SOM} is trained with normal traffic data and by considering the best matching unit or clustering region and the quantization error, the type of traffic is determined.",
}

@Article{inspek179_bibuniq_1279,
  author =       "Depren O. and Topallar M. and Anarim E. and Ciliz M. K.",
  title =        "An intelligent intrusion detection system ({IDS}) for anomaly and misuse detection in computer networks",
  journal =      "Expert Systems with Applications",
  pages =        "713--722",
  volume =       "29",
  number =       "4",
  month =        "November",
  year =         "2005",
  publisher =    "Elsevier",
  abstract =     "In this paper, we propose a novel intrusion detection system (IDS) architecture utilizing both anomaly and misuse detection approaches. This hybrid intrusion detection system architecture consists of an anomaly detection module, a misuse detection module and a decision support system combining the results of these two detection modules. the proposed anomaly detection module uses a self-organizing map ({SOM}) structure to model normal behavior. Deviation from the normal behavior is classified as an attack. the proposed misuse detection module uses J. 48 decision tree algorithm to classify various types of attacks. the principle interest of this work is to benchmark the performance of the proposed hybrid IDS architecture by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers. A rule-based decision support system (DSS) is also developed for interpreting the results of both anomaly and misuse detection modules. Simulation results of both anomaly and misuse detection modules based on the KDD 99 Data Set are given. It is observed that the proposed hybrid approach gives better performance over individual approaches. [All rights reserved Elsevier].",
}

@InProceedings{inspek88_bibuniq_1196,
  author =       "Desjardins G. and Godin R. and Proulx R.",
  title =        "A self-organizing map for concept classification in information retrieval",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks 2005",
  pages =        "1570--1574",
  volume =       "3",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Information retrieval is concerned with the classification processes and the selective recovery of information. Improvements in this field are mainly sought at the core level of the engine's classification capabilities and by query enhancement processes. the later one became the prime interest of researchers since less progress has been made on the former one. Both make substantial use of manual interventions, which results in a less automated overall process. in this paper, we propose a new model based on the self-organizing map paradigm to discover the concepts embedded in a collection of documents. the terms of the corpus are directly classified into concepts, without manual category labelling. Then the concepts serve as a new knowledge representation for information retrieval. This model has been tested on a TREC-6 subcollection (text retrieval conference). As expected, the retrieval using the concepts representation does not outperform the corresponding full term retrieval. It is a step toward terms classification using a self-organizing map and contributes to fully automate the discovery of concepts in text collections.",
}

@InProceedings{inspek328_bibuniq_978,
  author =       "Dewen-Hu Gang-Wang",
  title =        "On nonlinear independent component analysis using self-organizing map",
  booktitle =    "Fifth World Congress on Intelligent Control and Automation",
  pages =        "91--95",
  volume =       "1",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Nonlinear independent component analysis (NICA) is a generalization of basic independent component analysis ({ICA}), and many methods have been proposed to recover signals from nonlinear mixtures. the work is performed on a post-nonlinear model, and the emphasis is focused on the method of self-organizing map ({SOM}). Two issues are addressed, (1) factor rotation is a crucial step in the process of inverse transform, and (2) density distortion is introduced by post-nonlinear transform. As {SOM} is a homeomorphism from input space to the maps, it cannot perform factor rotation and remove distortion. Experiments show that the rough nonlinear separation ability comes mostly from the decorrelation via whitening rather than from the SOM, and the process of NICA using {SOM} is in essence a second-order statistical method.",
}

@InProceedings{inspek433_bibuniq_1076,
  author =       "Dewen-Hu {Shuang-Chen, Zongtan-Zhou}",
  editor =       "C. {Yin, F. ; Wang, J. ; Guo}",
  title =        "Diffusion and growing self-organizing map: a nitric oxide based neural model",
  booktitle =    "Advances in Neural Networks {ISNN}, International Symposium on Neural Networks. Proceedings Lecture Notes in Comput. Sci.",
  pages =        "199--204",
  volume =       "1",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This paper presents a new diffusion and growing neural network model called DGSOM for self-organization, where more generalized ideas of short-range competition and long-range cooperation are adopted while introducing the diffusion mechanism of intrinsic NO as its most remarkable characteristic. the new DGSOM model can compartmentalize input space rationally and efficiently, and generated topological connections among neurons can reflect the dimensionality and structure of input signals. Experiments and simulations indicated that the embedding of NO diffusion mechanism improve the performance of self-organization remarkably, especially the flexibility and rapidity of response for dynamic distribution.",
}

@Article{Di00a_bibuniq_3994,
  author =       "Di Wang and Chai Quek and Geok See Ng",
  title =        "Novel self-organizing Takagi-Sugeno-Kang fuzzy neural networks based on {ART}-like clustering",
  journal =      "Neural Processing Letters",
  pages =        "39--51",
  volume =       "20",
  month =        "August",
  number =       "1",
  year =         "2004",
}

@Misc{webform_10151_bibuniq_4258,
  author =       "Didier Verleysen",
  title =        "Using the Self-Organizing Maps to prove empirically the market inefficiency: Evidence from Paris Stock Exchange",
  howpublished = "in Proc. of 12th Connectionist Approaches in Economics and Management - {ASCEG} 2005",
  pages =        "",
  note =         "",
  month =        "November",
  year =         "2005",
}

@InProceedings{merkl03_bibuniq_4315,
  author =       "Dieter Merkl and Shao Hui He and Michael Dittenbach and Andreas Rauber",
  title =        "Adaptive Hierarchical Incremental Grid Growing: An architecture for high-dimensional data visualization",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek272_bibuniq_1347,
  author =       "Diganta-Saha Nirmalya-Chowdhury",
  editor =       "A. Gelbukh",
  title =        "Unsupervised text classification using {K}ohonen's self organizing network",
  booktitle =    "Computational Linguistics and Intelligent Text Processing. 6th International Conference, {CICL}ing 2005",
  pages =        "715--718",
  volume =       "3406",
  year =         "2005",
  publisher =    "Spring-Verlag, Berlin, Germany",
  abstract =     "A text classification method using {K}ohonen's self organizing network is presented here. the proposed method can classify a set of text documents into a number of classes depending on their contents where the number of such classes is not known a priori. Text documents from various faculties of games are considered for experimentation. the method is found to provide satisfactory results for large size of data.",
}

@Article{inspek562_bibuniq_774,
  author =       "Dittenbach M.",
  title =        "The growing hierarchical self-organizing map: uncovering hierarchical structure in data",
  journal =      "{OEGAI} Journal",
  pages =        "25--28",
  month =        "October",
  volume =       "22",
  number =       "3",
  year =         "2003",
  publisher =    "Osterreichische Gesellschaft fur Artificial Intelligence",
  abstract =     "Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces, with current approaches hardly incorporating these requirements within a single model. in this paper we present the growing hierarchical self-organizing map (GHSOM) along with a demonstration of its potential with an application from the information retrieval domain, which is prototypical both of the high-dimensional feature spaces frequently encountered in today's applications as well as of the hierarchical nature of data. the main feature of this novel architecture is its capability of growing both in terms of map size as well as in a three-dimensional tree-structure in order to represent the hierarchical structure present in a data collection during an unsupervised training process.",
}

@InProceedings{inspek71_bibuniq_1182,
  author =       "Dittenbach M. and Rauber A. and Polzlbauer G.",
  title =        "Investigation of alternative strategies and quality measures for controlling the growth process of the growing hierarchical self-organizing map",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "2954--2959",
  volume =       "5",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The self-organizing map ({SOM}) is a very popular neural network model for data analysis and visualization of high-dimensional input data. the growing hierarchical self-organizing map (GHSOM) - being one of the many architectures based on the {SOM} - has the property of dynamically adapting its architecture during training by map growth as well as creating a hierarchical structure of maps, thus reflecting hierarchical relations in the data. This allows for viewing portions of the data at different levels of granularity. We review different {SOM} quality measures and also investigate alternative strategies as candidates for guiding the growth process of the GHSOM in order to improve the hierarchical representation of the data.",
}

@Article{inspek636_bibuniq_836,
  author =       "Dokur Z. and Olmez T.",
  title =        "Classification of respiratory sounds by using an artificial neural network",
  journal =      "International Journal of Pattern Recognition and Artificial Intelligence",
  pages =        "567--580",
  volume =       "17",
  number =       "4",
  month =        "June",
  year =         "2003",
  publisher =    "World Scientific",
  abstract =     "In this paper, a classification method for respiratory sounds (RSs) in patients with asthma and in healthy subjects is presented. Wavelet transform is applied to a window containing 256 samples. Elements of the feature vectors are obtained from the wavelet coefficients. the best feature elements are selected by using dynamic programming. Grow and Learn (GAL) neural network, {K}ohonen network and multi-layer perceptron (MLP) are used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.",
}

@Article{inspek682_bibuniq_879,
  author =       "Dokur Z. and Olmez T.",
  title =        "Segmentation of {MR} and {CT} images by using a quantiser neural network",
  journal =      "Neural Computing \& Applications",
  pages =        "168--177",
  volume =       "11",
  number =       "3--4",
  year =         "2003",
  publisher =    "Springer-Verlag",
  abstract =     "A quantiser neural network (QNN) is proposed for the segmentation of MR (magnetic resonance) and CT (computer topography) images. Elements of a feature vector are formed by image intensities at one neighborhood of the pixel of interest. QNN is a neural network structure, which is trained by genetic algorithms. Each node in the first layer of the QNN forms a hyperplane (HP) in the input space. There is a constraint on the HPs in a QNN. the HP is represented by only one parameter in d-dimensional input space. Genetic algorithms are used to find the optimum values of the parameters, which represent these nodes. the novel neural network is comparatively examined with a multilayer perceptron and a {K}ohonen network for the segmentation of MR and CT head images. It is observed that the QNN gives the best classification performance with fewer nodes after a short training time.",
}

@InProceedings{inspek585_bibuniq_793,
  author =       "Doser A. B.",
  editor =       "M. H. Hamza",
  title =        "Decision-aiding algorithm for interpreting physiological data hazardous situations",
  booktitle =    "Proceedings of the Fifth {IASTED} International Conference on Signal and Image Processing",
  pages =        "434--439",
  year =         "2003",
  publisher =    "ACTA Press, Anaheim, CA, USA",
  abstract =     "A method is presented which combines self-organizing map techniques with a new clustering approach to interpret physiological data. the application is a decision making aid for team leaders of first responders (firefighters, police, etc. ) who must decide which action to take regarding their team members in a stressful and dangerous situation. Actual physiological data was collected from volunteers who wore an armband collection device while undergoing regular activities. Results demonstrate that the scheme developed can be a useful tool to aid in the classification of individual body states. Additionally, results suggest that physiological data mapping is highly unique and that a network trained to one person does not readily transfer to another.",
}

@InProceedings{inspek868_bibuniq_667,
  author =       "Douzono H. and Hara S. and Kuriyama Y. and Tokushima H. and Noguchi Y.",
  title =        "A clustering method of chromosome fluorescence profiles using self organizing map",
  booktitle =    "Proceedings of the 2002 International Joint Conference on Neural Networks. {IJCNN}'02",
  pages =        "1080--1085",
  volume =       "2",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The clustering method by the self-organizing map algorithm of chromosome profiles measured by slit-scan flowcytometer is proposed. Chromosome profile represents the distribution of the fluorescence intensities along the lengthwise. To examine the performance of the cytometer developed in our laboratory, we made clustering experiments of the measured profiles. We developed a {SOM} based clustering algorithm using chromosome physical model, which can estimate the chromosome models and rotation angles, but the results were considered to be not complete. For this problem, we propose a new algorithm, which estimate the models and rotation angles step by step. We examine our algorithm using the virtual profiles and profiles measured by our cytometer.",
}

@Article{inspek240_bibuniq_923,
  author =       "Drigas A. and Vrettaros J.",
  title =        "An intelligent tool for building e-learning contend-material using natural language in digital libraries [content read as contend]",
  journal =      "{WSEAS} Transactions on Information Science and Applications",
  pages =        "1197--1205",
  volume =       "1",
  number =       "5",
  year =         "2004",
  month =        "November",
  publisher =    "{WSEAS}",
  abstract =     "In this paper is developed an intelligent searching tool using the self-organizing map ({SOM}) algorithm, as a prototype e-content retrieval tool. the proposed searching tool has the ability to adjust and scale into any e-learning platform that requires concept-based queries. the {SOM} algorithm has been used successfully for the document organization as well as for document retrieval. in the proposed methodology, maps are used for the automatic replacement of the unstructured, the half structured and the multidimensional data of text in such a way that similar entries in the map are represented near between them. the performance and the functionality of the document organization and retrieval tool employing the {SOM} architecture, is presented. Furthermore, experiments performed to test the time performance of a learning algorithm used for the direct creation of teams of terms and texts enabling efficient searching and retrieval of the documents.",
}

@InProceedings{inspek521_bibuniq_752,
  author =       "Du R. Ming-Ge and Yangsheng-Xu",
  title =        "Fault detection using hierarchical self-organizing map",
  booktitle =    "Proceedings. 2003 {IEEE} International Conference on Robotics, Intelligent Systems and Signal Processing",
  pages =        "565--570",
  volume =       "1",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The appropriate features are essential for pattern classification and signal modeling. Stamping operations eagerly need the condition monitoring system in practice to guarantee the product quality; however, its processes are nearly intractable and the features of its signals are not easy selected. the self-organizing map ({SOM}) is an excellent tool in data exploratory due to its property of mapping the complex relationships in high dimensional space onto simple geometric relationships in a low dimensional space. A hierarchical {SOM} was developed in the paper: the prototype vectors of the bottom layer {SOM} are considered as the features, which are clustered at the top layer SOMs. the results demonstrate that the proposed approach working effectively in the condition monitoring. This suggests that the hierarchical {SOM} is worthy of more applications.",
}

@InProceedings{inspek6_bibuniq_1136,
  author =       "Duran O. and Petrou M.",
  title =        "A time-efficient clustering method for pure class selection",
  booktitle =    "{IGARSS} 2005. {IEEE} International Geoscience and Remote Sensing Symposium",
  pages =        "CD-ROM",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In order to detect a target or anomaly in a hyper-spectral image the classes associated with the background have to be identified. We propose a computationally efficient methodology to determine the background classes present in the image. the method is based on the assumption that mixed and anomaly pixels are relatively rare in comparison with the abundance of the background class pixels. the method considers the background classes as groups of distinct measurements and consists of robust clustering of a randomly picked small percentage of the image pixels. the resulting clusters may be considered as representatives of the background of the image. Several clustering techniques are investigated and experimental results using hyperspectral data are presented. the proposed technique using a self-organising map is then compared with a state-of the art endmember extraction technique.",
}

@InProceedings{inspek270_bibuniq_934,
  author =       "Dutta R. and Gardner J. W. and Hines E. L.",
  editor =       "M. J. {Rocha, D. ; Sarro, P. M. ; Vellekoop}",
  title =        "{ENT} bacteria classification using a neural network based Cyranose 320 electronic nose",
  booktitle =    "Proceedings of the {IEEE} Sensors",
  pages =        "324--325",
  volume =       "1",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320, comprising an array of thirty-two polymer carbon black composite sensors has been used to identify 3 species of bacteria responsible for ear, nose and throat (ENT) infections when present in standard agar solution. Swab samples were collected from the infected areas of the ENT patient ear, nose and throat regions. Gathered data were from a very complex mixture of different chemical compounds. An innovative data clustering approach was investigated for these bacteria data by combining the principal component analysis ({PCA}) based 3{D} scatter plot, fuzzy C means (FCM) and self-organizing map ({SOM}) network. Using these three data clustering algorithms simultaneously, better classification of three ENT bacteria classes were represented. Then, three supervised classifiers, namely multi-layer perceptron (MLP), probabilistic neural network (PNN) and radial basis function network (RBF), were used to classify the three bacteria classes. A comparative evaluation of the classifiers was conducted for this application.",
}

@InProceedings{inspek634_bibuniq_834,
  author =       "Dutta R. and Hines E. L. and Gardner J. W. and Kashwan K. R. and Bhuyan M.",
  title =        "Determination of tea quality by using a neural network based electronic nose",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "404--409",
  volume =       "1",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In these paper we have used a metal oxide sensor based electronic nose (EN) to analyse five tea samples with different qualities, namely, drier month, drier month again over fired, well fermented normal fired in oven, well fermented over fired in oven, and under fermented normal fired in oven. the flavour of team is determined mainly by its taste and smell, which generated by hundreds of volatile organic compounds (VOCs) and non-volatile organic compounds present in tea. These VOCs are present in different ratios and determine the quality of the tea. For example Assamica (Sri Lanka and Assam tea) and Assamica Sinesis (Dajeeling and Japanese tea) are two different species of tea giving different flavour notes. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human organoleptic profiling panels. These methods are expensive in terms of time and labour and also inaccurate because of lack of either sensitivity or quantitative information. in this paper an investigation has been made to determine the flavours of different tea samples using an EN and to explore the possibility of replacing existing analytical and profiling panel methods. the technique uses as array of 4 metal oxide sensors (MOS), each of, which has an electrical resistance that has partial sensitivity to the headspace of tea. the signals from the sensor array are then conditioned by suitable interface circuitry. the data were processed using principal component analysis ({PCA}), fuzzy C means algorithm (FCM). We also explored the use of self-organizing map ({SOM}) method along with a radial basis function network (RBF) and a probabilistic neural network (PNN) classifier. Using FCM and {SOM} feature extraction techniques along with RBF neural network we achieved 100\% correct classification for the five different tea samples with different qualities. These results prove that our EN is capable of discriminating between the flavours of teas manufactured under different processing conditions, viz. over-fermented, over-fired, under fermented etc.",
}

@InProceedings{inspek759_bibuniq_579,
  author =       "Dzemyda G.",
  editor =       "A. {Haav, H-M. ; Kalja}",
  title =        "Method of visual data mining for the analysis of curricula",
  booktitle =    "Databases and Information Systems. Proceedings of the Fifth International Baltic Conference",
  pages =        "201--212",
  volume =       "1",
  year =         "2002",
  publisher =    "Inst. Cybernetics at Tallinn Tech. Univ, Tallinn, Estonia",
  abstract =     "The data mining method proposed in this paper allows us to discover knowledge on the interaction and similarity of subjects. Its advantage is a possibility to visualize the interlocation of subjects. the investigator has a possibility to make the proper decision on a basis of multidimensional numerical data presented visually. Moreover, the paper gives an illustrative example of a successful application of the visual analysis of correlation matrices. in fact, the method has been developed for quality analysis of the studies via the analysis of databases of examination results. It integrates two methods for data mapping: Sammon's (1969) mapping and the self-organizing map. the method is based on the visualization of a set of subjects characterized by their correlation matrix obtained on a basis of examination results. As a result, our method makes it possible to evaluate the level of humanity and mathematization of different computer science subjects.",
}

@InProceedings{inspek626_bibuniq_826,
  author =       "Dzemyda G. and Kurasova O.",
  title =        "Visualization of multidimensional data taking into account the learning flow of the self-organizing neural network",
  booktitle =    "Journal of {WSCG}",
  pages =        "117--124",
  volume =       "11",
  number =       "1",
  year =         "2003",
  publisher =    "Univ. West Bohemia",
  abstract =     "We discuss the visualization of multidimensional vectors taking into account the learning flow of the self-organizing neural network. A new algorithm realizing a combination of the self-organizing map ({SOM}) and Sammon's mapping has been proposed. It takes into account the intermediate learning results of the SOM. the experiments have showed that the algorithm gives lower mean projection errors as compared with a consequent application of the {SOM} and Sammon's mapping. This is the essential advantage of the new algorithm, i. e. we succeed to eliminate the influence of the {"}magic factor{"} alpha (0< alpha <or=1) on Sammon's mapping results. For larger values of alpha ( alpha >1), the mean projection error grows. However, in this case the new algorithm operates more stable and gives smaller values of the mean projection error.",
}

@Article{bayram04a_bibuniq_156,
  author =       "E. Bayram and P. Santago and R. Harris and Y. D. Xiao and A. J. Clauset and J. D. Schmitt",
  title =        "Genetic algorithms and self-organizing maps: a powerful combination for modeling complex {QSAR} and {QSPR} problems",
  journal =      "Journal of Computer-Aided Molecular Design",
  year =         "2004",
  volume =       "18",
  number =       "7",
  month =        "July",
  pages =        "483--493",
}

@Article{fatzinger05a_bibuniq_75,
  author =       "E. C. Fatzinger and E. V. Hill",
  title =        "Low proof load prediction of ultimate loads of fiberglass/epoxy resin {I}-beams using acoustic emission",
  journal =      "Journal of Testing and Evaluation",
  year =         "2005",
  volume =       "33",
  number =       "5",
  month =        "September",
  pages =        "340--347",
}

@Article{Dimitriadou04a_bibuniq_1483,
  author =       "E. Dimitriadou and M. Barth and C. Windischberger and K. Hornik and E. Moser",
  title =        "A quantitative comparison of functional {MRI} cluster analysis",
  journal =      "Artificial Intelligence in Medicine. May 2004; 31(1): 57-71",
  year =         "2004",
  volume =       "",
  pages =        "",
  abstract =     "The aim of this work is to compare the efficiency and power of several cluster analysis techniques on fully artificial (mathematical) and synthesized (hybrid) functional magnetic resonance imaging ({fMRI}) data sets. the clustering algorithms used are hierarchical, crisp (neural gas, self-organizing maps, hard competitive learning, k-means, maximin-distance, CLARA) and fuzzy (c-means, fuzzy competitive learning). To compare these methods we use two performance measures, namely the correlation coefficient and the weighted Jaccard coefficient (wJC). Both performance coefficients (PCs) clearly show that the neural gas and the k-means algorithm perform significantly better than all the other methods using our setup. For the hierarchical methods the ward linkage algorithm performs best under our simulation design. in conclusion, the neural gas method seems to be the best choice for {fMRI} cluster analysis, given its correct classification of activated pixels (true positives (TPs)) whilst minimizing the misclassification of inactivated pixels (false positives (FPs)), and in the stability of the results achieved.",
}

@InProceedings{Dominguez00b_bibuniq_3929,
  author =       "E. Dominguez and J. Munoz",
  title =        "Bidirectional neural network for clustering problems",
  booktitle =    "Advances in Artificial Intelligence Iberamia 2004, 9th Ibero-American Conference on {AI}. Proceedings Lecture Notes in Artificial Intelligence",
  volume =       "3315",
  pages =        "788--798",
  year =         "2004",
}

@Article{fatemizadeh03a_bibuniq_343,
  author =       "E. Fatemizadeh and C. Lucas and H. Soltanian-Zadeh",
  title =        "Automatic landmark extraction from image data using modified growing neural gas network",
  journal =      "{IEEE} Transactions on Information Technology in Biomedicine",
  year =         "2003",
  volume =       "7",
  number =       "2",
  month =        "June",
  pages =        "77--85",
}

@Article{fukusaki05a_bibuniq_30,
  author =       "E. Fukusaki and A. Kobayashi",
  title =        "Plant meta-bolomics: Potential for practical operation",
  journal =      "Journal of Bioscience and Bioengineering",
  year =         "2005",
  volume =       "100",
  number =       "4",
  month =        "October",
  pages =        "347--354",
}

@Article{koua04a_bibuniq_142,
  author =       "E. L. Koua and M. J. Kraak",
  title =        "Alternative visualization of large geospatial datasets",
  journal =      "Cartographic Journal",
  year =         "2004",
  volume =       "41",
  number =       "3",
  month =        "December",
  pages =        "217--228",
}

@Article{lopez-rubio04a_bibuniq_186,
  author =       "E. Lopez-Rubio and J. M. Ortiz-De-Lazcano-Lobato and J. Munoz-Perez and J. A. Gomez-Ruiz",
  title =        "Principal components analysis competitive learning",
  journal =      "Neural Computation",
  year =         "2004",
  volume =       "16",
  number =       "11",
  month =        "November",
  pages =        "2459--2481",
}

@Article{lopezrubio04b_bibuniq_263,
  author =       "E. Lopez-Rubio and J. Munoz-Perez and A. Gomez-Ruiz",
  title =        "A principal components analysis self-organizing map",
  journal =      "Neural Networks",
  year =         "2004",
  volume =       "17",
  number =       "2",
  month =        "March",
  pages =        "261--270",
}

@InProceedings{lopez-rubio02a_bibuniq_4945,
  author =       "E. Lopez-Rubio and J. Munoz-Perez and J. A. Gomez-Ruiz",
  title =        "The Principal Components Analysis self-organizing map",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "865--870",
  abstract =     "",
}

@Article{lopez-rubio03a_bibuniq_241,
  author =       "E. Lopez-Rubio and J. Munoz-Perez and J. A. Gomez-Ruiz and E. Dominguez-Merino",
  title =        "New learning rules for the {ASSOM} network",
  journal =      "Neural Computing \& Applications",
  year =         "2003",
  volume =       "12",
  number =       "2",
  month =        "November",
  pages =        "109--118",
}

@InProceedings{pampalk02a_bibuniq_4946,
  author =       "E. Pampalk and A. Rauber and D. Merkl",
  title =        "Using smoothed data histograms for cluster visualization in Self-Organizing Maps",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "871--876",
  abstract =     "",
}

@InProceedings{reid05a_bibuniq_84,
  author =       "E. Reid and H. C. Chen",
  title =        "Mapping the contemporary terrorism research domain: Researchers, publications, and institutions analysis",
  booktitle =    "Intelligence and Security Informatics, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "417--429",
}

@Article{uchino05a_bibuniq_133,
  author =       "E. Uchino and N. Suetake and C. Ishigaki",
  title =        "Pruning rule for {kMER}-based acquisition of the global topographic feature map",
  journal =      "{IEICE} Transactions on Information and Systems",
  year =         "2005",
  volume =       "E88D",
  number =       "3",
  month =        "March",
  pages =        "675--678",
}

@Article{seo05a_bibuniq_46,
  author =       "E. Y. Seo and J. H. Namkung and K. M. Lee and W. H. Lee and M. Im and S. H. Kee and G. T. Park and J. M. Yang and Y. J. Seo and J. K. Park and C. D. Kim and J. H. Lee",
  title =        "Analysis of calcium-inducible genes in keratinocytes using suppression subtractive hybridization and c{DNA} microarray",
  journal =      "Genomics",
  year =         "2005",
  volume =       "86",
  number =       "5",
  month =        "November",
  pages =        "528--538",
}

@InProceedings{uchino03_bibuniq_4310,
  author =       "Eiji Uchino and Kazuaki Yano and Tadahiro Azetsu",
  title =        "Tone quality evaluation of bone conduction voice converted by twin units {SOM}",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{inspek496_bibuniq_737,
  author =       "Eklund T. and Back B. and Vanharanta H. and Visa A.",
  title =        "Using the self-organizing map as a visualization tool in financial benchmarking",
  journal =      "Information Visualization",
  pages =        "171--181",
  volume =       "2",
  number =       "3",
  month =        "September",
  year =         "2003",
  publisher =    "Palgrave Macmillan",
  abstract =     "We illustrate the use of the self-organizing map technique for financial performance analysis and benchmarking. We build a database of financial ratios indicating the performance of 91 international pulp and paper companies for the time period 1995-2001. We then use the self-organizing map technique to analyze and benchmark the performance of the five largest pulp and paper companies in the world. the results of the study indicate that by using the self-organizing maps, we are able to structure, analyze, and visualize large amounts of multidimensional financial data in a meaningful manner.",
}

@InProceedings{inspek41_bibuniq_706,
  author =       "El-Gamal M. A. and Abdel-Malek H. L. and Sorour M. A.",
  editor =       "N. Hamdy",
  title =        "Automatic circuit tuning using unsupervised learning procedures",
  booktitle =    "Proceedings of the 46th International Midwest Symposium on Circuits and Systems",
  pages =        "125--128",
  volume =       "1",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This work describes a novel technique for automating the post-fabrication circuit tuning process. A training set that characterizes the behavior of the circuit under test is first constructed. the data in this set consists of input measurement vectors with no output attributes, and is clustered via unsupervised learning algorithm in order to explore its underlying structure and correlations. the generated clusters are efficiently labeled and directly utilized in circuit tuning by calculating the value(s) of the tuning parameter(s). Three prominent and fundamentally different unsupervised learning algorithms, namely, the self-organizing map, the Gaussian mixture model, and the fuzzy C-means algorithm are tried and their performance is compared. Experimental results demonstrate that the proposed technique provides a robust and efficient circuit tuning approach.",
}

@Article{inspek237_bibuniq_1327,
  author =       "El-Gamal M. A. and Abdel-Malek H. L. and Sorour M. A.",
  title =        "A neural-network-based approach for post-fabrication circuit tuning",
  journal =      "Neural Computing \& Applications",
  pages =        "25--35",
  volume =       "14",
  number =       "1",
  month =        "April",
  year =         "2005",
  publisher =    "Springer-Verlag",
  abstract =     "A hierarchical neural-network-based approach for circuit tuning at the post-fabrication stage is proposed. in this approach, measurements that characterize the behavior of the circuit under test are first selected. the best candidates of circuit parameters for tuning are also determined. A training set comprising the selected circuit measurements is then constructed. These measurements are calculated during simulations in which the circuit parameter values are uniformly distributed in a tolerance region around their nominal values. the training set is fed to a self-organizing map neural network to cluster the measurements. the generated clusters are manipulated and classified via a hierarchical circuit tuning procedure. Based on this classification, tuning values for the tuning parameters are calculated. Situations in which the circuit cannot be tuned are also addressed. Experimental results indicate that the developed approach provides a robust and efficient technique for circuit tuning.",
}

@InProceedings{pampalk03_bibuniq_4297,
  author =       "Elias Pampalk",
  title =        "Aligned Self-Organizing Maps",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek434_bibuniq_1077,
  author =       "Ellis G. and Dix A.",
  editor =       "J. J. {Banissi, E. ; Borner, K. ; Chen, C. ; Dastbaz, M. ; Clapworthy, G. ; Faiola, A. ; Izquierdo, E. ; Maple, C. ; Roberts, J. ; Moore, C. ; Ursyn, A. ; Zhang}",
  title =        "Visualising Web visitations: a probabilistic approach",
  booktitle =    "Proceedings. Eighth International Conference on Information Visualisation",
  pages =        "599--604",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "This paper presents a technique, the Quantum Web Field, designed to give an ambient visualisation of the current activity on a Web site. It uses the paths of past visitors to the site and a self--organising map to build a diffuse `probabilistic' mapping of pages to cells in a 2{D} matrix, where highly traversed page-links tend to be closer to each other. the paths of current visitors appear as intelligible trails giving a sense of purposeful human activity rather than offering detailed analysis. the visualisation is not constrained by either the complexity or the number of pages in the site.",
}

@InProceedings{corchado03_bibuniq_4276,
  author =       "Emilio Corchado and Colin Fyfe and Donald MacDonald",
  title =        "Maximum Likelihood Kernel Scale Invariant Maps",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek38_bibuniq_1157,
  author =       "Enhai-Liu {Lukui-Shi, Pilian-He}",
  editor =       "R. {Zhang, S. ; Jarvis}",
  title =        "An incremental nonlinear dimensionality reduction algorithm based on {ISOMAP}",
  booktitle =    "{AI} 2005: Advances in Artificial Intelligence. 18th Australian Joint Conference on Artificial Intelligence. Proceedings Lecture Notes in Artificial Intelligence",
  pages =        "892--895",
  volume =       "3809",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "Recently, there are several nonlinear dimensionality reduction algorithms that can discover the low-dimensional coordinates on a manifold based on training samples, such as ISOMAP, LLE, Laplacian eigenmaps. However, most of these algorithms work in batch mode. in this paper, we presented an incremental nonlinear dimensionality reduction algorithm to efficiently map new samples into the embedded space. the method permits one to select some landmark points and to only preserve geodesic distances between new data and landmark points. Self-organizing map algorithm is used to choose landmark points. Experiments demonstrate that the proposed algorithm is effective.",
}

@InProceedings{inspek716_bibuniq_891,
  author =       "Erberich S. G. and Bluml S. and Nelson M. D.",
  title =        "Analysis of brain {fMRI} time-series using {HRF} knowledge-based correlation classifier on unsupervised self-organizing neural network map",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering",
  pages =        "350--358",
  year =         "2003",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "Brain imaging and particular functional MRI ({fMRI}), which acquires brain volumes in time, reveals new understanding of the functional/structural relation in neuroscience. During {fMRI} imaging physiological state changes occur in the brain regions activated from the task paradigm which the subject performs in the scanner. These state changes can be depicted in the small veins of the activated region due to the blood oxygen level dependent (BOLD) effect. For each brain voxel in the {fMRI} experiment one accumulates a time series vector which has to be analyzed for similarity to the original task paradigm vector and its characteristic hemodynamic response function (HRF). Various analysis methods have been discussed for {fMRI} analysis, model-based statistical or unsupervised data-driven techniques. the purpose of this paper is to introduce a new method which combines two different approaches. We use an unsupervised self-organizing map ({SOM}) neural network to reduce the time series vector space by non-linear pattern recognition into a 2{D} table of representative time series wave-forms. Using a-priori knowledge of the HRF, either derived from a theoretical wave-form model or estimated from a brain region of interest (ROI), one can use correlation analysis between the time series patterns of the {SOM} table and the HRF to depict regions of activation specific to the HRF. An optional second {SOM} training with a reduce number of neurons of the best-matching time series to the HRF classification refines the second neural network pattern table. the learned time series pattern of each neuron and the corresponding brain voxels are superimposed onto the subject's brain image for visual investigation.",
}

@InProceedings{inspek747_bibuniq_569,
  author =       "Erberich S. G. and Willmes K. and Thron A. and Oberschelp W. and Huang H. K.",
  title =        "Knowledge-based approach for functional {MRI} analysis by {SOM} neural network using prior labels from Talairach stereotaxic space",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering",
  pages =        "363--373",
  year =         "2002",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "Among the methods proposed for the analysis of functional MR we have previously introduced a model-independent analysis based on the self-organizing map ({SOM}) neural network technique. the {SOM} neural network can be trained to identify the temporal patterns in voxel time-series of individual functional MRI ({fMRI}) experiments. the separated classes consist of activation, deactivation and baseline patterns corresponding to the task-paradigm. While the classification capability of the {SOM} is not only based on the distinctness of the patterns themselves but also on their frequency of occurrence in the training set, a weighting or selection of voxels of interest should be considered prior to the training of the neural network to improve pattern learning. Weighting of interesting voxels by means of autocorrelation or F-test significance levels has been used successfully, but still a large number of baseline voxels is included in the training. the purpose of this approach is to avoid the inclusion of these voxels by using three different levels of segmentation and mapping from Talairach space: (1) voxel partitions at the lobe level, (2) voxel partitions at the gyrus level and (3) voxel partitions at the cell level (Brodmann areas). the results of the {SOM} classification based on these mapping levels in comparison to training with all brain voxels are presented in this paper.",
}

@InProceedings{oja03_bibuniq_4261,
  author =       "Erkki Oja",
  title =        "Data and Image Mining with {SOM}",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{cuardosvargas03_bibuniq_4294,
  author =       "Ernesto Cuadros-Vargas and Roseli Ap. Francelin Romero and Klaus Obermayer",
  title =        "Speeding up algorithms of {SOM} Family for Large and High Dimensional Databases",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek326_bibuniq_976,
  author =       "Ersahin K. and Scheuchl B. and Cumming I.",
  title =        "Incorporating texture information into polarimetric radar classification using neural networks",
  booktitle =    "IGARSS 2004. 2004 {IEEE} International Geoscience and Remote Sensing",
  pages =        "560--563",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Most of the recent research on polarimetric SAR classification focused on pixel-based techniques using the covariance matrix representation. Since multiple channels are inherently provided in polarimetric data, conventional techniques for increasing the dimensionality of the observation, such as texture feature extraction, were ignored. in this paper, we have demonstrated the potential of texture classification through gray level cooccurrence probabilities (GLCP), and proposed an unsupervised scheme using the self-organizing map ({SOM}) neural network. the increase in separability of the feature space is shown via the Fisher criterion and also verified by increased classification performance. Compared to the Wishart classifier, promising classification results are obtained from the Flevoland data set.",
}

@InProceedings{bacao05a_bibuniq_89,
  author =       "F. Bacao and V. Lobo and M. Painho",
  title =        "Self-organizing maps as substitutes for k-means clustering",
  booktitle =    "Computational Science - {ICCS} 2005, Pt. 3, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "209--217",
}

@Article{bacao05b_bibuniq_147,
  author =       "F. Bacao and V. Lobo and M. Painho",
  title =        "The self-organizing map, the {Geo-SOM}, and relevant variants for geosciences",
  journal =      "Computers \& Geosciences",
  year =         "2005",
  volume =       "31",
  number =       "2",
  month =        "March",
  pages =        "155--163",
}

@InProceedings{bacao05c_bibuniq_177,
  author =       "F. Bacao and V. Lobo and M. Painho",
  title =        "Geo-self-organizing map ({Geo-SOM}) for building and exploring homogeneous regions",
  booktitle =    "Geographic Information Science, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "137--148",
}

@Article{camastra03a_bibuniq_369,
  author =       "F. Camastra and A. Vinciarelli",
  title =        "Combining neural gas and learning vector quantization for cursive character recognition",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "51",
  month =        "April",
  pages =        "147--159",
}

@InProceedings{fessant05a_bibuniq_138,
  author =       "F. Fessant and F. Clerot and C. Dousson",
  title =        "Mining of an alarm log to improve the discovery of frequent patterns",
  booktitle =    "Advances in Data Mining, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "707--722",
}

@InProceedings{Florez00a_bibuniq_3928,
  author =       "F. Florez Revuelta and M. J. Garcia Chamizo and J. Garcia Rodriguez and A. Hernandez Saez",
  title =        "Geodesic topographic product: an improvement to measure topology preservation of self-organizing neural networks",
  booktitle =    "Advances in Artificial Intelligence Iberamia-2004. 9th-Ibero-American Conference on AI. Proceedings Lecture Notes in Artificial Intelligence-Vol. 3315. 2004: 841-50",
  year =         "2004",
}

@InProceedings{luengo04a_bibuniq_258,
  author =       "F. Luengo and A. S. Cofino and J. M. Gutierrez",
  title =        "Grid oriented implementation of self-organizing maps for data mining in meteorology",
  booktitle =    "Grid Computing, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "161--184",
}

@Article{marini05a_bibuniq_94,
  author =       "F. Marini and J. Zupan and A. L. Magri",
  title =        "Class-modeling using {K}ohonen artificial neural networks",
  journal =      "Analytica Chimica Acta",
  year =         "2005",
  volume =       "544",
  number =       "1-2",
  month =        "July 15",
  pages =        "306--314",
}

@Article{c2_bibuniq_1792,
  author =       "F. Michon {M. Cottrell, P. Letrémy, S. Macaire, C. Meilland}",
  title =        "Le temps de travail des formes particulières d'emploi",
  journal =      "Economie et Statistique",
  year =         "2002",
  number =       "352-353",
  pages =        "169--190",
}

@Article{supek04a_bibuniq_194,
  author =       "F. Supek and K. Vlahovicek",
  title =        "Inca: synonymous codon usage analysis and clustering by means of self-organizing map",
  journal =      "Bioinformatics",
  year =         "2004",
  volume =       "20",
  number =       "14",
  month =        "September",
  pages =        "2329--2330",
}

@Misc{webform_9563_bibuniq_4253,
  author =       "Fabien Moutarde and Alfred Ultsch",
  title =        "{U}*{F} clustering: a new performant {"}cluster-mining{"} method based on segmentation of Self-Organizing Maps",
  howpublished = "Proceedings of the 5th Workshop on Self-Organizing Maps ({WSOM}'05)",
  pages =        "25--32",
  note =         "",
  year =         "2005",
}

@InProceedings{boudemai03_bibuniq_4299,
  author =       "Farid Boudjemaï and Philippe Biela Enberg and Jack-Gérard Postaire",
  title =        "Self Organizing Spherical Map Architecture for 3{D} Object Modeling",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek673_bibuniq_872,
  author =       "Faulkner A. and Bhandarkar S.",
  editor =       "D. J. {Krol, M. ; Mitra, S. ; Lee}",
  title =        "An interactive tool for segmentation, visualization, and navigation of magnetic resonance images",
  booktitle =    "Proceedings 16th {IEEE} Symposium on Computer Based Medical Systems {CBMS}",
  pages =        "340--345",
  year =         "2003",
  publisher =    "IEEE, Los Alamitos, CA, USA",
  abstract =     "An interactive tool for the segmentation, visualization and navigation of magnetic resonance (MR) images is presented. Previous work has shown the hierarchical self-organizing map (HSOM) to be highly effective in segmenting MR images at multiple scales or levels of abstraction. the resulting abstraction tree represents the multiscale segmentation of the MR image. A segmented MR image at any desired scale or level of detail can be obtained by appropriate traversal of the abstraction tree. the interactive tool permits traversal of the abstraction tree using a user-friendly graphical user interface (GUI) allowing the user to view the segmented MR image or any portion thereof at the desired level of detail. the tool could be used by radiologists to effectively sift through large amounts of MR image data to arrive at accurate diagnoses in an expeditious manner.",
}

@Article{inspek707_bibuniq_540,
  author =       "Fayos J. and Cano F. H.",
  title =        "Crystal-packing prediction by neural networks",
  journal =      "Crystal Growth \& Design",
  pages =        "591--519",
  volume =       "2",
  numner =       "6",
  month =        "November",
  year =         "2002",
  publisher =    "American Chem. Soc",
  abstract =     "In this work we propose the use of a neural network to predict the crystal mode of packing of small organic molecules from just their 3-D molecular structure. A sample of 31 molecules, of quite different chemical characters and known crystal structures, has been employed. These molecules, encoded by the I-D Fourier transform of their 3-D point charge distributions, are used as input in a {K}ohonen neural network. Although no molecular packing information is given, the resulting similarity output maps self-classify the molecules after the type of H-bonding pattern they present, or according to their observed molecular packing mode, when no H-bonds exist. the corresponding crystal packings of these molecules were encoded similarly by the Fourier transform of a finite cluster of molecules sought from their known crystal structures. the self-classification of these encoded packings, on the {K}ohonen map, presents good correlation with the classification found for the isolated molecules, and both correlate well also with the visually observed types of packings in the crystal structure. Thus, it seems that the Fourier transform of an isolated molecule includes enough packing information to allow its classification into packing modes. Finally, the same neural network, trained with part of the set of 31 molecules, supervised with their crystal packing, is used to predict the encoded packing of molecules not included in the training, in order to classify them into a mode of packing.",
}

@InProceedings{inspek474_bibuniq_730,
  author =       "Feng X. and Jiang H. and Dong X. and Tonellato P. J.",
  editor =       "H. {Valafar, F. ; Valafar}",
  title =        "A new self-organizing map and fuzzy logic based data mining approach for genetic and phenotypic analysis of rat strains",
  booktitle =    "International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences {METMBS}",
  pages =        "54--59",
  year =         "2003",
  publisher =    "CSREA Press, USA",
  abstract =     "We present a new data mining algorithm, based on self-organizing map ({SOM}) and fuzzy logic, to study phenotypic differences of rat strain data, with the goal of exploring their genetic characteristics. the new algorithm is a two-step approach. First, we train a self-organizing map ({SOM}) with all possible traits. the weight vectors of the trained {SOM} will represent the center of each rat cluster. Then we apply fuzzy logic inference to interpret the weights of the SOM, and calculate the impact rating (IR) and keep rating (KR) of each phenotypic trait. Those two derived measures represent the degree of significance of each trait in separating rat clusters. We first studied 2 inbred rat trains, e. g. Brown Norway (BN) and Dahl salt sensitive (SS). Among 37 renal traits, 4 have higher Keep Ratings thus significantly impacted and separated the two rat clusters. the second study was to explore genetic factors of 3 rat strains: BN, SS, and SS13/sup BN/. These results were consistent with the understanding in physiological study, and with more inspirational explanations.",
}

@Article{inspek565_bibuniq_1134,
  author =       "Ferguson K. L. and Allinson N. M.",
  title =        "Efficient video compression codebooks using {SOM}-based vector quantisation",
  journal =      "{IEE} Proceedings Vision, Image and Signal Processing",
  pages =        "102--108",
  volume =       "151",
  number =       "2",
  year =         "2004",
  month =        "April",
  publisher =    "IEE",
  abstract =     "A new rate-constrained self-organising map ({SOM}) learning algorithm, incorporating a noise-mixing model, is presented as a vector quantiser for very low bit-rate video codecs. A {SOM}-based approach will exhibit a higher resilience against local minima under low resolution conditions. Practical implementation details and results are also described.",
}

@InProceedings{inspek546_bibuniq_506,
  author =       "Ferner N. J. and Klein B. E. and Hubbard L.",
  editor =       "N. Younan",
  title =        "Classification of nuclear opacity using slit lamp images",
  booktitle =    "Proceedings of the Fourth {IASTED} International Conference Signal and Image Processing",
  pages =        "554--559",
  year =         "2002",
  publisher =    "ACTA Press, Anaheim, CA, USA",
  abstract =     "We present a method to classify images using intensity values of features that have been localized in slit lamp images of the lens. A multi-step algorithm is used to train a linear self-organizing map to determine the mapping from the image feature space to a scalar grading scale. Using a large set of images for unsupervised training, we evaluate our resulting function using a small set of images whose classification has been provided by human experts. Results are presented showing that a high correlation is achieved.",
}

@Article{inspek876_bibuniq_675,
  author =       "Fessant F. and Midenet S.",
  title =        "Self-organising map for data imputation and correction in surveys",
  journal =      "Neural Computing \& Applications",
  pages =        "300--310",
  volume =       "10",
  number =       "4",
  year =         "2002",
  publisher =    "Springer-Verlag",
  abstract =     "This paper is dedicated to erroneous data detection and imputation methods in surveys. We describe experiments conducted under the scope of a European project for studying new statistical methods based on neural networks. We show that the self-organising map can be used successfully for these tasks. A self-organising map is calibrated according to the available observations, described through a set of correlated variables handled together. the map can then be used both to detect erroneous data and to impute values to partial observations. We apply these principles to a real size transport survey database. We show that the performance of our imputation model compares well to other classical methods, and that the use of a se f-organising map for data correction provides a performing system for data validation, data correction and data analysis.",
}

@InProceedings{inspek781_bibuniq_599,
  author =       "Foody G. A. and Cutler M. E.",
  title =        "Remote sensing of biodiversity: using neural networks to estimate the diversity and composition of a Bornean tropical rainforest from Landsat {TM} data",
  booktitle =    "{IEEE} International Geoscience and Remote Sensing Symposium. 24th Canadian Symposium on Remote Sensing. Proceedings",
  pages =        "497--499",
  volume =       "1",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Two types of neural network were used to derive measures of biodiversity from Landsat TM data of a tropical rainforest. A feedforward neural network was used to estimate species richness while a {K}ohonen neural network was used to provide information on species composition. the results indicate the potential of remote sensing as a source of maps of biodiversity.",
}

@InProceedings{inspek743_bibuniq_565,
  author =       "Foucher C. and Le-Guennec D. and Vaucher G.",
  editor =       "J. J. Villanueva",
  title =        "Fast image vector quantization with self-organizing maps",
  booktitle =    "Proceedings of Second {IASTED} International Conference Visualization, Imaging, and Image Processing",
  pages =        "229--232",
  year =         "2002",
  publisher =    "ACTA Press, Anaheim, CA, USA",
  abstract =     "Vector quantization (VQ) is an efficient technique for lossy image compression, but it often suffers from computing complexity. We propose to reduce coding time in image vector quantization by using natural inter-block correlations and a topologically ordered codebook. Such a codebook is obtained by a self-organizing, map ({SOM}), a neural unsupervised learning algorithm. During coding, when block content changes smoothly, the search for a code vector is limited to the previously used code vector's neighbourhood instead of the entire codebook (exhaustive search). in both exhaustive and non exhaustive coding modes, the Partial Distance Search (PDS) is used to find the nearest neighbour. the algorithm was tested and we obtained a coding time reduction of up to 54\% comparing to PDS and 85\% comparing to full search.",
}

@InProceedings{inspek267_bibuniq_1346,
  author =       "Franzmeier M. and Witkowski U. and Ruckert U.",
  editor =       "F. {Cabestany, J. ; Prieto, A. ; Sandoval}",
  title =        "Explorative data analysis based on self-organizing maps and automatic map analysis",
  booktitle =    "Computational Intelligence and Bioinspired Systems. 8th International Work Conference on Artificial Neural Networks, {IWANN} 2005. Proceedings Lecture Notes in Computer Science",
  pages =        "725--733",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "In the field of explorative data analysis self-organizing maps have been used successfully for a lot of applications. in our case, we apply the self-organizing map for the analysis of semiconductor fabrication data by training recorded high dimensional data sets. Usually, the training result is displayed by using appropriate visualization techniques and the results are evaluated manually. Especially for large data sets an automated post-processing of the training result is essential. in this paper an automatic training result analysis based on specific image processing is introduced. Dependencies between component maps are calculated by structure overlapping analysis based on the segmentation of component maps. This novel method has been integrated into the data analysis software DanI, that simulates self-organizing maps for data analysis with several pre-processing and post-processing capabilities.",
}

@Article{inspek297_bibuniq_951,
  author =       "Freeman R. T. and Yin H.",
  title =        "Adaptive topological tree structure for document organisation and visualisation",
  journal =      "Neural Networks",
  pages =        "1255--1271",
  volume =       "17",
  number =       "8--9",
  year =         "2004",
  month =        "October",
  publisher =    "Elsevier",
  abstract =     "The self-organising map ({SOM}) is finding more and more applications in a wide range of fields, such as clustering, pattern recognition and visualisation. It has also been employed in knowledge management and information retrieval. We propose an alternative to existing 2-dimensional {SOM} based methods for document analysis. the method, termed adaptive topological tree structure (ATTS), generates a taxonomy of underlying topics from a set of unclassified, unstructured documents. the ATTS consists of a hierarchy of adaptive self-organising chains, each of which is validated independently using a proposed entropy-based {B}ayesian information criterion. A node meeting the expansion criterion spans a child chain, with reduced vocabulary and increased specialisation. the ATTS creates a topological tree of topics, which can be browsed like a content hierarchy and reflects the connections between related topics at each level. A review is also given on the existing neural network based methods for document clustering and organisation. Experimental results on real-world datasets using the proposed ATTS method are presented and compared with other approaches. the results demonstrate the advantages of the proposed validation criteria and the efficiency of the ATTS approach for document organisation, visualisation and search. It shows that the proposed methods not only improve the clustering results but also boost the retrieval.",
}

@InProceedings{inspek90_bibuniq_1198,
  author =       "Fukumi M. and Nagao T. and Mitsukura Y. and Khosla R.",
  editor =       "L. C. {Khosla, R. ; Howlett, R. J. ; Jain}",
  title =        "Drift ice detection using a self-organizing neural network",
  booktitle =    "Knowledge Based Intelligent Information and Engineering Systems. 9th International Conference, {KES} 2005. Proceedings Part I Lecture Notes in Artificial Intelligence",
  pages =        "1268--1274",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This paper proposes a segmentation method of SAR (synthetic aperture radar) images based on a {SOM} (self-organizing map) neural network. SAR images are obtained by observation using a microwave sensor. For teacher data generation, they are segmented into the drift ice (thick and thin) and sea regions manually, and then their features are extracted from partitioned data. However, they are not necessarily effective for neural network learning because they might include incorrectly segmented data. Therefore, a multi-step {SOM} is used as a learning method to improve reliability of teacher data, and carry out classification. This process enables us to fix all erroneous data and segment the SAR image data using just data. the validity of this method was demonstrated by means of computer simulations using the actual SAR images.",
}

@InProceedings{inspek72_bibuniq_1183,
  author =       "Fuller E. and Yerramalla S. and Cukic B. and Gururajan S.",
  title =        "An approach to predicting non-deterministic neural network behavior",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "2921--2926",
  volume =       "5",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper describes a methodology for generating indicators of performance for the dynamic cell structures neural network, a type of growing self-organizing map. the performance indicators are based on the learning architecture of the neural network and are validated using correlation measures of Murphy's rule. Time estimates for neural network convergence are generated based on the current data conditions and the confidence in the neural network, which is provided by the performance indicators. Analytical and experimental results are presented for the dynamic cell structures neural network during its training from the Carnegie Mellon University two-spirals benchmark data.",
}

@InProceedings{inspek351_bibuniq_1001,
  author =       "Funada A. and Muramatsu D. and Matsumoto T.",
  title =        "The reduction of memory and the improvement of recognition rate for {HMM} on-line handwriting recognition",
  booktitle =    "Proceedings. Ninth International Workshop on Frontiers in Handwriting Recognition",
  pages =        "383--388",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "The purpose of this project is two fold. the first purpose is to reduce the memory size of our previous handwriting recognition algorithm based on an {HMM} using self-organizing map ({SOM}) density tying. the second is to improve recognition capability by incorporating additional information. {SOM} density tying reduced the dictionary size to 1/7 of the original size, with a recognition rate of 90. 45\%, only slightly less than the original recognition rate of 91. 51\%. Our additional feature increased recognition capability to 91. 34\%.",
}

@InProceedings{inspek87_bibuniq_1195,
  author =       "Furukawa T. and Tokunaga K. and Morishita K. and Yasui S.",
  title =        "Modular network {SOM} (mn{SOM}): from vector space to function space",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "1581--1586",
  volume =       "3",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Kohonen's self-organizing map ({SOM}), which performs topology-preserving transformation from a high dimensional data vector space to a low-dimensional map space, provides a powerful tool for data analysis, classification and visualization in many application fields. Despite its power, {SOM} can only deal with vectorized data, although many expansions have been proposed for various data-type cases. This study aims to develop a novel generalization of {SOM} called modular network {SOM} (mnSOM), which enables users to deal with general data classes in a consistent manner. mnSOM has an array structure consisting of function modules that are trainable neural networks, e. g. multi-layer perceptrons (MLPs), instead of the vector units of the conventional {SOM} family. in the case of MLP-modules, mnSOM learns a group of systems or functions in terms of the input-output relationships, and at the same time, mnSOM generates a feature map that shows distances between the learned systems. Thus, mnSOM with MLP modules is an {SOM} in function space rather than in vector space. From this point of view, the conventional {SOM} of {K}ohonen's can be regarded as a special case of mnSOM, the modules consisting of fixed-value bias units. in this paper, mnSOM with MLP modules is described along with some application examples.",
}

@Article{inspek132_bibuniq_1235,
  author =       "Fyfe C.",
  title =        "Two topographic maps for data visualisation",
  journal =      "Computing and Information Systems Technical Report",
  pages =        "1--25",
  number =       "31",
  month =        "May",
  year =         "2005",
  publisher =    "Univ. Paisley",
  abstract =     "We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the generative topographic mapping (GTM). But whereas the GTM is an extension of a mixture of experts, our new model is an extension of a product of experts. We show visualisation results on some real data sets and compare with the GTM. We then introduce a second mapping based on harmonic averages and show that it too creates a topographic mapping of the data. We compare these mappings on real and artificial data sets.",
}

@Article{barreto04a_bibuniq_198,
  author =       "G. A. Barreto and A. F. R. Araujo",
  title =        "Identification and control of dynamical systems using the self-organizing map",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2004",
  volume =       "15",
  number =       "5",
  month =        "September",
  pages =        "1244--1259",
}

@InProceedings{barreto04b_bibuniq_236,
  author =       "G. A. Barreto and A. F. R. Araujo",
  title =        "Predictive modeling and planning of robot trajectories using the Self-Organizing Map",
  booktitle =    "Innovations in Applied Artificial Intelligence, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "751--755",
}

@Article{barreto05a_bibuniq_65,
  author =       "G. A. Barreto and J. C. M. Mota and L. G. M. Souza and R. A. Frota and L. Aguayo",
  title =        "Condition monitoring of 3{G} cellular networks through competitive neural models",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2005",
  volume =       "16",
  number =       "5",
  month =        "September",
  pages =        "1064--1075",
}

@InProceedings{Carpenter00a_bibuniq_4064,
  author =       "G. A. Carpenter and S. Martens and O. J. Ogas",
  title =        "Self-organizing hierarchical knowledge discovery by an {ARTMAP} image fusion system",
  booktitle =    "Seventh International Conference on Information Fusion",
  pages =        "325--242",
  volume =       "1",
  year =         "2004",
}

@Article{bortolan02a_bibuniq_407,
  author =       "G. Bortolan and W. Pedrycz",
  title =        "Fuzzy descriptive models: An interactive framework of information granulation",
  journal =      "{IEEE} Transactions on Fuzzy Systems",
  year =         "2002",
  volume =       "10",
  number =       "6",
  month =        "December",
  pages =        "743--755",
}

@Article{bortolan02b_bibuniq_486,
  author =       "G. Bortolan and W. Pedrycz",
  title =        "An interactive framework for an analysis of {ECG} signals",
  journal =      "Artificial Intelligence in Medicine",
  year =         "2002",
  volume =       "24",
  number =       "2",
  month =        "February",
  pages =        "109--132",
}

@Article{Chicco04a_bibuniq_1563,
  author =       "G. Chicco and R. Napoli and F. Piglione and P. Postolache and M. Scutariu and C. Toader",
  title =        "Load pattern-based classification of electricity customers",
  journal =      "{IEEE} Transactions on Power-Systems",
  year =         "2004",
  volume =       "19",
  number =       "2",
  month =        "May",
  pages =        "1232--1239",
  abstract =     "Accurate knowledge of the customers' consumption patterns represents a worthwhile asset for electricity providers in competitive electricity markets. Various approaches can be used for grouping customers that exhibit similar electrical behavior into customer classes. in this paper, we focus on two approaches for customer classification-a modified follow-the-leader algorithm and the self-organizing maps. We include an overview of basic theory for these methods and discuss the performance of the customer classification on the real case of a set of customers supplied by a distribution company. We compare the results obtained from the two approaches by means of two suitably defined adequacy indicators and discuss the potential applications of the surveyed approaches.",
}

@InProceedings{barreto02a_bibuniq_4956,
  author =       "G. D. Barreto and A. F. R. Araujo",
  title =        "Nonlinear modeling of dynamic systems with the self-organizing map",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "975--980",
  abstract =     "",
}

@Article{dong05a_bibuniq_88,
  author =       "G. Dong and M. Xie",
  title =        "Color clustering and learning for image segmentation based on neural networks",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2005",
  volume =       "16",
  number =       "4",
  month =        "July",
  pages =        "925--936",
}

@Article{dzemyda05a_bibuniq_139,
  author =       "G. Dzemyda",
  title =        "Multidimensional data visualization in the statistical analysis of curricula",
  journal =      "Computational Statistics \& Data Analysis",
  year =         "2005",
  volume =       "49",
  number =       "1",
  month =        "April",
  pages =        "265--281",
}

@Article{dzemyda04a_bibuniq_152,
  author =       "G. Dzemyda",
  title =        "Visualization of correlation-based environmental data",
  journal =      "Environmetrics",
  year =         "2004",
  volume =       "15",
  number =       "8",
  month =        "December",
  pages =        "827--836",
}

@InProceedings{dzemyda04d_bibuniq_226,
  author =       "G. Dzemyda",
  title =        "Visual analysis of the multidimensional meteorological data",
  booktitle =    "Computational Science - {ICCS} 2004, Pt. 1, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "45--55",
}

@InProceedings{dzemyda04b_bibuniq_205,
  author =       "G. Dzemyda and O. Kurasova",
  title =        "Parallelization of the {SOM}-based integrated mapping",
  booktitle =    "Artificial Intelligence and Soft Computing - Icaisc 2004, Lecture Notes in Artificial Intelligence",
  year =         "2004",
  pages =        "11--22",
}

@Article{lin05a_bibuniq_90,
  author =       "G. F. Lin and L. H. Chen",
  title =        "Time series forecasting by combining the radial basis function network and the self-organizing map",
  journal =      "Hydrological Processes",
  year =         "2005",
  volume =       "19",
  number =       "10",
  month =        "June",
  pages =        "1925--1937",
}

@Article{haydon05a_bibuniq_153,
  author =       "G. H. Haydon and Y. Hiltunen and M. R. Lucey and D. Collett and B. Gunson and N. Murphy and P. G. Nightingale and J. Neuberger",
  title =        "Self-organizing maps can determine outcome and match recipients and donors at orthotopic liver transplantation",
  journal =      "Transplantation",
  year =         "2005",
  volume =       "79",
  number =       "2",
  month =        "January",
  pages =        "213--218",
}

@Article{schmitz02a_bibuniq_435,
  author =       "G. H. Schmitz and N. Schutze and U. Petersohn",
  title =        "New strategy for optimizing water application under trickle irrigation",
  journal =      "Journal of Irrigation and Drainage Engineering - {ASCE}",
  year =         "2002",
  volume =       "128",
  number =       "5",
  month =        "September-October",
  pages =        "287--297",
}

@InProceedings{Heidemann04a_bibuniq_1468,
  author =       "G. Heidemann and H. Bekel and I. Bax and A. Saalbach",
  title =        "Hand gesture recognition: self-organising maps as a graphical user interface for the partitioning of large training data sets",
  booktitle =    "Proceedings of the 17th International Conference on Pattern-Recognition",
  year =         "2004",
  pages =        "487--490",
  volume =       "4",
  abstract =     "Gesture recognition is a difficult task in computer vision due to the numerous degrees of freedom of a human hand. Fortunately, human gesture covers only a small part of the theoretical {"}configuration space{"} of a hand, so an appearance based representation of human gesture becomes tractable. A major problem, however, is the acquisition of appropriate labelled image data from which an appearance based representation can be built. in this paper we apply self-organising maps for a visualisation of large amounts of segmented hands performing pointing gestures. Using a graphical interface, an easy labelling of the data set is facilitated. the labelled set is used to train a neural classification system, which is itself embedded in a larger architecture for the recognition of gestural reference to objects.",
}

@Article{bowden02a_bibuniq_461,
  author =       "G. J. Bowden and H. R. Maier and G. C. Dandy",
  title =        "Optimal division of data for neural network models in water resources applications",
  journal =      "Water Resources Research",
  year =         "2002",
  volume =       "38",
  pages =        "10.1029/2001WR000266",
  number =       "2",
  month =        "February",
}

@Article{jeney02a_bibuniq_392,
  author =       "G. Jeney and J. Levendovszky and L. Pap and E. C. van der Meulen",
  title =        "Adaptive near-optimal multiuser detection using a stochastic and hysteretic Hopfield net receiver",
  journal =      "Eurasip Journal on Applied Signal Processing",
  year =         "2002",
  volume =       "2002",
  number =       "12",
  month =        "December",
  pages =        "1401--1414",
}

@Article{knopf04a_bibuniq_270,
  author =       "G. K. Knopf and A. Sangole",
  title =        "Interpolating scattered data using 2{D} self-organizing feature maps",
  journal =      "Graphical Models",
  year =         "2004",
  volume =       "66",
  number =       "1",
  month =        "January",
  pages =        "50--69",
}

@Article{knopf05a_bibuniq_98,
  author =       "G. K. Knopf and P. C. Igwe",
  title =        "Deformable mesh for virtual shape sculpting",
  journal =      "Robotics and Computer Integrated Manufacturing",
  year =         "2005",
  volume =       "21",
  number =       "4-5",
  month =        "August-October",
  pages =        "302--311",
}

@Article{kastberger03a_bibuniq_361,
  author =       "G. Kastberger and S. Radloff and G. Kranner",
  title =        "Individuality of wing patterning in Giant honey bees (Apis laboriosa)",
  journal =      "Apidologie",
  year =         "2003",
  volume =       "34",
  number =       "3",
  month =        "May-June",
  pages =        "311--318",
}

@Article{loot02a_bibuniq_431,
  author =       "G. Loot and J. L. Giraudel and S. Lek",
  title =        "A non-destructive morphometric technique to predict Ligula intestinalis {L}. plerocercoid load in roach (Rutilus rutilus {L}. ) abdominal cavity",
  journal =      "Ecological Modelling",
  year =         "2002",
  volume =       "156",
  number =       "1",
  month =        "October 15",
  pages =        "1--11",
}

@Article{foody03a_bibuniq_342,
  author =       "G. M. Foody and M. E. J. Cutler",
  title =        "Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing",
  journal =      "Journal of Biogeography",
  year =         "2003",
  volume =       "30",
  number =       "7",
  month =        "July",
  pages =        "1053--1066",
}

@Article{mercier05a_bibuniq_157,
  author =       "G. Mercier and L. Hubert-Moy and T. Houet and P. Gouery",
  title =        "Estimation and monitoring of bare soil/vegetation ratio with Spot Vegetation and Hrvir",
  journal =      "{IEEE} Transactions on Geoscience and Remote Sensing",
  year =         "2005",
  volume =       "43",
  number =       "2",
  month =        "February",
  pages =        "348--354",
}

@Article{burton02a_bibuniq_446,
  author =       "G. R. Burton and Y. Guan and R. Nagarajan and R. E. McGehee",
  title =        "Microarray analysis of gene expression during early adipocyte differentiation",
  journal =      "Gene",
  year =         "2002",
  volume =       "293",
  number =       "1-2",
  month =        "June",
  pages =        "21--31",
}

@Article{kumar04a_bibuniq_144,
  author =       "G. S. Kumar and P. K. Kalra and S. G. Dhande",
  title =        "Curve and surface reconstruction from points: an approach based on self-organizing maps",
  journal =      "Applied Soft Computing",
  year =         "2004",
  volume =       "5",
  number =       "1",
  month =        "December",
  pages =        "55--66",
}

@Article{tanaka05a_bibuniq_82,
  author =       "G. Tanaka and K. Aihara",
  title =        "Multistate associative memory with parametrically coupled map networks",
  journal =      "International Journal of Bifurcation and Chaos",
  year =         "2005",
  volume =       "15",
  number =       "4",
  month =        "April",
  pages =        "1395--1410",
}

@InProceedings{inspek405_bibuniq_1051,
  author =       "Gabbai J. M. E. and Wright W. A. and Allinson N. M.",
  editor =       "H. {Yang, Z. R. ; Everson, R. ; Yin}",
  title =        "Visualisation of multi-agent system organisations using a self-organising map of Pareto solutions",
  booktitle =    "Intelligent Data Engineering and Automated Learning {IDEAL} 2004. 5th International Conference. Proceedings Lecture Notes in Comput. Sci.",
  pages =        "841--847",
  volume =       "3177",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "The structure and performance of organisations - natural or man-made -are intricately linked, and these multifaceted interactions are increasingly being investigated using multiagent system concepts. This paper shows how a selection of generic structural metrics for organisations can be explored using a combination of Pareto frontier exemplars; extensive simulations of simple goal-orientated multiagent systems, and expose of organisational types through self-organising map clusters can provide insights into desirable structures for such objectives as robustness and efficiency.",
}

@InProceedings{inspek564_bibuniq_776,
  author =       "Gabriel J. and Gomes R. C. and Mitra S. K.",
  title =        "Analog multilayer perceptron implementation of low complexity {VQ} for image compression",
  booktitle =    "Proceedings 2003 International Conference on Image Processing vol. 3",
  pages =        "279--282",
  volume =       "3",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This work proposes a multilayer perception (MLP) method to perform direct mapping from an analog image block into a vector index, i. e. implement vector quantization (VQ) without the need for carrying out first an analog-to-digital (A/D) conversion of each analog sample. the design focuses on an analog hardware implementation and is based on observing that the use of a careful binary index assignment causes hyperplanes associated with the formation of the same bit at the MLP output to have similar spatial orientation. Experimental results show that, for the same rate-distortion performance, MLPs can be implemented with less than half the computational complexity of the {K}ohonen self-organizing map (KSOM) approaches advanced earlier.",
}

@Article{inspek695_bibuniq_532,
  author =       "Galda H. and Murao H. and Tamaki H. and Kitamura S.",
  title =        "Application of self-organizing maps to the segmentation of color images",
  journal =      "Memoirs of the Faculty of Engineering, Kobe University",
  pages =        "57--63",
  number =       "49",
  month =        "November",
  year =         "2002",
  publisher =    "Kobe Univ",
  abstract =     "Malignant melanoma is a skin cancer that can be mistaken as a nevus even by a dermatologist. For this reason dermatologists use a dermoscope to visualize the pigmented structures of the skin. However, even when using a dermoscope. it can still be difficult to classify a skin lesion as malignant or benign. Therefore it seems desirable to support. the diagnosis by digital image processing. the first step in a computerized image analysis is the segmentation of the image into regions of the same color. in this research the self-organizing map is used for this purpose and two weight vector initialization methods are tested.",
}

@Article{inspek555_bibuniq_770,
  author =       "Galda H. and Murao H. and Tamaki H. and Kitamura S.",
  title =        "Skin image segmentation using a self-organizing map and genetic algorithms",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan",
  pages =        "2056--2062",
  volume =       "123 C",
  number =       "11",
  year =         "2003",
  month =        "November",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "In order to distinguish malignant from benign skin lesions dermatologists use a microscope that shows the pigmented structure of the skin. However, it can be difficult to classify a skin lesion as benign or malignant using a dermoscopic image alone. This motivates computer analysis of dermoscopic images by digital image processing. the first step for a computer analysis is the segmentation of the image into regions of the same color, i. e. regions of the same color should be assigned the same gray level and regions of different colors should be assigned different gray levels. the number of colors is not known in advance. This paper presents a color clustering method that determines the number of colors automatically. First the RGB image is transformed into the L/sup */u/sup */v/sup */ color space and segmented by a self-organizing map ({SOM}). After completion of the training a genetic algorithm groups the {SOM} neurons into clusters searching for a grouping that optimizes the Davies-Bouldin index. Various genetic algorithms are presented and evaluated for this purpose.",
}

@InProceedings{inspek480_bibuniq_1109,
  author =       "Gale T. M. and Davey N. and Laws K. R. and Loomes M. and Frank R. J.",
  editor =       "V. S. {Yager, R. R. ; Sgurev}",
  title =        "Self-organising map representations of greyscale images reflect human similarity judgements",
  booktitle =    "2nd International {IEEE} Conference on 'Intelligent Systems'",
  pages =        "66--70",
  volume =       "1",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this study we assessed a {K}ohonen network's ability to represent visual similarity between grayscale pictures and whether these representations were associated with human ratings of perceived similarity. We trained a {K}ohonen network ({SOM}) with 370 standardized grayscale pictures deriving from 70 basic level object categories (e. g. dog, apple, chair, etc. ) and measured, for each category, the average Euclidean distance of the {SOM} output patterns to provide an index of the visual similarity between exemplars of the same basic level category. We then asked human subjects to provide visual similarity ratings for the same categories of stimuli and compared these with the measures extracted from the SOM. the significant correlation between the {SOM} and human measures suggests that a {SOM} may be a useful way of modeling certain stages of human visual categorization. Interestingly, the human ratings showed category-specific differences in the level of similarity ascribed to living and nonliving things. However, this pattern was not reflected in the {SOM} representations of the same stimuli. This has important implications for theories of object recognition and, specifically, our understanding of category-specific naming impairments.",
}

@InProceedings{inspek515_bibuniq_499,
  author =       "Gallagher M. and Deacon P.",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "Neural networks and the classification of mineralogical samples using x-ray spectra",
  booktitle =    "{ICONIP'02}. Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        "2683--2687",
  volume =       "5",
  year =         "2002",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "The automatic classification of large numbers of mineral samples is a practical problem in mining research. A system currently in use is based on simple statistical tests. Although the system performs well under typical conditions, the data collection procedure can be very time-consuming. This time can be significantly reduced, but at a cost of introducing noise into the data, leading to a degradation in classification performance. This paper reports on an initial investigation into the application of neural network techniques to the mineral identification task, and compares the performance of these methods to the current system. the results are very encouraging and suggest that a more powerful classifier might allow die data collection process to be significantly sped up without significant loss of classification accuracy for the overall system.",
}

@InProceedings{inspek866_bibuniq_665,
  author =       "Garavaglia S. B.",
  title =        "A quantum-inspired self-organizing map ({QISOM})",
  booktitle =    "Proceedings of the 2002 International Joint Conference on Neural Networks. {IJCNN}'02",
  pages =        "1779--1784",
  volume =       "2",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A variation of the {K}ohonen map, the quantum-inspired self-organizing map (QISOM) is a topological map of {"}local{"} quantum states. the two-dimensional map is initialized to random quantum states with its training data and the training process selects each winner using a decoherence operator that reduces the quantum states to classic non-probabilistic states. the QISOM is not used to cluster new data; it is used as a current state of the population that is updated in real time. the QISOM is useful when observations from a very large population are presented in small batches. the result is that the prevalence of very rare events may be more accurate than with traditional statistical sampling in batches. However, this is accomplished at the cost of the absolute prevalence accuracy of more common events, although rank ordering of prevalence is preserved. the assumption for employing a QISOM is that the primary concern is to capture the prevalence of very rare critical and costly events. the QISOM is demonstrated with data on selected health and social problems of children. Results are compared with bootstrapping.",
}

@InProceedings{inspek647_bibuniq_847,
  author =       "Garavaglia S. B.",
  title =        "Identifying riskier combinations of risky behavior using a self-organizing map",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "75--80",
  volume =       "1",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A variation on the self-organizing map ({SOM}) introduced by T. {K}ohonen [1982, 1995] was developed using real-valued, categorical, and binary data in each vector as a tool for multivariate descriptive analysis. Different similarity measures were applied to each type of data and all were combined and normalized to produce a single score as the final similarity measure. the data source is a national telephone survey on health status and behaviors [2001]. One state, New Jersey, was selected for {SOM} development to both limit the number of vectors and focus on a region of interest. Several nodes and neighbors in the {SOM} topology revealed combinations of risk that might work synergistically to produce a much higher level of risk. Examples include firearms in the home combined with stress and lack of rest and/or alcohol abuse.",
}

@InProceedings{inspek402_bibuniq_1048,
  author =       "Garavaglia S. B. and Synthelabo S.",
  title =        "Generational trends in obesity in the United States: analysis with a wavelet coefficient self-organizing map",
  booktitle =    "2004 {IEEE} International Joint Conference on Neural Networks",
  pages =        "769--774",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Increasing prevalence of obesity is considered to be a major public health problem, particularly in the United States [Flegel, C. M. et al., 2000 and Louise, W 2003], Many factors have been considered in causing this unhealthy condition, but an overall aging population could be a dominant cause, as it is well known that most people gain weight as they age. However, when the US population is analyzed as generational cohorts (based on year of birth), not simply as age cohorts growing older each year, some different patterns emerge. Trends based on age cohorts and generational cohorts are compared with geography (state) as a controlling factor. As trends in population weight can serve as a general health status {"}signal{"}, a wavelet-based approach to analysis was selected, using Haar wavelet coefficients as the self-organizing map weight vectors. Similarity was determined by the Kullback-Leibler information statistic. the result is that generations prior to the {"}Baby Boomer{"} generation, who were not exposed to more recent unhealthy food consumption patterns as younger people, are less likely to be obese, in spite of their age. in addition, geography also plays a role. Understanding behavioral and attitudinal factors in obesity could lead to more targeted and effective public health campaigns.",
}

@InProceedings{inspek2_bibuniq_903,
  author =       "Garcia H. L. and Gonzalez I. M.",
  title =        "An introduction to biological wastewater treatment explained by {SOM} and clustering algorithms",
  booktitle =    "2004 {IEEE} International Symposium on Industrial Electronics",
  pages =        "525--530",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The objective of this paper is the establishment of the relationships among the main variables of a wastewater biological treatment process using, as a first approach, data from a continuous aerobic reactor that was simulated by means of the activated sludge model No 1 (ASM no 1). Self-organizing map ({SOM}) and clustering techniques were used to carry out this task.",
}

@Article{inspek377_bibuniq_1025,
  author =       "Garcia-Lamont J. and Antonio J. and Cadenas M. and Gomez-Castaneda F.",
  title =        "Analogue {CMOS} prototype vision chip with fuzzy {K}ohonen network processing for grey level image segmentation",
  journal =      "International Journal of Electronics",
  pages =        "697--717",
  volume =       "19",
  number =       "12",
  month =        "December",
  year =         "2004",
  publisher =    "Taylor \& Francis",
  abstract =     "In this paper, the design and realization of an analogue CMOS prototype vision chip for grey level image segmentation for two regions is presented with fuzzy-Kohonen network processing as an alternative to neuromorphic and cellular neural networks approaches for vision chips. This alternative is due to the compensation that fuzziness gives in order to utilize simple analogue circuits with small layout areas instead of robust analogue circuits with bigger layout areas required. This helps to relieve the signal to noise ratio when crisp computations are present. the vision chip is based on the elaboration of an array of basic cells that work simultaneously. Every basic cell has embedded one vertical phototransistor and one network implemented under weak inversion techniques. the chip processes one image in 35 mu s with consumption of 733 mu W per cell, signal to noise ratio of 43 dB and dynamic range of 49 dB.",
}

@InProceedings{inspek455_bibuniq_725,
  author =       "Garcia-Perez D. and Mosquera A.",
  editor =       "M. H. Hamza",
  title =        "Colour image retrieval by self-organizing maps",
  booktitle =    "Proceedings of the {IASTED} International Conference on Signal, Processing, Pattern Recognition, and Applications",
  pages =        "61--65",
  year =         "2003",
  publisher =    "ACTA Press, Anaheim, CA, USA",
  abstract =     "The actual necessity for systems that searches for images by visual features is making the growing of a new research field exclusivity centered in this problem. We introduce a new method to organize the image database of a content-based information retrieval system. Our first implementation of an image retrieval systems uses a L*a*b* colour histogram as image feature. the feature vectors are organized by a neural net, a self-organizing map. This net classifies the image database in subgroups of images where the system can apply any type of ranking metric to show the results to the final user.",
}

@Article{inspek658_bibuniq_857,
  author =       "Gasiorowski D.",
  title =        "Phoneme probability estimation in neural speech recognition system",
  journal =      "Elektronika",
  pages =        "37--39",
  volume =       "44",
  number =       "5--6",
  year =         "2003",
  publisher =    "SIGMA NOT",
  abstract =     "This thesis presents modern methods for phoneme probability estimation with artificial neural networks (ANN) for use in an automatic speech recognition (ASR) system. the digital signal processing technology provides feature extraction, called mel frequency cepstral coefficients (MFCC). Presented are two kinds of neural estimations: multi-state time delay neural network (MS-TDNN) and {K}ohonen self-organizing map ({SOM}) with time-delay windows.",
}

@Article{inspek670_bibuniq_869,
  author =       "Gasteiger J. and Teckentrup A. and Terfloth L. and Spycher S.",
  title =        "Neural networks as data mining tools in drug design",
  journal =      "Journal of Physical Organic Chemistry",
  pages =        "232--245",
  volume =       "16",
  number =       "4",
  month =        "April",
  year =         "2003",
  publisher =    "Wiley",
  abstract =     "Neural networks are powerful data mining tools with a wide range of applications in drug design. This paper largely concentrates on self-organizing neural networks that can be used for investigating datasets both by unsupervised and by supervised learning. the representation of chemical structures is the key to success in establishing useful relationships. Applications are shown for exploring different structure representations, for establishing quantitative structure-activity relationships and for handling compounds having multicategory activities the applications comprise the separation of compounds according to different biological activities, the location of biologically active compounds in large chemical spaces, the analysis of high-throughput screening data and the classification of compounds according to mode of toxic action.",
}

@Misc{webform_7525_bibuniq_4243,
  author =       "Gautama T. and Van Hulle M. M.",
  title =        "Batch Map Extensions of the Kernel-based Maximum Entropy Learning Rule.",
  howpublished = "{IEEE} Transactions on Neural Networks",
  pages =        "",
  note =         "",
  year =         "in press",
}

@InProceedings{inspek559_bibuniq_510,
  author =       "Gavat I. H. and Dumitru C. O. O.",
  editor =       "F. {Callaos, N. ; Hernandez-Encinas, L. ; Yetim}",
  title =        "Continuous speech segmentation algorithms based on artificial neural networks",
  booktitle =    "6th World Multiconference on Systemics, Cybernetics and Informatics",
  pages =        "111--114",
  volume =       "14",
  year =         "2002",
  publisher =    "Int. Inst. Inf. \& Syst, Orlando, FL, USA",
  abstract =     "In this paper, we present the performances obtained using different segmentation algorithms based on neural networks applied to continuous speech in the Romanian language. We have experimented with two kinds of algorithm. the first is a broad syllable segmentation, realized with neural networks in the form of multilayer perceptrons (MLP) and radial basis function neural networks (RBF). in both situations, based on information obtained over five frames, three frames, or two frames, we achieved correct segmentation scores that exceed 90\%. the second is a hierarchical system, with three hierarchy levels. On the first level, realized with a MLP, the decisions are: voiced, unvoiced and silence. On the second level, realized with a self-organizing map ({SOM}) as vectorial quantizer, the decisions are: vowels, plosives, liquids, nasals and silence. On the third level, realized with radial basis function neural networks, a definitive phonemic segmentation in 48 phonetic classes is accomplished, with good results. the correct segmentation score exceeding 85\%.",
}

@Article{inspek262_bibuniq_1343,
  author =       "Ge-Peng {Shenfang-Yuan, Lei-Wang}",
  title =        "Neural network method based on a new damage signature for structural health monitoring",
  journal =      "Thin Walled Structures",
  pages =        "553--563",
  volume =       "43",
  number =       "4",
  month =        "April",
  year =         "2005",
  publisher =    "Elsevier",
  abstract =     "Adopting wide-band Lamb wave based active monitoring technology, this study focuses on a neural network method based on a new damage signature for on-line damage detection applied to thin-walled composite structures. Honeycomb sandwich and carbon fiber composite structures are studied. Two kinds of damage are considered: delamination and impact damage. A new damage signature is introduced to determine the presence and extent of damage in composites, while eliminating the influence of different distances between the actuator and sensor. Neural network method is researched to take advantage of this new damage signature combined with several other signatures to decide the damage mode. {K}ohonen neural network is developed. the proposed method is shown to be effective, reliable, and straightforward for the specimens considered in the present study, which are composed of different materials and suffer various levels of damage. [All rights reserved Elsevier].",
}

@InProceedings{inspek230_bibuniq_1323,
  author =       "Gengui-Zhou {Zhiqing-Meng, Hongcan-Zhu, Yihua-Zhu}",
  editor =       "Z. {Wang, J. ; Liao, X. ; Yi}",
  title =        "A clustering algorithm for {C}hinese text based on {SOM} neural network and density",
  booktitle =    "Advances in Neural Networks {ISNN} 2005. Second International Symposium on Neural Networks. Proceedings, Part II Lecture Notes in Computer Science",
  pages =        "251--256",
  volume =       "2",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This paper introduces a clustering algorithm for {C}hinese text based on both {SOM} (self-organizing map) neural network and density. the algorithm contains two stages. During the first stage, {C}hinese text are transformed into text vectors, which are used as training data of {SOM} and mapped by training {SOM} so that an initial clustering result for text data, i. e., a virtual coordinates set, is obtained. Then, during the second stage, the virtual coordinates set is further clustered according to density. It should be pointed out that the proposed algorithm in the first stage is different from the existing ones. Moreover, in the second stage, it outperforms other algorithms in computing time due to decreasing dimension. Numerical experiment shows that the algorithm is efficient for clustering text data and high multi-dimensional data.",
}

@InProceedings{simon03_bibuniq_4271,
  author =       "Geoffroy Simon and Amaury Lendasse and Marie Cottrell and Jean-Claude Fort and Michel Verleysen",
  title =        "Double {SOM} for long-term time series perception",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Misc{webform_10053_bibuniq_4257,
  author =       "Geoffroy Simon and John A. Lee and Michel Verleysen",
  title =        "On the need of unfolding preprocessing for time series clustering",
  howpublished = "in Proc. of Workshop on Self-Organizing Maps {WSOM}2005",
  pages =        "251--258",
  note =         "",
  year =         "04-08 Septembre 2005",
}

@InProceedings{Poe05WSOM_bibuniq_1804,
  author =       "Georg P{\"o}lzlbauer and Michael Dittenbach and Andreas Rauber",
  title =        "Gradient visualization of grouped component planes on the {SOM} lattice",
  booktitle =    "Proceedings of the Fifth Workshop on Self-Organizing Maps ({WSOM}'05)",
  year =         "2005",
  month =        "September",
  address =      "Paris, France",
  editor =       "Marie Cottrell",
  pages =        "331--338",
}

@InProceedings{Poe05IJCNN_bibuniq_1805,
  author =       "Georg P{\"o}lzlbauer and Michael Dittenbach and Andreas Rauber",
  title =        "A visualization technique for Self-Organizing Maps with vector fields to obtain the cluster structure at desired levels of detail",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks ({IJCNN}'05)",
  ISBN =         "0-7803-9049-0",
  year =         "2005",
  month =        "July",
  address =      "Montreal, Canada",
  pages =        "1558--1563",
  publisher =    "IEEE Computer Society",
}

@InCollection{KrellRebSffMi_CIARP03_bibuniq_25,
  author =       "Gerald Krell and Ren{\'{e}} Rebmann and Udo Seiffert and Bernd Michaelis",
  title =        "Improving Still Image Coding by an {SOM}-controlled Associative Memory",
  booktitle =    "Progress in Pattern Recognition, Speech and Image Analysis",
  publisher =    "Springer-Verlag",
  year =         "2003",
  editor =       "Alberto Sanfeliu and Jos{\'{e}} Ruiz-Shulcloper",
  series =       "Lecture Notes in Computer Science 2905",
  pages =        "571--579",
  address =      "Berlin Heidelberg, Germany",
  location =     "Havana, Cuba",
  abstract =     "Archiving of image data often requires a suitable data reduction to minimise the memory requirements. However, these compression procedures entail compression artefacts which make machine processing of the captured documents more difficult and reduce subjective image quality for the human viewer. A method is presented which can reduce the occurring compression artefacts. the corrected image yields as output of an auto-associative memory that is controlled by a Self-Organising Map ({SOM})",
  ISSN =         "0302-9743",
  file =         F,
}

@InProceedings{inspek604_bibuniq_807,
  author =       "Girado J. I. and Sandin D. J. and DeFanti T. A. and Wolf L. K.",
  title =        "Real-time camera-based face detection using a modified {LAMSTAR} neural network system",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering",
  pages =        "35--46",
  year =         "2003",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "This paper describes a cost-effective, real-time (640 x 480 at 30 Hz) upright frontal face detector as part of an ongoing project to develop a video-based, tetherless 3{D} head position and orientation tracking system. the work is specifically targeted for auto-stereoscopic displays and projection-based virtual reality systems. the proposed face detector is based on a modified LAMSTAR neural network system. At the input stage, after achieving image normalization and equalization, a sub-window analyzes facial features using a neural network. the sub-window is segmented, and each part is fed to a neural network layer consisting of a {K}ohonen self-organizing map ({SOM}). the output of the {SOM} neural networks are interconnected and related by correlation-links, and can hence determine the presence of a face with enough redundancy to provide a high detection rate. To avoid tracking multiple faces simultaneously, the system is initially trained to track only the face centered in a box superimposed on the display. the system is also rotationally and size invariant to a certain degree.",
}

@InProceedings{inspek787_bibuniq_605,
  author =       "Girolami M.",
  title =        "Latent variable models for the topographic organisation of discrete and strictly positive data",
  booktitle =    "Neurocomputing",
  pages =        "185--198",
  number =       "48",
  month =        "October",
  year =         "2002",
  publisher =    "Elsevier",
  abstract =     "This paper deals with learning dense low-dimensional representations of high-dimensional positive data. the positive data may be continuous, discrete binary or count based. in addition to the low-dimensional data model, a topographic ordering of the representation is desired. the primary motivation for this work is the requirement for a low-dimensional interpretation of sparse vector space models of text documents which may take the form of binary, count based or real multivariate data. the generative topographic mapping (GTM) was developed and introduced as a principled alternative to the self-organising map for, principally, visualising high-dimensional continuous data. the GTM is one method by which a topographically organised low-dimensional data representation may be realised. Based on the continuous GTM data model a nonlinear latent variable model for modelling high-dimensional binary data is presented. the non-negative factorisation of a positive matrix which ensures a topographic ordering of the constituent factors is also presented. Experimental demonstrations of both methods are provided based on representing binary coded handwritten digits and the topographic organisation and visualisation of a collection of text based documents.",
}

@Article{inspek579_bibuniq_789,
  author =       "Gkoutos G. V. and Rzepa H. and Clark R. M. and Adjei O. and Johal H.",
  title =        "Chemical machine vision: automated extraction of chemical metadata from raster images",
  journal =      "Journal of Chemical Information and Computer Sciences",
  pages =        "1342--1355",
  volume =       "43",
  number =       "5",
  month =        "September",
  year =         "2003",
  publisher =    "ACS",
  abstract =     "We present a novel application of machine vision methods for the identification of chemical composition diagrams from two-dimensional digital raster images. the method is based on the use of Gabor wavelets and an energy function to derive feature vectors from digital images. These are used for training and classification purposes using a {K}ohonen network for classification with the Euclidean distance norm. We compare this method with previous approaches to transforming such images to a molecular connection table, which are designed to achieve complete atom connection table fidelity but at the expense of requiring human interaction. the present texture-based approach is complementary in attempting to recognize higher order features such as the presence of a chemical representation in the original raster image. This information can be used for providing chemical metadata descriptors of the original image as part of a robot-based Internet resource discovery tool.",
}

@InProceedings{inspek119_bibuniq_1222,
  author =       "Glass J. O. and Helton K. J. Chin-Shang-Li and Reddick W. E.",
  title =        "Computer-aided diagnosis of leukoencephalopathy in children treated for acute lymphoblastic leukemia",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering",
  pages =        "939--946",
  volume =       "5747",
  number =       "1",
  year =         "2005",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "The purpose of this study was to use objective quantitative MR imaging methods to develop a computer-aided diagnosis tool to differentiate white matter (WM) hyperintensities as either leukoencephalopathy (LE) or normal maturational processes in children treated for acute lymphoblastic leukemia with intravenous high dose methotrexate. A combined imaging set consisting of T1, T2, PD, and FLAIR MR images and WM, gray matter, and cerebrospinal fluid a priori maps from a spatially normalized atlas were analyzed with a neural network segmentation based on a {K}ohonen Self-Organizing Map. Segmented regions were manually classified to identify the most hyperintense WM region and the normal appearing genu region. Signal intensity differences normalized to the genu within each examination were generated for two time points in 203 children. An unsupervised hierarchical clustering algorithm with the agglomeration method of McQuitty was used to divide data from the first examination into normal appearing or LE groups. A C-support vector machine (C-SVM) was then trained on the first examination data and used to classify the data from the second examination. the overall accuracy of the computer-aided detection tool was 83. 5\% (299/358) with sensitivity to normal WM of 86. 9\% (199/229) and specificity to LE of 77. 5\% (100/129) when compared to the readings of two expert observers. These results suggest that subtle therapy-induced leukoencephalopathy can be objectively and reproducibly detected in children treated for cancer using this computer-aided detection approach based on relative differences in quantitative signal intensity measures normalized within each examination.",
}

@InProceedings{inspek364_bibuniq_1012,
  author =       "Glass J. O. and Reddick W. E. and Reeves C. and Ching-Hon-Pui",
  title =        "Improving the segmentation of therapy-induced leukoencephalopathy using apriori information and a gradient magnitude threshold",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering",
  pages =        "1738--1745",
  volume =       "5370",
  number =       "1",
  year =         "2004",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "Reliably quantifying therapy-induced leukoencephalopathy in children treated for cancer is a challenging task due to its varying MR properties and similarity to normal tissues and imaging artifacts. T1, T2, PD, and FLAIR images were analyzed for a subset of 15 children from an institutional protocol for the treatment of acute lymphoblastic leukemia. Three different analysis techniques were compared to examine improvements in the segmentation accuracy of leukoencephalopathy versus manual tracings by two expert observers. the first technique utilized no apriori information and a white matter mask based on the segmentation of the first serial examination of each patient. MR images were then segmented with a {K}ohonen Self-Organizing Map. the other two techniques combine apriori maps from the ICBM atlas spatially normalized to each patient and resliced using SPM99 software. the apriori maps were included as input and a gradient magnitude threshold calculated on the FLAIR images was also utilized. the second technique used a 2-dimensional threshold, while the third algorithm utilized a 3-dimensional threshold. Kappa values were compared for the three techniques to each observer, and improvements were seen with each addition to the original algorithm (Observer 1: 0. 651, 0. 653, 0. 744; Observer 2: 0. 603, 0. 615, 0. 699).",
}

@Article{inspek390_bibuniq_1037,
  author =       "Glass J. O. and Reddick W. E. and Reeves C. and Pui C. H.",
  title =        "Improving the segmentation of therapy-induced leukoencephalopathy in children with acute lymphoblastic leukemia using a priori information and a gradient magnitude threshold",
  journal =      "Magnetic Resonance in Medicine",
  pages =        "1336--1341",
  volume =       "52",
  number =       "6",
  month =        "December",
  year =         "2004",
  publisher =    "Wiley",
  abstract =     "Reliably quantifying therapy-induced leukoencephalopathy is a challenging task due to the similarity between its MR properties and those of normal tissues. Multispectral MR images were analyzed for 15 children treated for acute lymphoblastic leukemia. Three different analysis techniques were compared to examine improvements in the segmentation accuracy of leukoencephalopathy versus manual tracings by two experienced observers. the original technique used a white matter mask based on the segmentation of the first serial examination of each patient and no a priori information. the modified techniques combine spatially normalized a priori maps as input and a gradient magnitude threshold. the second technique used a 2{D} threshold, while the third algorithm utilized a 3{D} threshold. MR images were segmented with a {K}ohonen self-organizing map for all three algorithms. Kappa values were compared for the three techniques to each observer and statistically significant improvements were seen between the original and third algorithms (Observer 1: 0. 651, 0. 744, P=0. 015; Observer 2: 0. 603, 0. 699, P=0. 024). More accurate and reliable quantification reduces the amount of variance in MR measures and facilitates clinical trials to determine the clinical significance of leukoencephalopathy in this vulnerable population.",
}

@Article{inspek150_bibuniq_1253,
  author =       "Godin N. and Huguet S. and Gaertner R.",
  title =        "Integration of the {K}ohonen's self-organising map and k-means algorithm for the segmentation of the {AE} data collected during tensile tests on cross-ply composites",
  journal =      "NDT\&E International",
  pages =        "299--309",
  volume =       "38",
  number =       "4",
  month =        "June",
  year =         "2005",
  publisher =    "Elsevier",
  abstract =     "The acoustic emission (AE) technique is a useful way for the investigation of local damage in materials. This study deals with the ability of a {K}ohonen's map to classify recorded AE signals collected during tensile tests on cross-ply glass/epoxy composites in order to monitor the chronology of the damaging process. An unsupervised clustering analysis shows that AE signals are distributed into three clusters. the proposed two-stage procedure is a combination of the Self-Organising Map ({SOM}) and the k-means methods. in the present work, Kohonen's map is applied as an unsupervised clustering method for the AE signals generated in cross-ply composite specimens during tensile tests. the input vectors of the signal descriptors used in the clustering procedure are calculated from the signal waveforms. the k-means method is then applied on the neurones of the map in order to delimit the clusters and to visualise the topology of the map. [All rights reserved Elsevier].",
}

@InProceedings{inspek582_bibuniq_792,
  author =       "Gomes J. G. R. C. and Mitra S. K.",
  editor =       "M. {Galias, Z. ; Garda, B. ; Kadeja, B. ; Ogorzalek}",
  title =        "Low-complexity image compression without {A}/{D} conversion using analog multilayer perceptron",
  booktitle =    "Proceedings of the 16th European Conference on Circuit Theory and Design, {ECCTD'03}",
  pages =        "281--284",
  volume =       "3",
  year =         "2003",
  publisher =    "Univ. Mining \& Metallurgy, Cracow, Poland",
  abstract =     "This paper proposes a multilayer perceptron (MLP) method to perform direct mapping from an analog image block into a vector index, i. e. implement vector quantization (VQ) without the need for carrying out first an analog-to-digital (A/D) conversion of each analog sample. the design focuses on an analog hardware implementation and is based on observing that the use of a careful binary index assignment causes hyperplanes associated with the formation of the same bit at the MLP output to have similar spatial orientation. Computer simulation results from color image databases show that, for the same rate-distortion performance, MLPs can be implemented with less than half the computational complexity of the {K}ohonen self-organizing map (KSOM) based approaches advanced earlier.",
}

@InProceedings{inspek898_bibuniq_686,
  author =       "Gruen R. and Kubota T.",
  title =        "A neural network approach to system performance analysis",
  booktitle =    "Proceedings {IEEE} {S}outheast{C}on",
  pages =        "349--354",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Neural networks are used in a wide variety of situations to solve complex problems. Some of the categories for which neural networks are used include: prediction software, classification algorithms, data association environments, data conceptualization environments, and data filtering problems. This work described in this paper implements a neural network that spans both the prediction and data association problems. the neural network approach to system performance analysis takes performance data from computer systems and uses a {K}ohonen based neural network to analyze the performance data and attempts to find bottlenecks in the computer system. the data performance analysis results are present as line graphs that can be interpreted by computer experts to determine bottlenecks within the computer system, and can intelligently suggest upgrades to improve any subsystem that suffers from poor performance. the aim of this work is to provide a {"}proof of concept{"} for use in IT assessments, but can also be applied to any situation involving computer performance analysis.",
}

@Article{inspek497_bibuniq_738,
  author =       "Guerrero-Bote V. P. and Lopez-Pujalte C. and de-Moya-Anegon F. and Herrero-Solana V.",
  title =        "Comparison of neural models for document clustering",
  journal =      "International Journal of Approximate Reasoning",
  pages =        "287--305",
  number =       "2--3",
  volume =       "34",
  year =         "2003",
  month =        "November",
  publisher =    "Elsevier",
  abstract =     "We compared the application of different algorithms to document clustering. the algorithms studied were fuzzy C-means, fuzzy ART, fuzzy ART for fuzzy clusters, fuzzy max-min, and the {K}ohonen neural network (only the first is not a neural network). We generated a testbed from LISA, using some of the descriptors corresponding to the different records for the comparison of the results. the best results were found with {K}ohonen's algorithm which also organizes the clusters topologically. We end by discussing in more detail the possibilities offered by Kohonen's algorithm.",
}

@Article{inspek766_bibuniq_585,
  author =       "Guo Ji-lian {Zhu-Jia-yuan, Zhang-Heng-xi, Tian-Song}",
  title =        "Data distribution simulation identification using combined structure neural networks",
  journal =      "Systems Engineering and Electronics",
  pages =        "96--99",
  volume =       "24",
  number =       "10",
  month =        "October",
  year =         "2002",
  publisher =    "Science Press",
  abstract =     "This paper studies the data distributions using combined-structure neural networks. First, the paper sets up data distribution modes and simulation identification training set based on statistical data such as kurtosis, skewness, quantile and cumulative probability. Then it clusters distribution functions using a {K}ohonen self-organizing map. Furthermore, it classifies the clustered modes respectively using BP neural networks. Finally, it identifies lots of random data series through computer simulation. the result indicates that the proposed approach is more successful for selecting input data distributions compared with other methods.",
}

@InProceedings{inspek271_bibuniq_935,
  author =       "Guru S. M. and Hsu A. and Halgamuge S. and Fernando S.",
  editor =       "S. {Palaniswami, M. ; Krishnamachari, B. ; Sowmya, A. ; Challa}",
  title =        "Clustering sensor networks using growing self-organising map",
  booktitle =    "Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference",
  pages =        "91--96",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Sensor networks consist of wireless enabled sensor nodes with limited energy. As sensors could be deployed in a large area, data transmitting and receiving are energy consuming operations. One of the methods to save energy is to reduce the transmission distance of each node by grouping nodes into clusters. Each cluster has a cluster-head (CH), which communicates with all the other nodes of that cluster and transmits the data to the remote base station. We describe the adaptation of a growing self-organising map (GSOM) to cluster the wireless sensor nodes and to identify the cluster-heads. We compare the results with a well-known clustering algorithm. We also describe the energy minimization criterion for clustering.",
}

@InProceedings{Allende04a_bibuniq_1397,
  author =       "H. Allende and C. Rogel and S. Moreno and R. Salas",
  title =        "Robust neural gas for the analysis of data with outliers",
  booktitle =    "Proceedings. 24th International Conference of the {C}hilean Computer Science Society",
  year =         "2004",
  volume =       "",
  pages =        "149--155",
  abstract =     "Learning the structure of real world data is difficult both to recognize and describe. the structure may contain high dimensional clusters that are related in complex ways. Furthermore, real data sets may contain several outliers. Vector quantization techniques has been successfully applied as a data mining tool. in particular the neural gas (NG) is a variant of the self organizing map ({SOM}) where the neighborhoods are adaptively defined during training through the ranking order of the distance of prototypes from the given training sample. Unfortunately, the learning algorithm of the NG is sensitive to the presence of outliers as we show in this paper. Due to the influence of the outliers in the learning process, the topology of the employed network does not conserve the topology of the manifold of the data which is presented. in this paper, we propose to robustify the learning algorithm where the parameter estimation process is resistant to the presence of outliers in the data. We call this algorithm robust neural gas (RNG). We illustrate our technique on synthetic and real data sets.",
}

@InProceedings{allende04a_bibuniq_166,
  author =       "H. Allende and S. Moreno and C. Rogel and R. Salas",
  title =        "Robust self-organizing maps",
  booktitle =    "Progress in Pattern Recognition, Image Analysis and Applications, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "1336--1341",
}

@Article{Barbara02a_bibuniq_1714,
  author =       "H. Barbara and T. Villmann",
  title =        "Generalized relevance learning vector quantization",
  journal =      "Neural Networks",
  year =         "2002",
  volume =       "15",
  number =       "8--9",
  month =        "October",
  pages =        "1059--1068",
  abstract =     "We propose a new scheme for enlarging generalized learning vector quantization (G{LVQ}) with weighting factors for the input dimensions. the factors allow an appropriate scaling of the input dimensions according to their relevance. They are adapted automatically during training according to the specific classification task whereby training can be interpreted as stochastic gradient descent on an appropriate error function. This method leads to a more powerful classifier and to an adaptive metric with little extra cost compared to standard G{LVQ}. Moreover, the size of the weighting factors indicates the relevance of the input dimensions. This proposes a scheme for automatically pruning irrelevant input dimensions. the algorithm is verified on artificial data sets and the iris data from the UCI repository. Afterwards, the method is compared to several well known algorithms which determine the intrinsic data dimension on real world satellite image data.",
}

@Article{bekel05a_bibuniq_60,
  author =       "H. Bekel and G. Heidemann and H. Ritter",
  title =        "Interactive image data labeling using self-organizing maps in an augmented reality scenario",
  journal =      "Neural Networks",
  year =         "2005",
  volume =       "18",
  number =       "5-6",
  month =        "June-July",
  pages =        "566--574",
}

@Article{bensmail05a_bibuniq_107,
  author =       "H. Bensmail and J. Golek and M. M. Moody and J. O. Semmes and A. Haoudi",
  title =        "A novel approach for clustering proteomics data using {B}ayesian fast Fourier transform",
  journal =      "Bioinformatics",
  year =         "2005",
  volume =       "21",
  number =       "10",
  month =        "May",
  pages =        "2210--2224",
}

@Article{chang02b_bibuniq_484,
  author =       "H. C. Chang and D. C. Kopaska-Merkel and H. C. Chen",
  title =        "Identification of lithofacies using {K}ohonen self-organizing maps",
  journal =      "Computers \& Geosciences",
  year =         "2002",
  volume =       "28",
  number =       "2",
  month =        "March",
  pages =        "223--229",
}

@Article{chen03b_bibuniq_363,
  author =       "H. C. Chen and A. M. Lally and B. Zhu and M. Chau",
  title =        "HelpfulMed: Intelligent searching for medical information over the Internet",
  journal =      "Journal of the American Society for Information Science and Technology",
  year =         "2003",
  volume =       "54",
  number =       "7",
  month =        "May",
  pages =        "683--694",
}

@Article{chen03a_bibuniq_299,
  author =       "H. C. Chen and H. Y. Fan and M. Chau and D. Zeng",
  title =        "Testing a Cancer Meta Spider",
  journal =      "International Journal of Human-Computer Studies",
  year =         "2003",
  volume =       "59",
  number =       "5",
  month =        "November",
  pages =        "755--776",
}

@Article{yang05a_bibuniq_50,
  author =       "H. C. Yang and C. H. Lee",
  title =        "A text mining approach for automatic construction of hypertexts",
  journal =      "Expert Systems With Applications",
  year =         "2005",
  volume =       "29",
  number =       "4",
  month =        "November",
  pages =        "723--734",
}

@Article{yang05d_bibuniq_73,
  author =       "H. C. Yang and C. H. Lee",
  title =        "Automatic category theme identification and hierarchy generation for {C}hinese text categorization",
  journal =      "Journal of Intelligent Information Systems",
  year =         "2005",
  volume =       "25",
  number =       "1",
  month =        "July",
  pages =        "47--67",
}

@Article{yang04a_bibuniq_193,
  author =       "H. C. Yang and C. H. Lee",
  title =        "A text mining approach on automatic generation of web directories and hierarchies",
  journal =      "Expert Systems With Applications",
  year =         "2004",
  volume =       "27",
  number =       "4",
  month =        "November",
  pages =        "645--663",
}

@InProceedings{yang03a_bibuniq_280,
  author =       "H. C. Yang and C. H. Lee",
  title =        "Mining environmental texts of images in web pages for image retrieval",
  booktitle =    "Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "607--613",
}

@Article{jin04a_bibuniq_231,
  author =       "H. D. Jin and W. H. Shum and K. S. Leung and M. L. Wong",
  title =        "Expanding Self-Organizing Map for data visualization and cluster analysis",
  journal =      "Information Sciences",
  year =         "2004",
  volume =       "163",
  number =       "1-3",
  month =        "June 14",
  pages =        "157--173",
}

@Article{eidenberger04a_bibuniq_178,
  author =       "H. Eidenberger",
  title =        "Statistical analysis of content-based {MPEG-7} descriptors for image retrieval",
  journal =      "Multimedia Systems",
  year =         "2004",
  volume =       "10",
  number =       "2",
  month =        "August",
  pages =        "84--97",
}

@Article{kang05a_bibuniq_124,
  author =       "H. G. Kang and D. Kim",
  title =        "Real-time multiple people tracking using competitive condensation",
  journal =      "Pattern Recognition",
  year =         "2005",
  volume =       "38",
  number =       "7",
  month =        "July",
  pages =        "1045--1058",
}

@Article{galda04a_bibuniq_197,
  author =       "H. Galda and H. Murao and H. Tamaki and S. Kitamura",
  title =        "Dermoscopic image segmentation by a self-organizing map and fuzzy genetic clustering",
  journal =      "Ieice Transactions on Information and Systems",
  year =         "2004",
  volume =       "E87D",
  number =       "9",
  month =        "September",
  pages =        "2195--2203",
}

@Article{ghaziri03a_bibuniq_398,
  author =       "H. Ghaziri and I. H. Osman",
  title =        "A neural network algorithm for the traveling salesman problem with backhauls",
  journal =      "Computers \& Industrial Engineering",
  year =         "2003",
  volume =       "44",
  number =       "2",
  month =        "February",
  pages =        "267--281",
}

@InProceedings{Yeung04a_bibuniq_1413,
  author =       "H. H. T. Yeung and P. W. M. Tsang",
  title =        "Distributed representation of syntactic structure by tensor product representation and non-linear compression",
  booktitle =    "Proceedings of the Nineteenth National Conference on Artificial Intelligence {AAAI}-04. Sixteenth Innovative Applications of Artificial Intelligence Conference {IAAI}-04",
  year =         "2004",
  volume =       "",
  pages =        "437--442",
  abstract =     "Representing lexicons and sentences with the subsymbolic approach (using techniques such as self organizing map ({SOM}) or artificial neural network (ANN)) is a relatively new but important research area in natural language processing. the performance of this approach however, is highly dependent on whether representations are well formed so that members within each cluster are corresponding to sentences or phrases of similar meaning. Despite the moderate success and the rapid advancement of contemporary computing power, it is still difficult to establish an efficient learning method so that natural language can be represented in a way close to the benchmark exhibited by human beings. One of the major problems is due to the general lack of effective method(s) to encapsulate semantic information into quantitative expressions or structures. in this paper, we propose to alleviate this problem with a novel technique based on tensor product representation and non-linear compression. the method is capable of encoding sentences into distributed representations that are closely associated with the semantic contents, being more comprehensible and analyzable from the perspective of human intelligence.",
}

@Article{hikawa05a_bibuniq_59,
  author =       "H. Hikawa",
  title =        "Fpga implementation of self organizing map with digital phase locked loops",
  journal =      "Neural Networks",
  year =         "2005",
  volume =       "18",
  number =       "5-6",
  month =        "June-July",
  pages =        "514--522",
}

@InProceedings{Hulsen00a_bibuniq_3983,
  author =       "H. Hulsen",
  title =        "Design of a fuzzy logic-based bidirectional mapping for {K}ohonen networks",
  booktitle =    "Proceedings of the-2004 {IEEE} International-Symposium on Intelligent Control",
  year =         "2004",
  pages =        "425--430",
}

@Article{igarashi05a_bibuniq_109,
  author =       "H. Igarashi",
  title =        "Visualization of optimal solutions using self-organizing maps in computational electromagnetism",
  journal =      "{IEEE} Transactions on Magnetics",
  year =         "2005",
  volume =       "41",
  number =       "5",
  month =        "May",
  pages =        "1816--1819",
}

@InProceedings{Inoue00a_bibuniq_4008,
  author =       "H. Inoue and H. Narihisa",
  title =        "Self-organizing neural grove: efficient multiple classifier system using pruned self-generating neural trees",
  booktitle =    "Parallel-Problem-Solving from Nature-Ppsn-Viii. 8th International Conference. Proceedings Lecture Notes in Comput. Sci.",
  pages =        "1113--1122",
  volume =       "3342",
  year =         "2004",
}

@InProceedings{inoue03_bibuniq_4293,
  author =       "H. Inoue and H. Narihisa",
  title =        "{SONG}: Self-Organizing Neural Grove",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{Iwata00a_bibuniq_3922,
  author =       "H. Iwata and S. Sugano",
  title =        "A system design for tactile recognition of human-robot contact state",
  booktitle =    "Proceedings of the {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems {IROS}",
  volume =       "1",
  pages =        "7--12",
  year =         "2003",
}

@Article{rajaniemi02a_bibuniq_487,
  author =       "H. J. Rajaniemi and P. Mähönen",
  title =        "Classifying gamma-ray bursts using self-organizing maps",
  journal =      "Astrophysical Journal",
  year =         "2002",
  volume =       "566",
  number =       "1",
  month =        "February",
  pages =        "202--209",
}

@InProceedings{yin03a_bibuniq_307,
  author =       "H. J. Yin",
  title =        "Nonlinear multidimensional data projection and visualisation",
  booktitle =    "Intelligent Data Engineering and Automated Learning, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "1241--1248",
}

@Article{yin02a_bibuniq_490,
  author =       "H. J. Yin",
  title =        "{ViSOM} - {A} novel method for multivariate data projection and structure visualization",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2002",
  volume =       "13",
  number =       "1",
  month =        "January",
  pages =        "237--243",
}

@Article{junninen04a_bibuniq_243,
  author =       "H. Junninen and H. Niska and K. Tuppurainen and J. Ruuskanen and M. Kolehmainen",
  title =        "Methods for imputation of missing values in air quality data sets",
  journal =      "Atmospheric Environment",
  year =         "2004",
  volume =       "38",
  number =       "18",
  month =        "June",
  pages =        "2895--2907",
}

@InProceedings{kim03a_bibuniq_305,
  author =       "H. Kim and C. Y. Choo and S. S. Chen",
  title =        "An integrated Digital Library server with {OAI} and self-organizing capabilities",
  booktitle =    "Research and Advanced Technology for Digital Libraries, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "185--200",
}

@Article{garcia04a_bibuniq_224,
  author =       "H. L. Garcia and L. M. Gonzalez",
  title =        "Self-organizing map and clustering for wastewater treatment monitoring",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2004",
  volume =       "17",
  number =       "3",
  month =        "April",
  pages =        "215--225",
}

@Article{zhuang02a_bibuniq_464,
  author =       "H. L. Zhuang and W. J. Ang and M. Ohshima and M. S. Chiu",
  title =        "Modeling and control of a nonlinear process based on the extended self-organizing map network",
  journal =      "Industrial \& Engineering Chemistry Research",
  year =         "2002",
  volume =       "41",
  number =       "12",
  month =        "June 12",
  pages =        "2941--2947",
}

@Article{zhao04a_bibuniq_203,
  author =       "H. M. Zhao and S. Ram",
  title =        "Clustering schema elements for semantic integration of heterogeneous data sources",
  journal =      "Journal of Database Management",
  year =         "2004",
  volume =       "15",
  number =       "4",
  month =        "October-December",
  pages =        "88--106",
}

@Article{Madokoro04a_bibuniq_1570,
  author =       "H. Madokoro and K. Sato and M. Ishii and S. Kadowaki",
  title =        "Segmentation of head magnetic resonance image using self-mapping characteristics",
  journal =      "Transactions of the Institute of Electronics, Information and Communication Engineers",
  year =         "2004",
  volume =       "J87D-II",
  pages =        "117--125",
  month =        "January",
  abstract =     "In this paper, we proposed a segmentation method for head magnetic resonance (MR) images. Our method used self mapping characteristic of a self-organization map ({SOM}), and it does not need the setting of the representative point by the operator. We considered the continuity and boundary in the brain tissues by the definition of the local block. in the evaluation experiment, we obtained the segmentation result of matching anatomical structure information. in addition, after our method applied the clinical MR images, it was possible to obtain the effective and objective result for supporting the diagnosis of the brain atrophy by the doctor.",
}

@Article{lu03a_bibuniq_401,
  author =       "H. Q. Lu and Y. Z. Wu and S. C. Chen",
  title =        "A new method based on {SOM} network to generate coarse meshes for overlapping unstructured multigrid algorithm",
  journal =      "Applied Mathematics and Computation",
  year =         "2003",
  volume =       "140",
  number =       "2--3",
  month =        "August",
  pages =        "353--360",
}

@Article{qiu03a_bibuniq_180,
  author =       "H. Qiu and J. Lee and J. Lin and G. Yu",
  title =        "Robust performance degradation assessment methods for enhanced rolling element bearing prognostics",
  journal =      "Advanced Engineering Informatics",
  year =         "2003",
  volume =       "17",
  number =       "3--4",
  month =        "July-October",
  pages =        "127--140",
}

@Article{ressom03b_bibuniq_341,
  author =       "H. Ressom and D. L. Wang and P. Natarajan",
  title =        "Clustering gene expression data using adaptive double self-organizing map",
  journal =      "Physiological Genomics",
  year =         "2003",
  volume =       "14",
  number =       "1",
  month =        "June",
  pages =        "35--46",
}

@Article{ressom03a_bibuniq_338,
  author =       "H. Ressom and D. Wang and P. Natarajan",
  title =        "Adaptive double self-organizing maps for clustering gene expression profiles",
  journal =      "Neural Networks",
  year =         "2003",
  volume =       "16",
  number =       "5--6",
  month =        "June-July",
  pages =        "633--640",
}

@Article{shah-hosseini03a_bibuniq_378,
  author =       "H. Shah-Hosseini and R. Safabakhsh",
  title =        "Tasom: {A} new time adaptive self-organizing map",
  journal =      "{IEEE} Transactions on Systems Man and Cybernetics, Part B: Cybernetics",
  year =         "2003",
  volume =       "33",
  number =       "2",
  month =        "April",
  pages =        "271--282",
}

@Article{shah-hosseini02a_bibuniq_436,
  author =       "H. Shah-Hosseini and R. Safabakhsh",
  title =        "Automatic multilevel thresholding for image segmentation by the growing time adaptive self-organizing map",
  journal =      "{IEEE} Transactions on Pattern Analysis and Machine Intelligence",
  year =         "2002",
  volume =       "24",
  number =       "10",
  month =        "October",
  pages =        "1388--1393",
}

@InProceedings{shimizu04a_bibuniq_168,
  author =       "H. Shimizu and T. Hirasawa and K. Nagahisa and S. Shioya",
  title =        "Analysis of responses of complex bionetworks to changes in environmental conditions",
  booktitle =    "Biologically Inspired Approaches TO Advanced Information Technology, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "1061--1085",
}

@Article{suzuki05a_bibuniq_38,
  author =       "H. Suzuki and R. Saito and M. Tomita",
  title =        "A problem in multivariate analysis of codon usage data and a possible solution",
  journal =      "Febs Letters",
  year =         "2005",
  volume =       "579",
  number =       "28",
  month =        "November",
  pages =        "6499--6504",
}

@Article{tamukoh04a_bibuniq_176,
  author =       "H. Tamukoh and K. Horio and T. Yamakawa",
  title =        "Fast learning algorithms for self-organizing map employing rough comparison {WTA} and its digital hardware implementation",
  journal =      "Ieice Transactions on Electronics",
  year =         "2004",
  volume =       "E87C",
  number =       "11",
  month =        "November",
  pages =        "1787--1794",
}

@Article{tokutaka02a_bibuniq_438,
  author =       "H. Tokutaka and K. Obu-Cann and K. Fujimura and Y. Ikeda and K. Yoshihara and C. A. Metal Mat Grp SASJ",
  title =        "Application of self-organizing maps ({SOM}s) to chemical spectral analysis of elements in the Periodic Table",
  journal =      "Surface and Interface Analysis",
  year =         "2002",
  volume =       "34",
  number =       "1",
  month =        "August",
  pages =        "610--614",
}

@Article{shin04a_bibuniq_246,
  author =       "H. W. Shin and S. Y. Sohn",
  title =        "Segmentation of stock trading customers according to potential value",
  journal =      "Expert Systems With Applications",
  year =         "2004",
  volume =       "27",
  number =       "1",
  month =        "July",
  pages =        "27--33",
}

@Article{huang02a_bibuniq_473,
  author =       "H. Y. Huang and Y. S. Chen and W. H. Hsu",
  title =        "Color image segmentation using a self-organizing map algorithm",
  journal =      "Journal of Electronic Imaging",
  year =         "2002",
  volume =       "11",
  number =       "2",
  month =        "April",
  pages =        "136--148",
}

@InProceedings{inspek85_bibuniq_1193,
  author =       "Hadzic F. and Dillon T. S.",
  title =        "{CSOM}: self-organizing map for continuous data",
  booktitle =    "3rd {IEEE} International Conference on Industrial Informatics {INDIN}",
  pages =        "740--745",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Nowadays, lots of data is being collected for different industrial and commercial purposes, where the aim is to discover useful patterns from data which leads to discovery of valuable domain knowledge. Unsupervised learning is a useful method for these tasks as it requires no target class and it clusters the feature values that occur frequently together. Clustering methods have been successfully used for this task due to the powerful property of creating spatial representations of the features and the abstractions detected from the input space. Self-organising map ({SOM}) is one of the most popular clustering techniques where abstractions are formed by mapping high dimensional input patterns into a lower dimensional set of output clusters. Most of the current uses of {SOM} for this task concentrated on clustering categorical features. fn this paper we present a new learning mechanism for self-organizing map which is useful when the aim is to extract patterns from a data set characterized by continuous input features. Furthermore the method used for network pruning and rule optimization is described.",
}

@InProceedings{inspek418_bibuniq_1062,
  author =       "Hagenbuchner M. and Ah-Chung-Tsoi",
  title =        "A supervised self-organizing map for structures",
  booktitle =    "2004 {IEEE} International Joint Conference on Neural Networks",
  pages =        "1923--1928",
  volume =       "3",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper proposes an improvement of a supervised learning technique for self organizing maps. the ideas presented in this paper differ from {K}ohonen's approach to supervision in that a. ) a rejection term is used, and b. ) rejection affects the training only locally. This approach produces superior results because it does not affect network weights globally, and hence, prevents the addition of noise to the learning process of remote neurons. We implemented the ideas into self-organizing maps for structured data (SOM-SD) which is a more general form of self-organizing maps capable of processing graphs. the capabilities of the proposed ideas are demonstrated by utilizing a relatively large real world learning problem from the area of image recognition. It is shown that the proposed method produces better classification performances while being more robust and flexible than other supervised approaches to SOM.",
}

@InProceedings{inspek640_bibuniq_840,
  author =       "Haimoudi E. K.",
  title =        "Variant of the {K}ohonen network with two additional components in vectors of realization set",
  booktitle =    "Experience of Designing and Application of {CAD} Systems in Microelectronics. Proceedings of the 7th International Conference",
  pages =        "253--255",
  year =         "2003",
  publisher =    "Lviv Polytechnic Nat. Univ, Lviv, Ukraine",
  abstract =     "The problems of using {K}ohonen networks in the tasks of pattern recognition have been considered. A new variant of the network with additional components in the input patterns has been developed. the results of solution of test tasks for standard and modified variants of the {K}ohonen network have been shown.",
}

@InProceedings{tamukou03_bibuniq_4313,
  author =       "Hakaru Tamukou and Keiichi Horio and Takeshi Yamakawa",
  title =        "Fast Learning Algorithm for Self-Organizing Maps and its Digital Hardware Design",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek584_bibuniq_512,
  author =       "Hanchang-Liu Huilin-Ye",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "A {SOM}-based method for feature selection",
  booktitle =    "{ICONIP}'02. Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        "1295--1299",
  volume =       "3",
  year =         "2002",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "This paper presents a method, called feature competitive algorithm (FCA), for feature selection, which is based on an unsupervised neural network, the self--organising map ({SOM}). the FCA is capable of selecting the most important features describing target concepts from a given whole set of features via the unsupervised learning. the FCA is simple to implement and fast in feature selection as the learning can be done automatically and no need for training data. A quantitative measure, called average distance distortion ratio, is figured out to assess the quality of the selected feature set. An asymptotic optimal feature set can then be determined on the basis of the assessment. This addresses an open research issue in feature selection. This method has been applied to a real case, a software document collection consisting of a set of UNIX command manual pages. the results obtained from a retrieval experiment based on this collection demonstrated some very promising potential.",
}

@Article{inspek348_bibuniq_998,
  author =       "Hanchang-Liu Huilin-Ye",
  title =        "A fuzzy-related thesaurus for query refinement",
  journal =      "Neural Processing Letters",
  pages =        "97--107",
  volume =       "19",
  number =       "2",
  year =         "2004",
  month =        "April", 
  publisher =    "Kluwer Academic Publishers",
  abstract =     "Query refinement is essential for information retrieval. in this study, a fuzzy-related thesaurus based query refinement mechanism is proposed. This thesaurus can be dynamically generated during the retrieval process for a document collection that is classified by an unsupervised neural network, the self-organising map. in contrast with general relational thesaurus, the fuzzy-related thesaurus is more effective and efficient. the relationships between the terms are based on the classification of a document collection, and thus, the generated thesaurus naturally has more power to enhance retrieval quality. the recognition of the relationships can be done automatically without human involvement, which significantly reduces the cost associated with the construction of the thesaurus. An evaluation on the query refinement mechanism based on the fuzzy-related thesaurus has been conducted and the preliminary result is promising. A significant improvement on retrieval performance was observed when a fuzzy-related thesaurus was used for query refinement for a software document collection.",
}

@InProceedings{inspek760_bibuniq_580,
  author =       "Hansen T. R.",
  editor =       "A. {Haav, H-M. ; Kalja}",
  title =        "Swapper - self-organizing automatic context visualization",
  booktitle =    "Databases and Information Systems. Proceedings of the Fifth International Baltic Conference",
  pages =        "189--200",
  volume =       "1",
  year =         "2002",
  publisher =    "Inst. Cybernetics at Tallinn Tech. Univ, Tallinn, Estonia",
  abstract =     "The amount of unstructured texts has recently largely increased due to technologies like the Internet and large databases. Many data mining approaches have tried to organize documents according to different categories, but even narrow categories sometimes contain several thousand items. This project is about trying to automatically generate a map visualizing the different contexts, which is represented in a collection of documents. This approach can be used to give an overview of a set of documents without having to go into all the details. At the same time this approach might yield some results in knowledge discovery, because if several different documents contain similar information, this information will be grouped together as a context. the project uses the {K}ohonen self-organizing map ({SOM}) to generate the map, but also introduces some new techniques like the notion of dynamic self-organizing map to handle large feature vectors and the notion of distance table as an alternative to using histograms in the attempt to build the feature vectors.",
}

@Article{inspek13_bibuniq_1141,
  author =       "Hara T. and Hirose A.",
  title =        "Adaptive plastic-landmine visualizing radar system: effects of aperture synthesis and feature-vector dimension reduction",
  journal =      "{IEICE} Transactions on Electronics",
  pages =        "2282--2288",
  volume =       "E88 C",
  numner =       "12",
  month =        "December",
  year =         "2005",
  publisher =    "Inst. Electron. Inf. \& Commun. Eng",
  abstract =     "We propose an adaptive plastic-landmine visualizing radar system employing a complex-valued self-organizing map (CSOM) dealing with a feature vector that focuses on variance of spatial- and frequency-domain inner products (V-CSOM) in combination with aperture synthesis. the dimension of the feature vector is greatly reduced in comparison with that of our previous texture feature-vector CSOM (T-CSOM). in experiments, we first examine the effect of aperture synthesis on the complex-amplitude texture in space and frequency domains. We also compare the calculation cost and the visualization performance of V- and T-CSOMs. Then we discuss merits and drawbacks of the two types of CSOMs with/without the aperture synthesis in the adaptive plastic-landmine visualization task. the V-CSOM with aperture synthesis is found promising to realize a useful plastic-landmine detection system.",
}

@InProceedings{inspek548_bibuniq_766,
  author =       "Harandi M. T. and Gharavi-Alkhansari M.",
  title =        "Low bitrate image compression using self-organized {K}ohonen maps",
  booktitle =    "Proceedings 2003 International Conference on Image Processing",
  pages =        "267--270",
  volume =       "3",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper, we propose a new image compression algorithm based on {K}ohonen self-organized maps. the compression is based on vector quantization (VQ) of the DCT coefficients of image blocks, where the VQ is implemented by a {K}ohonen network. At low bitrates, our proposed method performs better than an earlier compression scheme developed by Amerijckx et al. (1998) and shows better subjective results in comparison to JPEG.",
}

@InProceedings{inspek769_bibuniq_588,
  author =       "Haritopoulos M. and Allinson N. M. Hujun-Yin",
  title =        "Self-organizing map applied to image denoising",
  booktitle =    "Neural Networks for Signal Processing {XII}, Proceedings of the 2002 {IEEE} Signal Processing Society Workshop",
  pages =        "525--534",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "We treat self-organizing maps (SOMs) as means for denoising of images corrupted by multiplicative noise. To achieve this goal, we propose a scheme for blind source separation based on a nonlinear topology preserving mapping as it is performed by SOMs. Despite the assumption that only two noisy frames of the same image scene are available, we show that by a suitable post-processing step based on the estimates provided by the SOM, one can obtain enhanced versions of the originally noisy scenes. Our work is illustrated by application results of the proposed method to test and real images.",
}

@InProceedings{chaput03_bibuniq_4317,
  author =       "Harold H. Chaput and Benjamin Kuipers and Risto Miikkulainen",
  title =        "Constructivist Learning: {A} Neural Implementation of the Schema Mechanism",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Misc{webform_3916_bibuniq_4214,
  author =       "Haruna Matsushita and Yoko Uwate and Yoshifumi Nishio",
  title =        "Research on Improvement in Self-Organization Capability Using Two {SOM}s",
  howpublished = "Proceedings of {RISP} International Workshop on Nonlinear Circuits and Signal Processing ({NCSP'05})",
  pages =        "307--310",
  note =         "",
  year =         "2005",
}

@Misc{webform_4013_bibuniq_4215,
  author =       "Haruna Matsushita and Yoshifumi Nishio",
  title =        "Competing Behavior of Two Kinds of {SOM}s and its Application to Clustering",
  howpublished = "Proceedings of Workshop on Self-Organizing Maps ({WSOM}'05)",
  pages =        "355--362",
  note =         "",
  year =         "2005",
}

@Misc{webform_4207_bibuniq_4216,
  author =       "Haruna Matsushita and Yoshifumi Nishio",
  title =        "Estimation of Embedding Dimension Using Self-Organizing Map",
  howpublished = "Proceedings of International Symposium on Nonlinear Theory and its Applications ({NOLTA'05})",
  pages =        "138--141",
  note =         "",
  year =         "2005",
}

@Article{inspek796_bibuniq_614,
  author =       "Hasegawa K. and Matsuoka S. and Arakawa M. and Funatsu K.",
  title =        "New molecular surface-based 3{D}-{QSAR} method using {K}ohonen neural network and 3-way {PLS}",
  journal =      "Computers \& Chemistry",
  pages =        "583--589",
  volume =       "26",
  number =       "6",
  year =         "2002",
  month =        "November",
  publisher =    "Elsevier",
  abstract =     "Comparative molecular field analysis (CoMFA) has been widely used as a standard three dimensional quantitative structure-activity relationship (3D-QSAR) method. Although CoMFA is a useful technique, it does not always reflect real ligand-receptor interaction. Molecular interactions between the ligand and receptor are mainly occurred near the van der Waals surface of ligand. All grid points surrounding whole molecule in CoMFA are not important as molecular descriptors. If each molecule is represented by physico-chemical parameters on molecular surface, more precise and realistic 3D-QSAR is possible. We developed a new surface-based 3D-QSAR method using {K}ohonen neural network (KNN) and three-way partial least squares (3-way PLS). This method was applied to 25 dopamine 2 (D2) receptor antagonists for validation. First, the 3{D} coordinates of all sampling points on the van der Waals surface were projected into the 2{D} map by KNN. Each node in the map was coded by the associated molecular electrostatic potential (MEP) value of the original sampling point. Then, the correlation between the MEP values of all 2{D} maps and D2 receptor antagonist activities was analyzed by 3-way PLS. the statistics of the 3-way PLS model was excellent and the coefficients back-projected on the van der Waals surface had reasonable 3{D} distribution. Lastly, all data was divided into the calibration and validation sets by D-optimal designs and the activities of validation set were predicted. the external validation suggested that 3-way PLS is better than standard (2-way) PLS for prediction.",
}

@InProceedings{inspek96_bibuniq_1203,
  author =       "Hashemi R. R. and Bahar M. and De-Agostino S.",
  title =        "An extended self-organizing map ({ESOM}) for hierarchical clustering",
  booktitle =    "The International Conference on System, Man and Cybernetics",
  volume =       "3",
  pages =        "2856--2860",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The bottom-up hierarchical clustering methodology that is introduced in this paper is an extension of self-organizing map neural network (ESOM) and it provides remedy for two different major problems. the first one is related to the hierarchical clustering and the second one is related to the self-organizing map ({SOM}) neural network that is able to perform a clustering task. the crucial problem that the hierarchical clustering approaches (top-down and bottom-up) are faced with is the fact that once a merging or decomposing of two clusters takes place, it is impossible to undo or redo it. the crucial problem for {SOM} stems from the fact that the initial clusters' weight vectors, that are generated randomly, highly influence the outcome of the {SOM} clustering.",
}

@InProceedings{inspek567_bibuniq_778,
  author =       "Hashemi R. R. and Bahar M. and Tyler A. and Bahrami A. and Tang N. and Hinson W.",
  editor =       "A. {Srimani, P. K. ; Bein, W. ; Hashemi, R. ; Lawrence, E. ; Cannataro, M. ; Regentova, E. ; Spink}",
  title =        "Development of group's signature for evaluation of skin cancer in mice caused by ultraviolet radiation",
  booktitle =    "Proceedings {ITCC 2003}. International Conference on Information Technology:-Coding and Computing",
  pages =        "617--620",
  year =         "2003",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "In this research effort, the effect of UVC (260 nm) on the skin of one month old Balb/c mice exposed for a total of 100 hours is studied. the goal is to identify those independent variables in the experimental group that have a significant change in their measurements in compare to the measurements of their counterparts in the control group. To meet the goal, we create signatures for both experimental and control groups using the {K}ohonen self-organizing map. the comparison of signatures to each other delivers the significant changes in the independent variables between the two groups. the findings are compared with another set of findings obtained from using analysis of variance. the results reveal that using signature approach that is created based on the {SOM} methodology, is a viable tool for this type of analysis.",
}

@InProceedings{inspek672_bibuniq_871,
  author =       "Hatonen K. and Laine S. and Simila T.",
  editor =       "S. W. Kercel",
  title =        "Using the LogSig-function to integrate expert knowledge to self-organizing map ({SOM}) based analysis",
  booktitle =    "{SMCia/03} Proceedings of the 2003 {IEEE} International Workshop on Soft Computing in Industrial Applications",
  pages =        "145--150",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Pre-treatment of data is an important step of data analysis. Most of the analysis methods measure distances between data points: a variable scaled to have higher variance than other variables will dominate such analysis. Pre-treatment states what levels of variance are significant in each variable, and which data points are fit for the analysis. This requires inclusion of process understanding in the data analysis process. This paper proposes the inclusion of this knowledge by using the logarithmic sigmoid (LogSig) function. the proposed method is compared to the common method of scaling variables to unit variance by studying data from a GSM-network using the self-organizing map ({SOM}).",
}

@InProceedings{Hau04a_bibuniq_1455,
  author =       "Hau San Wong and K. K. T. Cheung and Yang Sha and H. H. S. Ip",
  title =        "Indexing and retrieval of 3{D} models by unsupervised clustering with hierarchical {SOM}",
  booktitle =    "Proceedings of the 17th International Conference on Pattern-Recognition",
  year =         "2004",
  volume =       "4",
  pages =        "613--616",
  abstract =     "A hierarchical indexing structure for 3{D} model retrieval based on the hierarchical self organizing map (HSOM) is proposed. the proposed approach organizes the database into a hierarchy so that head models are partitioned by coarse features initially and finer scale features are used in lower levels. the aim is to traverse a small subset of the database during retrieval. This is made possible by exploiting the multi-resolution capability of spherical wavelet features to successively approximate the salient characteristics of the head models, which are encoded in the form of weight vectors associated with the nodes at different levels (from coarse to fine) of the HSOM. To avoid premature commitment to a possibly erroneous model class, search is propagated from a subset of nodes at each level, which is selected based on a fuzzy membership measure between the query feature vector and weight vector, instead of taking the winner-take-all approach. Experiments show that, in addition to efficiency improvement, model retrieval based on the HSOM approach is able to achieve a much higher accuracy compared with the case where no indexing is performed.",
}

@InProceedings{inspek493_bibuniq_497,
  author =       "Hauta-Kasari M. and Karttunen P.",
  title =        "Broad-band color filter design for spectral camera",
  booktitle =    "Proceedings of {ICIS}'02: International Congress of Imaging Science",
  pages =        "486--487",
  year =         "2002",
  address =      "Tokyo",
  publisher =    "Soc. Photographic Sci. \& Technol. Japan, Tokyo, Japan",
  abstract =     "We present a comparative study for designing broad-band color filters for a spectral camera. in color filter design we apply clustering techniques for a priori measured spectral databases, and therefore, the design is adaptive to an application. Designed color filters contain only positive coefficients and they can be directly implemented optically. For clustering, we use a self-organizing map ({SOM}), c-means, and a pairwise nearest neighbor (PNN) methods. the cluster centroids are used as color filters to represent the color clusters in a spectral database. in the experimental part of the study, the spectral databases measured from the Munsell Book of Color and from Macbeth Color Checker board are used. the computationally designed color filters can be implemented optically, for example, using a liquid crystal spatial light modulator (LCSLM). the low-dimensional component image set acquired using broadband filters can be used in spectral image reconstruction or it can be directly used for pattern recognition tasks.",
}

@InProceedings{inspek610_bibuniq_812,
  author =       "Hayakawa Y. and Ogata T. and Sugano S.",
  title =        "Flexible assembly work cooperation based on work state identifications by a self-organizing map",
  booktitle =    "Proceedings 2003 {IEEE}/{ASME} International Conference on Advanced Intelligent Mechatronics {AIM}",
  pages =        "1031--1036",
  volume =       "2",
  month =        "July",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This study presents a method of realizing flexible assembly work cooperation in cases where neither the assembly process, nor the final form of the completed task is pre-defined in advance. To realize such systems, there exists an issue of identifying work states during the assembly, and determine when and what kind of support is necessary. As an approach of solving such issues, identifying the work states from a work model built by self-organizing assembly motions of human is taken. Examples of the work state identifications and a support system, which judges the situational necessity of support and selects whether to hand out or holds assembly parts are shown. the support is carried out based on the work states identified by the self-organized map. Experiments indicate that work state identification by a self-organizing map is effective in flexibly cooperating with human during assembly work.",
}

@InProceedings{inspek414_bibuniq_1058,
  author =       "Hegde A. and Erdogmus D. and Principe J. C.",
  title =        "Synchronization analysis of epileptic {ECOG} data using {SOM}-based {SI} measure",
  booktitle =    "Conference Proceedings. 26th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society",
  pages =        "952--955 ",
  volume =       "2",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The exact spatio-temporal changes leading to epileptic seizures, although widely studied, are not well understood yet. We propose to investigate the mechanisms leading to epileptic seizures by using a self-organising map ({SOM}) based similarity index (SI) measure. While it is shown that this measure is statistically as accurate as the original SI measure, it is also computationally faster and therefore applicable for real-time analyses. Application of {SOM}-based SI measure on epileptic seizure data reveals interesting aspects of synchronization and de-synchronization at various spatio-temporal levels.",
}

@InProceedings{inspek464_bibuniq_727,
  author =       "Hegde A. and Erdogmus D. and Rao Y. N. and Principe J. C. and Gao J.",
  title =        "{SOM}-based similarity index measure: quantifying interactions between multivariate structures",
  booktitle =    "2003 {IEEE} {XIII} Workshop on Neural Networks for Signal Processing",
  pages =        "819--828",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper addresses the issue of quantifying asymmetric functional relationships between signals. We specifically consider a previously proposed similarity index that is conceptually powerful, yet computationally very expensive. the complexity increases with the square of the number of samples in the signals. in order to counter this difficulty, a self-organizing map that is trained to model the statistical distribution of the signals of interest is introduced in the similarity index evaluation procedure. the {SOM} based technique is equally accurate, but computationally less expensive compared to the conventional measure. These results are demonstrated by comparing the original and {SOM}-based similarity index approaches on synthetic chaotic signal and real {EEG} signal mixtures.",
}

@InProceedings{ritter03_bibuniq_4264,
  author =       "Helge Ritter",
  title =        "Theory and Applications of Non-Euclidean {SOM}s",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{inspek102_bibuniq_908,
  author =       "Hendry D. C.",
  title =        "Comparator trees for winner-take-all circuits",
  journal =      "Theoretical Computer Science",
  pages =        "389--403",
  volume =       "328",
  number =       "1--2",
  month =        "November",
  year =         "2004",
  publisher =    "Elsevier",
  abstract =     "This paper presents architectures for comparator trees capable of finding the minimum value of a large number of inputs. Such circuits are of general applicability although the intended application for which the circuits were designed is the winner-take-all functions of a digital implementation of a neural network based on the self-organising map. Mechanisms for reducing delay based on look-ahead logic within individual comparators and mechanisms based on multiplexor architectures of a comparator are compared for both propagation delay and area.",
}

@Article{inspek667_bibuniq_866,
  author =       "Hendry D. C. and Cambio R.",
  title =        "Reduced power {SOM}/{LVQ} arrays through distance thresholding",
  journal =      "Electronics Letters",
  pages =        "1524--1525",
  volueme =      "39",
  number =       "21",
  year =         "2003",
  month =        "October",
  publisher =    "IEE",
  abstract =     "The distance thresholding method is introduced as a means of reducing power dissipation for self-organising map ({SOM})/learning vector quantisation ({LVQ}) calculations using a single instruction multiple data (SIMD) array. the method requires little additional hardware support. For three standard SOM/{LVQ} benchmarks power reductions are approximately 45\%.",
}

@InProceedings{inspek73_bibuniq_1184,
  author =       "Hikawa H.",
  title =        "A new pulse mode self organizing map hardware with digital phase locked loops",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "2855--2860",
  volume =       "5",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The self-organizing map ({SOM}) has found applicability in a wide range of application areas. This paper proposes a new {SOM} hardware with phase modulated pulse signal and digital phase-locked loops (DPLLs). the system uses the DPLL as a computing element because the operation of the DPLL is very similar to that of SOM's computation. the system also uses square waveform phase to hold the value of the each input vector element. the proposed {SOM} architecture is described in VHDL and its feasibility is verified by simulation. Results show that the proposed {SOM} has good quantization capability.",
}

@InProceedings{Hiong04a_bibuniq_1382,
  author =       "Hiong Sen Tan and S. E. George",
  title =        "Investigating learning parameters in a standard 2-d {SOM} model to select good maps and avoid poor ones",
  booktitle =    "AI-2004:-Advances in Artificial Intelligence. 17th-Australian-Joint Conference on Artificial Intelligence. Proceedings Lecture Notes in Artificial Intelligence",
  year =         "2004",
  volume =       "3339",
  pages =        "425--437",
  abstract =     "In the self organising map ({SOM}), applying different learning parameters to the same input will lead to different maps. the question of how to select the best map is important. A map is good if it is relatively accurate in representing the input and ordered. A measure or measures are needed to quantify the accuracy and the 'order' of maps. This paper investigates the learning parameters in standard 2-dimensional SOMs to find the learning parameters that lead to optimal arrangements. An example of choosing a map in a real world application is also provided.",
}

@Article{inspek129_bibuniq_1232,
  author =       "Hiramatsu A. and Notomi K. and Saito K.",
  title =        "A proposal of color correction method with self-organizing maps for personal user's visibility on the Web browsing",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan",
  pages =        "935--940",
  volume =       "125 C",
  number =       "6",
  year =         "2005",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "In this article, we describe a Web browsing method with an automatic color correction (palette changing) for personal user's visibility. Especially we describe a paired comparison test and {SOM} (self-organizing map) analysis for achromatic character colors and chromatic background colors. We are implementing and evaluating a Web application system for this method as CGI (common gateway interface) software on a HTTP server of the Internet. Since a user profiling is required beforehand for every user, we considered about this problem with SOM.",
}

@InProceedings{nishio03_bibuniq_4282,
  author =       "Hirokazu Nishio and Altaf-Ul-Amin and Tetsuo Sato and Ken-nosuke Wada and Yoshiko Wada and Kotaro Minato and Kazuo Kobayashi and Naotake Ogasawara and Shigehiko Kanaya",
  title =        "Visualization of Gene Classification Based on Expression Profile Using {BL}-{SOM}",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{inspek613_bibuniq_814,
  author =       "Hirose A. and Nagashima T.",
  title =        "Predictive self-organizing map for vector quantization of migratory signals and its application to mobile communications",
  journal =      "{IEEE} Transactions on Neural Networks",
  pages =        "1532--1540",
  month =        "November",
  volume =       "14",
  number =       "6",
  year =         "2003",
  publisher =    "IEEE",
  abstract =     "This paper proposes a predictive self-organizing map (P-SOM) that performs an adaptive vector quantization of migratory time-sequential signals whose stochastic properties such as average values of signals in each cluster are varying continuously. the P-SOM possesses not only the weight corresponding to the signal values themselves but also those related to the time-derivative information. All the weights self-organize to predict appropriate future reference vectors. the prediction using the time-derivative weights enables the separation of continuously varying components form random noise components, resulting in a better performance of the adaptive vector quantization. That is to say, the stationary random noise components are captured by the ordinary weights, whereas the migrating components are captured by the first (and higher) order time-derivative ones. An application to a mobile communication receiver using quasi-coherent detection is presented. By utilizing both the ordinary and time-derivative weights consistently, the P-SOM generates a predictive reference vectors and quantizes the migratory signals adaptively. Simulation experiments on the bit-error rates (BERs) demonstrate that a P-SOM adaptive demodulator has a superior capability to track phase rotations caused by the Doppler effect. A theoretical noise analysis is also reported for the conventional {SOM} and the P-SOM. It is found that the calculation results are approximately in good agreement with the experimental ones.",
}

@InProceedings{douzono03_bibuniq_4285,
  author =       "Hiroshi DOUZONO and Shigeomi HARA and Hisao TOKUSHIMA and Yoshio NOGUCHI",
  title =        "A Design Method of {DNA} chips for Sequence Analyses Using Self Organizing Maps",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{email4_bibuniq_1767,
  author =       "Hiroshi Masuyama {Masahiro Michihata, Tsutomu Miyoshi}",
  title =        "Limited Scope Learning Algorithm for Self-Organizing Map",
  booktitle =    "Proceedings of Joint 1st International Conference on Soft Computing and Intelligent Systems and 3rd International Symposium on Advanced Intelligent Systems ({SCIS} \& {ISIS2002})",
  pages =        "24P4--3",
  year =         "2002",
  address =      "Tsukuba, Japan",
  month =        "October",
}

@InProceedings{email5_bibuniq_1768,
  author =       "Hiroshi Masuyama {Masahiro Michihata, Tsutomu Miyoshi}",
  title =        "Consideration about {SOM} Learning with Several Dimensional Data",
  booktitle =    "Proceedings of Joint 1st International Conference on Soft Computing and Intelligent Systems and 3rd International Symposium on Advanced Intelligent Systems ({SCIS} \& {ISIS2002})",
  pages =        "24P4--1",
  year =         "2002",
  address =      "Tsukuba, Japan",
  month =        "October",
}

@InProceedings{email3_bibuniq_1766,
  author =       "Hiroshi Masuyama {Tsutomu Miyoshi, Hidenori Kawai}",
  title =        "Efficient {SOM} Learning by Data Order Adjustment",
  booktitle =    "Proceedings of 2002 {IEEE} World Congress on Computational Intelligence ({WCCI2002})",
  pages =        "784--784",
  year =         "2002",
  address =      "Honolulu, Hawaii",
  month =        "May",
}

@InProceedings{wakuya03_bibuniq_4268,
  author =       "Hiroshi Wakuya and Hiroyuki Harada",
  title =        "A New Architecture of Self-Organizing Map for Temporal Signal Processing",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{kurosawa03_bibuniq_4290,
  author =       "Hitoshi Kurosawa and Y. Maniwa and K. Fujimura and H. Tokutaka and M. Ohkita",
  title =        "Construction of checkup system by Self-Organizing Maps",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{inspek575_bibuniq_785,
  author =       "Hoffmann M. and Kovacs E.",
  title =        "Developable surface modelling by neural network",
  journal =      "Mathematical and Computer Modelling",
  pages =        "849--853",
  volume =       "38",
  number =       "7--9",
  month =        "October",
  year =         "2003",
  publisher =    "Elsevier",
  abstract =     "In this paper, the construction of developable NURBS surface for a set of tangent planes is presented. the problem is handled by the concept of duality of projective spaces. Using a special distance function, a curve modelling is solved by a {K}ohonen neural network approach.",
}

@InProceedings{inspek457_bibuniq_495,
  author =       "Hogo M. A. and Snorek M.",
  editor =       "F. {Callaos, N. ; Hernandez-Encinas, L. ; Yetim}",
  title =        "Hand written Arabic numeral recognition using three clustering techniques",
  booktitle =    "6th World Multiconference on Systemics, Cybernetics and Informatics",
  pages =        "394--399",
  volume =       "16",
  year =         "2002",
  publisher =    "Int. Inst. Inf. \& Syst, Orlando, FL, USA",
  abstract =     "We consider the development of an offline handwritten isolated Arabic numerals recognition system (single digit). the developed recognition system consists of two main stages, preprocessing stage (thinning, smoothing, feature extraction and features normalization phases), and building of multilevel neural networks classifier stage (clustering and fine classification steps), using three different techniques for the clustering process. Two clustering methods of them are heuristic clustering depending on the knowledge about the characteristics of the numeral set. the third clustering technique is the {K}ohonen network ({SOM}). the performance evaluation of the developed system indicated that it is better than single backpropagation neural networks classifier system using the same set of features and it has a high classification rate. Also it shows that the heuristic clustering methods in some cases is better than the automatic clustering techniques.",
}

@InProceedings{inspek450_bibuniq_1091,
  author =       "Homma N. and Gupta M. M.",
  editor =       "M. {Dick, S. ; Kurgan, L. ; Musilek, P. ; Pedrycz, W. ; Reformat}",
  title =        "Fuzzy self-organizing map in cerebral cortical structure for pattern recognition",
  booktitle =    "{NAFIPS} 2004. 2004 Annual Meeting of the North American Fuzzy Information Processing Society",
  pages =        "539--544",
  volume =       "2",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper demonstrates that a novel neural structure can be useful for formation of long-term memory. An essential core of the proposed model is a control signal for learning or memory formation, inspired by a possible biological learning mechanism observed in cerebral cortices and hippocampus. From a biological point of view, the control signal can be generated by feedback connections between different cortical regions that correspond to specific functions in memory formation. the model involves the feedback from cognitive results based on the current long-term memories, representing the current knowledge, to a control signal that decides whether the present input should be memorized or not. Simulation results show that the model can possess some biologically observed features of human memory system.",
}

@InBook{inspek889_bibuniq_680,
  author =       "Honda R. and Iijima Y. and Konishi O.",
  editor =       "A. {Arikawa, S. ; Shinohara}",
  title =        "Mining of topographic feature from heterogeneous imagery and its application to lunar craters",
  booktitle =    "Progress discovery science. Final report of the Japanese discovery science project Lecture Notes in Artificial Intelligence",
  pages =        "395--407",
  year =         "2002",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "In this study, a crater detection system for a large-scale image database is proposed. the original images are grouped according to spatial frequency patterns, and both the optimized parameter sets and noise reduction techniques are used to identify candidate craters. False candidates are excluded using a self-organizing map ({SOM}) approach. the results show that despite the fact that an accurate classification is achievable using the proposed technique, future improvements in detection process of the system are needed.",
}

@Article{inspek226_bibuniq_1319,
  author =       "Hong G. Y. and Fong B. and Fong A. C. M.",
  title =        "An intelligent video categorization engine",
  journal =      "Kybernetes",
  pages =        "784--802",
  volume =       "34",
  number =       "6",
  year =         "2005",
  publisher =    "Emerald",
  abstract =     "Purpose - We describe an intelligent video categorization engine (IVCE) that uses the learning capability of artificial neural networks (ANNs) to classify suitably preprocessed video segments into a predefined number of semantically meaningful events (categories). Design/methodology/approach - We provide a survey of existing techniques that have been proposed, either directly or indirectly, towards achieving intelligent video categorization. We also compare the performance of two popular ANNs: {K}ohonen's self-organizing map ({SOM}) and fuzzy adaptive resonance theory (Fuzzy ART). in particular, the ANNs are trained offline to form the necessary knowledge base prior to online categorization. Findings - Experimental results show that accurate categorization can be achieved near instantaneously. Research limitations the main limitation of this research is the need for a finite set of predefined categories. Further research should focus on generalization of such techniques. Originality/value - Machine understanding of video footage has tremendous potential for three reasons. First, it enables interactive broadcast of video. Second, it allows unequal error protection for different video shots/segments during transmission to make better use of limited channel resources. Third, it provides intuitive indexing and retrieval for video on demand applications.",
}

@InProceedings{Hong04a_bibuniq_1451,
  author =       "Hong Qiang Wang and De Shuang Huang and Xing Ming Zhao and Xin Huang",
  title =        "A novel clustering analysis based on {PCA} and {SOM}s for gene expression patterns",
  booktitle =    "Advances in Neural Networks-{ISNN}-2004. International-Symposium on Neural-Networks. Proceedings Lecture Notes in Comput. Sci.",
  year =         "2004",
  volume =       "2",
  pages =        "476--481",
  abstract =     "This paper proposes a novel clustering analysis algorithm based on principal component analysis ({PCA}) and self-organizing maps (SOMs) for clustering the gene expression patterns. This algorithm uses the {PCA} technique to direct the determination of the clusters such that the SOMs clustering analysis is not blind any longer. the integration of the {PCA} and the SOMs makes it possible to powerfully mine the underlying gene expression patterns with the practical meanings. in particular, our proposed algorithm can provide the informative clustering results like a hierarchical tree. Finally, the application on the leukemia data indicates that our proposed algorithm is efficient and effective, and it can expose the gene groups associated with the class distinction between the acute lymphoblastic leukemia (ALL) samples and the acute myeloid leukemia (AML) samples.",
}

@InProceedings{Hong00a_bibuniq_4024,
  author =       "Hong Zhou and T. Kawamura",
  title =        "Linear distortion compensation based on {SOM} for digital wireless communications",
  booktitle =    "{IEEE} International-Symposium on Communications and Information Technologies",
  volume =       "2",
  pages =        "1155--1159",
  year =         "2004",
}


@InProceedings{inspek382_bibuniq_1030,
  author =       "Hong-Zhou",
  editor =       "C. {Yin, F. ; Wang, J. ; Guo}",
  title =        "Neural compensation of linear distortion in digital communications",
  booktitle =    "Advances in Neural Networks {ISNN} 2004. International Symposium on Neural Networks. Proceedings Lecture Notes in Comput. Sci.",
  pages =        "305--310",
  volume =       "2",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "In this paper, we proposed a novel and simple linear distortion compensation scheme utilizing {K}ohonen's neural network for digital communications. the scheme compensates the distortions that exist in modulator and demodulator, at the decision stage of the receiver at the same time of data transmission and decision process. the scheme can compensate not only static but also changing distortions. Computer simulations using QPSK signal have confirmed the effectiveness of the proposed scheme.",
}

@InProceedings{inspek752_bibuniq_574,
  author =       "Honkela J. and Tuulos V. H.",
  editor =       "K. {Beulieu, M. ; Baeza-Yates, R. ; Myaeng, S. H. ; Jarvelin}",
  title =        "{GS} Textplorer-adaptive framework for information retrieval",
  booktitle =    "Proceedings of {SIGIR} 2002. Twenty Fifth Annual International {ACM} {SIGIR} Conference on Research and Development in Information Retrieval",
  pages =        "456--456",
  year =         "2002",
  publisher =    "ACM, New York, NY, USA",
  abstract =     "The {WEBSOM} is a method developed originally at Helsinki University of Technology for analyzing and visualizing large document collections. in the {WEBSOM} method, the self-organizing map algorithm is used to automatically organize collections of documents on a map to enable easy exploration and search of the collection. GS Textplorer enables automatic creation of maps of tens of thousands, even millions of documents. Motivation for the GS Textplorer framework raises from the visual information retrieval paradigm, based on the {WEBSOM} method. the aim was to design a robust and adaptable system which could easily be tailored for different information retrieval applications. Its versatile system architecture is discussed.",
}

@InProceedings{inspek394_bibuniq_1040,
  author =       "Honkela T. and Hyvarinen A.",
  title =        "Linguistic feature extraction using independent component analysis",
  booktitle =    "2004 {IEEE} International Joint Conference on Neural Networks",
  pages =        "279--284",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Our aim is to find syntactic and semantic relationships of words based on the analysis of corpora. We propose the application of independent component analysis, which seems to have clear advantages over two classic methods: latent semantic analysis and self-organizing maps. Latent semantic analysis is a simple method for automatic generation of concepts that are useful, e. g., in encoding documents for information retrieval purposes. However, these concepts cannot easily be interpreted by humans. Self-organizing maps can be used to generate an explicit diagram which characterizes the relationships between words. the resulting map reflects syntactic categories in the overall organization and semantic categories in the local level. the self-organizing map does not, however, provide any explicit distinct categories for the words. Independent component analysis applied on word context data gives distinct features which reflect syntactic and semantic categories. Thus, independent component analysis gives features or categories that are both explicit and can easily be interpreted by humans. This result can be obtained without any human supervision or tagged corpora that would have some predetermined morphological, syntactic or semantic information.",
}

@Article{inspek28_bibuniq_1150,
  author =       "Horio K. and Masui I. and Kumamoto M. and Yamakawa T.",
  title =        "Hierarchical pattern classification method using weighted distance measure based self-organizing maps",
  journal =      "Transactions of the Institute of Electronics, Information and Communication Engineers",
  pages =        "2260--2268",
  volume =       "J88 D-II",
  number =       "11",
  month =        "November",
  year =         "2005",
  publisher =    "Inst. Electron. Inf. \& Commun. Eng",
  abstract =     "In this paper, a new self-organizing map which is based on a weighted distance measure is proposed. in real applications, input vectors often include some unnecessary elements, thus it is useful to employ coefficients for corresponding elements of input vectors. A genetic algorithm which is effective for optimization problem is used to decide the desired coefficients. Furthermore, a new classification method, in which the self-organizing maps based on weighted distance measure are arranged hierarchically, is proposed. the effectiveness and the validity of the proposed classification method are verified by applying it to classification of the patients of the jaw deformity.",
}

@InProceedings{inspek173_bibuniq_912,
  author =       "Horiuchi T. and Ogawa T. and Kanada H.",
  title =        "Analysis of impact perforation images using self-organizing map",
  booktitle =    "{SICE} Annual Conference",
  pages =        "605--608",
  volume =       "1",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The estimation of characteristics from the impact perforation process of materials by the high-speed photograph system has been studied. in this method, the characteristics of the material are estimated from the successive images that are taken after the small steel ball perforates into the material specimen. in this study, we propose to use the self-organization map to distinguish the steel ball, the background and the fragment of the specimen in the impact perforation images.",
}

@Article{inspek755_bibuniq_899,
  author =       "Horvath D. and Gmitra M.",
  title =        "The neural network two-scale model of the magnetic dot array",
  journal =      "Journal of Magnetism and Magnetic Materials",
  pages =        "195--213",
  volume =       "256",
  number =       "1--3",
  month =        "January",
  year =         "2003",
  publisher =    "Elsevier",
  abstract =     "We have constructed and investigated numerically a two-scale model of the truncated periodic planar array of the square dots on the square lattice, where large-interdot and small-intradot scales are described by particular models. the large-scale degrees of freedom are block spins interacting via dipolar interactions, whereas the small scales are described by the effective spin lattice Hamiltonian. the adaptive coupling of models belonging to different scales is mediated by interface model. Its main part is the {K}ohonen neural network. the low-energy states are investigated (for assemblies of 3*3 and 12*12 dots) using hybrid algorithm combining the energy minimization and neural network predictions. the effectiveness of simulation and variety of intradot configurations were enhanced by considering the point group symmetry aspect related to dot shape. the simulation for a given set of parameters recovers the state formed by {"}S{"} and {"}C{"} intradot configurations arranged into antiferromagnetically ordered array chains.",
}

@Article{inspek902_bibuniq_689,
  author =       "Hosaka K. and Goya T. and Umehara D. and Kawai M.",
  title =        "An efficient method for network topology identification based on {SOM} algorithm",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan",
  pages =        "208--216",
  volume =       "112 C",
  number =       "2",
  month =        "February",
  year =         "2002",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "In this paper we consider a large number of wireless terminals that are interconnected by a multihop wireless network called an ad-hoc network. Design of routing protocols is a crucial problem in ad-hoc networks. Location information of wireless terminals is an effective measure for ad-hoc network routing. This paper presents a method to identify network topology implying terminal location and connections among terminals. A modified self-organizing map ({SOM}) algorithm is proposed to apply to the network topology identification. This method exploits information of the received power levels of the signals that are transmitted by other terminals. This paper has evaluated how network topology is identified by using an example graph made by random numbers. the results show that only one bit information about the received power level in each terminal can identify network topology accurately with average error of about 10\% for more terminals than a certain value.",
}

@InProceedings{inspek332_bibuniq_982,
  author =       "Hosoda K. and Sumioka H. and Morita A. and Asada M.",
  title =        "Acquisition of human-robot joint attention through real-time natural interaction",
  booktitle =    "{IEEE}/{RSJ} International Conference on Intelligent Robots and Systems {IROS}",
  pages =        "2867--2872",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Joint attention, a process to attend to the object that the other attends to is supposed to be important for human-robot communication as well as for human-human communication. We propose an architecture for acquiring joint attention within a certain time period for realizing natural human-robot interaction. the architecture has two featured modules: a self-organizing map that makes the leaning time shorter and an automatic visual attention selector that let the agent communicate with a human synchronously. We implemented the proposed architecture in a real robot agent and found that 30 minutes was enough for acquiring joint attention with two objects. We can conclude from preliminary experiments that even if the gaze preference of the robot is different from that of the human caregiver, it can acquire joint attention.",
}

@InProceedings{inspek727_bibuniq_552,
  author =       "Hosokawa M. and Hoshi T.",
  title =        "Polarimetric {SAR} data classification method using the self-organizing map",
  booktitle =    "{IEEE} International Geoscience and Remote Sensing Symposium. 24th Canadian Symposium on Remote Sensing",
  pages =        "3468--3470",
  volume =       "6",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper, we introduce a supervised classification method, which differentiates polarimetric SAR data into three categories using a self-organizing map ({SOM}) and a counter propagation learning approach after identifying the appropriate scattering classes. This classifier produces category maps corresponding to the {K}ohonen layers using training data for each scattering class. the SAR data are classified by inputting both like- and cross-polarization power elements into the learned SOM. in the experiment, PI-SAR data are employed since the resolution of aerial SAR data is higher than that of SAR data obtained from space. the proposed method yields higher-accuracy classifications than do conventional methods.",
}

@Article{inspek214_bibuniq_1307,
  author =       "Hsiao-Wen-Chung {Kai-Hsiang-Chuang, Ming-Ting-Wu, Yi-Ru-Lin, Kai-Sheng-Hsieh, Ming-Long-Wu, Shang-Yueh-Tsai, Cheng-Wen-Ko}",
  title =        "Application of model-free analysis in the {MR} assessment of pulmonary perfusion dynamics",
  journal =      "Magnetic Resonance in Medicine",
  pages =        "299--308",
  volume =       "54",
  number =       "2",
  month =        "August",
  year =         "2005",
  publisher =    "Wiley",
  abstract =     "Dynamic contrast-enhanced (DCE) MRI has been used to quantitatively evaluate pulmonary perfusion based on the assumption of a gamma-variate function and an arterial input function (AIF) for deconvolution. However, these assumptions may be too simplistic and may not be valid in pathological conditions, especially in patients with complex inflow patterns (such as in congenital heart disease). Exploratory data analysis methods make minimal assumptions on the data and could overcome these pitfalls. in this work, two temporal clustering methods- {K}ohonen clustering network (KCN) and Fuzzy C-Means (FCM) - were concatenated to identify pixel time-course patterns. the results from seven normal volunteers show that this technique is superior for discriminating vessels and compartments in the pulmonary circulation. Patient studies with five cases of acquired or congenital pulmonary perfusion disorders demonstrate that pathologies can be highlighted in a concise map that combines information of the mean transit time (MTT) and pulmonary blood volume (PBV). the method was found to provide greater insight into the perfusion dynamics that might be overlooked by current model-based analyses, and may serve as a basis for optimal hemodynamic quantitative modeling of the interrogated perfusion compartments.",
}

@Article{inspek491_bibuniq_1115,
  author =       "Hu-Jie Yang-Shang-ming",
  title =        "Java parallel implementations of {K}ohonen self-organizing feature maps",
  journal =      "Journal of Electronic Science and Technology of China",
  pages =        "29--35",
  volume =       "2",
  number =       "2",
  month =        "June",
  year =         "2004",
  publisher =    "Editorial Board of Journal of Univ. of Electron. Sci. and Technol. China",
  abstract =     "The {K}ohonen self-organizing map ({SOM}) is an important tool to find a mapping from high-dimensional space to low dimensional space. the time a {SOM} requires increases with the number of neurons. A parallel implementation of the algorithm can make it faster. This paper investigates the most recent parallel algorithms on SOMs. Using Java network programming utilities, improved parallel and distributed system are set up to simulate these algorithms. From the simulations, we conclude that those algorithms form good feature maps.",
}

@Article{inspek814_bibuniq_626,
  author =       "Huguet S. and Godin N. and Gaertner R. and Salmon L. and Villard D.",
  title =        "Use of acoustic emission to identify damage modes in glass fibre reinforced polyester [using {SOFM} signal classification]",
  journal =      "Composites Science and Technology",
  pages =        "1433--1444",
  volume =       "62",
  number =       "10--11",
  year =         "2002",
  publisher =    "Elsevier",
  abstract =     "The purpose of this work is the use of acoustic emission signal parameters to identify and characterise the various damage mechanisms in stressed glass fibre reinforced polymer composite. Data from acoustic emission are used as inputs in a {K}ohonen self-organising map which automatically separates the acoustic emission signals, enabling a correlation with the failure mode. These results open perspectives for real-time damage recognition in complex composite materials.",
}

@InProceedings{Hui00a_bibuniq_3979,
  author =       "Hui Zeng and D. Rine",
  title =        "Estimation of software defects fix effort using neural networks",
  booktitle =    "Proceedings of the 28th-Annual International Computer Software and Applications Conference. {COMPSAC}-2004",
  pages =        "20--21",
  volume =       "2",
  year =         "2004",
}

@InProceedings{inspek397_bibuniq_1043,
  author =       "Hui-Wang {Yi-Ming-Gu, Yu-Hong-Zhao}",
  title =        "Visual method for process monitoring and its application to Tennessee Eastman challenge problem",
  booktitle =    "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",
  pages =        "3423--3428",
  volume =       "6",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Online process monitoring is extremely important for the successful operation of any process. in this paper a visual data-based method suitable for online monitoring of complex systems is proposed. the self-organizing map is used to project a high-dimensional vector of process data onto a 2{D} visualization space in which different process conditions are represented by different regions. the process state can be indicated by the trajectory in visualization space. the effectiveness of the proposed method is illustrated by the application on the Tennessee Eastman process. Online monitoring and fault detection can be carried in a more intuitionistic and practical manner by using this method.",
}

@InProceedings{inspek144_bibuniq_1247,
  author =       "Huilin-Ye",
  editor =       "F. {Gallagher, M. ; Hogan, J. Maire}",
  title =        "Multi-level document classifications with self-organising maps",
  booktitle =    "Intelligent Data Engineering and Automated Learning {IDEAL} 2005. 6th International Conference. Proceedings Lecture Notes in Computer Science Vol. 3578. 2005: 367--74",
  pages =        "357--374",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "The self-organizing map ({SOM}) is widely used to classify document collections. Such classifications are usually coarse-grained and cannot accommodate accurate document retrieval. A document classification scheme based on multi-level nested self-organizing map (MNSOM) is proposed to solve the problem. An MNSOM consists of a top map and a set of nested maps organized at different levels. the clusters on the top map of an MNSOM are at a relatively general level achieving retrieval recall, and the nested maps further elaborate the clusters into more specific groups, thus enhancing retrieval precision. the MNSOM was tested by a software document collection. the experimental results reveal that the MNSOM significantly improved the retrieval performance in comparison with the single {SOM} based classification.",
}

@InProceedings{yin03_bibuniq_4301,
  author =       "Hujun Yin",
  title =        "Resolution Enhancement for the Vi{SOM}",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "CD-ROM",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek577_bibuniq_787,
  author =       "Hulsen H. and Garnica S. and Fatikow S.",
  title =        "Extended {K}ohonen networks for the pose control of microrobots in a nanohandling station",
  booktitle =    "Proceedings of the 2003 {IEEE} International Symposium on Intelligent Control",
  pages =        "116--121",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A microrobot-based nanohandling station, which is able to handle objects in the micrometer range and below, is introduced. A functional description of the microrobot's mobile platform is given as well as an overview over the control system architecture and the low-level control part. the application of an extended {K}ohonen network for the adaptive open-loop pose control of the mobile platform is presented with theoretical background and implementation details. Its performance is analysed by means of simulation results.",
}

@InProceedings{inspek767_bibuniq_586,
  author =       "Hussain M. and Eakins J. and Sexton G.",
  title =        "Visual clustering of trademarks using the self-organizing map",
  booktitle =    "Image and Video Retrieval International Conference, {CIVR} 2002. Proceedings Lecture Notes in Computer Science",
  pages =        "147--156",
  year =         "2002",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This paper describes the experiments used to investigate ways in which digitised trademark images can be visually clustered on a 2{D} surface, using topological properties of the self-organizing map. Experiments were carried out on a set of original and edge detected binary trademark images, as well as their moment invariants, angular radial transformations and wavelet feature vectors. A radial based precision-recall measure was also used to evaluate the results objectively. Initial results are encouraging.",
}

@InProceedings{inspek605_bibuniq_808,
  author =       "Hussin M. F. and Kamel M.",
  title =        "Document clustering using hierarchical {SOMART} neural network",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "2238--2242",
  volume =       "3",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Availability of large full-text document collections in electronic form has created a need for tools and techniques that assist users in organizing these collections. Document clustering is one of the popular methods used for this purpose. in this paper, we propose the neural network based document clustering method by using a hierarchically organized network built up from independent Self-Organizing Map ({SOM}) and Adaptive Resonance Theory (ART) neural networks. We present clustering results using the REUTERS corpus and show an improvement in clustering performance using both entropy and F-measure as evaluation measures.",
}

@InProceedings{inspek416_bibuniq_1060,
  author =       "Hussin M. F. and Kamel M. S.",
  title =        "Integrating phrases to enhance {HSOMART}-based document clustering",
  booktitle =    "2004 {IEEE} International Joint Conference on Neural Networks",
  pages =        "2347--2352",
  volume =       "3",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Document clustering is one of the popular techniques that assist users in organizing collections of documents. Two successful models of unsupervised neural networks, self-organizing map ({SOM}) and adaptive resonance theory (ART), have shown promising results in this task. Most of the existing neural network based document clustering techniques rely on a {"}bag of words{"} document representation. Each word in the document is considered as a separate feature, ignoring the word order. in this paper, we investigate the use of phrases rather than words as document features applied to our proposed document clustering technique, called hierarchical SOMarT (HSOMarT), which is a hierarchical network built up from independent {SOM} and ART neural networks. We describe a phrase grammar extraction technique, and the proposed HSOMarT. the experimental results of clustering documents from the REUTERS corpus using the extracted phrases as features show an improvement in the clustering performance evaluated using the entropy and F-measure.",
}

@InProceedings{inspek343_bibuniq_993,
  author =       "Hussin M. F. and Kamel M. S. and Nagi M. H.",
  editor =       "S. K. {Pal, N. R. ; Kasabov, N. ; Mudi, R. K. ; Pal, S. ; Parui}",
  title =        "An efficient two-level {SOM}ar{T} document clustering through dimensionality reduction",
  booktitle =    "Neural Information Processing. 11th International Conference, {ICONIP} 2004. Proceedings Lecture Notes in Computer Science",
  pages =        "158--165",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "Document clustering is one of the popular techniques that can unveil inherent structure in the underlying data. Two successful models of unsupervised neural networks, self-organizing map ({SOM}) and adaptive resonance theory (ART) have shown promising results in this task. the high dimensionality of the data has always been a challenging problem in document clustering. It is common to overcome this problem using dimension reduction methods. in this paper, we propose a new two-level neural network based document clustering architecture that can be used for high dimensional data. Our solution is to use {SOM} in the first level as a dimension reduction method to produce multiple output clusters, then use ART in the second level to produce the final clusters using the reduced vector space. the experimental results of clustering documents from the RETURES corpus using our proposed architecture show an improvement in the clustering performance evaluated using the entropy and the f\_measure.",
}

@InProceedings{Hwajeong03a_bibuniq_1537,
  author =       "Hwajeong Lee and Daehwan Kim and Daijin Kim and Sung Yang Bang",
  title =        "Real-time automatic vehicle management system using vehicle tracking and car plate number identification",
  booktitle =    "Proceedings-2003 International Conference on Multimedia and Expo",
  pages =        "353--356",
  year =         "2003",
  volume =       "2",
  pages =        "",
  abstract =     "This paper proposes a real-time vehicle management system using a vehicle tracking and a car plate number identification technique. the system uses two cameras: one for tracking vehicles and another for capturing LP (license plate). We track the vehicles by applying the CONDENSATION algorithm over the vehicle's movement image captured from the first camera. To render the CONDENSATION algorithm more effective, we build a discrete vehicle shape model by training vehicle patterns with a {SOM} (self organizing map), which makes the system suitable for real-time application. Next, we take the probabilistic dynamic model such as {HMM} (hidden {M}arkov model) to reflect the temporal change in shape of various vehicles. As a vehicle reaches the designated target line, a signal is sent to the second camera for capturing the vehicle's front side. the captured image is transferred to an LPR (vehicle LP recognition system) which recognizes the vehicle's category and LP. LPR system detects the vehicle LP using the only the vertical edge of the captured vehicle image, and effectively accomplishes the character segmentation of the LP region using the geometric transformation without respect to the position and angle of the CCD camera. the segmented characters are recognized using the SVM (support vector machine). By combining these two techniques, we construct a real-time automatic vehicle management system that can be used to control vehicle parking and searching for specific vehicles.",
}

@InProceedings{Hyungchul02a_bibuniq_1733,
  author =       "Hyungchul Kim and C. Singh",
  title =        "Probabilistic security analysis using {SOM} and Monte Carlo simulation",
  booktitle =    "{IEEE} Power Engineering Society Winter Meeting. Conference Proceedings",
  year =         "2002",
  volume =       "2",
  pages =        "755--760",
  abstract =     "This paper proposes a new probabilistic method involving transient stability and voltage stability for power system security assessment by combining Monte Carlo simulation and self organizing map ({SOM}). This overcomes the problem of large amount of computation time required for Monte Carlo simulation. {SOM} learns to recognize groups of similar input vectors in such a way that neurons physically near each other in the neuron layer respond to similar input vectors. Data classification by {SOM} can reduce sampling data, which reduces computation time for the reliability security index when using classified data. A case study of the {IEEE} RTS is given to demonstrate the efficiency of this approach.",
}

@InProceedings{inspek563_bibuniq_775,
  author =       "I-Shyan-Hwang San-Nan-Lee",
  title =        "Stochastic generative model of cost-effective {OADM} using a three-dimensional neural network in a {WDM} access network",
  booktitle =    "{CLEO}/Pacific Rim 2003. The 5th Pacific Rim Conference on Lasers and Electro Optics",
  pages =        "873",
  volume =       "2",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "For more than a decade twelve years, all-optical networks have greatly matured. the WDM access network is a high performance network with various channels for providing offering different services, including data, voice, video and others. Rings permit slots to be synchronized: check meaning even at extremely high data rates; hence, they support efficient and flexible use of the available bandwidth for packet communication. the WDM access network can provide multiple channels and increase bandwidth to improve its performance. the access node (such as optical add/drop multiplexer (OADM)) transfers data between the feeder network and the distribution network. the OADM has four main parts-multiplexing (MUX), demultiplexing (DEMUX), the 2 * 2 optical switch and the electronic IP router. Reducing the delay of the OADM can increase the performance of the WDM access network. This work uses a three-dimensional neural network algorithm with increased performance to develop new stochastic generative model that aggregates data on cost-effective OADM. It is based on {K}ohonen self-organizing maps because they are unsupervised. the self-organizing map neural network has three input parameters-source node, destination node and the number of channels are considered to evaluate the performance of the network; they are translation time, switching times and conversion times. When a packet is transferred to the OADM on the channel and then transferred to another OADM, the same channel must be free; otherwise the 3-D neural network algorithm is used to select another free channel to transfer it. the random packet generation processor generates packets randomly from a distribution network. in the simulation, when the number of the access node increased, the load becomes heavier and collisions occur frequently. When the load is low (the maximum number of randomly generated packets is 50), the performance of a access network that uses a three-dimensional neural network is similar to that of one that uses a random generating network. However, when the load is heavy (maximum number of randomly generated packets is 500), an access network that uses the three-dimensional neural network outperforms one using a randomly generating algorithm or the two-dimensional neural network.",
}

@InProceedings{sulistijono05a_bibuniq_47,
  author =       "I. A. Sulistijono and N. Kubota",
  title =        "Human clustering for a partner robot based on computational intelligence",
  booktitle =    "Fuzzy Systems and Knowledge Discovery, Pt. 1, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2005",
  pages =        "265--278",
}

@InProceedings{martinez04a_bibuniq_281,
  author =       "I. C. Martinez and M. A. Castro and C. D. Castillo",
  title =        "Improving self-confidence: An advise-based evolutionary model",
  booktitle =    "Progress in Artificial Intelligence, Lecture Notes in Artificial Intelligence",
  year =         "2004",
  pages =        "175--188",
}

@InProceedings{blanco02a_bibuniq_4960,
  author =       "I. D. Blanco and A. A. C. Vega and A. B. D. Gonzalez",
  title =        "Correlation visualization of high dimensional data using topographic maps",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "1005--1010",
  abstract =     "",
}

@Article{Farkas03a_bibuniq_1623,
  author =       "I. Farkas",
  title =        "Lexical acquisition and developing semantic map",
  journal =      "Neural Network World",
  year =         "2003",
  volume =       "13",
  number =       "3",
  pages =        "235--245",
  abstract =     "We describe a self-organizing neural network model that addresses the process of early lexical acquisition in young children. the growing lexicon is modeled by combined semantic word representations based on distributional statistics of words and on grounded semantic features of words. Changing semantic word representations are assumed to model the maturation of word meaning and serve as inputs to the growing semantic map. the model has been tested on a real child-directed parental language corpus and as a result, the map demonstrates the emergence and reorganization of various word categories, as quantified by two measures.",
}

@Article{Costa03a_bibuniq_1601,
  author =       "I. G. Costa and F. A. T. de Carvalho and M. C. P. de Souto",
  title =        "Comparative study on proximity indices for cluster analysis of gene expression time series",
  journal =      "Journal of Intelligent \& Fuzzy-Systems",
  year =         "2003",
  volume =       "13",
  number =       "2--4",
  pages =        "133--142",
  abstract =     "In the computational analysis of gene expression time series, the main aspect in finding co-expressed genes is the proximity (similarity or dissimilarity) index used in the clustering method. in this context, the proximity indices should find genes that have similar patterns of expression change through time. There is a number of proximity indices used for such a task. However, the majority of these works has given emphasis on the biological results, with no critical evaluation of the suitability of the proximity index used. As a consequence, so far, there is no validity study on which proximity indices are more suitable for the analysis of gene expression time series. Based on this, a comparative study of proximity indices broadly used in the literature is accomplished in this work. More specifically, versions of three distinct proximity indices are compared: Euclidean distance, Pearson correlation and angular separation. in order to evaluate the results, an adaptation of the k-fold cross-validation procedure suitable for unsupervised methods is used. the accuracies of the proximity indices are assessed with the use of an external index, which measures the agreement between the clustering results and gene annotation data.",
}

@Article{kuzmanovski05a_bibuniq_100,
  author =       "I. Kuzmanovski and M. Trpkovska and B. Soptrajanov",
  title =        "Optimization of supervised self-organizing maps with genetic algorithms for classification of urinary calculi",
  journal =      "Journal of Molecular Structure",
  year =         "2005",
  volume =       "744",
  month =        "June",
  pages =        "833--838",
}

@Article{lapidot02a_bibuniq_449,
  author =       "I. Lapidot and H. Guterman and A. Cohen",
  title =        "Unsupervised speaker recognition based on competition between self-organizing maps",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2002",
  volume =       "13",
  number =       "4",
  month =        "July",
  pages =        "877--887",
}

@Article{silven03a_bibuniq_365,
  author =       "I. Silven and M. Niskanen and H. Kauppinen",
  title =        "Wood inspection with non-supervised clustering",
  journal =      "Machine Vision and Applications",
  year =         "2003",
  volume =       "13",
  number =       "5-6",
  month =        "March",
  pages =        "275--285",
}

@Article{pletnev02a_bibuniq_479,
  author =       "I. V. Pletnev and V. V. Zernov",
  title =        "Classification of metal ions according to their complexing properties: a data-driven approach",
  journal =      "Analytica Chimica Acta",
  year =         "2002",
  volume =       "455",
  number =       "1",
  month =        "March",
  pages =        "131--142",
}

@Article{valova05a_bibuniq_57,
  author =       "I. Valova and D. Szer and N. Gueorguieva and A. Buer",
  title =        "A parallel growing architecture for self-organizing maps with unsupervised learning",
  journal =      "Neurocomputing",
  year =         "2005",
  volume =       "68",
  month =        "October",
  pages =        "177--195",
}

@InProceedings{inspek133_bibuniq_1236,
  author =       "Iftekharuddin K. M. and Islam M. A. and Shaik J. and Parra C. and Ogg R.",
  title =        "Automatic brain tumor detection in {MRI}: methodology and statistical validation",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering",
  pages =        "2012--2022",
  volume =       "5747",
  number =       "1",
  year =         "2005",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "Automated brain tumor segmentation and detection are immensely important in medical diagnostics because it provides information associated to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. in this work, we propose a novel automated brain tumor segmentation technique based on multiresolution texture information that combines fractal Brownian motion (fBm) and wavelet multiresolution analysis. Our wavelet-fractal technique combines the excellent multiresolution localization property of wavelets to texture extraction of fractal. We prove the efficacy of our technique by successfully segmenting pediatric brain MR images (MRIs) from St. Jude Children's Research Hospital. We use self-organizing map ({SOM}) as our clustering tool wherein we exploit both pixel intensity and multiresolution texture features to obtain segmented tumor. Our test results show that our technique successfully segments abnormal brain tissues in a set of T1 images. in the next step, we design a classifier using Feed-Forward (FF) neural network to statistically validate the presence of tumor in MRI using both the multiresolution texture and the pixel intensity features. We estimate the corresponding receiver operating curve (ROC) based on the findings of true positive fractions and false positive fractions estimated from our classifier at different threshold values. An ROC, which can be considered as a gold standard to prove the competence of a classifier, is obtained to ascertain the sensitivity and specificity of our classifier. We observe that at threshold 0. 4 we achieve true positive value of 1. 0 (100\%) sacrificing only 0. 16 (16\%) false positive value for the set of 50 T1 MRI analyzed in this experiment.",
}

%%% TAHAN JAATIIN %%%

@InProceedings{inspek706_bibuniq_539,
  author =       "Iivarinen J. and Pakkanen J.",
  title =        "Content-based retrieval of defect images",
  booktitle =    "ACIVS'2002:-Advanced Concepts for Intelligent Vision Systems. 2002: 62--7",
  pages =        "CD--ROM",
  year =         "2002",
  publisher =    "Univ. Gent, Gent, Belgium",
  abstract =     "A content-based retrieval system for defect images is proposed. the system is based on PicSOM, a generic image retrieval system for large, unannotated databases. When a set of defect images is shown to PicSOM, it retrieves another set of defect images that are similar with respect to their shape and internal structure. For each different feature set a tree-structured self-organizing map is formed. Retrieval results from each of these tree-structured self-organizing maps are then combined, and relevance feedback is used to adapt each query iteratively to a user's view of similar defects. Experiments with a defect image database of 13000 images show that the system works rapidly with good retrieval results.",
}

@InProceedings{inspek593_bibuniq_514,
  author =       "Ikeda Y. and Tokutaka H. and Fujimura K. and Maniwa Y.",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "Constructing analysis systems with the self-organizing map based on Web technology",
  booktitle =    "{ICONIP}'02. Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        ""778--781,
  year =         "2002",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "The capacity of the telecommunication line has recently grown to accommodate a growing number of domestic users of the Internet in Japan. This has been necessitated by the shift of the information and communication networks hardware from the traditional coaxial cable to the optical fiber. However, software (service) is insufficient, and actual usefulness of the system has not been established so far. A system prototype for offering various chemical analysis systems is constructed by using the self-organizing map ({SOM}) that is based on the Web technology.",
}

@Article{inspek459_bibuniq_1093,
  author =       "Il-Hwan-Kim Jae-Kang-Lee",
  title =        "Design of reinforcement learning controller with self-organizing map",
  journal =      "Transactions of the Korean Institute of Electrical Engineers, D. May 2004; 53(5): 353--60",
  pages =        "",
  year =         "2004",
  publisher =    "Korean Inst. Electr. Eng",
  abstract =     "This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and environment as input data. For fast learning in neural network training, it is necessary to reduce learning data. in this paper, we use the self-organizing map to partition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. and neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum on the cart system is simulated. the designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.",
}

@Article{inspek812_bibuniq_624,
  author =       "In-Keun-Yu {Chang-Il-Kim, Ki-Chul-Seong}",
  title =        "A study on the demand forecasting control using a composite fuzzy model",
  journal =      "Transactions of the Korean Institute of Electrical Engineers, A. Sept. 2002; 51(9): 417--24",
  pages =        "",
  year =         "2002",
  publisher =    "Korean Inst. Electr. Eng",
  abstract =     "This paper presents an industrial peak load management system for the peak demand control. {K}ohonen neural network and wavelet transform based techniques are adopted for industrial peak load forecasting that will be used as input data of the peak demand control. Firstly, one year of historical load data of a steel company were sorted and clustered into several groups using {K}ohonen neural network and then wavelet transforms are applied with Biorthogonal 1. 3 mother wavelet in order to forecast the peak load of one minute ahead. in addition, for the peak demand control, composite fuzzy model is proposed and implemented in this work. the results are compared with those of conventional model, fuzzy model and composite model, respectively. the outcome of the study clearly indicates that the composite fuzzy model approach can be used as an attractive and effective means of the peak demand control.",
}

@InProceedings{inspek403_bibuniq_1049,
  author =       "Inokuchi R. and Miyamoto S.",
  title =        "{LVQ} clustering and {SOM} using a kernel function",
  booktitle =    "2004 {IEEE} International Conference on Fuzzy Systems",
  pages =        "1497--1500",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper aims at discussing clustering algorithm based on learning vector quantization ({LVQ}) using a kernel function in support vector machines. Furthermore, self-organizing map ({SOM}) using a kernel function is considered. Examples of clustering using different techniques are shown and effects of the kernel function are discussed.",
}

@InProceedings{inspek831_bibuniq_643,
  author =       "Inui M. Hong-Du and Ohkita M. and Fujimura K. and Tokutaka H.",
  title =        "Short-term prediction of oil temperature change of an indoor transformer by self-organizing map ({SOM})",
  booktitle =    "IEEE Power Engineering Society Winter Meeting. Conference Proceedings",
  pages =        "1366--1371",
  volume =       "2",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper considers an application of the Self-Organizing Map ({SOM}), an effective technique for clustering of multi-dimensional data, to the short-term prediction of the oil temperature change of an indoor transformer. Due to the heavy load during the summer, the {SOM} is obtained from the learning with oil temperature and atmospheric temperature in the summer season. the prediction of the oil temperature of the transformer can be realized by the {SOM} based on the maximum and minimum values of the forecast atmospheric temperature announced by the meteorological observatory. Using this technique, the change of the oil temperature of the transformer is well predicted, and the prediction accuracy is higher than that obtained using the conventional method.",
}

@InProceedings{inspek352_bibuniq_1002,
  author =       "Ishii K. and Nishida S. and Ura T.",
  title =        "A self-organizing map based navigation system for an underwater robot",
  booktitle =    "2004 {IEEE} International Conference on Robotics and Automation {IEEE} Vol. 5",
  pages =        "5726",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Autonomous underwater vehicles (AUVs) have great advantages for activities in deep sea, and expected as the attractive tool. However, AUVs have various problems which should be solved. in this paper, the self-organizing map ({SOM}) is applied as the clustering method for the navigation system. the {SOM} is known as one of the effective methods to extract the principle feature from many parameters and decrease the dimension of parameters. Through the competitive learning algorithms, the obtained map is tuned to express specific features of the input signals. We have been investigating the possibility of a navigation system based on {SOM} through simulations and experiments with an AUV called {"}twin-burger{"}. the learning algorithm of usual {SOM} is unsupervised learning. However, supervised learning algorithms should be introduced because the relationship between distances information and desirable behavior of the robot, that is, the relationship from inputs to outputs should be acquired and learned. in this paper, a supervised learning algorithm is introduced into {SOM} and a method to adapt the local map to its environment by learning and evaluating the trajectory of robot is proposed. in the proposed method, the {"}initial map{"} is made static and digital vale as teaching data. in order to include more information of environment in the initial map, the trajectories of robot are evaluated, and the evaluation is utilized in the learning process. This method enables the map to have both the effect of dynamics of robot and environmental information. the efficiency of the method is investigated through the simulations and experiments.",
}

@InProceedings{inspek608_bibuniq_518,
  author =       "Ishii K. and Nishida S. and Watanabe K. and Ura T.",
  title =        "A collision avoidance system based on self-organizing map and its application to an underwater vehicle",
  booktitle =    "7th International Conference on Control, Automation, Robotics and Vision {IEEE} vol. 2",
  pages =        "3 vol. 1718",
  year =         "2002",
  publisher =    "Nanyang Technological Univ, Singapore",
  abstract =     "Underwater vehicles are expected as the attractive tools for the operation in the extreme environment such as the deep ocean survey. in order to realize the useful and practical robots, which can work in the ocean, underwater vehicles should take their action by judging the changing condition from their own sensors and actuators, and are desirable to make their behavior with limited efforts of the operators, because of features caused by the working environment. Therefore, the mobile robot should be autonomous and adaptive to their environment. Development of the path planning system, which can navigate the vehicle without the collision to the obstacles, is one of the most important problems in order to realize the autonomous underwater vehicles. in this paper, Self-Organizing Map ({SOM}) proposed by {K}ohonen is applied to the navigation, which takes the distances to the surroundings as inputs, and outputs the direction for the robot to proceed. the efficiency of path planning system is investigated through the simulation and experiments with an underwater vehicle {"}Twin-Burger{"}.",
}

@InProceedings{inspek583_bibuniq_511,
  author =       "Ishikawa M. and Sasaki N.",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "Gesture recognition based on {SOM} using multiple sensors",
  booktitle =    "{ICONIP}'02. Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        "1300--1304",
  year =         "2002",
  volume =       "3",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "Gesture recognition is important, because it is a useful communication medium between humans and computers. in this paper we use multiple sensors, i. e., PSD cameras for detecting LEDs on a body and DataGloves for both hands. One of the major difficulties in gesture recognition is temporal segmentation from continuous motion. We use training samples with manually attached labels as prior knowledge. A self-organizing map({SOM}) is constructed based on training samples. Test gestural data is segmented by systematic search to obtain the best match with a reference vector in the resulting {SOM}. A comparative study is done between the use of a single {SOM} and 3 {SOM}s for representing spatio-temporal information obtained from PSD cameras and DataGloves.",
}

@Article{inspek223_bibuniq_1316,
  author =       "Itagaki T. and Mori H.",
  title =        "Reconstructing clusters for preconditioned short-term load forecasting",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan, Part B. 2005; 125 B(3): 302--8",
  pages =        "",
  year =         "2005",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "This paper presents a new preconditioned method for short-term load forecasting that focuses on more accurate predicted value. in recent years, the deregulated and competitive power market increases the degree of uncertainty. As a result, more sophisticated short-term load forecasting techniques are required to deal with more complicated load behavior. To alleviate the complexity of load behavior, this paper presents a new preconditioned model. in this paper, clustering results are reconstructed to equalize the number of learning data after clustering with the Kohonen-based neural network. That enhances a short-term load forecasting model at each reconstructed cluster. the proposed method is successfully applied to real data of one-step ahead daily maximum load forecasting.",
}

@InProceedings{inspek21_bibuniq_1146,
  author =       "Iwasaki H. and Ohki H. and Sueda N.",
  editor =       "M. H. Hamza",
  title =        "A system of autonomous state space construction with a self-organizing map in reinforcement learning",
  booktitle =    "Proceedings of the Eight {IASTED} International Conference on Intelligent Systems and Control",
  pages =        "454--459",
  year =         "2005",
  publisher =    "ACTA Press, Anaheim, CA, USA",
  abstract =     "A state space construction is a very important problem in the application of reinforcement learning to real tasks. A system for state space construction with self-organizing map is proposed in this paper. in this system, an agent constructs state space from its own experience autonomously. in the experimentations, the system verifies the agent's ability to construct a suitable state space from any unknown situation. Subsequently, it can improve ability and steadiness of learning, and robustness to noise. Furthermore, it is capable of reconstructing the state space to fit any change most in the environment.",
}

@InProceedings{inspek736_bibuniq_560,
  author =       "Iyer S. V. and Mohan B. K.",
  title =        "Urban landuse monitoring using neural network classification",
  booktitle =    "{IEEE} International Geoscience and Remote Sensing Symposium. 24th Canadian Symposium on Remote Sensing",
  pages =        "2959--2961",
  volume =       "5",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The pressure on land in the city of Mumbai (Bombay), India has given rise to an alternative satellite city called Navi Mumbai (New Bombay). the area is being developed in a planned manner by delineating specific areas for various purposes. the strategy permits private land holding and development within the designated areas. This appears to have resulted in construction according to individual needs and priorities and somewhat unsystematic development and changes in landuse. This paper describes the application of neural network techniques to assess the landuse landcover changes using remotely sensed data. This helps in arresting changes, if any, which disturb the ecological balance. Remotely sensed data of Mumbai area of IRS-1A LISS II has been used. An area of 240 sq. km has been classified using supervised classification technique and backpropagation and {K}ohonen neural network techniques. the performance of the networks have been optimized with appropriate values of parameters such as number of hidden nodes, error tolerance and learning rate in order to achieve a high degree of accuracy. the results of classification by the three methods were judiciously interpreted. in all, there were seven classes - starting with water body (creek), water body (lake) and adjoining marshy land, the landuse changed gradually to vegetation, urban areas, dense urban areas and, lastly, the hilly areas of the Western Ghats. the study showed depletion of vegetation and hilly areas due to tree felling and quarrying; reclamation of marshy lands for urban development resulting in removal of mangrove vegetation; urban areas developing essentially near transportation nodes resulting in congestion.",
}

@Article{bednar04a_bibuniq_184,
  author =       "J. A. Bednar and A. Kelkar and R. Miikkulainen",
  title =        "Scaling self-organizing maps to model large cortical networks",
  journal =      "Neuroinformatics",
  year =         "2004",
  volume =       "2",
  number =       "3",
  month =        "FAL",
  pages =        "275--301",
}

@Misc{webform_7038_bibuniq_4240,
  author =       "J. A. Lee and C. Archambeau and M. Verleysen",
  title =        "Locally Linear Embedding versus Isotop",
  howpublished = "Proc. ESANN 2003, European Symposium on Artificial Neural Networks",
  pages =        "527--534",
  note =         "",
  year =         "2003",
}

@Misc{webform_6744_bibuniq_4238,
  author =       "J. A. Lee and M. Verleysen",
  title =        "Nonlinear Projection with the Isotop Method",
  howpublished = "Lecture Notes in Computer Science 2415",
  pages =        "933--938",
  note =         "",
  year =         "2002",
}

@Misc{webform_6940_bibuniq_4239,
  author =       "J. A. Lee and M. Verleysen",
  title =        "Generalization of the Lp norm for time series and its application to Self-Organizing Maps",
  howpublished = "Proc. {WSOM} 2005, Workshop on Self-Organizing Maps",
  pages =        "733--740",
  note =         "",
  year =         "2005",
}

@Article{abonyi03a_bibuniq_290,
  author =       "J. Abonyi and S. Nemeth and C. Vincze and P. Arva",
  title =        "Process analysis and product quality estimation by Self-Organizing Maps with an application to polyethylene production",
  journal =      "Computers in Industry",
  year =         "2003",
  volume =       "52",
  number =       "3",
  month =        "December",
  pages =        "221--234",
}

@Article{wang03b_bibuniq_382,
  author =       "J. B. Wang and J. Delabie and H. C. Aasheim and E. Smeland and O. Myklebost",
  title =        "Clustering of the {SOM} easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study",
  journal =      "BMC Bioinformatics",
  year =         "2003",
  volume =       "19",
  number =       "4",
  month =        "March 1",
  pages =        "449--458",
}

@Article{barhak02a_bibuniq_414,
  author =       "J. Barhak and A. Fischer",
  title =        "Adaptive reconstruction of freeform objects with 3{D} {SOM} neural network grids",
  journal =      "Computers \& Graphics-UK",
  year =         "2002",
  volume =       "26",
  number =       "5",
  month =        "October",
  pages =        "745--751",
}

@InProceedings{claussen02a_bibuniq_4954,
  author =       "J. C. Claussen and H. G. Schuster",
  title =        "Asymptotic level density of the elastic net self-organizing feature map",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "939--944",
  abstract =     "",
}

@Article{claussen05a_bibuniq_151,
  author =       "J. C. Claussen and T. Villmann",
  title =        "Magnification control in winner relaxing neural gas",
  journal =      "Neurocomputing",
  year =         "2005",
  volume =       "63",
  month =        "January",
  pages =        "125--137",
}

@InProceedings{Fort02a_bibuniq_1688,
  author =       "J. C. Fort and P. Letremy and M. Cottrel",
  title =        "Advantages and drawbacks of the Batch {K}ohonen algorithm",
  booktitle =    "10th-European-Symposium on Artificial Neural Networks. Esann'2002. Proceedings. 2002: 223-30",
  year =         "2002",
  volume =       "",
  pages =        "",
  abstract =     "The {K}ohonen algorithm ({SOM}) was originally defined as a stochastic algorithm which works in all on-line way and which was designed to model some plastic features of the human brain. in fact it is nowadays extensively used for data mining, data visualization, and exploratory data analysis. Some users are tempted to use the batch version of the {K}ohonen algorithm (KBATCH) since it is a deterministic algorithm which can go faster in some cases. After (Fort et al., 2001), which tried to elucidate the mathematical nature of the Batch variant, we give some elements of comparison for both algorithms, using theoretical arguments, simulated data and real data.",
}

@Article{Lamirel00a_bibuniq_3970,
  author =       "J. C. Lamirel and S. Al Shehabi and C. Francois and X. Polanco",
  title =        "Using a compound approach based on elaborated neural network for Webometrics: an example issued from the {EICSTES} project",
  journal =      "Scientometrics-. Nov. Dec. 2004; 61(3): 427-41",
  year =         "2004",
}

@Article{wiemer03a_bibuniq_362,
  author =       "J. C. Wiemer",
  title =        "The time-organized map algorithm: Extending the self-organizing map to spatiotemporal signals",
  journal =      "Neural Computation",
  year =         "2003",
  volume =       "15",
  number =       "5",
  month =        "May",
  pages =        "1143--1171",
}

@Article{wiemer02a_bibuniq_458,
  author =       "J. C. Wiemer and W. von Seelen",
  title =        "Topography from time-to-space transformations",
  journal =      "Neurocomputing",
  year =         "2002",
  volume =       "44",
  month =        "June",
  pages =        "1017--1022",
}

@Article{chakma03a_bibuniq_315,
  author =       "J. Chakma and K. Umemura",
  title =        "Factor controlled hierarchical {SOM} visualization for large set of data",
  journal =      "Ieice Transactions on Information and Systems",
  year =         "2003",
  volume =       "E86D",
  number =       "9",
  month =        "September",
  pages =        "1796--1803",
}

@InProceedings{choi04a_bibuniq_214,
  author =       "J. Choi and J. H. Yi",
  title =        "A two-stage dimensional reduction approach to low-dimensional representation of facial images",
  booktitle =    "Biometric Authentication, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "246--262",
}

@InProceedings{herrero-jaraba03a_bibuniq_383,
  author =       "J. E. Herrero-Jaraba and C. Orrite-Urunuela and D. Buldain and A. Roy-Yahza",
  title =        "Human recognition by gait analysis using neural networks",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "1361--1373",
}

@InProceedings{Taylor04a_bibuniq_1465,
  author =       "J. G. Taylor",
  title =        "{CHIMERA}: creating a new generation chip by brain guidance",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks {IEEE} vol. 3",
  year =         "2004",
  volume =       "3",
  pages =        "",
  abstract =     "We present an outline for an adaptive chip architecture, based on general principles of global brain processing. the chip composed of sets of single neurons, which are initially grouped into columns, and these further grouped into modules. the overall architecture of the chip is based on the attention control system of the brain, together with its division into posterior low-level processing and classifying modules (of {SOM} type) and high-level cognitive modules (of recurrent type). Various balances and neuron complexity is discussed, as further aspects of the processing styles to be used (causal learning laws, salience maps and emotionality, working memory structures, inhibitory competition, neuromodulators).",
}

@InProceedings{cho04a_bibuniq_154,
  author =       "J. H. Cho and H. J. Park and K. B. Kim",
  title =        "Vector quantization using enhanced {SOM} algorithm",
  booktitle =    "Parallel and Distributed Computing: Applications and Technologies, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "199--211",
}

@Article{huang03a_bibuniq_275,
  author =       "J. H. Huang and H. Shimizu and S. Shioya",
  title =        "Clustering gene expression pattern and extracting relationship in gene network based on artificial neural networks",
  journal =      "Journal of Bioscience and Bioengineering",
  year =         "2003",
  volume =       "96",
  number =       "5",
  month =        "November",
  pages =        "421--428",
}

@InProceedings{kim03b_bibuniq_321,
  author =       "J. H. Kim and B. R. Moon",
  title =        "New usage of {SOM} for genetic algorithms",
  booktitle =    "Genetic and Evolutionary Computation - Gecco 2003, Pt. I, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "333--363",
}

@Article{lee05c_bibuniq_118,
  author =       "J. H. Lee and S. C. Park",
  title =        "Intelligent profitable customers segmentation system based on business intelligence tools",
  journal =      "Expert Systems With Applications",
  year =         "2005",
  volume =       "29",
  number =       "1",
  month =        "July",
  pages =        "145--152",
}

@InProceedings{himberg03_bibuniq_4307,
  author =       "J. Himberg and J. A. Flanagan and J. Mäntyjärvi",
  title =        "Towards context awareness using Symbol Clustering Map",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{huiskonen05a_bibuniq_167,
  author =       "J. Huiskonen and P. Niemi and T. Pirttila",
  title =        "The role of {C}-products in providing customer service - refining the inventory policy according to customer-specific factors",
  journal =      "International Journal of Production Economics",
  year =         "2005",
  volume =       "93-94",
  month =        "January 8",
  pages =        "139--149",
}

@Article{rhee05a_bibuniq_160,
  author =       "J. I. Rhee and K. I. Lee and C. K. Kim and Y. S. Yim and S. W. Chung and J. Q. Wei and K. H. Bellgardt",
  title =        "Classification of two-dimensional fluorescence spectra using self-organizing maps",
  journal =      "Biochemical Engineering Journal",
  year =         "2005",
  volume =       "22",
  number =       "2",
  month =        "January",
  pages =        "135--144",
}

@Article{Liszka02a_bibuniq_1735,
  author =       "J. J. Liszka Hackzell and D. P. Martin",
  title =        "technique",
  journal =      "Journal of Medical-Systems. Aug. 2002; 26(4): 337-47",
  year =         "2002",
  volume =       "",
  pages =        "",
  abstract =     "Low back pain represents a significant medical problem, both in its prevalence and its cost to society. Most episodes of acute low back pain resolve without significant long-term functional impact. However, a minority of patients experience extended chronic pain and disability. in this paper, we have explored new techniques of patient assessment that may prospectively identify this minority of patients at risk of developing poor outcomes. We studied 15 patients with acute low back pain and 25 patients with chronic low back pain over four month's time. Patients monitored their pain and activity levels continuously over the first three weeks. Pain and functional status were assessed at baseline and at three weeks following enrollment. Follow-up assessment of functional status and progress were performed at two and four months. the pain and activity levels were categorized using a self-organizing-map neural network. A backpropagation neural network was trained with the categorization and outcome data. There was a good correlation between the true and predicted values for general health (r=0. 96, p<0. 01) and mental health (r=0. 80, p<0. 01). No significant correlation was found if activity and pain data were not entered into the analysis. Our results show that neural network techniques can be applied effectively to categorizing patients with acute and chronic low back pain. It is our hope that future research will allow these categorizations to be tied to prognostic and therapeutic decisions in patients who present with episodes of back pain.",
}

@Article{kim03d_bibuniq_389,
  author =       "J. Kim and T. Chen",
  title =        "A {VLSI} architecture for video-object segmentation",
  journal =      "{IEEE} Transactions on Circuits and Systems for Video Technology",
  year =         "2003",
  volume =       "13",
  number =       "1",
  month =        "January",
  pages =        "83--96",
}

@Article{laiho05a_bibuniq_115,
  author =       "J. Laiho and K. Raivio and P. Lehtimaki and K. Hatonen and I. Simula",
  title =        "Advanced analysis methods for 3{G} cellular networks",
  journal =      "{IEEE} Transactions on Wireless Communications",
  year =         "2005",
  volume =       "4",
  number =       "3",
  month =        "May",
  pages =        "930--942",
}

@Article{li02a_bibuniq_463,
  author =       "J. Li and J. A. Johnson",
  title =        "Time-dependent changes in {ARE}-driven gene expression by use of a noise-filtering process for microarray data",
  journal =      "Physiological Genomics",
  year =         "2002",
  volume =       "9",
  number =       "3",
  month =        "June 3",
  pages =        "137--144",
}

@InProceedings{Li03a_bibuniq_1558,
  author =       "J. Li and Q. Liang and M. T. Manry and T. H. Kim",
  title =        "A semiblind demodulator aided by protocols for wireless {ATM} network",
  booktitle =    "14th {IEEE}-2003 International-Symposium on Personal, Indoor and Mobile-Radio-Communications. Proceedings {IEEE} vol. 3",
  year =         "2003",
  volume =       "3",
  pages =        "",
  abstract =     "In this paper, we study the demodulation problem in time-division-multiple-access (TDMA) wireless asynchronous transfer mode (ATM) networks. We propose a self-organizing-map ({SOM}) based demodulator. A linear interpolation with decision feedback (LIDF) combined with {SOM} algorithm (LIDF-SOM) is developed, in which the {SOM} network is initialized with some known symbols. Such known symbols are obtained by exploiting the medium access control (MAC) and data link control (DLC) protocols. Our scheme has three advantages, it: (1) avoids training symbols for uplink data bursts, (2) has reduced computational costs compared with common blind demodulators, and (3) blocks propagation estimation error through cells. Simulation results show that LIDF-SOM has better performances (0. 2-0. 4 dB gain) over time-varying channel as compared to the LIDF algorithm.",
}

@Article{Carazo03a_bibuniq_1501,
  author =       "J. M. Carazo",
  title =        "In the quest of order: the role of pattern recognition in microscopy image analysis",
  journal =      "Microscopy and Microanalysis. 2003; 9: 8-9",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "The role of pattern in the images is detected by microscopy image analysis. the informational signature is present either on an image or on a sequence of images. It is through the real {"}quest of order{"} within the set of images how these patterns are discovered. These patterns are used as a tool for the quantitative analysis of the images, leading to applications ranging from their classification into groups to efficient indexing and storage into new microscopy databases. the two basic usages of pattern recognition approach is referred to as classification or cluster analysis and segmentation. in the first approach we classify images and in the second case we classify pixels. the principal component analysis ({PCA}) is used to define trends of changes in a collection of elements according to a variance reduction principle. This paper presents a couple of quantitative SOMs (self organizing maps) which are based on functional optimization of well defined cost functions.",
}

@InProceedings{Chamizo03a_bibuniq_1624,
  author =       "J. M. G. Chamizo and A. F. Guillo and J. A. Lopez and F. M. Perez",
  title =        "Architecture for image labeling in real conditions",
  booktitle =    "Computer-Vision-Systems. Third International Conference, Icvs-2003. Proceedings Lecture Notes in Computer Science Vol. 2626. 2003: 131-40",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "A general model for the segmentation and labeling of acquired images in real conditions is proposed. These images could be obtained in adverse environmental conditions, such as faulty illumination, non- homogeneous scale, etc. the system is based on surface identification of the objects in the scene using a database. This database stores features from series of each surface perceived with successive optical parameter values: the collection of each surface perceived at successive distances, and at successive illumination intensities, etc. We propose the use of non-specific descriptors, such as brightness histograms, which could be systematically used in a wide range of real situations and the simplification of database queries by obtaining context information. Self-organizing maps have been used as a basis for the architecture, in several phases of the process. Finally, we show an application of the architecture for labeling scenes obtained in different illumination conditions and an example of a deficiently illuminated outdoor scene.",
}

@InProceedings{muruzabal05a_bibuniq_140,
  author =       "J. Muruzabal",
  title =        "On the emulation of {K}ohonen's self-organization via single-map metropolis-Hastings algorithms",
  booktitle =    "Computational Science -- {ICCS} 2001, Proceedings Pt. 2, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "305--315",
}

@Misc{webform_932_bibuniq_4188,
  author =       "J. N. Kok {E. V. Samsonova, T. Bäck, M. W. Beukers, A. P. IJzerman}",
  title =        "Combining and comparing cluster methods in a receptor database",
  howpublished = "Advances in Intelligent Data Analysis V. 5th International Symposium on Intelligent Data Analysis, IDA 2003. Proceedings Lecture Notes in Computer Science Vol. 2810",
  pages =        "341--351",
  note =         "",
  year =         "2003",
}

@Article{nikkila02a_bibuniq_417,
  author =       "J. Nikkila and P. Törönen and Samuel Kaski and J. Venna and E. Castren and G. Wong",
  title =        "Analysis and visualization of gene expression data using Self-Organizing Maps",
  journal =      "Neural Networks",
  year =         "2002",
  volume =       "15",
  number =       "8-9",
  month =        "October-November",
  pages =        "953--966",
}

@Article{kim05a_bibuniq_91,
  author =       "J. O. Kim and B. R. Lee and C. H. Chung",
  title =        "Real-time interactive motion transitions by a uniform posture map",
  journal =      "Future Generation Computer Systems",
  year =         "2005",
  volume =       "21",
  number =       "7",
  month =        "July",
  pages =        "1106--1116",
}

@InProceedings{Pakkanen03a_bibuniq_1546,
  author =       "J. Pakkanen and J. Iivarinen",
  title =        "Content-based retrieval of surface defect images with {MPEG}-7 descriptors",
  booktitle =    "Proceedings of the Spie-The International-Society for Optical-Engineering. 2003",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "In this paper a prototype system is described for the management and content-based retrieval of defect images in huge image databases. This is a real problem in surface inspection applications, since modern inspection systems may produce up to thousands of defect images in a day. We are using a noncommercial, generic content-based image retrieval (CBIR) system called PicSOM that is modified to fit to the special requirements of our application. the system is tested with a small pre-classified database of surface defect images using the MPEG-7 features. the scalability of the system is also examined using a larger database. Results indicate that the system works with a high level of success.",
}

@Article{peltonen04a_bibuniq_169,
  author =       "J. Peltonen and A. Klami and Samuel Kaski",
  title =        "Improved learning of Riemannian metrics for exploratory analysis",
  journal =      "Neural Networks",
  year =         "2004",
  volume =       "17",
  number =       "8-9",
  month =        "October-November",
  pages =        "1087--1100",
}

@InProceedings{pulkkinen02a_bibuniq_4759,
  author =       "J. Pulkkinen and M. Lappalainen and A. M. A. Hakkinen and N. Lundbom and R. A. Kauppinen and Y. Hiltunen",
  title =        "Application of self-organising maps in automated chemical shift correction of in vivo {H}-1 {MR} spectra",
  booktitle =    "Intelligent Data Engineering and Automated Learning - Ideal 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "423--428",
  abstract =     "",
}

@Article{iglesias-rozas03a_bibuniq_311,
  author =       "J. R. Iglesias-Rozas and B. Maier",
  title =        "Histological and immunohistological profiles of human glioblastomas investigated with a {K}ohonen self-organizing map ({KSOM})",
  journal =      "Acta Neuropathologica",
  year =         "2003",
  volume =       "106",
  number =       "4",
  month =        "October",
  pages =        "414--414",
}

@InProceedings{Kerr00a_bibuniq_3998,
  author =       "J. R. Kerr and C. H. Luk and D. Hammerstrom and M. Pavel",
  title =        "Advanced integrated enhanced vision systems",
  booktitle =    "Proceedings of the-Spie-The International-Society for Optical-Engineering. 2003",
  year =         "2003",
}

@Article{stack03a_bibuniq_308,
  author =       "J. R. Stack and R. G. Harley and P. Springer and J. A. Mahaffey",
  title =        "Estimation of wooden cross-arm integrity using artificial neural networks and laser vibrometry",
  journal =      "{IEEE} Transactions on Power Delivery",
  year =         "2003",
  volume =       "18",
  number =       "4",
  month =        "October",
  pages =        "1539--1544",
}

@InProceedings{Raitio04a_bibuniq_1467,
  author =       "J. Raitio and R. Vigario and J. Sarela and T. Honkela",
  title =        "Assessing similarity of emergent representations based on unsupervised learning",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks {IEEE} 597-602",
  year =         "2004",
  volume =       "",
  pages =        "",
  abstract =     "According to a connectionist view, mental states consist of the activations of neural units in a connectionist network. We consider the similarity of representations that emerge in unsupervised, self-organization process of neural lattices when exposed to color spectrum stimuli. Self-organizing maps ({SOM}) are trained with color spectrum input, using various vectorial encodings for representation of the input. Further, the {SOM} is used for heteroassociative mapping to associate color spectrum with color names. Recall of association between the spectra and colors is assessed. It shows that the {SOM} learns representations for both stimuli and color names, and is able to associate them successfully. the resulting organization is compared through correlation of the activation patterns of the neural maps when responding to color spectrum stimuli. Experiments show that the emerged representations for stimuli are similar with respect to the partitioning of activation-space measure almost independently of the encoding used for input representation. This adds a new example in favour of the usability of the state space semantics.",
}

@Article{ruiz-cabello02a_bibuniq_468,
  author =       "J. Ruiz-Cabello and J. Regadera and C. Santisteban and M. Grana and R. P. de Alejo and I. Echave and P. Aviles and I. Rodriguez and I. Santos and D. Gargallo and M. Cortijo",
  title =        "Monitoring acute inflammatory processes in mouse muscle by {MR} imaging and spectroscopy: a comparison with pathological results",
  journal =      "{NMR} in Biomedicine",
  year =         "2002",
  volume =       "15",
  number =       "3",
  month =        "May",
  pages =        "204--214",
}

@Article{laaksonen04a_bibuniq_170,
  author =       "J. T. Laaksonen and J. M. Koskela and E. Oja",
  title =        "Class distributions on {SOM} surfaces for feature extraction and object retrieval",
  journal =      "Neural Networks",
  year =         "2004",
  volume =       "17",
  number =       "8-9",
  month =        "October-November",
  pages =        "1121--1133",
}

@Article{Tani03a_bibuniq_1682,
  author =       "J. Tani",
  title =        "Learning to generate articulated behavior through the bottom-up and top-down interaction processes",
  journal =      "Neural Networks. Jan. 2003; 16(1): 11-23",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "A novel hierarchical neural network architecture for sensory-motor learning and behavior generation is proposed. Two levels of forward model neural networks are operated on different time scales while parametric interactions are allowed between the two network levels in the bottom-up and top-down directions. the models are examined through experiments of behavior learning and generation using a real robot arm equipped with a vision system. the results of the learning experiments showed that the behavioral patterns are learned by self-organizing the behavioral primitives in the lower level and combining the primitives sequentially in the higher level. the results contrast with prior work by Pawelzik et al. (1996), Tani and Nolfi (1998), and Wolpert and Kawato (1998) in that the primitives are represented in a distributed manner in the network in the present scheme whereas, in the prior work, the primitives were localized in specific modules in the network. Further experiments of online planning show that the behavior could be generated robustly against a background of real world noise while the behavior plans could be modified flexibly in response to changes in the environment. It is concluded that the interaction between the bottom-up process of recalling the past and the top-down process of predicting the future enables both robust and flexible situated behavior.",
}

@InProceedings{Poe05IKNOW_bibuniq_1806,
  author =       "Georg P{\"o}lzlbauer and Andreas Rauber and Michael Dittenbach",
  title =        "A {S}{O}{M}-view of Oilfield Data: {A} Novel Vector Field Visualization for Self-Organizing Maps and its Applications in the Petroleum Industry",
  booktitle =    "Proceedings of the Fifth International Conference on Knowledge Management (I-KNOW'05)",
{
?? Expected closing brace or parenthesis in entry ``@InProceedings{Poe05IKNOW_bibuniq_1806,''.
ISNN} = {0948-695x},
        year = {2005},
        month= {June 29 -- July 1},
        address = {Graz, Austria},
        editor = {Klaus Tochtermann and Hermann Maurer},
        pages = {502---509},
        publisher = {J. UCS - Journal of Universal Computer Science}

}





@InProceedings{Poe05ESANN_bibuniq_1807,
  author =       "Georg P{\"o}lzlbauer and Andreas Rauber and Michael Dittenbach",
  title =        "Graph projection techniques for Self-Organizing Maps",
  booktitle =    "Proceedings of the European Symposium on Artificial Neural Networks (ESANN'05)",
  ISBN =         "2-930307-05-6",
  year =         "2005",
  month =        "April" # " 27--29",
  address =      "Bruges, Belgium",
  editor =       "Michel Verleysen",
  pages =        "533--538",
  publisher =    "d-side publications",
  thanks =       "MUSCLE",
}

@InProceedings{Poe04WDA_bibuniq_1808,
  author =       "Georg P{\"o}lzlbauer",
  title =        "Survey and Comparison of Quality Measures for Self-Organizing Maps",
  booktitle =    "Proceedings of the Fifth Workshop on Data Analysis (WDA'04)",
  year =         "2004",
  month =        "June" # " 24--27",
  address =      "Sliezsky dom, Vysok\'e Tatry, Slovakia",
  editor =       "J\'an Parali\v{c} and Georg P{\"o}lzlbauer and Andreas Rauber",
  pages =        "67--82",
  ISBN =         "80-89066-87-9",
  publisher =    "Elfa Academic Press",
  thanks =       "none",
}

@Masterthesis{Poe04_bibuniq_1809,
  author =       "Georg P{\"o}lzlbauer",
  title =        "Application of Self-Organizing Maps to a Political Dataset",
  school =       "Vienna University of Technology",
  year =         "2004",
}

@InProceedings{KlankeRitter2005-PPS_bibuniq_1810,
  author =       "Stefan Klanke and Helge Ritter",
  title =        "{PSOM}$^+$: Parametrized Self-Organizing Maps for noisy and incomplete data",
  booktitle =    "Proceedings of the 5th Workshop on Self-Organizing Maps ({WSOM} 05)",
  month =        "September",
  year =         "2005",
  address =      "Paris, France",
}

@Misc{k_bibuniq_1811,
  author =       "Y. Bennani",
  title =        "{R}éseaux de neurones artificiels Chapitre dans",
  howpublished = "Encyclopédie d'Informatique et Systèmes de l'Information",
  year =         "2005",
}

@Misc{k_bibuniq_1812,
  author =       "L. Denhi and Y. Benani and F. Krief",
  title =        "Une approche neuronale adaptative de routage minimisant la consommation d'énergie dans les réseaux de capteurs",
  howpublished = "28/2-3/3, colloque GRES 2005 : Gestion de REseaux et de Services, {LUCHON}",
  year =         "2005",
}

@InProceedings{k_bibuniq_1813,
  author =       "L. Dehni and F. Krief and Y. Bennani",
  title =        "Power Control and Clustering in Wireless Sensor Networks",
  booktitle =    "Proceedings of Med-Hoc-Net 2005 : Mediterranean Ad Hoc Networking Workshop",
  year =         "2005",
  address =      "Ile de Porquerolles, France.",
  month =        "June" # " 21-24",
}

@InProceedings{k_bibuniq_1814,
  author =       "L. Denhi and Y. Bennani and F. Krief",
  title =        "Low Energy Adaptive Connectionist Clustering for Wireless Sensor Networks",
  OPTcrossref =  "",
  OPTkey =       "",
  booktitle =    "Proceedings of {IEEE/IFIP} {MATA} 2005 : 2nd International Workshop on Mobility Aware Technologies and Applications",
  pages =        "405--415",
  year =         "2005",
  OPTeditor =    "",
  OPTvolume =    "",
  OPTnumber =    "",
  OPTseries =    "",
  address =      "Montreal, Canada",
  month =        "October",
  OPTorganization = "",
  publisher =    "Springer-Verlag",
  OPTnote =      "",
  OPTannote =    "",
}

@InProceedings{k_bibuniq_1815,
  author =       "S. Guerif and Y. Bennani and E. Janvier",
  title =        "Weighting features during clustering",
  OPTcrossref =  "",
  OPTkey =       "",
  booktitle =    "Proc. {WSOM}'05, International Workshop On Self- Organizing Maps",
  OPTpages =     "",
  year =         "2005",
  OPTeditor =    "",
  OPTvolume =    "",
  OPTnumber =    "",
  OPTseries =    "",
  address =      "Paris, France",
  month =        "September" # " 5-8",
  OPTorganization = "",
  OPTpublisher = "",
  OPTnote =      "",
  OPTannote =    "",
}

@InProceedings{k_bibuniq_1816,
  author =       "F. Zehraoui and Y. Bennani",
  title =        "{M-SOM}: Matricial Self Organizing Map for sequences clustering and classification",
  OPTcrossref =  "",
  OPTkey =       "",
  booktitle =    "Proc. of {IJCNN}'04, International Joint Conference on Neural Network",
  OPTpages =     "",
  year =         "2004",
  OPTeditor =    "",
  OPTvolume =    "",
  OPTnumber =    "",
  OPTseries =    "",
  address =      "Budapest, Hungary",
  OPTmonth =     "July 25-29",
  OPTorganization = "",
  OPTpublisher = "",
  OPTnote =      "",
  OPTannote =    "",
}

@InProceedings{k_bibuniq_1817,
  author =       "K. Benabdeslem and Y. Bennani",
  title =        "An incremental {SOM} for web navigation patterns clustering",
  OPTcrossref =  "",
  OPTkey =       "",
  booktitle =    "26th International Conference on Information Technology Interfaces {ITI} 2004",
  OPTpages =     "",
  year =         "2004",
  OPTeditor =    "",
  OPTvolume =    "",
  OPTnumber =    "",
  OPTseries =    "",
  address =      "Cavtat/Dubrovnik, Croatia",
  month =        "June" # " 7-10",
  OPTorganization = "",
  OPTpublisher = "",
  OPTnote =      "",
  OPTannote =    "",
}

@InProceedings{k_bibuniq_1818,
  author =       "F. Zeraoui and Y. Bennani",
  title =        "{SOM-ART}: Incorporation des propriétés de plasticité et de stabilité dans une carte auto-organisatrice",
  OPTcrossref =  "",
  OPTkey =       "",
  booktitle =    "Atelier FDC : Fouille de Données Complexes dans un processus d'extraction de connaissances ({EGC} 2004)",
  pages =        "169--180",
  year =         "2004",
  OPTeditor =    "",
  OPTvolume =    "",
  OPTnumber =    "",
  OPTseries =    "",
  address =      "Clermont-Ferrand",
  month =        "January",
  OPTorganization = "",
  OPTpublisher = "",
  OPTnote =      "",
  OPTannote =    "",
}

@InProceedings{k_bibuniq_1819,
  author =       "A. Zebulon and Y. Bennani and K. Benabdeslem",
  title =        "Hybrid Connectionist Approach for Knowledge Discovery from Web Navigation Patterns",
  OPTcrossref =  "",
  OPTkey =       "",
  booktitle =    "Proc. of {ACS/IEEE} International Conference on Computer Systems and Applications",
  OPTpages =     "",
  year =         "2003",
  OPTeditor =    "",
  OPTvolume =    "",
  OPTnumber =    "",
  OPTseries =    "",
  address =      "Tunisia",
  month =        "July" # " 14-18",
  OPTorganization = "",
  OPTpublisher = "",
  OPTnote =      "",
  OPTannote =    "",
}

@InProceedings{k_bibuniq_1820,
  author =       "K. Benabdeslem and Y. Bennani and E. Janvier",
  title =        "Visualization and Analysis of Web Navigation Data",
  OPTcrossref =  "",
  OPTkey =       "",
  booktitle =    "International Conference on Artificial Neural Networks {ICANN}",
  pages =        "486--491",
  year =         "2002",
  OPTeditor =    "",
  OPTvolume =    "",
  OPTnumber =    "",
  OPTseries =    "",
  OPTaddress =   "",
  month =        "August",
  OPTorganization = "",
  OPTpublisher = "",
  OPTnote =      "",
  OPTannote =    "",
}

@InBook{k_bibuniq_1821,
  author =       "Y. S. Park and M. Gevrey and S. Lek and J. L. Giraudel",
  title =        "Modelling community structure in freshwater ecosystems",
  chapter =      "Evaluation of relevant species in communities: development of structuring indices for the classification of communities using a self-organizing map",
  publisher =    "Springer",
  year =         "2005",
  pages =        "369--380",
}

@PhdThesis{_bibuniq_1825,
  author =       "Muriel Gevrey",
  title =        "Modéliser la diversité et la structure des communautés aquatiques en utilisant la technique des réseaux de neurones artificiels",
  school =       "LAboratoire DYnamique de la BIOdiversité (LADYBIO), université Paul Sabatier",
  year =         "2003",
  address =      "Toulouse, France",
  month =        "June",
}

@Article{k_bibuniq_1826,
  author =       "Kawakami J. and Hoshi K. and Ishiyama A. and Miyagishima K. and Sato K.",
  title =        "Application of a self-organizing map to quantitative structure-activity relationship analysis of carboquinone and benzodiazepine",
  journal =      "Chem. Pharm. Bull. 52",
  year =         "2004",
  pages =        "751--755",
}

@Article{k_bibuniq_1827,
  author =       "Sato K. {Hoshi K, Kawakami J, Kumagai M, Kasahara S, Nisimura N, Nakamura H}",
  title =        "An analysis of thyroid function diagnosis using Bayesian-type and {SOM}-type neural networks",
  journal =      "Chem. Pharm. Bull. 53",
  year =         "2005",
  pages =        "1570--1574",
}

@InProceedings{k_bibuniq_1828,
  author =       "Eero Carlson and Pekka Rahkila",
  title =        "Visual Explorations in Real Estate Landscape",
  booktitle =    "Proceedings of the 5th Workshop On Self-Organizing Maps, {WSOM} 2005",
  pages =        "123--130",
  year =         "2005",
  editor =       "Marie Cottrell",
  address =      "Paris, France",
  month =        "September",
  organization = "Paris 1 Panthéon-Sorbonne University",
}

@MastersThesis{k_bibuniq_1829,
  author =       "Pasi Lehtimäki",
  title =        "Self-Organizing Operator Maps in Complex System Analysis",
  school =       "Helsinki University of Technology",
  year =         "2002",
}

@InProceedings{Hatonen2003_bibuniq_1831,
  author =       "Kimmo H{\"a}t{\"o}nen and Sampsa Laine and Timo Simil{\"a}",
  title =        "Using the logsig-function to integrate expert knowledge to self-organizing map based analysis",
  booktitle =    "IEEE International Workshop on Soft Computing in Industrial Applications (SMCia)",
  OPTcrossref =  "",
  OPTkey =       "",
  pages =        "145--150",
  year =         "2003",
  OPTeditor =    "",
  OPTvolume =    "",
  OPTnumber =    "",
  OPTseries =    "",
  OPTaddress =   "Binghamton, NY, USA",
  OPTmonth =     "June 23--25",
  OPTorganization = "",
  OPTpublisher = "",
  OPTnote =      "",
  OPTannote =    "",
}

%  bibtex/b_pubmed_clean. bib =====================================





@Article{pmid14571370_bibuniq_1837,
  author =       "Takashi Abe and Shigehiko Kanaya and Makoto Kinouchi and Yuta Ichiba and Tokio Kozuki and Toshimichi Ikemura",
  title =        "A novel bioinformatic strategy for unveiling hidden genome signatures of eukaryotes: self-organizing map of oligonucleotide frequency",
  journal =      "Genome Inform Ser Workshop Genome Inform",
  year =         "2002",
  volume =       "13",
  pages =        "12--20",
}

@Article{pmid12437783_bibuniq_1838,
  author =       "Ritaban Dutta and Evor L Hines and Julian W Gardner and Pascal Boilot",
  title =        "Bacteria classification using Cyranose 320 electronic nose",
  journal =      "Biomed Eng Online",
  year =         "2002",
  volume =       "1",
  pages =        "4",
  month =        "October",
}

@Article{pmid12416687_bibuniq_1843,
  author =       "Eric de Bodt and Marie Cottrell and Michel Verleysen",
  title =        "Statistical tools to assess the reliability of self-organizing maps",
  journal =      "Neural Networks",
  year =         "2002",
  volume =       "15",
  number =       "8-9",
  pages =        "967--978",
  month =        "October",
}

@Article{pmid11855964_bibuniq_1850,
  author =       "Frank Hoehn and Ekkehard Lindner and Hermann A Mayer and Thomas Hermle and Wolfgang Rosenstiel",
  title =        "Neural networks evaluating {NMR} data: an approach to visualize similarities and relationships of sol-gel derived inorganic-organic and organometallic hybrid polymers",
  journal =      "Journal of Chem Inf Comput Sci",
  year =         "2002",
  volume =       "42",
  number =       "1",
  pages =        "36--45",
  month =        "January",
}

@Article{pmid11928520_bibuniq_1853,
  author =       "R Brian Potter and Sorin Draghici",
  title =        "A {SOFM} approach to predicting {HIV} drug resistance",
  journal =      "Pac Symp Biocomput",
  year =         "2002",
  pages =        "77--87",
}

@Article{pmid12009974_bibuniq_1854,
  author =       "Louise Urruty and Jean-Luc Giraudel and Sovan Lek and Philippe Roudeillac and Michel Montury",
  title =        "Assessment of strawberry aroma through {SPME}/{GC} and {ANN} methods. Classification and discrimination of varieties",
  journal =      "Journal of Agric Food Chem",
  year =         "2002",
  volume =       "50",
  number =       "11",
  pages =        "3129--3136",
  month =        "May",
}

@Article{pmid12375091_bibuniq_1857,
  author =       "Francisco Sanchez-Martos and Pedro A Aguilera and Antonia Garrido-Frenich and Jose A Torres and Antonio Pulido-Bosch",
  title =        "Assessment of groundwater quality by means of self-organizing maps: application in a semiarid area",
  journal =      "Environ Manage",
  year =         "2002",
  volume =       "30",
  number =       "5",
  pages =        "716--726",
  month =        "November",
}

@Article{pmid12185458_bibuniq_1863,
  author =       "Thomas M Harris and Geoffrey Childs",
  title =        "Global gene expression patterns during differentiation of {F9} embryonal carcinoma cells into parietal endoderm",
  journal =      "Funct Integr Genomics",
  year =         "2002",
  volume =       "2",
  number =       "3",
  pages =        "105--119",
  month =        "August",
}

@Article{pmid12160707_bibuniq_1864,
  author =       "A Pascual-Montano and K A Taylor and H Winkler and R D Pascual-Marqui and J-M Carazo",
  title =        "Quantitative self-organizing maps for clustering electron tomograms",
  journal =      "Journal of Struct Biol",
  year =         "2002",
  volume =       "138",
  number =       "1-2",
  pages =        "114--122",
  month =        "April",
}

@Article{pmid12820129_bibuniq_1868,
  author =       "Dimitris K Agrafiotis",
  title =        "Stochastic proximity embedding",
  journal =      "Journal of Comput Chem",
  year =         "2003",
  volume =       "24",
  number =       "10",
  pages =        "1215--1221",
  month =        "July",
}

@Article{pmid14502965_bibuniq_1870,
  author =       "Brian Castellani and John Castellani",
  title =        "Data mining: qualitative analysis with health informatics data",
  journal =      "Qual Health Res",
  year =         "2003",
  volume =       "13",
  number =       "7",
  pages =        "1005--1018",
  month =        "September",
}

@Article{pmid12906730_bibuniq_1872,
  author =       "Steen Rasmussen and Michael J Raven and Gordon N Keating and Mark A Bedau",
  title =        "Collective intelligence of the artificial life community on its own successes, failures, and future",
  journal =      "Artif Life",
  year =         "2003",
  volume =       "9",
  number =       "2",
  pages =        "207--235",
  month =        "Spring",
}

@Article{pmid14565701_bibuniq_1874,
  author =       "Liem T Tran and C Gregory Knight and Robert V O'Neill and Elizabeth R Smith and Michael O'Connell",
  title =        "Self-organizing maps for integrated environmental assessment of the Mid-Atlantic region",
  journal =      "Environ Manage",
  year =         "2003",
  volume =       "31",
  number =       "6",
  pages =        "822--835",
  month =        "June",
}

@Article{pmid14556688_bibuniq_1879,
  author =       "Steen Rasmussen and Liaohai Chen and Martin Nilsson and Shigeaki Abe",
  title =        "Bridging nonliving and living matter",
  journal =      "Artif Life",
  year =         "2003",
  volume =       "9",
  number =       "3",
  pages =        "269--316",
  month =        "Summer",
}

@Article{pmid12940808_bibuniq_1880,
  author =       "Franco Mongini and Andrea Deregibus and Fabio Raviola and Tullia Mongini",
  title =        "Confirmation of the distinction between chronic migraine and chronic tension-type headache by the McGill Pain Questionnaire",
  journal =      "Headache",
  year =         "2003",
  volume =       "43",
  number =       "8",
  pages =        "867--877",
  month =        "September",
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}

@Article{pmid16360395_bibuniq_2015,
  author =       "David Velazquez-Fernandez and Cecilia Laurell and Janos Geli and Anders Hoog and Jacob Odeberg and Magnus Kjellman and Joakim Lundeberg and Bertil Hamberger and Peter Nilsson and Martin Backdahl",
  title =        "Expression profiling of adrenocortical neoplasms suggests a molecular signature of malignancy",
  journal =      "Surgery",
  year =         "2005",
  volume =       "138",
  number =       "6",
  pages =        "1087--1094",
  month =        "December",
}

@Article{pmid16084474_bibuniq_2016,
  author =       "Jason W H Wong and Hugh M Cartwright",
  title =        "Deterministic projection by growing cell structure networks for visualization of high-dimensionality datasets",
  journal =      "Journal of Biomed Inform",
  year =         "2005",
  volume =       "38",
  number =       "4",
  pages =        "322--330",
  month =        "August",
}

%  bibtex/cfyfe. bib =====================================



@Article{fyfe:cesarsom_bibuniq_2017,
  author =       "C. Garcia-Osorio and C. Fyfe",
  title =        "The combined use of self-organising maps and Andrews' curves.",
  journal =      "International Journal of Neural Systems",
  year =         "2005",
}

@Article{fyfe:gtmchange_bibuniq_2018,
  author =       "C. Fyfe",
  title =        "Making the Generative Topographic Mapping more responsive to the data",
  journal =      "{WSEAS} Transactions on Computers",
  year =         "2005",
  volume =       "4",
  number =       "10",
  pages =        "1223--1233",
  month =        "October",
}

@Article{fyfe:hannahemi_bibuniq_2019,
  author =       "Y. Han and E. Corchado and C. Fyfe",
  title =        "Forecasting using Twinned Principal Curves and Twinned Self Organising Maps",
  journal =      "Neurocomputing",
  year =         "2004",
  number =       "57",
  pages =        "37--47",
}

@Article{fyfe:marianmaltajournal_bibuniq_2021,
  author =       "M. Pe\~{n}a and C. Fyfe",
  title =        "Developments of the Generalised Harmonic Topographic Mapping",
  journal =      "{WSEAS} Transactions on Computers",
  year =         "2005",
  volume =       "4",
  number =       "11",
  pages =        "1548--1555",
}

@InProceedings{fyfe:emiicann2003_bibuniq_2023,
  author =       "E. Corchado and C. Fyfe",
  title =        "Relevance and Kernel Self Organising Maps",
  booktitle =    "13th International Conference on Artificial Neural Networks, ICANN/{ICONIP}03",
  year =         "2003",
}

@InProceedings{fyfeemiiwann2003_bibuniq_2024,
  author =       "E. Corchado and C. Fyfe",
  title =        "Progressive Concept Formation in Self-organising Maps",
  booktitle =    "International Workshop on Artificial Neural Networks",
  year =         "2003",
  publisher =    "Springer Verlag",
}

@InProceedings{fyfe:emilioideal2003_bibuniq_2025,
  author =       "E. Corchado and C. Fyfe",
  title =        "Initialising Self-organising Maps",
  booktitle =    "Fourth International Conference on Intelligent Data Engineering and Automated Learning, {IDEAL2003}",
  year =         "2003",
}

@InProceedings{fyfe:emiliokes2002_bibuniq_2026,
  author =       "E. Corchado and C. Fyfe",
  title =        "The Scale Invariant Map and Maximum Likelihood Hebbian Learning",
  booktitle =    "The Sixth International Conference on Knowledge-based Intelligent Information and Engineering Systems, {KES2002}",
  year =         "2002",
}

@InProceedings{fyfe:emiwsom2003_bibuniq_2027,
  author =       "E. Corchado and C. Fyfe and D. MacDonald",
  title =        "Maximum Likelihood Kernel Scale Invariant Feature Maps",
  booktitle =    "3rd Intrernational Workshop on Self Organising Maps, {WSOM2003}",
  year =         "2003",
}

@InProceedings{fyfe:gaylegtm_bibuniq_2028,
  author =       "G. Leen and C. Fyfe",
  title =        "Training an {AI} player to play pong using the {GTM}",
  booktitle =    "{IEEE} Symposium on Computational Intelligence and Games",
  year =         "2005",
}

@InProceedings{fyfe:gtmcorfu_bibuniq_2029,
  author =       "C. Fyfe",
  title =        "Matching the dimensionalilty of maps with that of the data",
  booktitle =    "{WSEAS} Multiconference on Computing",
  year =         "2005",
}

@InProceedings{fyfe:linaeunite_bibuniq_2030,
  author =       "L. Petrakieva and C. Fyfe",
  title =        "Bagging and Bumping Self-organising Maps",
  booktitle =    "European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems, {EUNITE2003}",
  year =         "2003",
  editor =       "B Gabrys and A. Nuernberger",
}

@InProceedings{fyfe:marian1_bibuniq_2031,
  author =       "M. Pe\~{n}a and C. Fyfe",
  title =        "The Harmonic Topographic Map",
  booktitle =    "The Irish conference on Artificial Intelligence and Cognitive Science, {AICS05}",
  year =         "2005",
}

@InProceedings{fyfe:marian2_bibuniq_2032,
  author =       "M. Pe\~{n}a and C. Fyfe",
  title =        "Tight clusters and smooth manifolds with the harmonic topographic map",
  booktitle =    "{WSEAS} Multiconference on Computing",
  year =         "2005",
}

@InProceedings{fyfe:marian4_bibuniq_2033,
  author =       "M. Pe\~{n}a and C. Fyfe",
  title =        "Faster clustering of complex data with the generalised harmonic topographic mapping ({G}-HaTo{M})",
  booktitle =    "{WSEAS} Multiconference on Computing",
  year =         "2005",
}

@InProceedings{fyfe:topoe_bibuniq_2034,
  author =       "C. Fyfe",
  title =        "Topographic Product of Experts",
  booktitle =    "International Conference on Artificial Neural Networks, ICANN2005",
  year =         "2005",
}

@InProceedings{fyfe:topoe2_bibuniq_2035,
  author =       "C. Fyfe",
  title =        "Properties of the Topographic Product of Experts",
  booktitle =    "Workshop on Self-Organizing Maps, {WSOM}2005",
  year =         "2005",
}

@InProceedings{shao02a_bibuniq_2039,
  author =       "J. F. Shao and Jiang Han",
  title =        "The application of {SOM} networks on rock blastability classification",
  booktitle =    "Proceedings of the Annual Conference on Explosives and Blasting Technique",
  year =         "2002",
  editor =       "",
  volume =       "I",
  pages =        "407--413",
  organization = "Lab. of Mechanics of Lille, Univ. of Sci. and Technol. of Lille",
  publisher =    "International Society of Explosives Engineers",
  address =      "",
}

@Article{bonabeau02a_bibuniq_2041,
  author =       "Eric Bonabeau",
  title =        "Graph multidimensional scaling with self-organizing maps",
  journal =      "Information Sciences",
  year =         "2002",
  volume =       "143",
  number =       "1-4",
  month =        "June",
  pages =        "159--180",
  organization = "Icosystem Corporation",
  publisher =    "Elsevier Science Inc.",
  address =      "",
}

@Article{guerrero_bote02a_bibuniq_2046,
  author =       "V. P. Guerrero Bote and F. De Moya Anegon and V. Herrero Solana",
  title =        "Document organization using {K}ohonen's algorithm",
  journal =      "Information Processing and Management",
  year =         "2002",
  volume =       "38",
  number =       "1",
  month =        "January",
  pages =        "79--89",
  organization = "Library and Information Sci. Faculty, University of Extremadura",
  publisher =    "",
  address =      "",
}

@Article{lubkin02a_bibuniq_2047,
  author =       "Jeremy Lubkin and Gert Cauwenberghs",
  title =        "{VLSI} implementation of fuzzy adaptive resonance and learning vector quantization",
  journal =      "Analog Integrated Circuits and Signal Processing",
  year =         "2002",
  volume =       "30",
  number =       "2",
  month =        "February",
  pages =        "149--157",
  organization = "Electrical and Computer Engineering, Johns Hopkins University",
  publisher =    "",
  address =      "",
}

@Article{marin02a_bibuniq_2050,
  author =       "F. J. Marin and F. Garcia-Lagos and G. Joya and F. Sandoval",
  title =        "Global model for short-term load forecasting using artificial neural networks",
  journal =      "IEE Proceedings: Generation, Transmission and Distribution",
  year =         "2002",
  volume =       "149",
  number =       "2",
  month =        "March",
  pages =        "121--125",
  organization = "Dpto. de Electronica, E. T. S. I. Informatica, Universidad de Malaga",
  publisher =    "Institution of Electrical Engineers",
  address =      "",
}

@Article{khuwaja02a_bibuniq_2051,
  author =       "G. A. Khuwaja",
  title =        "An adaptive combined classifier system for invariant face recognition",
  journal =      "Digital Signal Processing: A Review Journal",
  year =         "2002",
  volume =       "12",
  number =       "1",
  month =        "January",
  pages =        "21--46",
  organization = "Department of Physics, Kuwait University",
  publisher =    "",
  address =      "",
}

@Article{papamarkos02a_bibuniq_2052,
  author =       "Nikos Papamarkos and Antonis E. Atsalakis and Charalampos P. Strouthopoulos",
  title =        "Adaptive color reduction",
  journal =      "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics",
  year =         "2002",
  volume =       "32",
  number =       "1",
  month =        "February",
  pages =        "44--56",
  organization = "Electric Circuits Analysis Lab., Department of Electrical Comp., Democritus Univ. of Thrace",
  publisher =    "Institute of Electrical and Electronics Engineers Inc.",
  address =      "",
}

@Article{buessler02a_bibuniq_2054,
  author =       "Jean-Luc Buessler and Jean-Philippe Urban and Julien Gresser",
  title =        "Additive composition of supervised self-organizing maps",
  journal =      "Neural Processing Letters",
  year =         "2002",
  volume =       "15",
  number =       "1",
  month =        "February",
  pages =        "9--20",
  organization = "TROP Research Group",
  publisher =    "",
  address =      "",
}

@Article{aires-de-sousa02a_bibuniq_2056,
  author =       "Joao Aires-de-Sousa and Johann Gasteiger",
  title =        "Prediction of enantiomeric selectivity in chromatography: Application of conformation-dependent and conformation-independent descriptors of molecular chirality",
  journal =      "Journal of Molecular Graphics and Modelling",
  year =         "2002",
  volume =       "20",
  number =       "5",
  month =        "",
  pages =        "373--388",
  organization = "Departamento de Quimica, Campus Fac. de Ciencias e Tecnologia, Universidade Nova de Lisboa",
  publisher =    "",
  address =      "",
}

@Article{wang02a_bibuniq_2058,
  author =       "Shouhong Wang",
  title =        "Nonlinear pattern hypothesis generation for data mining",
  journal =      "Data and Knowledge Engineering",
  year =         "2002",
  volume =       "40",
  number =       "3",
  month =        "March",
  pages =        "273--283",
  organization = "Dept. of Mktg. /Bus. Info. Systems, Charlton College of Business, Univ. of Massachusetts Dartmouth",
  publisher =    "",
  address =      "",
}

@Article{toyama02a_bibuniq_2060,
  author =       "Kentaro Toyama and Andrew Blake",
  title =        "Probabilistic tracking with exemplars in a metric space",
  journal =      "International Journal of Computer Vision",
  year =         "2002",
  volume =       "48",
  number =       "1",
  month =        "June",
  pages =        "9--19",
  organization = "Microsoft Research",
  publisher =    "Kluwer Academic Publishers",
  address =      "",
}

@Article{ito02a_bibuniq_2061,
  author =       "Junji Ito and Kunihiko Kaneko",
  title =        "Spontaneous structure formation in a network of chaotic units with variable connection strengths",
  journal =      "Physical Review Letters",
  year =         "2002",
  volume =       "88",
  number =       "2",
  month =        "January" # " 14",
  pages =        "287011--287014",
  organization = "Dept. of Pure and Applied Sciences, College of Arts and Sciences, University of Tokyo",
  publisher =    "",
  address =      "",
}

@Article{levy02a_bibuniq_2062,
  author =       "J. C. S. Levy and Sovirith Tan and Pui-Man Lam",
  title =        "Monte-Carlo investigation of vertical correlations in self-organized multilayer growth of islands",
  journal =      "Physica A: Statistical Mechanics and its Applications",
  year =         "2002",
  volume =       "303",
  number =       "1-2",
  month =        "January" # " 1",
  pages =        "105--118",
  organization = "Department of Physics, Southern University",
  publisher =    "",
  address =      "",
}

@Article{kasemo02a_bibuniq_2063,
  author =       "Bengt Kasemo",
  title =        "Biological surface science",
  journal =      "Surface Science",
  year =         "2002",
  volume =       "500",
  number =       "1-3",
  month =        "March" # " 10",
  pages =        "656--677",
  organization = "Department of Applied Physics, Chalmers University of Technology, Goteborg University",
  publisher =    "Elsevier Science B. V.",
  address =      "",
}

@Article{skupin02a_bibuniq_2064,
  author =       "Andre Skupin",
  journal =      "Pollution Engineering",
  year =         "2002",
  volume =       "34",
  number =       "2",
  month =        "February",
  pages =        "50--58",
  organization = "Department of Geography, University of New Orleans",
  publisher =    "Cahner Publishing Co.",
  address =      "",
}

@Article{vuori02a_bibuniq_2065,
  author =       "Vuokko Vuori and Jorma Laaksonen and Jari Kangas",
  title =        "Influence of erroneous learning samples on adaptation in on-line handwriting recognition",
  journal =      "Pattern Recognition",
  year =         "2002",
  volume =       "35",
  number =       "4",
  month =        "April",
  pages =        "915--925",
  organization = "Lab. of Computer and Info. Science, Helsinki University of Technology",
  publisher =    "",
  address =      "",
}

@Article{nishiwaki02a_bibuniq_2066,
  author =       "Daisuke Nishiwaki and Atsushi Sato and Jun Tsukumo",
  title =        "Research and development for {OCR} technologies",
  journal =      "NEC Research and Development",
  year =         "2002",
  volume =       "43",
  number =       "1",
  month =        "January",
  pages =        "29--32",
  organization = "",
  publisher =    "NEC Creative Ltd.",
  address =      "",
}

@Article{khuwaja02b_bibuniq_2067,
  author =       "G. A. Khuwaja and M. S. Laghari",
  title =        "A parameter-based combined classifier for invariant facial expression and gender recognition",
  journal =      "International Journal of Pattern Recognition and Artificial Intelligence",
  year =         "2002",
  volume =       "16",
  number =       "1",
  month =        "February",
  pages =        "27--51",
  organization = "Physics Department, Kuwait University",
  publisher =    "World Scientific Publishing Co. Pte. Ltd",
  address =      "",
}

@Article{patane02a_bibuniq_2068,
  author =       "Giuseppe Patane and Marco Russo",
  title =        "Distributed unsupervised learning using the multisoft machine",
  journal =      "Information Sciences",
  year =         "2002",
  volume =       "143",
  number =       "1-4",
  month =        "June",
  pages =        "181--196",
  organization = "Department of Physics, University of Messina",
  publisher =    "Elsevier Science Inc.",
  address =      "",
}

%  bibtex/csa02. bib =====================================



@Article{Steiner02a_bibuniq_2069,
  author =       "F. M. Steiner and B. C. Schlick Steiner and A. Nikiforov and R. Kalb and R. Mistrik",
  title =        "Cuticular hydrocarbons of Tetramorium ants from Central Europe: Analysis of {GC}-{MS} data with self-organizing maps ({SOM}) and implications for systematics",
  journal =      "Journal of Chemical Ecology",
  year =         "2002",
  number =       "12",
  volume =       "28",
  pages =        "2569--2584",
  abstract =     "Cuticular hydrocarbons were extracted from workers of 63 different nests of five species of Tetramorium ants (Hymenoptera: Formicidae) from Austria, Hungary, and Spain. the GC-MS data were classified (data mining) by self-organizing maps ({SOM}). {SOM} neurons derived from primary neuron separation were subjected to hierarchical {SOM} (HSOM) and were grouped to neuron areas on the basis of vicinity in the hexagonal output grid. While primary neuron separation and HSOM resulted in classifications on a level more sensitive than species differences, neuron areas resulted in chemical phenotypes apparently of the order of species. These chemical phenotypes have implications for systematics: while the chemical phenotypes for T. ferox and T. moravicum correspond to morphological determination, in T. caespitum and T. impurum a total of six chemical phenotypes is found. Three hypotheses are discussed to explain this disparity between morphological and chemical classifications, including in particular the possibility of hybridization and the existence of cryptic species. Overall, the GC-MS profiles classified by {SOM} prove to be a practical alternative to morphological determination (T. ferox, T. moravicum) and indicate the need to revisit systematics (T. caespitum, T. impurum). DN: Database Name Biological Sciences",
}

@Article{Martoglio02a_bibuniq_2070,
  author =       "A. M. Martoglio and J. W. Miskin and S. K. Smith and D. J. C. MacKay",
  title =        "A decomposition model to track gene expression signatures: preview on observer-independent classification of ovarian cancer",
  journal =      "Bioinformatics",
  year =         "2002",
  number =       "12",
  volume =       "18",
  pages =        "1617--1624",
  abstract =     "A number of algorithms and analytical models have been employed to reduce the multidimensional complexity of {DNA} array data and attempt to extract some meaningful interpretation of the results. These include clustering, principal components analysis, self-organizing maps, and support vector machine analysis. Each method assumes an implicit model for the data, many of which separate genes into distinct clusters defined by similar expression profiles in the samples tested. A point of concern is that many genes may be involved in a number of distinct behaviours, and should therefore be modelled to fit into as many separate clusters as detected in the multidimensional gene expression space. the analysis of gene expression data using a decomposition model that is independent of the observer involved would be highly beneficial to improve standard and reproducible classification of clinical and research samples. DN: Database Name ASFA: Aquatic Sciences and Fisheries Abstracts",
}

@Article{Richardson02a_bibuniq_2071,
  author =       "A. J. Richardson and M. C. Pfaff and J. G. Field and N. F. Silulwane and F. A. Shillington",
  title =        "Identifying characteristic chlorophyll a profiles in the coastal domain using an artificial neural network",
  journal =      "Journal of Plankton Research",
  year =         "2002",
  number =       "12",
  volume =       "24",
  pages =        "1289--1303",
  abstract =     "To estimate primary production in the marine environment, knowledge of the vertical distribution of phytoplankton is needed. the measurement of ocean colour by satellite remote sensing makes it possible to map the near-surface phytoplankton distribution, although the subsurface vertical structure cannot be measured. in this study, we investigated the shape of vertical chlorophyll profiles from the Benguela upwelling system seasonally and with respect to environmental variables such as surface temperature and chlorophyll, and mixed layer and water column depth. We used an artificial neural network technique called a self-organizing map to identify characteristic classes of vertical chlorophyll profiles, and then classify existing profiles into these representative classes. the self-organizing map identified a continuum of patterns, ranging from those with small deep peaks ( approximately 1 mg m super(-3), 45 m) to those with large near-surface peaks ( approximately 9 mg m super(-3), 7 m). Although profile shape varied seasonally, profiles were very variable within each season, making chlorophyll profiles averaged seasonally meaningless. A canonical succession in profile shape following upwelling of cold water in spring and summer could be identified, with large surface peaks in cool water and small deep peaks in warm water. the approach presented here can be used in a semi-quantitative manner to predict the subsurface chlorophyll field from known (water column depth) or easily measured variables from satellites (surface temperature or surface chlorophyll), as the relative frequency of each characteristic profile under different environmental conditions is presented. This approach enables prediction of profile shapes in the dynamic coastal domain and thus superior regional estimates of primary production. DN: Database Name Biological Sciences",
}

@Article{Finn02a_bibuniq_2077,
  author =       "P. W. Finn and H. He and C. Ma and T. Mueller and J. R. Stone and H. C. Liou and M. R. Boothby and DL* Perkins",
  title =        "Molecular profiling of the role of the {NF}- Kappa {B} family of transcription factors during alloimmunity",
  journal =      "Journal of Leukocyte Biology",
  year =         "2002",
  number =       "5",
  volume =       "72",
  pages =        "",
  abstract =     "Allograft rejection involves a complex network of multiple immune regulators and effector mechanisms. in the current study, we focused on the role of nuclear factor (NF)- Kappa B/Rel. Previous studies had established that deficiency of the p50 NF- Kappa B family member prolonged allograft survival only modestly. However, because of its crucial role in signal transduction in inflammatory and immune responses, we hypothesized that other NF- Kappa B/Rel family members may produce more profound effects on alloimmunity. Therefore, in addition to p50, we analyzed the role of c-Rel, which is expressed predominantly in lymphocytes. Also, to investigate NF- Kappa B activation in T cells, we examined transgenic mice that express a trans-dominant inhibitor of NF- Kappa B [I Kappa B( Delta N)] regulated by a T cell-restricted promoter. Allograft survival was prolonged indefinitely in the c-Rel-deficient and I Kappa B( Delta N)-transgenic recipients. To determine the molecular basis of NF- Kappa B modulation of rejection, we analyzed a panel of 58 parameters including effector molecules, chemokines, cytokines, receptors, and cellular markers using hierarchical clustering algorithms and self-organizing maps in p50 super(-/-), c-Rel super(-/-), and I Kappa B( Delta N)-transgenic, experimental groups plus allogeneic-, syngeneic-, and lymphocyte-deficient (alymphoid) control groups. Surprisingly, profiles of gene expression in the c-Rel recipients (which have indefinite graft survival) were similar to the p50 super(-/-) and allogeneic recipients (which rapidly reject grafts). As expected, gene expression in the I Kappa B( Delta N) recipients (which also have indefinite graft survival) was similar to profiles of nonrejecting syngeneic and alymphoid recipients. Importantly, self-organizing maps identified a small subset of genes including several chemokine receptors and cytokines with expression profiles that correlate with graft survival. Thus, our results demonstrate a crucial role for NF- Kappa B in acute allograft rejection, identify different molecular mechanisms of rejection by distinct NF- Kappa B family members, and identify a small subset of inducible genes whose inhibition is linked to graft acceptance. DN: Database Name Biological Sciences",
}

@Article{McShane02a_bibuniq_2078,
  author =       "L. M. McShane and M. D. Radmacher and B. Freidlin and R. Yu and M. C. Li and R. Simon",
  title =        "Methods for assessing reproducibility of clustering patterns observed in analyses of microarray data",
  journal =      "Bioinformatics",
  year =         "2002",
  number =       "11",
  volume =       "18",
  pages =        "1462--1469",
  abstract =     "Recent technological advances such as cDNA microarray technology have made it possible to simultaneously interrogate thousands of genes in a biological specimen. A cDNA microarray experiment produces a gene expression 'profile'. Often interest lies in discovering novel subgroupings, or 'clusters', of specimens based on their profiles, for example identification of new tumor taxonomies. Cluster analysis techniques such as hierarchical clustering and self-organizing maps have frequently been used for investigating structure in microarray data. However, clustering algorithms always detect clusters, even on random data, and it is easy to misinterpret the results without some objective measure of the reproducibility of the clusters. DN: Database Name Environmental Sciences and Pollution Mgmt",
}

@Article{Kalelkar02a_bibuniq_2083,
  author =       "S. Kalelkar and E. R. Dow and J. Grimes and M. Clapham and H. Hu",
  title =        "Automated analysis of proton {NMR} spectra from combinatorial rapid parallel synthesis using self-organizing maps.",
  journal =      "Journal of combinatorial chemistry",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "It is now quite routine to acquire proton {NMR} spectra of compounds in 96-well plates prepared in a rapid parallel synthesis fashion using a flow-{NMR} automation setup. However, the analysis of 96 {NMR} spectra obtained in this manner is often laborious and painstakingly slow. We have developed a new, automated method for rapidly analyzing 96 {NMR} spectra of compounds synthesized in an 8 x 12 matrix using self-organizing maps ({SOM}). This unsupervised neural network is capable of clustering together {NMR} spectra containing a common pattern of -R groups and identifying outliers from within such clusters. Analysis of these outlier spectra can quickly help indicate the presence of undesired products, impurities, starting materials, and other unexpected errors in a 96-well plate synthesis by focusing the chemists' attention on the aberrant {NMR} spectra. Thus, {SOM} can be a valuable tool in performing efficient quality control on combinatorial libraries. DN: Database Name MEDLINE",
}

@Article{Valentini02a_bibuniq_2084,
  author =       "G. Valentini",
  title =        "Gene expression data analysis of human lymphoma using support vector machines and output coding ensembles.",
  journal =      "Artificial intelligence in medicine",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "The large amount of data generated by {DNA} microarrays was originally analysed using unsupervised methods, such as clustering or self-organizing maps. Recently supervised methods such as decision trees, dot-product support vector machines (SVM) and multi-layer perceptrons (MLP) have been applied in order to classify normal and tumoural tissues. We propose methods based on non-linear SVM with polynomial and Gaussian kernels, and output coding (OC) ensembles of learning machines to separate normal from malignant tissues, to classify different types of lymphoma and to analyse the role of sets of coordinately expressed genes in carcinogenic processes of lymphoid tissues. Using gene expression data from Lymphochip, a specialised {DNA} microarray developed at Stanford University School of Medicine, we show that SVM can correctly separate normal from tumoural tissues, and OC ensembles can be successfully used to classify different types of lymphoma. Moreover, we identify a group of coordinately expressed genes related to the separation of two distinct subgroups inside diffuse large B-cell lymphoma (DLBCL), validating a previous Alizadeh's hypothesis about the existence of two distinct diseases inside DLBCL. (Copyright 2002 Elsevier Science B. V. )DN: Database Name MEDLINE",
}

@Article{Salamalekis02a_bibuniq_2089,
  author =       "E. Salamalekis and P. Thomopoulos and D. Giannaris and I. Salloum and G. Vasios and A. Prentza and D. Koutsouris",
  title =        "Computerised intrapartum diagnosis of fetal hypoxia based on fetal heart rate monitoring and fetal pulse oximetry recordings utilising wavelet analysis and neural networks.",
  journal =      "Bjog : an international Journal of obstetrics and gynaecolonnaris",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "OBJECTIVE: To develop a computerised system that will assist the early diagnosis of fetal hypoxia and to investigate the relationship between the fetal heart rate variability and the fetal pulse oximetry recordings. DESIGN: Retrospective off-line analysis of cardiotocogram and FSpO2 recordings. SETTING: the Maternity Unit of the 2nd Department of Obstetrics and Gynaecology, Aretaieion Hospital, University of Athens. POPULATION: Sixty-one women of more than 37 weeks of gestation were monitored throughout labour. METHODS: Multiresolution wavelet analysis was applied in each 10-minute period of second stage of labour focussing on long term variability changes in different frequency ranges and statistical analysis was performed in the associated 10-minute FSpO2 recordings. Self-organising map neural network was used to categorise the different 10-minute fetal heart rate patterns and the associated 10-minute FSpO2 recordings. MAin OUTCOME MEASURES: Umbilical artery pH of [Lt] or = 7. 20 and Apgar score at 5 minutes of [Lt] or = 7 formed the inclusion criteria of the risk group. RESULTS: After using k-means clustering algorithm, the two-dimensional output layer of the self-organising map neural network was divided into three distinct clusters. All the cases that mapped in cluster 3 belonged in the risk group except one. the sensitivity of the system was 83. 3% and the specificity 97. 9% for the detection of risk group cases. CONCLUSIONS: A relationship between the fetal heart rate variability in different frequency ranges and the time in which FSpO2 is less than 30% was noticed. Fetal pulse oximetry seems to be an important additional source of information. Computerised analysis of the fetal heart rate monitoring and pulse oximetry recordings is a promising technique in objective intrapartum diagnosis of fetal hypoxia. Further evaluation of this technique is mandatory to evaluate its efficacy and reliability in interpreting fetal heart rate recordings. DN: Database Name MEDLINE",
}

@Article{Kusumoputro02a_bibuniq_2090,
  author =       "B. Kusumoputro and H. Budiarto and W. Jatmiko",
  title =        "Fuzzy-neuro {LVQ} and its comparison with fuzzy algorithm {LVQ} in artificial odor discrimination system.",
  journal =      "ISA Transactions",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "The human sensory test is often used for obtaining the sensory quantities of odors, however, the fluctuation of results due to the expert's condition can cause discrepancies among panelists. Authors have studied the artificial odor discrimination system using a quartz resonator sensor and a back-propagation neural network as the recognition system, however, the unknown category of odor is always recognized as the known category of odor. in this paper, a kind of fuzzy algorithm for learning vector quantization ({LVQ}) is developed and used as a pattern classifier. in this type of fuzzy {LVQ}, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistics of the measurement error directly. During learning, the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated by shifting the central position of the fuzzy reference vector toward or away from the input vector, and by modifying its fuzziness. Two types of fuzziness modifications are used, i. e., a constant modification factor and a variable modification factor. This type of fuzzy-neuro (FN) {LVQ} is different in nature from fuzzy algorithm (FA) {LVQ}, and in this paper, the performance of FN{LVQ} network is compared with that of FA{LVQ} in an artificial odor recognition system. Experimental results show that both FA{LVQ} and FN{LVQ} could provide high recognition probability in determining various known categories of odors, however, the FN{LVQ} neural system has the ability to recognize the unknown category of odor that could not be recognized by the FA{LVQ} neural system. DN: Database Name MEDLINE",
}

@Article{Lee02a_bibuniq_2095,
  author =       "J. A. Lee and M. Verleysen",
  title =        "Self-organizing maps with recursive neighborhood adaptation.",
  journal =      "Neural networks : the official Journal of the International Neural Network Society",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "Self-organizing maps (SOMs) are widely used in several fields of application, from neurobiology to multivariate data analysis. in that context, this paper presents variants of the classic {SOM} algorithm. With respect to the traditional SOM, the modifications regard the core of the algorithm, (the learning rule), but do not alter the two main tasks it performs, i. e. vector quantization combined with topology preservation. After an intuitive justification based on geometrical considerations, three new rules are defined in addition to the original one. They develop interesting properties such as recursive neighborhood adaptation and non-radial neighborhood adaptation. in order to assess the relative performances and speeds of convergence, the four rules are used to train several maps and the results are compared according to several error measures (quantization error and topology preservation criterions). DN: Database Name MEDLINE",
}

@Article{Herrero02a_bibuniq_2104,
  author =       "J. Herrero and J. Dopazo",
  title =        "Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns.",
  journal =      "Journal of proteome researrrero",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "Self-organizing maps ({SOM}) constitute an alternative to classical clustering methods because of its linear run times and superior performance to deal with noisy data. Nevertheless, the clustering obtained with {SOM} is dependent on the relative sizes of the clusters. Here, we show how the combination of {SOM} with hierarchical clustering methods constitutes an excellent tool for exploratory analysis of massive data like {DNA} microarray expression patterns. DN: Database Name MEDLINE",
}

@Article{Moran02a_bibuniq_2105,
  author =       "J. L. Moran and Y. Li and A. A. Hill and W. M. Mounts and C. P. Miller",
  title =        "Gene expression changes during mouse skeletal myoblast differentiation revealed by transcriptional profiling.",
  journal =      "Physiological genomics, 2002 Aug 14, 10(2):103-11. Epounts, WM",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "Studies described here utilize high-density oligonucleotide arrays to characterize changes in global mRNA expression patterns during proliferation, cell cycle withdrawal, and terminal differentiation in mouse C2C12 myoblasts. Statistical analyses revealed 629 sequences differentially regulated between proliferating and differentiating myoblasts. These genes were clustered using self-organizing maps to identify sets of coregulated genes and were assigned to functional categories that were analyzed for distribution across expression clusters. Clusters were identified with statistically significant enrichment of functional categories including muscle contraction, cell adhesion, extracellular matrix function, cellular metabolism, mitochondrial transport, {DNA} replication, cell cycle control, mRNA transcription, and unexpectedly, immune regulation. in addition, functional category enrichment data can be used to predict gene function for numerous differentially regulated expressed sequence tags. the results provide new insight into how genes involved in these cellular processes may play a role in skeletal muscle growth and differentiation. DN: Database Name MEDLINE",
}

@Article{Brustle02a_bibuniq_2107,
  author =       "M. Brustle and B. Beck and T. Schindler and W. King and T. Mitchell and T. Clark",
  title =        "Descriptors, physical properties, and drug-likeness.",
  journal =      "Journal of medicinal chemisttle",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "We have investigated techniques for distinguishing between drugs and nondrugs using a set of molecular descriptors derived from semiempirical molecular orbital (AM1) calculations. the drug data set of 2105 compounds was derived from the World Drug Index (WDI) using a procedure designed to select real drugs. the nondrug data set was the Maybridge database. We have first investigated the dimensionality of physical properties space based on a set of 26 descriptors that we have used successfully to build absorption, distribution, metabolism, and excretion-related quantitative structure-property relationship models. We discuss the general nature of the descriptors for physical property space and the ability of these descriptors to distinguish between drugs and nondrugs. the third most significant principal component of this set of descriptors serves as a useful numerical index of drug-likeness, but no others are able to distinguish between drugs and nondrugs. We have therefore extended our set of descriptors to a total of 66 and have used recursive partitioning to identify the descriptors that can distinguish between drugs and nondrugs. This procedure pointed to two of the descriptors that play an important role in the principal component found above and one more from the set of 40 extra descriptors. These three descriptors were then used to train a {K}ohonen artificial neural net for the entire Maybridge data set. Projecting the drug database onto the map obtained resulted in a clear distinction not only between drugs and nondrugs but also, for instance, between hormones and other drugs. Projection of 42 131 compounds from the WDI onto the {K}ohonen map also revealed pronounced clustering in the regions of the map assigned as druglike. DN: Database Name MEDLINE",
}

@Article{Tomida02a_bibuniq_2108,
  author =       "S. Tomida and T. Hanai and H. Honda and T. Kobayashi",
  title =        "Analysis of expression profile using fuzzy adaptive resonance theory.",
  journal =      "Bioinformatics",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "MOTIVATION: It is well understood that the successful clustering of expression profiles give beneficial ideas to understand the functions of uncharacterized genes. in order to realize such a successful clustering, we investigate a clustering method based on adaptive resonance theory (ART) in this report. RESULTS: We apply Fuzzy ART as a clustering method for analyzing the time series expression data during sporulation of Saccharomyces cerevisiae. the clustering result by Fuzzy ART was compared with those by other clustering methods such as hierarchical clustering, k-means algorithm and self-organizing maps (SOMs). in terms of the mathematical validations, Fuzzy ART achieved the most reasonable clustering. We also verified the robustness of Fuzzy ART using noised data. Furthermore, we defined the correctness ratio of clustering, which is based on genes whose temporal expressions are characterized biologically. Using this definition, it was proved that the clustering ability of Fuzzy ART was superior to other clustering methods such as hierarchical clustering, k-means algorithm and SOMs. Finally, we validate the clustering results by Fuzzy ART in terms of biological functions and evidence. AVAILABILITY: the software is available at http//www. nubio. nagoya-u. ac. jp/proc/index. htmlDN: Database Name MEDLINE",
}

@Article{Christopher02a_bibuniq_2110,
  author =       "K. Christopher and T. F. Mueller and C. Ma and Y. Liang and D. L. Perkins",
  title =        "Analysis of the Innate and Adaptive Phases of Allograft Rejection by Cluster Analysis of Transcriptional Profiles",
  journal =      "Journal of Immunology",
  year =         "2002",
  number =       "1",
  volume =       "169",
  pages =        "522--530",
  abstract =     "Both clinical and experimental observations suggest that allograft rejection is a complex process with multiple components that are, at least partially, functionally redundant. Studies using graft recipients deficient in various genes including chemokines, cytokines, and other immune-associated genes frequently produce a phenotype of delayed, but not indefinitely prevented, rejection. Only a small subset of genetic deletions (for example, TCR alpha or beta, MHC I and II, B7-1 and B7-2, and recombinase-activating gene) permit permanent graft acceptance suggesting that rejection is orchestrated by a complex network of interrelated inflammatory and immune responses. To investigate this complex process, we have used oligonucleotide microarrays to generate quantitative mRNA expression profiles following transplantation. Patterns of gene expression were confirmed with real-time PCR data. Hierarchical clustering algorithms clearly differentiated the early and late phases of rejection. Self-organizing maps identified clusters of coordinately regulated genes. Genes up-regulated during the early phase included genes with prior biological functions associated with ischemia, injury, and Ag-independent innate immunity, whereas genes up-regulated in the late phase were enriched for genes associated with adaptive immunity. DN: Database Name Biological Sciences",
}

@Article{Sultan02a_bibuniq_2111,
  author =       "M. Sultan and D. A. Wigle and C. A. Cumbaa and M. Maziarz and J. Glasgow and M. S. Tsao and I. Jurisica",
  title =        "Binary tree-structured vector quantization approach to clustering and visualizing microarray data",
  journal =      "Bioinformatics",
  year =         "2002",
  number =       "1",
  volume =       "18",
  pages =        "",
  abstract =     "With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into 'meaningful' groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified. DN: Database Name MEDLINE",
}

@Article{Vuckovic02a_bibuniq_2115,
  author =       "A. Vuckovic and V. Radivojevic and A. C. Chen and D. Popovic",
  title =        "Automatic recognition of alertness and drowsiness from {EEG} by an artificial neural network.",
  journal =      "Medical engineering \& physics",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "We present a novel method for classifying alert vs drowsy states from 1 s long sequences of full spectrum {EEG} recordings in an arbitrary subject. This novel method uses time series of interhemispheric and intrahemispheric cross spectral densities of full spectrum {EEG} as the input to an artificial neural network (ANN) with two discrete outputs: drowsy and alert. the experimental data were collected from 17 subjects. Two experts in {EEG} interpretation visually inspected the data and provided the necessary expertise for the training of an ANN. We selected the following three ANNs as potential candidates: (1) the linear network with Widrow-Hoff (WH) algorithm; (2) the non-linear ANN with the Levenberg-Marquardt (LM) rule; and (3) the Learning Vector Quantization ({LVQ}) neural network. We showed that the {LVQ} neural network gives the best classification compared with the linear network that uses WH algorithm (the worst), and the non-linear network trained with the LM rule. Classification properties of {LVQ} were validated using the data recorded in 12 healthy volunteer subjects, yet whose {EEG} recordings have not been used for the training of the ANN. the statistics were used as a measure of potential applicability of the {LVQ}: the t-distribution showed that matching between the human assessment and the network output was 94. 37+/-1. 95%. This result suggests that the automatic recognition algorithm is applicable for distinguishing between alert and drowsy state in recordings that have not been used for the training. DN: Database Name MEDLINE",
}

@Article{Terry02a_bibuniq_2119,
  author =       "A. M. R. Terry and P. K. McGregor",
  title =        "Census and monitoring based on individually identifiable vocalizations: the role of neural networks",
  journal =      "Animal Conservation",
  year =         "2002",
  number =       "2",
  volume =       "5",
  pages =        "103--111",
  abstract =     "Vocal individuality is widely suggested as a method for identifying individuals within a population. But few studies have explored its performance in real or simulated conservation situations. Here we simulated the use of vocal individuality to monitor the calling corncrake (Crex crex), a secretive and endangered land rail. Our data set contained 600 calls from 30 individuals and was used to simulate a population of corncrakes being counted and monitored. We tested three different neural network models for their ability to discriminate between and to identify individuals. Neural networks are non-linear classification tools widely applied to both biological and non-biological identification tasks. Backpropagation and probabilistic neural networks were used to simulate the reidentification of members of a known population (monitoring) and a {K}ohonen network was used to simulate the counting of a population of unknown size (census). We found that both backpropagation and probabilistic networks identified all individuals correctly all the time, irrespective of sample size. {K}ohonen networks were more variable in performance but estimated population size to within one individual of the actual size. Our results indicate that neural networks can be used effectively together with recordings of vocalizations in census and monitoring tasks. DN: Database Name Biological Sciences",
}

@Article{Marengo02a_bibuniq_2124,
  author =       "E. Marengo and M. C. Gennaro and V. Gianotti and S. Angioi and G. Pavese and A. Indaco",
  title =        "Chemometric investigations on environmental data from Carlo Alberto irrigation canal (Alessandria, Piedmont, Italy).",
  journal =      "Annali di chimiF",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "The Carlo Alberto Canal connects Bormida and Tanaro rivers in Piedmont (ITALY). It was created for irrigation purposes but since its waters are suspected to be polluted, a sampling campaign was performed by the ARPA of Alessandria. the physico-chemical parameters analysed along 3 years (1998-2000) were investigated by multivariate chemometric methods. {PCA} showed that the waters situation depends heavily on the sampling period. Also a {K}ohonen self-organising map confirmed the clustering observed, providing insights into the causes of the clusterisation. New samplings are now being performed and a larger set of environmental variables is determined on each sample. DN: Database Name MEDLINE",
}

@Article{Roche02a_bibuniq_2125,
  author =       "O. Roche and G. Trube and J. Zuegge and P. Pflimlin and A. Alanine and G. Schneider",
  title =        "A virtual screening method for prediction of the {HERG} potassium channel liability of compound libraries.",
  journal =      "Chembiochem : a European Journal of chemical biolo",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "A computer-based method has been developed for prediction of the hERG (human ether-a-go-go related gene) K(+)-channel affinity of low molecular weight compounds. hERG channel blockage is a major concern in drug design, as such blocking agents can cause sudden cardiac death. Various techniques were applied to finding appropriate molecular descriptors for modeling structure-activity relationships: substructure analysis, self-organizing maps ({SOM}), principal component analysis ({PCA}), partial least squares fitting (PLS), and supervised neural networks. the most accurate prediction system was based on an artificial neural network. in a validation study, 93 % of the nonblocking agents and 71 % of the hERG channel blockers were correctly classified. This virtual screening method can be used for general compound-library shaping and combinatorial library design. DN: Database Name MEDLINE",
}

@InProceedings{Shillington02a_bibuniq_2128,
  author =       "F. A. Shillington",
  title =        "Selected results from the {ENVIFISH} (1998-2001) Project",
  booktitle =    "Southern African Marine Science Symposium",
  year =         "2002",
  volume =       "",
  pages =        "",
  abstract =     "ENVIFISH* was a three-year European Union funded project with three African partners (Angola, Namibia and South Africa) and five European partners (Italy, Germany, UK, Portugal and Norway) designed to study the environmental conditions and fluctuations in pelagic fish stocks in the Benguela and Angola current systems. the main objective of the ENVIFISH project was to develop appropriate methodologies for improving the sustainable management of small pelagic fish, based on the identification and quantification of key environmental conditions that influence fluctuations in their recruitment and distribution in the Benguela and Angolan systems. in ENVIFISH, a retrospective analysis of fisheries, satellite, Oceanographic and meteorological data covering the period 1982-1999 was carried out. A significant result of the project was the introduction of an artificial neural network - {K}ohonen Self-Organising Map ({SOM}) analysis, which has been applied to a variety of data sets including SST, vertical chlorophyll profiles, altimeter derived sea level, QuikScat winds. Results of the NOAA satellite derived SST climatology over the Benguela System will be presented. Environmental Conditions and Fluctuations of Distribution in Small Pelagic Fish Stocks. CA: Corporate Author Southern African Network for Coastal and Oceanographic Research (SANCOR), (South Africa); Benguela Environment Fisheries Interaction and Training Programme (BENEFIT), (Nambia)DN: Database Name MEDLINE",
}

@Article{Kuruvilla02a_bibuniq_2129,
  author =       "F. G. Kuruvilla and P. J. Park and S. L. Schreiber",
  title =        "Vector algebra in the analysis of genome-wide expression data.",
  journal =      "Genome biology, 2002, 3(3)",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "BACKGROUND: Data from thousands of transcription-profiling experiments in organisms ranging from yeast to humans are now publicly available. How best to analyze these data remains an important challenge. A variety of tools have been used for this purpose, including hierarchical clustering, self-organizing maps and principal components analysis. in particular, concepts from vector algebra have proven useful in the study of genome-wide expression data. RESULTS: Here we present a framework based on vector algebra for the analysis of transcription profiles that is geometrically intuitive and computationally efficient. Concepts in vector algebra such as angles, magnitudes, subspaces, singular value decomposition, bases and projections have natural and powerful interpretations in the analysis of microarray data. Angles in particular offer a rigorous method of defining 'similarity' and are useful in evaluating the claims of a microarray-based study. We present a sample analysis of cells treated with rapamycin, an immunosuppressant whose effects have been extensively studied with microarrays. in addition, the algebraic concept of a basis for a space affords the opportunity to simplify data analysis and uncover a limited number of expression vectors to span the transcriptional range of cell behavior. CONCLUSIONS: This framework represents a compact, powerful and scalable construction for analysis and computation. As the amount of microarray data in the public domain grows, these vector-based methods are relevant in determining statistical significance. These approaches are also well suited to extract biologically meaningful information in the analysis of signaling networks. DN: Database Name AIDS and Cancer Research Abstracts",
}

@Article{Damrauer02a_bibuniq_2132,
  author =       "S. M. Damrauer and R. DeFina and Hongzhen He and K. J. Haley and D. L. Perkins",
  title =        "Molecular profiles of allograft rejection following inhibition of {CD40} ligand costimulation differentiated by cluster analysis",
  journal =      "Journal of Leukocyte Biology",
  year =         "2002",
  number =       "2",
  volume =       "71",
  pages =        "348--358",
  abstract =     "Recent technological advances in biomedical research, such as genome sequences and {DNA} microarrays, have dramatically increased the size of relevant databases. A major challenge is the extraction of a limited number of parameters from these databases that can differentiate and diagnose complex biological states. in a model of cardiac transplantation investigating immunosuppression by inhibition of CD40 ligand costimulation, we have applied a combination of cluster algorithms and self-organizing maps to analyze a panel of 60 candidate genes. Dendrograms generated by cluster analysis distinguished different molecular bases of rejection. Using self-organizing maps, we identified nine genes (CD4, CCR3, CCR5, LT beta, MIP-1 alpha, MIP-2, CD8 alpha, IP-10, and RANTES), each with a unique profile of transcriptional expression, that reproduce the differentiation of states of rejection in dendrograms. Using histology and immunohistochemistry, we correlated differential regulation of CD4 and CD8 at the levels of mRNA and protein. Our strategy of data reduction successfully decreased the number of genes to nine, which are sufficient to differentiate distinct states of rejection in our experimental protocol. DN: Database Name Biological Sciences",
}

@Article{Mahfouf02a_bibuniq_2135,
  author =       "M. Mahfouf and M. F. Abbod and D. A. Linkens",
  title =        "The design of supervisory rule-based control in the operating theatre via an anaesthesia simulator",
  journal =      "Expert Systems",
  year =         "2002",
  number =       "1",
  volume =       "19",
  pages =        "11--20",
  abstract =     "The development of online drug administration strategies in operating theatres represents a highly safety-critical situation. the usefulness of different levels of simulation prior to clinical trials has been shown in previous studies in muscle relaxant anaesthesia. Thus, in earlier work on predictive self-tuning control for muscle relaxation a dual computer real-time simulation was undertaken, subsequent to algorithm validation via off-line simulation. in the present approach a supervised rule-based control algorithm is used. the control software was implemented on the actual machine to be used in theatre, while another computer acted as a real-time patient simulator. This set-up has further advantages of providing accurate timing and also finite data accuracy via the ADC/DAC interface, or the equivalent digital lines. Also, it provides for controller design fast simulation studies compared to the real-time application. in this paper, a new architecture which combines several hierarchical levels for control (a Mamdani-type fuzzy controller), adaptation (self-organizing fuzzy logic control) and performance monitoring (fault detection, isolation and accommodation) is developed and applied to a computer real-time simulation platform for muscle relaxant anaesthesia. Experimental results showed that the proposed algorithm fulfilled successfully the requirements for autonomy, i. e. automatic control, adaptation and supervision, and proved effective in dealing with the faults and disturbances which are normally encountered in operating theatres during surgery. DN: Database Name MEDLINE",
}

@Article{Karakitsos02a_bibuniq_2140,
  author =       "P. Karakitsos and A. Kyroudes and A. Pouliakis and E. B. Stergiou and Z. Voulgaris and C. Kittas",
  title =        "Potential of the learning vector quantizer in the cell classification of endometrial lesions in postmenopausal women.",
  journal =      "Analytical and quantitative cytology and histology",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "OBJECTIVE: To investigate the potential of artificial neural networks for cell identification in endometrial lesions from postmenopausal women. STUDY DESIGN: the study was performed on cytologic material obtained by the Gynoscann endometrial cell samplerfrom 12 cases of atrophic endometrium, 48 cases of hyperplasia without cytologic atypia (18 cases of simple hyperplasia and 30 cases of complex hyperplasia), 12 cases of hyperplasia with cytologic atypia (complex atypical hyperplasia) and 48 cases of adenocarcinoma (30 cases of well-differentiated, 12 cases of moderately differentiated and 6 cases of poorly differentiated carcinoma). From each case approximately 100 cells were examined using a custom image analysis system. A learning vector quantizer ({LVQ}) identified the collected data. RESULTS: Investigation of cells from Endometrial Alterations with {LVQ} proved that according to the nuclear characteristics, as expressed by morphometric and textural measures, the endometrial cells from postmenopausal women may be identified as belonging to one of thefollowing three groups: atrophy, hyperplasia without cytologic atypia (simple and complex hyperplasia) and malignant neoplastic lesions (atypical complex and adenocarcinoma). CONCLUSION: the role of nuclear morphologic features in the cytologic diagnosis of endometrial alterations was confirmed. the overlap in thefeature space observed indicates that cell characteristics do not form strictly separate clusters. Thatfact explains the difficulty that morphologists have with the reproducible identification of cells from endometrial lesions in postmenopausal women. Application of {LVQ} offers a good classification at the cell level and promises to be a powerful toolfor classification on the individual patient level andfor the clarification of the natural history of endometrial pathology. DN: Database Name MEDLINE",
}

@Article{Kim02a_bibuniq_2141,
  author =       "J. H. Kim and I. S. Kohane and L. Ohno Machado",
  title =        "Visualization and evaluation of clusters for exploratory analysis of gene expression data",
  journal =      "Journal of biomedical information",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "Clustering algorithms have been shown to be useful to explore large-scale gene expression profiles. Visualization and objective evaluation of clusters are two important considerations when users are selecting different clustering algorithms, but they are often overlooked. the developments of a framework and software tools that implement comprehensive data visualization and objective measures of cluster quality are crucial. in this paper, we describe a theoretical framework and formalizations for consistently developing clustering algorithms. A new clustering algorithm was developed within the proposed framework. We demonstrate that a theoretically sound principle can be uniformly applied to the developments of cluster-optimization function, comprehensive data-visualization strategy, and objective cluster-evaluation measures as well as actual implementation of the principle. Cluster consistency and quality measures of the algorithm are rigorously evaluated against those of popular clustering algorithms for gene expression data analysis (K-means and self-organizing maps), in four data sets, yielding promising results. DN: Database Name AIDS and Cancer Research Abstracts",
}

@Article{McKee02a_bibuniq_2142,
  author =       "C. M. McKee and R. Defina and H. He and K. J. Haley and J. R. Stone and D. L. Perkins",
  title =        "Prolonged Allograft Survival in {TNF} Receptor 1-Deficient Recipients Is Due to Immunoregulatory Effects, Not to Inhibition of Direct Antigraft Cytotoxicity",
  journal =      "Journal of Immunology",
  year =         "2002",
  number =       "1",
  volume =       "168",
  pages =        "483--489",
  abstract =     "TNF- alpha and lymphotoxin (LT) alpha have been shown to be important mediators of allograft rejection. TNF-R1 is the principal receptor for both molecules. Mice with targeted genetic deletions of TNF-R1 demonstrate normal development of T and B lymphocytes but exhibit functional defects in immune responses. However, the role of TNF-R1-mediated signaling in solid organ transplant rejection has not been defined. To investigate this question, we performed vascularized heterotopic allogeneic cardiac transplants in TNF-R1-deficient (TNF-R1 super(-/-)) and wild-type mice. Because all allografts in our protocol expressed TNF-R1, direct antigraft effects of TNF- alpha and LT alpha were not prevented. However, immunoregulatory effects on recipient inflammatory cells by TNF-R1 engagement was eliminated in TNF-R1 super(-/-) recipients. in our study, cardiac allograft survival was significantly prolonged in TNF-R1 super(-/-) recipients. Despite this prolonged allograft survival, we detected increased levels of CD8 T cell markers in allografts from TNF-R1 super(-/-) recipients, suggesting that effector functions, but not T cell recruitment, were blocked. We also demonstrated the inhibition of multiple chemokines and cytokines in allografts from TNF-R1 super(-/-) recipients including RANTES, IFN-inducible protein-10, lymphotactin, and IL-1R antagonist, as well as altered levels of chemokine receptors. We correlated gene expression with the physiologic process of allograft rejection using self-organizing maps and identified distinct patterns of gene expression in allografts from TNF-R1 super(-/-) recipients. These findings indicate that in our experimental system TNF- alpha and LT alpha exert profound immunoregulatory effects through TNF-R1. DN: Database Name Biological Sciences",
}

@Article{Bednar02a_bibuniq_2146,
  author =       "James A. Bednar and Amol Kelkar and Risto Miikkulainen",
  title =        "Modeling large cortical networks with growing self-organizing maps",
  journal =      "Neurocomputing",
  year =         "2002",
  number =       "44--46",
  volume =       "",
  pages =        "315--321",
  abstract =     "Self-organizing computational models with specific intracortical connections can explain many features of visual cortex. However, due to their computation and memory requirements, it is difficult to use such detailed models to study large-scale object segmentation and recognition. This paper describes GLISSOM, a method for scaling a small RF-LISSOM model network into a larger one during self-organization, dramatically reducing time and memory needs while obtaining equivalent results. With GLISSOM it should be possible to simulate all of human V1 at the single-column level using existing supercomputers. the scaling equations GLISSOM uses also allow comparison of biological maps and parameters between individuals and species with different brain region sizes. DN: Database Name Biological Sciences",
}

@InProceedings{Ishii02a_bibuniq_2148,
  author =       "K. Ishii and S. Nishida and K. Yano and K. Watanabe and T. Ura",
  title =        "A navigation system for an underwater vehicle using the self-organizing map",
  booktitle =    "Proceedings of the International Offshore and Polar Engineering Conference",
  year =         "2002",
  volume =       "",
  pages =        "284--289",
  abstract =     "Underwater vehicles are expected as the attractive tools for the operation in the extreme environment such as the deep ocean survey. in order to realize the useful and practical robots which can work in the ocean, underwater vehicles should take their action by judging the changing condition from their own sensors and actuators, and are desirable to make their behavior with limited efforts of the operators, because of features caused by the working environment. Therefore, the mobile robot should be autonomous and adaptive to their environment. Development of the path planning system which can navigate the vehicle without the collision to the obstacles is one of the most important problems in order to realize the autonomous underwater vehicles. in this paper, Self-Organizing Map ({SOM}) proposed by {K}ohonen are applied to the navigation which takes the distances to the surroundings as inputs, and outputs the direction for the robot to proceed. the efficiency of path planning system is investigated through the simulation and experiments with an underwater vehicle {"}Twin-Burger{"}. DN: Database Name Plant Science",
}

@Article{Moshou02a_bibuniq_2149,
  author =       "D. Moshou and H. Ramon and J. De Baerdemaeker",
  title =        "A weed species spectral detector based on neural networks",
  journal =      "Precision Agriculture, 3(3)"y
?? Expected comma after last field ``journal''.
ear =         {2002},
  number =       {},
  volume =       {},
  pages =        {209--223},
  abstract =     {A new neural network architecture for classification purposes is proposed. the Self-Organizing Map ({SOM}) neural network is used in a supervised way for a classification task. the neurons of the {SOM} become associated with local linear mappings (LLM). Error information obtained during training is used in a novel learning algorithm to train the classifier. the proposed method achieves fast convergence and good generalization. the classification method is then applied in a precision farming application, the classification of crops and different kinds of weeds by using spectral reflectance measurements. the classification performance of the proposed method is proven superior compared to other neural classifiers. Also, the proposed method compares favorably with the results obtained by using an optimal {B}ayesian classifier. DN: Database Name Plant Science}

}



@Article{Yang02a_bibuniq_2150,
  author =       "C. C. Yang and S. O. Prasher and J. Landry and H. S. Ramaswamy",
  title =        "Development of neural networks for weed recognition in corn fields",
  journal =      "Transactions of the American Society of Agricultural Engineers",
  year =         "2002",
  number =       "3",
  volume =       "45",
  pages =        "859--864",
  abstract =     "The main objective of this project was to develop a weed recognition system based on artificial neural networks to assist in the precision application of herbicides in corn fields. Digital images were collected in May 1998 using a commercially available digital camera. the intensities of the three primary colors (red, green, and blue) were compared for each pixel of the images. the three intensities of a pixel remained unchanged when, in the pixel, the green intensity was greater than each of the other two; otherwise, the three intensities of the pixel were set to zero. Background objects, except plants, were thus removed from the images. the resulting pixel intensities of the modified images were used as the inputs for Learning Vector Quantization ({LVQ}) artificial neural networks (ANNs). ANNs were trained to distinguish corn from weeds, as well as to differentiate between weed species. the success rate for a single ANN in distinguishing a given weed species from corn was as high as 90%, and as high as 80% in distinguishing any of four weed species from corn. Better success rates might be obtainable with more elaborate schemes for data input and/or structural improvements such as cascading. the image-processing time for the ANNs was as short as 0. 48 s per image, thus making it useful for real-time data processing and application of herbicides. the development of such ANNs for weed recognition could be useful in precision farming to guide site-specific herbicide application and ultimately reduce the total amount of herbicide applied as well as lowering the risk of pollution. DN: Database Name MEDLINE",
}

@Article{Sturn02a_bibuniq_2152,
  author =       "A. Sturn and J. Quackenbush and Z. Trajanoski",
  title =        "Genesis: cluster analysis of microarray data.",
  journal =      "Bioinformatics (Oxford, Englan A; Quackenbush, J; Trajanoski, Z",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "A versatile, platform independent and easy to use Java suite for large-scale gene expression analysis was developed. Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines. the results of the clustering are transparent across all implemented methods and enable the analysis of the outcome of different algorithms and parameters. Additionally, mapping of gene expression data onto chromosomal sequences was implemented to enhance promoter analysis and investigation of transcriptional control mechanisms. DN: Database Name MEDLINE",
}

@Article{Dougherty02a_bibuniq_2153,
  author =       "E. R. Dougherty and J. Barrera and M. Brun and S. Kim and R. M. Cesar and Y. Chen and M. Bittner and J. M. Trent",
  title =        "Inference from clustering with application to gene-expression microarrays.",
  journal =      "Journal of computational biology : a Journal of computational molecular cell biology",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different underlying classes, whereas those in the same cluster come from the same class. Stochastically, the underlying classes represent different random processes. the inference is that clusters represent a partition of the sample points according to which process they belong. This paper discusses a model-based clustering toolbox that evaluates cluster accuracy. Each random process is modeled as its mean plus independent noise, sample points are generated, the points are clustered, and the clustering error is the number of points clustered incorrectly according to the generating random processes. Various clustering algorithms are evaluated based on process variance and the key issue of the rate at which algorithmic performance improves with increasing numbers of experimental replications. the model means can be selected by hand to test the separability of expected types of biological expression patterns. Alternatively, the model can be seeded by real data to test the expected precision of that output or the extent of improvement in precision that replication could provide. in the latter case, a clustering algorithm is used to form clusters, and the model is seeded with the means and variances of these clusters. Other algorithms are then tested relative to the seeding algorithm. Results are averaged over various seeds. Output includes error tables and graphs, confusion matrices, principal-component plots, and validation measures. Five algorithms are studied in detail: K-means, fuzzy C-means, self-organizing maps, hierarchical Euclidean-distance-based and correlation-based clustering. the toolbox is applied to gene-expression clustering based on cDNA microarrays using real data. Expression profile graphics are generated and error analysis is displayed within the context of these profile graphics. A large amount of generated output is available over the web. DN: Database Name MEDLINE",
}

@Article{Yanikoglu02a_bibuniq_2155,
  author =       "B. Yanikoglu and B. Erman",
  title =        "Minimum energy configurations of the 2-dimensional {HP}-model of proteins by self-organizing networks.",
  journal =      "Journal of computational biology : a Journal of computational molecular cell biology",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "We use self-organizing maps ({SOM}) as an efficient tool to find the minimum energy configurations of the 2-dimensional HP-models of proteins. the usage of the {SOM} for the protein folding problem is similar to that for the Traveling Salesman Problem. the lattice nodes represent the cities whereas the neurons in the network represent the amino acids moving towards the closest cities, subject to the HH interactions. the valid path that maximizes the HH contacts corresponds to the minimum energy configuration of the protein. We report promising results for the cases when the protein completely fills a lattice and discuss the current problems and possible extensions. in all the test sequences up to 36 amino acids, the algorithm was able to find the global minimum and its degeneracies. DN: Database Name MEDLINE",
}

@Article{Mattfeldt02a_bibuniq_2156,
  author =       "T. Mattfeldt and H. Wolter and D. Trijic and H. W. Gottfried and H. A. Kestler",
  title =        "Chromosomal regions in prostatic carcinomas studied by comparative genomic hybridization, hierarchical cluster analysis and self-organizing feature maps.",
  journal =      "Anal Cell Path",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "Comparative genomic hybridization (CGH) is an established genetic method which enables a genome-wide survey of chromosomal imbalances. For each chromosome region, one obtains the information whether there is a loss or gain of genetic material, or whether there is no change at that place. Therefore, large amounts of data quickly accumulate which must be put into a logical order. Cluster analysis can be used to assign individual cases (samples) to different clusters of cases, which are similar and where each cluster may be related to a different tumour biology. Another approach consists in a clustering of chromosomal regions by rewriting the original data matrix, where the cases are written as rows and the chromosomal regions as columns, in a transposed form. in this paper we applied hierarchical cluster analysis as well as two implementations of self-organizing feature maps as classical and neuronal tools for cluster analysis of CGH data from prostatic carcinomas to such transposed data sets. Self-organizing maps are artificial neural networks with the capability to form clusters on the basis of an unsupervised learning rule. We studied a group of 48 cases of incidental carcinomas, a tumour category which has not been evaluated by CGH before. in addition we studied a group of 50 cases of pT2N0-tumours and a group of 20 pT3N0-carcinomas. the results show in all case groups three clusters of chromosomal regions, which are (i) normal or minimally affected by losses and gains, (ii) regions with many losses and few gains and (iii) regions with many gains and few losses. Moreover, for the pT2N0- and pT3N0-groups, it could be shown that the regions 6q, 8p and 13q lay all on the same cluster (associated with losses), and that the regions 9q and 20q belonged to the same cluster (associated with gains). For the incidental cancers such clear correlations could not be demonstrated. DN: Database Name MEDLINE",
}

@Article{Pantazi02a_bibuniq_2158,
  author =       "S. Pantazi and Y. Kagolovsky and J. R. Moehr",
  title =        "Cluster analysis of Wisconsin Breast Cancer dataset using self-organizing maps.",
  journal =      "Studies in health technology and inform",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "This work deals with multidimensional data analysis, precisely cluster analysis applied to a very well known dataset, the Wisconsin Breast Cancer dataset. After the introduction of the topics of the paper the cluster analysis concept is shortly explained and different methods of cluster analysis are compared. Further, the {K}ohonen model of self-organizing maps is briefly described together with an example and with explanations of how the cluster analysis can be performed using the maps. After describing the data set and the methodology used for the analysis we present the findings using textual as well as visual descriptions and conclude that the approach is a useful complement for assessing multidimensional data and that this dataset has been overused for automated decision benchmarking purposes, without a thorough analysis of the data it contains. DN: Database Name MEDLINE",
}

@Article{Marengo02b_bibuniq_2159,
  author =       "E. Marengo and M. Aceto and V. Maurino",
  title =        "Classification of Nebbiolo-based wines from Piedmont ({I}taly) by means of solid-phase microextraction-gas chromatography-mass spectrometry of volatile compounds.",
  journal =      "Journal of chromatography",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "Sixty-eight samples of wines from Piedmont (Italy) were analysed to determine their content of volatile compounds, using the solid-phase microextraction (SPME) technique coupled with gas chromatography-mass spectrometry (GC-MS). Samples were from five groups of wines: Barolo, Barbaresco, Nebbiolo d'Alba, Roero and Langhe Nebbiolo, all produced from the Nebbiolo grape in the Langhe and Roero areas (province of Cuneo, Piedmont) but differing in vintage (respectively, 3 years, 2 years, 1 year, 8 months and few months) and production zone. Thirty-five analytes were identified; peak area data, corrected for internal standard, were used for pattern recognition treatments. Principal components analysis, hierarchical cluster analysis, {K}ohonen self organising map, stepwise linear discriminant analysis and soft independent modelling of class analogy were applied to the data, revealing a good separation between the five groups. A main factor, strictly connected to wine vintage, was identified and found to be related to some analytes.",
}

@Article{Liu02a_bibuniq_2170,
  author =       "Zh Q. Liu",
  title =        "Adaptive subspace self-organizing map and its applications in face recognition.",
  journal =      "International Journal of Image and Graphics. Vol. 2, P. A. ; Garrido Frenich, A. ; Torres, J. A. ; Pulido Bosch, A.",
  year =         "2002",
  number =       "4",
  volume =       "",
  pages =        "519--540",
  abstract =     "Recently {K}ohonen proposed the Adaptive Subspace Self-Organizing Map (ASSOM) for extracting subspace detectors from the input data. in the ASSOM, all subspaces represented by the neurons are constrained to intersect the origin in the feature space. As a result, it cannot compensate for the mean present in the data set. in this paper we propose affined subspaces for constructing a set of linear manifolds. This gives rise to a modified ASSOM known as the Adaptive Manifold Self-Organizing Map (AMSOM). in some cases, AMSOM performs many orders of magnitude better than ASSOM. We apply AMSOM to face recognition. Since some face images may share a manifold due to similarities present in the images, we, use a multi-layer neural network to divide the manifold into sub-areas each of which corresponds to a single class, e. g., a face class for Smith. Our experiment results show that this approach performs better than those obtained using the standard Principal Component Analysis ({PCA}) method. DN: Database Name CSA Technology Research Database",
}

@Article{Singh02a_bibuniq_2171,
  author =       "D. Singh and S. P Singh",
  title =        "Self organization and learning methods in short term electric load forecasting: {A} review",
  journal =      "Electric Power Components and Systems. Vol. 30, . 2, P. A. ; Garrido Frenich, A. ; Torres, J. A. ; Pulido Bosch, A.",
  year =         "2002",
  number =       "10",
  volume =       "",
  pages =        "1075--1089",
  abstract =     "This paper reviews unconventional methods used in short-term load forecasting. the basic theory of these methods and their suitability to short term load forecasting is discussed. Application and the basic formulation strategy adopted for the purpose are also discussed. These methods are classified into supervised and unsupervised learning and self-organizing with optimization categories. Different models of artificial neural network, fuzzy logic, evolutionary programming, simulated annealing, learning machine, and expert system have been dealt within appropriate classification of each. DN: Database Name CSA Technology Research Database",
}

@Article{Ren02a_bibuniq_2173,
  author =       "Hui Ren and Jia Liu Gu and Er Min He and Zhi Yu Zhang",
  title =        "Classification and identification of the rotor rub-impacting chaotic signals with noise",
  journal =      "Journal of Aerospace Power. Vol. 17, Jia Liu; He, Er Min; Zhang, Zhi Yu",
  year =         "2002",
  number =       "4",
  volume =       "",
  pages =        "442--446",
  abstract =     "In this paper, we will try to identify the chaotic signals of a rub-impacting rotor system using learning vector quantization ({LVQ}), which is based on competitive learning and clustering analysis. the theory, analysis, and results of the computation are given. We have mainly studied the situations of the different classification when the response signals are in different ratios of signal to noise. Our research shows that the {LVQ} neural network not only can identify rub-impacting chaotic signals but also work well for signals with noise. So, the network supplies a direct method for classifying such nonlinear signals of the rub-impacting rotor system and the possibility of the practical application for a real rotor system. (Author)AN: Accession Number A03-11702 (AH)DN: Database Name Aerospace \& High Technology Database",
}

@Article{Yang02a_bibuniq_2174,
  author =       "Bo suk Yang and Kwangkeun Kim and Raj B. K. N. Rao",
  title =        "Condition classification of reciprocating compressors using radial basis function neural network",
  journal =      "International Journal of Comadem. Vol. 5, Kwangkeun; Rao, Raj B. K. N.",
  year =         "2002",
  number =       "4",
  volume =       "",
  pages =        "12--20",
  abstract =     "This paper proposes a condition classification algorithm using the radial basis function neural network (RBFN) for the reciprocating compressors of refrigerators. the RBFN is compared to other classification methods, such as the self-organizing feature map (SOFM) and the learning vector quantization ({LVQ}) networks, and its superiority is verified by real examples. the efficient construction method of the RBFN, based on the hidden layer and the spread constant, is investigated. AN: Accession Number A03-35144 (AH)DN: Database Name CSA Technology Research Database",
}

@Article{Oja02a_bibuniq_2175,
  author =       "Erkki Oja",
  title =        "Unsupervised learning in neural computation",
  journal =      "Theoretical Computer Science. Vol. 287, , Kwangkeun; Rao, Raj B. K. N.",
  year =         "2002",
  number =       "1",
  volume =       "",
  pages =        "187--207",
  abstract =     "In this article, we consider unsupervised learning from the point of view of applying neural computation on signal and data analysis problems. the article is an introductory survey, concentrating on the main principles and categories of unsupervised learning. in neural computation, there are two classical categories for unsupervised learning methods and models: first, extensions of principal component analysis and factor analysis, and second, learning vector coding or clustering methods that are based on competitive learning. These are covered in this article. the more recent trend in unsupervised learning is to consider this problem in the framework of probabilistic generative models. If it is possible to build and estimate a model that explains the data in terms of some latent variables, key insights may be obtained into the true nature and structure of the data. This approach is also briefly reviewed. DN: Database Name ASFA 2: Ocean Technology, Policy and Non-Living Resources",
}

@InProceedings{Gold02a_bibuniq_2178,
  author =       "N. Gold and K. Bennett",
  title =        "Hypothesis-based concept assignment in software maintenance",
  booktitle =    "IEE Proceedings: Software. Vol. 149, ett, K",
  year =         "2002",
  volume =       "",
  pages =        "103--111",
  abstract =     "Software maintenance accounts for a significant proportion of the lifetime cost of a software system. Software comprehension is required in many parts of the maintenance process and is one of the most expensive activities. Many tools have been developed to help the maintainer reduce the time and cost of this task, but of the numerous tools and methods available one group has received relatively little attention: those using plausible reasoning to address the concept assignment problem. We present a concept assignment method for COBOL II: hypothesis-based concept assignment (HB-CA). An implementation of a prototype tool is described, and the results from a comprehensive evaluation using commercial COBOL II sources summarised. in particular, we identify areas of a standard maintenance process where such methods would be appropriate, and discuss the potential cost savings that may result. DN: Database Name CSA Technology Research Database",
}

@Article{Huguet02a_bibuniq_2179,
  author =       "S. Huguet and N. Godin and R. Gaertner and L. Salmon and D. Villard",
  title =        "Use of acoustic emission to identify damage modes in glass fibre reinforced polyester",
  journal =      "Composites Science and Technology (UK). Vol. 62, tner, R; Salmon, L; Villard, D",
  year =         "2002",
  number =       "10",
  volume =       "",
  pages =        "1433--1444",
  abstract =     "The purpose of this work is the use of acoustic emission signal parameters to identify and characterize the various damage mechanisms in stressed glass fibre reinforced polymer composite. Data from acoustic emission are used as inputs in a {K}ohonen self-organizing map which automatically separate the acoustic emission signals, enabling a correlation with the failure mode. These results open perspectives for real-time damage recognition in complex composite materials. DN: Database Name BioEngineering Abstracts",
}

@Article{Suganthan02a_bibuniq_2185,
  author =       "P. N Suganthan",
  title =        "Shape indexing using self-organizing maps",
  journal =      "IEEE Transactions on Neural Networks. Vol. 13, la, Markus; Oja, Erkki",
  year =         "2002",
  number =       "4",
  volume =       "",
  pages =        "835--840",
  abstract =     "In this paper, we propose a novel approach to generating topology preserving mapping of structural shapes using the self-organizing maps ({SOM}). the structural information of the geometrical shapes is captured by the relational vectors. These relational attribute vectors are quantised using an SOM. Using this quantization SOM, a histogram is generated for every shape. These histograms are treated as inputs to train another {SOM} which yields a topology preserving mapping of the geometric shapes. By appropriately choosing the relational vectors, it is possible to generate the mapping invariant to some chosen transformations such as rotation, translation, scale, affine, or perspective. Experimental results using trademark objects are presented to demonstrate the performance of the proposed methodology. DN: Database Name CSA Technology Research Database",
}

@Article{Vailaya02a_bibuniq_2187,
  author =       "Aditya Vailaya and Hong Jiang Zhang and Changjiang Yang and Feng I. Liu and Anil K. Jain",
  title =        "Automatic image orientation detection",
  journal =      "IEEE Transactions on Image Processing. Vol. 11, Hong Jiang; Yang, Changjiang; Liu, Feng I; Jain, Anil K",
  year =         "2002",
  number =       "7",
  volume =       "",
  pages =        "746--755",
  abstract =     "We present an algorithm for automatic image orientation estimation using a {B}ayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer ({LVQ}) can be used to estimate the class-conditional densities of the observed features needed for the {B}ayesian methodology. We further show how principal component analysis ({PCA}) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. the proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques. DN: Database Name CSA Technology Research Database",
}

@Article{Ren02b_bibuniq_2189,
  author =       "H. Ren and J. L. Gu and E. M. He and Z. Y Zhang",
  title =        "Classification and identification of rotor rub impacting chaotic signals with noise.",
  journal =      "Hangkong Dongli Xuebao/Journal of Aerospace Power. Vol. 17",
  year =         "2002",
  number =       "4",
  volume =       "",
  pages =        "442--446",
  abstract =     "We try to identify the chaotic signals of the rub-impacting rotor system using learning vector quantization ({LVQ}) which is based on competitive learning and clustering analysis. the theory analysis and computation results are given. We have mainly studied the situations of different classification when the response signals are in different ratios of signal to noise. Our research shows that the {LVQ} neural network can not only identify rub-impacting chaotic signals but also work well for the signals with noise. So the network supplies a direct method for classifying such nonlinear signals of the rub-impacting rotor system and a possibility of practical application for a real rotor system. DN: Database Name CSA Technology Research Database",
}

@Article{Amini02a_bibuniq_2190,
  author =       "J. Amini and M. R. Seradjian",
  title =        "Kohonen self organizing for automatic identification of cartographic objects.",
  journal =      "International Journal of Engineering Transaction B: Applications. Vol. 15, g; Liu, Feng I; Jain, Anil K",
  year =         "2002",
  pages =        "109--116",
  abstract =     "Automatic identification and localization of cartographic objects in aerial and satellite images have gained increasing attention in recent years in digital photogrammetry and remote sensing. Although the automatic extraction of man made objects in essence is still an unresolved issue, the man made objects can be extracted from aerial photos and satellite images. Recently, the high-resolution satellite images, typically at most 3 meters in panchromatic band ground sample distance (GSD) and up to four multispectral bands in the visible and near infrared spectrum, are suitable for detection and identification of objects. This paper presents a new algorithm for identification of cartographic objects based on Artificial Neural Network (ANN). the algorithm is divided in two modules: image simplification by the Wavelet transform, Mathematical Morphology (MM) operators, and identification of object by the {K}ohonen Self Organizing Map (KSOM) and split and merge method. the study area included two parts of an orthoimage from Kish, Iran. DN: Database Name CSA Technology Research Database",
}

@InProceedings{Brusilovsky02a_bibuniq_2191,
  author =       "Peter Brusilovsky and Riccardo Rizzo",
  title =        "Map-based horizontal navigation in educational hypertext",
  booktitle =    "Hypertext 2002: Proceedings of the Thirteenth ACM Conference on Hypertext and Hypermedia; College Park, MD; United States; 11-15 June 2002.",
  year =         "2002",
  volume =       "",
  pages =        "1--10",
  abstract =     "This paper discusses the problem of horizontal (non-hierarchical) navigation in modern educational courseware. We will look at why horizontal links disappear, how to support horizontal navigation in modern hyper-courseware, and our earlier attempts to provide horizontal navigation in Web-based electronic textbooks. Here, we present map-based navigation - a new approach to support horizontal navigation in open corpus educational courseware that we are currently investigating. We will describe the mechanism behind this approach, present a system KnowledgeSea that implements this approach, and provide some results of a classroom study of this system. DN: Database Name CSA Technology Research Database",
}

@Article{Zadeh02a_bibuniq_2193,
  author =       "Ali N. Zadeh and Bijan Jabbari and Raymond Pickholtz and Branimir Vojcic",
  title =        "Self-organizing packet radio ad hoc networks with overlay ({SOPRANO})",
  journal =      "IEEE Communications Magazine. Vol. 40, abbari, Bijan; Pickholtz, Raymond; Vojcic, Branimir",
  year =         "2002",
  number =       "6",
  volume =       "",
  pages =        "149--157",
  abstract =     "The SOPRANO project involves a novel adaptive and scalable wireless network architecture utilizing a mixture of cellular and multihop packet radio system topologies with the potential to support a variety of applications including high-data-rate Internet and multimedia traffic at a reasonable degree of implementation complexity. This article discusses the potential benefits of this structure and addresses several relevant issues necessary to support such a network. More specifically, it focuses on connection establishment and self-organization, investigates the formulation of an optimum transmission strategy, and examines some of the techniques by which we can augment the capacity or enhance the system performance in this multihop network. We also present capacity bounds that illustrate how these techniques help in trading off conserved power for a multifold capacity advantage. DN: Database Name Aerospace \& High Technology Database",
}

@Article{Hayashida02a_bibuniq_2200,
  author =       "N. Hayashida and H. Takagi",
  title =        "Acceleration of {EC} convergence with landscape visualization and human intervention",
  journal =      "Applied Soft Computing. Vol. 1, a, N; Takagi, H",
  year =         "2002",
  number =       "4",
  volume =       "",
  pages =        "245--256",
  abstract =     "We propose Visualized EC/IEC as an evolutionary computation (EC) and interactive EC (IEC) with visualizing individuals in a multi-dimensional searching space in a 2-D space. This visualization helps us envision the landscape of an n-D searching space, so that it is easier for us to join an EC search by indicating the possible global optimum estimated in the 2-D mapped space. We first compare four mapping methods from the points of view of computational time, convergence speed, and visual easiness to grasp whole EC landscape with five benchmark functions and 28 subjects. Then, we choose self- organizing maps for the projection of individuals onto a 2-D space and experimentally evaluate the effect of visualization using a benchmark function. the experimental result shows that the convergence speed of GA with human search on the Visualized space is at least five times faster than a conventional GA. DN: Database Name CSA Technology Research Database",
}

@Article{Tino02a_bibuniq_2202,
  author =       "Peter Tino and Ian Nabney",
  title =        "Hierarchical {GTM}: Constructing localized nonlinear projection manifolds in a principled way",
  journal =      "IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 24, 0, tury, M",
  year =         "2002",
  number =       "5",
  volume =       "",
  pages =        "639--656",
  abstract =     "It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets and, therefore, a hierarchical visualization system is desirable. in this paper, we extend an existing locally linear hierarchical visualization system PhiVis [1] in several directions: 1) We allow for nonlinear projection manifolds. the basic building block is the Generative Topographic Mapping (GTM). 2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. 3) Using tools from differential geometry, we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the ancestor visualization plots which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 18-dimensional data sets. DN: Database Name CSA Technology Research Database",
}

@InProceedings{Oh02a_bibuniq_2204,
  author =       "S. K. Oh and W. Pedrycz and H. S Park",
  title =        "Self-organising networks in modelling experimental data in software engineering",
  booktitle =    "IEE Proceedings: Computers and Digital Techniques. Vol. 149, lications and Reviews. Vol. 32, apan; 26-31 May 2002. States; 11-15 June 2002.",
  year =         "2002",
  volume =       "",
  pages =        "61--78",
  abstract =     "Experimental software datasets describing software projects in terms of their complexity and development time have been the subject of intensive modelling. A. number of various modelling methodologies and detailed modelling designs have been proposed including neural networks and fuzzy models. the authors introduce self-organising networks (SON) that result from a synergy of fuzzy inference schemes and polynomial neural networks (PNNs). the latter has included an efficient scheme of selecting input variables of the model being realised on a basis of a group method of data handling (GMDH) algorithm. the authors discuss a detailed architecture of the S. O. N. and propose a comprehensive learning algorithm. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the S. O. N. are not predetermined (as is the case in a popular topology of a multilayer perceptron). the experimental results include well-known software data such as the one describing software modules of the medical imaging system (MIS) and the NASA data set concerning software cost estimation. the experimental results reveal that the proposed model exhibits high accuracy. DN: Database Name C. S. A. Technology Research Database",
}

@Article{Arakawa02a_bibuniq_2206,
  author =       "M. Arakawa and H. Nakayama and H. Ishikawa",
  title =        "Optimum design using radial basis function network and adaptive range genetic algorithms. {III} - Usage of data generation by using self-organizing maps",
  journal =      "Transactions of the Japan Society of Mechanical Engineers. C. Vol. 68, and Reviews. Vol. 32, apan; 26-31 May 2002. States; 11-15 June 2002.",
  year =         "2002",
  pages =        "184--191",
  abstract =     "Genetic algorithms (GAs) have been studied widely to report their effectiveness in a variety of fields. As optimizers, they give us much privilege in considering types of design variables for multi-peaked problems. However, there exist some shortcomings, such as the treatment of continuous variables, the formulation of the fitness function, and the number of function calls. in previous studies, we have been developed Adaptive Range GAs for the continuous variable problem by using RBF (Radial Basis Function) networks as approximation tools in solving large-scale constraint optimization problems. From these results, we have shown the effectiveness in obtaining good results. However, it is very difficult to give data within a feasible region only by using random numbers for the RBF, and almost 80 percent of them are consumed in vain. in order to raise their ratio, we introduce a {SOM} (Self Organizing Map) as a classification tool to choose data within a feasible region. in this study, we show the effectiveness of the proposed method based upon a famous benchmark test problem. AN: Accession Number A03-27244 (AH)DN: Database Name CSA Technology Research Database",
}

@Article{Hosokawa02a_bibuniq_2207,
  author =       "M. Hosokawa and Sh. Zama and T. Hoshi",
  title =        "Supervised landform classification method using neural network and its application to estimation of seismic ground motion.",
  journal =      "Journal of Structural and Construction Engineering, i, T.",
  year =         "2002",
  number =       "555",
  volume =       "",
  pages =        "69--76",
  abstract =     "This paper presents a supervised classification method using a neural network to classify typical landforms based on a land cover map and a Digital Elevation Model (DEM). the proposed method classified the landform of Kobe city in Japan into hill, plateau, fan and reclaimed land. As a result, a Self-Organizing Map ({SOM}) produces the higher classification accuracy than Back Propagation method. Furthermore, we adopted these classified landforms for a ground motion estimation in Kobe during the 1995 Hyogoken Nanbu earthquake, and could obtain detailed ground motion distribution compared with the one based on the Digital National Land Information (DNLI). DN: Database Name Paperbase/Pira Collection",
}

@Article{Lansiluoto02a_bibuniq_2208,
  author =       "A. Lansiluoto and B. Back and H. Vanharanta and A. Visa",
  title =        "An analysis of economic trends in the pulp and paper sector using self-organizing maps",
  journal =      "Pap. Puu, cÔ",
  year =         "2002",
  number =       "4",
  volume =       "84",
  pages =        "",
  abstract =     "Encompassing the period 1990-2000, this study aimed to compare whether there were differences between the trends of the pulp and paper sector in eight countries: Austria, Canada, Finland, France, Germany, Japan, Sweden and the US. Seventeen variables were used; six relating to costs and prices, eight relating to production and productivity and three other variables (output to input ratio both capital to work input ratio and annual working time). the Self-Organising Maps ({SOM}) techniques was used which enables analysis of many variables simultaneously. Results indicated that economic fluctuations have at least slight international origin. It was also noticed that all countries have primarily moved towards the same upward direction. It was found that the shorter the distance between countries, the more similarities in trends. the findings of this study generally indicate that countries' economic situation should be considered before operating on new continents. (2 fig, 1 tab, 36 ref)DN: Database Name CSA Technology Research Database",
}

@Article{Huang02a_bibuniq_2209,
  author =       "Hsin Yuan Huang and Anu Khendry and Thomas G. Robertazzi",
  title =        "Layernet: {A} self-organizing protocol for small ad hoc networks",
  journal =      "IEEE Transactions on Aerospace and Electronic Systems. Vol. 38, zi, Thomas G",
  year =         "2002",
  number =       "2",
  volume =       "",
  pages =        "378--387",
  abstract =     "A self-organizing protocol is proposed for small ad hoc networks. Among this protocol's original features is the initial creation of an asynchronous sparse tree topology followed by a transition to a more fully connected network and synchronous scheduling. An efficient address bundling technique and unique reliability simulation results for this protocol are also presented. DN: Database Name CSA Technology Research Database",
}

@Article{Huang02b_bibuniq_2211,
  author =       "Jiansheng Huang and Michael Negnevitsky and D. Thong Nguyen",
  title =        "A neural-fuzzy classifier for recognition of power quality disturbances",
  journal =      "IEEE Transactions on Power Delivery. Vol. 17, evitsky, Michael; Nguyen, D Thong",
  year =         "2002",
  number =       "2",
  volume =       "",
  pages =        "609--616",
  abstract =     "This paper presents a neural-fuzzy technology-based classifier for the recognition of power quality disturbances. the classifier adopts neural networks in the architecture of frequency sensitive competitive leaning and learning vector quantization ({LVQ}). With given size of codewords, the neural networks are trained to determine the optimal decision boundaries separating different categories of disturbances. To Cope with the uncertainties in the involved pattern recognition, the neural network outputs, instead of being taken as the final classification, are used to activate the fuzzy-associative-memory (FAM) recalling for identifying the most possible type that the input waveform may belong to. Furthermore, the input waveforms are preprocessed by the wavelet transform for feature extraction so as to improve the classifier with respect to recognition accuracy and scheme simplicity. Each subband of the transform coefficients is then utilized to recognize the associated disturbances. DN: Database Name CSA Technology Research Database",
}

@Article{Botros02a_bibuniq_2214,
  author =       "K. K. Botros and G. Kibrya and A. Glover",
  title =        "A demonstration of artificial neural-networks-based data mining for gas-turbine-driven compressor stations.",
  journal =      "Journal of Engineering for Gas Turbines and Power (Transactions of the Asme). Vol. 124, 32, apan; 26-31 May 2002. States; 11-15 June 2002.",
  year =         "2002",
  number =       "2",
  volume =       "",
  pages =        "284--297",
  abstract =     "This paper presents a successful demonstration of application of neural networks to perform various data mining functions on an RB211 gas-turbine-driven compressor station. Radial basis function networks were optimized and were capable of performing the following functions: backup of critical parameters; detection of sensor faults; prediction of complete engine operating health with few variables; and estimation of parameters that cannot be measured. A {K}ohonen {SOM} technique has also been applied to recognize the correctness and validity of any data once the network is trained on a good set of data. This was achieved by examining the activation levels of the winning unit on the output layer of the network. Additionally, it would also be possible to determine the suspicious, faulty or corrupted parameter(s) in the cases which are not recognized by the network by simply examining the activation levels of the input neurons. DN: Database Name Paperbase/Pira Collection",
}

@Article{Avcibas02a_bibuniq_2216,
  author =       "I. Avcibas and B. Sankur and K. Sayood",
  title =        "Statistical Evaluation of Image Quality Measures",
  journal =      "J. Electron. Imaging",
  year =         "2002",
  number =       "2",
  volume =       "11",
  pages =        "",
  abstract =     "Image quality measures are categorised, measures defined for grey-scale images are extended to the multispectral case, and novel image quality measures are proposed and categorized into pixel difference-based, correlation-based, edge-based, spectral-based, context-based, and human visual system (HVS)-based measures. These measures are compared statistically for still-image compression applications. the statistical behaviour of the measures and their sensitivity to coding artefacts are investigated through analysis of variance techniques. Their similarities or differences are illustrated by plotting their {K}ohonen maps. Measures are identified that give consistent scores across an image class, and that are sensitive to coding artefacts. Measures based on the phase spectrum, the multiresolution distance or the HVS filtered mean square error are computationally simple, and are more responsive to coding. 59 refs. DN: Database Name CSA Technology Research Database",
}

@Article{Chen02a_bibuniq_2218,
  author =       "J. Chen and I. Hagiwara and X. Su and Q. Shi",
  title =        "A bispectrum feature extraction enhanced structure damage detection approach.",
  journal =      "Jsme International Journal, Series C: Mechanical Systems Machine Elements \& Manufacturing. Vol. 45, May 2002. States; 11-15 June 2002.",
  year =         "2002",
  number =       "1",
  volume =       "",
  pages =        "121--126",
  abstract =     "The subject of structure defect diagnosis has been extensively investigated in the field of nondestructive testing (NDT). in this paper, a new approach for detecting structure damage is proposed, which is based on the combination of the bispectrum feature extraction method and the learning vector quantization ({LVQ}) identification method. Because bispectrum analysis possesses the capability of restraining Gaussian noise, it may be employed to enhance the performance of the feature extraction method. in simulation, by using the proposed method, it has been shown that very high accuracy of structure damage identification can be obtained compared with the modal assurance criterion (MAC) formed from the modal parameters method, especially in the case of low signal-to-noise ratio environment. DN: Database Name CSA Technology Research Database",
}

@Article{Wang02a_bibuniq_2219,
  author =       "Y. Wang and Y. Xing and X. Ruan",
  title =        "Gate type selection based on fuzzy mapping.",
  journal =      "Chinese Journal of Mechanical Engineering (English Edition). Vol. 15, nts \& Manufacturing. Vol. 45, May 2002. States; 11-15 June 2002.",
  year =         "2002",
  number =       "1",
  volume =       "",
  pages =        "31--34",
  abstract =     "Gate type selection is very important for mould design. Improper gate type may lead to poor product quality and low production efficiency. Although numerical simulation approach could be used to optimize gate location, the determination of gate type is still up to designers' experience. A novel method for selecting gate type based on fuzzy logic is proposed. the proposed methodology follows three steps: Design requirements for gate is extracted and generalized; Possible gate types (design schemes) are presented; the fuzzy mapping relationship between gate design requirements and gate design scheme is established based on fuzzy composition and fuzzy relation transition matrices that are assigned by domain experts. DN: Database Name Aerospace \& High Technology Database",
}

@InProceedings{Penn02a_bibuniq_2220,
  author =       "B. S Penn",
  title =        "Using self-organizing maps for anomaly detection in hyperspectral imagery",
  booktitle =    "2002 {IEEE} Aerospace Conference Proceedings - Volume 3, Big Sky, MT; United States; 9-16 Mar. 2002. 45, May 2002. States; 11-15 June 2002.",
  year =         "2002",
  volume =       "",
  pages =        "3--1531",
  abstract =     "Hyperspectral imagery datasets contain tremendous amounts of information. Unfortunately, due to the homogeneity of the Earth's surface many pixels in such imagery function as background and serve to obscure or hide a desired target. in many cases, the primary reason for collecting the hyperspectral imagery is to find a pixel or two representing statistical scene anomalies. Few commercial applications focus on this essential aspect of hyperspectral image processing. More often, the primary focus of commercial image processing packages is the more abundant pixels to the exclusion of the few anomalous pixels. Toward this end we have developed a system based on Serf-Organizing Maps ({SOM}) that cluster the data then evaluates the relationship of the data to the cluster centers. Those pixels located farthest from the cluster centers are found on the outer surface of the convex hull enclosing the hyperspectral dataset. in addition, these outlying pixels represent anomalies within the dataset and their location in proximity to an individual cluster center may merely be serendipitous. This procedure for locating anomalous pixel is demonstrated in a 1998 AVIRIS scene of the Copper Flat porphyry copper deposit. the mineral gypsum was previously identified during ground studies and is found only in proximity to a pond in the center of the Copper Flat Mine and along a streambed south of the mine. Using the anomaly detector we were able to pinpoint the location of the gypsum. This approach is applicable to other domains besides geology and mineral exploration. (Author)AN: Accession Number A03-11059 (AH)DN: Database Name BioEngineering Abstracts",
}

@Article{Skupin02a_bibuniq_2225,
  author =       "Andre Skupin",
  title =        "A cartographic approach to visualizing conference abstracts",
  journal =      "Pollution Engineering. Vol. 34, Andre",
  year =         "2002",
  number =       "2",
  volume =       "",
  pages =        "50--58",
  abstract =     "The map metaphor has become popular in recent works on information visualization. However, as one attempt to visualize information spaces in a low-dimensional display space, crucial impediments to the metaphor's usefulness appear. A cartographic approach to mapping nongeographic information which helps to manage graphic complexity in visualizations is presented. It aids domain comprehension by forcing to use the same cognitive skills when viewing geographic maps. DN: Database Name Aerospace \& High Technology Database",
}

@Article{Wu02b_bibuniq_2226,
  author =       "H. Wu and Y. Liu and Y. Ding and X. Zhang",
  title =        "Application study of {SOM} artificial neural net in airliner fault diagnosis (self-organizing map)",
  journal =      "Journal of Nanjing University of Aeronautics \& Astronautics. Vol. 34, ol. 32, haralampos P",
  year =         "2002",
  number =       "1",
  volume =       "",
  pages =        "31--34",
  abstract =     "It is unavoidable for a modern airliner to malfunction in its routine use, and fault diagnosis is a very important guarantee for its flight safety. the general fault diagnosis method for an airliner is to combine the probabilistic statistical method with expertise. It shows that the trained {K}ohonen self-organizing map ({SOM}) artificial neural net reflects the probability density of the input samples through its output without need of the knowledge of the prior probability of the input samples, and it can be applied in function approximation as well. the {SOM} is used to calculate the probability of the airliner fault occurrence, and in associating the probability with the expertise. the technical feasibility of the method is proved by an actual fault diagnosis. (Author)AN: Accession Number A02-30355 (AH)DN: Database Name Aerospace \& High Technology Database",
}

@Article{Bahadori02a_bibuniq_2227,
  author =       "M. Y. Bahadori and U. Hegde and A. L Laganelli",
  title =        "Robust intelligent systems for feature extraction",
  journal =      "Aiaa Aerospace Sciences Meeting \& Exhibit, 40th, Reno, NV; United States; 14-17 Jan. 20s P",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "The application of intelligent systems for feature extraction in different experimental data sets is explored. Various techniques have been utilized for obtaining characteristic patterns. These patterns are generated using different approaches such as support and confidence levels, clustering, self-organizing maps, neural networks, and collaborative filtering. in these approaches, frequent item sets are identified, where in many instances, significant association rules and unique patterns are uncovered. the paper presents some examples of the application of these feature extraction methods to a number of problems in different fields. (Author)RP: Report Number AIAA Paper 2002-1070AN: Accession Number A02-14275 (AH)DN: Database Name Aerospace \& High Technology Database",
}

@Article{Obayashi02a_bibuniq_2228,
  author =       "S. Obayashi and D. Sasaki",
  title =        "Self-organizing map of Pareto solutions obtained from multiobjective supersonic wing design",
  journal =      "Aiaa Aerospace Sciences Meeting \& Exhibit, 40th, Reno, NV; United States; 14-17 Jan. 20s P",
  year =         "2002",
  number =       "",
  volume =       "",
  pages =        "",
  abstract =     "A Self-Organizing Map ({SOM}) has been applied to analyze 766 Pareto solutions obtained from the four-objective aerodynamic optimization of supersonic wings using Evolutionary Algorithms. Three-dimensional Pareto front (tradeoff surface) is mapped onto the 2{D} {SOM} where global tradeoffs are successfully visualized. Furthermore, from the clusters obtained in the SOM, the design variables are mapped onto another SOM. This leads to clusters of design variables which indicate the relative importance of design variables and their interactions. {SOM} is confirmed to be a versatile datamining tool for aeronautical engineering. (Author)RP: Report Number AIAA Paper 2002-0991AN: Accession Number 200204-11-0839 (MT); A02-14130 (AH)DN: Database Name BioEngineering Abstracts",
}

@InProceedings{Lim02a_bibuniq_2235,
  author =       "S. Lim and N. M. Nasrabadi and R. M Mersereau",
  title =        "Boosting algorithm for {LVQ} {ATR} classifiers",
  booktitle =    "Spie Proceedings Series, m, S; Nasrabadi, N M; Mersereau, R M",
  year =         "2002",
  volume =       "",
  pages =        "193--202",
  abstract =     "Boosting has emerged as a popular combination technique to refine weak classifiers. Pioneered by Freund and Schapire, numerous variations of the AdaBoost algorithm have emerged, such as Breiman's arc-fs algorithms. the central theme of these methods is the generation of an ensemble of a weak learning algorithm using modified versions of the original training set, with emphasis placed on the more difficult instances. the validation stage then aggregates results from each element of the ensemble using some predetermined rule. in this paper the wavelet decomposition based codebook classifier proposed by Chan et al. is used as the learning algorithm. Starting with the whole training set, modifications to the training set are made at each iteration by re-sampling the original training data set with replacement. the weights used in the re-sampling are determined using different algorithms, including AdaBoost and arc-fs. the accuracies of the ensembles generated are then determined using various combination techniques such as simple voting and weighted sums. Boosting improves upon the two classifier methods (K-means and {LVQ}) by exploiting their inherent codebook nature. (Author)AN: Accession Number A03-16726 (AH); 200309-81-1184 (CI); 200309-34-0126 (EA)DN: Database Name CSA Technology Research Database",
}

@Article{Endo02a_bibuniq_2237,
  author =       "Masahiro Endo and Masahiro Ueno and Takaya Tanabe",
  title =        "A clustering method using hierarchical self-organizing maps",
  journal =      "Journal of {VLSI} Signal Processing Systems for Signal, Image, and Video Technology. Vol. 32, Alexandre",
  year =         "2002",
  number =       "1",
  volume =       "",
  pages =        "105--118",
  abstract =     "We describe a method of clustering that uses self-organizing maps (SOMs) in a method of image classification. To ensure that this clustering method is fast, we defined a hierarchical {SOM} and used it to construct the clustering method (M. Endo, M. Ueno, T. Tanabe, and M. Yamamoto, in Proc. of the {IEEE} Int. Workshop on Neural Networks for Signal Processing X, 2000, pp. 261-270). We define the clustering method in detail and outline its behavior as determined on the basis of both theory and experiment. We also propose a cooperative learning algorithm for the hierarchical SOM. Experiments on artificial image data confirmed the basic performance and adaptability of the {SOM} in clustering images. We also confirmed, both experimentally and theoretically, that our method is faster SOM, for the objects used in these experiments, than a method based on a non-hierarchical SOM. DN: Database Name CSA Technology Research Database",
}

@Article{Van02a_bibuniq_2238,
  author =       "Marc M. Van Hulle and Temujin Gautama",
  title =        "Monitoring the formation of kernel-based topographic maps with application to hierarchical clustering of music signals",
  journal =      "Journal of {VLSI} Signal Processing Systems for Signal, Image, and Video Technology. Vol. 32",
  year =         "2002",
  number =       "1",
  volume =       "",
  pages =        "119--134",
  abstract =     "When using topographic maps for clustering purposes, which is now being considered in the data mining community, it is crucial that the maps are free of topological defects. Otherwise, a contiguous cluster could become split into separate clusters. We introduce a new algorithm for monitoring the degree of topology preservation of kernel-based maps during learning. the algorithm is applied to a real-world example concerned with the identification of 3 musical instruments and the notes played by them, in an unsupervised manner, by means of a hierarchical clustering analysis, starting from the music signal's spectrogram. DN: Database Name CSA Technology Research Database",
}

@Article{Liu02a_bibuniq_2240,
  author =       "Xiaohui Liu and Gongxian Cheng and John X. Wu",
  title =        "Analyzing outliers cautiously",
  journal =      "IEEE Transactions on Knowledge and Data Engineering. Vol. 14, n X",
  year =         "2002",
  number =       "2",
  volume =       "",
  pages =        "432--437",
  abstract =     "Outliers are difficult to handle because some of them can be measurement errors, while others may represent phenomena of interest, something {"}significant{"} from the viewpoint of the application domain. Statistical and computational methods have been proposed to detect outliers, but further analysis of outliers requires much relevant domain knowledge. in our previous work. we suggested a knowledge-based method for distinguishing between the measurement errors and phenomena of interest by modeling {"}real measurements{"} - how measurements should be distributed in an application domain, in this paper, we make this distinction by modeling measurement errors instead. This is a cautious approach to outlier analysis, which has been successfully applied to a medical problem and may find interesting applications in other domains such as science, engineering, finance, and economics. DN: Database Name CSA Technology Research Database",
}

@Article{Atsalakis02a_bibuniq_2241,
  author =       "A. Atsalakis and N. Papamarkos and I. Andreadis",
  title =        "On estimation of the number of image principal colors and color reduction through self-organized neural networks",
  journal =      "International Journal of Imaging Systems and Technology. Vol. 12, atelopoulos, S; Zakopoulos, N; Moulopoulos, S",
  year =         "2002",
  number =       "3",
  volume =       "",
  pages =        "117--127",
  abstract =     "A new technique suitable for reduction of the number of colors in a color image is presented in this article. It is based on the use of the image Principal Color Components (PCC), which consist of the image color components and additional image components extracted with the use of proper spatial features. the additional spatial features are used to enhance the quality of the final image. First, the principal colors of the image and the principal colors of each PCC are extracted. Three algorithms were developed and tested for this purpose. Using {K}ohonen self-organizing feature maps (SOFM) as classifiers, the principal color components of each PCC are obtained and a look-up table, containing the principal colors of the PCC, is constructed. the final colors are extracted from the look-up table entries through a SOFM by setting the number of output neurons equal to the number of the principal colors obtained for the original image. To speed up the entire algorithm and reduce memory requirements, a fractal scanning subsampling technique is employed. the method is independent of the color scheme; it is applicable to any type of color images and can be easily modified to accommodate any type of spatial features. Several experimental and comparative results exhibiting the performance of the proposed technique are presented. DN: Database Name CSA Technology Research Database",
}

@InProceedings{Low02a_bibuniq_2242,
  author =       "Kian Hsiang Low and Wee Kheng Leow and Ang Jr. and Marcelo H.",
  title =        "Integrated planning and control of mobile robot with self-organizing neural network",
  booktitle =    "Proceedings - {IEEE} International Conference on Robotics and Automation. Vol. 4, ; Zakopoulos, N; Moulopoulos, S",
  year =         "2002",
  volume =       "",
  pages =        "3870--3875",
  abstract =     "Despite the many significant advances made in robotics research, few works have focused on the tight integration of task planning and motion control. Most integration works involve the task planner providing discrete commands to the low-level controller, which performs kinematics and control computations to command the motor and joint actuators. This paper presents a framework of the integrated planning and control for mobile robot navigation. Unlike existing integrated approaches, it produces a sequence of checkpoints instead of a complete path at the planning level. At the motion control level, a neural network is trained to perform motor control that moves the robot from one checkpoint to the next. This method allows for a tight integration between high-level planning and low-level control, which permits real-time performance and easy modification of motion path while the robot is enroute to the goal position. DN: Database Name CSA Technology Research Database",
}

@InProceedings{Amit02a_bibuniq_2243,
  author =       "R. Amit and Maja J. Mataric",
  title =        "Parametric primitives for motor representation and control",
  booktitle =    "Proceedings - {IEEE} International Conference on Robotics and Automation. Vol. 1, ; Zakopoulos, N; Moulopoulos, S",
  year =         "2002",
  volume =       "",
  pages =        "863--868",
  abstract =     "The use of motor primitives for the generation of complex movements is a relatively new and interesting idea for dimensionality reduction in robot control. We propose a framework in which adaptive primitives learn and represent synergetic arm movements. A simple and fixed set of postural and oscillatory primitives form the substrate through which all control is elicited. Higher level adaptive primitives interact and control the primitive substrate in order to handle complex movement sequences. We implemented this model on a simulated 20 DOF humanoid character with dynamics. We present results of the experiments involving the presentation and learning of synergetic arm movements. DN: Database Name CSA Technology Research Database",
}

@InProceedings{Hayes02a_bibuniq_2244,
  author =       "Adam T. Hayes and Parsa Dormiani Tabatabaei",
  title =        "Self-organized flocking with agent failure: Off-line optimization and demonstration with real robots",
  booktitle =    "Proceedings - {IEEE} International Conference on Robotics and Automation. Vol. 4, ; Zakopoulos, N; Moulopoulos, S",
  year =         "2002",
  volume =       "",
  pages =        "3900--3905",
  abstract =     "This paper presents an investigation of flocking by teams of autonomous mobile robots using principles of Swarm Intelligence. First, we present a simple flocking task, and we describe a leaderless distributed flocking algorithm (LD) that is more conducive to implementation on embodied agents that the established algorithms used in computer animation. Next, we use an embodied simulator and reinforcement learning techniques to optimize LD performance under different conditions, showing that this method can be used not only to improve performance but also to gain insight into which algorithm components contribute most to system behavior. Finally, we demonstrate that a group of real robots executing LD with emulated sensors can successfully flock (even in the presence of individual agent failure) and that systematic characterization (and therefore optimization) of real robot flocking performance is achievable. DN: Database Name Aerospace \& High Technology Database",
}

@InProceedings{Thiebaut02a_bibuniq_2245,
  author =       "Carole Thiebaut and Michel Boer and Sylvie Roques",
  title =        "Steps toward the development of an automatic classifier for astronomical sources",
  booktitle =    "Spie Proceedings Series. Vol. Spie-4847, Boer, Michel; Roques, Sylvie",
  year =         "2002",
  volume =       "",
  pages =        "379--390",
  abstract =     "We present the progress we have made in implementing a new kind of automatic classifier for astronomical objects. the developed classifier will work both in the image and time domain and take into account the geometrical and temporal characteristics of the sources. We have first constructed a 2{D} classifier which is based on a Self Organizing Map. the developed network is able to learn through experience and to discriminate between astronomical objects such as stars, galaxies, saturated objects or blended objects. in order to recognize and classify variable objects, the method had to be improved. We present the next step of classification through our 3{D} (geometry - time) classifier. the temporal characteristics of the sources are obtained by different analysis of their light curves: time domain, frequency, and time-frequency analysis. We add the geometrical and temporal characteristics to obtain a complete classification of the sources. We plan to use the difference image analysis to obtain block of images and analyze them directly through the classifier. Such a complete classification has not yet been realized in the astronomical domain. in general our method works better than other automatic methods and allows a more complete discrimination through astronomical sources. (Author)RP: Report Number SPIE-4847AN: Accession Number A03-17483 (AH); 200309-81-1151 (CI)DN: Database Name CSA Technology Research Database",
}

@InProceedings{Crook02a_bibuniq_2247,
  author =       "Paul A. Crook and Stephen Marsland and Gillian Hayes and Ulrich Nehmzow",
  title =        "A tale of two filters - on-line novelty detection",
  booktitle =    "Proceedings - {IEEE} International Conference on Robotics and Automation. Vol. 4, w, Ulrich",
  year =         "2002",
  volume =       "",
  pages =        "3894--3899",
  abstract =     "For mobile robots, as well as other learning systems, the ability to highlight unexpected features of their environment - novelty detection - is very useful. One particularly important application for a robot equipped with novelty detection is inspection, highlighting potential problems in an environment. in this paper two novelty filters, both of which are capable of on-line and off-line novelty detection, are compared for two robot inspection tasks, one using sonar and the other camera images. the benefits and problems of using each of the filters are discussed and demonstrated. DN: Database Name CSA Technology Research Database",
}

@Article{Blanco02a_bibuniq_2248,
  author =       "I. D. Blanco and A. A. C. Vega and A. D. Gonzalez and L. R. Loredo and F. O. Carrera and J. A Rodriguez",
  title =        "Visual predictive maintenance tool based on {SOM} projection techniques.",
  journal =      "Revue de Metallurgie, Cahiers d'Informations Techniques. Vol. 99, oredo, L R; Carrera, F O; Rodriguez, J A",
  year =         "2002",
  pages =        "52--53",
  abstract =     "This paper presents a portable condition monitoring system named MAprEX which was developed as the result of the cooperation between the University of Oviedo and Aceralia inside of a research project funded by the ECSC program of the EC the system integrates powerful monitoring and data visualization methods based on the Self-Organizing Map ({SOM}). in this paper the architecture of the developed system is described in detail as well as the visualization methods implemented. DN: Database Name CSA Technology Research Database",
}

@InProceedings{Lehrasab02a_bibuniq_2251,
  author =       "N. Lehrasab and H. P. B. Dassanayake and C. Roberts and S. Fararooy and C. J Goodman",
  title =        "Industrial fault diagnosis: pneumatic train door case study.",
  booktitle =    "Proceedings of the Institution of Mechanical Engineers F, Journal of Rail and Rapid Transit. Vol. 216, J A",
  year =         "2002",
  volume =       "",
  pages =        "175--183",
  abstract =     "A practical, robust method of fault detection and diagnosis of a class of pneumatic train door commonly found in rapid transit systems is presented. the methodology followed is intended to be applied within a practical system where computation is distributed across a local data network for economic reasons. the health of the system is ascertained by extracting features from the trajectory profiles of the train door. This is incorporated into a low-level fault detection scheme, which relies upon using simple parity equations. Detailed diagnostics are carried out once a fault has been detected; for this purpose neural network models are utilized. This method of detection and diagnosis is implemented in a distributed architecture resulting in a practical, low-cost industrial solution. It is feasible to integrate the results of the diagnosis process directly into an operator's maintenance information system (MIS), thus producing a proactive maintenance regime. DN: Database Name CSA Technology Research Database",
}

@Article{Buessler02a_bibuniq_2252,
  author =       "J. L. Buessler and J. P. Urban",
  title =        "Neurobiology suggests the design of modular architectures for neural control.",
  journal =      "Advanced Robotics. Vol. 16, sler, J. L. ; Urban, J. P.",
  year =         "2002",
  number =       "3",
  volume =       "",
  pages =        "297--307",
  abstract =     "The existence of modular structures in the biological world strongly suggests that the training of this kind of structure is actually feasible. It is a key indication for the development of neural network applications, especially in the field of robotics. Indeed, a single network can only efficiently treat problems with few independent variables; the combination of several networks is necessary to address more complex tasks. We investigate learning techniques and show that using a particular form of architecture can ease the training of a modular structure: a bi-directional structure that allows combining several neural networks. the approach is illustrated with {K}ohonen's self-organizing maps for a robotic visual servoing task. DN: Database Name CSA Technology Research Database",
}

@Article{Lopez02a_bibuniq_2253,
  author =       "J. Lopez Coronado and J. L. Pedreno Molina and A. Guerrero Gonzalez and P. Gorce",
  title =        "A neural model for visual-tactile-motor integration in robotic reaching and grasping tasks.",
  journal =      "Robotica. Vol. 20, G",
  year =         "2002",
  number =       "1",
  volume =       "",
  pages =        "23--31",
  abstract =     "This paper presents a neural model to solve the visual-tactile-motor coordination problem in robotic applications. the proposed neural controller is based on the VAMC (Vector Associative Map) model. This algorithm is based on the human biological system and has the ability of learning the mapping that establishes the relationship between the spatial and the motor coordinates. These spatial inputs are composed of visual and force parameters. the LINCE stereohead carries out a visual detection process, detecting the positions of the object and of the manipulator. the artificial tactile skins placed over the two fingers of the gripper measure the force distribution when an object is touched. the neural controller has been implemented for robotic operations of reaching and object grasping. the reaching process is fed back in order to minimize the Difference Vector (DV) between the visual projections of the object and the manipulator. the stable grasping task processes the force distribution maps detected in the contact with the two surfaces of the gripper, in order to direct the object into the robotic fingers. Experimental results have demonstrated the robustness of the model and the accuracy of the final pick and place process. DN: Database Name CSA Technology Research Database",
}

@Article{Rughooputh02a_bibuniq_2254,
  author =       "H. C. Sh. Rughooputh and S. D. Dh. V. Rughooputh",
  title =        "Neural network process vision systems for flotation process.",
  journal =      "Kybernetes. Vol. 31",
  year =         "2002",
  number =       "3",
  volume =       "",
  pages =        "529--535",
  abstract =     "Froth flotation is a process whereby valuable minerals are separated from waste by exploiting natural differences or by chemically inducing differences in hydrophobicity. Flotation processes are difficult to model because of the stochastic nature of the froth structures and the ill-defined chemorheology of these systems. in this paper a hierarchical configuration hybrid neural network has been used to interpret froth images in a copper flotation process. This hierarchical neural network uses two Pulse-Coupled Neural Networks (PCNNs) as preprocessors that 'convert' the froth images into corresponding binary barcodes. Our technique demonstrates the effectiveness of the hybrid neural network for process vision, and hence, its potential for use for real time automated interpretation of froth images and for flotation process control in the mining industry. the system is simple, inexpensive and is very reliable. DN: Database Name Aerospace \& High Technology Database",
}

@InProceedings{Hogan02a_bibuniq_2257,
  author =       "R. Hogan and T. Roush",
  title =        "{SOM} classification of Martian {TES} data",
  booktitle =    "33rd Lunar and Planetary Science Conference; Abstracts of Papes. Vol. 34, ainen, J.",
  year =         "2002",
  volume =       "",
  pages =        "",
  abstract =     "A classification scheme based on unsupervised self-organizing maps ({SOM}) is described. Results from its application to the ASU mineral spectral database are presented. Applications to the Martian TES data are discussed. RP: Report Number LPI Contribution No. 1109AN: Accession Number A03-31503 (AH)DN: Database Name CSA Technology Research Database",
}

@Article{Xing02a_bibuniq_2258,
  author =       "F. H. Xing and G. y. Wang",
  title =        "Structural choice based on knowledge discovery system.",
  journal =      "Journal of Harbin Institute of Technology (New Series). Vol. 9, of AIJ), inen, J.",
  year =         "2002",
  number =       "3",
  volume =       "",
  pages =        "263--266",
  abstract =     "Structural choice is a significant decision having an important influence on structural function, social economics, structural reliability and construction cost. A Case Based Reasoning system with its retrieval part constructed with a KDD subsystem, is put forward to make a decision for a large scale engineering project. A typical CBR system consists of four parts: case representation, case retriever, evaluation, and adaptation. A case library is a set of parameterized excellent and successful structures. For a structural choice, the key point is that the system must be able to detect the pattern classes hidden in the case library and classify the input parameters into classes properly. That is done by using the KDD Data Mining algorithm based on Self-Organizing Feature Maps (SOFM), which makes the whole system more adaptive, self-organizing, self-learning and open. DN: Database Name CSA Technology Research Database",
}

@Article{Matthews02a_bibuniq_2259,
  author =       "P. C. Matthews and L. T. M. Blessing and K. M. Wallace",
  title =        "The introduction of a design heuristics extraction method.",
  journal =      "Advanced Engineering Informatics. Vol. 16, essing, L. T. M. ; Wallace, K. M.",
  year =         "2002",
  number =       "1",
  volume =       "",
  pages =        "3--19",
  abstract =     "This paper introduces a novel method for analyzing conceptual design data. Given a database of previous designs, this method identifies relationships between design components within this database. Further, the method transforms these relationships into explicit design knowledge that can be used to generate a 'heuristic-based' model of the design domain for use at the conceptual stage. This can be viewed as a knowledge extracting method for the conceptual design stage. Such a method is particularly interesting, as the conceptual stage typically lacks explicit models to describe the trade-offs that must be made when designing. the method uses either principal components analysis or self-organizing maps to identify the relationships, and this paper describes all the elements required by the method to successfully extract and verify design knowledge from design databases. DN: Database Name Polymer Library (formerly Rapra Abstracts)",
}

@Article{Sasaki04a_bibuniq_2263,
  author =       "Y. Sasaki and A. Matsuo",
  title =        "Optimization and Knowledge Discovery to the Design Problem of Fuel Injector in the Supersonic Combustor",
  journal =      "Nihon Koku Uchu Gakkaishi Rombunshi (Journal of the Japan Society for Aeronautical and Space Sciences) (Japan). Vol. 52",
  year =         "2004",
  number =       "607",
  volume =       "",
  pages =        "371--376",
  abstract =     "A Multi-Objective Genetic Algorithm (MOGA) is applied in search of the optimal fuel injector shape in the supersonic combustor. the optimal shape is investigated in terms of rapid mixing of fuel jet and supersonic airstream. Two goals, those are to maximize the jet core height and to minimize the total pressure loss, are adopted for the optimization. the numbers of maximum injector holes are restricted to 1, 2, and 3 during the evolutive process. Pareto fronts under the 2 and 3 maximum holes restriction evolve successfully. Self-Organizing Map ({SOM}) is applied, in order to realize the role of optimal solutions in actual design, to analysis of structure on design space by means of design database obtained in evolutive process of optimization. the design database is mapped onto the two-dimensional SOM, where design space is successfully visualized. the visualization that clarifies relativity of performance parameters acquires valuable design information. AN: Accession Number A05-34-13657 (AH)DN: Database Name Computer and Information Systems Abstracts",
}

@Article{Wang04a_bibuniq_2265,
  author =       "Z. Wang and X. P Wang",
  title =        "Texture segmentation based on energy features and neural networks.",
  journal =      "Tiedao xuebao (Journal of the China Railway Society) (China). Vol. 26, eronautical and Space Sciences) (Japan). Vol. 52",
  year =         "2004",
  number =       "3",
  volume =       "",
  pages =        "67--70",
  abstract =     "Feature illustration is an efficient measure to describe the essential property of texture. A new method is proposed for texture segmentation based on energy features by combining the sensitive selectivity of Gabor filters for different frequencies and orientations with the flexibility and adaptability of the self-organizing feature mapping neural network. First, texture energy features are extracted by Gabor filters. Second, the self-organizing neural networks are employed to perform feature clustering, and finally texture segmentation is accomplished by minimum distance classification. Simulations show that this method can efficiently segment multi-texture images into several regions according to their different texture properties, and its segmentation error ratio is lower than that of the method of combining co-occurrence matrix features extraction with K-means clustering. DN: Database Name Computer and Information Systems Abstracts",
}

@Article{Yang04a_bibuniq_2270,
  author =       "H. Yang and C. Lee",
  title =        "Mining text documents for thematic hierarchies using self-organizing maps.",
  journal =      "Computing Reviews. Vol. 45, , H; Lee, C",
  year =         "2004",
  number =       "2",
  volume =       "",
  pages =        "117--118",
  abstract =     "The automatic classification of documents is a hot topic in natural language processing research right now, and consequently, so is the automatic generation of document classification schemes. Hierarchical classifications, rather than simple clusters, are of special interest, as they have much greater utility. in this paper, the authors use {K.}ohonen's (2001) self-organizing maps as their basic method of clustering documents. They can then form hierarchies by relying on the fact that proximate clusters in the map will also be similar. Then, thematic names are given to each node in the hierarchy. This is based on a separate map, in which words found to be important in the first phase are clustered. Yang and Lee try their method out on a small and a large set of news articles in {C.}hinese (100 and 3268 documents, respectively). the resulting hierarchies are deemed satisfactory, with respect to the formulation of the problem in self-organizing map ({S. O. M.}) terms, but the absence of any kind of evaluation by human judges, which is simply postponed to future work, is a large hole in the research. AN: Accession Number A04-82-42198 (AH)DN: Database Name BioEngineering Abstracts",
}

@Article{Triantafyllou03a_bibuniq_2284,
  author =       "I. Triantafyllou and G. Carayannis",
  title =        "Architectures and techniques for monolingual and multilingual information retrieval systems in a {SOM} framework [self-organizing map].",
  journal =      "Wseas Transactions on Systems. Vol. 2, I; Carayannis, G",
  year =         "2003",
  number =       "3",
  volume =       "",
  pages =        "589--597",
  abstract =     "The most important issue in Information Retrieval systems is the representation of the information that the documents and/or queries withhold. A new architecture for classification systems is introduced trying to improve the discrimination capacity of the initial search space by employing a Self-Organizing Map. At the same time different aspects of representation techniques are investigated in order to conclude to a new compelling combination. in query based IR systems the contribution of SOMs is mainly concentrated in Relevance Feedback techniques. Multilingual systems are also considered. Their major variation from monolingual systems is the use of bilingual lexicons to deal multilinguality. the enlightening use of parallel corpora and tools for name extraction and recognition can also contribute to this purpose. AN: Accession Number A05-82-14987 (AH); 200503-32-03233 (CI)DN: Database Name Environmental Engineering Abstracts",
}

@Article{Zhang03a_bibuniq_2287,
  author =       "J. Zhang and J. L. Kang and M. N Wang",
  title =        "An {RBF} network based nonlinear modeling of diesel engine.",
  journal =      "Transactions of {C}hinese Society for Internal Combustion Engines. Vol. 21, nnell, M",
  year =         "2003",
  number =       "3",
  volume =       "",
  pages =        "221--226",
  abstract =     "This paper deals with a problem of nonlinear modeling of diesel engine. A new nonlinear model is proposed based on the radial basis function (RBF) network. the model identified as a black-box model with input-output training data does not require the priori knowledge. A modified self-organizing map (MSOM) network is developed for the determination of the RBF centers. Based on the MSOM, the clustering algorithm selects the RBF centers automatically according to the distribution of training data in the input-output space and the given approximating error. Then the linear least squares method is employed to estimate these weights. Simulating results indicate that the nonlinear model depicts the dynamics of diesel engine accurately in the whole operating range. This nonlinear model can be used to design and analyze the control system of diesel engine. DN: Database Name BioEngineering Abstracts",
}

@Article{Beckonert03a_bibuniq_2295,
  author =       "O. Beckonert and J. Monnerjahn and U. Bonk and D. Leibfritz",
  title =        "Visualizing metabolic changes in breast-cancer tissue using super(1){H}-{NMR} spectroscopy and self-organizing maps",
  journal =      "NMR in Biomedicine . Vol. 16, rjahn, J; Bonk, U; Leibfritz, D",
  year =         "2003",
  number =       "1",
  volume =       "",
  pages =        "1--11",
  abstract =     "In-vitro {NMR} spectroscopic examinations of tissue extracts can be combined with appropriate pattern-recognition and visualization techniques in order to monitor characteristic metabolic differences between tissue classes. in the present study, such techniques are applied to a set of 88 breast-tissue samples with the intention of identifying typical differences between various tissue classes. the set contains 49 breast-tumor samples of various tumor grades and 39 samples of healthy tissue. the metabolite compositions of the tissue extracts were investigated using a dual extraction technique and high-resolution super(1)H- {NMR} spectroscopy. the spectra of the hydrophilic and the lipophilic compounds were assigned to three groups according to different malignancy grades of the respective tissue samples. the group characteristics were analyzed using the k- nearest-neighbor method and self-organizing-map visualizations. the results show an increase of UDP-hexose, phosphocholine and phosphoethanolamine concentrations according to the tumor grade. Higher concentrations of taurine were detected in the malignant samples. Myo-inositol and glucose content were elevated in control samples compared with malignant tissue. Both compounds also characterized different subgroups in the pool of unaffected tissue samples depending upon fat content or fibrosis. Several lipid metabolites showed a characteristic elevation with high malignancy. DN: Database Name Computer and Information Systems Abstracts",
}

%  bibtex/csa2005. bib =====================================





@Article{Pan05a_bibuniq_2299,
  author =       "Wei Pan and Weihua Li and Haobin Shi and Jianfeng Yan",
  title =        "Optimizing radial basis function networks to recognize network attacks for intrusion detection",
  journal =      "Proc. Spie. Vol. Spie-5985",
  year =         "2005",
  number =       "",
  volume =       "",
  pages =        "347--351",
  abstract =     "",
}

@Article{Lee05a_bibuniq_2301,
  author =       "Chien Sing Lee and Yashwant PRASAD Singh",
  title =        "A. neural-linear approach to student modelling for the Onto{ID} authoring tool",
  journal =      "Wseas Transactions on Computers. Vol. 4",
  year =         "2005",
  number =       "10",
  volume =       "",
  pages =        "1399--1408",
  abstract =     "",
}

@Article{Rensen05a_bibuniq_2303,
  author =       "Judith Rensen and Stefan Luther and Detlef Lohse",
  title =        "The effect of bubbles on developed turbulence",
  journal =      "Journal of Fluid Mechanics. Vol. 538",
  year =         "2005",
  number =       "",
  volume =       "",
  pages =        "153--187",
  abstract =     "",
}

@Article{Barandela05a_bibuniq_2305,
  author =       "Ricardo Barandela and Francesc J. Ferri and J. SALVADORAF Sanchez",
  title =        "Decision Boundary Preserving Prototype Selection for Nearest Neighbor Classification",
  journal =      "International Journal of Pattern Recognition and Artificial Intelligence. Vol. 19",
  year =         "2005",
  number =       "6",
  volume =       "",
  pages =        "787--806",
  abstract =     "",
}

@Article{Wu05a_bibuniq_2307,
  author =       "Jun Wu and Bin Liang",
  title =        "Analysis of Slope Stability Based on the Theory of Rough Sets With Self-Organizing {MAP} Networks",
  journal =      "Wuhu China Anhui Shifan Daxue Xuebao (Ziran Kexue Ban) / (Journal of Anhui Normal University) (Natural Science) (China). Vol. 28",
  year =         "2005",
  number =       "3",
  volume =       "",
  pages =        "290--293",
  abstract =     "",
}

@Article{Nongnuch05a_bibuniq_2309,
  author =       "A. Nongnuch and A. Surarerks",
  title =        "A Novel Approach of Density Estimation for Vector Quantization",
  journal =      "Wseas Transactions on Computers. Vol. 4",
  year =         "2005",
  number =       "9",
  volume =       "",
  pages =        "1179--1186",
  abstract =     "",
}

@Article{Honkanen05a_bibuniq_2310,
  author =       "Honkanen Markus and Saarenrinne Pentti and Stoor Thomas and Niinimaki Jouko",
  title =        "Recognition of highly overlapping ellipse-like bubble images",
  journal =      "Measurement Science and Technology. Vol. 16",
  year =         "2005",
  number =       "9",
  volume =       "",
  pages =        "1760--1770",
  abstract =     "",
}

@Article{Kuo05a_bibuniq_2313,
  author =       "R. J. Kuo and J. L. Liao and C. Tu",
  title =        "Integration of {ART2} neural network and genetic {K}-means algorithm for analyzing Web browsing paths in electronic commerce",
  journal =      "Decision Support Systems. Vol. 40",
  year =         "2005",
  number =       "2",
  volume =       "",
  pages =        "355--374",
  abstract =     "",
}

@Article{Zhu05a_bibuniq_2314,
  author =       "Zhu Bin and Chen Hsinchun",
  title =        "Using 3{D} interfaces to facilitate the spatial knowledge retrieval: a geo-referenced knowledge repository system",
  journal =      "Decision Support Systems. Vol. 40",
  year =         "2005",
  number =       "2",
  volume =       "",
  pages =        "167--182",
  abstract =     "",
}

@Article{Kannan05a_bibuniq_2315,
  author =       "S. R Kannan",
  title =        "Extended Bidirectional Associative Memories: {A} Study On Poor Education",
  journal =      "Mathematical and Computer Modelling. Vol. 42",
  year =         "2005",
  number =       "3",
  volume =       "",
  pages =        "389--395",
  abstract =     "",
}

@Article{Balbinot05a_bibuniq_2317,
  author =       "L. Balbinot and P. Smichowski and S. Farias and M. A. Z. Arruda and C. Vodopivez and R. J Poppi",
  title =        "Classification of Antarctic algae by applying {K}ohonen neural network with 14 elements determined by inductively coupled plasma optical emission spectrometry",
  journal =      "Spectrochimica Acta, Part B, Atomic Spectroscopy. Vol. 60",
  year =         "2005",
  number =       "5",
  volume =       "",
  pages =        "725--730",
  abstract =     "",
}

@Article{El05a_bibuniq_2319,
  author =       "S. M. A. B. D. El Moetty and A. A. ABOU Ali and A. A. Fahmy and A. A. ABOU El Nour",
  title =        "Fractal Neural Processor",
  journal =      "Journal of Engineering and Applied Science. Vol. 52",
  year =         "2005",
  number =       "3",
  volume =       "",
  pages =        "457--474",
  abstract =     "",
}

@Article{Chen05a_bibuniq_2320,
  author =       "Shou Yu Chen and Qing Guo Li",
  title =        "Fuzzy clustering neural network and its application to water resources assessment.",
  journal =      "Shuili Xuebao (J. Hydraul. Eng. ). Vol. 36",
  year =         "2005",
  number =       "6",
  volume =       "",
  pages =        "662--666",
  abstract =     "",
}

@Article{Tutu05a_bibuniq_2322,
  author =       "H. Tutu and E. M. Cukrowska and V. Dohnal and J. Havel",
  title =        "Application of artificial neural networks for classification of uranium distribution in the Central Rand goldfield, South Africa",
  journal =      "Environmental Modeling and Assessment. Vol. 10",
  year =         "2005",
  number =       "2",
  volume =       "",
  pages =        "143--152",
  abstract =     "",
}

@Article{Zhang05a_bibuniq_2323,
  author =       "Rui Hua Zhang and Yun Ting Song and Xi Chen",
  title =        "A fast method for comprehensive reliability assessment of power generation and transmission system using {SOM}",
  journal =      "Electric Power. Vol. 38",
  year =         "2005",
  number =       "6",
  volume =       "",
  pages =        "12--16",
  abstract =     "",
}

@Article{Jiang05a_bibuniq_2324,
  author =       "Hui Lan Jiang and Min An and Xiao Jin Liu and Xin Zhao and Jian Hai Zhang",
  title =        "Calculation of energy losses in distribution systems based on {RBF} network with dynamic clustering algorithm.",
  journal =      "Zhongguo Dianji Gongcheng Xuebao (Proc. Chin. Soc. Electr. Eng. ). Vol. 25",
  year =         "2005",
  number =       "10",
  volume =       "",
  pages =        "35--39",
  abstract =     "",
}

@Article{Sheridan05a_bibuniq_2329,
  author =       "Cormac Sheridan and Marion OFarrell and Elfed Lewis and William B. Lyons and Colin Flanagan and Nick Jackman",
  title =        "A comparison of k-{NN}, backpropagation, and self-organising map classification methods using an optical fibre based sensor system utilised in an industrial large scale oven",
  journal =      "Proc. Spie. Vol. Spie-5826",
  year =         "2005",
  number =       "",
  volume =       "",
  pages =        "706--713",
  abstract =     "",
}

@Article{Jiang05b_bibuniq_2331,
  author =       "Yuan Jiang and Zhao Yang Zhang and Pei Liang Qiu and Dong Fang Zhou",
  title =        "Clustering algorithms used in data mining.",
  journal =      "Dianzi Yu Xinxi Xuebao (J. Electron. Inf. Technol. ). Vol. 27",
  year =         "2005",
  number =       "4",
  volume =       "",
  pages =        "655--662",
  abstract =     "",
}

@Article{Qian05a_bibuniq_2332,
  author =       "Xiao Dong Qian and Zheng Ou Wang",
  title =        "Fast Latent Semantic Indexing in Text Processing Based on Random Mapping",
  journal =      "Tianjin Daxue Xuebao (Journal of Tianjin University of Science and Technology). Vol. 38",
  year =         "2005",
  number =       "4",
  volume =       "",
  pages =        "372--376",
  abstract =     "",
}

@Article{Frankowiak05a_bibuniq_2334,
  author =       "Marcos Frankowiak and Roger Grosvenor and Paul Prickett",
  title =        "A review of the evolution of microcontroller-based machine and process monitoring",
  journal =      "International Journal of Machine Tools \& Manufacture. Vol. 45",
  year =         "2005",
  number =       "4",
  volume =       "",
  pages =        "573--582",
  abstract =     "",
}

@Article{Gao05a_bibuniq_2335,
  author =       "Zhiming Gao and Shizhe Song and Yunhai Xu",
  title =        "Electrochemical Impedance Spectroscopy Analysis of Coating Deterioration Process With {K}ohonen Neural Networks",
  journal =      "Journal of {C}hinese Society for Corrosion and Protection. Vol. 25",
  year =         "2005",
  number =       "2",
  volume =       "",
  pages =        "106--109",
  abstract =     "",
}

@Article{Li05a_bibuniq_2336,
  author =       "Jian Lin Li and Yu Ling Li and Chun Li and Zhong Chao Zhang",
  title =        "Research on {CPN}-{SVM} technique applied in {VSC}.",
  journal =      "Zhongguo Dianji Gongcheng Xuebao (Proc. Chin. Soc. Electr. Eng. ). Vol. 25",
  year =         "2005",
  number =       "6",
  volume =       "",
  pages =        "71--74",
  abstract =     "",
}

@InProceedings{Faro05a_bibuniq_2338,
  author =       "A. Faro and D. Giordano and F. Maiorana",
  title =        "Discovering Complex Regularities by Adaptive Self Organizing Classification",
  booktitle =    "WEC '05: the Second World Enformatika Conference; Istanbul; Turkey; 25-27 Feb. 20",
  year =         "2005",
  volume =       "",
  pages =        "",
  abstract =     "",
}

@InProceedings{Bhatnagar05a_bibuniq_2339,
  author =       "Vasudha Bhatnagar and Ahmed Sultan Al Hegami and Naveen Kumar",
  title =        "A Hybrid Approach for Quantification of Novelty in Rule Discovery",
  booktitle =    "WEC '05: the Second World Enformatika Conference; Istanbul; Turkey; 25-27 Feb. 20",
  year =         "2005",
  volume =       "",
  pages =        "",
  abstract =     "",
}

@Article{Tan05b_bibuniq_2343,
  author =       "Xiao Yang Tan and Jun Liu and Fu Yan Zhang",
  title =        "Finding Important Sub-Areas for Face Recognition from Single Training Image Per Person",
  journal =      "Journal of Nanjing University of Aeronautics \& Astronautics. Vol. 37",
  year =         "2005",
  number =       "1",
  volume =       "",
  pages =        "44--47",
  abstract =     "",
}

@Article{Tang05a_bibuniq_2344,
  author =       "Yong Tang and Jun Xian Hou and Wen Zhuo Liu",
  title =        "Modeling of distribution network and var compensator and induction motor in the load model for power system digital simulation.",
  journal =      "Zhongguo Dianji Gongcheng Xuebao (Proc. Chin. Soc. Electr. Eng. ). Vol. 25",
  year =         "2005",
  number =       "3",
  volume =       "",
  pages =        "8--12",
  abstract =     "",
}

@Article{Zhang05d_bibuniq_2345,
  author =       "Hong Bo Zhang and Zhen Qi Hu and Qiu Ji Chen and Hong Quan Xie and Chang Hua Liu",
  title =        "Application of artificial neural network technology in land reclamation of mining area",
  journal =      "Liaoning Gongcheng Jishu Daxue Xuebao/Journal of Liaoning Technical University (China). Vol. 24",
  year =         "2005",
  number =       "1",
  volume =       "",
  pages =        "26--28",
  abstract =     "",
}

@Article{Bowden05a_bibuniq_2346,
  author =       "G. J. Bowden and G. C. Dandy and H. R Maier",
  title =        "Input determination for neural network models in water resources applications. Part 1--background and methodology",
  journal =      "Journal of Hydrology. Vol. 301",
  year =         "2005",
  number =       "1",
  volume =       "",
  pages =        "75--92",
  abstract =     "",
}

@Article{Bowden05b_bibuniq_2347,
  author =       "G. J. Bowden and H. R. Maier and G. C Dandy",
  title =        "Input determination for neural network models in water resources applications. Part 2. Case study: forecasting salinity in a river",
  journal =      "Journal of Hydrology. Vol. 301",
  year =         "2005",
  number =       "1",
  volume =       "",
  pages =        "93--107",
  abstract =     "",
}

@Article{Xiao05a_bibuniq_2348,
  author =       "Hui Xiao and Yunfa Hu",
  title =        "Data mining based on segmented time warping distance in time series database.",
  journal =      "Jisuanji Yanjiu yu Fazhan (Comput. Res. Dev. ). Vol. 42",
  year =         "2005",
  number =       "1",
  volume =       "",
  pages =        "72--78",
  abstract =     "",
}

%  bibtex/hutmscthesis. bib =====================================





@MastersThesis{lindhknuutila05_bibuniq_2350,
  author =       "Tiina Lindh-{K.}nuutila",
  title =        "Simulating the Emergence of a Shared Conceptual System in a Multi-Agent Environment",
  school =       "Helsinki University of Technology",
  year =         "2005",
  address =      "Espoo, Finland",
  month =        "October",
}

@MastersThesis{k_bibuniq_2351,
  author =       "Terje Bergström",
  title =        "Context Awareness in Symbian {OS} Based Smart-phones",
  school =       "Helsinki University of Technology",
  year =         "2002",
  address =      "Espoo, Finland",
}

@MastersThesis{k_bibuniq_2352,
  author =       "Jukka Kuusisto",
  title =        "Recognition of dialogue topics with learning methods",
  school =       "Helsinki University of Technology",
  year =         "2002",
  address =      "Espoo, Finland",
}

@MastersThesis{k_bibuniq_2353,
  author =       "Aleksi Saari",
  title =        "Topographic Mappings for Analyzing Clinical Patient Data",
  school =       "Helsinki University of Technology",
  year =         "2002",
  address =      "Espoo, Finland",
}

@MastersThesis{k_bibuniq_2354,
  author =       "Kalle Lyytikäinen",
  title =        "From Customer Requirements to Product Concept: Soft Computing Tool for Recruiting",
  school =       "Helsinki University of Technology",
  year =         "2002",
  address =      "Espoo, Finland",
}

@MastersThesis{k_bibuniq_2355,
  author =       "Vesa Paatero",
  title =        "A comparison of term weighting methods using {WEBSOM} maps",
  school =       "Helsinki University of Technology",
  year =         "2003",
  address =      "Espoo, Finland",
}

@MastersThesis{k_bibuniq_2356,
  author =       "Lasse Rasinen",
  title =        "Magazine Sales Outlet Assortment Optimization",
  school =       "Helsinki University of Technology",
  year =         "2004",
  address =      "Espoo, Finland",
}

@MastersThesis{k_bibuniq_2357,
  author =       "Hugo Gävert",
  title =        "Bankruptcy Prediction and Cluster Analysis of Small and Medium-Sized Enterprises Based on Financial Statements",
  school =       "Helsinki University of Technology",
  year =         "2004",
  address =      "Espoo, Finland",
}

@MastersThesis{k_bibuniq_2358,
  author =       "Heikki Verta",
  title =        "Trace based off-line analysis of component-based systems",
  school =       "Helsinki University of Technology",
  year =         "2004",
  address =      "Espoo, Finland",
}

@MastersThesis{k_bibuniq_2359,
  author =       "Timo Similä",
  title =        "The impact of research and development on growth in {F}innish manufacturing firms",
  school =       "Helsinki University of Technology",
  year =         "2004",
  address =      "Espoo, Finland",
}

@MastersThesis{k_bibuniq_2360,
  author =       "Sami Virpioja",
  title =        "New methods for statistical natural language modeling",
  school =       "Helsinki University of Technology",
  year =         "2005",
  address =      "Espoo, Finland",
}

%  bibtex/isi02. bib =====================================



@Article{butte02a_bibuniq_2614,
  author =       "A. Butte",
  title =        "The use and analysis of microarray data",
  journal =      "Nature Reviews Drug Discovery",
  year =         "2002",
  volume =       "1",
  number =       "12",
  month =        "December",
  abstract =     "",
}

@Article{dibona02a_bibuniq_2621,
  author =       "S. Di Bona and O. Salvetti",
  title =        "Neural method for three-dimensional image matching",
  journal =      "Journal of Electronic Imaging",
  year =         "2002",
  volume =       "11",
  number =       "4",
  month =        "October",
  abstract =     "",
}

@Article{rhodius02a_bibuniq_2626,
  author =       "V. Rhodius and T. K. Van Dyk and C. Gross and R. A. LaRossa",
  title =        "Impact of genomic technologies on studies of bacterial gene epression",
  journal =      "Annual Review of Microbiology",
  year =         "2002",
  volume =       "56",
  abstract =     "",
}

@Article{mateos02a_bibuniq_2627,
  author =       "A. Mateos and J. Dopazo and R. Jansen and Y. H. Tu and M. Gerstein and G. Stolovitzky",
  title =        "Systematic learning of gene functional classes from {DNA} array expression data by using multilayer perceptrons",
  journal =      "Genome Research",
  year =         "2002",
  volume =       "12",
  number =       "11",
  month =        "November",
  abstract =     "",
}

@InProceedings{porrmann02a_bibuniq_2628,
  author =       "M. Porrmann and U. Witkowski and H. Kalte and U. Ruckert",
  title =        "Dynamically reconfigurable hardware - {A} new perspective for neural network implementations",
  booktitle =    "Field-Programmable Logic and Applications, Proceedings, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "1048--1057",
  abstract =     "",
}

@Article{aupetit02a_bibuniq_2638,
  author =       "M. Aupetit and P. Couturier and P. Massotte",
  title =        "gamma-Observable neighbours for vector quantization",
  journal =      "Neural Networks",
  year =         "2002",
  volume =       "15",
  number =       "8-9",
  month =        "October" # "-" # nov,
  abstract =     "",
}

@Article{vanhulle02a_bibuniq_2639,
  author =       "M. A. Van Hulle",
  title =        "Kernel-based topographic map formation achieved with an information-theoretic approach",
  journal =      "Neural Networks",
  year =         "2002",
  volume =       "15",
  number =       "8-9",
  month =        "October" # "-" # nov,
  abstract =     "",
}

@Article{brandt02a_bibuniq_2645,
  author =       "S. Brandt and J. Laaksonen and E. Oja",
  title =        "Statistical shape features for content-based image retrieval",
  journal =      "Journal of Mathematical Imaging and Vision",
  year =         "2002",
  volume =       "17",
  number =       "2",
  month =        "September",
  abstract =     "",
}

@Article{khuwaja02a_bibuniq_2646,
  author =       "G. A. Khuwaja and M. S. Laghari",
  title =        "Adaptive classifier integration for invariant face recognition ({ACIIFR})",
  journal =      "International Journal of Pattern Recognition and Artificial Intelligence",
  year =         "2002",
  volume =       "16",
  number =       "6",
  month =        "September",
  abstract =     "",
}

@Article{dzemyda02a_bibuniq_2651,
  author =       "G. Dzemyda and O. Kurasova",
  title =        "Comparative analysis of the graphical result presentation in the {SOM} software",
  journal =      "Informatica",
  year =         "2002",
  volume =       "13",
  number =       "3",
  abstract =     "",
}

@Article{xu02a_bibuniq_2653,
  author =       "H. Q. Xu and P. R. Wu and C. F. J. Wu and C. Tidwell and Y. X. Wang",
  title =        "A smooth response surface algorithm for constructing a gene regulatory network",
  journal =      "Physiological Genomics",
  year =         "2002",
  volume =       "11",
  number =       "1",
  month =        "October" # " 2",
  abstract =     "",
}

@Article{hu02a_bibuniq_2659,
  author =       "Y. C. Hu and R. S. Chen and Y. T. Hsu and G. H. Tzeng",
  title =        "Grey self-organizing feature maps",
  journal =      "Neurocomputing",
  year =         "2002",
  volume =       "48",
  month =        "October",
  abstract =     "",
}

@Article{szu02a_bibuniq_2660,
  author =       "H. Szu and P. Chanyagorn and I. Kopriva",
  title =        "Sparse coding blind source separation through Powerline",
  journal =      "Neurocomputing",
  year =         "2002",
  volume =       "48",
  month =        "October",
  abstract =     "",
}

@Article{guerrero02a_bibuniq_2661,
  author =       "V. P. Guerrero and F. Moya-Anegon and V. Herrero-Solana",
  title =        "Automatic extraction of relationships between terms by means of {K}ohonen's algorithm",
  journal =      "Library \& Information Science Research",
  year =         "2002",
  volume =       "24",
  number =       "3",
  abstract =     "",
}

@Article{polanski02a_bibuniq_2663,
  author =       "J. Polanski and F. Zouhiri and L. Jeanson and D. Desmaele and J. D'Angelo and J. F. Mouscadet and R. Gieleciak and J. Gasteiger and M. Le Bret",
  title =        "Use of the {K}ohonen neural network for rapid screening of ex vivo anti-{HIV} activity of styrylquinolines",
  journal =      "Journal of Medicinal Chemistry",
  year =         "2002",
  volume =       "45",
  number =       "21",
  month =        "October" # " 10",
  abstract =     "",
}

@Article{hewitson02a_bibuniq_2679,
  author =       "B. C. Hewitson and R. G. Crane",
  title =        "Self-organizing maps: applications to synoptic climatology",
  journal =      "Climate Research",
  year =         "2002",
  volume =       "22",
  number =       "1",
  month =        "August" # " 8",
  abstract =     "",
}

@Article{tung02a_bibuniq_2681,
  author =       "W. L. Tung and C. Quek",
  title =        "GenSo{FNN}: {A} generic self-organizing fuzzy neural network",
  journal =      "IEEE Transactions on Neural Networks",
  year =         "2002",
  volume =       "13",
  number =       "5",
  month =        "September",
  abstract =     "",
}

@Article{hameri02a_bibuniq_2686,
  author =       "A. P. Hameri and M. Puittinen and M. Syrjalahti",
  title =        "Organizational emergence in networked collaboration",
  journal =      "International Journal of Communication Systems",
  year =         "2002",
  volume =       "15",
  number =       "7",
  month =        "September",
  abstract =     "",
}

@Article{distante02a_bibuniq_2688,
  author =       "C. Distante and P. Siciliano and K. C. Persaud",
  title =        "Dynamic cluster recognition with multiple self-organising maps",
  journal =      "Pattern Analysis and Applications",
  year =         "2002",
  volume =       "5",
  number =       "3",
  month =        "June",
  abstract =     "",
}

@Article{levenson02a_bibuniq_2689,
  author =       "A. S. Levenson and I. L. Kliakhandler and K. M. Svoboda and K. M. Pease and S. A. Kaiser and J. E. Ward and V. C. Jordan",
  title =        "Molecular classification of selective oestrogen receptor modulators on the basis of gene expression profiles of breast cancer cells expressing oestrogen receptor alpha",
  journal =      "British Journal of Cancer",
  year =         "2002",
  volume =       "87",
  number =       "4",
  month =        "August" # " 12",
  abstract =     "",
}

@Article{saban02a_bibuniq_2690,
  author =       "R. Saban and N. P. Gerard and M. R. Saban and N. B. Nguyen and D. J. DeBoer and B. K. Wershil",
  title =        "Mast cells mediate substance {P}-induced bladder inflammation through an {NK1} receptor-independent mechanism",
  journal =      "American Journal of Physiology-Renal Physiology",
  year =         "2002",
  volume =       "283",
  number =       "4",
  month =        "October",
  abstract =     "",
}

@Article{lopez-rubio02a_bibuniq_2692,
  author =       "E. Lopez-Rubio and J. Munoz-Perez and J. Antonio Gomez-Ruiz",
  title =        "Self-organizing dynamic graphs",
  journal =      "Neural Processing Letters",
  year =         "2002",
  volume =       "16",
  number =       "2",
  month =        "October",
  abstract =     "",
}

@Article{tamames02a_bibuniq_2695,
  author =       "J. Tamames and D. Clark and J. Herrero and J. Dopazo and C. Blaschke and J. M. Fernandez and J. C. Oliveros and A. Valencia",
  title =        "Bioinformatics methods for the analysis of expression arrays: data clustering and information extraction",
  journal =      "Journal of Biotechnology",
  year =         "2002",
  volume =       "98",
  number =       "2-3",
  month =        "September" # " 25",
  abstract =     "",
}

@Article{clauset02a_bibuniq_2699,
  author =       "A. J. Clauset and W. S. Caldwell and J. D. Schmitt",
  title =        "Novel use of self-organizing feature maps in {QSAR} studies.",
  journal =      "Abstracts of Papers of the American Chemical Society",
  year =         "2002",
  volume =       "224",
  month =        "August" # " 18",
  abstract =     "",
}

@Article{eom02a_bibuniq_2702,
  author =       "K. Eom and J. W. Jung and H. Sirisena",
  title =        "Intelligent system for adapting to a user's characteristics",
  journal =      "Neural Computing \& Applications",
  year =         "2002",
  volume =       "11",
  number =       "1",
  month =        "July",
  abstract =     "",
}

@Article{xu02c_bibuniq_2704,
  author =       "X. L. Xu and J. M. Olson and L. P. Zhao",
  title =        "A regression-based method to identify differentially expressed genes in microarray time course studies and its application in an inducible Huntington's disease transgenic model",
  journal =      "Human Molecular Genetics",
  year =         "2002",
  volume =       "11",
  number =       "17",
  month =        "August" # " 15",
  abstract =     "",
}

@Article{marzi02a_bibuniq_2713,
  author =       "H. Marzi",
  title =        "Development of a real-time monitoring system",
  journal =      "Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture",
  year =         "2002",
  volume =       "216",
  number =       "6",
  abstract =     "",
}

@Article{yang02b_bibuniq_2716,
  author =       "M. S. Yang and J. H. Yang",
  title =        "A fuzzy-soft learning vector quantization for control chart pattern recognition",
  journal =      "International Journal of Production Research",
  year =         "2002",
  volume =       "40",
  number =       "12",
  month =        "August",
  abstract =     "",
}

@Article{huang02a_bibuniq_2717,
  author =       "Y. H. Huang and T. D. Chiueh",
  title =        "A new audio coding scheme using a forward masking model and perceptually weighted vector quantization",
  journal =      "IEEE Transactions on Speech and Audio Processing",
  year =         "2002",
  volume =       "10",
  number =       "5",
  month =        "July",
  abstract =     "",
}

@Article{moukovski02a_bibuniq_2725,
  author =       "A. Moukovski and D. M. Gorinevski and M. A. Giese and W. von Seelen",
  title =        "Formation of pinwheels of preferred orientation by learning sparse neural representations of natural images",
  journal =      "Neurocomputing",
  year =         "2002",
  volume =       "44",
  month =        "June",
  abstract =     "",
}

@Article{tversky02a_bibuniq_2727,
  author =       "T. Tversky and R. Miikkulainen",
  title =        "Modeling directional selectivity using self-organizing delay-adaptation maps",
  journal =      "Neurocomputing",
  year =         "2002",
  volume =       "44",
  month =        "June",
  abstract =     "",
}

@Article{yano02a_bibuniq_2729,
  author =       "N. Yano and K. J. Fadden-Paiva and M. Endoh and H. Sakai and K. Kurokawa and L. D. Dworkin and A. Rifai",
  title =        "Profiling the Ig{A} nephropathy renal transcriptome: analysis by complementary {DNA} array hybridization",
  journal =      "Nephrology",
  year =         "2002",
  volume =       "7",
  month =        "August",
  abstract =     "",
}

@Article{goumas02a_bibuniq_2732,
  author =       "S. K. Goumas and M. E. Zervakis and G. S. Stavrakakis",
  title =        "Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction",
  journal =      "IEEE Transactions on Instrumentation and Measurement",
  year =         "2002",
  volume =       "51",
  number =       "3",
  month =        "June",
  abstract =     "",
}

@Article{gu02a_bibuniq_2733,
  author =       "C. C. Gu and D. C. Rao and G. Stormo and C. Hicks and M. A. Province",
  title =        "Role of gene expression microarray analysis in finding complex disease genes",
  journal =      "Genetic Epidemiology",
  year =         "2002",
  volume =       "23",
  number =       "1",
  month =        "June",
  abstract =     "",
}

@Article{schollhorn02a_bibuniq_2734,
  author =       "W. I. Schollhorn and B. M. Nigg and D. J. Stefanyshyn and W. Liu",
  title =        "Identification of individual walking patterns using time discrete and time continuous data sets",
  journal =      "Gait \& Posture",
  year =         "2002",
  volume =       "15",
  number =       "2",
  month =        "April",
  abstract =     "",
}

@Article{axelson02a_bibuniq_2737,
  author =       "D. Axelson and I. J. Bakken and I. S. Gribbestad and B. Ehrnholm and G. Nilsen and J. Aasly",
  title =        "Applications of neural network analyses to in vivo {H}-1 magnetic resonance spectroscopy of {P}arkinson disease patients",
  journal =      "Journal of Magnetic Resonance Imaging",
  year =         "2002",
  volume =       "16",
  number =       "1",
  month =        "July",
  abstract =     "",
}

@Article{delcarpio02a_bibuniq_2738,
  author =       "C. A. Del Carpio and T. Hennig and S. Fickel and A. Yoshimori",
  title =        "A combined bioinformatic approach oriented to the analysis and design of peptides with high affinity to {MHC} class {I} molecules",
  journal =      "Immunology and Cell Biology",
  year =         "2002",
  volume =       "80",
  number =       "3",
  month =        "June",
  abstract =     "",
}

@Article{castle02a_bibuniq_2739,
  author =       "A. L. Castle and M. R. Carver and D. L. Mendrick",
  title =        "Toxicogenomics: a new revolution in drug safety",
  journal =      "Drug Discovery Today",
  year =         "2002",
  volume =       "7",
  number =       "13",
  month =        "July" # " 1",
  abstract =     "",
}

@Article{brodnjak-voncina02a_bibuniq_2740,
  author =       "D. Brodnjak-Voncina and D. Dobcnik and M. Novic and J. Zupan",
  title =        "Chemometrics characterisation of the quality of river water",
  journal =      "Analytica Chimica Acta",
  year =         "2002",
  volume =       "462",
  number =       "1",
  month =        "June" # " 26",
  abstract =     "",
}

@Article{paulino02a_bibuniq_2742,
  author =       "W. F. Paulino and E. Detmann and S. D. Valadares and R. D. Lana",
  title =        "Whole soybean and cottonseed in multiple supplements for fattening of crossbred cattle under grazing",
  journal =      "Revista Brasileira DE Zootecnia-Brazilian Journal of Animal Science",
  year =         "2002",
  volume =       "31",
  number =       "1",
  month =        "January" # "-" # feb,
  abstract =     "",
}

@Article{wang02b_bibuniq_2747,
  author =       "C. C. Wang and G. P. J. Too",
  title =        "Rotating machine fault detection based on {HOS} and artificial neural networks",
  journal =      "Journal of Intelligent Manufacturing",
  year =         "2002",
  volume =       "13",
  number =       "4",
  month =        "August",
  abstract =     "",
}

@Article{bunke02a_bibuniq_2748,
  author =       "H. Bunke and X. Y. Jiang and K. Abegglen and A. Kandel",
  title =        "On the weighted mean of a pair of strings",
  journal =      "Pattern Analysis and Applications",
  year =         "2002",
  volume =       "5",
  number =       "1",
  abstract =     "",
}

@Article{ultsch02a_bibuniq_2750,
  author =       "A. Ultsch and F. Roske",
  title =        "Self-organizing feature maps predicting sea levels",
  journal =      "Information Sciences",
  year =         "2002",
  volume =       "144",
  number =       "1-4",
  month =        "July",
  abstract =     "",
}

@Article{banerjee02a_bibuniq_2751,
  author =       "N. Banerjee and M. X. Zhang",
  title =        "Functional genomics as applied to mapping transcription regulatory networks",
  journal =      "Current Opinion in Microbiology",
  year =         "2002",
  volume =       "5",
  number =       "3",
  month =        "June",
  abstract =     "",
}

@Article{zupan02a_bibuniq_2752,
  author =       "J. Zupan",
  title =        "2{D} mapping of large quantities of multi-variate data",
  journal =      "Croatica Chemica Acta",
  year =         "2002",
  volume =       "75",
  number =       "2",
  month =        "June",
  abstract =     "",
}

@Article{uchida02a_bibuniq_2753,
  author =       "S. Uchida and T. Shimizu",
  title =        "Modification of the {K}ohonen algorithm for the travelling salesman problem",
  journal =      "Journal of the Korean Physical Society",
  year =         "2002",
  volume =       "40",
  number =       "6",
  month =        "June",
  abstract =     "",
}

@Article{ngan02a_bibuniq_2755,
  author =       "S. C. Ngan and E. S. Yacoub and W. F. Auffermann and X. P. Hu",
  title =        "Node merging in {K}ohonen's self-organizing mapping of {fMRI} data",
  journal =      "Artificial Intelligence in Medicine",
  year =         "2002",
  volume =       "25",
  number =       "1",
  month =        "May",
  abstract =     "",
}

@Article{tereshko02a_bibuniq_2761,
  author =       "V. Tereshko and N. M. Allinson",
  title =        "Combining lateral and elastic interactions: Topology-preserving elastic nets",
  journal =      "Neural Processing Letters",
  year =         "2002",
  volume =       "15",
  number =       "3",
  month =        "June",
  abstract =     "",
}

@Article{todd02a_bibuniq_2762,
  author =       "R. Todd and D. T. W. Wong",
  title =        "{DNA} hybridization arrays for gene expression analysis of human oral cancer",
  journal =      "Journal of Dental Research",
  year =         "2002",
  volume =       "81",
  number =       "2",
  month =        "February",
  abstract =     "",
}

@Article{alexandridis02a_bibuniq_2764,
  author =       "A. P. Alexandridis and C. I. Siettos and H. K. Sarimveis and A. G. Boudouvis and G. V. Bafas",
  title =        "Modelling of nonlinear process dynamics using {K}ohonen's neural networks, fuzzy systems and Chebyshev series",
  journal =      "Computers \& Chemical Engineering",
  year =         "2002",
  volume =       "26",
  number =       "4-5",
  month =        "May" # " 15",
  abstract =     "",
}

@Article{saban02b_bibuniq_2766,
  author =       "M. R. Saban and N. B. Nguyen and T. G. Hammond and R. Saban",
  title =        "Gene expression profiling of mouse bladder inflammatory responses to {LPS}, substance {P}, and antigen-stimulation",
  journal =      "American Journal of Pathology",
  year =         "2002",
  volume =       "160",
  number =       "6",
  month =        "June",
  abstract =     "",
}

@Article{shilling02a_bibuniq_2767,
  author =       "P. D. Shilling and J. R. Kelsoe",
  title =        "Functional genomics approaches to understanding brain disorders",
  journal =      "Pharmacogenomics",
  year =         "2002",
  volume =       "3",
  number =       "1",
  month =        "January",
  abstract =     "",
}

@Article{lansiluoto02a_bibuniq_2769,
  author =       "A. Lansiluoto and B. Back and H. Vanharanta and A. Visa",
  title =        "An analysis of economic trends in the pulp and paper sector using self-organiziong maps",
  journal =      "Paperi JA PUU-Paper and Timber",
  year =         "2002",
  volume =       "84",
  number =       "4",
  abstract =     "",
}

@Article{vanhulle02c_bibuniq_2770,
  author =       "M. M. Van Hulle",
  title =        "Kernel-based topographic map formation by local density modeling",
  journal =      "Neural Computation",
  year =         "2002",
  volume =       "14",
  number =       "7",
  month =        "July",
  abstract =     "",
}

@Article{kuo02a_bibuniq_2771,
  author =       "R. J. Kuo and L. M. Ho and C. M. Hu",
  title =        "Cluster analysis in industrial market segmentation through artificial neural network",
  journal =      "Computers \& Industrial Engineering",
  year =         "2002",
  volume =       "42",
  number =       "2-4",
  month =        "June",
  abstract =     "",
}

@Article{soulet02a_bibuniq_2774,
  author =       "D. Soulet and S. Rivest",
  title =        "Perspective: How to make microarray, serial analysis of gene expression, and proteomic relevant to day-to-day endocrine problems and physiological systems",
  journal =      "Endocrinology",
  year =         "2002",
  volume =       "143",
  number =       "6",
  month =        "June",
  abstract =     "",
}

@Article{dozono02a_bibuniq_2785,
  author =       "H. Dozono and Y. Noguchi and S. Hara",
  title =        "A flexible design method of {DNA} chip using discrete valued self organizing maps",
  journal =      "Cytometry",
  year =         "2002",
  abstract =     "",
}

@Article{su02a_bibuniq_2787,
  author =       "M. C. Su and H. T. Chang and C. H. Chou",
  title =        "A novel measure for quantifying the topology preservation of self-organizing feature maps",
  journal =      "Neural Processing Letters",
  year =         "2002",
  volume =       "15",
  number =       "2",
  abstract =     "",
}

@Article{fryer02a_bibuniq_2791,
  author =       "R. M. Fryer and J. Randall and T. Yoshida and L. L. Hsiao and J. Blumenstock and K. E. Jensen and T. Dimofte and R. V. Jensen and S. R. Gullans",
  title =        "Global analysis of gene expression: Methods, interpretation, and pitfalls",
  journal =      "Experimental Nephrology",
  year =         "2002",
  volume =       "10",
  number =       "2",
  abstract =     "",
}

@Article{espinosa02a_bibuniq_2793,
  author =       "G. Espinosa and A. Arenas and F. Giralt",
  title =        "An integrated {SOM}-fuzzy {ARTMAP} neural system for the evaluation of toxicity",
  journal =      "Journal of Chemical Information and Computer Sciences",
  year =         "2002",
  volume =       "42",
  number =       "2",
  month =        "March" # "-" # apr,
  abstract =     "",
}

@Article{questier02a_bibuniq_2797,
  author =       "F. Questier and Q. Guo and B. Walczak and D. L. Massart and C. Boucon and S. de Jong",
  title =        "The Neural-Gas network for classifying analytical data",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  year =         "2002",
  volume =       "61",
  number =       "1-2",
  month =        "February" # " 28",
  abstract =     "",
}

@Article{warner02a_bibuniq_2800,
  author =       "E. E. Warner and B. K. Dieckgraefe",
  title =        "Application of genome-wide gene expression profiling by high-density {DNA} Arrays to the treatment and study of inflammatory bowel disease",
  journal =      "Inflammatory Bowel Diseases",
  year =         "2002",
  volume =       "8",
  number =       "2",
  month =        "March",
  abstract =     "",
}

@Article{villmann02b_bibuniq_2813,
  author =       "T. Villmann",
  title =        "Evolutionary algorithms using a neural network like migration scheme",
  journal =      "Integrated Computer-Aided Engineering",
  year =         "2002",
  volume =       "9",
  number =       "1",
  abstract =     "",
}

@Article{polanski03a_bibuniq_2828,
  author =       "J. Polanski and A. Bak",
  title =        "Modeling steric and electronic effects in {3D}-and {4D}-{QSAR} schemes: Predicting benzoic p{K}(a) values and steroid {CBG} binding affinities",
  journal =      "Journal of Chemical Information and Computer Sciences",
  year =         "2003",
  volume =       "43",
  number =       "6",
  month =        "November" # "-" # dec,
  abstract =     "",
}

@Article{zhang03a_bibuniq_2829,
  author =       "X. Y. Zhang and A. F. Clark and T. Yorio",
  title =        "Interactions of endothelin-1 with dexamethasone in primary cultured human trabecular meshwork cells",
  journal =      "Investigative Ophthalmology \& Visual Science",
  year =         "2003",
  volume =       "44",
  number =       "12",
  month =        "December",
  abstract =     "",
}

@Article{park03a_bibuniq_2832,
  author =       "Y. S. Park and J. B. Chang and S. Lek and W. X. Cao and S. Brosse",
  title =        "Conservation strategies for endemic fish species threatened by the Three Gorges Dam",
  journal =      "Conservation Biology",
  year =         "2003",
  volume =       "17",
  number =       "6",
  month =        "December",
  abstract =     "",
}

@InProceedings{decarvalho03a_bibuniq_2836,
  author =       "L. A. V. de Carvalho and R. S. Wedemann and R. Donangelo and D. Q. Mendes",
  title =        "Dopaminergic noise control of memory in psychic aparatus functioning",
  booktitle =    "Knowledge-Based Intelligent Information and Engineering Systems, Pt. 2, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "1172--1179",
  abstract =     "",
}

@InProceedings{elfadil03a_bibuniq_2837,
  author =       "N. Elfadil and D. Isa",
  title =        "Automated knowledge acquisition based on unsupervised neural network and expert system paradigms",
  booktitle =    "Knowledge-Based Intelligent Information and Engineering Systems, Pt. 1, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "134--140",
  abstract =     "",
}

@InProceedings{shin03a_bibuniq_2838,
  author =       "J. Shin",
  title =        "A study of the compression method for a reference character dictionary used for on-line character recognition",
  booktitle =    "Knowledge-Based Intelligent Information and Engineering Systems, Pt. 1, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "300--309",
  abstract =     "",
}

@InProceedings{velasquez03a_bibuniq_2839,
  author =       "J. D. Velasquez and H. Yasuda and T. Aoki and R. Weber and E. Vera",
  title =        "Using self organizing feature maps to acquire knowledge about visitor behavior in a web site",
  booktitle =    "Knowledge-Based Intelligent Information and Engineering Systems, Pt. 1, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "951--958",
  abstract =     "",
}

@InProceedings{adorno03a_bibuniq_2840,
  author =       "M. C. Adorno and M. Resta",
  title =        "A note on the sensitivity to parameters in the convergence of self-organizing maps",
  booktitle =    "Knowledge-Based Intelligent Information and Engineering Systems, Pt. 1, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "1088--1094",
  abstract =     "",
}

@Article{luo03a_bibuniq_2841,
  author =       "J. C. Luo and J. Zheng and Y. Leung and C. H. Zhou",
  title =        "A knowledge-integrated stepwise optimization model for feature mining in remotely sensed images",
  journal =      "International Journal of Remote Sensing",
  year =         "2003",
  volume =       "24",
  number =       "23",
  month =        "December",
  abstract =     "",
}

@Article{ang03a_bibuniq_2843,
  author =       "K. K. Ang and C. Quek and M. Pasquier",
  title =        "{POPFNN}-{CRI}({S}): Pseudo outer product based fuzzy neural network using the compositional rule of inference and singleton fuzzifier",
  journal =      "{IEEE} Transactions on Systems Man and Cybernetics Part B-Cybernetics",
  year =         "2003",
  volume =       "33",
  number =       "6",
  month =        "December",
  abstract =     "",
}

@Article{hasegawa03a_bibuniq_2847,
  author =       "K. Hasegawa and K. Morikami and Y. Shiratori and T. Ohtsuka and Y. Aoki and N. Shimma",
  title =        "3{D}-{QSAR} study of antifungal {N}-myristoyltransferase inhibitors by comparative molecular surface analysis",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  year =         "2003",
  volume =       "69",
  number =       "1-2",
  month =        "November" # " 28",
  abstract =     "",
}

@InProceedings{ramadas03a_bibuniq_2851,
  author =       "M. Ramadas and S. Ostermann and B. Tjaden",
  title =        "Detecting anomalous network traffic with self-organizing maps",
  booktitle =    "Recent Advances in Intrusion Detection, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "36--54",
  abstract =     "",
}

@Article{zavros03a_bibuniq_2858,
  author =       "Y. Zavros and S. Rathinavelu and J. Y. Kao and A. Todisco and J. {Del Valle} and J. V. Weinstock and M. J. Low and J. L. Merchant",
  title =        "Treatment of Helicobacter gastritis with {IL}-4 requires somatostatin",
  journal =      "Proceedings of the National Academy of Sciences oftheUnited States of America",
  year =         "2003",
  volume =       "100",
  number =       "22",
  month =        "October" # " 28",
  abstract =     "",
}

@Article{liu03b_bibuniq_2859,
  author =       "D. H. Liu and R. P. Singh and A. H. Khan and K. Bhavsar and A. J. Lusis and R. C. Davis and D. J. Smith",
  title =        "Identifying loci for behavioral traits using genome-tagged mice",
  journal =      "Journal of Neuroscience Research",
  year =         "2003",
  volume =       "74",
  number =       "4",
  month =        "November" # " 15",
  abstract =     "",
}

@Article{khuwaja03a_bibuniq_2861,
  author =       "G. A. Khuwaja",
  title =        "Adaptive {LVQ} classifier for invariant face recognition",
  journal =      "Cybernetics and Systems",
  year =         "2003",
  volume =       "34",
  number =       "8",
  month =        "December",
  abstract =     "",
}

@Article{li03a_bibuniq_2866,
  author =       "S. T. Li and J. T. Kwok and H. L. Zhu and Y. N. Wang",
  title =        "Texture classification using the support vector machines",
  journal =      "Pattern Recognition",
  year =         "2003",
  volume =       "36",
  number =       "12",
  month =        "December",
  abstract =     "",
}

@Article{wang03a_bibuniq_2867,
  author =       "S. H. Wang",
  title =        "Application of self-organising maps for data mining with incomplete data sets",
  journal =      "Neural Computing \& Applications",
  year =         "2003",
  volume =       "12",
  number =       "1",
  month =        "September",
  abstract =     "",
}

@Article{conde03a_bibuniq_2868,
  author =       "L. Conde and A. Mateos and J. Herrero and J. Dopazo",
  title =        "Improved class prediction in {DNA} microarray gene expression data by unsupervised reduction of the dimensionality followed by supervised learning with a perceptron",
  journal =      "Journal of {VLSI} Signal Processing Systems for Signal Image and Video Technology",
  year =         "2003",
  volume =       "35",
  number =       "3",
  month =        "November",
  abstract =     "",
}

@Article{ster03a_bibuniq_2871,
  author =       "B. Ster and A. Dobnikar",
  title =        "Adaptive radial basis decomposition by learning vector quantization",
  journal =      "Neural Processing Letters",
  year =         "2003",
  volume =       "18",
  number =       "1",
  month =        "August",
  abstract =     "",
}

@Article{sara03a_bibuniq_2872,
  author =       "G. Sara and S. Vizzini and A. Mazzola",
  title =        "Sources of carbon and dietary habits of new Lessepsian entry Brachidontes pharaonis (Bivalvia, Mytilidae) in the western Mediterranean",
  journal =      "Marine Biology",
  year =         "2003",
  volume =       "143",
  number =       "4",
  month =        "October",
  abstract =     "",
}

@Article{schneider03b_bibuniq_2873,
  author =       "G. Schneider and M. Nettekoven",
  title =        "Ligand-based combinatorial design of selective purinergic receptor ({A}(2{A})) antagonists using self-organizing maps",
  journal =      "Journal of Combinatorial Chemistry",
  year =         "2003",
  volume =       "5",
  number =       "3",
  month =        "May" # "-" # jun,
  abstract =     "",
}

@InProceedings{christodoulou03b_bibuniq_2874,
  author =       "C. I. Christodoulou and E. Kyriacou and M. S. Pattichis and C. S. Pattichis and A. Nicolaides",
  title =        "A comparative study of morphological and other texture features for the characterization of atherosclerotic carotid plaques",
  booktitle =    "Computer Analysis of Images and Patterns, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "503--511",
  abstract =     "",
}

@InProceedings{leitao03a_bibuniq_2875,
  author =       "A. P. Leitao and S. Tilie and S. S. Ieng and V. Vigneron",
  title =        "Detecting and classifying road turn directions from a sequence of images",
  booktitle =    "Computer Analysis of Images and Patterns, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "555--562",
  abstract =     "",
}

@InProceedings{yang03b_bibuniq_2876,
  author =       "H. C. Yang and C. H. Lee",
  title =        "Discovering image semantics from web pages using a text mining approach",
  booktitle =    "Advances in WEB-AGE Information Management, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "495--502",
  abstract =     "",
}

@InProceedings{chan03a_bibuniq_2879,
  author =       "B. K. Y. Chan and W. W. S. Chu and L. Xu",
  title =        "Empirical comparison between two computational strategies for topological self-organization",
  booktitle =    "Intelligent Data Engineering and Automated Learning, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "410--414",
  abstract =     "",
}

@InProceedings{pulkkinen03a_bibuniq_2880,
  author =       "J. Pulkkinen and M. Lappalainen and A. M. Hakkinen and N. Lundbom and R. A. Kauppinen and Y. Hiltunen",
  title =        "Quantification of human brain metabolites from in vivo {H}-1 {NMR} magnitude spectra using self-organising maps",
  booktitle =    "Intelligent Data Engineering and Automated Learning, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "522--529",
  abstract =     "",
}

@InProceedings{chau03a_bibuniq_2881,
  author =       "R. Chau and C. H. Yeh",
  title =        "A concept-based inter-lingua and its applications to multilingual text and Web mining",
  booktitle =    "Intelligent Data Engineering and Automated Learning, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "756--760",
  abstract =     "",
}

@Article{tani03a_bibuniq_2882,
  author =       "J. Tani and M. Ito",
  title =        "Self-organization of behavioral primitives as multiple attractor dynamics: {A} robot experiment",
  journal =      "{IEEE} Transactions on Systems Man and Cybernetics Part A-Systems and Humans",
  year =         "2003",
  volume =       "33",
  number =       "4",
  month =        "July",
  abstract =     "",
}

@Article{herbert03a_bibuniq_2883,
  author =       "D. A. Herbert and M. Williams and E. B. Rastetter",
  title =        "A model analysis of {N} and {P} limitation on carbon accumulation in Amazonian secondary forest after alternate land-use abandonment",
  journal =      "Biogeochemistry",
  year =         "2003",
  volume =       "65",
  number =       "1",
  month =        "August",
  abstract =     "",
}

@Article{magallanes03a_bibuniq_2885,
  author =       "J. F. Magallanes and J. Zupan and D. Gomez and S. Reich and L. Dawidowski and N. Groselj",
  title =        "The mean angular distance among objects and its relationships with {K}ohonen artificial neural networks",
  journal =      "Journal of Chemical Information and Computer Sciences",
  year =         "2003",
  volume =       "43",
  number =       "5",
  month =        "September" # "-" # oct,
  abstract =     "",
}

@Article{lei03a_bibuniq_2888,
  author =       "T. Lei and J. K. Udupa",
  title =        "Performance evaluation of finite normal mixture model-based image segmentation techniques",
  journal =      "{IEEE} Transactions on Image Processing",
  year =         "2003",
  volume =       "12",
  number =       "10",
  month =        "October",
  abstract =     "",
}

@Article{hasegawa03b_bibuniq_2890,
  author =       "K. Hasegawa and S. Matsuoka and M. Arakawa and K. Funatsu",
  title =        "Multi-way {PLS} modeling of structure-activity data by incorporating electrostatic and lipophilic potentials on molecular surface",
  journal =      "Computational Biology and Chemistry",
  year =         "2003",
  volume =       "27",
  number =       "3",
  month =        "July",
  abstract =     "",
}

@Article{rakovska03a_bibuniq_2892,
  author =       "A. Rakovska and D. Javitt and P. Raichev and R. Ang and A. Balla and J. Aspromonte and S. Vizi",
  title =        "Physiological release of striatal acetylcholine (in vivo): effect of somatostatin on dopaminergic-cholinergic interaction",
  journal =      "Brain Research Bulletin",
  year =         "2003",
  volume =       "61",
  number =       "5",
  month =        "September" # " 30",
  abstract =     "",
}

@Article{pintore03a_bibuniq_2895,
  author =       "M. Pintore and N. Piclin and E. Benfenati and G. Gini and J. R. Chretien",
  title =        "Predicting toxicity against the fathead Minnow by Adaptive Fuzzy Partition",
  journal =      "Qsar \& Combinatorial Science",
  year =         "2003",
  volume =       "22",
  number =       "2",
  month =        "April",
  abstract =     "",
}

@Article{chen03b_bibuniq_2897,
  author =       "Y. Chen and A. T. Chwang",
  title =        "Particle image velocimetry system with self-organized feature map algorithm",
  journal =      "Journal of Engineering Mechanics-Asce",
  year =         "2003",
  volume =       "129",
  number =       "10",
  month =        "October",
  abstract =     "",
}

@Article{sharma03a_bibuniq_2898,
  author =       "S. Sharma and S. V. Barai",
  title =        "Studies of air quality predictors based on neural networks",
  journal =      "International Journal of Environment and Pollution",
  year =         "2003",
  volume =       "19",
  number =       "5",
  abstract =     "",
}

@InProceedings{pal03a_bibuniq_2901,
  author =       "S. Pal and J. Das and K. Majumdar",
  title =        "A hybrid neural architecture and its application to temperature prediction",
  booktitle =    "Artificail Neural Networks and Neural Information Processing - {ICAN/ICONIP} 2003, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "581--588",
  abstract =     "",
}

@InProceedings{albayrak03a_bibuniq_2902,
  author =       "S. Albayrak",
  title =        "Unsupervised clustering methods for medical data: An application to thyroid gland data",
  booktitle =    "Artificail Neural Networks and Neural Information Processing - {ICAN/ICONIP} 2003, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "695--701",
  abstract =     "",
}

@InProceedings{paplinski03a_bibuniq_2904,
  author =       "A. P. Paplinski and L. Gustafsson",
  title =        "Detailed learning in narrow fields - Towards a neural network model of autism",
  booktitle =    "Artificail Neural Networks and Neural Information Processing - {ICAN/ICONIP} 2003, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "830--838",
  abstract =     "",
}

@InProceedings{nurnberger03a_bibuniq_2905,
  author =       "A. Nurnberger and M. Detyniecki",
  title =        "Weighted self-organizing maps: Incorporating user feedback",
  booktitle =    "Artificail Neural Networks and Neural Information Processing - {ICAN/ICONIP} 2003, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "883--890",
  abstract =     "",
}

@Article{petrelli03a_bibuniq_2906,
  author =       "M. Petrelli and D. Perugini and B. Moroni and G. Poli",
  title =        "Determination of travertine provenance from ancient buildings using self-organizing maps and fuzzy logic",
  journal =      "Applied Artificial Intelligence",
  year =         "2003",
  volume =       "17",
  number =       "8-9",
  month =        "September" # "-" # oct,
  abstract =     "",
}

@Article{irvine03a_bibuniq_2907,
  author =       "W. M. Irvine and A. Carraminana and L. Carrasco and F. P. Schloerb",
  title =        "The Large Millimeter Telescope El Gran Telescopio Milimetrico: {A} new instrument for astrobiology",
  journal =      "Origins of Life and Evolution of the Biosphere",
  year =         "2003",
  volume =       "33",
  number =       "6",
  month =        "December",
  abstract =     "",
}

@Article{fasulo03a_bibuniq_2909,
  author =       "W. H. Fasulo and S. E. Hemby",
  title =        "Time-dependent changes in gene expression profiles of midbrain dopamine neurons following haloperidol administration",
  journal =      "Journal of Neurochemistry",
  year =         "2003",
  volume =       "87",
  number =       "1",
  month =        "October",
  abstract =     "",
}

@Article{polanski03b_bibuniq_2913,
  author =       "J. Polanski",
  title =        "Self-organizing neural networks for pharmacophore mapping",
  journal =      "Advanced Drug Delivery Reviews",
  year =         "2003",
  volume =       "55",
  number =       "9",
  month =        "September" # " 12",
  abstract =     "",
}

@Article{simelius03a_bibuniq_2915,
  author =       "K. Simelius and M. Stenroos and L. Reinhardt and J. Nenonen and I. Tierala and M. Makijarvi and L. Toivonen and T. Katila",
  title =        "Spatiotemporal characterization of paced cardiac activation with body surface potential mapping and self-organizing maps",
  journal =      "Physiological Measurement",
  year =         "2003",
  volume =       "24",
  number =       "3",
  month =        "August",
  abstract =     "",
}

@InProceedings{turtinen03a_bibuniq_2918,
  author =       "M. Turtinen and T. Maenpaa and M. Pietikainen",
  title =        "Texture classification by combining local binary pattern features and a self-organizing map",
  booktitle =    "Image Analysis, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "1162--1169",
  abstract =     "",
}

@InProceedings{rodrigues03a_bibuniq_2922,
  author =       "F. Rodrigues and J. Duarte and V. Figueiredo and Z. Vale and M. Cordeiro",
  title =        "A comparative analysis of clustering algorithms applied to load profiling",
  booktitle =    "Machine Learning and Data Mining in Pattern Recognition, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "73--85",
  abstract =     "",
}

@InProceedings{barsi03a_bibuniq_2923,
  author =       "A. Barsi",
  title =        "Neural self-organization using graphs",
  booktitle =    "Machine Learning and Data Mining in Pattern Recognition, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "343--352",
  abstract =     "",
}

@InProceedings{romero03a_bibuniq_2927,
  author =       "G. Romero and M. G. Arenas and P. A. Castillo and J. J. Merelo",
  title =        "Visualization of neural net evolution",
  booktitle =    "Computational Methods in Neural Modeling, Pt. 1, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "534--541",
  abstract =     "",
}

@InProceedings{soria-frisch03a_bibuniq_2928,
  author =       "A. Soria-Frisch and M. Koppen",
  title =        "Morphological clustering of the {SOM} for multi-dimensional image segmentation",
  booktitle =    "Computational Methods in Neural Modeling, Pt. 1, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "582--589",
  abstract =     "",
}

@InProceedings{pedreno-molina03a_bibuniq_2929,
  author =       "J. L. Pedreno-Molina and A. Guerrero-Gonzalez and O. A. Florez-Giraldo and J. Molina-Vilaplana",
  title =        "Sensory-motor control scheme based on {K}ohonen maps and {AVITE} model",
  booktitle =    "Artificial Neural Nets Problem Solving Methods, Pt. II, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "185--192",
  abstract =     "",
}

@InProceedings{baldassarri03a_bibuniq_2930,
  author =       "P. Baldassarri and P. Puliti and A. Montesanto and G. Tascini",
  title =        "Self-organizing maps versus growing neural gas in a robotic application",
  booktitle =    "Artificial Neural Nets Problem Solving Methods, Pt. II, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "201--208",
  abstract =     "",
}

@InProceedings{theis03a_bibuniq_2931,
  author =       "F. J. Theis and M. R. Alvarez and C. G. Puntonet and E. W. Lang",
  title =        "An adaptive approach to blind source separation using a self-organzing map and a neural gas",
  booktitle =    "Artificial Neural Nets Problem Solving Methods, Pt. II, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "695--702",
  abstract =     "",
}

@Article{dieterle03a_bibuniq_2932,
  author =       "F. Dieterle and S. Muller-Hagedorn and H. M. Liebich and G. Gauglitz",
  title =        "Urinary nucleosides as potential tumor markers evaluated by learning vector quantization",
  journal =      "Artificial Intelligence in Medicine",
  year =         "2003",
  volume =       "28",
  number =       "3",
  month =        "July",
  abstract =     "",
}

@Article{cowling03a_bibuniq_2934,
  author =       "M. Cowling and R. Sitte",
  title =        "Comparison of techniques for environmental sound recognition",
  journal =      "Pattern Recognition Letters",
  year =         "2003",
  volume =       "24",
  number =       "15",
  month =        "November",
  abstract =     "",
}

@InProceedings{mollineda03a_bibuniq_2935,
  author =       "R. A. Mollineda and E. Vidal and C. Martinez-Hinarejos",
  title =        "Adaptive learning for string classification",
  booktitle =    "Pattern Recognition and Image Analysis, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "564--571",
  abstract =     "",
}

@InProceedings{penas03a_bibuniq_2936,
  author =       "M. Penas and M. J. Carreiro and M. G. Penedo",
  title =        "Gabor wavelets and auto-organised structures for directional primitive extraction",
  booktitle =    "Pattern Recognition and Image Analysis, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "722--732",
  abstract =     "",
}

@InProceedings{deleon03a_bibuniq_2937,
  author =       "P. J. P. de Leon and J. M. Inesta",
  title =        "Feature-driven recognition of music styles",
  booktitle =    "Pattern Recognition and Image Analysis, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "773--781",
  abstract =     "",
}

@InProceedings{ahmad03a_bibuniq_2938,
  author =       "K. Ahmad and M. Casey and B. Vrusias and P. Saragiotis",
  title =        "Combining multiple modes of information using unsupervised neural classifiers",
  booktitle =    "Multiple Classifier Systems, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "236--245",
  abstract =     "",
}

@InProceedings{george03a_bibuniq_2940,
  author =       "S. E. George",
  title =        "Clustering on-line dynamically constructed handwritten music notation with the self-organising feature map",
  booktitle =    "Developments in Applied Artificial Intelligence, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "93--103",
  abstract =     "",
}

@InProceedings{sas03a_bibuniq_2943,
  author =       "C. Sas and G. O'Hare and R. Reilly",
  title =        "Online trajectory classification",
  booktitle =    "Computational Sicence - {ICCS} 2003, Pt. III, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "1035--1044",
  abstract =     "",
}

@Article{levita03a_bibuniq_2944,
  author =       "L. Levita and I. Mania and D. G. Rainnie",
  title =        "Subtypes of substance {P} receptor immunoreactive interneurons in the rat basolateral amygdala",
  journal =      "Brain Research",
  year =         "2003",
  volume =       "981",
  number =       "1-2",
  month =        "August" # " 15",
  abstract =     "",
}

@InProceedings{fan03a_bibuniq_2945,
  author =       "N. P. Fan and J. Rosca",
  title =        "Enhanced {VQ}-based algorithms for speech independent speaker identification",
  booktitle =    "Audio-AND Video-Based Biometric Person Authentication, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "470--477",
  abstract =     "",
}

@InProceedings{yu03a_bibuniq_2947,
  author =       "X. Yu and S. X. Yang",
  title =        "Motion recognition from video sequences",
  booktitle =    "Advances in Artificial Intelligence, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "532--536",
  abstract =     "",
}

@InProceedings{je03a_bibuniq_2948,
  author =       "S. K. Je and C. J. Seo and J. Y. Lee and E. Y. Cha",
  title =        "Self-organizing coefficient for semi-blind watermarking",
  booktitle =    "WEB Technologies and Applications, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "275--286",
  abstract =     "",
}

@InProceedings{yang03d_bibuniq_2949,
  author =       "H. Yang and M. J. Zhang",
  title =        "Intelligent search for distributed information sources using heterogeneous neural networks",
  booktitle =    "WEB Technologies and Applications, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "513--524",
  abstract =     "",
}

@Article{givehchi03a_bibuniq_2950,
  author =       "A. Givehchi and A. Dietrich and P. Wrede and G. Schneider",
  title =        "ChemSpaceShuttle: {A} tool for data mining in drug discovery by classification, projection, and 3{D} visualization",
  journal =      "{QSAR} \& Combinatorial Science",
  year =         "2003",
  volume =       "22",
  number =       "5",
  month =        "July",
  abstract =     "",
}

@Article{korolev03a_bibuniq_2951,
  author =       "D. Korolev and K. V. Balakin and Y. Nikolsky and E. Kirillov and Y. A. Ivanenkov and N. P. Savchuk and A. A. Ivashchenko and T. Nikolskaya",
  title =        "Modeling of human cytochrome {P450}-mediated drug metabolism using unsupervised machine learning approach",
  journal =      "Journal of Medicinal Chemistry",
  year =         "2003",
  volume =       "46",
  number =       "17",
  month =        "August" # " 14",
  abstract =     "",
}

@Article{tran03a_bibuniq_2955,
  author =       "L. T. Tran and C. G. Knight and R. V. O'Neill and E. R. Smith and M. O'Connell",
  title =        "Self-organizing maps for integrated environmental",
  journal =      "Environmental Management",
  year =         "2003",
  volume =       "31",
  number =       "6",
  month =        "June",
  abstract =     "",
}

@Article{ivanenkov03a_bibuniq_2956,
  author =       "Y. A. Ivanenkov and K. V. Balakin and A. V. Skorenko and  {Tkachenko, } and N. P. Savchuk and A. A. Ivashchenko and Y. Nikolsky",
  title =        "Application of advanced machine learning algorithm for profiling specific {GPCR}-active compounds",
  journal =      "Chimica Oggi-Chemistry Today",
  year =         "2003",
  volume =       "21",
  number =       "6",
  month =        "June",
  abstract =     "",
}

@Article{linder03a_bibuniq_2957,
  author =       "J. D. Linder and J. C. Klapow and S. D. Linder and C. M. Wilcox",
  title =        "Incomplete response to endoscopic sphincterotomy in patients with sphincter of Oddi dysfunction: Evidence for a chronic pain disorder",
  journal =      "American Journal of Gastroenterology",
  year =         "2003",
  volume =       "98",
  number =       "8",
  month =        "August",
  abstract =     "",
}

@Article{carlton03a_bibuniq_2958,
  author =       "S. M. Carlton and S. Zhou and B. Kraemer and R. E. Coggeshall",
  title =        "A role for peripheral somatostatin receptors in counter-irritation-induced analgesia",
  journal =      "Neuroscience",
  year =         "2003",
  volume =       "120",
  number =       "2",
  abstract =     "",
}

@Article{liang03a_bibuniq_2961,
  author =       "X. J. Liang and J. S. N. Jean",
  title =        "Mapping of generalized template matching onto reconfigurable computers",
  journal =      "{IEEE} Transactions on Very Large Scale Integration ({VLSI}) Systems",
  year =         "2003",
  volume =       "11",
  number =       "3",
  month =        "June",
  abstract =     "",
}

@Article{baisden03a_bibuniq_2962,
  author =       "W. T. Baisden and R. Amundson",
  title =        "An analytical approach to ecosystem biogeochemistry modeling",
  journal =      "Ecological Applications",
  year =         "2003",
  volume =       "13",
  number =       "3",
  month =        "June",
  abstract =     "",
}

@Article{liu03c_bibuniq_2963,
  author =       "C. L. Liu and K. Nakashima and H. Sako and H. Fujisawa",
  title =        "Handwritten digit recognition: benchmarking of state of the-art techniques",
  journal =      "Pattern Recognition",
  year =         "2003",
  volume =       "36",
  number =       "10",
  month =        "October",
  abstract =     "",
}

@Article{holubar03a_bibuniq_2964,
  author =       "P. Holubar and L. Zani and M. Hager and W. Frochl and Z. Radak and R. Braun",
  title =        "Start-up and recovery of a biogas-reactor using a hierarchical neural network-based control tool",
  journal =      "Journal of Chemical Technology and Biotechnology",
  year =         "2003",
  volume =       "78",
  number =       "8",
  month =        "August",
  abstract =     "",
}

@Article{kotzab03a_bibuniq_2965,
  author =       "H. Kotzab and N. Skjoldager and T. Vinum",
  title =        "The development and empirical validation of an e-based supply chain strategy optimization model",
  journal =      "Industrial Management \& Data Systems",
  year =         "2003",
  volume =       "103",
  number =       "5-6",
  abstract =     "",
}

@Article{garcia-villada03a_bibuniq_2968,
  author =       "E. Garcia-Villada",
  title =        "Experiential learning in foreign language education.",
  journal =      "Tesol Quarterly",
  year =         "2003",
  volume =       "37",
  number =       "2",
  month =        "SUM",
  abstract =     "",
}

@InProceedings{jiang03a_bibuniq_2969,
  author =       "Y. Jiang and K. J. Chen and Z. H. Zhou",
  title =        "{SOM} based image segmentation",
  booktitle =    "Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "640--643",
  abstract =     "",
}

@Article{niang03a_bibuniq_2970,
  author =       "A. Niang and L. Gross and S. Thiria and F. Badran and C. Moulin",
  title =        "Automatic neural classification of ocean colour reflectance spectra at the top of the atmosphere with introduction of expert knowledge",
  journal =      "Remote Sensing of Environment",
  year =         "2003",
  volume =       "86",
  number =       "2",
  month =        "July" # " 30",
  abstract =     "",
}

@Article{zhang03b_bibuniq_2971,
  author =       "Y. X. Zhang and Y. H. Zhao",
  title =        "Classification in multidimensional parameter space: Methods and examples",
  journal =      "Publications of the Astronomical Society of the Pacific",
  year =         "2003",
  volume =       "115",
  number =       "810",
  month =        "August",
  abstract =     "",
}

@Article{ishihi03a_bibuniq_2973,
  author =       "Y. Ishihi",
  title =        "Feeding of the bivalve Theora lubrica on benthic microalgae: isotopic evidence",
  journal =      "Marine Ecology-Progress Series",
  year =         "2003",
  volume =       "255",
  abstract =     "",
}

@Article{lin03b_bibuniq_2976,
  author =       "X. Lin and H. D. White and J. Buzydlowski",
  title =        "Real-time author co-citation mapping for online searching",
  journal =      "Information Processing \& Management",
  year =         "2003",
  volume =       "39",
  number =       "5",
  month =        "September",
  abstract =     "",
}

@Article{solberg03a_bibuniq_2979,
  author =       "B. Solberg and A. Moiseyev and A. M. I. Kallio",
  title =        "Economic impacts of accelerating forest growth in Europe",
  journal =      "Forest Policy and Economics",
  year =         "2003",
  volume =       "5",
  number =       "2",
  month =        "July",
  abstract =     "",
}

@Article{buckhaults03a_bibuniq_2980,
  author =       "P. Buckhaults and Z. Zhang and Y. C. Chen and T. L. Wang and B. St Croix and S. Saba and A. Bardelli and P. J. Morin and K. Polyak and R. H. Hruban and V. E. Velculescu and I. M. Shih",
  title =        "Identifying tumor origin using a gene expression-based classification map",
  journal =      "Cancer Research",
  year =         "2003",
  volume =       "63",
  number =       "14",
  month =        "July" # " 15",
  abstract =     "",
}

@Article{mannan03a_bibuniq_2981,
  author =       "B. Mannan and A. K. Ray",
  title =        "Crisp and fuzzy competitive learning networks for supervised classification of multispectral {IRS} scenes",
  journal =      "International Journal of Remote Sensing",
  year =         "2003",
  volume =       "24",
  number =       "17",
  month =        "September" # " 10",
  abstract =     "",
}

@Article{aiello03a_bibuniq_2982,
  author =       "A. Aiello and D. Grimaldi",
  title =        "Frequency error measurement in {GMSK} signals in a multipath propagation environment",
  journal =      "{IEEE} Transactions on Instrumentation and Measurement",
  year =         "2003",
  volume =       "52",
  number =       "3",
  month =        "June",
  abstract =     "",
}

@Article{kim03c_bibuniq_2983,
  author =       "J. Kim and T. Chen",
  title =        "Combining static and dynamic features using neural networks and edge fusion for video object extraction",
  journal =      "IEE Proceedings-Vision Image and Signal Processing",
  year =         "2003",
  volume =       "150",
  number =       "3",
  month =        "June",
  abstract =     "",
}

@Article{vandegrift03a_bibuniq_2986,
  author =       "K. J. Vandegrift and T. L. Cravener and R. M. Hulet and W. B. Roush",
  title =        "Analysis of the nonlinear dynamics of daily broiler growth and feed intake",
  journal =      "Poultry Science",
  year =         "2003",
  volume =       "82",
  number =       "7",
  month =        "July",
  abstract =     "",
}

@Article{caudell03a_bibuniq_2987,
  author =       "T. P. Caudell and Y. H. Xiao and M. J. Healy",
  title =        "eLoom and Flatland: specification, simulation and visualization engines for the study of arbitrary hierarchical neural architectures",
  journal =      "Neural Networks",
  year =         "2003",
  volume =       "16",
  number =       "5-6",
  month =        "June" # "-" # jul,
  abstract =     "",
}

@Article{diaz03a_bibuniq_2990,
  author =       "C. Diaz and J. E. Conde and D. Estevez and S. J. P. Olivero and J. P. P. Trujillo",
  title =        "Application of multivariate analysis and artificial neural networks for the differentiation of red wines from the Canary Islands according to the island of origin",
  journal =      "Journal of Agricultural and Food Chemistry",
  year =         "2003",
  volume =       "51",
  number =       "15",
  month =        "July" # " 16",
  abstract =     "",
}

@Article{papamarkos03a_bibuniq_2992,
  author =       "N. Papamarkos",
  title =        "Document gray-scale reduction using a neuro-fuzzy technique",
  journal =      "International Journal of Pattern Recognition and Artificial Intelligence",
  year =         "2003",
  volume =       "17",
  number =       "4",
  month =        "June",
  abstract =     "",
}

@Article{khalid03a_bibuniq_2994,
  author =       "A. Khalid and M. Peterson and A. Slivka",
  title =        "Secretin-stimulated magnetic resonance pancreaticogram to assess pancreatic duct outflow obstruction in evaluation of idiopathic acute recurrent pancreatitis - {A} pilot study",
  journal =      "Digestive Diseases and Sciences",
  year =         "2003",
  volume =       "48",
  number =       "8",
  month =        "August",
  abstract =     "",
}

@Article{plermkamon03a_bibuniq_2995,
  author =       "S. Plermkamon and N. Afzulpurkar",
  title =        "An intelligent real-time tracking and grasping system for a robotic work cell",
  journal =      "Advanced Robotics",
  year =         "2003",
  volume =       "17",
  number =       "5",
  abstract =     "",
}

@Article{podlasz03a_bibuniq_2996,
  author =       "P. Podlasz and K. Wasowicz and J. Kaleczyc and M. Lakomy and R. Bukowski",
  title =        "Localization of immunoreactivities for neuropeptides and neurotransmitter-synthesizing enzymes in the pterygopalatine ganglion of the pig",
  journal =      "Veterinarni Medicina",
  year =         "2003",
  volume =       "48",
  number =       "4",
  month =        "April",
  abstract =     "",
}

@Article{jeon03a_bibuniq_2997,
  author =       "B. S. Jeon and J. H. Bae and M. W. Suh",
  title =        "Automatic recognition of woven fabric patterns by an artificial neural network",
  journal =      "Textile Research Journal",
  year =         "2003",
  volume =       "73",
  number =       "7",
  month =        "July",
  abstract =     "",
}

@Article{sato03a_bibuniq_2998,
  author =       "A. Sato and A. Inoue and T. Suzuki and T. Hosoi",
  title =        "NeoFace - Development of face detection and recognition engine",
  journal =      "NEC Research \& Development",
  year =         "2003",
  volume =       "44",
  number =       "3",
  month =        "July",
  abstract =     "",
}

@Article{luo03c_bibuniq_2999,
  author =       "X. C. Luo and C. Singh and A. D. Patton",
  title =        "Power system reliability evaluation using learning vector quantization and Monte Carlo simulation",
  journal =      "Electric Power Systems Research",
  year =         "2003",
  volume =       "66",
  number =       "2",
  month =        "August",
  abstract =     "",
}

@Article{garner03a_bibuniq_3006,
  author =       "B. Garner",
  title =        "A novel approach to training neurons with biological plausibility",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "52-4",
  month =        "June",
  abstract =     "",
}

@Article{seo03a_bibuniq_3009,
  author =       "S. Seo and K. Obermayer",
  title =        "Soft learning vector quantization",
  journal =      "Neural Computation",
  year =         "2003",
  volume =       "15",
  number =       "7",
  month =        "July",
  abstract =     "",
}

@Article{roggo03a_bibuniq_3010,
  author =       "Y. Roggo and L. Duponchel and C. Ruckebusch and J. P. Huvenne",
  title =        "Statistical tests for comparison of quantitative and qualitative models developed with near infrared spectral data",
  journal =      "Journal of Molecular Structure",
  year =         "2003",
  volume =       "654",
  number =       "1-3",
  month =        "June" # " 25",
  abstract =     "",
}

@Article{durr03a_bibuniq_3011,
  author =       "V. Durr and A. F. Krause and J. Schmitz and H. Cruse",
  title =        "Neuroethological concepts and their transfer to walking machines",
  journal =      "International Journal of Robotics Research",
  year =         "2003",
  volume =       "22",
  number =       "3-4",
  month =        "March" # "-" # apr,
  abstract =     "",
}

@Article{mascagni03a_bibuniq_3013,
  author =       "F. Mascagni and A. J. McDonald",
  title =        "Immunohistochemical characterization of cholecystokinin containing neurons in the rat basolateral amygdala",
  journal =      "Brain Research",
  year =         "2003",
  volume =       "976",
  number =       "2",
  month =        "June" # " 27",
  abstract =     "",
}

@Article{monti03a_bibuniq_3015,
  author =       "S. Monti and P. Tamayo and J. Mesirov and T. Golub",
  title =        "Consensus clustering: {A} resampling-based method for class discovery and visualization of gene expression microarray data",
  journal =      "Machine Learning",
  year =         "2003",
  volume =       "52",
  number =       "1-2",
  month =        "July" # "-" # aug,
  abstract =     "",
}

@Article{jinno03a_bibuniq_3017,
  author =       "S. Jinno and T. Kosaka",
  title =        "Patterns of expression of neuropeptides in {GABA}ergic nonprincipal neurons in the mouse hippocampus: Quantitative analysis with optical disector",
  journal =      "Journal of Comparative Neurology",
  year =         "2003",
  volume =       "461",
  number =       "3",
  month =        "June" # " 30",
  abstract =     "",
}

@Article{taylor03a_bibuniq_3018,
  author =       "A. W. Taylor and D. G. Yee",
  title =        "Somatostatin is an imunosuppressive factor in aqueous humor",
  journal =      "Investigative Ophthalmology \& Visual Science",
  year =         "2003",
  volume =       "44",
  number =       "6",
  month =        "June",
  abstract =     "",
}

@Article{sverud03a_bibuniq_3019,
  author =       "O. Sverud and R. M. MacCallum",
  title =        "Towards optimal views of proteins",
  journal =      "Bioinformatics",
  year =         "2003",
  volume =       "19",
  number =       "7",
  month =        "May" # " 1",
  abstract =     "",
}

@Article{draghici03a_bibuniq_3020,
  author =       "S. Draghici and F. Graziano and S. Kettoola and I. Sethi and G. Towfic",
  title =        "Mining {HIV} dynamics using independent component analysis",
  journal =      "Bioinformatics",
  year =         "2003",
  volume =       "19",
  number =       "8",
  month =        "May" # " 22",
  abstract =     "",
}

@Article{liu03d_bibuniq_3024,
  author =       "Z. H. Liu and C. G. Zheng and L. X. Zhou",
  title =        "A joint {PDF} model for turbulent spray evaporation/combustion",
  journal =      "Proceedings of the Combustion Institute",
  year =         "2003",
  volume =       "29",
  abstract =     "",
}

@Article{daszykowski03a_bibuniq_3026,
  author =       "M. Daszykowski and B. Walczak and D. L. Massart",
  title =        "A journey into low-dimensional spaces with autoassociative neural networks",
  journal =      "Talanta",
  year =         "2003",
  volume =       "59",
  number =       "6",
  month =        "May" # " 1",
  abstract =     "",
}

@Article{prabhakar03a_bibuniq_3027,
  author =       "S. Prabhakar and A. K. Jain and S. Pankanti",
  title =        "Learning fingerprint minutiae location and type",
  journal =      "Pattern Recognition",
  year =         "2003",
  volume =       "36",
  number =       "8",
  month =        "August",
  abstract =     "",
}

@Article{vakil-baghmisheh03a_bibuniq_3028,
  author =       "M. T. Vakil-Baghmisheh and N. Pavesic",
  title =        "Premature clustering phenomenon and new training algorithms for {LVQ}",
  journal =      "Pattern Recognition",
  year =         "2003",
  volume =       "36",
  number =       "8",
  month =        "August",
  abstract =     "",
}

@Article{fogle-hansson03a_bibuniq_3030,
  author =       "M. Fogle-Hansson and P. White and A. Hermansson",
  title =        "Pathogens in acute otitis media - impact of intermittent penicillin {V} prophylaxis on infant nasopharyngeal flora",
  journal =      "International Journal of Pediatric Otorhinolaryngology",
  year =         "2003",
  volume =       "67",
  number =       "5",
  month =        "May",
  abstract =     "",
}

@Article{curry03a_bibuniq_3031,
  author =       "B. Curry and F. Davies and M. Evans and L. Moutinho and P. Phillips",
  title =        "The {K}ohonen self-organising map as an alternative to cluster analysis: an application to direct marketing",
  journal =      "International Journal of Market Research",
  year =         "2003",
  volume =       "45",
  number =       "2",
  abstract =     "",
}

@Article{barreto03a_bibuniq_3035,
  author =       "G. D. Barreto and A. F. R. Araujo and H. J. Ritter",
  title =        "Self-organizing feature maps for modeling and control of robotic manipulators",
  journal =      "Journal of Intelligent \& Robotic Systems",
  year =         "2003",
  volume =       "36",
  number =       "4",
  month =        "April",
  abstract =     "",
}

@Article{dejonge03a_bibuniq_3039,
  author =       "F. De Jonge and L. Van Nassauw and J. G. De Man and B. Y. De Winter and F. Van Meir and I. Depoortere and T. L. Peeters and P. A. Pelckmans and E. Van Marck and J. P. Timmermans",
  title =        "Effects of Schistosoma mansoni infection on somatostatin and somatostatin receptor 2{A} expression in mouse ileum",
  journal =      "Neurogastroenterology and Motility",
  year =         "2003",
  volume =       "15",
  number =       "2",
  month =        "April",
  abstract =     "",
}

@Article{ahn03a_bibuniq_3041,
  author =       "K. Ahn and S. Yokota",
  title =        "Application of discrete event control to the insertion task of electric line using 6-link electro-hydraulic manipulators with dual arm",
  journal =      "{JSME} International Journal Series C-Mechanical Systems Machine Elements and Manufacturing",
  year =         "2003",
  volume =       "46",
  number =       "1",
  month =        "March",
  abstract =     "",
}

@Article{ambalavanar03a_bibuniq_3043,
  author =       "R. Ambalavanar and M. Moritani and A. Raines and T. Hilton and D. Dessem",
  title =        "Chemical phenotypes of muscle and cutaneous afferent neurons in the rat trigeminal ganglion",
  journal =      "Journal of Comparative Neurology",
  year =         "2003",
  volume =       "460",
  number =       "2",
  month =        "May" # " 26",
  abstract =     "",
}

@Article{li03c_bibuniq_3044,
  author =       "T. S. Li and C. Y. Chen and C. T. Su",
  title =        "Comparison of neural and statistical algorithms for supervised classification of multi-dimensional data",
  journal =      "International Journal of Industrial Engineering-Theory Applications and Practice",
  year =         "2003",
  volume =       "10",
  number =       "1",
  month =        "March",
  abstract =     "",
}

@Article{chen03d_bibuniq_3045,
  author =       "Q. W. Chen and A. E. Mynett",
  title =        "Integration of data mining techniques and heuristic knowledge in fuzzy logic modelling of eutrophication in Taihu Lake",
  journal =      "Ecological Modelling",
  year =         "2003",
  volume =       "162",
  number =       "1-2",
  month =        "April" # " 1",
  abstract =     "",
}

@Article{bote-lorenzo03a_bibuniq_3046,
  author =       "M. L. Bote-Lorenzo and Y. A. Dimitriadis and E. Gomez-Sanchez",
  title =        "Automatic extraction of human-recognizable shape and execution prototypes of handwritten characters",
  journal =      "Pattern Recognition",
  year =         "2003",
  volume =       "36",
  number =       "7",
  month =        "July",
  abstract =     "",
}

@Article{villmann03a_bibuniq_3047,
  author =       "T. Villmann and E. Merenyi and B. Hammer",
  title =        "Neural maps in remote sensing image analysis",
  journal =      "Neural Networks",
  year =         "2003",
  volume =       "16",
  number =       "3-4",
  month =        "April" # "-" # may,
  abstract =     "",
}

@Article{bullen03a_bibuniq_3049,
  author =       "R. J. Bullen and D. Cornford and I. T. Nabney",
  title =        "Outlier detection in scatterometer data: neural network approaches",
  journal =      "Neural Networks",
  year =         "2003",
  volume =       "16",
  number =       "3-4",
  month =        "April" # "-" # may,
  abstract =     "",
}

@Article{mattfeldt03a_bibuniq_3050,
  author =       "T. Mattfeldt",
  title =        "Classification of binary spatial textures using stochastic geometry, nonlinear deterministic analysis and artificial neural networks",
  journal =      "International Journal of Pattern Recognition and Artificial Intelligence",
  year =         "2003",
  volume =       "17",
  number =       "2",
  month =        "March",
  abstract =     "",
}

@Article{zhang03d_bibuniq_3051,
  author =       "Y. X. Zhang and Y. H. Zhao",
  title =        "Learning vector quantization for classifying astronomical objects",
  journal =      "Chinese Journal of Astronomy and Astrophysics",
  year =         "2003",
  volume =       "3",
  number =       "2",
  month =        "April",
  abstract =     "",
}

@Article{kaleczyc03a_bibuniq_3054,
  author =       "J. Kaleczyc and K. Wasowicz and M. Klimczuk and K. Czaja and M. Lakomy",
  title =        "Immunohistochemical characterisation of cholinergic neurons in the anterior pelvic ganglion of the male pig",
  journal =      "Folia Histochemica ET Cytobiologica",
  year =         "2003",
  volume =       "41",
  number =       "2",
  abstract =     "",
}

@Article{niedernostheide03a_bibuniq_3055,
  author =       "F. Niedernostheide and H. J. Schulze and O. Freyd and M. Bode and A. V. Gorbatyuk",
  title =        "Realization of a neural algorithm by means of front-propagation in a thyristor-based hybrid system",
  journal =      "Chaos Solitons \& Fractals",
  year =         "2003",
  volume =       "17",
  number =       "2-3",
  month =        "July",
  abstract =     "",
}

@Article{sade03a_bibuniq_3056,
  author =       "J. Sade and E. Russo and C. Fuchs and D. Cohen",
  title =        "Is secretory otitis media a single disease entity?",
  journal =      "Annals of Otology Rhinology and Laryngology",
  year =         "2003",
  volume =       "112",
  number =       "4",
  month =        "April",
  abstract =     "",
}

@Article{amritkar03a_bibuniq_3057,
  author =       "R. E. Amritkar and S. Jalan",
  title =        "Self-organized and driven phase synchronization in coupled map networks",
  journal =      "Physica A-Statistical Mechanics and ITS Applications",
  year =         "2003",
  volume =       "321",
  number =       "1-2",
  month =        "April" # " 1",
  abstract =     "",
}

@Article{lees03a_bibuniq_3058,
  author =       "A. Lees and G. Barton and L. Kershaw",
  title =        "The use of {K}ohonen neural network analysis to qualitatively characterize technique in soccer kicking",
  journal =      "Journal of Sports Sciences",
  year =         "2003",
  volume =       "21",
  number =       "4",
  month =        "April",
  abstract =     "",
}

@Article{imbierowicz03a_bibuniq_3060,
  author =       "K. Imbierowicz and U. T. Egle",
  title =        "Childhood adversities in patients with fibromyalgia and somatoform pain disorder",
  journal =      "European Journal of Pain",
  year =         "2003",
  volume =       "7",
  number =       "2",
  abstract =     "",
}

@Article{lee03a_bibuniq_3063,
  author =       "C. H. Lee and H. C. Yang",
  title =        "A multilingual text mining approach based on self-organizing maps",
  journal =      "Applied Intelligence",
  year =         "2003",
  volume =       "18",
  number =       "3",
  month =        "May" # "-" # jun,
  abstract =     "",
}

@Article{kronenberg03a_bibuniq_3064,
  author =       "J. Kronenberg and L. Migirov",
  title =        "The role of mastoidectomy in cochlear implant surgery",
  journal =      "Acta OTO-Laryngologica",
  year =         "2003",
  volume =       "123",
  number =       "2",
  abstract =     "",
}

@Article{atukorale03a_bibuniq_3065,
  author =       "A. S. Atukorale and T. Downs and P. N. Suganthan",
  title =        "Boosting the {HONG} network",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "51",
  month =        "April",
  abstract =     "",
}

@Article{mazzatorta03a_bibuniq_3071,
  author =       "P. Mazzatorta and M. Vracko and A. Jezierska and E. Benfenati",
  title =        "Modeling toxicity by using supervised {K}ohonen Neural Networks",
  journal =      "Journal of Chemical Information and Computer Sciences",
  year =         "2003",
  volume =       "43",
  number =       "2",
  month =        "March" # "-" # apr,
  abstract =     "",
}

@Article{ting03a_bibuniq_3074,
  author =       "S. B. Ting and T. Wilanowski and L. Cerruti and L. L. Zhao and J. M. Cunningham and S. M. Jane",
  title =        "The identification and characterization of human Sister of Mammalian Grainyhead ({SOM}) expands the grainyhead-like family of developmental transcription factors",
  journal =      "Biochemical Journal",
  year =         "2003",
  volume =       "370",
  month =        "March" # " 15",
  abstract =     "",
}

@Article{bin-sagheer03a_bibuniq_3076,
  author =       "S. T. Bin-Sagheer and P. G. Brady and J. J. Mamel and B. Robinson",
  title =        "Reduction in the incidence of pancreatitis in patients undergoing sphincter of Oddi manometry: {A} successful quality improvement project",
  journal =      "Southern Medical Journal",
  year =         "2003",
  volume =       "96",
  number =       "3",
  month =        "March",
  abstract =     "",
}

@Article{seo03b_bibuniq_3078,
  author =       "S. Seo and M. Bode and K. Obermayer",
  title =        "Soft nearest prototype classification",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2003",
  volume =       "14",
  number =       "2",
  month =        "March",
  abstract =     "",
}

@Article{murtagh03a_bibuniq_3080,
  author =       "F. Murtagh and T. Taskaya and P. Contreras and J. Mothe and K. Englmeier",
  title =        "Interactive visual user interfaces: {A} survey",
  journal =      "Artificial Intelligence Review",
  year =         "2003",
  volume =       "19",
  number =       "4",
  month =        "June",
  abstract =     "",
}

@Article{tomita03a_bibuniq_3081,
  author =       "R. Tomita and S. Fujisaki and K. Tanjoh and M. Fukuzawa",
  title =        "Studies on gastrointestinal hormone and jejunal interdigestive migrating motor complex in patients with or without early dumping syndrome after total gastrectomy with Roux-en-{Y} reconstruction for early gastric cancer",
  journal =      "American Journal of Surgery",
  year =         "2003",
  volume =       "185",
  number =       "4",
  month =        "April",
  abstract =     "",
}

@Article{hong03a_bibuniq_3082,
  author =       "Y. S. T. Hong and M. R. Rosen and R. Bhamidimarri",
  title =        "Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis",
  journal =      "Water Research",
  year =         "2003",
  volume =       "37",
  number =       "7",
  month =        "April",
  abstract =     "",
}

@Article{scherer03a_bibuniq_3086,
  author =       "R. Scherer and B. Graimann and J. E. Huggins and S. P. Levine and G. Pfurtscheller",
  title =        "Frequency component selection for an {EC}o{G}-based brain-computer interface",
  journal =      "Biomedizinische Technik",
  year =         "2003",
  volume =       "48",
  number =       "1-2",
  month =        "January" # "-" # feb,
  abstract =     "",
}

@Article{suzuki03a_bibuniq_3087,
  author =       "Y. Suzuki and N. Washio and M. Hashimoto and K. Ohtsuka",
  title =        "Three-dimensional eye movement analysis of superior oblique myokymia",
  journal =      "American Journal of Ophthalmology",
  year =         "2003",
  volume =       "135",
  number =       "4",
  month =        "April",
  abstract =     "",
}

@Article{cannizzaro03a_bibuniq_3088,
  author =       "C. Cannizzaro and B. C. Tel and S. Rose and B. Y. Zeng and P. Jenner",
  title =        "Increased neuropeptide {Y} m{RNA} expression in striatum in Parkinson's disease",
  journal =      "Molecular Brain Research",
  year =         "2003",
  volume =       "110",
  number =       "2",
  month =        "February" # " 20",
  abstract =     "",
}

@Article{hu03a_bibuniq_3090,
  author =       "J. C. Hu and C. W. Wu and W. C. Gau and C. P. Chen and L. J. Chen and C. H. Li and T. C. Chang and C. J. Chu",
  title =        "Self-organized nanomolecular films on low-dielectric constant porous methyl silsesquioxane at room temperature",
  journal =      "Journal of the Electrochemical Society",
  year =         "2003",
  volume =       "150",
  number =       "4",
  month =        "April",
  abstract =     "",
}

@Article{niculescu03a_bibuniq_3091,
  author =       "S. P. Niculescu",
  title =        "Artificial neural networks and genetic algorithms in {QSAR}",
  journal =      "Journal of Molecular Structure-Theochem",
  year =         "2003",
  volume =       "622",
  number =       "1-2",
  month =        "March" # " 7",
  abstract =     "",
}

@Article{lopez-molinero03a_bibuniq_3093,
  author =       "A. Lopez-Molinero and J. Pino and A. Castro and J. R. Castillo",
  title =        "Artificial neural networks applied to the classification of emission lines in inductively coupled plasma-atomic emission spectroscopy",
  journal =      "Analytical Letters",
  year =         "2003",
  volume =       "36",
  number =       "1",
  abstract =     "",
}

@Article{wu03b_bibuniq_3094,
  author =       "H. Q. Wu and Y. Liu and Y. L. Ding",
  title =        "A method of aircraft unit fault diagnosis",
  journal =      "Aircraft Engineering and Aerospace Technology",
  year =         "2003",
  volume =       "75",
  number =       "1",
  abstract =     "",
}

@Article{chertov03a_bibuniq_3097,
  author =       "O. Chertov and A. Komarov and M. Kolstrom and S. Pitkanen and H. Strandman and S. Zudin and S. Kellomaki",
  title =        "Modelling the long-term dynamics of populations and communities of trees in boreal forests based on competition for light and nitrogen",
  journal =      "Forest Ecology and Management",
  year =         "2003",
  volume =       "176",
  number =       "1-3",
  month =        "March" # " 17",
  abstract =     "",
}

@Article{lesinskas03a_bibuniq_3098,
  author =       "E. Lesinskas",
  title =        "Factors affecting the results of nonsurgical treatment of secretory otitis media in adults",
  journal =      "Auris Nasus Larynx",
  year =         "2003",
  volume =       "30",
  number =       "1",
  month =        "February",
  abstract =     "",
}

@Article{lleti03a_bibuniq_3099,
  author =       "R. Lleti and L. A. Sarabia and M. C. Ortiz and R. Todeschini and M. P. Colombini",
  title =        "Application of the {K}ohonen artificial neural network in the identification of proteinaceous binders in samples of panel painting using gas chromatography-mass spectrometry",
  journal =      "Analyst",
  year =         "2003",
  volume =       "128",
  number =       "3",
  abstract =     "",
}

@Article{hsu03a_bibuniq_3105,
  author =       "A. L. Hsu and S. K. Halgamuge",
  title =        "Enhancement of topology preservation and hierarchical dynamic self-organising maps for data visualisation",
  journal =      "International Journal of Approximate Reasoning",
  year =         "2003",
  volume =       "32",
  number =       "2-3",
  month =        "February",
  abstract =     "",
}

@Article{rajer-kanduc03a_bibuniq_3107,
  author =       "K. Rajer-Kanduc and J. Zupan and N. Majcen",
  title =        "Separation of data on the training and test set for modelling: a case study for modelling of five colour properties of a white pigment",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  year =         "2003",
  volume =       "65",
  number =       "2",
  month =        "February" # " 28",
  abstract =     "",
}

@Article{karayiannis03a_bibuniq_3112,
  author =       "N. B. Karayiannis and M. M. Randolph-Gips",
  title =        "Soft learning vector quantization and clustering algorithms based on non-Euclidean norms: Multinorm algorithms",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2003",
  volume =       "14",
  number =       "1",
  month =        "January",
  abstract =     "",
}

@Article{rishikesh03a_bibuniq_3117,
  author =       "N. Rishikesh and Y. V. Venkatesh",
  title =        "A computational model for the development of simple-cell receptive fields spanning the regimes before and after eye-opening",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "50",
  month =        "January",
  abstract =     "",
}

@Article{vieira03a_bibuniq_3118,
  author =       "A. Vieira and N. Barradas",
  title =        "A training algorithm for classification of high-dimensional data",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "50",
  month =        "January",
  abstract =     "",
}

@Article{malcangio03a_bibuniq_3120,
  author =       "M. Malcangio",
  title =        "{GDNF} and somatostatin in sensory neurones",
  journal =      "Current Opinion in Pharmacology",
  year =         "2003",
  volume =       "3",
  number =       "1",
  month =        "February",
  abstract =     "",
}

@Article{jamsa-jounela03a_bibuniq_3123,
  author =       "S. L. Jämsä-Jounela and M. Vermasvuori and P. Endén and S. Haavisto",
  title =        "A process monitoring system based on the {K}ohonen self-organizing maps",
  journal =      "Control Engineering Practice",
  year =         "2003",
  volume =       "11",
  number =       "1",
  month =        "January",
  pages =        "83--92",
  abstract =     "",
}

@Article{aires-de-sousa03a_bibuniq_3124,
  author =       "J. Aires-de-Sousa and L. Aires-de-Sousa",
  title =        "Representation of {DNA} sequences with virtual potentials and their processing by ({SEQREP}) {K}ohonen self-organizing maps",
  journal =      "Bioinformatics",
  year =         "2003",
  volume =       "19",
  number =       "1",
  month =        "January",
  abstract =     "",
}

@Article{roggo03b_bibuniq_3125,
  author =       "Y. Roggo and L. Duponchel and J. P. Huvenne",
  title =        "Comparison of supervised pattern recognition methods with McNemar's statistical test - Application to qualitative analysis of sugar beet by near-infrared spectroscopy",
  journal =      "Analytica Chimica Acta",
  year =         "2003",
  volume =       "477",
  number =       "2",
  month =        "February" # " 3",
  abstract =     "",
}

@Article{hwang03b_bibuniq_3126,
  author =       "S. Hwang and T. J. Cutright",
  title =        "Statistical implications of pyrene and phenanthrene sorptive phenomena: Effects of sorbent and solute properties",
  journal =      "Archives of Environmental Contamination and Toxicology",
  year =         "2003",
  volume =       "44",
  number =       "2",
  month =        "February",
  abstract =     "",
}

@Article{olmez03b_bibuniq_3127,
  author =       "T. Olmez and Z. Dokur",
  title =        "Classification of heart sounds using an artificial neural network",
  journal =      "Pattern Recognition Letters",
  year =         "2003",
  volume =       "24",
  number =       "1-3",
  month =        "January",
  abstract =     "",
}

%  bibtex/isi0304. bib =====================================



@Article{hammer04a_bibuniq_3128,
  author =       "B. Hammer and A. Micheli and A. Sperduti and M. Strickert",
  title =        "Recursive self-organizing network models",
  journal =      "Neural Networks",
  year =         "2004",
  volume =       "17",
  number =       "8-9",
  month =        "October" # "-" # nov,
  pages =        "1061--1085",
  abstract =     "",
}

@Article{cottrell04a_bibuniq_3131,
  author =       "M. Cottrell and S. Ibbou and P. Letremy",
  title =        "{SOM}-based algorithms for qualitative variables",
  journal =      "Neural Networks",
  year =         "2004",
  volume =       "17",
  number =       "8-9",
  month =        "October" # "-" # nov,
  pages =        "1149--1167",
  abstract =     "",
}

@Article{simon04a_bibuniq_3132,
  author =       "G. Simon and A. Lendasse and M. Cottrell and J. C. Fort and M. Verleysen",
  title =        "Double quantization of the regressor space for long-term time series prediction: method and proof of stability",
  journal =      "Neural Networks",
  year =         "2004",
  volume =       "17",
  number =       "8-9",
  month =        "October" # "-" # nov,
  pages =        "1169--1181",
  abstract =     "",
}

@Article{polcicova04a_bibuniq_3133,
  author =       "G. Polcicova and P. Tino",
  title =        "Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns",
  journal =      "Neural Networks",
  year =         "2004",
  volume =       "17",
  number =       "8-9",
  month =        "October" # "-" # nov,
  pages =        "1183--1199",
  abstract =     "",
}

@Article{blazej04a_bibuniq_3140,
  author =       "M. Blazej and M. Jurascik and J. Annus and J. Markos",
  title =        "Measurement of mass transfer coefficient in an airlift reactor with internal loop using coalescent and non-coalescent liquid media",
  journal =      "Journal of Chemical Technology and Biotechnology",
  year =         "2004",
  volume =       "79",
  number =       "12",
  month =        "December",
  pages =        "1405--1411",
  abstract =     "",
}

@Article{normanton04a_bibuniq_3141,
  author =       "A. S. Normanton and B. Barber and A. Bell and A. Spaccarotella and L. Holappa and J. Laine and H. Peters and N. Link and F. Ors and A. Lopez and J. J. Laraudogoitia",
  title =        "Developments in online surface and internal quality forecasting of continuously cast semis",
  journal =      "Ironmaking \& Steelmaking",
  year =         "2004",
  volume =       "31",
  number =       "5",
  pages =        "376--382",
  abstract =     "",
}

@Article{faba-perez05a_bibuniq_3142,
  author =       "C. Faba-Perez and V. P. Guerrero-Bote and F. de Moya-Anegon",
  title =        "Self-organizing maps of Web spaces based on formal characteristics",
  journal =      "Information Processing \& Management",
  year =         "2005",
  volume =       "41",
  number =       "2",
  month =        "March",
  pages =        "331--346",
  abstract =     "",
}

@InProceedings{zheng04a_bibuniq_3143,
  author =       "J. Zheng and M. Z. Hu and B. X. Fang and H. L. Zhang",
  title =        "Anomaly detection using fast {SOFM}",
  booktitle =    "Grid and Cooperative Computing GCC 2004 Workshops, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "530--537",
  abstract =     "",
}

@Article{wang04a_bibuniq_3144,
  author =       "Y. Wang and M. Toledo-Rodriguez and A. Gupta and C. Z. Wu and G. Silberberg and J. Y. Luo and H. Markram",
  title =        "Anatomical, physiological and molecular properties of Martinotti cells in the somatosensory cortex of the juvenile rat",
  journal =      "Journal of Physiology-London",
  year =         "2004",
  volume =       "561",
  number =       "1",
  month =        "November" # " 15",
  pages =        "65--90",
  abstract =     "",
}

@Article{liu05a_bibuniq_3147,
  author =       "T. C. Liu and R. K. Li",
  title =        "A new {ART}-counterpropagation neural network for solving a forecasting problem",
  journal =      "Expert Systems With Applications",
  year =         "2005",
  volume =       "28",
  number =       "1",
  month =        "January",
  pages =        "21--27",
  abstract =     "",
}

@Article{kurth04a_bibuniq_3149,
  author =       "C. Kurth and V. Wegerer and U. Reulbach and P. Lewczuk and J. Kornhuber and B. J. Steinhoff and S. Bleich",
  title =        "Analysis of hippocampal atrophy in alcoholic patients by a {K}ohonen feature map",
  journal =      "Neuroreport",
  year =         "2004",
  volume =       "15",
  number =       "2",
  month =        "February" # " 9",
  pages =        "367--371",
  abstract =     "",
}

@InProceedings{khosla04a_bibuniq_3152,
  author =       "R. Khosla and T. Goonesekera",
  title =        "Integration of psychology, artificial intelligence and soft computing for recruitment and benchmarking of salespersons",
  booktitle =    "Knowledge-Based Intelligent Information and Engineering Systems, Pt. 2, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "1--8",
  abstract =     "",
}

@InProceedings{adorno04a_bibuniq_3153,
  author =       "M. C. Adorno and M. Resta",
  title =        "Reliability and convergence on {K}ohonen Maps: An empirical study",
  booktitle =    "Knowledge-Based Intelligent Information and Engineering Systems, Pt. 1, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "426--433",
  abstract =     "",
}

@InProceedings{shon04a_bibuniq_3154,
  author =       "M. K. Shon and J. Murata",
  title =        "Behavior learning of autonomous agents in continuous state using function approximation",
  booktitle =    "Knowledge-Based Intelligent Information and Engineering Systems, Pt. 1, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "1213--1219",
  abstract =     "",
}

@Article{hansen04a_bibuniq_3155,
  author =       "C. Hansen and A. Fu and C. Li and W. T. Dixon and R. Christopherson and S. S. Moore",
  title =        "Global gene expression patterns spanning 3{T3}-{L1} preadipocyte differentiation",
  journal =      "Canadian Journal of Animal Science",
  year =         "2004",
  volume =       "84",
  number =       "3",
  month =        "September",
  pages =        "367--376",
  abstract =     "",
}

@Article{doi04a_bibuniq_3157,
  author =       "H. Doi and E. Kikuchi and C. Mizota and N. Satoh and S. Shikano and N. Yurlova and E. Yadrenkina and E. Zuykova",
  title =        "Carbon, nitrogen, and sulfur isotope changes and hydro-geological processes in a saline lake chain",
  journal =      "Hydrobiologia",
  year =         "2004",
  volume =       "529",
  number =       "1",
  month =        "November",
  pages =        "225--235",
  abstract =     "",
}

@Article{lee04b_bibuniq_3158,
  author =       "I. S. K. Lee and H. Y. K. Lau",
  title =        "Adaptive state space partitioning for reinforcement learning",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2004",
  volume =       "17",
  number =       "6",
  month =        "September",
  pages =        "577--588",
  abstract =     "",
}

@Article{guedes04a_bibuniq_3159,
  author =       "R. P. Guedes and M. I. Marchi and G. G. Viola and L. L. Xavier and M. Achaval and W. A. Partata",
  title =        "Somatostatin-, calcitonin gene-related peptide, and gamma-aminobutyric acid-like immunoreactivitity in the frog lumbosacral spinal cord: distribution and effects of sciatic nerve transection",
  journal =      "Comparative Biochemistry and Physiology B-Biochemistry \& Molecular Biology",
  year =         "2004",
  volume =       "138",
  number =       "1",
  month =        "May",
  pages =        "19--28",
  abstract =     "",
}

@Article{bloom04a_bibuniq_3162,
  author =       "J. Z. Bloom",
  title =        "Tourist market segmentation with linear and non-linear techniques",
  journal =      "Tourism Management",
  year =         "2004",
  volume =       "25",
  number =       "6",
  month =        "December",
  pages =        "723--733",
  abstract =     "",
}

@Article{wu04a_bibuniq_3163,
  author =       "C. S. Wu and Q. X. Hu and J. S. Sun and T. Polte and D. Rehfeldt",
  title =        "Intelligent monitoring and recognition of the short-circuiting gas-metal are welding process",
  journal =      "Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture",
  year =         "2004",
  volume =       "218",
  number =       "9",
  month =        "September",
  pages =        "1145--1151",
  abstract =     "",
}

@Article{li04b_bibuniq_3164,
  author =       "L. X. Li and C. K. Mechefske and W. D. Li",
  title =        "Electric motor faults diagnosis using artificial neural networks",
  journal =      "Insight",
  year =         "2004",
  volume =       "46",
  number =       "10",
  month =        "October",
  pages =        "616--621",
  abstract =     "",
}

@InProceedings{martin-herrero04a_bibuniq_3165,
  author =       "J. Martin-Herrero and M. Ferreiro-Arman and J. L. Alba-Castro",
  title =        "Grading textured surfaces with automated soft clustering in a supervised {SOM}",
  booktitle =    "Image Analysis and Recognition, Pt. 2, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "323--330",
  abstract =     "",
}

@Article{smith04a_bibuniq_3167,
  author =       "K. D. Smith and N. G. Hall and S. de Lestang and I. C. Potter",
  title =        "Potential bias in estimates of the size of maturity of crabs derived from trap samples",
  journal =      "Ices Journal of Marine Science",
  year =         "2004",
  volume =       "61",
  number =       "6",
  month =        "September",
  pages =        "906--912",
  abstract =     "",
}

@Article{wu04b_bibuniq_3168,
  author =       "X. L. Wu and K. B. Griffin and M. D. Garcia and J. J. Michal and Q. J. Xiao and R. W. Wright and Z. H. Jiang",
  title =        "Census of orthologous genes and self-organizing maps of biologically relevant transcriptional patterns in chickens (Gallus gallus)",
  journal =      "Gene",
  year =         "2004",
  volume =       "340",
  number =       "2",
  month =        "October" # " 13",
  pages =        "213--225",
  abstract =     "",
}

@Article{williamson04a_bibuniq_3170,
  author =       "D. M. Williamson and I. I. Bejar and A. Sax",
  title =        "Automated tools for subject matter expert evaluation of automated scoring",
  journal =      "Applied Measurement in Education",
  year =         "2004",
  volume =       "17",
  number =       "4",
  pages =        "323--357",
  abstract =     "",
}

@Article{lee04c_bibuniq_3171,
  author =       "S. W. Lee and D. Palmer-Brown and C. M. Roadknight",
  title =        "Performance-guided neural network for rapidly self-organising active network management",
  journal =      "Neurocomputing",
  year =         "2004",
  volume =       "61",
  month =        "October",
  pages =        "5--20",
  abstract =     "",
}

@Article{marcos04a_bibuniq_3173,
  author =       "Z. Marcos and K. Pffeifer and M. E. Bodegas and M. P. Sesma and L. Guembe",
  title =        "Cellular prion protein is expressed in a subset of neuroenclocrine cells of the rat gastrointestinal tract",
  journal =      "Journal of Histochemistry \& Cytochemistry",
  year =         "2004",
  volume =       "52",
  number =       "10",
  month =        "October",
  pages =        "1357--1365",
  abstract =     "",
}

@Article{cauli04a_bibuniq_3175,
  author =       "B. Cauli and X. K. Tong and A. Rancillac and N. Serluca and B. Lambolez and J. Rossier and E. Hamel",
  title =        "Cortical {GABA} interneurons in neurovascular coupling: Relays for subcortical vasoactive pathways",
  journal =      "Journal of Neuroscience",
  year =         "2004",
  volume =       "24",
  number =       "41",
  month =        "October" # " 13",
  pages =        "8940--8949",
  abstract =     "",
}

@Article{masliukov04a_bibuniq_3176,
  author =       "P. M. Masliukov and J. P. Timmermans",
  title =        "Immunocytochemical properties of stellate ganglion neurons during early postnatal development",
  journal =      "Histochemistry and Cell Biology",
  year =         "2004",
  volume =       "122",
  number =       "3",
  month =        "September",
  pages =        "201--209",
  abstract =     "",
}

@Article{yang05a_bibuniq_3178,
  author =       "B. S. Yang and W. W. Hwang and D. J. Kim and A. C. Tan",
  title =        "Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines",
  journal =      "Mechanical Systems and Signal Processing",
  year =         "2005",
  volume =       "19",
  number =       "2",
  month =        "March",
  pages =        "371--390",
  abstract =     "",
}

@Article{gramatica04a_bibuniq_3179,
  author =       "P. Gramatica and P. Pilutti and E. Papa",
  title =        "Validated {QSAR} prediction of {OH} tropospheric degradation of {VOC}s: Splitting into training-test sets and consensus modeling",
  journal =      "Journal of Chemical Information and Computer ScienceS",
  year =         "2004",
  volume =       "44",
  number =       "5",
  month =        "September" # "-" # oct,
  pages =        "1794--1802",
  abstract =     "",
}

@Article{mlinsek04a_bibuniq_3180,
  author =       "G. Mlinsek and M. Novic and M. Kotnik and T. Solmajer",
  title =        "Enzyme binding selectivity prediction: alpha-thrombin vs trypsin inhibition",
  journal =      "Journal of Chemical Information and Computer ScienceS",
  year =         "2004",
  volume =       "44",
  number =       "5",
  month =        "September" # "-" # oct,
  pages =        "1872--1882",
  abstract =     "",
}

@InProceedings{smigiel04a_bibuniq_3181,
  author =       "E. Smigiel and A. Belaid and H. Hamza",
  title =        "Self-organizing maps and ancient documents",
  booktitle =    "Document Analysis Systems VI, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "125--134",
  abstract =     "",
}

@InProceedings{bottou04a_bibuniq_3182,
  author =       "L. Bottou",
  title =        "Stochastic learning",
  booktitle =    "Advanced LectureS on Machine Learning, Lecture Notes in Artificial Intelligence",
  year =         "2004",
  pages =        "146--168",
  abstract =     "",
}

@Article{il'in04a_bibuniq_3183,
  author =       "S. V. Il'in and M. N. Rychagov",
  title =        "Segmentation of acoustic images by neural network processing",
  journal =      "Acoustical Physics",
  year =         "2004",
  volume =       "50",
  number =       "5",
  month =        "September" # "-" # oct,
  pages =        "528--534",
  abstract =     "",
}

@Article{ho04a_bibuniq_3187,
  author =       "W. Y. Ho",
  title =        "Using {K}ohonen neural network and principle component analysis to characterize divergent thinking",
  journal =      "Creativity Research Journal",
  year =         "2004",
  volume =       "16",
  number =       "2-3",
  pages =        "283--292",
  abstract =     "",
}

@Article{brett04a_bibuniq_3191,
  author =       "D. R. Brett and R. G. West and P. J. Wheatley",
  title =        "The automated classification of astronomical light curves using {K}ohonen self-organizing maps",
  journal =      "Monthly Notices of the Royal Astronomical Society",
  year =         "2004",
  volume =       "353",
  number =       "2",
  month =        "September" # " 11",
  pages =        "369--376",
  abstract =     "",
}

@InProceedings{king04a_bibuniq_3193,
  author =       "R. D. King and M. Ouali",
  title =        "Poly-transformation",
  booktitle =    "Intelligent Data Engineering and Automated Learning Ideal 2004, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "99--107",
  abstract =     "",
}

@InProceedings{lisboa04a_bibuniq_3195,
  author =       "P. J. G. Lisboa and S. Patel",
  title =        "Cluster-based visualisation of marketing data",
  booktitle =    "Intelligent Data Engineering and Automated Learning Ideal 2004, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "552--558",
  abstract =     "",
}

@Article{lee04d_bibuniq_3198,
  author =       "S. C. Lee and Y. H. Suh and J. K. Kim and K. J. Lee",
  title =        "A cross-national market segmentation of online game industry using {SOM}",
  journal =      "Expert Systems With Applications",
  year =         "2004",
  volume =       "27",
  number =       "4",
  month =        "November",
  pages =        "559--570",
  abstract =     "",
}

@Article{pearson04a_bibuniq_3202,
  author =       "C. A. Pearson",
  title =        "Roger Duffy of {SOM} weaves together art, architecture, and landscape in a crystalline new upper school at Greenwich Academy",
  journal =      "Architectural Record",
  year =         "2004",
  volume =       "192",
  number =       "6",
  month =        "June",
  pages =        "228--233",
  abstract =     "",
}

@InProceedings{zehraoui04a_bibuniq_3204,
  author =       "F. Zehraoui and R. Kanawati and S. Salotti",
  title =        "{CAS}ep2: Hybrid case-based reasoning system for sequence processing",
  booktitle =    "Advances in Case-Based Reasoning, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "449--463",
  abstract =     "",
}

@Article{giurcaneanu04a_bibuniq_3207,
  author =       "C. D. Giurcaneanu and I. Tabus and J. Astola and J. Ollila and M. Vihinen",
  title =        "Fast iterative gene clustering based on information theoretic criteria for selecting the cluster structure",
  journal =      "Journal of Computational Biology",
  year =         "2004",
  volume =       "11",
  number =       "4",
  pages =        "660--682",
  abstract =     "",
}

@Article{shigei04a_bibuniq_3209,
  author =       "N. Shigei and H. Miyajima and M. Maeda",
  title =        "Numerical evaluation of incremental Vector Quantization using stochastic relaxation",
  journal =      "Ieice Transactions on Fundamentals of Electronics Communications and Computer ScienceS",
  year =         "2004",
  volume =       "E87A",
  number =       "9",
  month =        "September",
  pages =        "2364--2371",
  abstract =     "",
}

@Article{balakin04a_bibuniq_3212,
  author =       "K. V. Balakin and S. Ekins and A. Bugrim and Y. A. Ivanenkov and D. Korolev and Y. V. Nikolsky and A. V. Skorenko and A. A. Ivashchenko and N. P. Savchuk and T. Nikolskaya",
  title =        "Kohonen maps for prediction of binding to human cytochrome {P450} 3{A4}",
  journal =      "Drug Metabolism and Disposition",
  year =         "2004",
  volume =       "32",
  number =       "10",
  month =        "October",
  pages =        "1183--1189",
  abstract =     "",
}

@InProceedings{cao04a_bibuniq_3214,
  author =       "Y. K. Cao and X. F. Liao and Y. F. Li",
  title =        "An e-mail filtering approach using neural network",
  booktitle =    "Advances in Neural Networks - {ISNN} 2004, Pt. 2, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "688--694",
  abstract =     "",
}

@InProceedings{kirk04a_bibuniq_3215,
  author =       "J. S. Kirk and J. M. Zurada",
  title =        "Topography-enhanced {BMU} search in self-organizing maps",
  booktitle =    "Advances in Neural Networks - {ISNN} 2004, Pt. 2, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "695--700",
  abstract =     "",
}

@InProceedings{shao04a_bibuniq_3216,
  author =       "C. Shao and H. K. Huang",
  title =        "Improvement of data visualization based on {SOM}",
  booktitle =    "Advances in Neural Networks - {ISNN} 2004, Pt. 2, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "707--712",
  abstract =     "",
}

@InProceedings{yang04b_bibuniq_3217,
  author =       "X. H. Yang and Z. H. Sun and Y. X. Sun",
  title =        "A freeway traffic incident detection algorithm based on neural networks",
  booktitle =    "Advances in Neural Networks - {ISNN} 2004, Pt. 2, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "912--919",
  abstract =     "",
}

@InProceedings{wang04b_bibuniq_3218,
  author =       "J. Wang and D. W. Yan",
  title =        "A high precision prediction method by using combination of {ELMAN} and {SOM} neural networks",
  booktitle =    "Advances in Neural Networks - {ISNN} 2004, Pt. 2, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "943--949",
  abstract =     "",
}

@InProceedings{xie04a_bibuniq_3219,
  author =       "J. G. Xie and J. Wang and Z. D. Qiu",
  title =        "Effectiveness of neural networks for prediction of corporate financial distress in China",
  booktitle =    "Advances in Neural Networks - {ISNN} 2004, Pt. 2, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "994--999",
  abstract =     "",
}

@InProceedings{yu04a_bibuniq_3220,
  author =       "J. Yu and P. W. Hao",
  title =        "On soft learning vector quantization based on reformulation",
  booktitle =    "Advances in Neural Networks - {ISNN} 2004, Pt. 1, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "168--173",
  abstract =     "",
}

@InProceedings{zhang04a_bibuniq_3221,
  author =       "D. Q. Zhang and S. C. Chen and Z. H. Zhou",
  title =        "Fuzzy-kernel learning vector quantization",
  booktitle =    "Advances in Neural Networks - {ISNN} 2004, Pt. 1, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "180--185",
  abstract =     "",
}

@InProceedings{qian04a_bibuniq_3225,
  author =       "T. Qian and R. Xu and C. Kwan and B. Linnell and R. Young",
  title =        "Toxic vapor classification and concentration estimation for Space Shuttle and International Space Station",
  booktitle =    "Advances in Neural Networks - {ISNN} 2004, Pt. 1, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "543--551",
  abstract =     "",
}

@InProceedings{wang04c_bibuniq_3227,
  author =       "Z. Wang and D. X. Liu and X. N. Feng",
  title =        "Improved {SOM} clustering for software component catalogue",
  booktitle =    "Advances in Neural Networks - {ISNN} 2004, Pt. 1, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "846--851",
  abstract =     "",
}

@Article{li04c_bibuniq_3230,
  author =       "X. Li and R. B. Ambrose and R. Araujo",
  title =        "Modeling mineral nitrogen export from a forest terrestrial ecosystem to streams",
  journal =      "Transactions of the Asae",
  year =         "2004",
  volume =       "47",
  number =       "3",
  month =        "May" # "-" # jun,
  pages =        "727--739",
  abstract =     "",
}

@Article{lubell04a_bibuniq_3232,
  author =       "S. Lubell",
  title =        "Skyscraper Museum finally gets its own home (Buillding designed by {SOM} opened April-2nd on the Southern tip of Manhattan)",
  journal =      "Architectural Record",
  year =         "2004",
  volume =       "192",
  number =       "5",
  month =        "May",
  pages =        "34--34",
  abstract =     "",
}

@Article{yoon04a_bibuniq_3233,
  author =       "W. K. Yoon and H. J. Kim and H. Y. Son and K. S. Jeong and S. J. Park and T. H. Kim and S. H. Kim and S. R. Kim and S. Y. Ryu",
  title =        "Somatostatin controls {LFA}-1 gene expression by altering neuraminidase expression in spleen cells",
  journal =      "Anticancer Research",
  year =         "2004",
  volume =       "24",
  number =       "4",
  month =        "July" # "-" # aug,
  pages =        "2331--2335",
  abstract =     "",
}

@Article{vanvugt04a_bibuniq_3238,
  author =       "H. H. {Van Vugt} and H. J. M. Swarts and B. J. M. Van de Heijning and E. M. Van der Beek",
  title =        "Centrally applied somatostatin inhibits the estrogen-induced luteinizing hormone surge via hypothalamic gonadotropin-releasing hormone cell activation in female rats",
  journal =      "Biology of Reproduction",
  year =         "2004",
  volume =       "71",
  number =       "3",
  month =        "September",
  pages =        "813--819",
  abstract =     "",
}

@Article{zhang04b_bibuniq_3239,
  author =       "Y. Zhang and Y. Zhao",
  title =        "Automated clustering algorithms for classification of astronomical objects",
  journal =      "Astronomy \& Astrophysics",
  year =         "2004",
  volume =       "422",
  number =       "3",
  month =        "August",
  pages =        "1113--1121",
  abstract =     "",
}

@InProceedings{gorzalczany04a_bibuniq_3241,
  author =       "M. B. Gorzalczany and F. Rudzinski",
  title =        "Application of genetic algorithms and {K}ohonen networks to cluster analysis",
  booktitle =    "Artificial Intelligence and Soft Computing - Icaisc 2004, Lecture Notes in Artificial Intelligence",
  year =         "2004",
  pages =        "556--561",
  abstract =     "",
}

@InProceedings{gorzalczany04b_bibuniq_3242,
  author =       "M. B. Gorzalczany and F. Rudzinski",
  title =        "Modified {K}ohonen networks for complex cluster-analysis problems",
  booktitle =    "Artificial Intelligence and Soft Computing - Icaisc 2004, Lecture Notes in Artificial Intelligence",
  year =         "2004",
  pages =        "562--567",
  abstract =     "",
}

@InProceedings{hammer04b_bibuniq_3243,
  author =       "B. Hammer and M. Strickert and T. Villmann",
  title =        "Relevance {LVQ} versus {SVM}",
  booktitle =    "Artificial Intelligence and Soft Computing - Icaisc 2004, Lecture Notes in Artificial Intelligence",
  year =         "2004",
  pages =        "592--597",
  abstract =     "",
}

@Article{lee04e_bibuniq_3246,
  author =       "E. S. Lee and Y. Fukui and B. C. Lee and J. M. Lim and W. S. Hwang",
  title =        "Promoting effect of amino acids added to a chemically defined medium on blastocyst formation and blastomere proliferation of bovine embryos cultured in vitro",
  journal =      "Animal Reproduction Science",
  year =         "2004",
  volume =       "84",
  number =       "3-4",
  month =        "September",
  pages =        "257--267",
  abstract =     "",
}

@Article{kumar04a_bibuniq_3247,
  author =       "N. Kumar and L. Behera",
  title =        "Visual-motor coordination using a quantum clustering based neural control scheme",
  journal =      "Neural Processing Letters",
  year =         "2004",
  volume =       "20",
  number =       "1",
  month =        "August",
  pages =        "11--22",
  abstract =     "",
}

@Article{mcdonald04a_bibuniq_3249,
  author =       "A. J. McDonald and F. Mascagni and J. F. Muller",
  title =        "Immunocytochemical localization of {GABA}({B}){R1} receptor subunits in the basolateral amygdala",
  journal =      "Brain Research",
  year =         "2004",
  volume =       "1018",
  number =       "2",
  month =        "August" # " 27",
  pages =        "147--158",
  abstract =     "",
}

@Article{marzi04a_bibuniq_3251,
  author =       "H. Marzi",
  title =        "Real-time fault detection and isolation in industrial machines using learning vector quantization",
  journal =      "Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture",
  year =         "2004",
  volume =       "218",
  number =       "8",
  month =        "August",
  pages =        "949--959",
  abstract =     "",
}

@Article{carlton04a_bibuniq_3252,
  author =       "S. A. Carlton and S. T. Zhou and J. H. Du and G. L. Hargett and G. C. Ji and R. E. Coggeshall",
  title =        "Somatostatin modulates the transient receptor potential vanilloid 1 ({TRPV1}) ion channel",
  journal =      "Pain",
  year =         "2004",
  volume =       "110",
  number =       "3",
  month =        "August",
  pages =        "616--627",
  abstract =     "",
}

@Article{riebroy04a_bibuniq_3256,
  author =       "S. Riebroy and S. Benjakul and W. Visessanguan and K. Kijrongrojana and M. Tanaka",
  title =        "Some characteristics of commercial Som-fug produced in Thailand",
  journal =      "Food Chemistry",
  year =         "2004",
  volume =       "88",
  number =       "4",
  month =        "December",
  pages =        "527--535",
  abstract =     "",
}

@Article{ahn04a_bibuniq_3258,
  author =       "K. Ahn and D. C. T. Tu",
  title =        "Improvement of the control performance of pneumatic artificial muscle manipulators using an intelligent switching control method",
  journal =      "{KSME} International Journal",
  year =         "2004",
  volume =       "18",
  number =       "8",
  month =        "August",
  pages =        "1388--1400",
  abstract =     "",
}

@Article{kurth04b_bibuniq_3260,
  author =       "C. Kurth and V. Wegerer and B. J. Steinhoff and S. Bleich",
  title =        "Data-analyses with artificial neural networks (Kohonen feature map) failed to show an association between hippocampal volume reduction and first-onset alcohol withdrawal seizures",
  journal =      "Epilepsia",
  year =         "2004",
  volume =       "45",
  pages =        "100--100",
  abstract =     "",
}

@Article{kim04c_bibuniq_3261,
  author =       "K. H. Kim and S. H. Won and J. K. Lim and T. Takahashi",
  title =        "An architectural design of control software for automated container terminals",
  journal =      "Computers \& Industrial Engineering",
  year =         "2004",
  volume =       "46",
  number =       "4",
  month =        "July",
  pages =        "741--754",
  abstract =     "",
}

@Article{zhang04c_bibuniq_3263,
  author =       "J. Y. Zhang and M. J. Hall",
  title =        "Regional flood frequency analysis for the Gan-Ming River basin in China",
  journal =      "Journal of Hydrology",
  year =         "2004",
  volume =       "296",
  number =       "1-4",
  month =        "August" # " 20",
  pages =        "98--117",
  abstract =     "",
}

@Article{moradkhani04a_bibuniq_3266,
  author =       "H. Moradkhani and K. Hsu and H. V. Gupta and S. Sorooshian",
  title =        "Improved streamflow forecasting using self-organizing radial basis function artificial neural networks",
  journal =      "Journal of Hydrology",
  year =         "2004",
  volume =       "295",
  number =       "1-4",
  month =        "August" # " 10",
  pages =        "246--262",
  abstract =     "",
}

@Article{czarnecki03a_bibuniq_3268,
  author =       "J. E. Czarnecki",
  title =        "{SOM} designs world's tallest tower for Dubai developer",
  journal =      "Architectural Record",
  year =         "2003",
  volume =       "191",
  number =       "7",
  month =        "July",
  pages =        "30--30",
  abstract =     "",
}

@Article{chiu04a_bibuniq_3273,
  author =       "L. Chiu and T. Y. Yum and T. F. Cheng and O. Xue and C. H. Chan",
  title =        "An injection locked subharmonic self-oscillating mixer for multi-band operation",
  journal =      "Microwave and Optical Technology Letters",
  year =         "2004",
  volume =       "42",
  number =       "5",
  month =        "September" # " 5",
  pages =        "415--419",
  abstract =     "",
}

@Article{yan04a_bibuniq_3275,
  author =       "A. X. Yan and J. Gasteiger and M. Krug and S. Anzali",
  title =        "Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods",
  journal =      "Journal of Computer-Aided Molecular Design",
  year =         "2004",
  volume =       "18",
  number =       "2",
  month =        "February",
  pages =        "75--87",
  abstract =     "",
}

@Article{duta04a_bibuniq_3276,
  author =       "M. Duta and C. Alford and S. Wilson and L. Tarassenko",
  title =        "Neural network analysis of the mastoid {EEG} for the assessment of vigilance",
  journal =      "International Journal of Human-Computer Interaction",
  year =         "2004",
  volume =       "17",
  number =       "2",
  pages =        "171--195",
  abstract =     "",
}

@Article{brehmer04a_bibuniq_3277,
  author =       "A. Brehmer and R. Croner and A. Dimmler and T. Papadopoulos and F. Schrodl and W. Neuhuber",
  title =        "Immunohistochemical characterization of putative primary afferent (sensory) myenteric neurons in human small intestine",
  journal =      "Autonomic Neuroscience-Basic \& Clinical",
  year =         "2004",
  volume =       "112",
  number =       "1-2",
  month =        "May" # " 31",
  pages =        "49--59",
  abstract =     "",
}

@InProceedings{alaghbari04a_bibuniq_3278,
  author =       "Z. Al Aghbari and A. Makinouchi",
  title =        "Linearization approach for efficient {KNN} search of high-dimensional data",
  booktitle =    "Advances in WEB-AGE Information Management: Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "229--238",
  abstract =     "",
}

@Article{teti04a_bibuniq_3280,
  author =       "R. Teti and D. D'Addona",
  title =        "Intelligent classification of neural network models for mild steel behaviour in hot forming",
  journal =      "Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture",
  year =         "2004",
  volume =       "218",
  number =       "6",
  month =        "June",
  pages =        "619--630",
  abstract =     "",
}

@Article{fernandez04a_bibuniq_3282,
  author =       "F. Fernandez and P. Isasi",
  title =        "Evolutionary design of nearest prototype classifiers",
  journal =      "Journal of Heuristics",
  year =         "2004",
  volume =       "10",
  number =       "4",
  month =        "July",
  pages =        "431--454",
  abstract =     "",
}

@Article{lee04f_bibuniq_3283,
  author =       "R. Lee and J. Liu",
  title =        "i{JADE} Weather{MAN}: {A} weather forecasting system using intelligent multiagent-based fuzzy neuro network",
  journal =      "{IEEE} Transactions on Systems Man and Cybernetics Part C-Applications and Reviews",
  year =         "2004",
  volume =       "34",
  number =       "3",
  month =        "August",
  pages =        "369--377",
  abstract =     "",
}

@Article{deoliveira04a_bibuniq_3284,
  author =       "R. de Oliveira and O. Frazao and J. L. Santos and A. T. Marques",
  title =        "Optic fibre sensor for real-time damage detection in smart composite",
  journal =      "Computers \& Structures",
  year =         "2004",
  volume =       "82",
  number =       "17-19",
  month =        "July",
  pages =        "1315--1321",
  abstract =     "",
}

@Article{lamirel04a_bibuniq_3285,
  author =       "J. C. Lamirel and C. Francois and S. A. L. Shehabi and M. Hoffmann",
  title =        "New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping",
  journal =      "Scientometrics",
  year =         "2004",
  volume =       "60",
  number =       "3",
  pages =        "445--462",
  abstract =     "",
}

@InProceedings{caromel04a_bibuniq_3286,
  author =       "D. Caromel and L. Mateu and E. Tanter",
  title =        "Sequential object monitors",
  booktitle =    "Ecoop 2004 - Object-Oriented Programming, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "316--340",
  abstract =     "",
}

@Article{russell03a_bibuniq_3287,
  author =       "J. S. Russell",
  title =        "{SOM} designs new Silverstein tower for 7 {WTC} site",
  journal =      "Architectural Record",
  year =         "2003",
  volume =       "191",
  number =       "1",
  month =        "January",
  pages =        "34--34",
  abstract =     "",
}

@Article{verma04a_bibuniq_3297,
  author =       "B. Verma and M. Blumenstein and M. Ghosh",
  title =        "A novel approach for structural feature extraction: Contour vs. direction",
  journal =      "Pattern Recognition Letters",
  year =         "2004",
  volume =       "25",
  number =       "9",
  month =        "July",
  pages =        "975--988",
  abstract =     "",
}

@InProceedings{choi04b_bibuniq_3302,
  author =       "K. H. Choi and M. H. Shin and S. H. Bae and C. H. Kwon and I. H. Ra",
  title =        "Similarity retrieval based on {SOM}-based {R}*-tree",
  booktitle =    "Computational Science - {ICCS} 2004, Pt. 3, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "234--241",
  abstract =     "",
}

@Article{li04e_bibuniq_3304,
  author =       "F. Z. Li and K. H. Hu and W. F. Su and S. Y. Li and N. Cai and Z. Y. Huang and Y. X. Hu",
  title =        "Automatic recognition of small cell carcinoma based on the self-organizing neural network",
  journal =      "BIO-Medical Materials and Engineering",
  year =         "2004",
  volume =       "14",
  number =       "2",
  pages =        "175--184",
  abstract =     "",
}

@Article{wyns04a_bibuniq_3305,
  author =       "B. Wyns and S. Sette and L. Boullart and D. Baeten and I. E. A. Hoffman and F. De Keyser",
  title =        "Prediction of diagnosis in patients with early arthritis using a combined {K}ohonen mapping and instance-based evaluation criterion",
  journal =      "Artificial Intelligence in Medicine",
  year =         "2004",
  volume =       "31",
  number =       "1",
  month =        "May",
  pages =        "45--55",
  abstract =     "",
}

@InProceedings{kapetanovic04a_bibuniq_3306,
  author =       "I. M. Kapetanovic and S. Rosenfeld and G. Izmirlian",
  title =        "Overview of commonly used bioinformatics methods and their applications",
  booktitle =    "Applications of Bioinformatics in Cancer Detection, Annals of the NEW York Academy of ScienceS",
  year =         "2004",
  pages =        "10--21",
  abstract =     "",
}

@Article{demesquita04a_bibuniq_3308,
  author =       "M. E. de Mesquita and S. A. Junior and F. R. G. Silva and M. A. C. dos Santos and R. O. Freire and N. B. C. Junior and G. F. de Sa",
  title =        "Synthesis, sparkle model and spectroscopic studies of the Eu(hfc)(3)center dot bipy{O}(2)complex",
  journal =      "Journal of Alloys and Compounds",
  year =         "2004",
  volume =       "374",
  number =       "1-2",
  month =        "July" # " 14",
  pages =        "320--324",
  abstract =     "",
}

@Article{derbel04a_bibuniq_3309,
  author =       "F. Derbel",
  title =        "Performance improvement of fire detectors by means of gas sensors and neural networks",
  journal =      "Fire Safety Journal",
  year =         "2004",
  volume =       "39",
  number =       "5",
  month =        "July",
  pages =        "383--398",
  abstract =     "",
}

@Article{fermo04a_bibuniq_3310,
  author =       "P. Fermo and F. Cariati and D. Ballabio and V. Consonni and G. B. Gianni",
  title =        "Classification of ancient Etruscan ceramics using statistical multivariate analysis of data",
  journal =      "Applied Physics A-Materials Science \& Processing",
  year =         "2004",
  volume =       "79",
  number =       "2",
  month =        "July",
  pages =        "299--307",
  abstract =     "",
}

@Article{smith04b_bibuniq_3313,
  author =       "F. A. Smith and J. W. C. White",
  title =        "Modem calibration of phytolith carbon isotope signatures for {C}-3/{C}-4 paleograssland reconstruction",
  journal =      "Palaeogeography Palaeoclimatology Palaeoecology",
  year =         "2004",
  volume =       "207",
  number =       "3-4",
  month =        "May" # " 20",
  pages =        "277--304",
  abstract =     "",
}

@InProceedings{wang04f_bibuniq_3320,
  author =       "X. L. Wang and Y. W. Luo and Z. Q. Xu",
  title =        "{SOM}: {A} novel model for defining topological line-region relations",
  booktitle =    "Computational Science and ITS Applications - Iccsa 2004, Pt. 3, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "335--344",
  abstract =     "",
}

@Article{godin04a_bibuniq_3322,
  author =       "N. Godin and S. Huguet and R. Gaertner and L. Salmon",
  title =        "Clustering of acoustic emission signals collected during tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers",
  journal =      "NDT \& E International",
  year =         "2004",
  volume =       "37",
  number =       "4",
  month =        "June",
  pages =        "253--264",
  abstract =     "",
}

@InProceedings{jalili-kharaajoo04a_bibuniq_3323,
  author =       "M. Jalili-Kharaajoo",
  title =        "Application of direction basis function neural network to adaptive identification and control",
  booktitle =    "Innovations in Applied Artificial Intelligence, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "11--19",
  abstract =     "",
}

@InProceedings{barreto04b_bibuniq_3324,
  author =       "G. A. Barreto and J. C. M. Mota and L. G. M. Souza and R. A. Frota",
  title =        "Nonstationary time series prediction using local models based on competitive neural networks",
  booktitle =    "Innovations in Applied Artificial Intelligence, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "1146--1155",
  abstract =     "",
}

@Article{schmid-saugeon04a_bibuniq_3326,
  author =       "P. Schmid-Saugeon and A. Zakhor",
  title =        "Dictionary design for matching pursuit and application to motion-compensated video coding",
  journal =      "{IEEE} Transactions on Circuits and Systems for Video Technology",
  year =         "2004",
  volume =       "14",
  number =       "6",
  month =        "June",
  pages =        "880--886",
  abstract =     "",
}

@Article{corne04a_bibuniq_3327,
  author =       "S. A. Corne and S. J. Carver and W. E. Kunin and J. J. Lennon and W. W. S. van Hees",
  title =        "Predicting forest attributes in southeast Alaska using artificial neural networks",
  journal =      "Forest Science",
  year =         "2004",
  volume =       "50",
  number =       "2",
  month =        "April",
  pages =        "259--276",
  abstract =     "",
}

@Article{muangsan04a_bibuniq_3329,
  author =       "N. Muangsan and C. Beclin and H. Vaucheret and D. Robertson",
  title =        "Geminivirus {VIGS} of endogenous genes requires {SGS2}/{SDE1} and {SGS3} and defines a new branch in the genetic pathway for silencing in plants",
  journal =      "Plant Journal",
  year =         "2004",
  volume =       "38",
  number =       "6",
  month =        "June",
  pages =        "1004--1014",
  abstract =     "",
}

@InProceedings{gao04b_bibuniq_3332,
  author =       "X. L. Gao and H. K. Miao and Y. H. Chen",
  title =        "Structured object-{Z} software specification language",
  booktitle =    "Grid and Cooperative Computing, Pt. 1, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "956--963",
  abstract =     "",
}

@Article{marengo04a_bibuniq_3333,
  author =       "E. Marengo and C. Soave and M. C. Gennaro and E. Robotti and M. Bobba and M. Lenti",
  title =        "Comparison of different calibration methods for the determination by {FT}-{NIR} spectroscopy of the hydroxyl number in polyester resins",
  journal =      "Annali DI Chimica",
  year =         "2004",
  volume =       "94",
  number =       "3",
  month =        "March",
  pages =        "219--228",
  abstract =     "",
}

@Article{pawlak04a_bibuniq_3335,
  author =       "M. Pawlak and R. F. Schmidt",
  title =        "Octreotide, a somatostatin analogue, attenuates movement evoked discharges of fine afferent units from inflamed knee joints of rats",
  journal =      "Neuroscience Letters",
  year =         "2004",
  volume =       "361",
  number =       "1-3",
  month =        "May" # " 6",
  pages =        "180--183",
  abstract =     "",
}

@Article{brus04a_bibuniq_3336,
  author =       "D. J. Brus and M. J. W. Jansen",
  title =        "Uncertainty and sensitivity analysis of spatial predictions of heavy metals in wheat",
  journal =      "Journal of Environmental Quality",
  year =         "2004",
  volume =       "33",
  number =       "3",
  month =        "May" # "-" # jun,
  pages =        "882--890",
  abstract =     "",
}

@Article{datta04a_bibuniq_3338,
  author =       "S. Datta and M. K. Banerjee",
  title =        "Kohonen network modelling for the strength of thermomechanically processed {HSLA} steel",
  journal =      "Isij International",
  year =         "2004",
  volume =       "44",
  number =       "5",
  pages =        "846--851",
  abstract =     "",
}

@Article{vanloocke04a_bibuniq_3340,
  author =       "P. Van Loocke",
  title =        "Visualization of data on basis of fractal growth",
  journal =      "Fractals-Complex Geometry Patterns and Scaling in Nature and Society",
  year =         "2004",
  volume =       "12",
  number =       "1",
  month =        "March",
  pages =        "123--136",
  abstract =     "",
}

@Article{marengo04b_bibuniq_3341,
  author =       "E. Marengo and M. Bobba and E. Robotti and M. Lenti",
  title =        "Hydroxyl and acid number prediction in polyester resins by near infrared spectroscopy and artificial neural networks",
  journal =      "Analytica Chimica Acta",
  year =         "2004",
  volume =       "511",
  number =       "2",
  month =        "May" # " 31",
  pages =        "313--322",
  abstract =     "",
}

@Article{joghataie04a_bibuniq_3343,
  author =       "A. Joghataie and M. T. Kamali",
  title =        "Eigenvalue determination by mixed modular networks",
  journal =      "Iranian Journal of Science and Technology",
  year =         "2004",
  volume =       "28",
  number =       "B1",
  month =        "WIN",
  pages =        "31--41",
  abstract =     "",
}

@Article{heikkila04a_bibuniq_3344,
  author =       "J. Heikkila and I. Silven",
  title =        "A real-time system for monitoring of cyclists and pedestrians",
  journal =      "Image and Vision Computing",
  year =         "2004",
  volume =       "22",
  number =       "7",
  month =        "July" # " 1",
  pages =        "563--570",
  abstract =     "",
}

@Article{he04a_bibuniq_3345,
  author =       "J. He and A. H. Tan and C. L. Tan",
  title =        "Modified {ART} 2{A} growing network capable of generating a fixed number of nodes",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2004",
  volume =       "15",
  number =       "3",
  month =        "May",
  pages =        "728--737",
  abstract =     "",
}

@Article{milano04a_bibuniq_3346,
  author =       "M. Milano and P. Koumoutsakos and J. Schmidhuber",
  title =        "Self-organizing nets for optimization",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2004",
  volume =       "15",
  number =       "3",
  month =        "May",
  pages =        "758--765",
  abstract =     "",
}

@Article{springer04a_bibuniq_3347,
  author =       "E. Springer and Y. K. Chen and D. Mahnke and M. R. Antillon and R. J. Shah",
  title =        "{ERCP} with sphincter of Oddi manometry ({SOM}) at an ambulatory endoscopy center ({AEC}): An assessment of complications",
  journal =      "Gastrointestinal Endoscopy",
  year =         "2004",
  volume =       "59",
  number =       "5",
  month =        "April",
  pages =        "AB192--AB192",
  abstract =     "",
}

@Article{helyes04a_bibuniq_3350,
  author =       "Z. Helyes and A. Szabo and J. Nemeth and B. Jakab and E. Pinter and A. Banvolgyi and L. Kereskai and G. Keri and J. Szolcsanyi",
  title =        "Antiinflammatory and analgesic effects of somatostatin released from capsaicin-sensitive sensory nerve terminals in a Freund's adjuvant-induced chronic arthritis model in the rat",
  journal =      "Arthritis and Rheumatism",
  year =         "2004",
  volume =       "50",
  number =       "5",
  month =        "May",
  pages =        "1677--1685",
  abstract =     "",
}

@InProceedings{suutala04a_bibuniq_3352,
  author =       "J. Suutala and S. Pirttikangas and J. Riekki and J. Roning",
  title =        "Reject-optional {LVQ}-based two-level classifier to improve reliability in footstep identification",
  booktitle =    "Pervasive Computing, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "182--187",
  abstract =     "",
}

@Article{song04a_bibuniq_3354,
  author =       "X. Z. Song and S. O. Farwell",
  title =        "Pyrolysis gas chromatography atomic emission detection method for determination of {N}-containing components of humic and fulvic acids",
  journal =      "Journal of Analytical and Applied Pyrolysis",
  year =         "2004",
  volume =       "71",
  number =       "2",
  month =        "June",
  pages =        "901--915",
  abstract =     "",
}

@Article{bock04a_bibuniq_3355,
  author =       "T. Bock",
  title =        "A new approach for exploring multivariate data: self-organising maps",
  journal =      "International Journal of Market Research",
  year =         "2004",
  volume =       "46",
  number =       "2",
  pages =        "189--203",
  abstract =     "",
}

@Article{yang04d_bibuniq_3356,
  author =       "H. T. Yang and S. C. Chen and W. N. Tsai",
  title =        "Classification of direct load control curves for performance evaluation",
  journal =      "{IEEE} Transactions on Power Systems",
  year =         "2004",
  volume =       "19",
  number =       "2",
  month =        "May",
  pages =        "811--817",
  abstract =     "",
}

@InProceedings{deleon04a_bibuniq_3358,
  author =       "P. J. P. de Leon and J. A. Inesta",
  title =        "Musical style classification from symbolic data: {A} two-styles case study",
  booktitle =    "Computer Music Modeling and Retrieval, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "167--178",
  abstract =     "",
}

@Article{zhua04a_bibuniq_3359,
  author =       "M. X. Zhua and X. Jiang and G. L. Ji",
  title =        "Experimental investigation on aluminum release from haplic acrisols in southeastern China",
  journal =      "Applied Geochemistry",
  year =         "2004",
  volume =       "19",
  number =       "6",
  month =        "June",
  pages =        "981--990",
  abstract =     "",
}

@Article{deboishebert04a_bibuniq_3361,
  author =       "V. De Boishebert and L. Urruty and J. L. Giraudel and M. Montury",
  title =        "Assessment of strawberry aroma through solid-phase microextraction-gas chromatography and artificial neuron network methods. Variety classification versus growing years",
  journal =      "Journal of Agricultural and Food Chemistry",
  year =         "2004",
  volume =       "52",
  number =       "9",
  month =        "May" # " 5",
  pages =        "2472--2478",
  abstract =     "",
}

@Article{wu04c_bibuniq_3362,
  author =       "X. W. Wu and Y. D. Chen and B. R. Brooks and Y. A. Su",
  title =        "The local maximum clustering method and its application in microarray gene expression data analysis",
  journal =      "Eurasip Journal on Applied Signal Processing",
  year =         "2004",
  volume =       "2004",
  number =       "1",
  month =        "January" # " 1",
  pages =        "53--63",
  abstract =     "",
}

@Article{yamamoto04a_bibuniq_3363,
  author =       "K. Yamamoto and T. Masaoka and M. Manaka and H. Oonishi and I. Clarke and H. Shoji and K. Kawanabe and A. Imakiire",
  title =        "Micro-wear features on unique 100-Mrad cups - Two retrieved cups compared to hip simulator wear study",
  journal =      "Acta Orthopaedica Scandinavica",
  year =         "2004",
  volume =       "75",
  number =       "2",
  month =        "April",
  pages =        "134--141",
  abstract =     "",
}

@Article{krassas04a_bibuniq_3365,
  author =       "G. E. Krassas",
  title =        "Somatostatin analogs: {A} new tool for the management of Graves' ophthalmopathy",
  journal =      "Journal of Endocrinological Investigation",
  year =         "2004",
  volume =       "27",
  number =       "3",
  month =        "March",
  pages =        "281--287",
  abstract =     "",
}

@Article{xiong04a_bibuniq_3366,
  author =       "H. L. Xiong and M. N. S. Swamy and M. O. Ahmad",
  title =        "Competitive splitting for codebook initialization",
  journal =      "{IEEE} Signal Processing Letters",
  year =         "2004",
  volume =       "11",
  number =       "5",
  month =        "May",
  pages =        "474--477",
  abstract =     "",
}

@Article{shin04b_bibuniq_3369,
  author =       "H. Shin and J. Kim and H. W. Lee and J. Lee and S. W. Kim and H. Song",
  title =        "Granular transfer molding of multimodal powders",
  journal =      "Ceramics International",
  year =         "2004",
  volume =       "30",
  number =       "3",
  pages =        "461--467",
  abstract =     "",
}

@Article{fioretti04a_bibuniq_3370,
  author =       "G. Fioretti",
  title =        "The investment acceleration principle revisited by means of a neural network",
  journal =      "Neural Computing \& Applications",
  year =         "2004",
  volume =       "13",
  number =       "1",
  month =        "April",
  pages =        "16--23",
  abstract =     "",
}

@Article{faba-perez04a_bibuniq_3371,
  author =       "C. Faba-Perez and V. P. Guerrero-Bote and F. De Moya-Anegon",
  title =        "Methods for analysing web citations: {A} study of web-coupling in a closed environment",
  journal =      "Libri",
  year =         "2004",
  volume =       "54",
  number =       "1",
  month =        "March",
  pages =        "43--53",
  abstract =     "",
}

@Article{fang04a_bibuniq_3373,
  author =       "G. L. Fang and W. Gao and D. B. Zhao",
  title =        "Large vocabulary sign language recognition based on fuzzy decision trees",
  journal =      "{IEEE} Transactions on Systems Man and Cybernetics Part A-Systems and Humans",
  year =         "2004",
  volume =       "34",
  number =       "3",
  month =        "May",
  pages =        "305--314",
  abstract =     "",
}

@Article{decos04a_bibuniq_3374,
  author =       "E. de Cos and A. Suarez and S. Sancho",
  title =        "Envelope transient analysis of self-oscillating mixers",
  journal =      "{IEEE} Transactions on Microwave Theory and Techniques",
  year =         "2004",
  volume =       "52",
  number =       "4",
  month =        "April",
  pages =        "1090--1100",
  abstract =     "",
}

@Article{marini04a_bibuniq_3378,
  author =       "F. Marini and J. Zupan and A. L. Magri",
  title =        "On the use of counterpropagation artificial neural networks to characterize Italian rice varieties",
  journal =      "Analytica Chimica Acta",
  year =         "2004",
  volume =       "510",
  number =       "2",
  month =        "May" # " 17",
  pages =        "231--240",
  abstract =     "",
}

@Article{polanski04a_bibuniq_3380,
  author =       "J. Polanski and R. Gieleciak and M. Wyszomirski",
  title =        "Mapping dye pharmacophores by the comparative molecular surface analysis (Co{MSA}): application to the heterocyclic monoazo dyes",
  journal =      "Dyes and Pigments",
  year =         "2004",
  volume =       "62",
  number =       "1",
  month =        "July",
  pages =        "61--76",
  abstract =     "",
}

@Article{hammond04a_bibuniq_3381,
  author =       "M. H. Hammond and C. J. Riedel and S. L. Rose-Pehrsson and F. W. Williams",
  title =        "Training set optimization methods for a probabilistic neural network",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  year =         "2004",
  volume =       "71",
  number =       "1",
  month =        "April" # " 30",
  pages =        "73--78",
  abstract =     "",
}

@Article{knuuttila04a_bibuniq_3387,
  author =       "J. E. A. Knuuttila and P. Toronen and E. Castren",
  title =        "Effects of antidepressant drug imipramine on gene expression in rat prefrontal cortex",
  journal =      "Neurochemical Research",
  year =         "2004",
  volume =       "29",
  number =       "6",
  month =        "June",
  pages =        "1235--1244",
  abstract =     "",
}

@Article{clement04a_bibuniq_3390,
  author =       "R. T. Clement",
  title =        "Gauguin Tahiti.",
  journal =      "Library Journal",
  year =         "2004",
  volume =       "129",
  number =       "6",
  month =        "April" # " 1",
  pages =        "88--88",
  abstract =     "",
}

@Article{chi04a_bibuniq_3392,
  author =       "F. H. Chi and G. L. Amy",
  title =        "Transport of anthracene and benz(a)anthracene through iron-quartz and three aquifer materials in laboratory columns",
  journal =      "Chemosphere",
  year =         "2004",
  volume =       "55",
  number =       "4",
  month =        "April",
  pages =        "515--524",
  abstract =     "",
}

@Article{jinno04a_bibuniq_3393,
  author =       "S. Jinno and T. Kosaka",
  title =        "Patterns of colocalization of neuronal nitric oxide synthase and somatostatin-like immunoreactivity in the mouse hippocampus: Quantitative analysis with optical disector",
  journal =      "Neuroscience",
  year =         "2004",
  volume =       "124",
  number =       "4",
  pages =        "797--808",
  abstract =     "",
}

@Article{boyle04a_bibuniq_3395,
  author =       "J. F. Boyle and N. L. Rose and P. G. Appleby and H. J. B. Birks",
  title =        "Recent environmental change and human impact on Svalbard: the lake-sediment geochemical record",
  journal =      "Journal of Paleolimnology",
  year =         "2004",
  volume =       "31",
  number =       "4",
  month =        "May",
  pages =        "515--530",
  abstract =     "",
}

@Article{jung04a_bibuniq_3396,
  author =       "H. Jung and J. Kim and H. Choi",
  title =        "Reaction kinetics of ozone in variably saturated porous media",
  journal =      "Journal of Environmental Engineering-Asce",
  year =         "2004",
  volume =       "130",
  number =       "4",
  month =        "April",
  pages =        "432--441",
  abstract =     "",
}

@Article{shabin04a_bibuniq_3397,
  author =       "M. A. Shabin and H. R. Maier and M. B. Jaksa",
  title =        "Data division for developing neural networks applied to geotechnical engineering",
  journal =      "Journal of Computing in Civil Engineering",
  year =         "2004",
  volume =       "18",
  number =       "2",
  month =        "April",
  pages =        "105--114",
  abstract =     "",
}

@Article{gustafsson04a_bibuniq_3398,
  author =       "L. Gustafsson and A. P. Paplinski",
  title =        "Self-organization of an artificial neural network subjected to attention shift impairments and familiarity preference, characteristics studied in autism",
  journal =      "Journal of Autism and Developmental Disorders",
  year =         "2004",
  volume =       "34",
  number =       "2",
  month =        "April",
  pages =        "189--198",
  abstract =     "",
}

@Article{verburg04a_bibuniq_3400,
  author =       "P. S. J. Verburg and J. A. Arnone and D. Obrist and D. E. Schorran and R. D. Evans and D. Leroux-Swarthout and D. W. Johnson and Y. Q. Luo and J. S. Coleman",
  title =        "Net ecosystem carbon exchange in two experimental grassland ecosystems",
  journal =      "Global Change Biology",
  year =         "2004",
  volume =       "10",
  number =       "4",
  month =        "April",
  pages =        "498--508",
  abstract =     "",
}

@Article{erbek04a_bibuniq_3401,
  author =       "F. S. Erbek and C. Ozkan and M. Taberner",
  title =        "Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities",
  journal =      "International Journal of Remote Sensing",
  year =         "2004",
  volume =       "25",
  number =       "9",
  month =        "May",
  pages =        "1733--1748",
  abstract =     "",
}

@Article{vracko04a_bibuniq_3402,
  author =       "M. Vracko and D. Mills and S. C. Basak",
  title =        "Structure-mutagenicity modelling using counter propagation neural networks",
  journal =      "Environmental Toxicology and Pharmacology",
  year =         "2004",
  volume =       "16",
  number =       "1-2",
  month =        "March",
  pages =        "25--36",
  abstract =     "",
}

@Article{sonesson04a_bibuniq_3404,
  author =       " {Sonesson, } and J. C. Fouron and G. Teyssier and A. Skoll and C. Chartrand",
  title =        "Immediate and short-term effects of pulmonary artery banding on left ventricular performance in foetal sheep",
  journal =      "Acta Paediatrica",
  year =         "2004",
  volume =       "93",
  number =       "4",
  month =        "April",
  pages =        "540--544",
  abstract =     "",
}

@Article{hardman-mountford03a_bibuniq_3406,
  author =       "N. J. Hardman-Mountford and A. J. Richardson and D. C. Boyer and A. Kreiner and H. J. Boyer",
  title =        "Relating sardine recruitment in the Northern Benguela to satellite-derived sea surface height using a neural network pattern recognition approach",
  journal =      "Progress in Oceanography",
  year =         "2003",
  volume =       "59",
  number =       "2-3",
  pages =        "241--255",
  abstract =     "",
}

@Article{roncaglioni04a_bibuniq_3407,
  author =       "A. Roncaglioni and M. Novic and M. Vracko and E. Benfenati",
  title =        "Classification of potential endocrine disrupters on the basis of molecular structure using a nonlinear modeling method",
  journal =      "Journal of Chemical Information and Computer ScienceS",
  year =         "2004",
  volume =       "44",
  number =       "2",
  month =        "March" # "-" # apr,
  pages =        "300--309",
  abstract =     "",
}

@Article{groselj04a_bibuniq_3408,
  author =       "N. Groselj and J. Zupan and S. Reich and L. Dawidowski and D. Gomez and J. Magallanes",
  title =        "2{D} mapping by {K}ohonen Networks of the air quality data from a large city",
  journal =      "Journal of Chemical Information and Computer ScienceS",
  year =         "2004",
  volume =       "44",
  number =       "2",
  month =        "March" # "-" # apr,
  pages =        "339--346",
  abstract =     "",
}

@Article{galvao04a_bibuniq_3411,
  author =       "R. K. H. Galvao and T. Yoneyama",
  title =        "A competitive wavelet network for signal clustering",
  journal =      "{IEEE} Transactions on Systems Man and Cybernetics Part B-Cybernetics",
  year =         "2004",
  volume =       "34",
  number =       "2",
  month =        "April",
  pages =        "1282--1288",
  abstract =     "",
}

@Article{lopez-tellez04a_bibuniq_3412,
  author =       "J. F. Lopez-Tellez and J. Vela and J. C. del Rio and B. Ramos and D. Baglietto-Vargas and C. Santa-Maria and D. Ruano and A. Gutierrez and J. Vitorica",
  title =        "Postnatal development of the alpha 1 containing {GABA}({A}) receptor subunit in rat hippocampus",
  journal =      "Developmental Brain Research",
  year =         "2004",
  volume =       "148",
  number =       "1",
  month =        "January" # " 31",
  pages =        "129--141",
  abstract =     "",
}

@Article{wang04g_bibuniq_3413,
  author =       "K. Wang and A. Salhi and E. S. Fraga",
  title =        "Process design optimisation using embedded hybrid visualisation and data analysis techniques within a genetic algorithm optimisation framework",
  journal =      "Chemical Engineering and Processing",
  year =         "2004",
  volume =       "43",
  number =       "5",
  month =        "May",
  pages =        "657--669",
  abstract =     "",
}

@Article{podrzaj04a_bibuniq_3414,
  author =       "P. Podrzaj and I. Polajnar and J. Diaci and Z. Kariz",
  title =        "Expulsion detection system for resistance spot welding based on a neural network",
  journal =      "Measurement Science \& Technology",
  year =         "2004",
  volume =       "15",
  number =       "3",
  month =        "March",
  pages =        "592--598",
  abstract =     "",
}

@Article{kuo04a_bibuniq_3415,
  author =       "R. J. Kuo and K. Chang and S. Y. Chien",
  title =        "Integration of self-organizing feature maps and genetic-algorithm-based clustering method for market segmentation",
  journal =      "Journal of Organizational Computing and Electronic Commerce",
  year =         "2004",
  volume =       "14",
  number =       "1",
  pages =        "43--60",
  abstract =     "",
}

@Article{wang04h_bibuniq_3418,
  author =       "H. Y. Wang and F. Azuaje and N. Black",
  title =        "An integrative and interactive framework for improving biomedical pattern discovery and visualization",
  journal =      "{IEEE} Transactions on Information Technology in Biomedicine",
  year =         "2004",
  volume =       "8",
  number =       "1",
  month =        "March",
  pages =        "16--27",
  abstract =     "",
}

@Article{larre-larrouy04a_bibuniq_3421,
  author =       "M. C. Larre-Larrouy and E. Blanchart and A. Albrecht and C. Feller",
  title =        "Carbon and monosaccharides of a tropical Vertisol under pasture and market-gardening: distribution in secondary organomineral separates",
  journal =      "Geoderma",
  year =         "2004",
  volume =       "119",
  number =       "3-4",
  month =        "April",
  pages =        "163--178",
  abstract =     "",
}

@Article{liao04a_bibuniq_3422,
  author =       "G. Liao and S. Liu and T. Shi and G. Zhang",
  title =        "Gearbox condition monitoring using self-organizing feature maps",
  journal =      "Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science",
  year =         "2004",
  volume =       "218",
  number =       "1",
  month =        "January",
  pages =        "119--129",
  abstract =     "",
}

@Article{cui04a_bibuniq_3425,
  author =       "J. Cui and L. Sofer and S. S. Cloud and J. Burnside",
  title =        "Patterns of gene expression in the developing chick thymus",
  journal =      "Developmental Dynamics",
  year =         "2004",
  volume =       "229",
  number =       "3",
  month =        "March",
  pages =        "480--488",
  abstract =     "",
}

@Article{froehle04a_bibuniq_3427,
  author =       "C. A. Froehle and A. V. Roth",
  title =        "New measurement scales for evaluating perceptions of the technology-mediated customer service experience",
  journal =      "Journal of Operations Management",
  year =         "2004",
  volume =       "22",
  number =       "1",
  month =        "February",
  pages =        "1--21",
  abstract =     "",
}

@Article{liu04a_bibuniq_3429,
  author =       "D. H. Liu and R. P. Singh and A. H. Khan and A. J. Lusis and R. C. Davis and D. J. Smith",
  title =        "Mapping behavioral traits by use of genome-tagged mice",
  journal =      "American Journal of Geriatric Psychiatry",
  year =         "2004",
  volume =       "12",
  number =       "2",
  month =        "March" # "-" # apr,
  pages =        "158--165",
  abstract =     "",
}

@Article{mattfeldt03a_bibuniq_3430,
  author =       "T. Mattfeldt and H. W. Gottfried and H. Wolter and V. Schmidt and H. A. Kestler and J. Mayer",
  title =        "Classification of prostatic carcinoma with artificial neural networks using comparative genomic hybridization and quantitative stereological data",
  journal =      "Pathology Research and Practice",
  year =         "2003",
  volume =       "199",
  number =       "12",
  pages =        "773--784",
  abstract =     "",
}

@Article{greco04a_bibuniq_3431,
  author =       "L. S. L. Greco and E. M. Rodriguez",
  title =        "Reproductive performance in Cyrtograpsus angulatus and Cyrtograpsus altimanus (Brachyura, Varunidae) from Jabali Island, Argentina",
  journal =      "Journal of Crustacean Biology",
  year =         "2004",
  volume =       "24",
  number =       "1",
  month =        "February",
  pages =        "213--216",
  abstract =     "",
}

@Article{azcarraga04a_bibuniq_3432,
  author =       "A. P. Azcarraga and T. N. Yap and J. Tan and T. S. Chua",
  title =        "Evaluating keyword selection methods for {WEBSOM} text archives",
  journal =      "{IEEE} Transactions on Knowledge and Data Engineering",
  year =         "2004",
  volume =       "16",
  number =       "3",
  month =        "March",
  pages =        "380--383",
  abstract =     "",
}

@Article{pidsudko04a_bibuniq_3433,
  author =       "Z. Pidsudko",
  title =        "Distribution and chemical coding of neurons in intramural ganglia of the porcine urinary bladder trigone",
  journal =      "Folia Histochemica ET Cytobiologica",
  year =         "2004",
  volume =       "42",
  number =       "1",
  pages =        "3--11",
  abstract =     "",
}

@Article{baurin04a_bibuniq_3435,
  author =       "N. Baurin and J. C. Mozziconacci and E. Arnoult and P. Chavatte and C. Marot and L. Morin-Allory",
  title =        "2{D} {QSAR} consensus prediction for high-throughput virtual screening. An application to {COX}-2 inhibition modeling and screening of the {NCI} database",
  journal =      "Journal of Chemical Information and Computer ScienceS",
  year =         "2004",
  volume =       "44",
  number =       "1",
  month =        "January" # "-" # feb,
  pages =        "276--285",
  abstract =     "",
}

@Article{hatzichristos04a_bibuniq_3438,
  author =       "T. Hatzichristos",
  title =        "Delineation of demographic regions with {GIS} and computational intelligence",
  journal =      "Environment and Planning B-Planning \& Design",
  year =         "2004",
  volume =       "31",
  number =       "1",
  month =        "January",
  pages =        "39--49",
  abstract =     "",
}

@Article{crane03a_bibuniq_3439,
  author =       "R. G. Crane and B. C. Hewitson",
  title =        "Clustering and upscaling of station precipitation records to regional patterns using self-organizing maps ({SOM}s)",
  journal =      "Climate Research",
  year =         "2003",
  volume =       "25",
  number =       "2",
  month =        "December" # " 5",
  pages =        "95--107",
  abstract =     "",
}

@Article{nadal04b_bibuniq_3440,
  author =       "M. Nadal and G. Espinosa and M. Schuhmacher and J. L. Domingo",
  title =        "Patterns of {PCDD}s and {PCDF}s in human milk and food and their characterization by artificial neural networks",
  journal =      "Chemosphere",
  year =         "2004",
  volume =       "54",
  number =       "10",
  month =        "March",
  pages =        "1375--1382",
  abstract =     "",
}

@Article{kang04a_bibuniq_3441,
  author =       "B. N. Kang and H. J. Kim and K. S. Jeong and S. J. Park and S. H. Kim and S. R. Kim and T. H. Kim and S. Y. Ryu",
  title =        "Regulation of leukocyte function-associated antigen 1-mediated adhesion by somatostatin and substance {P} in mouse spleen cells",
  journal =      "Neuroimmunomodulation",
  year =         "2004",
  volume =       "11",
  number =       "2",
  pages =        "84--92",
  abstract =     "",
}

@Article{debodt04a_bibuniq_3442,
  author =       "E. de Bodt and M. Cottrell and P. Letremy and M. Verleysen",
  title =        "On the use of self-organizing maps to accelerate vector quantization",
  journal =      "Neurocomputing",
  year =         "2004",
  volume =       "56",
  month =        "January",
  pages =        "187--203",
  abstract =     "",
}

@InProceedings{chen03a_bibuniq_3449,
  author =       "Y. P. P. Chen",
  title =        "A hybrid framework using {SOM} and fuzzy theory for textual classification in data mining",
  booktitle =    "Modelling With Words: Learning, Fusion, and Reasoning Within A Formal Linguistic Representation Framework, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "153--167",
  abstract =     "",
}

@Article{ryding04a_bibuniq_3451,
  author =       "M. Ryding and P. White and O. Kalm",
  title =        "Eustachian tube function and tympanic membrane findings after chronic secretory otitis media",
  journal =      "International Journal of Pediatric Otorhinolaryngology",
  year =         "2004",
  volume =       "68",
  number =       "2",
  month =        "February",
  pages =        "197--204",
  abstract =     "",
}

@Article{tung04a_bibuniq_3452,
  author =       "W. L. Tung and C. Quek",
  title =        "Falcon: Neural fuzzy control and decision systems using {FKP} and {PFKP} clustering algorithms",
  journal =      "{IEEE} Transactions on Systems Man and Cybernetics Part B-Cybernetics",
  year =         "2004",
  volume =       "34",
  number =       "1",
  month =        "February",
  pages =        "686--695",
  abstract =     "",
}

@Article{godino-llorente04a_bibuniq_3454,
  author =       "J. I. Godino-Llorente and P. Gomez-Vilda",
  title =        "Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors",
  journal =      "{IEEE} Transactions on Biomedical Engineering",
  year =         "2004",
  volume =       "51",
  number =       "2",
  month =        "February",
  pages =        "380--384",
  abstract =     "",
}

@Article{coppini04a_bibuniq_3456,
  author =       "G. Coppini and S. Diciotti and G. Valli",
  title =        "Matching of medical images by self-organizing neural networks",
  journal =      "Pattern Recognition Letters",
  year =         "2004",
  volume =       "25",
  number =       "3",
  month =        "February",
  pages =        "341--352",
  abstract =     "",
}

@Article{waldrop04b_bibuniq_3457,
  author =       "M. P. Waldrop and M. K. Firestone",
  title =        "Microbial community utilization of recalcitrant and simple carbon compounds: impact of oak-woodland plant communities",
  journal =      "Oecologia",
  year =         "2004",
  volume =       "138",
  number =       "2",
  month =        "January",
  pages =        "275--284",
  abstract =     "",
}

@Article{hall04a_bibuniq_3459,
  author =       "R. J. Hall and A. Patwardhan",
  title =        "A two step approach for semi-automated particle selection from low contrast cryo-electron micrographs",
  journal =      "Journal of Structural Biology",
  year =         "2004",
  volume =       "145",
  number =       "1-2",
  month =        "January" # "-" # feb,
  pages =        "19--28",
  abstract =     "",
}

@Article{shiroma03a_bibuniq_3460,
  author =       "G. S. Shiroma and R. Y. Miyamoto and W. A. Shiroma",
  title =        "A 16-element two-dimensional active self-steering array using self-oscillating mixers",
  journal =      "{IEEE} Transactions on Microwave Theory and Techniques",
  year =         "2003",
  volume =       "51",
  number =       "12",
  month =        "December",
  pages =        "2476--2482",
  abstract =     "",
}

@Article{frantti04a_bibuniq_3461,
  author =       "T. Frantti and S. Kallio",
  title =        "Expert system for gesture recognition in terminal's user interface",
  journal =      "Expert Systems with Applications",
  year =         "2004",
  volume =       "26",
  number =       "2",
  month =        "February",
  pages =        "189--202",
  abstract =     "",
}

@Article{wang03a_bibuniq_3462,
  author =       "J. B. Wang and T. H. Bo and I. Jonassen and O. Myklebost and E. Hovig",
  title =        "Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data",
  journal =      "BMC Bioinformatics",
  year =         "2003",
  volume =       "4",
  month =        "December" # " 2",
  abstract =     "",
}

@InProceedings{antonietti03a_bibuniq_3465,
  author =       "A. Antonietti and C. Maretti",
  title =        "Degrees of similarity in knowledge transfer",
  booktitle =    "Neural Nets, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "338--347",
  abstract =     "",
}

@Article{kucherenko03a_bibuniq_3466,
  author =       "Y. A. Kucherenko and A. P. Pylaev and V. D. Murzakov and A. V. Belomestnih and V. N. Popov and A. A. Tyaktev",
  title =        "Experimental study into the Rayleigh-Taylor turbulent mixing zone heterogeneous structure",
  journal =      "Laser and Particle Beams",
  year =         "2003",
  volume =       "21",
  number =       "3",
  month =        "September",
  pages =        "375--379",
  abstract =     "",
}

@Article{kobayashi04a_bibuniq_3467,
  author =       "S. Kobayashi and H. Yoshizawa and S. Yamada",
  title =        "Pathology of lumbar nerve root compression - Part 2: morphological and immunohistochemical changes of dorsal root ganglion",
  journal =      "Journal of Orthopaedic Research",
  year =         "2004",
  volume =       "22",
  number =       "1",
  month =        "January",
  pages =        "180--188",
  abstract =     "",
}

@Article{bensmail03a_bibuniq_3468,
  author =       "H. Bensmail and A. Haoudi",
  title =        "Postgenomics: Proteomics and bioinformatics in cancer research",
  journal =      "Journal of Biomedicine and Biotechnology",
  year =         "2003",
  volume =       "2003",
  number =       "4",
  month =        "October" # " 29",
  pages =        "217--230",
  abstract =     "",
}

@Article{leski03a_bibuniq_3469,
  author =       "J. M. Leski",
  title =        "Generalized weighted conditional fuzzy clustering",
  journal =      "{IEEE} Transactions on Fuzzy Systems",
  year =         "2003",
  volume =       "11",
  number =       "6",
  month =        "December",
  pages =        "709--715",
  abstract =     "",
}

@Article{leski04a_bibuniq_3470,
  author =       "J. M. Leski",
  title =        "Fuzzy c-varieties/elliptotypes clustering in reproducing kernel Hilbert space",
  journal =      "Fuzzy Sets and Systems",
  year =         "2004",
  volume =       "141",
  number =       "2",
  month =        "January" # " 16",
  pages =        "259--280",
  abstract =     "",
}

@InProceedings{lingras03a_bibuniq_3471,
  author =       "P. Lingras and M. Hogo and M. Snorek and B. Leonard",
  title =        "Clustering supermarket customers using rough set based {K}ohonen networks",
  booktitle =    "Foundations of Intelligent Systems, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "169--173",
  abstract =     "",
}

@InProceedings{satoh03a_bibuniq_3474,
  author =       "H. Satoh and T. Nakata",
  title =        "Knowledge discovery on chemical reactivity from experimental reaction information",
  booktitle =    "Discovery Science, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "470--477",
  abstract =     "",
}

@Article{zhao03a_bibuniq_3475,
  author =       "X. W. Zhao and L. M. Zhou and T. L. Chen",
  title =        "Effects of interactive function forms in a self-organized critical model based on neural networks",
  journal =      "Communications in Theoretical Physics",
  year =         "2003",
  volume =       "40",
  number =       "5",
  month =        "November" # " 15",
  pages =        "607--613",
  abstract =     "",
}

@Article{kowianski04a_bibuniq_3476,
  author =       "P. Kowianski and J. M. Morys and S. Wojcik and J. Dziewiatkowski and A. Luczynska and E. Spodnik and J. P. Timmermans and J. Morys",
  title =        "Neuropeptide-containing neurons in the endopiriform region of the rat: morphology and colocalization with calcium-binding proteins and nitric oxide synthase",
  journal =      "Brain Research",
  year =         "2004",
  volume =       "996",
  number =       "1",
  month =        "January" # " 16",
  pages =        "97--110",
  abstract =     "",
}

@Article{bermejo04a_bibuniq_3477,
  author =       "S. Bermejo and J. Cabestany",
  title =        "Local averaging of ensembles of {LVQ}-based nearest neighbor classifiers",
  journal =      "Applied Intelligence",
  year =         "2004",
  volume =       "20",
  number =       "1",
  month =        "January" # "-" # feb,
  pages =        "47--58",
  abstract =     "",
}

@InProceedings{pirrone03a_bibuniq_3478,
  author =       "R. Pirrone and A. Chella",
  title =        "A neural architecture for segmentation and modelling of range data",
  booktitle =    "AI(Asterisk)IA 2003: Advances in Artificial Intelligence, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "130--141",
  abstract =     "",
}

@InProceedings{marques03a_bibuniq_3480,
  author =       "N. C. Marques and N. Chen",
  title =        "Border detection on remote sensing satellite data using self-organizing maps",
  booktitle =    "Progress in Artificial Intelligence, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "294--307",
  abstract =     "",
}

@InProceedings{kong03a_bibuniq_3484,
  author =       "J. Kong and D. G. Li and A. C. Watson",
  title =        "A firearm identification system based on neural network",
  booktitle =    "AI 2003: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "315--326",
  abstract =     "",
}

@InProceedings{tan03a_bibuniq_3485,
  author =       "H. S. Tan",
  title =        "{HL}abel{SOM}: Automatic labelling of self organising maps toward hierarchical visualisation for information retrieval",
  booktitle =    "AI 2003: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "532--543",
  abstract =     "",
}

@Article{papamarkos03a_bibuniq_3487,
  author =       "N. Papamarkos",
  title =        "A neuro-fuzzy technique for document binarisation",
  journal =      "Neural Computing \& Applications",
  year =         "2003",
  volume =       "12",
  number =       "3-4",
  month =        "December",
  pages =        "190--199",
  abstract =     "",
}

@Article{kanungo04a_bibuniq_3488,
  author =       "S. B. Kanungo and S. S. Tripathy and S. K. Mishra and B. Sahoo and Rajeev",
  title =        "Adsorption of Co2+, Ni2+, {CU2}+, and Zn2+ onto amorphous hydrous manganese dioxide from simple (1-1) electrolyte solutions",
  journal =      "Journal of Colloid and Interface Science",
  year =         "2004",
  volume =       "269",
  number =       "1",
  month =        "January" # " 1",
  pages =        "11--21",
  abstract =     "",
}

@Article{roth03a_bibuniq_3491,
  author =       "A. V. Roth and L. J. Menor",
  title =        "Insights into service operations management: {A} research agenda",
  journal =      "Production and Operations Management",
  year =         "2003",
  volume =       "12",
  number =       "2",
  month =        "SUM",
  pages =        "145--164",
  abstract =     "",
}

@Article{kim03b_bibuniq_3492,
  author =       "S. W. Kim and B. J. Oommen",
  title =        "A brief taxonomy and ranking of creative prototype reduction schemes",
  journal =      "Pattern Analysis and Applications",
  year =         "2003",
  volume =       "6",
  number =       "3",
  month =        "December",
  pages =        "232--244",
  abstract =     "",
}

@Article{kluver03a_bibuniq_3493,
  author =       "J. Kluver and C. Stoica",
  title =        "Simulations of group dynamics with different models",
  journal =      "Jasss-THE Journal of Artificial Societies and Social Simulation",
  year =         "2003",
  volume =       "6",
  number =       "4",
  month =        "October",
  abstract =     "",
}

@Article{larre-larrouy03a_bibuniq_3494,
  author =       "M. C. Larre-Larrouy and A. Albrecht and E. Blanchart and T. Chevallier and C. Feller",
  title =        "Carbon and monosaccharides of a tropical Vertisol under pasture and market-gardening: distribution in primary organomineral separates",
  journal =      "Geoderma",
  year =         "2003",
  volume =       "117",
  number =       "1-2",
  month =        "November",
  pages =        "63--79",
  abstract =     "",
}

@Article{lizarraga-cubedo03a_bibuniq_3495,
  author =       "H. A. Lizarraga-Cubedo and I. Tuck and N. Bailey and G. J. Pierce and J. A. M. Kinnear",
  title =        "Comparisons of size at maturity and fecundity of two Scottish populations of the European lobster, Homarus gammarus",
  journal =      "Fisheries Research",
  year =         "2003",
  volume =       "65",
  number =       "1-3",
  month =        "December",
  pages =        "137--152",
  abstract =     "",
}

@Article{wesolowski03a_bibuniq_3496,
  author =       "M. Wesolowski and B. Suchacz and P. Konieczynski",
  title =        "The application of artificial neural networks for the selection of key thermoanalytical parameters in medicinal plants analysis",
  journal =      "Combinatorial Chemistry \& High Throughput Screening",
  year =         "2003",
  volume =       "6",
  number =       "8",
  month =        "December",
  pages =        "811--820",
  abstract =     "",
}

@Article{li03a_bibuniq_3497,
  author =       "Z. Q. Li and F. Wei and Y. Jin",
  title =        "Numerical simulation of pulverized coal combustion and {NO} formation",
  journal =      "Chemical Engineering Science",
  year =         "2003",
  volume =       "58",
  number =       "23-24",
  month =        "November" # "-" # dec,
  pages =        "5161--5171",
  abstract =     "",
}

@Article{kowalski03a_bibuniq_3498,
  author =       "C. T. Kowalski and T. Orlowska-Kowalska",
  title =        "Neural networks application for induction motor faults diagnosis",
  journal =      "Mathematics and Computers in Simulation",
  year =         "2003",
  volume =       "63",
  number =       "3-5",
  month =        "November" # " 17",
  pages =        "435--448",
  abstract =     "",
}

@InProceedings{lesot03a_bibuniq_3499,
  author =       "M. J. Lesot and F. d'Alche-Buc and G. Siolas",
  title =        "Evaluation of topographic clustering and its kernelization",
  booktitle =    "Machine Learning: Ecml 2003, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "265--276",
  abstract =     "",
}

@Article{turtinen03a_bibuniq_3500,
  author =       "M. Turtinen and M. Pietikainen and O. Silven and T. Maenpaa and M. Niskanen",
  title =        "Paper characterisation by texture using visualisation-based training",
  journal =      "International Journal of Advanced Manufacturing Technology",
  year =         "2003",
  volume =       "22",
  number =       "11-12",
  month =        "December",
  pages =        "890--898",
  abstract =     "",
}

@Article{unhino03a_bibuniq_3501,
  author =       "E. Unhino and K. Yano and T. Azetsu",
  title =        "Twin unit self-organising map for voice conversion",
  journal =      "Electronics Letters",
  year =         "2003",
  volume =       "39",
  number =       "24",
  month =        "November" # " 27",
  pages =        "1767--1769",
  abstract =     "",
}

@InProceedings{polani03a_bibuniq_3502,
  author =       "D. Polani",
  title =        "Measuring self-organization via observers",
  booktitle =    "Advances in Artificial Life, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "667--675",
  abstract =     "",
}

@Article{faba-perez03a_bibuniq_3503,
  author =       "C. Faba-Perez and V. P. Guerrero-Bote and F. De Moya-Anegon",
  title =        "Data mining in a closed Web environment",
  journal =      "Scientometrics",
  year =         "2003",
  volume =       "58",
  number =       "3",
  pages =        "623--640",
  abstract =     "",
}

@Article{martin-valdivia03a_bibuniq_3504,
  author =       "M. T. Martin-Valdivia and M. Garcia-Vega and L. A. Urena-Lopez",
  title =        "{LVQ} for text categorization using a multilingual linguistic resource",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "55",
  number =       "3-4",
  month =        "October",
  pages =        "665--679",
  abstract =     "",
}

@Article{liu03a_bibuniq_3507,
  author =       "Z. Y. Liu and L. Xu",
  title =        "Topological local principal component analysis",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "55",
  number =       "3-4",
  month =        "October",
  pages =        "739--745",
  abstract =     "",
}

@Article{gonchar03a_bibuniq_3508,
  author =       "Y. Gonchar and A. Burkhalter",
  title =        "Distinct {GABA}ergic targets of feedforward and feedback connections between lower and higher areas of rat visual cortex",
  journal =      "Journal of Neuroscience",
  year =         "2003",
  volume =       "23",
  number =       "34",
  month =        "November" # " 26",
  pages =        "10904--10912",
  abstract =     "",
}

@Article{marini05a_bibuniq_3514,
  author =       "F. Marini and A. Roncaglioni and M. Novic",
  title =        "Variable selection and interpretation in structure-affinity correlation modeling of estrogen receptor binders",
  journal =      "Journal of Chemical Information and Modeling",
  year =         "2005",
  volume =       "45",
  number =       "6",
  month =        "November" # "-" # dec,
  abstract =     "",
}

@InProceedings{angelopoulou05a_bibuniq_3519,
  author =       "A. Angelopoulou and A. Psarrou and J. G. Rodriguez and K. Revett",
  title =        "Automatic landmarking of 2{D} medical shapes using the growing neural gas network",
  booktitle =    "Computer Vision for Biomedical Image Applications, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "210--219",
  abstract =     "",
}

@Article{buchala05a_bibuniq_3523,
  author =       "S. Buchala and N. Davey and R. J. Frank and M. Loomes and T. M. Gale",
  title =        "The role of global and feature based information in gender classification of faces: {A} comparison of human performance and computational models",
  journal =      "International Journal of Neural Systems",
  year =         "2005",
  volume =       "15",
  number =       "1-2",
  month =        "February" # "-" # apr,
  abstract =     "",
}

@Article{ma05a_bibuniq_3529,
  author =       "J. W. Ma and H. Bagan",
  title =        "Land-use classification using Aster data and self-organized neutral networks",
  journal =      "International Journal of Applied Earth Observation and Geoinformation",
  year =         "2005",
  volume =       "7",
  number =       "3",
  month =        "November",
  abstract =     "",
}

@InProceedings{khosla05a_bibuniq_3534,
  author =       "R. Khosla and C. D'Souza and M. Taghian",
  title =        "Intelligent consumer purchase intention prediction system tor green products",
  booktitle =    "Knowledge-Based Intelligent Information and Engineering Systems, Pt. 4, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2005",
  pages =        "752--757",
  abstract =     "",
}

@Article{oguz05a_bibuniq_3539,
  author =       "C. Oguz and M. A. Gallivan",
  title =        "A data-driven approach for reduction of molecular simulations",
  journal =      "International Journal of Robust and Nonlinear Control",
  year =         "2005",
  volume =       "15",
  number =       "15",
  month =        "October",
  abstract =     "",
}

@Article{azcarraga05a_bibuniq_3542,
  author =       "A. P. Azcarraga and M. H. Hsieh and S. L. Pan and R. Setiono",
  title =        "Extracting salient dimensions for automatic {SOM} labeling",
  journal =      "Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews",
  year =         "2005",
  volume =       "35",
  number =       "4",
  month =        "November",
  abstract =     "",
}

@Article{yan05a_bibuniq_3545,
  author =       "W. Yan and C. H. Chen and L. P. Khoo",
  title =        "A Web-enabled product definition and customization system for product conceptualization",
  journal =      "Expert Systems",
  year =         "2005",
  volume =       "22",
  number =       "5",
  month =        "November",
  abstract =     "",
}

@Article{kim05a_bibuniq_3546,
  author =       "Y. A. Kim and H. S. Song and S. H. Kim",
  title =        "Strategies for preventing defection based on the mean time to defection and their implementations on a self-organizing map",
  journal =      "Expert Systems",
  year =         "2005",
  volume =       "22",
  number =       "5",
  month =        "November",
  abstract =     "",
}

@InProceedings{karras05a_bibuniq_3547,
  author =       "D. A. Karras",
  title =        "A hierarchical support vector machine based solution for off-line inverse modeling in intelligent robotics applications",
  booktitle =    "Artificial Neural Networks: Formal Models and Their Applications - {ICANN} 2005, Pt. 2, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "619--624",
  abstract =     "",
}

@InProceedings{gas05a_bibuniq_3548,
  author =       "B. Gas and M. Chetouani and J. L. Zarader and C. Charbuillet",
  title =        "Predictive {K}ohonen map for speech features extraction",
  booktitle =    "Artificial Neural Networks: Formal Models and Their Applications - {ICANN} 2005, Pt. 2, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "793--798",
  abstract =     "",
}

@InProceedings{merlin05a_bibuniq_3549,
  author =       "P. Merlin and B. Maillet",
  title =        "Completing hedge fund missing net asset values using {K}ohonen maps and constrained randomization",
  booktitle =    "Artificial Neural Networks: Formal Models and Their Applications - {ICANN} 2005, Pt. 2, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "923--928",
  abstract =     "",
}

@Article{allouche05a_bibuniq_3555,
  author =       "M. K. Allouche and B. Moulin",
  title =        "Amalgamation in cartographic generalization using {K}ohonen's feature nets",
  journal =      "International Journal of Geographical Information Science",
  year =         "2005",
  volume =       "19",
  number =       "8-9",
  month =        "September" # "-" # oct,
  abstract =     "",
}

@InProceedings{bensalem05a_bibuniq_3562,
  author =       "Z. N. Ben Salem and F. Mouria-Beji and F. Kamoun",
  title =        "Spatio-temporal organization map: {A} speech recognition application",
  booktitle =    "Artificial Neural Networks: Biological Inspirations - {ICANN} 2005, Pt. 1, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "371--378",
  abstract =     "",
}

@InProceedings{germen05a_bibuniq_3565,
  author =       "E. Germen and D. G. Ece and O. N. Gerek",
  title =        "Self Organizing Map ({SOM}) approach for classification of power quality events",
  booktitle =    "Artificial Neural Networks: Biological Inspirations - {ICANN} 2005, Pt. 1, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "403--408",
  abstract =     "",
}

@InProceedings{lessmann05a_bibuniq_3568,
  author =       "B. Lessmann and A. Degenhard and P. Kessar and L. Pointon and M. Khazen and M. O. Leach and T. W. Nattkemper",
  title =        "{SOM}-based wavelet filtering for the exploration of medical images",
  booktitle =    "Artificial Neural Networks: Biological Inspirations - {ICANN} 2005, Pt. 1, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "671--676",
  abstract =     "",
}

@InProceedings{germen05b_bibuniq_3575,
  author =       "E. Germen",
  title =        "Improving the resultant quality of {K}ohonen's self organizing map using stiffness factor",
  booktitle =    "Advances in Natural Computation, Pt. 1, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "353--357",
  abstract =     "",
}

@Article{hrubes05a_bibuniq_3578,
  author =       "P. Hrubes",
  title =        "Recognition of geographical information system layers based on spatial analysis",
  journal =      "Neural Network World",
  year =         "2005",
  volume =       "15",
  number =       "1",
  abstract =     "",
}

@Article{bartkowiak05a_bibuniq_3579,
  author =       "A. Bartkowiak",
  title =        "Distal points viewed in {K}ohonen's self-organizing maps",
  journal =      "Neural Network World",
  year =         "2005",
  volume =       "15",
  number =       "4",
  abstract =     "",
}

@Article{lipinski05a_bibuniq_3580,
  author =       "P. Lipinski",
  title =        "Clustering of large number of stock market trading rules",
  journal =      "Neural Network World",
  year =         "2005",
  volume =       "15",
  number =       "4",
  abstract =     "",
}

@Article{papa05a_bibuniq_3586,
  author =       "E. Papa and F. Villa and P. Gramatica",
  title =        "Statistically validated {QSAR}s, based on theoretical descriptors, for modeling aquatic toxicity of organic chemicals in Pimephales promelas (fathead minnow)",
  journal =      "Journal of Chemical Information and Modeling",
  year =         "2005",
  volume =       "45",
  number =       "5",
  month =        "September" # "-" # oct,
  abstract =     "",
}

@Article{kyan05a_bibuniq_3590,
  author =       "M. Kyan and L. Guan and S. Liss",
  title =        "Refining competition in the self-organising tree map for unsupervised biofilm image segmentation",
  journal =      "Neural Networks",
  year =         "2005",
  volume =       "18",
  number =       "5-6",
  month =        "June" # "-" # jul,
  abstract =     "",
}

@Article{cinar05a_bibuniq_3598,
  author =       "O. Cinar",
  title =        "New tool for evaluation of performance of wastewater treatment plant: Artificial neural network",
  journal =      "Process Biochemistry",
  year =         "2005",
  volume =       "40",
  number =       "9",
  month =        "September",
  abstract =     "",
}

@Article{dimoglo05a_bibuniq_3600,
  author =       "A. Dimoglo and V. Kovalishyn and N. Shvets and V. Ahsen",
  title =        "The structure-inhibitory activity relationships study in a series of cyclooxygenase-2 inhibitors: {A} combined electronic-topological and neural networks approach",
  journal =      "Mini-Reviews in Medicinal Chemistry",
  year =         "2005",
  volume =       "5",
  number =       "10",
  month =        "October",
  abstract =     "",
}

@Article{tison05a_bibuniq_3602,
  author =       "J. Tison and Y. S. Park and M. Coste and J. G. Wasson and L. Ector and F. Rimet and F. Delmas",
  title =        "Typology of diatom communities and the influence of hydro-ecoregions: {A} study on the {F}rench hydrosystem scale",
  journal =      "Water Research",
  year =         "2005",
  volume =       "39",
  number =       "14",
  month =        "September",
  abstract =     "",
}

@Article{kwok05a_bibuniq_3604,
  author =       "T. Kwok and K. A. Smith",
  title =        "Optimization via intermittency with a self-organizing neural network",
  journal =      "Neural Computation",
  year =         "2005",
  volume =       "17",
  number =       "11",
  month =        "November",
  abstract =     "",
}

@Article{ibarra05a_bibuniq_3606,
  author =       "A. A. Ibarra and Y. S. Park and S. Brosse and Y. Reyjol and P. Lim and S. Lek",
  title =        "Nested patterns of spatial diversity revealed for fish assemblages in a west European river",
  journal =      "Ecology of Freshwater Fish",
  year =         "2005",
  volume =       "14",
  number =       "3",
  month =        "September",
  abstract =     "",
}

@Article{supek05a_bibuniq_3608,
  author =       "F. Supek and K. Vlahovicek",
  title =        "Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity",
  journal =      "BMC Bioinformatics",
  year =         "2005",
  volume =       "6",
  month =        "July" # " 19",
  abstract =     "",
}

@Article{wong05a_bibuniq_3611,
  author =       "J. W. H. Wong and H. M. Cartwright",
  title =        "Deterministic projection by growing cell structure networks for visualization of high-dimensionally datasets",
  journal =      "Journal of Biomedical Informatics",
  year =         "2005",
  volume =       "38",
  number =       "4",
  month =        "August",
  abstract =     "",
}

@Article{simon05a_bibuniq_3612,
  author =       "G. Simon and A. Lendasse and M. Cottrell and J. C. Fort and M. Verleysen",
  title =        "Time series forecasting: Obtaining long term trends with self-organizing maps",
  journal =      "Pattern Recognition Letters",
  year =         "2005",
  volume =       "25",
  number =       "12",
  month =        "September",
  abstract =     "",
}

@Article{dipalma05a_bibuniq_3613,
  author =       "F. Di Palma and G. De Nicolao and G. Miraglia and E. Pasquinetti and F. Piccinini",
  title =        "Unsupervised spatial pattern classification of electrical-wafer-sorting maps in semiconductor manufacturing",
  journal =      "Pattern Recognition Letters",
  year =         "2005",
  volume =       "25",
  number =       "12",
  month =        "September",
  abstract =     "",
}

@Article{hagenbuchner05a_bibuniq_3614,
  author =       "M. Hagenbuchner and A. C. Tsoi",
  title =        "A supervised training algorithm for self-organizing maps for structures",
  journal =      "Pattern Recognition Letters",
  year =         "2005",
  volume =       "25",
  number =       "12",
  month =        "September",
  abstract =     "",
}

@InProceedings{wermter05a_bibuniq_3618,
  author =       "S. Wermter and C. Weber and M. Elshaw and V. Gallese and F. Pulvermuller",
  title =        "Grounding neural robot language in action",
  booktitle =    "Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience, Lecture Notes in Artificial Intelligence",
  year =         "2005",
  pages =        "162--181",
  abstract =     "",
}

@InProceedings{campbell05a_bibuniq_3624,
  author =       "A. Campbell and E. Berglund and A. Streit",
  title =        "Graphics hardware implementation of the parameter-less self-organising map",
  booktitle =    "Intelligent Data Engineering and Automated Learning Ideal 2005, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "343--350",
  abstract =     "",
}

@InProceedings{lee05b_bibuniq_3625,
  author =       "H. J. Lee and S. Z. Cho",
  title =        "{SOM}-based novelty detection using novel data",
  booktitle =    "Intelligent Data Engineering and Automated Learning Ideal 2005, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "359--366",
  abstract =     "",
}

@Article{bay05a_bibuniq_3630,
  author =       "of Bay and R. Bayir",
  title =        "Kohonen network based fault diagnosis and condition monitoring of pre-engaged starter motors",
  journal =      "International Journal of Automotive Technology",
  year =         "2005",
  volume =       "6",
  number =       "4",
  month =        "August",
  abstract =     "",
}

@Article{rynkiewicz05a_bibuniq_3631,
  author =       "J. Rynkiewicz",
  title =        "Consistency of it least extended variance estimator.",
  journal =      "Comptes Rendus Mathematique LA French",
  year =         "2005",
  volume =       "341",
  number =       "2",
  month =        "July" # " 15",
  abstract =     "",
}

@InProceedings{moreno05a_bibuniq_3634,
  author =       "S. Moreno and H. Allende and C. Rogel and R. Salas",
  title =        "Robust Growing Hierarchical Self Organizing Map",
  booktitle =    "Computational Intelligence and Bioinspired Systems, Proceedings, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "341--348",
  abstract =     "",
}

@Article{terai05a_bibuniq_3639,
  author =       "S. Terai and I. Sakaida and H. Nishina and K. Okita",
  title =        "Lesson from the {GFP}/{CC}l4 model - Translational Research Project: the development of cell therapy using autologous bone marrow cells in patients with liver cirrhosis",
  journal =      "Journal of Hepato-Biliary-Pancreatic Surgery",
  year =         "2005",
  volume =       "12",
  number =       "3",
  abstract =     "",
}

@Article{pal05b_bibuniq_3640,
  author =       "N. R. Pal and A. Laha and J. Das",
  title =        "Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images",
  journal =      "International Journal of Remote Sensing",
  year =         "2005",
  volume =       "26",
  number =       "10",
  month =        "May" # " 20",
  abstract =     "",
}

@Article{kataoka05a_bibuniq_3643,
  author =       "H. Kataoka and K. Ichihara and O. Konishi and K. Ogura and T. Sugiura",
  title =        "Quality control of image-based laboratory data using the self-organizing map technique",
  journal =      "Clinical Chemistry",
  year =         "2005",
  volume =       "51",
  abstract =     "",
}

@Article{rallo05a_bibuniq_3663,
  author =       "R. Rallo and G. Espinosa and F. Giralt",
  title =        "Using an ensemble of neural based {QSAR}s for the prediction of toxicological properties of chemical contaminants",
  journal =      "Process Safety and Environmental Protection",
  year =         "2005",
  volume =       "83",
  number =       "B4",
  month =        "July",
  abstract =     "",
}

@Article{caetano05a_bibuniq_3681,
  author =       "S. Caetano and J. Aires-De-Sousa and A. Daszykowski and Y. V. Heyden",
  title =        "Prediction of enantio selectivity using chirality codes and Classification and Regression Trees",
  journal =      "Analytica Chimica Acta",
  year =         "2005",
  volume =       "544",
  number =       "1-2",
  month =        "July" # " 15",
  abstract =     "",
}

@Article{boudour05a_bibuniq_3688,
  author =       "M. Boudour and A. Hellal",
  title =        "Power system dynamic security mapping using synchronizing and damping torques technique",
  journal =      "Arabian Journal for Science and Engineering",
  year =         "2005",
  volume =       "30",
  number =       "1B",
  month =        "April",
  abstract =     "",
}

@Article{baker05a_bibuniq_3690,
  author =       "C. L. Baker and A. P. Shon and R. P. N. Rao",
  title =        "Learning temporal clusters with synaptic facilitation and lateral inhibition",
  journal =      "Neurocomputing",
  year =         "2005",
  volume =       "65",
  month =        "June",
  abstract =     "",
}

@Article{corma05a_bibuniq_3693,
  author =       "A. Corma and J. M. Serra and P. Serna and M. Moliner",
  title =        "Integrating high-throughput characterization into combinatorial heterogeneous catalysis: unsupervised construction of quantitative structure/property relationship models",
  journal =      "Journal of English",
  year =         "2005",
  volume =       "232",
  number =       "2",
  month =        "June" # " 10",
  abstract =     "",
}

@Article{zheng05a_bibuniq_3695,
  author =       "P. Z. Zheng and K. K. Wang and Q. Y. Zhang and Q. H. Huang and Y. Z. Du and Q. H. Zhang and D. K. Xiao and S. H. Shen and S. Imbeaud and E. Eveno and C. J. Zhao and Y. L. Chen and H. Y. Fan and S. Waxman and C. Auffray and G. Jin and S. J. Chen and Z. Chen and J. Zhang",
  title =        "Systems analysis of transcriptome and proteome in retinoic acid/arsenic trioxide-induced cell differentiation/apoptosis of promyelocytic leukemia",
  journal =      "Proceedings of the National Academy of Sciences of the United States of America",
  year =         "2005",
  volume =       "102",
  number =       "21",
  month =        "May" # " 24",
  abstract =     "",
}

@Article{mlakar04a_bibuniq_3696,
  author =       "P. Mlakar",
  title =        "Analysis of ambient {SO2} concentrations and winds in the complex surroundings of a thermal power plant",
  journal =      "Nuovo Cimento Della Societa Italiana DI Fisica C-Geophysics and Space Physics",
  year =         "2004",
  volume =       "27",
  number =       "6",
  month =        "November" # "-" # dec,
  abstract =     "",
}

@Article{lingras05a_bibuniq_3699,
  author =       "P. Lingras and M. Hogo and M. Snorek and C. West",
  title =        "Temporal analysis of clusters of supermarket customers: conventional versus interval set approach",
  journal =      "Information Sciences",
  year =         "2005",
  volume =       "172",
  number =       "1-2",
  month =        "June" # " 9",
  abstract =     "",
}

@Article{wang05e_bibuniq_3702,
  author =       "Y. H. Wang and Y. Li and S. L. Yang and L. Yang",
  title =        "Classification of substrates and inhibitors of {P}-glycoprotein using unsupervised machine learning approach",
  journal =      "Journal of Chemical Information and Modeling",
  year =         "2005",
  volume =       "45",
  number =       "3",
  month =        "May" # "-" # jun,
  abstract =     "",
}

@Article{kurnaz05a_bibuniq_3707,
  author =       "M. N. Kurnaz and Z. Dokur and T. Olmez",
  title =        "Segmentation of remote-sensing images by incremental neural network",
  journal =      "Pattern Recognition Letters",
  year =         "2005",
  volume =       "26",
  number =       "8",
  month =        "June",
  abstract =     "",
}

@Article{shalinie05a_bibuniq_3709,
  author =       "S. M. Shalinie",
  title =        "Modeling Connectionist neuro-fuzzy network and applications",
  journal =      "Neural Computing \& Applications",
  year =         "2005",
  volume =       "14",
  number =       "1",
  month =        "April",
  abstract =     "",
}

@Article{majumdar05a_bibuniq_3712,
  author =       "K. Majumdar and N. Das",
  title =        "Mobile user tracking using a hybrid neural network",
  journal =      "Wireless Networks",
  year =         "2005",
  volume =       "11",
  number =       "3",
  month =        "May",
  abstract =     "",
}

@Article{meyer-base05a_bibuniq_3723,
  author =       "A. Meyer-Base and K. Jancke and A. Wismuller and S. Foo and T. Martinetz",
  title =        "Medical image compression using topology-preserving neural networks",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2005",
  volume =       "18",
  number =       "4",
  month =        "June",
  abstract =     "",
}

@Article{low05a_bibuniq_3729,
  author =       "K. H. Low and W. K. Leow and M. H. Ang",
  title =        "An ensemble of cooperative extended {K}ohonen maps for complex robot motion tasks",
  journal =      "Neural Computation",
  year =         "2005",
  volume =       "17",
  number =       "6",
  month =        "June",
  abstract =     "",
}

@Article{claussen05a_bibuniq_3733,
  author =       "J. C. Claussen",
  title =        "Winner-relaxing self-organizing maps",
  journal =      "Neural Computation",
  year =         "2005",
  volume =       "17",
  number =       "5",
  month =        "May",
  abstract =     "",
}

@Article{ruanet05a_bibuniq_3739,
  author =       "V. V. Ruanet and E. Z. Kochieva and N. N. Ryzhova",
  title =        "The use of a self-organizing feature map for the treatment of the results of Rapd and Issr analyses in studies on the genomic polymorphism in the genus Capsicum {L}.",
  journal =      "Russian Journal of Genetics",
  year =         "2005",
  volume =       "41",
  number =       "2",
  month =        "February",
  abstract =     "",
}

@Article{sarasamma05a_bibuniq_3746,
  author =       "S. T. Sarasamma and Q. M. A. Zhu and J. Huff",
  title =        "Hierarchical Kohonenen net for anomaly detection in network security",
  journal =      "Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics",
  year =         "2005",
  volume =       "35",
  number =       "2",
  month =        "April",
  abstract =     "",
}

@Article{chen05a_bibuniq_3751,
  author =       "C. H. Chen and L. P. Khoo and W. Yan",
  title =        "Pdcs - a product definition and customisation system for product concept development",
  journal =      "Expert Systems With Applications",
  year =         "2005",
  volume =       "28",
  number =       "3",
  month =        "April",
  abstract =     "",
}

@Article{wu05e_bibuniq_3754,
  author =       "S. T. Wu and M. K. M. Rahman and T. W. S. Chow",
  title =        "Content-based image retrieval using growing hierarchical self-organizing quadtree map",
  journal =      "Pattern Recognition",
  year =         "2005",
  volume =       "38",
  number =       "5",
  month =        "May",
  abstract =     "",
}

@Article{kauko05a_bibuniq_3759,
  author =       "T. Kauko",
  title =        "Using the self-organising map to identify regularities across country-specific housing-market contexts",
  journal =      "Environment and Planning B-Planning \& Design",
  year =         "2005",
  volume =       "32",
  number =       "1",
  month =        "January",
  abstract =     "",
}

@Article{dacosta05a_bibuniq_3763,
  author =       "F. B. Da Costa and L. Terfloth and J. Gasteiger",
  title =        "Sesquiterpene lactone-based classification of three Asteraceae tribes: a study based on self-organizing neural networks applied to chemo systematics",
  journal =      "Phytochemistry",
  year =         "2005",
  volume =       "66",
  number =       "3",
  month =        "February",
  abstract =     "",
}

@Article{balakin04a_bibuniq_3766,
  author =       "K. V. Balakin and S. Ekins and A. Bugrim and Y. A. Ivanenkov and D. Korolev and Y. V. Nikolsky and A. V. Skorenko and A. A. Ivashchenko and N. P. Savchuk and T. Nikolskaya",
  title =        "Kohonen self organizing maps and neural networks for predicting human {CYP} affinity and rate of metabolism",
  journal =      "Drug Metabolism Reviews",
  year =         "2004",
  volume =       "36",
  month =        "August",
  abstract =     "",
}

@Article{barbedo05a_bibuniq_3769,
  author =       "J. G. A. Barbedo and A. Lopes",
  title =        "A new cognitive model for objective assessment of audio quality",
  journal =      "Journal of the Audio Engineering Society",
  year =         "2005",
  volume =       "53",
  number =       "1-2",
  month =        "January" # "-" # feb,
  abstract =     "",
}

@Article{muralidharan05a_bibuniq_3773,
  author =       "A. Muralidharan and P. J. Rousche",
  title =        "Decoding of auditory cortex signals with a Lamstar neural network",
  journal =      "Neurological Research",
  year =         "2005",
  volume =       "27",
  number =       "1",
  month =        "January",
  abstract =     "",
}

@Article{cottrell05a_bibuniq_3776,
  author =       "M. Cottrell and P. Letremy",
  title =        "How to use the {K}ohonen algorithm to simultaneously analyze individuals and modalities in a survey",
  journal =      "Neurocomputing",
  year =         "2005",
  volume =       "63",
  month =        "January",
  abstract =     "",
}

@Article{manni05a_bibuniq_3779,
  author =       "F. Manni and B. Toupance and A. Sabbagh and E. Heyer",
  title =        "New method for surname studies of ancient patrilineal population structures, and possible application to improvement of {Y}-chromosome sampling",
  journal =      "American Journal of Physical Anthropology",
  year =         "2005",
  volume =       "126",
  number =       "2",
  month =        "February",
  abstract =     "",
}

@Article{brodnjak-voncina05a_bibuniq_3787,
  author =       "D. Brodnjak-Voncina and Z. C. Kodba and M. Novic",
  title =        "Multivariate data analysis in classification of vegetable oils characterized by the content of fatty acids",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  year =         "2005",
  volume =       "75",
  number =       "1",
  month =        "January" # " 28",
  abstract =     "",
}

@Article{fayos05a_bibuniq_3793,
  author =       "J. Fayos and L. Infantes and F. H. Cano",
  title =        "Neural network prediction of secondary structure in crystals: Hydrogen-bond systems in pyrazole derivatives",
  journal =      "Crystal Growth \& Design",
  year =         "2005",
  volume =       "5",
  number =       "1",
  month =        "January" # "-" # feb,
  abstract =     "",
}

@Article{kauko04a_bibuniq_3796,
  author =       "T. Kauko",
  title =        "A comparative perspective on urban spatial housing market structure: Some more evidence of local sub-markets based on a neural network classification of Amsterdam",
  journal =      "Urban Studies",
  year =         "2004",
  volume =       "41",
  number =       "13",
  month =        "December",
  abstract =     "",
}

@Article{perl04a_bibuniq_3803,
  author =       "E. Perl",
  title =        "A neural network approach to movement pattern analysis",
  journal =      "Human Movement Science",
  year =         "2004",
  volume =       "23",
  number =       "5",
  month =        "November",
  abstract =     "",
}

@Article{galvez-fernandez04a_bibuniq_3804,
  author =       "C. Galvez-Fernandez and C. Spinola and J. M. Bonelo and F. J. M. Tapia and J. Vizoso",
  title =        "An approach to the analysis of thickness deviations in stainless steel coils based on self-organising map neural networks",
  journal =      "Neural Computing \& Applications",
  year =         "2004",
  volume =       "13",
  number =       "4",
  month =        "December",
  abstract =     "",
}

@Article{lin04a_bibuniq_3807,
  author =       "T. Lin and R. K. Aggarwal and C. H. Kim",
  title =        "Identification of the defective equipments in {GIS} using the self organising map",
  journal =      "IEE Proceedings-Generation Transmission and Distribution",
  year =         "2004",
  volume =       "151",
  number =       "5",
  month =        "September",
  abstract =     "",
}

@Article{maran04a_bibuniq_3810,
  author =       "E. Maran and M. Novic and P. Barbieri and J. Zupan",
  title =        "Application of counterpropagation artificial neural network for modelling properties of fish antibiotics",
  journal =      "SAR and {QSAR} in Environmental Research",
  year =         "2004",
  volume =       "15",
  number =       "5-6",
  month =        "October" # "-" # dec,
  abstract =     "",
}

@Article{sano04a_bibuniq_3811,
  author =       "K. Sano and S. Momose and H. Takizawa and H. Kobayashi and T. Nakamura",
  title =        "Efficient parallel processing of competitive learning algorithms",
  journal =      "Parallel Computing",
  year =         "2004",
  volume =       "30",
  number =       "12",
  month =        "December",
  abstract =     "",
}

@Article{swindale04a_bibuniq_3816,
  author =       "N. V. Swindale",
  title =        "How different feature spaces may be represented in cortical maps",
  journal =      "Network-Computation in Neural Systems",
  year =         "2004",
  volume =       "15",
  number =       "4",
  month =        "November",
  abstract =     "",
}

%  bibtex/manual0304. bib =====================================



@Article{Corchado00a_bibuniq_3820,
  author =       "E. Corchado and D. MacDonald and C. Fyfe",
  title =        "Internet agents who structure concept formation using kernel self-organising maps",
  journal =      "International Journal of Web Engineering and Technology. 2004; 1(4): 427-36",
  year =         "2004",
}

@InProceedings{Arboleya00a_bibuniq_3822,
  author =       "P. Arboleya and G. Diaz and J. Gomez Aleixandre and N. de Abajo",
  title =        "Power transformer overload forecasting using unsupervised learning neural networks",
  booktitle =    "16th International Conference on Electrical Machines. Conference Proceedings. 2004: 4 pp.",
  year =         "2004",
}

@InProceedings{Diaz00a_bibuniq_3823,
  author =       "G. Diaz and P. Arboleya and J. Gomez Aleixandre and N. de Abajo",
  title =        "Clustering events related to restricted earth fault and differential relaying on the protection of power transformer",
  booktitle =    "16th International Conference on Electrical Machines. Conference Proceedings. 2004: 5 pp.",
  year =         "2004",
}

@InProceedings{Naenna00a_bibuniq_3824,
  author =       "T. Naenna and M. J. Embrechts",
  title =        "Automated magnetocardiogram classifications with self-organizing maps ({SOM}s)",
  booktitle =    "Tencon 2004. 2004 {IEEE} Region 10 Conference 2",
  year =         "2004",
}

@Article{Villmann00a_bibuniq_3827,
  author =       "T. Villmann and B. Villmann and V. Slowik",
  title =        "Evolutionary algorithms with neighborhood cooperativeness according to neural maps",
  journal =      "Neurocomputing-. 2004; 57: 151-69",
  year =         "2004",
}

@InProceedings{Haidong00a_bibuniq_3829,
  author =       "Haidong Yang and Yueming Hu and Fei qi Deng and Xian Tian and Baorong Li",
  title =        "Fuzzy {SOFM}-{GIS} space cluster model and its application analysis",
  booktitle =    "2004-8th International Conference on Control, Automation, Robotics and Vision-Icarcv 1",
  year =         "2004",
}

@Article{Kamimura00a_bibuniq_3832,
  author =       "R. Kamimura",
  title =        "Cooperative information control for self-organizing maps",
  journal =      "Theoretical Computer Science. 29 Nov. 2004; 328(1-2): 225-65",
  year =         "2004",
}

@Article{Chan00a_bibuniq_3833,
  author =       "Chan Hee Lee and Soon Ho Jung",
  title =        "Off-line handwritten digit recognition by combining direction codes of strokes",
  journal =      "Journal of Kiss:-Software and Applications. Dec. 2004; 31(12): 1581-90",
  year =         "2004",
}

@Article{Sung00a_bibuniq_3835,
  author =       "Sung Kwun Oh and W. Pedrycz and B. T. Park",
  title =        "Relation-based neurofuzzy networks with evolutionary data granulation",
  journal =      "Mathematical and Computer-Modelling. Oct. 2004; 40(7-8): 891-921",
  year =         "2004",
}

@Article{Erzsebet00a_bibuniq_3836,
  author =       "Erzsebet Merenyi and Abha Jain and W. H. Farrand",
  title =        "Applications of {SOM} magnification to data mining",
  journal =      "Wseas Transactions on Systems. July 2004; 3(5): 2122-7",
  year =         "2004",
}

@Article{Dazi00a_bibuniq_3838,
  author =       "Dazi Li and K. Hirasawa and Jinglu Hu and K. Wada",
  title =        "An incremental learning of neural network with multiplication units for function approximation",
  journal =      "Research-Reports on Information-Science and Electrical Engineering of-Kyushu-University. Sept. 2003; 8(2): 135-40",
  year =         "2003",
}

@InProceedings{Sookhanaphibarn00a_bibuniq_3839,
  author =       "K. Sookhanaphibarn and T. Raicharoen and C. Lursinsap",
  title =        "A supervised neural network approach to invariant image recognition",
  booktitle =    "2004-8th International Conference on Control, Automation, Robotics and Vision-Icarcv 3",
  year =         "2004",
}

@InProceedings{Tseng00a_bibuniq_3840,
  author =       "G. C. Tseng",
  title =        "A comparative review of gene clustering in expression profile",
  booktitle =    "2004-8th International Conference on Control, Automation, Robotics and Vision-Icarcv {IEEE} 2",
  year =         "2004",
}

@Article{Kloptchenko00a_bibuniq_3844,
  author =       "A. Kloptchenko and T. Eklund and J. Karlsson and B. Back and H. Vanharanta and A. Visa",
  title =        "Combining data and text mining techniques for analysing financial reports",
  journal =      "International-Journal of Intelligent-Systems in Accounting, Finance and Management. Jan. March 2004; 12(1): 29-41",
  year =         "2004",
}

@InProceedings{Mokhnache00a_bibuniq_3857,
  author =       "L. Mokhnache and A. Boubakeur",
  title =        "Classification of transformer oil using self-organizing networks and {B}ayesian neural networks",
  booktitle =    "Aptadm'2004. Second International Conference on Advances in Processing-Testing and Application of Dielectric-Materials. 2004: 196-9",
  year =         "2004",
}

@InProceedings{Kwang00b_bibuniq_3858,
  author =       "Kwang Baek Kim and Gwang Ha Kim and Sung Kwan Je",
  title =        "Medical image vector quantizer using wavelet transform and enhanced {SOM} algorithm",
  booktitle =    "AI-2004:-Advances in Artificial Intelligence. 17th-Australian-Joint Conference on Artificial Intelligence. Proceedings Lecture Notes in Artificial Intelligence Vol. 3339. 2004: 98-108",
  year =         "2004",
}

@Article{Zuppa00a_bibuniq_3862,
  author =       "M. Zuppa and C. Distante and P. Siciliano and K. C. Persaud",
  title =        "Drift counteraction with multiple self-organising maps for an electronic nose",
  journal =      "Sensors and Actuators-B-Chemical. 15 March 2004; B98(2-3): 305-17",
  year =         "2004",
}

@Article{Huang00a_bibuniq_3864,
  author =       "Huang Yong and Chen Lin",
  title =        "Star pattern recognition algorithm based {SOFM} clustering function",
  journal =      "Optics and Precision-Engineering. June 2004; 12(3): 346-51",
  year =         "2004",
}

@InProceedings{Ruirui00a_bibuniq_3868,
  author =       "Ruirui Ji and Hong Zhu and Qieshi Zhang",
  title =        "Sorted evolutionary strategy based {SOFM} used for vector quantization",
  booktitle =    "Proceedings of International Conference on Information-Acquisition {IEEE} 331-4",
  year =         "2004",
}

@InProceedings{Simon00a_bibuniq_3870,
  author =       "G. Simon and J. A. Lee and M. Verleysen and M. Cottrell",
  title =        "Double quantization forecasting method for filling missing data in the {CATS} time series",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks vol. 2",
  year =         "2004",
}

@InProceedings{Meyer00a_bibuniq_3871,
  author =       "A. Meyer Base and S. S. Pilyugin and A. Wismuller",
  title =        "Stability analysis of a self-organizing neural network with feedforward and feedback dynamics",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks vol. 2",
  year =         "2004",
}

@InProceedings{Sasakawa00b_bibuniq_3872,
  author =       "T. Sasakawa and J. Hu and K. Hirasawa",
  title =        "Self-organized function localization neural network",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks vol. 2",
  year =         "2004",
}

@InProceedings{Kosuge00a_bibuniq_3875,
  author =       "S. Kosuge and Y. Osana",
  title =        "Chaotic associative memory using distributed patterns for image retrieval by shape information",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks vol. 2",
  year =         "2004",
}

@InProceedings{Chih00a_bibuniq_3876,
  author =       "Chih Ming Chen and Chin Ming Hong and Yung Feng Lu",
  title =        "A pruning structure of self-organizing {HCMAC} neural network classifier",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks vol. 2",
  year =         "2004",
}

@InProceedings{Dong00a_bibuniq_3878,
  author =       "Dong Won Kim and Gwi Tae Park",
  title =        "Hybrid architecture of the neural networks and self-organizing approximator technique: a new approach to nonlinear system modeling",
  booktitle =    "SMC'03 Conference Proceedings. 2003 {IEEE} International Conference on Systems, Man and Cybernetics. Conference-Theme System Security and Assurance-vol. 1",
  year =         "2003",
}

@Article{Tani00a_bibuniq_3881,
  author =       "J. Tani and M. Ito and Y. Sugita",
  title =        "Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using {RNNPB}",
  journal =      "Neural Networks. Oct. Nov. 2004; 17(8-9): 1273-89",
  year =         "2004",
}

}

@Article{tison04a_bibuniq_250,
  author =       "J. Tison and J. L. Giraudel and M. Coste and Y. S. Park and F. Delmas",
  title =        "Use of unsupervised neural networks for ecoregional zoning of hydrosystems through diatom communities: case study of Adour-Garonne watershed (France)",
  journal =      "Archiv FUR Hydrobiologie",
  year =         "2004",
  volume =       "159",
  number =       "3",
  month =        "March",
  pages =        "409--422",
}

@InProceedings{VayrynenHonkela04STEP_bibuniq_13,
  author =       "Jaakko J. V{\"{a}}yrynen and Timo Honkela",
  title =        "Word {ICA}",
  booktitle =    "Life, Cognition and Systems Sciences, Symposium Proceedings of the 11th {F}innish Artificial Intelligence Conference",
  year =         "2004",
  editor =       "Pekka Ala-Siuru Heikki Hy{\"{o}}tyniemi and Jouko Sepp{\"{a}}nen",
  pages =        "173--185",
}

@InCollection{Peltonen03wsom_bibuniq_1778,
  author =       "Jaakko Peltonen and Arto Klami and Samuel Kaski",
  title =        "Learning metrics for information visualization",
  booktitle =    "Proceedings of {WSOM}'03, Workshop on Self-Organizing Maps",
  pages =        "213--218",
  publisher =    "Kyushu Institute of Technology",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  note =         "(Proceedings on CD-ROM)",
}

@InCollection{Peltonen02icann_bibuniq_1783,
  author =       "Jaakko Peltonen and Arto Klami and Samuel Kaski",
  title =        "Learning more accurate metrics for self-organizing maps",
  booktitle =    "Artificial Neural Networks---ICANN 2002",
  pages =        "999--1004",
  publisher =    "Springer",
  year =         "2002",
  editor =       "J. R. Dorronsoro",
  address =      "Berlin",
}

@InProceedings{inspek111_bibuniq_910,
  author =       "Jack L. B. and Nandi A. K. and Wong M. L. D.",
  title =        "Automated novelty detection using a modified {K}ohonen self organizing map",
  booktitle =    "Eighth International Conference on Vibrations in Rotating Machinery IMechE Conference Transactions 2004--2. 2004: 313--22",
  pages =        "724",
  year =         "2004",
  publisher =    "Institution of Mechanical Engineers, Bury St Edmunds, UK",
  abstract =     "This paper proposes a novelty detection based method for machine condition monitoring (MCM) and a new feature extraction method based on higher order statistics of the power spectral density. This novel MCM method is structured on a modified {K}ohonen's self-organising map. It adopts a multi-dimensional dissimilarity measure for dual-class classification. the approach is designed to be highly modular and fits in well in a multi-sensor condition monitoring environment. Experiments using real world datasets with up to eight sensors have shown high accuracy in classification and robustness across different CM applications.",
}

@InProceedings{inspek367_bibuniq_1015,
  author =       "Jafar-Razmara",
  title =        "A model for classifying multisource remote sensing images by {K}ohonen neural networks",
  booktitle =    "{IGARSS} {IEEE} International Geoscience and Remote Sensing",
  pages =        "3849--3852",
  volume =       "6",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper a self-organized neural network for classifying of multisource remote sensing images is proposed. the method is a modified self-organizing map based on the {K}ohonen model that uses training patterns which belong to the known classes in the training phase of the network. in this way we will have a supervised trained system, which results in more accurate and rapid information. Acquiring this accuracy is more guaranteed by using multisource in remote sensing such as digital terrain model data, which is highly effective for extracting the features of the collected data. the model used for the classification of multisource remote sensing images was collected from two geographical locations in Iran and then its performance in classification was compared with two other methods: maximum likelihood (MLH) statistical method, and back-propagation neural network. the applied model proved to be the best in accuracy and speed.",
}

@InProceedings{inspek421_bibuniq_1065,
  author =       "Jakubiak A.",
  title =        "Classification of weather clutter models using neural networks",
  booktitle =    "Modern Problems of Radio Engineering, Telecommunications and Computer Science. Proceedings of the International Conference TCSET 2004 {IEEE} 264--6",
  pages =        "632",
  year =         "2004",
  publisher =    "Lviv Poltytechnic, Lviv, Ukraine",
  abstract =     "A decision system based on {K}ohonen {LVQ}2 neural network was used for the classification of weather clutter statistics. Three classes of distributions were distinguished: log-normal, Weibull and K. Data which were observed using L band radar. It was shown that the measured clutter amplitude samples obey a Weibull distribution, according to the classifier decision.",
}

@InProceedings{inspek439_bibuniq_1082,
  author =       "Jang-Myung-Lee {Sang-Joo-Kim, Jae-Ho-Lee}",
  editor =       "H. {Sugisaka, M. ; Tanaka}",
  title =        "Trajectory estimation of a moving object using {K}ohonen networks",
  booktitle =    "Ninth International Symposium on Artificial Life and Robotics {AROB} 9th'04. 2004: 221--4 Vol. 1",
  pages =        "799",
  year =         "2004",
  publisher =    "Oita Univ, Oita, Japan",
  abstract =     "A novel approach to estimate the real time moving trajectory of an object is proposed in this paper. the object position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Kalman filter and neural networks are utilized. Since the Kalman filter needs to approximate a non-linear system into a linear model to estimate the states, there always exist errors as well as uncertainties again. To resolve this problem, the neural networks are adopted in this approach, which have high adaptability with the memory of the input-output relationship. {K}ohonen network (self-organized map) is selected to learn the motion trajectory since it is spatially oriented. the superiority of the proposed algorithm is demonstrated through the real experiments.",
}

@InProceedings{inspek54_bibuniq_1167,
  author =       "Japkowicz N. Taeho-Jo",
  title =        "Text clustering with {NTSO} (neural text self organizer)",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "558--563",
  volume =       "1",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Text clustering is the process of segmenting a particular collection of texts into subgroups including content-based similar ones. This study proposes a new neural network, called NTSO (neural text self organizer), which is suitable for text clustering. This neural network uses string vectors instead of numerical vectors as its input vectors and its weight vectors are different from those of other unsupervised neural networks such as {K}ohonen networks and ART (adaptive resonance theory), although it is similar to {K}ohonen networks at the architecture level and in its learning process. Intuitively, text is better represented by a string vector than by a numerical vector. the representation of texts into numerical vectors leads to two main problems: sparse distribution and huge dimensionality of the feature vectors. This study proposes an unsupervised neural network that uses string vectors for text clustering, to address these problems.",
}

@InCollection{Salojarvi03wsom_bibuniq_1779,
  author =       "Jarkko Saloj{\"a}rvi and Ilpo Kojo and Jaana Simola and Samuel Kaski",
  title =        "Can relevance be inferred from eye movements in information retrieval?",
  booktitle =    "Proceedings of {WSOM}'03, Workshop on Self-Organizing Maps",
  pages =        "261--266",
  publisher =    "Kyushu Institute of Technology",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  note =         "(Proceedings on CD-ROM)",
}

@InCollection{Venna05wsom_bibuniq_1773,
  author =       "Jarkko Venna and Samuel Kaski",
  title =        "Local multidimensional scaling with controlled tradeoff between trustworthiness and continuity",
  booktitle =    "Proceedings of {WSOM}'05, 5th Workshop On Self-Organizing Maps",
  pages =        "695--702",
  year =         "2005",
  address =      "Paris",
}

@InProceedings{mariage03_bibuniq_4324,
  author =       "Jean-Jacques Mariage and Gilles Bernard",
  title =        "Automatic Strings",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{Jenhwa00a_bibuniq_3914,
  author =       "Jenhwa Guo and Hung Yuan Wei and Forng Chen Chiu and Sheng Wen Cheng",
  title =        "A maximum entropy method for multi-{AUV} grouping",
  journal =      "Oceans-'04-MTS/{IEEE}-Techno-Ocean-'04 {IEEE} Vol. 1",
  year =         "2004",
}

@InProceedings{inspek113_bibuniq_1216,
  author =       "Jeong S. and Obayashi S.",
  title =        "Efficient global optimization ({EGO}) for multi-objective problem and data mining",
  booktitle =    "{IEEE} Congress on Evolutionary Computation",
  pages =        "2138--2145",
  volume =       "3",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this study, a surrogate model is applied to multi-objective aerodynamic optimization design. For the balanced exploration and exploitation with the surrogate model, objective functions are converted to the Expected Improvements (EI) and these values are directly used as fitness values in the multi-objective optimization. Among the non-dominated solutions about EIs, additional sample points for the update of the Kriging model are selected. the present method is applied to a transonic airfoil design. in order to obtain the information about design space, two data mining techniques are applied to design results. One is analysis of variance (ANovA) and the other is self-organizing map ({SOM}).",
}

@Article{inspek60_bibuniq_1173,
  author =       "Jeong S. and Obayashi S. and Yamamoto K.",
  title =        "Aerodynamic optimization design with Kriging model",
  journal =      "Transactions of the Japan Society for Aeronautical and Space Sciences. Nov. 2005; 48(161): 161--8",
  pages =        "",
  year =         "2005",
  publisher =    "Japan Soc. Aeronaut. \& Space Sci",
  abstract =     "This study applied a Kriging model to optimization of a constrained aerodynamic design problem. the objective function and all constraint functions are considered statistically independent to avoid treating the complicated multivariate normal distribution in the constrained optimization problem. in the Kriging model of objective functions, expected improvements (EI) is calculated and in the Kriging model of constraint function, the probabilities of satisfying the constraints are calculated. Based on these values, an efficient exploration of the global optimum is performed. Two data mining techniques are used to investigate the information of design space such as the relationship between objective function and design variables: functional analysis of variance (ANovA), and self-organizing map ({SOM}). ANovA shows information quantitatively, while {SOM} shows it qualitatively. Based on the information, elimination of design variables with little effect on objective function is performed. the present method is applied to two-dimensional (2D) transonic airfoil design. the results showed the validity of the present method.",
}

@InProceedings{Ji03a_bibuniq_1596,
  author =       "Ji He and Ah Hwee Tan and Chew Lim Tan",
  title =        "Self-organizing neural networks for efficient clustering of gene expression data",
  booktitle =    "Proceedings of the International-Joint Conference on Neural Networks-2003 vol. 3",
  year =         "2003",
  volume =       "3",
  pages =        "",
  abstract =     "Clustering of gene expression patterns is of great value for the understanding of the various molecular biological processes. While a number of algorithms have been applied to gene clustering, there are relatively few studies on the application of neural networks to this task. in addition, there is a lack of quantitative evaluation of the gene clustering results. This paper proposes Adaptive Resonance Theory under Constraint (ART-C) for efficient clustering of gene expression data. We illustrate that ART-C can effectively identify gene functional groupings through a case study on rat CNS data. Based on a set of quantitative evaluation measures, we compare the performance of ART-C with those of K-Means, SOM, and conventional ART. Our comparative studies on the yeast cell cycle and the human hematopoietic differentiation data sets show that ART-C produces reasonably good quantitative performance. More importantly, compared with K-Means and SOM, ART-C shows a significantly higher learning efficiency, which is crucial for knowledge discovery from large scale biological databases.",
}

@InProceedings{Jiang03a_bibuniq_1400,
  author =       "Jiang Li and Dongdong Li and J. A. Khoja and Q. Liang and M. T. Manry and V. K. Prabhu",
  title =        "Overcoming co-channel interference in {TDMA} systems using {SOM} equalizer",
  booktitle =    "Proceedings-2003-Radio-Wireless. Rawcon Conference 123-6",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "This paper studies the co-channel interference (CCI) problem for time-division-multiple-access (TDMA) cellular mobile communication system with burst transmission. We present a method using self-organizing-map ({SOM}) to overcome CCI for such a system. the {SOM} is realized as a classification equalizer with a decision feedback adaptive filter. An extremely small number of unique words (UWs) are utilized to initialize the {SOM} equalizer. Simulation results show that the bit error rate (BER) of our proposed method is much better than that of the recently proposed nearest neighbor classification equalizer.",
}

@InProceedings{Jiang04a_bibuniq_1425,
  author =       "Jiang Li and Qilian Liang and M. T. Manry",
  title =        "A nonlinear filtering approach for demodulation over Rician flat fading channels",
  booktitle =    "Globecom-'04. {IEEE}-Global-Telecommunications Conference {IEEE} Vol. 2",
  year =         "2004",
  volume =       "",
  pages =        "",
  abstract =     "We study the demodulation problems for enhanced general packet radio services (EGPRS) wireless systems, where Rician flat fading channels are considered. A linear interpolation with decision feedback combined with a modified self-organizing-map (LIDF-MSOM) demodulator is implemented for such systems. Simulation results show that the performance of the proposed demodulator is much better than that of LIDF alone.",
}

@InProceedings{Jiang04b_bibuniq_1532,
  author =       "Jiang Li and Qilian Liang and M. T. Manry",
  title =        "Demodulation for wireless {ATM} network using modified {SOM} network",
  booktitle =    "{IEEE} International Conference on Acoustics, Speech, and Signal Processing. 2004: V-669-72 vol. 5",
  year =         "2004",
  volume =       "5",
  pages =        "",
  abstract =     "We study the demodulation problem in time division multiple access (TDMA) wireless asynchronous transfer mode (ATM) networks, where Rician flat fading channels are considered. A linear interpolation with decision feedback combined with a modified version of the self-organizing-map (LIDF-SOM) demodulator is proposed for such a system. We obtain the training sequence by exploiting medium access control (MAC) and data link control (DLC) protocols such that a semi-blind adaptive demodulator is implemented. Simulation results show that LIDF-SOM obtains 0. 4-1. 0 dB gain over Rician fading channels as compared to LIDF alone.",
}

@InProceedings{inspek591_bibuniq_799,
  author =       "Jiangtao-Ren {Jianming-Hu, Chunguang-Zong, Jingyan-Song, Zuo-Zhang}",
  title =        "An applicable short-term traffic flow forecasting method based on chaotic theory",
  booktitle =    "Proceedings of the 2003 {IEEE} International Conference on Intelligent Transportation Systems vol. 1",
  pages =        "2 vol. 1785",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Short-term traffic flow forecasting plays a very important role in urban traffic management and control. in this paper, According to the chaotic property of urban traffic flow, we compute the parameters of phrase space reconstruction for traffic flow system. Meanwhile, a local-forecasting method is introduced to predict urban road short-term traffic flow based on the theory of phrase space reconstruction. Self-organizing Map ({SOM}) network is introduced to seek the near neighbor. Case study using real traffic flow data from UTC-SCOOT system proves the validity of the method. the research in this paper is a significant attempt to forecast traffic flow from the viewpoint of non-linear time series.",
}

@InProceedings{inspek827_bibuniq_639,
  author =       "Jianyong-Li {Longhan-Cao, Changxiu-Cao, Zhen-Guo}",
  title =        "The research of fault diagnosis for fuel injection system of diesel engine with {ANN} based on rough sets theory",
  booktitle =    "Proceedings of the 4th World Congress on Intelligent Control and Automation vol. 1",
  pages =        "4 vol. 3353",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The core content of rough set theory is introduced, and the discrete method of continuous attribute value based on the {K}ohonen neural network is given. the rough set theory is used to simplify the attribute parameter reflecting operating conditions of a diesel engine, and a RBF neural network is used to realize automatic fault classification and fault diagnosis for the fuel injection system of a diesel engine. the example is shown that the system's ANN input node number can be reduced.",
}

@InProceedings{inspek398_bibuniq_1044,
  author =       "Jin-Chen {Yumin-Chen, Youchuan-Wan, Jianya-Gong}",
  editor =       "C. {Yin, F. ; Wang, J. ; Guo}",
  title =        "Comparison with two classification algorithms of remote sensing image based on neural network",
  booktitle =    "Advances in Neural Networks {ISNN} 2004. International Symposium on Neural Networks. Proceedings Lecture Notes in Comput. Sci.",
  pages =        "906--911",
  volume =       "1",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "The traditional approaches of classification are always unfavorable in the description of information distribution. This paper describes the BP neural network approach and the {K}ohonen neural network approach to the classification of remote sensing images. Two algorithms have their own traits and can be good used in the classification. A qualitative comparison demonstrates that both original images and the classified maps are visually well matched. A further quantitative analysis indicates that the accuracy of BP algorithm is better than the result of the {K}ohonen neural network.",
}

@InProceedings{inspek139_bibuniq_1242,
  author =       "Jin-shou-Yu Qiang-Lv",
  editor =       "Y. S. {Wang, L. ; Chen, K. ; Ong}",
  title =        "Fuzzy self-organizing map neural network using kernel {PCA} and the application",
  booktitle =    "Advances in Natural Computation. First International Conference, ICNC 2005. Proceedings, Part I-Lecture Notes in Computer Science",
  pages =        "81--90",
  volume =       "3610",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "The fuzzy self-organizing map neural network using kernel principal component analysis is presented and a hybrid-learning algorithm (K{PCA}-FSOM) divided into two stages is proposed to train this network. the first stage, the K{PCA} algorithm is applied to extract the features of nonlinear data. the second stage, combining both the fuzzy theory and locally-weight distortion index to extend {SOM} basic algorithm, the fuzzy {SOM} algorithm is presented to train the {SOM} network with features gained. A real life application of K{PCA}-FSOM algorithm in classifying data of acrylonitrile reactor is provided. the experimental results show this algorithm can obtain better clustering and network after training can more effectively monitor yields.",
}

@InProceedings{inspek99_bibuniq_1206,
  author =       "Jing-Hao-Fei {Xing-Ce-Wang, Ping-Guo, Xin-Yu-Liu}",
  title =        "Compressing the multirobot team formation state based on {SOM} network",
  booktitle =    "Proceedings of 2005 International Conference on Machine Learning and Cybernetics",
  pages =        "3277--3281",
  volume =       "6",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "As a platform of multirobots' cooperation and coordination, the multirobots team formation is paid more and more attention. Using the reinforcement learning to realize the team formation can strengthen not only the self-learning ability but also the self-adaptation. in this research field, however, there still exit problems such as low learning speed and the difficult convergence raised with the exponential space of reinforcement learning. Using the self-organizing map ({SOM}) network compressing state from exponential to multinomial speeds up the ergodic, consequently improves the learning rate. and the function of adding and deleting the neurons can compress more space. in the simulation of the experiment, the feasibility of these technologies is verified further. the expands of the methods are strong and can be used in the similar system.",
}

@InProceedings{inspek733_bibuniq_557,
  author =       "Jing-Lei-Feng {Jia-Yuan-Zhu, Heng-Xi-Zhang, Ji-Lian-Guo}",
  title =        "Data distributions automatic identification based on {SOM} and support vector machines",
  booktitle =    "Proceedings of 2002 International Conference on Machine Learning and Cybernetics",
  pages =        "340--344",
  volume =       "1",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "It is very important to identify probability distributions fast and efficiently in data analysis. the paper analyzes data distributions automatic identification using a combined structure mode via self-organizing map and support vector machines. First, the paper sets up data distributions identification training sets, which are based on summary statistics including kurtosis, skewness, quantile and cumulative probability. Then, different data distributions are clustered using a self-organizing map. Furthermore, the clusters are learned and classified respectively using support vector machines. Finally, identification of random data distribution time series is tested in combined structure mode. the results indicate that the approach of the paper is feasible and efficient for automatically identifying data distributions in comparison with other methods.",
}

@Article{inspek845_bibuniq_654,
  author =       "Jirina M.",
  title =        "Preprocessing of initial weights in the {SOM}",
  journal =      "Neural Network World. 2002; 12(3): 223--39",
  pages =        "",
  year =         "2002",
  publisher =    "UIVT AV CR - NNW",
  abstract =     "We introduce a new approach to the preprocessing (initial setting) of weight vectors and thus a speed-up of the well-known {SOM} (Kohonen's SOFM) neural network. the idea of the method (we call it Prep through this paper) consists in spreading a small lattice over the pattern space and consequently completing its inner meshes and boundaries to obtain a larger lattice. This large lattice is then tuned by its training for a short time. To justify the speed up of the Prep method we give a detailed time analysis. To demonstrate the suggested method we show its abilities on several representative examples.",
}

@InProceedings{Jiu03a_bibuniq_1585,
  author =       "Jiu zhen Liang",
  title =        "Chinese Web page classification based on self-organizing mapping neural networks",
  booktitle =    "Proceedings-Fifth International Conference on Computational Intelligence and Multimedia-Applications. Iccima-2003. 2003: 96-101",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "This paper deals with self--organizing mapping ({SOM}) neural network's topology and learning algorithm, and the application in the automatic classification of {C}hinese Web pages. {SOM} neural network has the advantages of simple structure, ordered mapping topology and low complexity of learning. It is suitable for many complex problems such as multi-class pattern recognition, high dimension input vector and large quantity of training data. the accuracy of clustering can be improved when combining SOM's unsupervised learning algorithm with {LVQ} learning algorithm. At the end of the paper, it is proposed the classification result of {SOM} neural network applied in the 5087 html pages of People's Daily Web edition, with the average precision 90. 08\% and the average recall 89. 85\%.",
}

@InProceedings{inspek653_bibuniq_853,
  author =       "Johansson J. and Jern M. and Treloar R. and Jansson M.",
  editor =       "J. {Banissi, E. ; Borner, K. ; Chen, C. ; Clapworthy, G. ; Maple, C. ; Lobben, A. ; Moore, C. ; Roberts, J. ; Ursyn, A. ; Zhang}",
  title =        "Visual analysis based on algorithmic classification",
  booktitle =    "Proceedings Seventh International Conference on Information Visualization IV 2003 International Conference on Computer Visualization and Graphics Applications",
  pages =        "86--93",
  year =         "2003",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Extracting actionable insight from large high dimensional data sets, and its use for more effective decision-making, has become a pervasive problem across many fields in research and industry. We describe an investigation of the application of tightly coupled statistical and visual analysis techniques to this task. the approach we choose is {"}unsupervised learning{"} where we investigate the advantages offered by close coupling of the self-organizing map algorithm with new combinations of visualization components and techniques for interactivity.",
}

@InProceedings{inspek426_bibuniq_1070,
  author =       "Johansson J. and Treloar R. and Jern M.",
  editor =       "J. J. {Banissi, E. ; Borner, K. ; Chen, C. ; Dastbaz, M. ; Clapworthy, G. ; Faiola, A. ; Izquierdo, E. ; Maple, C. ; Roberts, J. ; Moore, C. ; Ursyn, A. ; Zhang}",
  title =        "Integration of unsupervised clustering, interaction and parallel coordinates for the exploration of large multivariate data",
  booktitle =    "Proceedings. Eighth International Conference on Information Visualisation",
  pages =        "52--57",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Parallel coordinates are widely used in many applications for visualization of multivariate data. Because of the nature of parallel coordinates, the visualization technique is often used for data overview. However, when the number of tuples to be visualized becomes very large, this technique makes it difficult to distinguish the overall structure. in this paper we present a novel technique which uses a classification approach, the self-organizing map (an unsupervised learning algorithm), to solve this problem by creating an initial clustering of the data. By initially only visualizing the resulting representational clusters, the inherited global structure can be shown. Using linked views and allowing the user to perform drill-down and filtering on these representations reveals the single data items without loss of context.",
}

@InProceedings{flanagan03_bibuniq_4287,
  author =       "John A. Flanagan",
  title =        "A Non-Parametric Approach to Unsupervised Learning and Clustering of Symbol Strings and Sequences",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{Jong00a_bibuniq_3935,
  author =       "Jong Seok Lee and Hajoon Lee and Jae Young Kim and Dongkyung Nam and Cheol Hoon Park",
  title =        "Self-organizing neural networks by construction and pruning",
  journal =      "Ieice Transactions on Information and Systems. Nov. 2004; E87-D(11): 2489-98",
  year =         "2004",
}

@InProceedings{inspek135_bibuniq_1238,
  author =       "Jong-Wen-Cheng Jiann-Horng-Lin",
  editor =       "T. M. ; Mei-Ling-Shyu {Du-Zhang, Khoshgoftaar}",
  title =        "Adaptive fuzzy identification of nonlinear dynamical systems based on quantum mechanics",
  booktitle =    "Proceedings of the 2005 {IEEE} International Conference on Information Reuse and Integration",
  pages =        "380--385",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Importation of methods from statistical physics into machine learning has led to rapid advancement in algorithms for efficient learning of good representations for complex problems. This paper presents a systematic approach to constructing a self-organizing fuzzy identifier. the proposed identifier is built on a fuzzy system consisting of a quantum clustering network and radial basis function network. We develop the corresponding self-organizing algorithms. Quantum clustering network, a new fuzzy clustering neural network model, combines the idea of quantum mechanics and the structure of {K}ohonen clustering network. the strategy proposed in our approach for the update rules of {K}ohonen clustering network is derived from the fixed-point iteration for the solution of nonlinear equations. the model eliminates the sensitivity to the choice of the initial configuration and yields a dynamic fuzzy clustering solution. Quantum clustering network is used for the generation of fuzzy rules as well as the construction of radial basis function network for fuzzy inference. Furthermore, the complex dynamical behavior in the proposed quantum learning systems is investigated. the concept of self-organization in complex dynamical systems and the role of quantum mechanics are presented. It provides us with efficient tools to get better insight into learning dynamics. Simulation results show that the proposed method can provide the fuzzy models with satisfactory accuracy.",
}

@Article{laaksonen02a_bibuniq_448,
  author =       "Jorma Laaksonen and Markus Koskela and Erkki Oja",
  title =        "{PicSOM} - Self-organizing image retrieval with {MPEG-7} content descriptors",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2002",
  volume =       "13",
  number =       "4",
  month =        "July",
  pages =        "841--853",
}

@InProceedings{laaksonen03_bibuniq_4278,
  author =       "Jorma Laaksonen and Markus Koskela and Erkki Oja",
  title =        "Probability interpretation of distributions on {SOM} surfaces",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{lacassie03_bibuniq_4298,
  author =       "Juan Pablo Lacassie and Javier Ruiz del Solar and Barry Roser and Elena Belousova and Edwin Ortiz and Francisco Hervé",
  title =        "Discovering geochemical patterns using Self-organizing neural networks: a new perspective for analysis in the Earth Sciences",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{Vesanto2002d_bibuniq_1787,
  author =       "Juha Vesanto and Mika Sulkava",
  title =        "Distance matrix based clustering of the Self-Organizing Map",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002: International Conference, Proceedings",
  pages =        "951--956",
  year =         "2002",
  editor =       "Jos{\'e} R. Dorronsoro",
  volume =       "2415",
  series =       "Lecture Notes in Computer Science",
  address =      "Madrid, Spain",
  month =        "August",
  publisher =    "Springer-Verlag",
}

@InProceedings{Vesanto2003_bibuniq_1786,
  author =       "Juha Vesanto and Mika Sulkava and Jaakko Hollm{\'e}n",
  title =        "On the Decomposition of the Self-Organizing Map Distortion Measure",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "11--16",
  year =         "2003",
  address =      "Hibikino, Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek420_bibuniq_1064,
  author =       "Junior Ad M. B. and Neto A. D. D. and de-Melo J. D.",
  title =        "Surface reconstruction using neural networks and adaptive geometry meshes",
  booktitle =    "2004 {IEEE} International Joint Conference on Neural Networks",
  pages =        "807--807",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "We present a multi-resolution surface reconstruction method from point clouds in 3{D} space based on {K}ohonen's self-organizing neural networks. It uses a set of mesh operators and simple rules for selective mesh refinement. Experimental results show the method is very successful on reconstructing forms with different geometry.",
}

@InProceedings{pakkanen03_bibuniq_4318,
  author =       "Jussi Pakkanen",
  title =        "The Evolving Tree: a new kind of self-organizing neural network",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{smith03a_bibuniq_360,
  author =       "K. A. Smith and A. Ng",
  title =        "Web page clustering using a self-organizing map of user navigation patterns",
  journal =      "Decision Support Systems",
  year =         "2003",
  volume =       "35",
  number =       "2",
  month =        "May",
  pages =        "245--256",
}

@InProceedings{chiba05a_bibuniq_101,
  author =       "K. Chiba and S. Obayashi and K. Nakahashi and H. Morino",
  title =        "High-fidelity Multidisciplinary design optimization of wing shape for regional jet aircraft",
  booktitle =    "Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "7653--7658",
}

@InProceedings{Chokshi00a_bibuniq_4021,
  author =       "K. Chokshi and S. Wermter and C. Panchev and K. Burn",
  title =        "Self organising neural place codes for vision based robot navigation",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks vol. 4",
  year =         "2004",
}

@Article{lee05b_bibuniq_117,
  author =       "K. D. Lee and D. Booth and P. Alam",
  title =        "A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms",
  journal =      "Expert Systems With Applications",
  year =         "2005",
  volume =       "29",
  number =       "1",
  month =        "July",
  pages =        "1--16",
}

@Article{hsieh04b_bibuniq_272,
  author =       "K. H. Hsieh and F. C. Tien",
  title =        "Self-organizing feature maps for solving location-allocation problems with rectilinear distances",
  journal =      "Computers \& Operations Research",
  year =         "2004",
  volume =       "31",
  number =       "7",
  month =        "June",
  pages =        "1017--1031",
}

@Article{hosaka03a_bibuniq_397,
  author =       "K. Hosaka and T. Goya and D. Umehara and M. Kawai",
  title =        "An efficient method for network topology identification based on an {SOM} algorithm",
  journal =      "Electrical Engineering in Japan",
  year =         "2003",
  volume =       "142",
  number =       "4",
  month =        "March",
  pages =        "34--44",
}

@Article{hyun05a_bibuniq_66,
  author =       "K. Hyun and M. Y. Song and S. Kim and T. S. Chon",
  title =        "Using an artificial neural network to patternize long-term fisheries data from South Korea",
  journal =      "Aquatic Sciences",
  year =         "2005",
  volume =       "67",
  number =       "3",
  month =        "September",
  pages =        "382--389",
}

@Article{lee05a_bibuniq_76,
  author =       "K. I. Lee and Y. S. Yim and S. W. Chung and J. Q. Wei and J. I. Rhee",
  title =        "Application of artificial neural networks to the analysis of two-dimensional fluorescence spectra in recombinant {E} coli fermentation processes",
  journal =      "Journal of Chemical Technology and Biotechnology",
  year =         "2005",
  volume =       "80",
  number =       "9",
  month =        "September",
  pages =        "1036--1045",
}

@Article{kishida05a_bibuniq_162,
  author =       "K. Kishida",
  title =        "Techniques of document clustering: {A} review",
  journal =      "Library and Information Science",
  year =         "2005",
  volume =       "35",
  number =       "1",
  month =        "January",
  pages =        "106--120",
}

@Article{hsu02a_bibuniq_384,
  author =       "K. L. Hsu and H. V. Gupta and X. G. Gao and S. Sorooshian and B. Imam",
  title =        "Self-organizing linear output map (Solo): An artificial neural network suitable for hydrologic modeling and analysis",
  journal =      "Water Resources Research",
  year =         "2002",
  volume =       "38",
  number =       "12",
  month =        "December 19",
}

@Article{omori04a_bibuniq_165,
  author =       "K. Omori and S. Terai and T. Ishikawa and K. Aoyama and I. Sakaida and H. Nishina and K. Shinoda and S. Uchimura and Y. Hamamoto and K. Okita",
  title =        "Molecular signature associated with plasticity of bone marrow cell under persistent liver damage by self-organizing-map-based gene expression",
  journal =      "Febs Letters",
  year =         "2004",
  volume =       "578",
  number =       "1-2",
  month =        "December 3",
  pages =        "10--20",
}

@Article{haylett04a_bibuniq_221,
  author =       "K. R. Haylett and P. Valles and R. F. McCloy",
  title =        "The classification of oesophageal 24h {pH} measurements using a {K}ohonen self-organizing feature map",
  journal =      "Physiological Measurement",
  year =         "2004",
  volume =       "25",
  number =       "3",
  month =        "June",
  pages =        "709--719",
}

@Article{sato03a_bibuniq_344,
  author =       "K. Sato and M. Ishii and H. Madokoro",
  title =        "Testing and evaluation of a patrol robot system for hospitals",
  journal =      "Electronics and Communications in Japan Part III-Fundamental Electronic Science",
  year =         "2003",
  volume =       "100",
  number =       "3",
  month =        "March",
  pages =        "307--315",
}

@InProceedings{tada03_bibuniq_4292,
  author =       "K. Tada",
  title =        "Customer Portfolio Analysis by using Self-Organizing Maps",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Misc{webform_9857_bibuniq_4256,
  author =       "K. Tasdemir and E. Merenyi",
  title =        "Considering Topology in Clustering of the Self-Organizing Maps",
  howpublished = "Proc. 5th Workshop on Self-Organizing Maps ({WSOM}05)",
  pages =        "439--446",
  note =         "",
  year =         "2005",
}

@Misc{webform_5960_bibuniq_4232,
  author =       "K. Tokunaga and K. Kimotsuki and S. Yasui and T. Furukawa",
  title =        "Self-organizing map of a dynamical system with mn{SOM}",
  howpublished = "Proceedings of 4th Postech-Kyutech Joint Workshop 2004",
  pages =        "",
  note =         "",
  year =         "2004",
}

@Misc{webform_4699_bibuniq_4219,
  author =       "K. Tokunaga and T. Furukawa",
  title =        "Self-Organizing Adaptive Controllers: Application to the Inverted Pendulum",
  howpublished = "Proceedings of the Proceedings of the 5th Workshop on Self-Organizing Maps ({WSOM}05)",
  pages =        "",
  note =         "",
  year =         "2005",
}

@Misc{webform_4796_bibuniq_4220,
  author =       "K. Tokunaga and T. Furukawa",
  title =        "Nonlinear {ASSOM} Constituted of Autoassociative Neural Modules",
  howpublished = "Proceedings of the Proceedings of the 5th Workshop on Self-Organizing Maps ({WSOM}05)",
  pages =        "637--644",
  note =         "",
  year =         "2005",
}

@Misc{webform_4990_bibuniq_4222,
  author =       "K. Tokunaga and T. Furukawa",
  title =        "Realizing the nonlinear adaptive subspace {SOM} ({NL}-{ASSOM})",
  howpublished = "Proceedings of 2nd International Conference on Brain-inspired Information Technology (BrainIT2005)",
  pages =        "76",
  note =         "",
  year =         "2005",
}

@Article{prema02a_bibuniq_403,
  author =       "K. V. Prema and N. V. S. Reddy",
  title =        "Two-tier architecture for unconstrained handwritten character recognition",
  journal =      "Sadhana-Academy Proceedings in Engineering Sciences",
  year =         "2002",
  volume =       "27",
  month =        "October",
  pages =        "585--594",
}

@InProceedings{inspek702_bibuniq_535,
  author =       "Kaarna A. and Toivanen P. J. and Keraenen P.",
  title =        "Compression of multispectral {AVIRIS} images",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering. 2002; 4725: 588--99",
  pages =        "",
  year =         "2002",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "We have composed several lossy compression methods for multispectral images. These methods include the self-organizing map ({SOM}), principal component analysis ({PCA}) and the three-dimensional wavelet transform combined with traditional lossless coding methods. the two-dimensional DCT/JPEG, JPEG2000 and SPIHT compression methods were applied to eigenimages produced by the {PCA}. the information loss from the compression was measured with signal-to-noise-ratio (SNR) and peak-signal-to-noise ratio (PSNR). To get more illustrative error measures C-means clustering and Euclidean distance for spectral matching were used. the test image was an AVIRIS image with 224 bands and 512 lines in 614 columns. the {PCA} in the spectral dimension was the best method in terms of image quality and compression speed. If required, JPEG2000 or SPIHT can be applied in spatial dimensions to get better compression ratios.",
}

@InProceedings{inspek387_bibuniq_1034,
  author =       "Kaburlasos V. G. and Papadakis S. E.",
  title =        "gr{SOM}: a granular extension of the self-organizing map for structure identification applications",
  booktitle =    "2004 {IEEE} International Conference on Fuzzy Systems",
  pages =        "789--794",
  volume =       "2",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "An extension of the self-organizing map ({SOM}) is presented, namely granular {SOM} or grSOM for short, applicable beyond R/sup N/ to F/sup N/, where F denotes the set of fuzzy interval numbers (FINs). Rigorous analysis establishes that F is a metric mathematical lattice. A FIN is interpreted as a linguistic granule, which corresponds to a local probability distribution function. the grSOM can be used for structure identification in linguistic system modeling applications. Experimental results using the greedy grSOM algorithm compare favorably with the corresponding results by alternative algorithms from the literature in two benchmark classification problems; in addition, descriptive decision-making knowledge (fuzzy rules) is induced from the data.",
}

@Article{inspek855_bibuniq_660,
  author =       "Kakimoto T. and Uehara Y. and Kambayashi Y.",
  title =        "Improvement of generation speed and browsability of browsing space",
  journal =      "Transactions of the Information Processing Society of Japan. April 2002; 43(4): 1089--99",
  pages =        "",
  year =         "2002",
  publisher =    "Inf. Process. Soc. Japan",
  abstract =     "It is important to find out the information required by a user effectively from the huge amount of information which is increasing from 4 to 8 times in speed on the Internet. in the case of retrieving multimedia data however, it is insufficient to retrieve data by search key, and it is necessary to retrieve it by search key and to browse it. the paper reports on the improvement of speed to compute the self-organizing map ({SOM}) and the browsability of 3{D} browsing space. in order to ensure that overviewing of all the information is the most important aspect, it is not so important to compute {SOM} precisely, thereby we realized the speed up of computing {SOM} by deducting the amount of computing for the best position of learning and the utilization of sampling data. It is shown that a {SOM} with 10000 data items is computed at 500 times the speed of SOM-PAK. This result shows that we are able to compute {SOM} of 10000 data using an entry class personal computer.",
}

@InProceedings{inspek721_bibuniq_549,
  author =       "Kamaya H. Haeyeon-Lee and Abe K.",
  title =        "Performance of {LQ}-learning in {POMDP} environments",
  booktitle =    "{SICE} 2002. Proceedings of the 41st SICE Annual Conference",
  pages =        "819--822",
  volume =       "2",
  year =         "2002",
  publisher =    "Soc. Instrument \& Control Eng. (SICE), Tokyo, Japan",
  abstract =     "In this paper, we propose a new type of LQ-learning to solve POMDP. in the POMDP environment, the agent cannot observe the environment directly. in the LQ-learning, in order to discriminate partially observed states, the agent attaches label to each observation which perceived as the same ones. Unlike our previous LQ-learning, we make preparations of knowledge about the environment in advance. the knowledge is automatically acquired by Kohenen's Self-Organizing Map ({SOM}), which provides the knowledge about state transitions to the agent. Then, LQ-learning agent attaches labels to observations with reference to a map obtained by SOM.",
}

@Article{inspek764_bibuniq_583,
  author =       "Kamimoto N. and Omatu S. Bingchen-Wang",
  title =        "Quality test of interphones using two kinds of neural networks",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan, Part C. Oct. 2002; 122 C(10): 1742--7",
  pages =        "",
  year =         "2002",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "This paper is concerned with an automatic classification of interphones into good and bad clusters based on the spectral data using two kinds of neural networks. One is the self-organizing map ({SOM}) by {K}ohonen and the other is the layered neural network (LNN) using the error back-propagation method. the {SOM} is used to find the representative teaching data for each cluster in order to achieve the fast convergence of learning of the LNN and reduction of the network size. the LNN is used to classify the input data into good and bad clusters. From the real data classification of interphones, we can see that the proposed method using two kinds of NNs could classify the data more precisely compared with the case using only a conventional LNN.",
}

@InProceedings{inspek865_bibuniq_664,
  author =       "Kamimura R. and Kamimura T. and Uchida O.",
  title =        "Hybrid information processing systems to generate self-organizing maps: combining {SOM} and information maximization for coherent activation patterns",
  booktitle =    "Proceedings of the 2002 International Joint Conference on Neural Networks. {IJCNN}'02",
  pages =        "1785-1789",
  volume =       "2",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "We combine a self-organizing map ({SOM}) and information maximization to produce coherent competitive unit activation patterns in an artificial system. the new system is composed of a {SOM} component and an information maximization component. in the {SOM} component, the conventional {SOM} is used to cooperate neurons. in the information maximization component, information between input units and competitive units is increased as much as possible. the component plays a role to accentuate activation patterns obtained by the {SOM} component. We apply the new system to medical data analysis. Experimental results confirm that firing patterns obtained by the conventional {SOM} are reinforced and become clearer by the information maximization component.",
}

@Misc{webform_2839_bibuniq_4204,
  author =       "Kari Torkkola and Eugene Tuv",
  title =        "Visualization of Massive Mixed Type Semiconductor Manufacturing Data using Self Organising Maps",
  howpublished = "Neural Network Engineering Experiences (Proc. of the 8th Int. Conf. on Engineering Applications of Neural Networks)",
  pages =        "96--103",
  note =         "",
  year =         "September 8-10, 2003",
}

@Misc{webform_2937_bibuniq_4205,
  author =       "Kari Torkkola and Keshu Zhang and Chris Schreiner and Noel Massey",
  title =        "Extracting Interesting Vehicle Sensor Data Using Multivariate Stationarity",
  howpublished = "Proceedings of the 8th Annual {IEEE} Conference on Intelligent Transportation Systems (ITSC 2005)",
  pages =        "959--964",
  note =         "",
  year =         "2005",
}

@Article{inspek295_bibuniq_1352,
  author =       "Kato S. and Koike K. and Horiuchi T.",
  title =        "A study on two-stage self-organizing map and its application to clustering problems",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan, Part C. 2005; 125 C(1): 14--20",
  pages =        "",
  year =         "2005",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "This paper presents a two-stage self-organizing map algorithm what we call two-stage {SOM} which combines {K}ohonen's basic {SOM} (BSOM) and Aoki's {SOM} with threshold operation (THSOM). in the first stage of two-stage SOM, we use BSOM algorithm in order to acquire topological structure of input data, and then we apply THSOM algorithm so that inactivated code-vectors move to appropriate region reflecting the distribution of the input data. Furthermore, we show that two-stage {SOM} can be applied to clustering problems. Some experimental results reveal that two-stage {SOM} is effective for clustering problems in comparison with conventional methods.",
}

@Article{inspek737_bibuniq_561,
  author =       "Kato T. and Ueta G. and Ishii S.",
  title =        "Automatic diagnosis of fault locations in power transformer impulse test using self-organizing map",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan, Part B. Dec. 2002; 122 B(12): 1330--5",
  pages =        "",
  year =         "2002",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "An impulse test is applied to a power transformer for an acceptance test. When there is a difference between neutral currents for full and reduced impulses, there is a fault in the transformer winding. However it takes a lot of work to find the fault location, much work have been researched to automate the process by using a computer. This automatic diagnosis is a sort of pattern recognition technique and the point is how to find the relation between fault locations and measured current waveforms. This paper proposes to use {K}ohonen's self-organizing map ({SOM}) for this automatic diagnosis of fault locations in power transformer tests. in this process, the transfer function method, which computes a transfer characteristic from the applied voltage to the neutral current by dividing the two frequency spectra with the fast Fourier transform algorithm, is useful to avoid dependence on the applied voltage waveform. the following three techniques are proposed in the {SOM} computation: winding section division into several blocks is useful to simplify SOMs; input current waveforms are necessary for symmetrical winding positions to identify faults; and multiple SOMs are effective to improve the diagnosis reliability.",
}

@InProceedings{inspek341_bibuniq_991,
  author =       "Kawano H. and Horio K. and Yamakawa T.",
  editor =       "S. K. {Pal, N. R. ; Kasabov, N. ; Mudi, R. K. ; Pal, S. ; Parui}",
  title =        "Adaptive affine subspace self-organizing map with kernel method",
  booktitle =    "Neural Information Processing. 11th International Conference, {ICONIP} 2004. Proceedings Lecture Notes in Computer Science",
  pages =        "387--392",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "Adaptive subspace self-organizing map (ASSOM) is an evolution of self-organizing map, where each computational unit defines a linear subspace. Its modified version, where each unit defines an affine subspace instead of the subspace, has been proposed. the affine subspace in a unit is represented by a mean vector and a set of basis vectors. After training, these units result in a set of affine subspace detectors. in numerous cases, however, these are not enough to describe a class of patterns because of its linearity. in this paper, the adaptive affine subspace {SOM} (AASSOM) on the high-dimensional space with kernel method is proposed in order to achieve efficient classification. By using the kernel method, linear affine subspaces in the AASSOM can be extended to nonlinear affine subspaces easily. the effectiveness of the proposed method is verified by applying it to some simple classification problems.",
}

@Article{inspek161_bibuniq_1264,
  author =       "Kawano H. and Horio K. and Yamakawa T.",
  title =        "Nonlinear adaptive manifold self-organizing map with reproducing kernels and its application to pose invariant face recognition",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan, Part C. 2005; 125 C(6): 948--55",
  pages =        "",
  year =         "2005",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "Adaptive manifold self-organizing map (AMSOM) is an evolution of self-organizing maps, where each computational unit defines a linear affine subspace (manifold). the affine subspace in a unit is represented by a mean vector and a set of basis vectors. After training, these units result in a set of affine subspace detectors. in complex situations, however, these are not enough to describe a class of patterns because of its linearity. in this paper, the AMSOM in the high-dimensional space with reproducing kernels, referred to as nonlinear adaptive manifold self-organizing map (NAMSOM) is proposed in order to achieve efficient classification and visualization of relations between classes. By using the reproducing kernels, linear affine subspaces in the AMSOM can be extended to nonlinear affine subspaces easily. To verify the effectiveness of the proposed method, it was applied to face recognition under varying pose as a practical example.",
}

@Article{inspek294_bibuniq_1351,
  author =       "Kawano H. and Horio K. and Yamakawa T.",
  title =        "A pattern classification method using kernel adaptive-subspace self-organizing map",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan, Part C. 2005; 125 C(1): 149--50",
  pages =        "",
  year =         "2005",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "Adaptive-subspace self-organizing map (ASSOM) is a variant of self-organizing map, where each computational unit defines a linear subspace. the subspace in a unit is represented by a set of basis vectors. After training, these units result in a set of subspace detectors. in numerous cases, however, these are not enough to describe a class of patterns because of a linearity. in this paper, the ASSOM on the high-dimensional space with kernel method is proposed in order to achieve efficient classification. By using the kernel method, linear subspaces in the ASSOM can be extended to non-linear subspaces easily. This improves the representation of subspace. the effectiveness of the proposed method is verified by applying it to a well-known problem, or two spirals classification.",
}

@InProceedings{inspek353_bibuniq_1003,
  author =       "Kawano H. and Yamakawa T. and Horio K.",
  editor =       "J. S. {Jamshidi, M. ; Ollero, A. ; Dios, J. R. M. -d. ; Jamshidi}",
  title =        "Kernel-based Adaptive-Subspace Self-Organizing Map as a nonlinear subspace pattern recognition",
  booktitle =    "Proceedings of the World Automation Congress",
  pages =        "149--150",
  volume =       "18",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The adaptive-subspace self-organizing map (ASSOM) has been proposed for extracting subspace detectors from the input data. in the ASSOM, each computation unit, referred to as a neuron, has a linear subspace which consists of a set of basis vectors. After the training, each unit results in a subspace detector. in this paper, the ASSOM on high-dimensional feature space with kernel methods is proposed in order to achieve classification for more general data such as images. By using kernel methods, linear subspaces in the ASSOM are extended to nonlinear subspaces. This leads to an increased ability of representation as a subspace. the effectiveness of the proposed method is verified by applying it to a face recognition problem under varying illumination.",
}

@InProceedings{tokunaga03_bibuniq_4295,
  author =       "Kazuhiro Tokunaga and Furukawa Tetsuo and Syozo Yasui",
  title =        "Modular Network {SOM}: Extension of {SOM} to the Realm of Function Space",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{ishii03_bibuniq_4314,
  author =       "Kazuo Ishii and Syuhei Nishida and Tamaki Ura",
  title =        "An Adaptive Learning Method for {SOM} Based Navigation System and Its Application to an Underwater Robot",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{horio03_bibuniq_4289,
  author =       "Keiichi Horio and Takeshi Yamakawa",
  title =        "Hybrid System of {ASSOM} and Feedback {SOM} for Spatio-Temporal Pattern Classification",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{fukuoka03_bibuniq_4291,
  author =       "Kensuke Fukuoka and Matashige Oyabu",
  title =        "Kansei Evaluation Model Developed by Self-Organizing Map",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek723_bibuniq_894,
  author =       "Keraenen P. and Kaarna A. and Toivanen P. J.",
  title =        "Spectral similarity measures for classification in lossy compression of hyperspectral images",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering. 2003; 4885: 285--96",
  pages =        "",
  year =         "2003",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "Several powerful lossy compression methods have been developed for hyperspectral images. However, it is difficult to determine sufficient quality for reconstructed hyperspectral images. We have measured the information loss from the lossy compression with signal-to-noise-ratio (SNR) and peak-signal-to-noise-ratio (PSNR). To get more illustrative error measures unsupervised K-means clustering combined with spectral matching methods was used. Spectral matching methods include Euclidean distance, spectral similarity value (SSV) and spectral angle mapper (SAM). We used two AVIRIS radiance images, which were compressed with three different methods: the self-organizing map ({SOM}), principal component analysis ({PCA}) and three-dimensional wavelet transform combined with lossless BWT/Huffman encoding. the two-dimensional JPEG2000 compression method was applied to the eigenimages produced by the {PCA}. It was found that clustering combined with spectral matching is a good method to realize the image quality for many applications. the high classification accuracies have been achieved even at very high compression ratios. the SAM and the SSV are much more vulnerable for information loss caused by the lossy compression than the Euclidean distance. the results suggest that lossy compression is possible in many real-world segmentation applications. the {PCA} transform combined with JPEG2000 was the best compression method according to all error metrics.",
}

@InProceedings{inspek694_bibuniq_531,
  author =       "Kezunovic M. Xiangjun-Xu",
  title =        "Automated network drawing using self-organizing map",
  booktitle =    "3rd Conference and Exhibition on Power Generation, Transmission, Distribution and Energy Conversion. Med Power 2002. 2002: 6 pp.",
  pages =        "CD--ROM",
  year =         "2002",
  publisher =    "Nat. Tech. Univ. Athens, Athens, Greece",
  abstract =     "In this paper, a method for automatically creating circuit schematic diagrams from the topological information contained in network data files has been proposed. This method is based on a self-organizing map ({SOM}) neural network and the basic idea behind the method is to let the network span itself according to a given {"}shape{"} of the network grid. the topology of a network is defined by the connections between its nodes. By forming an {SOM} using the network connection topology and training it using data grids of desired {"}shape{"}, the positions of the nodes and their neighbors will be gradually updated until a desired diagram has been created.",
}

@InProceedings{inspek53_bibuniq_1166,
  author =       "Khalid S. and Naftel A.",
  editor =       "R. {Bres, S. ; Laurini}",
  title =        "Motion trajectory clustering for video retrieval using spatio-temporal approximations",
  booktitle =    "Visual Information and Information Systems. 8th International Conference, VISUAL 2005. Revised Selected Papers Lecture Notes in Computer Science Vol. 3736. 2005: 60--70",
  pages =        "60--70",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "A new technique is proposed for clustering and similarity retrieval of video motion clips based on spatio-temporal object trajectories. the trajectories are treated as motion time series and modelled using orthogonal basis polynomial approximations. Trajectory clustering is then carried out to discover patterns of similar object motion behaviour. the coefficients of the basis functions are used as input feature vectors to a self-organising map which can learn similarities between object trajectories in an unsupervised manner. Clustering in the basis coefficient space leads to efficiency gains over existing approaches that encode trajectories as point-based flow vectors. Experiments on pedestrian motion data gathered from video surveillance demonstrate the effectiveness of our approach. Applications to motion data mining in video surveillance databases are envisaged.",
}

@InProceedings{Kian00a_bibuniq_3930,
  author =       "Kian Hsiang Low and Wee Kheng Leow and M. H. Ang and Jr.",
  title =        "Task allocation via self-organizing swarm coalitions in distributed mobile sensor network",
  booktitle =    "Proceedings. Nineteenth-National Conference on Artificial Intelligence Aaai-04. Sixteenth-Innovative-Applications of Artificial Intelligence Conference {IAAI} 04. 2004: 28-33",
  year =         "2004",
}

@InProceedings{Kian00b_bibuniq_3963,
  author =       "Kian Hsiang Low and Wee Kheng Leow and M. H. Ang and Jr.",
  title =        "Continuous-spaced action selection for single- and multi-robot tasks using cooperative extended {K}ohonen maps",
  booktitle =    "{IEEE} International Conference on Networking, Sensing and Control Vol. 1",
  year =         "2004",
}

@Article{inspek277_bibuniq_938,
  author =       "Kiang M. Y. and Kumar A.",
  title =        "A comparative analysis of an extended {SOM} network and {K}-means analysis",
  journal =      "International Journal of Knowledge Based and Intelligent Engineering Systems. 2004; 8(1): 9--15",
  pages =        "",
  year =         "2004",
  publisher =    "IOS Press",
  abstract =     "The self-organizing map ({SOM}) network, a variation of neural computing networks, is a categorization network developed by Kohonen. the main function of {SOM} networks is to map the input data from an n-dimensional space to a lower dimensional plot while maintaining the original topological relations. in this research, we apply an extended {SOM} network that includes a grouping function to further cluster input data based on the relationships derived from a lower dimensional {SOM} map, to market segmentation problems. A computer program for implementing the extended {SOM} networks has been developed and it was first compared with K-means analysis in an experimental design using simulated data sets with known cluster solutions. Test results indicate that the extended {SOM} networks perform better when the data are skewed. We then further test the performance of the method with a real-world data set from a widely referenced machine-learning case. We believe the findings from this research can be applied to other problem domains as well.",
}

@InProceedings{inspek340_bibuniq_990,
  author =       "King I. Chi-Hang-Chan",
  editor =       "S. K. {Pal, N. R. ; Kasabov, N. ; Mudi, R. K. ; Pal, S. ; Parui}",
  title =        "Using biased support vector machine to improve retrieval result in image retrieval with self-organizing map",
  booktitle =    "Neural Information Processing. 11th International Conference, {ICONIP} 2004. Proceedings Lecture Notes in Computer Science Vol. 3316. 2004: 714--19",
  pages =        "714--719",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "The relevance feedback approach is a powerful technique in content-based image retrieval (CBIR) tasks. in past years, many intraquery learning techniques have been proposed to solve the relevance feedback problem. Among these techniques, support vector machines (SVM) have shown promising results in the area. More specifically, in relevance feedback applications the SVM are typically been used as binary classifiers with the balanced input data assumption. in other words, they do not consider the imbalanced dataset problem in relevance feedback, i. e., the non-relevant examples outnumbered the relevant examples. in this paper, we propose to apply our biased support vector machine (BSVM) to address this problem. Moreover, we apply our self-organizing map based inter-query technique to reorganize the feature vector space, in order to incorporate the information provided by past queries and improve the retrieval performance for future queries. the proposed combined scheme is evaluated against real world data with promising results demonstrating the effectiveness of our proposed approach.",
}

@InProceedings{inspek873_bibuniq_672,
  author =       "Kirk J. S. and Zurada J. M.",
  title =        "Motivation for a genetically-trained topography-preserving map",
  booktitle =    "Proceedings of the 2002 International Joint Conference on Neural Networks. {IJCNN}'02 vol. 1",
  pages =        "394--399",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "It is often observed that the lattice of a well-trained self-organizing map ({SOM}) preserves the topology of the data set. in this paper, we examine what is meant by this claim and discuss a related goal for a dimension-reducing mapping. We term this goal {"}topography preservation{"}, and attempt to fulfill it using a two-stage training method called genetically-trained topographic mapping. in the first stage of training, a clustering algorithm is used to map sets of input data points to each neuron. in the second stage, a genetic algorithm assigns adjacencies between the neurons of the output lattice according to the fitness defined by the topography preservation goal. Stock market data and an artificial data set are used to illustrate the relative strengths of the standard {SOM} and the new algorithm.",
}

@InProceedings{inspek461_bibuniq_1095,
  author =       "Kobayashi T. and Inui M. and Oda R. and Ohki M. and Ohkita M.",
  title =        "Diagnostic method for the faults in substation transformers by the self-organizing map ({SOM})",
  booktitle =    "The 2004--47th Midwest Symposium on Circuits and Systems",
  pages =        "345--348",
  volume =       "2",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Internal unusual diagnosis of the power transformers is important for carrying out stable supply of the electric power. in this paper, the method of perform unusual diagnosis of an oil filled transformer using the self-organizing map ({SOM}) is proposed.",
}

@InProceedings{inspek174_bibuniq_913,
  author =       "Kohonen O. and Hauta-Kasari M. and Miyazawa K. and Parkkinen J. and Jaskelainen T.",
  title =        "Methods to organize spectral image database",
  booktitle =    "CGIV 2004. Second European Conference on Color in Graphics, Imaging, and Vision and Sixth International Symposium on Multispectral Color Science",
  pages =        "372--375",
  year =         "2004",
  publisher =    "Society for Imaging Science and Technology, Springfield, VA, USA",
  abstract =     "Techniques for searching images from a spectral image database and calculating the distances between spectral images are proposed. the techniques are based on one- and two-dimensional self-organizing map ({SOM}). For one-dimensional SOM, the best matching unit (BMU) histogram for every spectral image in a database is created, and images of a database are ordered according to the histogram similarity. Two-dimensional {SOM} is trained by using BMU-histograms as a training data and the distance between spectral images is defined based on their location on the map. the results using real spectral image database are given.",
}

@InProceedings{inspek656_bibuniq_521,
  author =       "Koike K. and Kato S. and Horiuchi T.",
  title =        "A two-stage self-organizing map with threshold operation for data classification",
  booktitle =    "{SICE} 2002. Proceedings of the 41st SICE Annual Conference",
  pages =        "3097--3099",
  volume =       "5",
  year =         "2002",
  publisher =    "Soc. Instrument \& Control Eng. (SICE), Tokyo, Japan",
  abstract =     "This paper presents a two-stage self-organizing map algorithm with threshold operation. {K}ohonen's basic {SOM} algorithm (B{SOM}) is simple and effective for data classification problems of high-dimensional data. But inactivated cells appear for specific input data and it causes to decline the ability of data classification. in order to solve this problem, B{SOM} with threshold operation (TH{SOM}) was proposed recently. the TH{SOM} algorithm, however, tends to loose topological structure of input data. Our two-stage self-organizing map algorithm inherits both good points of B{SOM} and TH{SOM}. Numerical simulations reveal that the two-stage {SOM} can achieve small clustering error and high topology preservation in comparison with B{SOM} and TH{SOM}.",
}

@InProceedings{wada03_bibuniq_4279,
  author =       "Koji Wada and Koji Kurata and Masato Okada",
  title =        "Self-organization of globally continuous and locally distributed information representation",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{pmid16472234_bibuniq_4169,
  author =       "Konstantin V Balakin and Yan A Ivanenkov and Nikolay P Savchuk and Andrey A Ivashchenko and Sean Ekins",
  title =        "Comprehensive computational assessment of {ADME} properties using mapping techniques",
  journal =      "Curr Drug Discov Technology",
  year =         "2005",
  volume =       "2",
  number =       "2",
  pages =        "99--113",
  month =        "June",
}

@InProceedings{inspek335_bibuniq_985,
  author =       "Koskela M. and Laaksonen J. and Oja E.",
  title =        "Entropy-based measures for clustering and {SOM} topology preservation applied to content-based image indexing and retrieval",
  booktitle =    "Proceedings of the 17th International Conference on Pattern Recognition. 2004: 1005--8 Vol. 2",
  pages =        "1176",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Content-based image retrieval (CBIR) addresses the problem of finding images relevant to the users' information needs, based principally on low-level visual features for which automatic extraction methods are available. For the development of CBIR applications, an important issue is to have efficient and objective performance assessment methods for different features and techniques. in this paper, we study the efficiency of clustering methods for image indexing with entropy-based measures. Furthermore, the self-organizing map ({SOM}) as an indexing method is discussed further and an analysis method that takes into account also the spatial configuration of the data on the {SOM} is presented. the proposed methods enable computationally light measurement of indexing and retrieval performance for individual image features.",
}

@InProceedings{inspek68_bibuniq_1179,
  author =       "Kosuge S. and Osana Y.",
  title =        "Chaotic associative memory using internal patterns for image retrieval by color and shape information",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "3318--3323",
  volume =       "5",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper, we propose a chaotic associative memory using internal patterns for image retrieval (CAMIP-IR) by color and shape information. This model is based on the chaotic associative memory which can realize dynamic associations and the self-organizing map. in the proposed model, the similarity-based image retrieval can be realized using color and shape information.",
}

@InProceedings{inspek600_bibuniq_515,
  author =       "Kotani M. and Sugiyama A. and Ozawa S.",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "Analysis of {DNA} microarray data using self-organizing map and kernel based clustering",
  booktitle =    "{ICONIP}'02. Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        "755--759",
  volume =       "2",
  year =         "2002",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "We describe a method of combining a self-organizing map ({SOM}) and a kernel based clustering for analyzing and categorizing the gene expression data obtained from {DNA} microarray. the {SOM} is an unsupervised neural network learning algorithm and forms a mapping a high-dimensional data to a two-dimensional space. However, it is difficult to find clustering boundaries from results of the {SOM}. On the other hand, the kernel based clustering can partition the data nonlinearly. in order to understand the results of {SOM} easily, we apply the kernel based clustering to finding the clustering boundaries and show that the proposed method is effective for categorizing the gene expression data.",
}

@InProceedings{inspek425_bibuniq_1069,
  author =       "Koua E. L. and Kraak M. J.",
  editor =       "J. J. {Banissi, E. ; Borner, K. ; Chen, C. ; Dastbaz, M. ; Clapworthy, G. ; Faiola, A. ; Izquierdo, E. ; Maple, C. ; Roberts, J. ; Moore, C. ; Ursyn, A. ; Zhang}",
  title =        "A usability framework for the design and evaluation of an exploratory geovisualization environment",
  booktitle =    "Proceedings. Eighth International Conference on Information Visualisation",
  pages =        "153--158",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "The exploration of large geospatial data for finding patterns and understanding underlying processes is one of the challenges in geovisualization research. New methods are needed for effective extraction of patterns and appropriate visualization tools are necessary to support knowledge construction throughout the exploration process. Based on an approach to combine visual and computational methods, a visualization environment has been developed to support visual data mining and knowledge discovery tasks. This environment integrates non-geographic information spaces with maps and other graphics that allow users to explore patterns and attribute relationships. the development of the tool intends to facilitate knowledge construction using a number of steps that underline data mining and knowledge discovery methodology. in order to investigate the effectiveness of the design concept, an empirical usability testing is planed to assess the tool's ability to meet user performance and satisfaction. in this test, different options of map-based and interactive visualizations of the output of a self-organizing map ({SOM}) are used to explore a socio-demographic dataset. the study emphasizes the knowledge discovery process based on exploratory tasks and visualization operations. This paper describes the usability framework used to guide the design, and examines key aspects of the evaluation of such visual-computational environment.",
}

@InProceedings{mitsunaga03_bibuniq_4270,
  author =       "Kouich Mitsunaga and MeiHong Zheng and Osamu Hoshino",
  title =        "Temporal to spatiotemporal conversion of neuronal information for {FM} sound detection",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek639_bibuniq_839,
  author =       "Kreesuradej W. and Chutipongpattanakul S. and Kruaklai W.",
  title =        "A text processing {K}ohonen neural network",
  booktitle =    "Proceedings 2003 {IEEE} International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium vol. 1",
  pages =        "36--39",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper proposes a text processing {K}ohonen neural network. This algorithm works directly on textual information without mapping documents into some representation that has quantitative features. the input level of the proposed neural network can directly receive a qualitative value without mapping the qualitative value into numerical value. Then, based on {K}ohonen self organize feature maps and the concepts of dissimilarity measure for symbolic objects, the proposed neural network assigns cluster labels to the objects.",
}

@InProceedings{inspek454_bibuniq_724,
  author =       "Krell G. and Rebmann R. and Seiffert U. and Michaelis B.",
  editor =       "J. {Sanfeliu, A. ; Ruiz-Shulcloper}",
  title =        "Improving still image coding by an {SOM}-controlled associative memory",
  booktitle =    "Progress in Pattern Recognition, Speech and Image Analysis. 8th Iberoamerican Congress on Pattern Recognition, CIARP 2003. Proceedings Lecture Notes in Comput. Sci.",
  pages =        "571--579",
  year =         "2003",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "Archiving of image data often requires a suitable data reduction to minimise the memory requirements. However, these compression procedures entail compression artefacts, which make machine processing of the captured documents more difficult and reduce subjective image quality for the human viewer. A method is presented which can reduce the occurring compression artefacts. the corrected image yields as output of an auto-associative memory that is controlled by a self-organising map ({SOM}).",
}

@Article{lagus02a_bibuniq_492,
  author =       "Krista Lagus",
  title =        "Text retrieval using self-organized document maps",
  journal =      "Neural Processing Letters",
  year =         "2002",
  volume =       "14",
  number =       "2",
  month =        "February",
  pages =        "437--471",
}

@Article{lagus04a_bibuniq_230,
  author =       "Krista Lagus and Samuel Kaski and Teuvo Kohonen",
  title =        "Mining massive document collections by the {WEBSOM} method",
  journal =      "Information Sciences",
  year =         "2004",
  volume =       "163",
  number =       "1-3",
  month =        "June 14",
  pages =        "135--156",
}

@Article{Linden04_bibuniq_1803,
  author =       "Krister Lind\'en",
  title =        "Evaluation of Linguistic Features for Word Sense Disambiguation with Self-Organized Document Maps",
  journal =      "Computers and the Humanities",
  publisher =    "Kluwer Academic Publishers",
  volume =       "38",
  number =       "4",
  pages =        "417--435",
  month =        "November",
  year =         "2004",
}

@InProceedings{Linden02_bibuniq_1802,
  author =       "Krister Lind\'en and Krista Lagus",
  title =        "Word Sense Disambiguation in Document Space",
  booktitle =    "Proceedings of the 2002 {IEEE} International Conference on Systems, Man and Cybernetics",
  address =      "Hammamet, Tunisia",
  month =        "September",
  year =         "2002",
}

@InProceedings{linden03_bibuniq_4323,
  author =       "Krister Lindén",
  title =        "Word Sense Disambiguation with {THESSOM}",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek603_bibuniq_806,
  author =       "Kritboonyalai W. and Avatchanakorn V.",
  editor =       "H. Leung",
  title =        "Data mining for multi-level marketing planning in e-commerce",
  booktitle =    "Proceedings of the Seventh {IASTED} International Conference on Artificial Intelligence and Soft Computing",
  pages =        "307--312",
  year =         "2003",
  publisher =    "ACTA Press, Anaheim, CA, USA",
  abstract =     "The widespread usage of Internet under World Wide Web platform in business practice. E-commerce, brings in a huge mound of data to organizations. Data mining has become a highly focused issue. This paper aspires to propose data mining algorithm in e-commerce, with a focus on clustering task by {K}ohonen for B-to-C e-commerce. Basically, the algorithm has to be adjusted base on the business application and interested information. Selected variable, network and learning parameter are the major adjustment factors. Distributor network planning under multilevel marketing e-commerce is used as the application domain to testify the effectiveness of algorithm. the mining result is used as business intelligence to understand distributors' expansion propensity in order to use on the first stage of network expansion strategic planning. Furthermore, under certain condition the algorithm is easily picked up to use with the new incoming without conscious.",
}

@InProceedings{inspek165_bibuniq_911,
  author =       "Kubota N.",
  title =        "Self-organizing map for a vision-based partner robot",
  booktitle =    "{SICE} 2004 Annual Conference {IEEE} 3",
  pages =        "",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper proposes a self-organizing map for the visual perception of a partner robot. the robot should memorize its interacting human, and also the environment as a background, because the background restricts the perception of the robot and gives suitable information to the robot. First, the robot memorizes the background by using difference extraction of images, and color information of the image by using k-means algorithm. and then, the robot learns the topological structure of the color patterns clustered by k-means algorithm. the experimental results show that the robot can recognize the human and several postures efficiently.",
}

@InProceedings{inspek396_bibuniq_1042,
  author =       "Kubota N.",
  title =        "Visual perception for a partner robot based on computational intelligence",
  booktitle =    "2004 {IEEE} International Conference on Fuzzy Systems",
  pages =        "293--298",
  volume =       "1",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper proposes a method for visual perception for a partner robot interacting with a human. A robot with a physical body should extract information by using prediction based on the dynamics of its environment, because the computational cost can be reduced, imitation is a powerful tool for gestural interaction between children and for teaching behaviors to children by parent. Furthermore, others' action can be a hint for obtaining a new behavior that might not be the same as the original action. This paper proposes a visual perception method for a partner robot based on the interactive teaching mechanism of a human teacher. the proposed method is composed of a spiking neural network, a self-organizing map, a steady-state genetic algorithm, and softmax action selection strategy. Furthermore, we discuss the interactive learning of a human and a partner robot based on the proposed method through several experiment results.",
}

@InProceedings{inspek62_bibuniq_1174,
  author =       "Kubota N. and Abe M.",
  editor =       "L. C. {Khosla, R. ; Howlett, R. J. ; Jain}",
  title =        "Computational intelligence for cyclic gestures recognition of a partner robot",
  booktitle =    "Knowledge Based Intelligent Information and Engineering Systems. 9th International Conference, KES 2005. Proceedings Part I-Lecture Notes in Artificial Intelligence",
  pages =        "650--656",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This paper proposes a method for cyclic gestures recognition of a partner robot based on computational intelligence. the mobile robot used as a partner robot must make decisions suitable to the human intention in the facing environment. Therefore, it is necessary to recognize the gesture used as a tool for human communication. the proposed method is composed of a fuzzy spiking neural network, a self-organizing map, and a steady-state genetic algorithm. Experimental results show the effectiveness of the proposed method.",
}

@InProceedings{inspek94_bibuniq_1201,
  author =       "Kubota N. and Nishida K.",
  title =        "Human recognition of a partner robot based on relevance theory and neuro-fuzzy computing",
  booktitle =    "2005 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems. 2005: 2417--22",
  pages =        "CD--ROM",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper proposes a human recognition method of a partner robot for natural communication with human. Basically, human recognition is performed by using various types of information. in this paper, we use the color image of human face and pattern of conversation with the human. the proposed method is composed of k-means algorithm, spiking neural network, self-organizing map, and steady-state genetic algorithm. Furthermore, we show experimental results of the partner robot based on the proposed method.",
}

@InProceedings{inspek331_bibuniq_981,
  author =       "Kubota N. and Nojima Y. and Kojima F.",
  title =        "Imitative behavior generation for a vision-based partner robot",
  booktitle =    "2004 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems {IROS} {IEEE} vol. 3",
  pages =        "3080--3085",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper proposes a method for generating behaviors based on imitation of a partner robot interacting with a human. First of all, we discuss the role of imitation, and explain the method for imitative behavior generation of the robot based on computational intelligence. the robot searches for a human by using a CCD camera. A human hand motion pattern is extracted from a series of images taken from the CCD camera. Next, the position sequence of the extracted human hand is used as inputs to a spiking neural network in order to recognize it as a gesture. Furthermore, the trajectory for a behavior is generated and updated by a steady-state genetic algorithm based on human motions. Furthermore, a self-organizing map is used for clustering human hand motion patterns as gestures. Finally, we show several experimental results of imitative behavior generation through interaction with a human.",
}

@InProceedings{inspek638_bibuniq_838,
  author =       "Kukovecz A. and Smolik M. and Bokova S. N. and Obraztsova E. and Kataura H. and Achiba Y. and Kuzmany H.",
  title =        "Artificial neural networks in the analysis of the fine structure of the {SWCNT} Raman {G}-band",
  booktitle =    "{AIP} Conference Proceedings. 2003; (685): 211--14",
  pages =        "",
  year =         "2003",
  publisher =    "{AIP}",
  abstract =     "Although the diameter dispersion of the phonons composing the Raman G-band of single wall carbon nanotubes (SWCNTs) is well understood theoretically, systematic experimental studies on the subject are scarce. We investigated 6 different diameter samples between d = 1. 05-1. 57 nm with several excitation lasers and used artificial neural networks (ANN) to explore if there is a connection between the fine structure of the G-band and the sample diameter. An initial screening by a {K}ohonen self-organizing map revealed that ANN technology is able to identify spectra measured on the same sample. Based on this result several supervised learning algorithms were tested and finally we succeeded in building a resilient propagation ANN with one hidden layer which is able to predict the diameter distribution of a macroscopic SWCNT sample from the structure of its Raman G-band with acceptable accuracy. We suggest that with more extensive calibration this method could be developed into it useful auxiliary technique of SWCNT characterization.",
}

@Article{inspek741_bibuniq_563,
  author =       "Kulkarni U. V. and Bhoyar K. K.",
  title =        "Devanagari handwritten digits recognition using weighted neighborhood self-organizing map",
  journal =      "IETE Journal of Research. Nov. Dec. 2002; 48(6): 431--6",
  pages =        "",
  year =         "2002",
  publisher =    "Instn. Electron. \& Telecommun. Eng",
  abstract =     "In the conventional self-neuron organizing map ({SOM}) all the cells in the neighborhood of the winning neuron are updated by giving the same treatment to each of them. However, the proposed weighted neighborhood {SOM} (WNSOM) algorithm updates these cells by a varying factor, which is a function of the distance of the neighboring neuron from the winning neuron and the current neighborhood radius. Both linear and exponential functions of these parameters are tried. the proposed procedure using these functions offered better results than the conventional SOM. These results are also compared. with type-1 learning vector quantization ({LVQ}-1) and are found to be better than those obtained after fine-tuning, which requires thousands of iterations applied to the initial map created using the conventional SOM.",
}

@Article{Kuo03a_bibuniq_1590,
  author =       "Kuo Lung Wu and Miin Shen Yang",
  title =        "A fuzzy-soft learning vector quantization",
  journal =      "Neurocomputing-. Oct. 2003; 55(3-4): 681-97",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "This paper presents a batch competitive learning method called fuzzy-soft learning vector quantization (FS{LVQ}). the proposed FS{LVQ} is a batch type of clustering learning network by fusing the batch learning, soft competition and fuzzy membership functions. the comparisons between the well-known fuzzy {LVQ} and the proposed FS{LVQ} are made. in a series of designed simulations for the parameter estimations of normal mixtures, the performances including the accuracy (mean square error) and computational efficiency (number of iterations) are measured. FS{LVQ} shows good accuracy and high performance.",
}

@Article{inspek385_bibuniq_1032,
  author =       "Kurata K. and Oshiro N.",
  title =        "Separating visual information into position and direction by {SOM}",
  journal =      "Artificial Life and Robotics. 2004; 8(1): 5--8",
  pages =        "",
  year =         "2004",
  publisher =    "Springer-Verlag",
  abstract =     "A model is proposed to self-organize a map for the visual recognition of position and direction by a robot moving autonomously in a room. the robot is assumed to have visual sensors. the model is based on {K}ohonen's self-organizing map ({SOM}), which was proposed as a model of self-organization of the cortex. An ordinary {SOM} consists of a two-dimensional array of neuron-like feature detector units. in our model, however, units are arranged in a three-dimensional array, and a periodic boundary condition is assumed in one dimension. Also, some new learning rules are added. Our model is shown by a computer simulation to form a map which can extract from the visual input two factors of information separately, i. e., the position and direction of the robot. This is an example of so-called two-factor problems. in our algorithm, the difference in the topology of the information is used to separate two factors of information.",
}

@InProceedings{inspek437_bibuniq_1080,
  author =       "Kurd Z. and Kelly T. P.",
  editor =       "S. {Heisel, M. ; Liggesmeyer, P. ; Wittmann}",
  title =        "Using fuzzy self-organising maps for safety critical systems",
  booktitle =    "Computer Safety, Reliability, and Security. 23rd International Conference, SAFECOMP 2004. Proceedings Lecture Notes in Comput. Sci.",
  pages =        "17--30",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This paper defines a type of constrained artificial neural network (ANN) that enables analytical certification arguments whilst retaining valuable performance characteristics. Previous work has defined a safety lifecycle for ANNs without detailing a specific neural model. Building on this previous work, the underpinning of the devised model is based upon an existing neuro-fuzzy system called the fuzzy self-organising map (FSOM). the FSOM is type of 'hybrid' ANN which allows behaviour to be described qualitatively and quantitatively using meaningful expressions. Safety of the FSOM is argued through adherence to safety requirements - derived from hazard analysis and expressed using safety constraints. the approach enables the construction of compelling (product-based) arguments for mitigation of potential failure modes associated with the FSOM. the constrained FSOM has been termed a safety critical artificial neural network (SCANN). the SCANN can be used for nonlinear function approximation and allows certified learning and generalisation. A discussion of benefits for real-world applications is also presented within the paper.",
}

@InProceedings{inspek380_bibuniq_1028,
  author =       "Kurd Z. and Kelly T. P. and Austin J.",
  editor =       "H. {Yang, Z. R. ; Everson, R. ; Yin}",
  title =        "Exploiting safety constraints in fuzzy self-organising maps for safety critical applications",
  booktitle =    "Intelligent Data Engineering and Automated Learning {IDEAL} 2004. 5th International Conference. Proceedings Lecture Notes in Comput. Sci.",
  pages =        "266--271",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This paper defines a constrained artificial neural network (ANN) that can be employed for highly-dependable roles in safety critical applications. the derived model is based upon the fuzzy self-organising map (FSOM) and enables behaviour to be described qualitatively and quantitatively. By harnessing these desirable features, behaviour is bounded through incorporation of safety constraints - derived from safety requirements and hazard analysis. the constrained FSOM has been termed a safety critical artificial neural network (SCANN) and preserves valuable performance characteristics for non-linear function approximation problems. the SCANN enables construction of compelling (product-based) safety arguments for mitigation and control of identified failure modes. Illustrations of potential benefits for real-world applications are also presented.",
}

@Article{inspek444_bibuniq_720,
  author =       "Kusumoputro B. and Murni A.",
  title =        "Comparison of hybrid neural systems of {K}ohonen {SOM} with back-propagation learning in artificial odor recognition system",
  journal =      "{WSEAS} Transactions on Computers. Jan. 2003; 2(1): 175--81",
  pages =        "",
  year =         "2003",
  publisher =    "{WSEAS}",
  abstract =     "This report proposes a hybrid neural networks that is developed based on {K}ohonen self-organization network (KSOM), and utilized it as an adaptive recognition system. As the goals in the research on artificial neural networks are to improve the recognition capability of the network and at the same time minimize the time needed for learning the patterns, these goals could be achieved by combining the two types of networks learning, i. e. supervised learning and unsupervised learning. the developed hybrid neural system will henceforth be referred to as hybrid adaptive of unsupervised KSOM with winning probability function and supervised BP or KSOM(WPF)-BP. This hybrid neural system could estimate the cluster distribution of given data, and directed it into predefined number of cluster neurons through creation and deletion mechanism. Comparison with other developed hybrid neural system is done for determination of various odors from Martha Tilaar Cosmetics product in an artificial odor recognition system. the performance of our developed learning system in terms of its recognition ability and its learning time are explored in this report. It is shown clearly that the average recognition rate of the hybrid neural system could be increased up to 96. 8\% to discriminate the odors properly, the learning time is much more shorter than other hybrid neural system.",
}

@InProceedings{inspek689_bibuniq_528,
  author =       "Kuu-young-Young Yi-Yuan-Chen",
  editor =       "T. {Baozong, Y. ; Xiaofang}",
  title =        "An intelligent radar predictor for high-speed moving-target tracking",
  booktitle =    "{IEEE} Region 10 Conference on Computer, Communications, Control and Power Engineering",
  pages =        "1638--1641",
  volume =       "3",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Due to rapid the increase in missile speed, the air-defense radar system faces severe challenge in tracking these high-speed missiles. During tracking, the radar data are read into the system in a real-time manner sequentially, and thus only few data are available for trajectory estimation in every short time period. Therefore, in this paper, we propose an intelligent radar predictor, including a self-organizing map ({SOM}), to achieve accurate trajectory estimation under the strict time constraint. By knowing the dynamic model of the moving target, the SOM, an unsupervised neural network, learns to predict the target trajectory using a limited number of data. the performance of the {SOM} is compared with that of the Kalman filtering. Simulation results based on both the generated and real radar data demonstrate the effectiveness of the proposed intelligent radar predictor.",
}

@InProceedings{inspek816_bibuniq_628,
  author =       "Kwan H. K.",
  title =        "Fuzzy neural network for phoneme sequence recognition",
  booktitle =    "{IEEE} International Symposium on Circuits and Systems. Proceedings",
  pages =        "847--850",
  volume =       "2",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper, we present a novel speech recognition system based on the use of the fuzzy neural network for 2{D} phoneme sequence pattern recognition. the self-organizing map and then learning vector quantization are used to organize the phoneme feature vectors of short and long phonemes segmented from speech samples to obtain their phoneme maps. the 2{D} phoneme response sequences of the speech samples are formed optimally on the phoneme maps by the Viterbi search algorithm. These 2{D} phoneme response sequence curves are used as inputs to the fuzzy neural network for training and recognition of speech utterances. Simulations indicate up to 91. 7\% accuracy on 0-9 digit-voice recognition can be obtained.",
}

@InProceedings{inspek514_bibuniq_747,
  author =       "Kwan H. K. and Dong X.",
  title =        "Phoneme sequence pattern recognition using fuzzy neural network",
  booktitle =    "Proceedings of 2003 International Conference on Neural Networks and Signal Processing",
  pages =        "535--538",
  volume =       "1",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper, a 2-D phoneme sequence pattern recognition using the fuzzy neural network is presented. the self-organizing map and the learning vector quantization are used to organize the phoneme feature vectors of short and long phonemes segmented from speech samples to obtain the phoneme maps. the 2-D phoneme response sequences of the speech samples are formed on the phoneme maps by the Viterbi search algorithm. These 2-D phoneme response sequence curves are used as inputs to the fuzzy neural network for training and recognition of 0-9 digit-voice utterances. Simulation results are given.",
}

@InProceedings{Kwang00c_bibuniq_3957,
  author =       "Kwang Baek Kim and Dae Su Kim",
  title =        "Color image vector quantization using wavelet transform and enhanced self-organizing neural network",
  booktitle =    "Neural-Information-Processing. 11th International Conference, {ICONIP}-2004. Proceedings Lecture Notes in Computer Science Vol. 3316. 2004: 166-71",
  year =         "2004",
}

@InProceedings{inspek370_bibuniq_1018,
  author =       "Kylvaja M. and Hatonen K. and Kumpulainen P. and Laiho J. and Lehtimaki P. and Raivio K. and Vehvilainen P.",
  title =        "Trial report on self-organizing map based analysis tool for radio networks [{GSM} applications]",
  booktitle =    "2004 {IEEE} 59th Vehicular Technology Conference. {VTC} 2004 Spring",
  pages =        "2365--2369",
  volume =       "4",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Telecommunications networks move towards wireless applications and service and business driven network management. Operators provide new services on different access technologies, in a multi-technology environment. This shift in abstraction level towards services, away from technology and network element management, requires new tools and methods to support the operational tasks, of which service monitoring and traffic analysis is an especially important area. in this paper, the concept of a neural network based tool that aids the operators in detecting the quality of end-user experience (QoE) is introduced.",
}

@Article{chang02a_bibuniq_411,
  author =       "L. C. Chang and F. J. Chang",
  title =        "An efficient parallel algorithm for {LISSOM} neural network",
  journal =      "Parallel Computing",
  year =         "2002",
  volume =       "28",
  number =       "11",
  month =        "November",
  pages =        "1611--1633",
}

@Article{canetta05a_bibuniq_29,
  author =       "L. Canetta and N. Cheikhrouhou and R. Glardon",
  title =        "Applying two-stage {SOM}-based clustering approaches to industrial data analysis",
  journal =      "Production Planning \& Control",
  year =         "2005",
  volume =       "16",
  number =       "8",
  month =        "December",
  pages =        "774--784",
}

@Article{cinque04a_bibuniq_220,
  author =       "L. Cinque and G. Foresti and L. Lombardi",
  title =        "A clustering fuzzy approach for image segmentation",
  journal =      "Pattern Recognition",
  year =         "2004",
  volume =       "37",
  number =       "9",
  month =        "September",
  pages =        "1797--1807",
}

@InProceedings{denny04a_bibuniq_228,
  author =       "L. Denny and V. C. S. Lee",
  title =        "An alternative methodology for mining seasonal pattern using self-organizing map",
  booktitle =    "Advances in Knowledge Discovery and Data Mining, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2004",
  pages =        "1571--1580",
}

@Misc{webform_2349_bibuniq_4199,
  author =       "L. G. Souza and G. A. Barreto and J. C. Mota",
  title =        "Using the self-organizing map to design efficient {RBF} models for nonlinear channel equalization",
  howpublished = "Proceedings of the 5th Workshop on Self-Organizing Maps ({WSOM}'05)",
  pages =        "339--346",
  note =         "",
  year =         "2005",
}

@Article{wu05b_bibuniq_51,
  author =       "L. H. Wu and L. Liu and J. Li and Z. Y. Li",
  title =        "Modeling user multiple interests by an improved {GCS} approach",
  journal =      "Expert Systems With Applications",
  year =         "2005",
  volume =       "29",
  number =       "4",
  month =        "November",
  pages =        "757--767",
}

@Article{yih02a_bibuniq_465,
  author =       "L. H. Yih and K. Peck and T. C. Lee",
  title =        "Changes in gene expression profiles of human fibroblasts in response to sodium arsenite treatment",
  journal =      "Carcinogenesis",
  year =         "2002",
  volume =       "23",
  number =       "5",
  month =        "May",
  pages =        "867--876",
}

@InProceedings{Hetel00a_bibuniq_4089,
  author =       "L. Hetel and J. L. Buessler and J. P. Urban",
  title =        "Superposition-based order analysis in self-organizing maps",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks 787-92",
  year =         "2004",
}

@Article{cao03a_bibuniq_371,
  author =       "L. J. Cao",
  title =        "Support vector machines experts for time series forecasting",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "51",
  month =        "April",
  pages =        "321--339",
}

@Article{wickramasinghe04a_bibuniq_188,
  author =       "L. K. Wickramasinghe and R. Amarasiri and L. D. Alahakoon",
  title =        "A hybrid intelligent multiagent system for e-business",
  journal =      "Computational Intelligence",
  year =         "2004",
  volume =       "20",
  number =       "4",
  month =        "November",
  pages =        "603--623",
}

@Article{li05b_bibuniq_102,
  author =       "L. Li and Q. Jiang and G. L. Ding and L. Zhang and Z. G. Zhang and J. R. Ewing and R. A. Knight and A. Kapke and H. Soltanian-Zadeh and M. Chopp",
  title =        "Map-Isodata demarcates regional response to combination rt-{PA} and 7{E3} {F}(ab ')(2) treatment of embolic stroke in the rat",
  journal =      "Journal of Magnetic Resonance Imaging",
  year =         "2005",
  volume =       "21",
  number =       "6",
  month =        "June",
  pages =        "726--734",
}

@Article{zampighi04a_bibuniq_245,
  author =       "L. M. Zampighi and C. L. Kavanau and G. A. Zampighi",
  title =        "The {K}ohonen self-organizing map: a tool for the clustering and alignment of single particles imaged using random conical tilt",
  journal =      "Journal of Structural Biology",
  year =         "2004",
  volume =       "146",
  number =       "3",
  month =        "June",
  pages =        "368--380",
}

@Article{zhou04a_bibuniq_215,
  author =       "L. M. Zhou and T. L. Chen",
  title =        "Effects of interactive function forms and refractoryperiod in a self-organized critical model based on neural networks",
  journal =      "Communications in Theoretical Physics",
  year =         "2004",
  volume =       "42",
  number =       "1",
  month =        "July 15",
  pages =        "121--125",
}

@Article{wetmore05a_bibuniq_67,
  author =       "L. Wetmore and M. I. Heywood and A. N. Zincir-Heywood",
  title =        "Speeding up the self-organizing feature map using dynamic subset selection",
  journal =      "Neural Processing Letters",
  year =         "2005",
  volume =       "22",
  number =       "1",
  month =        "August",
  pages =        "17--32",
}

@Article{xiao03a_bibuniq_379,
  author =       "L. Xiao and K. K. Wang and Y. Teng and J. Zhang",
  title =        "Component plane presentation integrated self-organizing map for microarray data analysis",
  journal =      "Febs Letters",
  year =         "2003",
  volume =       "538",
  number =       "1-3",
  month =        "March 13",
  pages =        "117--124",
}

@InProceedings{inspek830_bibuniq_642,
  author =       "Laiho J. and Kylvaja M. and Hoglund A.",
  title =        "Utilization of advanced analysis methods in {UMTS} networks",
  booktitle =    "Vehicular Technology Conference. {IEEE} 55th Vehicular Technology Conference. {VTC} Spring 2002 vol. 2",
  pages =        "4 vol. 2118",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The scope of this paper is to introduce new analysis and visualization methods for WCDMA cellular networks. the proposed examples are mainly based on the self-organizing map ({SOM}) method, but also other neural and statistical methods are equally applicable. the main motivation for advanced methods is to increase the abstraction level from the raw network measurements, i. e. radio access network language, to network functional areas or a language closer to the business of network operator. Furthermore the vast amount of quality of service (QoS) and service combinations, that 3G will enable, require effective data handling procedures.",
}

@InProceedings{inspek711_bibuniq_543,
  author =       "Laine S.",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "Selecting the variables that train a self-organizing map ({SOM}) which best separates predefined clusters",
  booktitle =    "{ICONIP}'02. Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        "1961--1965",
  volume =       "4",
  year =         "2002",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "The paper presents how to find the variables that best illustrate a problem of interest when visualizing with the self-organizing map ({SOM}). the user defines what is interesting by labeling data points, e. g. with alphabets. These labels assign the data points into clusters. An optimization algorithm looks for the set of variables that best separates the clusters. These variables reflect the knowledge the user applied when labeling the data points. the paper measures the separability, not in the variable space, but on a {SOM} trained into this space. the found variables contain interesting information, and are well suited for the SOM. the trained {SOM} can comprehensively visualize the problem of interest, which supports discussion and learning from data. the approach is illustrated using the case of the Hitura mine; and compared with a standard statistical visualization algorithm, the Fisher discriminant analysis.",
}

@InProceedings{inspek342_bibuniq_992,
  author =       "Laine S. and Simila T.",
  editor =       "S. K. {Pal, N. R. ; Kasabov, N. ; Mudi, R. K. ; Pal, S. ; Parui}",
  title =        "Using {SOM}-based data binning to support supervised variable selection",
  booktitle =    "Neural Information Processing. 11th International Conference, {ICONIP} 2004. Proceedings Lecture Notes in Computer Science",
  pages =        "172--180",
  volume =       "3316",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "We propose a robust and understandable algorithm for supervised variable selection. the user defines a problem by manually selecting the variables Y that are used to train a self-organizing map ({SOM}), which best describes the problem of interest. This is an illustrative problem definition even in multivariate case. the user also defines another set X, which contains variables that may be related to the problem. Our algorithm browses subsets of X and returns the one, which contains most information of the user's problem. We measure information by mapping small areas of the studied subset to the {SOM} lattice. We return the variable set providing, on average, the most compact mapping. By analysis of public domain data sets and by comparison against other variable selection methods, we illustrate the main benefit of our method: understandability to the common user.",
}

@InProceedings{inspek872_bibuniq_671,
  author =       "Lang R. and Warwick K.",
  title =        "The plastic self organising map",
  booktitle =    "Proceedings of the 2002 International Joint Conference on Neural Networks. {IJCNN}'02 vol. 1",
  pages =        "727--732",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A novel extension to {K}ohonen's self-organising map, called the plastic self organising map (PSOM), is presented. PSOM is unlike any other network because it only has one phase of operation. the PSOM does not go through a training cycle before testing, like the {SOM} does and its variants. Each pattern is thus treated identically for all time. the algorithm uses a graph structure to represent data and can add or remove neurons to learn dynamic nonstationary pattern sets. the network is tested on a real world radar application and an artificial nonstationary problem.",
}

@InProceedings{inspek509_bibuniq_1122,
  author =       "Lange O. and Wismueller A. Anke-Meyer-Baese and Hurdal M. and Sumners D. and Auer D.",
  title =        "Model-free functional {MRI} analysis using improved fuzzy cluster analysis techniques",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering. 2004; 5421(1): 19--28",
  pages =        "",
  year =         "2004",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "Conventional model-based or statistical analysis methods for functional MRI ({fMRI}) are easy to implement, and are effective in analyzing data with simple paradigms. However, they are not applicable in situations in which patterns of neural response are complicated and when {fMRI} response is unknown. in this paper the Gath-Geva algorithm is adapted and rigorously studied for analyzing {fMRI} data. the algorithm supports spatial connectivity aiding in the identification of activation sites in functional brain imaging. A comparison of this new method with the fuzzy n-means algorithm, {K}ohonen's self-organizing map, fuzzy n-means algorithm with unsupervised initialization, minimal free energy vector quantizer and the {"}neural gas{"} network is done in a systematic {fMRI} study showing comparative quantitative evaluations. the most important findings in the paper are: (1) the Gath-Geva algorithms outperforms for a large number of codebook vectors all other clustering methods in terms of detecting small activation areas, and (2) for a smaller number of codebook vectors the fuzzy n-means with unsupervised initialization outperforms all other techniques. the applicability of the new algorithm is demonstrated on experimental data.",
}

@Article{inspek282_bibuniq_942,
  author =       "Lauzon N. and Anctil F. and Petrinovic J.",
  title =        "Characterization of soil moisture conditions at temporal scales from a few days to annual",
  journal =      "Hydrological Processes. 15 Dec. 2004; 18(17): 3235--54",
  pages =        "",
  year =         "2004",
  publisher =    "Wiley",
  abstract =     "This work proposes the analysis of soil moisture conditions based on the use of two recently developed descriptive techniques: (1) wavelet analysis and (2) self-organizing mapping through {K}ohonen neural networks. This analysis is applied to soil moisture profiles as well as supporting data, i. e. precipitation, temperature and flow observations, from an experimental site in the Orgeval watershed in France. With wavelet analysis and self-organizing mapping, a comprehensive description of the structure of soil moisture profile, its evolution over time, and how it relates to observations of precipitation, temperature and flow can be obtained. Soil moisture conditions, particularly in the Orgeval watershed, are an important feature of the hydrologic cycle. There might be a significant advantage to consider soil moisture information in a variety of hydrologic models, such as streamflow models often employed in simulation and prediction modes for operational purposes, and the analysis performed here provides some avenues leading to the consideration of this information.",
}

@Article{inspek526_bibuniq_1127,
  author =       "Lavine B. K. and Davidson C. E. and Westover D. J.",
  title =        "Spectral pattern recognition using self-organizing {MAPS}",
  journal =      "Journal of Chemical Information and Computer Sciences. May June 2004; 44(3): 1056--64",
  pages =        "",
  year =         "2004",
  publisher =    "ACS",
  abstract =     "A {K}ohonen neural network is an iterative technique used to map multivariate data. the network is able to learn and display the topology of the data. Self-organizing maps have advantages as well as drawbacks when compared to principal component plots. One advantage is that data preprocessing is usually minimal. Another is that an outlier will only affect one map unit and its neighborhood. However, outliers can have a drastic and disproportionate effect on principal component plots. Removing them does not always solve the problem for as soon as the worst outliers are deleted, other data points may appear in this role. the advantage of using self-organizing maps for spectral pattern recognition is demonstrated by way of two studies recently completed in our laboratory. in the first study, Raman spectroscopy and self-organizing maps were used to differentiate six common household plastics by type for recycling purposes. the second study involves the development of a potential method to differentiate acceptable lots from unacceptable lots of avicel using diffuse reflectance near-infrared spectroscopy and self-organizing maps.",
}

@Article{inspek572_bibuniq_1135,
  author =       "Lazzerini B. and Marcelloni F. and Marola G.",
  title =        "Calibration of positron emission tomograph detector modules using new neural method",
  journal =      "Electronics Letters. 18 March 2004; 40(6): 360--1",
  pages =        "",
  year =         "2004",
  publisher =    "IEE",
  abstract =     "A new method for calibration of positron emission tomography detector modules is proposed. A purposely-modified version of the standard neural self-organising map is adopted. Experimental results to assess accuracy and efficiency of the proposed method are included.",
}

@InProceedings{inspek128_bibuniq_1231,
  author =       "Lee J. and Ersoy O. K.",
  editor =       "S. {Kurnaz, S. ; Ince, F. ; Onbasioglu, S. ; Basturk}",
  title =        "Classification of remote sensing data by multistage self-organizing maps with rejection schemes",
  booktitle =    "RAST 2005. Proceedings of 2nd International Conference on Recent Advances in Space Technologies {IEEE} 534--9",
  pages =        "534--539",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A new classification method for remote sensing data is proposed. the proposed classifier consists of several stage neural networks (SNN) and rejection schemes. Rejection schemes are used to decide whether the input vector is hard to classify. By adopting rejection schemes, it is possible to detect the hard input vectors and reduce the possibility of misclassification, for example, due to input vectors which are linearly non-separable or close to boundaries between classes. Such input vectors are rejected by rejection schemes in each SNN and fed into the next SNN. Simultaneously, the input vectors accepted by rejection schemes are classified in each SNN. the self-organizing map ({SOM}) is used for learning of weight vectors. Experiments are done using the proposed method with two remote sensing data sets, and results are compared to those of other methods.",
}

@Article{inspek216_bibuniq_1309,
  author =       "Lehn-Schioler T. and Hegde A. and Erdogmus D. and Principe J. C.",
  title =        "Vector quantization using information theoretic concepts",
  journal =      "Natural Computing. 2005; 4(1): 39--51",
  pages =        "",
  year =         "2005",
  publisher =    "Kluwer Academic Publishers",
  abstract =     "The process of representing a large data set with a smaller number of vectors in the best possible way, also known as vector quantization, has been intensively studied in the recent years. Very efficient algorithms like the {K}ohonen self-organizing map ({SOM}) and the Linde Buzo Gray (LBG) algorithm have been devised. in this paper a physical approach to the problem is taken, and it is shown that by considering the processing elements as points moving in a potential field an algorithm equally efficient as the before mentioned can be derived. Unlike {SOM} and LBG this algorithm has a clear physical interpretation and relies on minimization of a well defined cost function. It is also shown how the potential field approach can be linked to information theory by use of the Parzen density estimator. in the light of information theory it becomes clear that minimizing the free energy of the system is in fact equivalent to minimizing a divergence measure between the distribution of the data and the distribution of the processing elements, hence, the algorithm can be seen as a density matching method.",
}

@InProceedings{inspek754_bibuniq_576,
  author =       "Lehtimaki P. and Raivio K. and Simula O.",
  editor =       "M. Verleysen",
  title =        "Mobile radio access network monitoring using the self-organizing map",
  booktitle =    "10th European Symposium on Artificial Neural Networks. {ESANN}'2002",
  pages =        "231--236",
  year =         "2002",
  publisher =    "d-side publications, Evere, Belgium",
  abstract =     "In this study, a method for process clustering and visualization using the self-organizing map ({SOM}) is described. the presented method is applied in clustering and monitoring of mobile cells of a mobile radio access network (RAN).",
}

@InProceedings{inspek543_bibuniq_1131,
  author =       "Lei J. Z. and Ghorbani A.",
  editor =       "A. A. Ghorbani",
  title =        "Network intrusion detection using an improved competitive learning neural network",
  booktitle =    "Proceedings. Second Annual Conference on Communication Networks and Services Research",
  pages =        "190--197",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "The paper presents a novel approach for detecting network intrusions based on a competitive learning neural network. the performance of this approach is compared to that of the self-organizing map ({SOM}), which is a popular unsupervised training algorithm used in intrusion detection. While obtaining a similarly accurate detection rate as the {SOM} does, the proposed approach uses only one fourth of the computation time of the SOM. Furthermore, the clustering result of this method is independent of the number of the initial neurons. This approach also exhibits the ability to detect known and unknown network attacks. the experimental results obtained by applying this approach to the KDD-99 data set demonstrate that the proposed approach performs exceptionally in terms of both accuracy and computation time.",
}

@Article{inspek758_bibuniq_578,
  author =       "Leung-Yee {Zhang-Yan-ning, Zhao-Rong-chun}",
  title =        "An efficient target recognition method for large scale data",
  journal =      "Acta Electronica Sinica. Oct. 2002; 30(10): 1533--5",
  pages =        "",
  year =         "2002",
  publisher =    "Chinese Inst. Electron",
  abstract =     "An efficient target recognition method for large scale data is proposed in this paper, which is based on the self-organizing map ({SOM}) neural network and support vector machines (SVM). the target data set is divided into clusters by the {SOM} first. Then, the support vector machines are applied to classify targets. the new method is used to classify the complex XOR problem, iris and appendicitis data, and the experimental results show that the new method can obtain better recognition results for the complex pattern classification of large scale data, and the training time is shorter than that using the support vector machine method only.",
}

@Article{Li00a_bibuniq_3909,
  author =       "Li Hong mei and Li Shi yu and Lin Wei qiang",
  title =        "Sustainable development evaluation using self-organizing feature map neural network",
  journal =      "Acta-Scientiarum-Naturalium-Universitatis-Sunyatseni. Nov. 2004; 43(6): 156-62",
  year =         "2004",
}

@Article{Li00b_bibuniq_4013,
  author =       "Li Qing and Zheng Nanning and You Qubo and Song Yonghong",
  title =        "Research and implementation of computer graphics partition algorithm",
  journal =      "Journal of Computer-Aided-Design-\& Computer Graphics. Aug. 2004; 16(8): 1040-4",
  year =         "2004",
}

@Article{inspek945_bibuniq_703,
  author =       "Li R. Y. and Al-Shamakhi N. Jung-Kim",
  title =        "Image compression using transformed vector quantization",
  journal =      "Image and Vision Computing. 1 Jan. 2002; 20(1): 37--45",
  pages =        "",
  year =         "2002",
  publisher =    "Elsevier",
  abstract =     "Vector quantization (VQ) is an important technique in digital image compression. To improve its performance, we would like to speed up the design process and achieve the highest compression ratio possible. To speed up the process, we used a fast {K}ohonen self-organizing neural network algorithm to achieve big saving in codebook construction time. To obtain better reconstructed images, we propose a new approach called the transformed vector quantization (TVQ), combining the features of transform coding and VQ. We use several data sets to demonstrate the feasibility of this TVQ approach. A comparison of reconstructed image quality is made between the TVQ and VQ. Also, a comparison is made between a TVQ and a standard JPEG approach.",
}

@InProceedings{inspek304_bibuniq_957,
  author =       "Li-Chaoyang Liu-Fang",
  editor =       "Yuan-Baozong; Ruan-Qiuqi; Tang-Xiaofang",
  title =        "A multiview face recognition based on combined feature with clonal selection",
  booktitle =    "2004--7th International Conference on Signal Processing Proceedings {IEEE} vol. 2",
  pages =        "3 vol. 1807",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The profile view of a face provides a complementary information, it is very important to face recognition. This paper construct novel the classification system combining both frontal mid profile views of faces can improve the classification accuracy and decrease cost tune. in this paper, we apply self-organizing map ({SOM}) and minor component (MC) to extract face feature from multiview and combine these features vector to a new combined feature set. Immune Clonal Selection Algorithm is applied to search for better feature sets trough rotation transformations, thus, one is help to reduce classifier cost. Then, support vector machine (SVM) is used as classifier, which have demonstrated high generalization capabilities. Simulation experiments were made on two different face test databases, achieving very high recognition result than {PCA} and Fisher methods with relative low classification cost.",
}

@Article{inspek813_bibuniq_625,
  author =       "Li-Guo-Jie {He-Qing-Fa, Lu-Song, Hao-Qin-Fen}",
  title =        "A new approach to automatic extraction of discriminant regions in image",
  journal =      "Chinese Journal of Computers. Aug. 2002; 25(8): 801--9",
  pages =        "",
  year =         "2002",
  publisher =    "Science Press",
  abstract =     "Image segmentation is a well-known hard problem in image processing. in order to automatically extract discriminant regions from an image, this paper presents a novel method of region extraction, which is based on a {SOM} (self-organizing map) reduction algorithm presented in the paper. Firstly, according to a multi-feature extraction algorithm, the raw image is transformed into a feature map, in which each feature vector consists of three sub-features: 1) color feature - dominant color of a sub-region, 2) texture feature - MRSAR parameters of a sub-region, 3) and position feature - center coordinate of a sub-region. Secondly, {SOM} training algorithm is performed against the feature map generated at the first step. A self-organizing map, in which the number of units is much smaller than that of feature vectors in the feature map, is created after {SOM} training. {SOM} training establishes a relationship between units in the {SOM} and feature vectors in the feature map. Those feature vectors, which are close with each other at the feature space, may map to the same unit of the SOM. Then, a family of reduced self-organizing maps is produced using a two-phase reduction algorithm of SOM. At the first phase, the unit, which has the least number of feature vectors at the map, will be reduced. At the second phase, two units, which are nearest at the feature space, will be merged. Those feature vectors mapping to the reduced unit will re-map to other neighbouring units according to a BMU match rule. Finally, in order to select an optimum one from a series of reduced self-organizing maps, an unsupervised cluster-validity analysis is performed. Pixels in the raw image can be grouped into different discriminant regions according to the relationship between the relevant feature map and the optimum reduced self-organizing map. At last, two evaluation experiments are given to verify the effectiveness of the new method.",
}

@Article{inspek826_bibuniq_638,
  author =       "Li-Zai-ming Chen-Ying",
  title =        "Content-based {MPEG} video variable bit rate ({VBR}) traffic model",
  journal =      "Journal of China Institute of Communications. March 2002; 23(3): 123--8",
  pages =        "",
  year =         "2002",
  publisher =    "People's Posts \& Telecommun. Publishing House",
  abstract =     "The modeling of video traffic is an important prerequisite for any network performance simulation, but it is very difficult to build a uniform model for the video traffic since video contents are variable. in this paper, according to the image texture and the motion complexity, a nonhomogeneous video sequence is divided into homogenous video clips with a 3*3 {K}ohonen self-organized neural network. By using a semi-Markov stochastic process for describing the transition probability among the video clips and the distribution law of their sojourn time and using an AR model for describing the inner process of each video clip, a new uniform moving picture experts group (MPEG) video traffic model can be built.",
}

@Article{inspek110_bibuniq_1214,
  author =       "Liebowitz J. {Wen-Shuan-Tseng, Hang-Nguyen} and Agresti W.",
  title =        "Distractions and motor vehicle accidents: Data mining application on fatality analysis reporting system ({FARS}) data files",
  journal =      "Industrial Management + Data Systems. 2005; 105(9): 1188--205",
  pages =        "1188--1205",
  volume =       "105",
  number =       "9",
  year =         "2005",
  publisher =    "Emerald",
  abstract =     "Purpose - This research applies data mining techniques to discover the relationship between driver inattention and motor vehicle accidents. Design/methodology/approach - the data used in this research is obtained from the Fatality Analysis Reporting System of the National Highway Traffic Safety Administration, focused on the Maryland and Washington, DC area from years 2000 to 2003. the data are first clustered using the {K}ohonen networks. Then, the patterns and rules of the data are explored by decision tree and neural network models. Findings - Results suggests that when inattention and physical/mental conditions take place at the same time, the driver has a higher tendency of being involved in a crash that collides into static objects. Furthermore, with regards to the manner of collision, the relative importance of colliding into a moving vehicle as the first harmful event is two times higher relative to that of colliding into a fixed object as the first harmful event in a crash. Research limitations/implications - the data used in this research are limited to fatal crashes that happened in Maryland and Washington, DC from years 2000 to 2003. Originality/value - This is one of the first research papers utilizing data mining techniques to explore the possible relationships between driver inattention and motor vehicle crashes.",
}

@Article{inspek765_bibuniq_584,
  author =       "Lin-Jun Guo-Fu-jun",
  title =        "Identification and location of fault on double circuit tower by multi-corresponding {BP} {ANN} method",
  journal =      "Power System Technology. Oct. 2002; 26(10): 14--17, 24",
  pages =        "",
  year =         "2002",
  publisher =    "Electr. Power Res. Inst",
  abstract =     "In high voltage power transmission the double circuit tower is increasingly applied, but the problem of fault identification and location under this mode are still not well solved. Because of the coupling between the two circuits on the same tower, the results of fault identification and location by only a single neural network are not satisfactory. After comparing the merit and demerit of applying BP network and {K}ohonen network to fault identification and location under this mode, a new method of fault identification and location under this mode is put forward. in this method the task of fault identification and location is distributed to multi-BP network, i. e., each pattern of the faults under this mode corresponds to a BP network respectively. Taking some fixed points in transmission line as marked points, the fuzzy value outputted from the trained BP network represents the possibility of a fault occurring at a marked point. Constituting the interpolation curves by use of fuzzy values, according to the relative position of the interpolation curves, the fault pattern can be determined and the faulty position is equal to the minimum of the curve from the determined fault pattern. A lot of simulation results show that with the presented method the fault pattern can be exactly and reliably determined and the accuracy of fault location is satisfactory.",
}

@Article{inspek224_bibuniq_1317,
  author =       "Lingras P. and Huang X.",
  title =        "Statistical, evolutionary, and neurocomputing clustering techniques: cluster-based vs object-based approaches",
  journal =      "Artificial Intelligence Review. March 2005; 23(1): 3--29",
  pages =        "",
  year =         "2005",
  publisher =    "Kluwer Academic Publishers",
  abstract =     "Modern day computers cannot provide optimal solution to the clustering problem. There are many clustering algorithms that attempt to provide an approximation of the optimal solution. These clustering techniques can be broadly classified into two categories. the techniques from first category directly assign objects to clusters and then analyze the resulting clusters. the methods from second category adjust representations of clusters and then determine the object assignments. in terms of disciplines, these techniques can be classified as statistical, genetic algorithms based, and neural network based. This paper reports the results of experiments comparing five different approaches: hierarchical grouping, object-based genetic algorithms, cluster-based genetic algorithms, {K}ohonen neural networks, and K-means method. the comparisons consist of the time requirements and within-group errors. the theoretical analyses were tested for clustering of highway sections and supermarket customers. All the techniques were applied to clustering of highway sections. the hierarchical grouping and genetic algorithms approaches were computationally infeasible for clustering a larger set of supermarket customers. Hence only {K}ohonen neural networks and K-means techniques were applied to the second set to confirm some of the results from previous experiments.",
}

@Article{inspek31_bibuniq_1151,
  author =       "Liszka-Hackzell J. J. and Martin D. P.",
  title =        "Analysis of nighttime activity and daytime pain in patients with chronic back pain using a self-organizing map neural network",
  journal =      "Journal of Clinical Monitoring and Computing. 2005; 19(6): 411--14",
  pages =        "",
  year =         "2005",
  publisher =    "Kluwer Academic Publishers",
  abstract =     "There may be a relationship between sleep and pain in patients with chronic back pain. We collected daytime pain and nighttime activity data from 18 patients diagnosed with chronic back pain. the patients were followed for 6 days and 5 nights. Pain levels were collected every 90 min between 0800 hours and 2200 hours using a computerized electronic diary. Activity levels were collected using a wrist accelerometer (Actiwatch AW-64). the Actiwatch sampled activity counts every 1 min. Patients were asked to wear the Actiwatch on their non-dominant arm. the pain level measurements were interpolated using cubic splines. A mean pain level was calculated for each period 0800 hours to 2200 hours as well as for the 6-day period. the difference between the mean pain levels for the 6-day period and each 0800 hours to 2200 hours period was calculated for each patient. Nighttime activity data were analyzed using the Actiwatch Sleep Analysis software. Correlations were calculated between the Actiwatch Sleep Analysis variables and the mean pain level differences for each patient and period. the correlation analysis was performed with SPSS 7. 5. We were unable to show any significant relationships. A different approach to analyze the data was used. A self-organizing map ({SOM}) neural network was trained using the original nighttime activity level time series from 10 randomly selected patients. Recall was then performed on all the activity level data. Correlations were calculated between the pain level variance for the 6-day period for each patient and the corresponding difference in the {SOM} output coordinates. the correlation was found to be r=0. 73, p<0. 01. We conclude that daytime pain levels are not directly correlated with sleep in the following night and that sleep is not directly correlated with daytime pain levels on the following day in this group of patients. There appears to be a correlation between the difference in nighttime activity levels and patterns and the daytime pain variance. Patients who experience large fluctuations in daytime pain levels also show a higher variability in their nighttime activity levels and patterns. Even though we were unable to show a direct relationship between daytime pain and sleep, it may be reasonable to assume that better pain control resulting in less daytime pain fluctuations can provide more stable nighttime activity levels and patterns in this limited group of patients. By using a neural network model, we were able to extract information from the nighttime activity levels even though a traditional statistical analysis was unsuccessful.",
}

@InProceedings{inspek558_bibuniq_509,
  author =       "Liu C. P. and Zhong W. and Kong L. and Xia D. S.",
  editor =       "F. {Callaos, N. ; Hernandez-Encinas, L. ; Yetim}",
  title =        "A fuzzy {K}ohonen neural network classification based on Dempster-Shafer theory in remote sensing image",
  booktitle =    "6th World Multiconference on Systemics, Cybernetics and Informatics. Proceedings",
  pages =        "156--161",
  volume =       "14",
  year =         "2002",
  publisher =    "Int. Inst. Inf. \& Syst, Orlando, FL, USA",
  abstract =     "A new adaptive classification fusion method for remote sensing images is proposed, based on the Dempster-Shafer theory of evidence and a fuzzy {K}ohonen neural network. the new method integrates ideas from the unsupervised neural network model and using neighborhood information in the framework of the Dempster-Shafer theory of evidence. This approach mainly consists in considering each neighbor of a pattern to be classified as an item of evidence supporting certain hypotheses concerning the class membership of that pattern. This evidence is represented by a basic probability assignment (BPA) and pooled using the Dempster rule of combination. Experiments with SPOT remote sensing images demonstrate the excellent performance of this classification scheme as compared with existing neural network techniques.",
}

@InProceedings{inspek696_bibuniq_533,
  author =       "Liu W. Q. Lixin-Xu and Svetha V.",
  editor =       "T. {Baozong, Y. ; Xiaofang}",
  title =        "A two-stage vector quantization approach via self-organizing map",
  booktitle =    "{ICSP}'02. 6th International Conference on Signal Processing Proceedings vol. 1",
  pages =        "913--916",
  volume =       "1",
  year =         "2002",
  publisher =    "IEEE, Piscatway, NJ, USA",
  abstract =     "A two-stage algorithm for vector quantization is proposed based on a self-organizing map ({SOM}) neural network. First, a conventional self-organizing map is modified to deal with dead codebooks in the learning process and is then used to obtain the codebook distribution structure for a given set of input data. Next, subblocks are classified based on the previous structure distribution with a prior criteria. Then, the conventional LBG algorithm is applied to these sub-blocks for data classification with initial values obtained via the SOM. Finally, extensive simulations illustrate that the proposed two-stage algorithm is very effective.",
}

@Article{inspek329_bibuniq_979,
  author =       "Liu-Li-fang {Tang-Bi-qiang, Deng-Chang-hong}",
  title =        "Application of compound neural network in power system transient stability assessment",
  journal =      "Power System Technology. Aug. 2004; 28(15): 62--6",
  pages =        "",
  year =         "2004",
  publisher =    "Electr. Power Res. Inst",
  abstract =     "A power system transient stability assessment method based on compound artificial neural network (ANN) is put forward. This compound ANN consists of {K}ohonen network and some radial basic function (RBF) networks and their advantages are integrated, so the ability of stability assessment is improved. the results of the simulations of central power grid by {K}ohonen network, RBF network and compound ANN respectively show that the proposed method is a superior one.",
}

@Article{inspek140_bibuniq_1243,
  author =       "Liu-Shang-wei {Sun-Ya-ming, Wang-Chen-li, Zhang-Zhi-sheng}",
  title =        "Clustering analysis of power system load series based on ant colony optimization algorithm",
  journal =      "Proceedings of the CSEE. Sept. 2005; 25(18): 40--5",
  pages =        "",
  year =         "2005",
  publisher =    "Chinese Soc. Electr. Eng",
  abstract =     "According to the performance of short-term load forecasting (STLF) model based on the principle of artificial neural networks(ANN), the forecasting accuracy is influenced by the distributed feature of load sample space, and the complex nonlinear relation which is formed by the sensibility of external weather factors to power load will also lead to the reduce of forecasting accuracy. To use power load series for characteristic clustering combination with pattern recognition may use as one method of solving the problem. in this paper, the characteristic clustering and its analysis to power load series based on ant colony optimization algorithm (ACOA) was presented. the load clustering performance of ACOA in actual load system has shown its superiority, which has more sensitivity and resolution to climatic anomaly circumstances, high temperature area, to festival and holiday condition than {K}ohonen neural network based clustered method, and which has more exquisite and even of the clustering characteristics on the similarity of load curve profile. the above clustering performance has a most important significance to improve the accuracy of STLF.",
}

@Article{inspek628_bibuniq_828,
  author =       "Liu-Ying-mei {Zhang-Hong-bin, He-Ren-mu}",
  title =        "The characteristics clustering and synthesis of electric dynamic loads based on {K}ohonen neural network",
  journal =      "Proceedings of the {CSEE}",
  pages =        "1--5",
  volume =       "23",
  number =       "5",
  month =        "May",
  year =         "2003",
  publisher =    "Chinese Soc. Electr. Eng",
  abstract =     "In this paper, a new method based on {K}ohonen self-organization neural network is presented for the characteristics clustering of dynamic loads. At first, the model of every group of load disturbance data is established, then the responses of the load models to the same voltage excitation and the pre-disturbance active power of the loads are incorporated into the feature vectors. At last, {K}ohonen neural network is introduced to cluster. the advantages of this method include: self-learning function, rapid computation and strong type recognition. Many sets of load data measured from North China Power System in three years (1996-1998) have been dealt with using the method. the results show load characteristics have rule though they are random and time-varying. the feasibility of the Measurement-Based Modeling approach is also proved. the use of typical load models will improve the power system simulation veracity.",
}

@InProceedings{inspek172_bibuniq_1274,
  author =       "Lobo V.",
  title =        "One dimensional Self-Organizing Maps to optimize marine patrol activities",
  booktitle =    "Oceans 2005 Europe",
  pages =        "",
  volume =       "1",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A method for planning routes for patrol vessels is proposed. This method is based on a Self-Organizing Map ({SOM}) solution for the Travelling Salesman Problem (TSP), although with significant changes. the locations of reported Search and Rescue (SAR) requests, together with the locations of reported occurrences of illegal fishing activities are used as guidelines for designing the path vessel should take. However, instead of forcing the patrol routes to pass exactly in those locations, as would happen in a TSP, the proposed method uses the locations as density estimators for where the patrol effort should be placed. It then obtains a patrol route that passes through the areas with greater density. We show the behaviour of the proposed method on artificial data, and then apply the method to some data from the Portuguese Navy, obtaining possible routes for its patrol vessels.",
}

@InProceedings{inspek266_bibuniq_1345,
  author =       "Lopez-Rubio E. and Ortiz-de-Lazcano-Lobato J. M. and del-Carmen-Vargas-Gonzalez M. and Lopez-Rubio J. M.",
  editor =       "F. {Cabestany, J. ; Prieto, A. ; Sandoval}",
  title =        "Intrinsic dimensionality maps with the {PCA}{SOM}",
  booktitle =    "Computational Intelligence and Bioinspired Systems. 8th International Work Conference on Artificial Neural Networks, IWANN 2005. Proceedings Lecture Notes in Computer Science",
  pages =        "750--757",
  volume =       "3512",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "The {PCA}SOM is a novel self-organizing neural model that performs principal components analysis ({PCA}). It is also related to the ASSOM network, but its training equations are simpler. the {PCA}SOM has the ability to learn self-organizing maps of the means and correlations of complex input distributions. Here we propose a method to extend this capability to build intrinsic dimensionality maps. These maps model the underlaying structure of the input. Experimental results are reported, which show the self-organizing map formation performed by the proposed network.",
}

@Article{inspek745_bibuniq_567,
  author =       "Luo-Lumin {Liu-Zhengyun, Bao-Xudong, Shu-Zhongli}",
  title =        "Fuzzy {K}ohonen clustering network for medullar cell image segmentation",
  journal =      "Journal of Southeast University Natural Science Edition. Nov. 2002; 32(6): 909--12",
  pages =        "",
  year =         "2002",
  publisher =    "Editorial Dept. J. Southeast Univ",
  abstract =     "A new method, based on image boundary characteristics, is presented to determine the numbers of fuzzy {K}ohonen clustering network (FKCN) output nodes. in this method, an image boundary model is presented, and the model parameters are acquired by using Zernike moments. the FKCN output numbers are then confirmed automatically, based on the traits of the parameters. the results show that the FKCN made by the proposed method can segment images automatically and is therefore feasible.",
}

@InProceedings{inspek202_bibuniq_1298,
  author =       "Luonan-Chen {Shu-Fan, Chengxiong-Mao}",
  editor =       "Z. {Wang, J. ; Liao, X. ; Yi}",
  title =        "Peak load forecasting using the self-organizing map",
  booktitle =    "Advances in Neural Networks {ISNN} 2005. Second International Symposium on Neural Networks. Proceedings, Part III Lecture Notes in Computer Science",
  pages =        "640--647",
  volume =       "3",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "This paper aims to study the short-term load forecasting of electricity by using an extended self-organizing map. We first adopt a traditional {K}ohonen self-organizing map ({SOM}) to learn time-series load data with weather information as parameters. Then, in order to improve the accuracy of the prediction, an extension of {SOM} algorithm based on error-correction learning rule is used, and the estimation of the peak load is achieved by averaging the output of all the neurons. Finally, as an implementation example, data of electricity demand from New York Independent System Operator (ISO) are used to verify the effectiveness of the learning and prediction for the proposed methods.",
}

@Article{motter02a_bibuniq_432,
  author =       "M. A. Motter and J. C. Principe",
  title =        "Predictive multiple model switching control with the self-organizing map",
  journal =      "International Journal of Robust and Nonlinear Control",
  year =         "2002",
  volume =       "12",
  number =       "11",
  month =        "September",
  pages =        "1029--1051",
}

@Article{tadross05a_bibuniq_64,
  author =       "M. A. Tadross and B. C. Hewitson and M. T. Usman",
  title =        "The interannual variability of the onset of the maize growing season over South Africa and Zimbabwe",
  journal =      "Journal of Climate",
  year =         "2005",
  volume =       "18",
  number =       "16",
  month =        "August 15",
  pages =        "3356--3372",
}

@InProceedings{Awad00a_bibuniq_4054,
  author =       "M. Awad and L. Khan and F. Bastani and I. Ling Yen",
  title =        "An effective support vector machines ({SVM}s) performance using hierarchical clustering",
  booktitle =    "Proceedings. 16th {IEEE} International Conference on Tools-with-Artificial Intelligence. 2004: 663-7",
  year =         "2004",
}

@Article{beccali04a_bibuniq_213,
  author =       "M. Beccali and M. Cellura and V. Lo Brano and A. Marvuglia",
  title =        "Forecasting daily urban electric load profiles using artificial neural networks",
  journal =      "Energy Conversion and Management",
  year =         "2004",
  volume =       "45",
  number =       "18-19",
  month =        "November",
  pages =        "2879--2900",
}

@Article{su02a_bibuniq_475,
  author =       "M. C. Su and C. H. Chou and H. T. Chang",
  title =        "A healing mechanism to improve the topological preserving property of feature maps",
  journal =      "Ieice Transactions on Information and Systems",
  year =         "2002",
  volume =       "E85D",
  number =       "4",
  month =        "April",
  pages =        "735--743",
}

@InProceedings{Chau02a_bibuniq_1723,
  author =       "M. Chau and H. Chen and J. Qin and Y. Zhou and Y. Qin and W. K. Sung and D. McDonald",
  title =        "Comparison of two approaches to building a vertical search tool: a case study in the nanotechnology domain",
  booktitle =    "Jcdl-2002. Proceedings of the Second-ACM/{IEEE}-CS-Joint Conference on Digital-Libraries. 2002: 135-44",
  year =         "2002",
  volume =       "",
  pages =        "",
  abstract =     "As the Web has been growing exponentially, it has become increasingly difficult to search for desired information. in recent years, many domain-specific (vertical) search tools have been developed to serve the information needs of specific fields. This paper describes two approaches to building a domain-specific search tool. We report our experience in building two different tools in the nanotechnology domain-(1) a server-side search engine, and (2) a client-side search agent. the designs of the two search systems are presented and discussed, and their strengths and weaknesses are compared. Some future research directions are also discussed.",
}

@Article{cheon04a_bibuniq_208,
  author =       "M. Cheon and M. Heo and E. J. Moon and S. Kim and K. Chung and I. Chang and H. Kim",
  title =        "Environment-dependent one-body score function for proteins by perceptron learning and protein threading",
  journal =      "Journal of the Korean Physical Society",
  year =         "2004",
  volume =       "45",
  number =       "2",
  month =        "August",
  pages =        "550--557",
}

@Article{cheon05a_bibuniq_40,
  author =       "M. Cheon and M. Heo and I. Chang and C. Kim",
  title =        "A self-organizing map of amino acids with their local environments in proteins by using pairwise-contact energy parameters",
  journal =      "Journal of the Korean Physical Society",
  year =         "2005",
  volume =       "47",
  number =       "5",
  month =        "November",
  pages =        "895--899",
}

@InBook{c5_bibuniq_1794,
  author =       "M. Cottrell S. Ponthieux",
  title =        "Journées de méthodologie statistique",
  chapter =      "Classification neuronale et Analyse de données traditionnelle: une application aux conditions de vie des ménages",
  year =         "2002",
  month =        "December",
}

@InProceedings{c7_bibuniq_1796,
  author =       "M. Cottrell {J. C. Fort, P. Letremy}",
  title =        "Avantages et inconvénients de la version batch de l'algorithme de {K}ohonen",
  booktitle =    "Actes du IXèm Congrès de la Société Francophone de Classification",
  pages =        "197--200",
  year =         "2002",
  address =      "Toulouse, France",
}

@Article{c1_bibuniq_1791,
  author =       "M. Cottrell {P. Letrémy, C. Meilland}",
  title =        "Using working patterns as a basis for differentiating part-time employment, {K}ohonen Maps, Genetic Algorithms and Perceptron: Methodological Reflections and New Empirical Developments in Economic and Management Science",
  journal =      "Special Issue of European Journal of Economic and Social Systems",
  year =         "2004",
  volume =       "17",
  number =       "1-2",
  pages =        "29--40",
}

@InProceedings{c8_bibuniq_1797,
  author =       "M. Cottrell {P. Letrémy, C. Meilland}",
  title =        "Des temps partiels différenciés par leur rythme de travail",
  booktitle =    "Actes de la Neuvième Conférence internationale ACSEG 2002",
  pages =        "49--58",
  address =      "Boulogne sur Mer, France",
}

@Article{delgado05a_bibuniq_96,
  author =       "M. Delgado and M. C. Pegalajar",
  title =        "A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference",
  journal =      "Pattern Recognition",
  year =         "2005",
  volume =       "38",
  number =       "9",
  month =        "September",
  pages =        "1444--1456",
}

@Article{dittenbach02a_bibuniq_422,
  author =       "M. Dittenbach and A. Rauber and D. Merkl",
  title =        "Uncovering hierarchical structure in data using the growing hierarchical self-organizing map",
  journal =      "Neurocomputing",
  year =         "2002",
  volume =       "48",
  month =        "October",
  pages =        "199--216",
}

@Article{drobics02a_bibuniq_433,
  author =       "M. Drobics and U. Bodenhofer and W. Winiwarter",
  title =        "Mining clusters and corresponding interpretable descriptions - a three-stage approach",
  journal =      "Expert Systems",
  year =         "2002",
  volume =       "19",
  number =       "4",
  month =        "September",
  pages =        "224--234",
}

@Article{egmont-petersen02a_bibuniq_441,
  author =       "M. Egmont-Petersen and D. de Ridder and H. Handels",
  title =        "Image processing with neural networks - a review",
  journal =      "Pattern Recognition",
  year =         "2002",
  volume =       "35",
  number =       "10",
  month =        "October",
  pages =        "2279--2301",
}

@Article{fernandez05a_bibuniq_71,
  author =       "M. Fernandez and A. Tundidor-Camba and J. M. Caballero",
  title =        "2{D} Autocorrelation modeling of the activity of trihalobenzocycloheptapyridine analogues as farnesyl protein transferase inhibitors",
  journal =      "Molecular Simulation",
  year =         "2005",
  volume =       "31",
  number =       "8",
  month =        "July",
  pages =        "575--584",
}

@InProceedings{Franzmeier00b_bibuniq_3931,
  author =       "M. Franzmeier and C. Pohl and M. Porrmann and U. Ruckert",
  title =        "Hardware accelerated data analysis",
  booktitle =    "International Conference on Parallel-Computing in Electrical-Engineering. 2004: 309-14",
  year =         "2004",
}

@Article{gaetz04a_bibuniq_277,
  author =       "M. Gaetz and G. L. Iverson and E. J. Rzempoluck and R. Remick and P. McLeane and W. Linden",
  title =        "Self-organizing neural network analyses of cardiac data in depression",
  journal =      "Neuropsychobiology",
  year =         "2004",
  volume =       "145",
  number =       "1-2",
  month =        "January-February",
  pages =        "19--28",
}

@Article{gevrey04a_bibuniq_4736,
  author =       "M. Gevrey and F. Rimet and Y. S. Park and J. L. Giraudel and L. Ector and S. Lek",
  title =        "Water quality assessment using diatom assemblages and advanced modelling techniques",
  journal =      "Freshwater Biology",
  year =         "2004",
  volume =       "49",
  number =       "2",
  month =        "February",
  abstract =     "",
}

@Article{hagenbuchner03a_bibuniq_353,
  author =       "M. Hagenbuchner and A. Sperduti and A. C. Tsoi",
  title =        "A self-organizing map for adaptive processing of structured data",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2003",
  volume =       "14",
  number =       "3",
  month =        "May",
  pages =        "491--505",
}

@Article{haritopoulos02a_bibuniq_419,
  author =       "M. Haritopoulos and H. J. Yin and N. M. Allinson",
  title =        "Image denoising using self-organizing map-based nonlinear independent component analysis",
  journal =      "Neural Networks",
  year =         "2002",
  volume =       "15",
  number =       "8-9",
  month =        "October-November",
  pages =        "1085--1098",
}

@InProceedings{hauta-kasari03a_bibuniq_320,
  author =       "M. Hauta-Kasari and K. Miyazawa and J. Parkkinen and T. Jaaskelainen",
  title =        "Searching technique in a spectral image database",
  booktitle =    "Image Analysis, Proceedings, Lecture Notes in Computer Science",
  year =         "2003",
  pages =        "867--877",
}

@Article{hoffmann05a_bibuniq_128,
  author =       "M. Hoffmann",
  title =        "Numerical control of {K}ohonen neural network for scattered data approximation",
  journal =      "Numerical Algorithms",
  year =         "2005",
  volume =       "39",
  number =       "1-3",
  month =        "July",
  pages =        "175--186",
}

@Article{huang03b_bibuniq_286,
  author =       "M. Huang and X. Liang and Y. Liang",
  title =        "A transferability study of model parameters for the variable infiltration capacity land surface scheme",
  journal =      "Journal of Geophysical Research-Atmospheres",
  year =         "2003",
  volume =       "108",
  number =       "D22",
  month =        "November 26",
}

@InProceedings{hussain04a_bibuniq_209,
  author =       "M. Hussain and J. P. Eakins",
  title =        "Visual clustering of trademarks using a component-based matching framework",
  booktitle =    "Image and Video Retrieval, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
}

@Article{Inal02a_bibuniq_1702,
  author =       "M. Inal and Y. S. Fatihoglu",
  title =        "Self organizing map and associative memory model hybrid classifier for speaker recognition",
  journal =      "6th-Seminar on Neural-Network-Applications in Electrical-Engineering. Neurel-200271-4",
  year =         "2002",
  volume =       "",
  pages =        "",
  abstract =     "In this study, self organizing map ({SOM}) and associative memory model (AMM) artificial neural networks (ANN) are used as hybrid classifier for several speaker recognition experiments. These include text dependent closed-set speaker identification and speaker verification of Turkish speaker set and text independent closed-set speaker identification of a subset of the TIMIT database. Turkish speaker set constitutes 10 speakers with their name and surname. Each utterance is repeated 8 times, 5 of them are used in training and. remaining in the test stages. the subset of the TIMIT database consists 38 speakers from New England region. Each speaker's 10 different utterances are equally selected for using in training and test session. Mel frequency cepstral coefficients (MFCC) method is used for feature extraction of the training and test vectors. When the study is compared with different studies for the same databases, this study gives good results as much as the others.",
}

@Article{allen03a_bibuniq_289,
  author =       "M. J. Allen and F. J. Marin and F. Garcia-Lagos and N. E. Gough and Q. Mehdi",
  title =        "Fuzzy processing for active vision",
  journal =      "Integrated Computer-Aided Engineering",
  year =         "2003",
  volume =       "17",
  number =       "6",
  month =        "December",
  pages =        "1748--1758",
}

@Article{carreira02a_bibuniq_4995,
  author =       "M. J. Carreira and M. Mirmehdi and B. T. Thomas and M. Penas",
  title =        "Perceptual primitives from an extended 4{D} Hough transform",
  journal =      "Image and Vision Computing",
  year =         "2002",
  volume =       "20",
  number =       "13-14",
  month =        "December 1",
  abstract =     "",
}

@InProceedings{Thompson74a_bibuniq_4103,
  author =       "M. J. Thompson and J. C. Sciortino and Jr.",
  title =        "Analysis of complex radar data sets using fuzzy adaptive resonance theory map",
  booktitle =    "Proceedings of the-Spie-The International-Society for Optical-Engineering. 2004",
  year =         "2004",
}

@InProceedings{bashar04a_bibuniq_4526,
  author =       "M. K. Bashar and N. Ohnishi and K. Agusa",
  title =        "Image retrieval by categorization using {LVQ} network with wavelet domain perceptual features",
  booktitle =    "Advances in Multimedia Information Processing - PCM 2004, Pt. 2, Proceedings, Lecture Notes in Computer Science",
  year =         "2004",
  pages =        "188--196",
  abstract =     "",
}

@Article{markey03a_bibuniq_375,
  author =       "M. K. Markey and J. Y. Lo and G. D. Tourassi and C. E. Floyd",
  title =        "Self-organizing map for cluster analysis of a breast cancer database",
  journal =      "Artificial Intelligence in Medicine",
  year =         "2003",
  volume =       "27",
  number =       "2",
  month =        "February",
  pages =        "113--127",
}

@Article{kaipainen03a_bibuniq_285,
  author =       "M. Kaipainen and T. Ilmonen",
  title =        "Period detection and representation by recurrent oscillatory self-organizing map",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "55",
  number =       "3-4",
  month =        "October",
  pages =        "699--710",
}

@Article{karilahti03a_bibuniq_381,
  author =       "M. Karilahti",
  title =        "Neural net analysis of integrated circuit yield dependence on Cmos process control parameters",
  journal =      "Microelectronics Reliability",
  year =         "2003",
  volume =       "43",
  number =       "1",
  month =        "January",
  pages =        "117--121",
}

@Article{kolehmainen03a_bibuniq_357,
  author =       "M. Kolehmainen and P. Ronkko and A. Raatikainen",
  title =        "Monitoring of yeast fermentation by ion mobility spectrometry measurement and data visualisation with Self-Organizing Maps",
  journal =      "Analytica Chimica Acta",
  year =         "2003",
  volume =       "484",
  number =       "1",
  month =        "May 7",
  pages =        "93--100",
}

@InProceedings{Koskela00b_bibuniq_3944,
  author =       "M. Koskela and J. Laaksonen and E. Oja",
  title =        "Use of image subset features in image retrieval with self-organizing maps",
  booktitle =    "Image and Video-Retrieval. Third International Conference, Civr-2004. Proceedings Lecture Notes in Comput. Sci. Vol. 3115. 2004: 508-16",
  year =         "2004",
}

@InProceedings{koskela02a_bibuniq_4957,
  author =       "M. Koskela and J. Laaksonen and E. Oja",
  title =        "Implementing relevance feedback as convolutions of local neighborhoods on Self-Organizing Maps",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "981--986",
  abstract =     "",
}

@Article{kubo03a_bibuniq_327,
  author =       "M. Kubo and Z. Aghbari and K. S. Oh and A. Makinouchi",
  title =        "Image retrieval by edge features using higher order autocorrelation in a {SOM} environment",
  journal =      "Ieice Transactions on Information and Systems",
  year =         "2003",
  volume =       "E86D",
  number =       "8",
  month =        "August",
  pages =        "1406--1415",
}

@Article{kurimo02a_bibuniq_434,
  author =       "M. Kurimo",
  title =        "Thematic indexing of spoken documents by using self-organizing maps",
  journal =      "Speech Communication",
  year =         "2002",
  volume =       "38",
  number =       "1-2",
  month =        "September",
  pages =        "29--45",
}

@InProceedings{bote-lorenzo02a_bibuniq_4943,
  author =       "M. L. Bote-Lorenzo and Y. A. Dimitriadis and E. Gomez-Sanchez",
  title =        "A hybrid two-stage fuzzy {ARTMAP} and {LVQ} neuro-fuzzy system for online handwriting recognition",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "438--443",
  abstract =     "",
}

@Article{siqueira02a_bibuniq_459,
  author =       "M. L. Siqueira and J. Scharcanski and P. O. A. Navaux",
  title =        "Echocardiographic image sequence segmentation and analysis using self-organizing maps",
  journal =      "Journal of {VLSI} Signal Processing Systems for Signal Image and Video Technology",
  year =         "2002",
  volume =       "32",
  number =       "1-2",
  month =        "August-September",
  pages =        "135--145",
}

@Article{lijeholm02a_bibuniq_455,
  author =       "M. Lijeholm and A. Lin and P. Ozdzynski and J. Beatty",
  title =        "Quantitative analysis of kernel properties in {K}ohonen's self-organizing map algorithm: Gaussian and difference of Gaussians neighborhoods",
  journal =      "Neurocomputing",
  year =         "2002",
  volume =       "44",
  month =        "June",
  pages =        "515--520",
}

@InProceedings{Awais00a_bibuniq_4027,
  author =       "M. M. Awais and S. Masud and S. Shamail and J. Akhtar",
  title =        "A hybrid multi-layered speaker independent Arabic phoneme identification system",
  booktitle =    "Intelligent-Data-Engineering and Automated-Learning-Ideal-2004. 5th International Conference. Proceedings Lecture Notes in Comput. Sci. Vol. 3177. 2004: 416-23",
  year =         "2004",
}

@InProceedings{El02a_bibuniq_1719,
  author =       "M. M. El Said and A. Kumar and A. S. Elmaghraby",
  title =        "An integrated neuro configuration management approach for hybrid mobile communication networks",
  booktitle =    "Proceedings-Iscc-2002-Seventh International-Symposium on Computers and Communications. 2002: 315-20",
  year =         "2002",
  volume =       "",
  pages =        "",
  abstract =     "Hybrid mobile communication is a mixed mode network version of wired and wireless components. Hybrid networks occur in many situations such as a military warfare theatre and extended range commercial cellular networks. This paper proposes an integrated configuration management approach for hybrid communication networks. the issues addressed and resolved by the proposed approach are: (i) an automated configuration management system using a clustering technique, self organizing map ({SOM}) implemented by neural networks; (ii) a novel network self-healing survivability approach to handle the network failure scenario; (iii) network performance integration in the design and reconfiguration of hybrid networks.",
}

@Article{Van02a_bibuniq_1736,
  author =       "M. M. Van Hulle",
  title =        "Joint entropy maximization in kernel-based topographic maps",
  journal =      "Neural-Computation. Aug. 2002; 14(8): 1887-906",
  year =         "2002",
  volume =       "",
  pages =        "",
  abstract =     "A new learning algorithm for kernel-based topographic map formation is introduced. the kernel parameters are adjusted individually so as to maximize the joint entropy of the kernel outputs. This is done by maximizing the differential entropies of the individual kernel outputs, given that the map's output redundancy, due to the kernel overlap, needs to be minimized. the latter is achieved by minimizing the mutual information between the kernel outputs. As a kernel, the (radial) incomplete gamma distribution is taken since, for a gaussian input density, the differential entropy of the kernel output will be maximal. Since the theoretically optimal joint entropy performance can be derived for the case of nonoverlapping gaussian mixture densities, a new clustering algorithm is suggested that uses this optimum as its {"}null{"} distribution. Finally, it is shown that the learning algorithm is similar to one that performs stochastic gradient descent on the Kullback-Leibler divergence for a heteroskedastic gaussian mixture density model.",
}

@Article{Van02b_bibuniq_1755,
  author =       "M. M. Van Hulle",
  title =        "Blind source separation and equiprobabilistic topographic maps",
  journal =      "Journal of {VLSI} Signal Processing-Systems for Signal, Image, and Video-Technology. May 2002; 31(1): 19-30",
  year =         "2002",
  volume =       "",
  pages =        "",
  abstract =     "Recently, a number of heuristic techniques have been devised in order to overcome some of the limitations of the blind source separation (BSS) algorithms that are rooted in the theory of independent component analysis ({ICA}). They are usually based on topographic maps and designed to separate mixtures of signals with either sub-Gaussian or super-Gaussian source densities. in the sub-Gaussian case, the coordinates of the winning neurons in the topographic map represent the estimates of the source signal amplitudes. in the super-Gaussian case, one relies on the topographic map's ability to detect the source directions in mixture space which, in turn, correspond to the column vectors of the mixing matrix in the linear case. We introduce a new topographic map-based heuristic for super-Gaussian BSS. It relies on the tendency of the mixture samples to cluster around the source directions. We demonstrate its performance on linear and mildly non-linear mixtures of speech signals, including the case where there are fewer mixtures than sources to be separated ({"}non-square{"} BSS).",
}

@Misc{webform_7331_bibuniq_4241,
  author =       "M. M. Van Hulle",
  title =        "Topographic map formation of factorized Edgeworth-expanded kernels",
  howpublished = "Neural networks",
  pages =        "",
  note =         "",
  year =         "in press",
}

@Misc{webform_7428_bibuniq_4242,
  author =       "M. M. Van Hulle",
  title =        "Edgeworth-expanded topographic map formation",
  howpublished = "{WSOM}05",
  pages =        "719--724",
  note =         "",
  year =         "2005",
}

@Article{Maeda00a_bibuniq_4073,
  author =       "M. Maeda and H. Miyajima",
  title =        "Adaptation neighborhoods of self-organizing maps for image restoration",
  journal =      "Wseas Transactions on Computers. April 2004; 3(2): 323-8",
  year =         "2004",
}

@Article{Markou03a_bibuniq_1587,
  author =       "M. Markou and S. Singh",
  title =        "Novelty detection: a review-part 2: neural network based approaches",
  journal =      "Signal Processing. Dec. 2003; 83(12): 2499-521",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. in this paper we focus on neural network-based approaches for novelty detection. Statistical approaches are covered in part 1 of the paper.",
}

@InProceedings{Martin04a_bibuniq_1421,
  author =       "M. Martin Merino and A. Munoz",
  title =        "Extending the {SOM} algorithm to non-Euclidean distances via the kernel trick",
  booktitle =    "Neural-Information-Processing. 11th International Conference, {ICONIP}-2004. Proceedings Lecture Notes in Computer Science Vol. 3316. 2004: 150-7",
  year =         "2004",
  volume =       "",
  pages =        "",
  abstract =     "The self organizing map is a nonlinear projection technique that allows to visualize the underlying structure of high dimensional data. However, the original algorithm relies on the use of Euclidean distances which often becomes a serious drawback for a number of real problems. in this paper, we present a new kernel version of the {SOM} algorithm that incorporates non-Euclidean dissimilarities keeping the simplicity of the classical version. To achieve this goal, the data are nonlinearly transformed to a feature space taking advantage of Mercer kernels, while the overall data structure is preserved. the new {SOM} algorithm has been applied to the challenging problem of word relation visualization. We report that the kernel {SOM} improves the map generated by other alternatives for certain classes of kernels.",
}

@Article{masugi04a_bibuniq_232,
  author =       "M. Masugi",
  title =        "Self-organizing map-based analysis of {IP}-network traffic in terms of time variation of self-similarity: {A} detrended fluctuation analysis approach",
  journal =      "Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences",
  year =         "2004",
  volume =       "E87A",
  number =       "6",
  month =        "June",
  pages =        "1546--1554",
}

@Article{masugi04b_bibuniq_239,
  author =       "M. Masugi",
  title =        "Detrended fluctuation analysis of {IP}-network traffic using a two-dimensional topology map",
  journal =      "Physica A-Statistical Mechanics and ITS Applications",
  year =         "2004",
  volume =       "337",
  number =       "3-4",
  month =        "June 15",
  pages =        "664--678",
}

@Article{masugi03a_bibuniq_316,
  author =       "M. Masugi",
  title =        "Energy spectrum-based analysis of musical sounds using self-organizing map",
  journal =      "Ieice Transactions on Information and Systems",
  year =         "2003",
  volume =       "E86D",
  number =       "9",
  month =        "September",
  pages =        "1934--1938",
}

@Article{masugi03b_bibuniq_348,
  author =       "M. Masugi",
  title =        "Self-organizing neural network-based analysis of electrostatic discharge for electromagnetic interference assessment",
  journal =      "Ieice Transactions on Communications",
  year =         "2003",
  volume =       "E86B",
  number =       "6",
  month =        "June",
  pages =        "1991--2000",
}

@Article{masugi02a_bibuniq_429,
  author =       "M. Masugi",
  title =        "{QoS} evaluation of {VoIP} communication employing self-organizing neural network",
  journal =      "{IEICE} Transactions on Communications",
  year =         "2002",
  volume =       "E85B",
  number =       "9",
  month =        "September",
  pages =        "1867--1871",
}

@InProceedings{Nadif00a_bibuniq_3904,
  author =       "M. Nadif and G. Govaert",
  title =        "Block clustering via the block {GEM} and two-way {EM} algorithms",
  booktitle =    "Book of Abstracts. ACS/{IEEE} International Conference on Computer-Systems and Applications. 2004: 32",
  year =         "2004",
}

@Misc{webform_100_bibuniq_4189,
  author =       "M. Nakano and F. Yasukata and and M. Fukumi",
  title =        "Recognition of Smiling Faces Using Neural Networks and {SPCA}",
  howpublished = "International Journal of Computational Intelligence and Applications, Vol. 4, No. 2",
  pages =        "153--164",
  note =         "{SOM} is used for recognizing smiling faces.",
  year =         "2004",
}

@Article{neuhaus05a_bibuniq_105,
  author =       "M. Neuhaus and H. Bunke",
  title =        "Self-organizing maps for learning the edit costs in graph matching",
  journal =      "{IEEE} Transactions on Systems Man and Cybernetics Part B-Cybernetics",
  year =         "2005",
  volume =       "35",
  number =       "3",
  month =        "June",
  pages =        "503--514",
}

@Misc{webform_5475_bibuniq_4227,
  author =       "M. Ohtani and T. Furukawa",
  title =        "{SOM}2: {SOM}-module mn{SOM}",
  howpublished = "Proceedings of 5th Postech-Kyutech Joint Workshop 2005",
  pages =        "",
  note =         "",
  year =         "2005",
}

@Article{oja05a_bibuniq_36,
  author =       "M. Oja and G. O. Sperber and J. Blomberg and Samuel Kaski",
  title =        "Self-organizing map-based discovery and visualization of human endogenous retroviral sequence groups",
  journal =      "International Journal of Neural Systems",
  year =         "2005",
  volume =       "15",
  number =       "3",
  month =        "June",
  pages =        "163--179",
}

@InCollection{Oja02bioinf_bibuniq_1782,
  author =       "M. Oja and P. T{\"o}r{\"o}nen and J. Nikkil{\"a} and E. Castr{\'e}n and S. Kaski",
  title =        "Learning metrics for {SOM}-based clustering and visualization of yeast gene expression data",
  booktitle =    "Proceedings of Bioinformatics 2002, Bergen, Norway, April 4-7",
  pages =        "78",
  year =         "2002",
}

@Article{Perez00a_bibuniq_3966,
  author =       "M. Perez",
  title =        "{SME}'s performance and neural classification",
  journal =      "European-Journal of Economic and Social-Systems. 2004; 17(1-2): 197-210",
  year =         "2004",
}

@Article{Reformat03a_bibuniq_1631,
  author =       "M. Reformat and W. Pedrycz and N. J. Pizzi",
  title =        "Software quality analysis with the use of computational intelligence",
  journal =      "Information and Software-Technology. 1 May 2003; 45(7): 405-17",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "Quality of individual objects composing a software system is one of the important factors that determine quality of this system. Quality of objects, on the other hand, can be related to a number of attributes, such as extensibility, reusability, clarity and efficiency. These attributes do not have representations suitable for automatic processing. There is a need to find a way to support quality related activities using data gathered during quality assurance processes, which involve humans. We propose an approach, which can be used to support quality assessment of individual objects. the approach exploits techniques of computational intelligence that are treated as a consortium of granular computing, neural networks and evolutionary techniques. in particular, self organizing maps and evolutionary based developed decision trees are used to gain a better insight into the software data and to support a process of classification of software objects. Genetic classifiers-a novel algorithmic framework-serve as 'filters' for software objects. These classifiers are built on data representing subjective evaluation of software objects done by humans. Using these classifiers, a system manager can predict quality of software objects and identify low quality objects for review and possible revision. the approach is applied to analyze an object oriented visualization based software system for biomedical data analysis.",
}

@Article{reich04a_bibuniq_212,
  author =       "M. Reich and K. Ohm and M. Angelo and P. Tamayo and J. P. Mesirov",
  title =        "GeneCluster 2. 0: an advanced toolset for bioarray analysis",
  journal =      "Bioinformatics",
  year =         "2004",
  volume =       "20",
  number =       "11",
  month =        "July 22",
  pages =        "1797--1798",
}

@InProceedings{Reuter00a_bibuniq_3912,
  author =       "M. Reuter and B. Rosendo",
  title =        "Computing with activities. {II}. Ruling robots by the activity patterns of hierarchical {SOM}s",
  booktitle =    "Proceedings of the World Automation Congress {IEEE} vol. 17",
  year =         "2004",
}

@Article{strickert05a_bibuniq_131,
  author =       "M. Strickert and B. Hammer",
  title =        "Merge {SOM} for temporal data",
  journal =      "Neurocomputing",
  year =         "2005",
  volume =       "64",
  month =        "March",
  pages =        "39--71",
}

@Article{strickert05b_bibuniq_150,
  author =       "M. Strickert and B. Hammer and S. Blohm",
  title =        "Unsupervised recursive sequence processing",
  journal =      "Neurocomputing",
  year =         "2005",
  volume =       "63",
  month =        "January",
  pages =        "69--97",
}

@Article{vahey02a_bibuniq_489,
  author =       "M. T. Vahey and M. E. Nau and L. L. Jagodzinski and J. Yalley-Ogunro and M. Taubman and N. L. Michael and M. G. Lewis",
  title =        "Impact of viral infection on the gene expression profiles of proliferating normal human peripheral blood mononuclear cells infected with {HIV} type 1 {RF}",
  journal =      "{AIDS} Research and Human Retroviruses",
  year =         "2002",
  volume =       "18",
  number =       "3",
  month =        "February",
  pages =        "179--192",
}

@Article{vahey03a_bibuniq_350,
  author =       "M. T. Vahey and M. E. Nau and M. Taubman and J. Yalley-Ogunro and P. Silvera and M. G. Lewis",
  title =        "Patterns of gene expression in peripheral blood mononuclear cells of rhesus macaques infected with {SIV}mac251 and exhibiting differential rates of disease progression",
  journal =      "{AIDS} Research and Human Retroviruses",
  year =         "2003",
  volume =       "19",
  number =       "5",
  month =        "May",
  pages =        "369--387",
}

@InProceedings{teranishi02a_bibuniq_4961,
  author =       "M. Teranishi and S. Omatu and T. Kosaka",
  title =        "Neuro-classification of bill fatigue levels based on acoustic wavelet components",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "1074--1079",
  abstract =     "",
}

@InProceedings{Topallar04a_bibuniq_1485,
  author =       "M. Topallar and M. O. Depren and E. Anarim and K. Ciliz",
  title =        "Host-based intrusion detection by monitoring Windows registry accesses",
  booktitle =    "Proceedings of the {IEEE}-12th Signal Processing and Communications-Applications Conference {IEEE} 728-31",
  year =         "2004",
  volume =       "",
  pages =        "",
  abstract =     "We propose a host-based intrusion detection system for Microsoft Windows. the proposed system detects attacks on a host machine by monitoring anomalous accesses to the Windows registry. First, a model of normal registry behavior is trained for a host and then this model is used to detect abnormal registry accesses. the system trains a normal model using data that contains no attacks and then checks each access to the registry to determine whether or not the behavior is abnormal and corresponds to an attack. A new approach to register anomaly detection (RAD) is proposed in the meaning of model generator and anomaly detector. A self organizing map ({SOM}), a type of artificial neural network model, is used as an anomaly detection algorithm. the system is trained on a set of normal registry accesses using {SOM} algorithm and then it is used to detect the behavior of malicious software. the results of this study show that the proposed system is effective in detecting the behavior of malicious software and has a low rate of false alarms compared to other host-based intrusion detection systems.",
}

@InProceedings{c10_bibuniq_1799,
  author =       "M. Verleysen {G. Simon, A. Lendasse, M. Cottrell}",
  title =        "Long-Term Time Series Forecasting Using Self-Organizing Maps: the Double Vector Quantization Method",
  booktitle =    "Proceedings of the conferencee {ANNPR}",
  year =         "2003",
  address =      "Florence",
  month =        "September",
}

@Article{vracko04a_bibuniq_267,
  author =       "M. Vracko and S. C. Basak",
  title =        "Similarity study of proteomic maps",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  year =         "2004",
  volume =       "70",
  number =       "1-2",
  month =        "January 28",
  pages =        "33--38",
}

@Article{Wu00a_bibuniq_3941,
  author =       "M. Wu and P. Rastgoufard",
  title =        "Optimum decision by artificial neural networks for reactive power control equipment to enhance power system stability and security performance",
  journal =      "{IEEE} Power Engineering Society-General-Meeting Vol. 2",
  year =         "2004",
}

@Article{cheon04b_bibuniq_229,
  author =       "M. Y. Cheon and I. S. Chang",
  title =        "Clustering of the protein design alphabets by using hierarchical self-organizing map",
  journal =      "Journal of the Korean Physical Society",
  year =         "2004",
  volume =       "44",
  number =       "6",
  month =        "June",
  pages =        "1577--1580",
}

@Article{hirai04a_bibuniq_219,
  author =       "M. Y. Hirai and M. Yano and D. B. Goodenowe and S. Kanaya and T. Kimura and M. Awazuhara and M. Arita and T. Fujiwara and K. Saito",
  title =        "Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana",
  journal =      "Proceedings of the National Academy of Sciences of the United States of America",
  year =         "2004",
  volume =       "101",
  number =       "27",
  month =        "July 6",
  pages =        "10205--10210",
}

@Article{kim04b_bibuniq_237,
  author =       "M. Y. Kim and H. Cho",
  title =        "Three-dimensional map building for mobile robot navigation environments using a self-organizing neural network",
  journal =      "Journal of Robotic Systems",
  year =         "2004",
  volume =       "21",
  number =       "6",
  month =        "June",
  pages =        "323--343",
}

@Article{email1_bibuniq_1764,
  author =       "MIYOSHI Tsutomu",
  title =        "Initial Node Exchange Using Learning Data and Convergence of {SOM} Learning",
  journal =      "{GESTS} International Transactions on Computer Science and Engineering",
  year =         "2005",
  volume =       "21",
  number =       "1",
  pages =        "216--224",
  month =        "November",
}

@Article{email2_bibuniq_1765,
  author =       "MIYOSHI Tsutomu",
  title =        "Learning Data Order and Convergence of {SOM} Learning",
  journal =      "{GESTS} International Transactions on Computer Science and Engineering",
  year =         "2005",
  volume =       "21",
  number =       "1",
  pages =        "118--197",
  month =        "November",
}

@InProceedings{email6_bibuniq_1769,
  author =       "MIYOSHI Tsutomu",
  title =        "Initial Node Exchange and Convergence of {SOM} Learning",
  booktitle =    "Proceedings of the 6th International Symposium on Advanced Intelligent Systems ({ISIS2005})",
  pages =        "316--319",
  year =         "2005",
  address =      "Yeosu, Korea",
  month =        "September",
}

@InProceedings{email7_bibuniq_1770,
  author =       "MIYOSHI Tsutomu",
  title =        "Order of Learning Data and Convergence of {SOM} Learning",
  booktitle =    "Proceedings of the 6th International Symposium on Advanced Intelligent Systems ({ISIS2005})",
  pages =        "756--759",
  year =         "2005",
  address =      "Yeosu, Korea",
  month =        "September",
}

@Article{inspek141_bibuniq_1244,
  author =       "Machon I. and Lopez H. and Robles A.",
  title =        "Treatment stage estimation in a sequencing batch reactor",
  journal =      "{WSEAS} Transactions on Computers. Aug. 2005; 4(8): 951--9",
  pages =        "",
  year =         "2005",
  publisher =    "{WSEAS}",
  abstract =     "Clustering algorithms are usually used for data classification. the estimation of the end-point of the treatment is a key factor in wastewater biological plants. Self-organizing map as well as clustering algorithms and validation indexes are useful tools for process supervision. All of them were used to carry out this estimation improving the efficiency of the treatment plant.",
}

@Article{inspek338_bibuniq_988,
  author =       "Mahdi A. E. and Picovici D.",
  title =        "Output-based objective measure for non-intrusive speech quality evaluation",
  journal =      "{WSEAS} Transactions on Acoustics and Music. July 2004; 1(3): 139--44",
  pages =        "",
  year =         "2004",
  publisher =    "{WSEAS}",
  abstract =     "This paper describes a newly developed output-based method for non-intrusive evaluation of speech quality of voice communication systems, and evaluates its performance. the method, which uses only the output of the system, is based on measuring perceptually motivated objective auditory distances between the voiced parts of the speech signal whose quality to be evaluated to appropriately matching reference vectors extracted from a pre-formulated codebook. the codebook is formed by optimally clustering large number of perceptually-based parametric vectors extracted from a database of clean speech signals. the auditory distance measures are then mapped into equivalent subjective score, represented by the Mean Opinion scores (MOS), using regression. the required clustering and matching processes are achieved by using an efficient neural network based data mining technique known as the Self-Organizing Map. Perceptual, speaker-independent parametric representation of the speech is achieved by using Linear Prediction (PLP) and Bark Spectrum analysis. Reported evaluation results show that the proposed system is robust against speaker, utterance and distortion variations, and outperforms the ITU-T P. 862 Perceptual Evaluation of Speech Quality (PESQ) for cases of speech degraded by channel impairments.",
}

@InProceedings{inspek599_bibuniq_805,
  author =       "Mahony S. and Smith T. J. and Mclnerney J. O. and Golden A.",
  title =        "A new approach to gene prediction using the self-organizing map",
  booktitle =    "Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 {IEEE} Bioinformatics Conference",
  pages =        "444-445",
  year =         "2003",
  publisher =    "IEEE Comput. Soc, Piscataway, NJ, USA",
  abstract =     "In this poster we present a gene prediction approach based on the self-organizing map that has the ability to automatically identify all the major patterns of content variation within a genome. the genome may then be scanned for regions displaying the same properties as one of these automatically identified models. Even using a relatively simple coding measure (codon usage), this method can predict the location of protein-coding sequences with a reasonably high accuracy. We also show other advantages of the approach, such as the ability to indicate genes that contain frame-shifts. We believe that this method has the potential to become a useful addition to the genome annotation process.",
}

@InProceedings{kinouchi03_bibuniq_4283,
  author =       "Makoto KINOUCHI and KUDO Yoshihiro",
  title =        "Much faster learning algorithm for Batch-Learning {SOM} and its application to Bioinformatics",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{kinouchi03b_bibuniq_4284,
  author =       "Makoto KINOUCHI and SHIGA Yuta and KUDO Yoshihiro",
  title =        "Introduction of virtual peptides as absolute indices in {SOM} for usage in bioinformatics",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek730_bibuniq_555,
  author =       "Man-Leung-Wong {Wing-Ho-Shum, Hui-Dong-Jin, Kwong-Sak-Leung}",
  editor =       "X. {Kumar, V. ; Tsumoto, S. ; Zhong, N. ; Yu, P. S. ; Wu}",
  title =        "A self-organizing map with expanding force for data clustering and visualization",
  booktitle =    "Proceedings 2002 {IEEE} International Conference on Data Mining",
  pages =        "434--441",
  year =         "2002",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "The self-organizing map ({SOM}) is a powerful tool in the exploratory phase of data mining. However, due to the dimensional conflict, neighborhood preservation cannot always lead to perfect topology preservation. in this paper we establish an expanding {SOM} (ESOM) to detect and preserve better topology correspondence between the two spaces. Our experiment results demonstrate that the ESOM constructs better mappings than the classic {SOM} in terms of both topological and quantization errors. Furthermore, clustering results generated by the ESOM are more accurate than those of the SOM.",
}

@InProceedings{vanhulle03_bibuniq_4263,
  author =       "Marc M. Van Hulle",
  title =        "Kernel-Based Topographic Maps: Theory, Algorithm and Applications",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{strickert03_bibuniq_4274,
  author =       "Marc Strickert and Barbara Hammer",
  title =        "Neural Gas for Sequences",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{StrSff_ESANN05_bibuniq_20,
  author =       "Marc Strickert and Neese Sreenivasulu and Winfriede Weschke and Udo Seiffert and Thomas Villmann",
  title =        "{Generalized Relevance LVQ} with Correlation Measures for Biological Data",
  booktitle =    "Proceedings of the 13. European Symposium on Artificial Neural Networks ESANN 2005",
  year =         "2005",
  editor =       "Michel Verleysen",
  pages =        "331--338",
  location =     "Bruges, Belgium",
  publisher =    "D-Side Publications",
  address =      "Evere, Belgium",
  ISBN =         "2-930307-05-6",
}

@InProceedings{StrSff_WSOM05_bibuniq_19,
  author =       "Marc Strickert and Sascha Teichmann and Neese Sreenivasulu and Udo Seiffert",
  title =        "{'DiPPP'} Online Self-Improving Linear Map for Distance-Preserving Data Analysis",
  booktitle =    "Proceedings of the 5th International Workshop on Self-Organizing Maps ({WSOM})",
  year =         "2005",
  editor =       "Marie Cottrell",
  pages =        "661--668",
  location =     "Paris, France",
  file =         F,
}

@Article{inspek412_bibuniq_1357,
  author =       "Marchesotti L. and Piva S. and Regazzoni C.",
  title =        "Structured context-analysis techniques in biologically inspired ambient-intelligence systems",
  journal =      "IEEE Transactions on Systems, Man \& Cybernetics, Party A-Systems \& Humans. Jan. ; 35(1): 106--20",
  pages =        "",
  year =         "2005",
  publisher =    "IEEE",
  abstract =     "In this paper, techniques and related issues for the definition of a contextual knowledge in ambient-intelligence systems are explored. A logical structure for this kind of system, inspired by a neurobiological brain model, is proposed. Through these considerations, the role and the importance of context awareness in the definition of an artificial organism showing adaptability, pervasiveness, and scalability features are described. Techniques for the definition of a multilayer context representation are explained and practically demonstrated with a test-bed. in the proposed system, a complex event classification is obtained through the fusion of heterogeneous data coming from a set of sensors thanks to the design of a self-organizing map ({SOM}). the {SOM} represents the core of the system and testing proofs show good results in the classification of the events taking place in the monitored environment.",
}

@InProceedings{cottrell03_bibuniq_4321,
  author =       "Marie Cottrell and Patrice Gaubert",
  title =        "Efficient estimators: the use of neural networks to construct pseudo panels",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek330_bibuniq_980,
  author =       "Mark J. W. {Jun-Xu, Xuemin-Shen} and Jun-Cai",
  title =        "Self-organizing map for mobile location estimation in {DS}-{CDMA} systems",
  booktitle =    "{GLOBECOM}'04. {IEEE} Global Telecommunications Conference",
  pages =        "3887--3891",
  volume =       "6",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A self-organizing map ({SOM}) scheme for mobile location estimation in a direct-sequence code division multiple access (DS-CDMA) system is proposed. the scheme performs nonlinear mapping between the measured pilot signal strengths from nearby base stations and the user's location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. in addition, the scheme exhibits superior performance in the non-line of sight (NLOS) situation. Numerical results under various terrestrial environments are presented to demonstrate the feasibility of the proposed {SOM} scheme.",
}

@Article{inspek790_bibuniq_608,
  author =       "Marsland S. and Shapiro J. and Nehmzow U.",
  title =        "A self-organising network that grows when required",
  journal =      "Neural Networks. Oct. Nov. 2002; 15(8--9): 1041--58",
  pages =        "",
  year =         "2002",
  publisher =    "Elsevier",
  abstract =     "The ability to grow extra nodes is a potentially useful facility for a self-organising neural network. A network that can add nodes into its map space can approximate the input space more accurately, and often more parsimoniously, than a network with predefined structure and size, such as the self-organising map. in addition, a growing network can deal with dynamic input distributions. Most of the growing networks that have been proposed in the literature add new nodes to support the node that has accumulated the highest error during previous iterations or to support topological structures. This usually means that new nodes are added only when the number of iterations is an integer multiple of some pre-defined constant, lambda. This paper suggests a way in which the learning algorithm can add nodes whenever the network in its current state does not sufficiently match the input. in this way the network grows very quickly when new data is presented, but stops growing once the network has matched the data. This is particularly important when we consider dynamic data sets, where the distribution of inputs can change to a new regime after some time. We also demonstrate the preservation of neighbourhood relations in the data by the network. the new network is compared to an existing growing network, the Growing Neural Gas (GNG), on a artificial dataset, showing how the network deals with a change in input distribution after some time. Finally, the new network is applied to several novelty detection tasks and is compared with both the GNG and an unsupervised form of the Reduced Coulomb Energy network on a robotic inspection task and with a Support Vector Machine on two benchmark novelty detection tasks.",
}

@InProceedings{inspek422_bibuniq_1066,
  author =       "Martin-Herrero J. and Ferreiro-Arman M. and Alba-Castro J. L.",
  title =        "A {SOFM} improves a real time quality assurance machine vision system",
  booktitle =    "Proceedings of the 17th International Conference on Pattern Recognition. 2004: 301--4 Vol. 4",
  pages =        "1176",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "We present a high speed machine vision system for the inspection and quality assurance of canned tuna, which is currently working at a rate over 1000 cans per minute. the system inspects the geometry of the can and its contents at a resolution of 4 pixels/mm. It is the evolution of a first prototype through the introduction of a {K}ohonen network, which maps texture features into a two dimensional grid where the user defines quality neighbourhoods. the inspection time, increased from 35 ms to 38 ms per can, allows the introduction of the system in the same production lines without affecting total performance, but with higher accuracy and user satisfaction.",
}

@Article{inspek67_bibuniq_1178,
  author =       "Martin-Valdivia M. T. and Martinez-Santiago F. and Urena-Lopez L. A.",
  title =        "Merging strategy for cross-lingual information retrieval systems based on learning vector quantization",
  journal =      "Neural Processing Letters. Oct. 2005; 22(2): 149--61",
  pages =        "",
  year =         "2005",
  publisher =    "Kluwer Academic Publishers",
  abstract =     "We present a new approach based on neural networks to solve the merging strategy problem for cross-lingual information retrieval (CLIR). in addition to language barrier issues in CLIR systems, how to merge a ranked list that contains documents in different languages from several text collections is also critical. We propose a merging strategy based on competitive learning to obtain a single ranking of documents merging the individual lists from the separate retrieved documents. the main contribution of the paper is to show the effectiveness of the learning vector quantization ({LVQ}) algorithm in solving the merging problem. in order to investigate the effects of varying the number of codebook vectors, we have carried out several experiments with different values for this parameter. the results demonstrate that the {LVQ} algorithm is a good alternative merging strategy.",
}

@InProceedings{atsumi03_bibuniq_4316,
  author =       "Masayasu Atsumi",
  title =        "Growing Competitive Spiking Neural Network for Saliency-based Scene Recognition",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{ishikawa03_bibuniq_4277,
  author =       "Masumi Ishikawa and Mizuki Tsutsumi",
  title =        "Integration of self-organization and supervised learning by attribute weights and dimensional reduction",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{inspek188_bibuniq_1286,
  author =       "Matsopoulos G. K. and Mouravliansky N. A. and Asvestas P. A. and Delibasis K. K. and Kouloulias V.",
  title =        "Thoracic non-rigid registration combining self-organizing maps and radial basis functions",
  journal =      "Medical Image Analysis. June 2005; 9(3): 237--54",
  pages =        "",
  year =         "2005",
  publisher =    "",
  abstract =     "An automatic three-dimensional non-rigid registration scheme is proposed in this paper and applied to thoracic computed tomography (CT) data of patients with stage III non-small cell lung cancer (NSCLC). According to the registration scheme, initially anatomical set of points such as the vertebral spine, the ribs, and shoulder blades are automatically segmented slice by slice from the two CT scans of the same patient in order to serve as interpolant points. Based on these extracted features, a rigid-body transformation is then applied to provide a pre-registration of the data. To establish correspondence between the feature points, the novel application of the self-organizing maps (SOMs) is adopted. in particular, the automatic correspondence of the interpolant points is based on the initialization of the {K}ohonen neural network model capable to identify 500 corresponding pairs of points approximately in the two CT sets. Then, radial basis functions (RBFs) using the shifted log function is subsequently employed for elastic warping of the image volume, using the correspondence between the interpolant points, as obtained in the previous phase. Quantitative and qualitative results are also presented to validate the performance of the proposed elastic registration scheme resulting in an alignment error of 6 mm, on average, over 15 CT paired datasets. Finally, changes of the tumor volume in respect to each reference dataset are estimated for all patients, which indicate inspiration and/or movement of the patient during acquisition of the data. Thus, the practical implementation of this scheme could provide estimations of lung tumor volumes during radiotherapy treatment planning. [All rights reserved Elsevier].",
}

@MastersThesis{polla05mscthesis_bibuniq_16,
  author =       "Matti P{\"{o}}ll{\"{a}}",
  title =        "Modeling Anticipatory Behavior with Self-Organizing Neural Networks",
  school =       "Helsinki University of Technology",
  year =         "2005",
  address =      "Espoo, Finland",
  month =        "May",
  URL =          "http://www. cis. hut. fi/mpolla/publications/polla_mscthesis. pdf",
}

@InProceedings{polla05akrr_bibuniq_17,
  author =       "Matti P{\"{o}}ll{\"{a}} and Tiina Lindh-Knuutila and Timo Honkela",
  title =        "Self-Refreshing {SOM} as a Semantic Memory Model",
  booktitle =    "Proceedings of {AKRR'05}, International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning",
  pages =        "171--174",
  year =         "2005",
  address =      "Espoo, Finland",
  month =        "June",
  URL =          "http://www. cis. hut. fi/AKRR05/papers/akrr05polla. pdf",
}

@InProceedings{inspek843_bibuniq_652,
  author =       "Mattone R.",
  title =        "The growing neural map: An on-line competitive clustering algorithm",
  booktitle =    "Proceedings 2002 {IEEE} International Conference on Robotics and Automation",
  pages =        "",
  volume =       "4",
  year =         "2002",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "On-line clustering is required whenever huge amounts or continuous flows of data must be classified and/or described in a compact way. Most available unsupervised competitive learning methods exhibit the problem that the units of the self-organizing map {"}accumulate{"} in the regions of the sample space characterized by higher data density, while in clustering it is generally desired that just one node represents each data cluster, independently of the relative density of points in the different clusters. This paper presents a novel on-line, hard-competitive algorithm that deals with this basic limitation, showing very good performance in clustering data from artificial distributions, as well as real data within the problem of motion-based scene segmentation in automated video surveillance.",
}

@InProceedings{inspek89_bibuniq_1197,
  author =       "Mazurkiewicz J.",
  editor =       "S. {Duch, W. ; Kacprzyk, J. ; Oja, E. ; Zadrozny}",
  title =        "Systolic realization of {K}ohonen neural network",
  booktitle =    "Artificial Neural Networks:-Formal Models and their Applications {ICANN} 2005--15th International Conference. Proceedings, Part II Lecture Notes in Computer Science",
  pages =        "1015--1020",
  year =         "2005",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "The paper is focused on partial parallel realization of retrieving phase as well as learning phase of {K}ohonen neural network algorithms. the method proposed is based on pipelined systolic arrays - an example of SIMD architecture. the discussion is realized based on operations which create the following steps of learning and retrieving algorithms. the data which are transferred among the calculation units are the second criterion of the problem.",
}

@InProceedings{inspek210_bibuniq_707,
  author =       "McGlinchey S. J.",
  editor =       "S. {Mehdi, Q. ; Gough, N. ; Natkin}",
  title =        "Learning of {AI} players from game observation data",
  booktitle =    "4th International Conference on Intelligent Games and Simulation. {GAME'ON} 2003. Proceedings",
  pages =        "106--110",
  year =         "2003",
  publisher =    "{EUROSIS-ETI}, Ghent, Belgium",
  abstract =     "To develop AI players for real-time games can be a difficult problem. Solutions using scripted rules often result in computer players whose style of play is unlike a human player. the level of performance may also be far better or worse than the performance of a human player. This work aims to train an AI player from game observation data recorded from games played by humans. the data is used to train a self-organising map ({SOM}), which is a widely used and understood neural network method. the method is applied to the game of {"}pong{"} with the objective of producing a computer pong player that plays in the style of the person that the training data was recorded from.",
}

@InProceedings{inspek269_bibuniq_933,
  author =       "Men-Hsieu-Ho {Shu-Ching-Kuo, Sheng-Tun-Li, Yi-Chung-Cheng}",
  editor =       "A. {Ishikawa, M. ; Hashimoto, S. ; Paprzycki, M. ; Barakova, E. ; Yoshida, K. ; Koppen, M. ; Corne, D. W. ; Abraham}",
  title =        "Knowledge discovery with {SOM} networks in financial investment strategy",
  booktitle =    "Fourth International Conference on Hybrid Intelligent Systems",
  pages =        "98--103",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Recently, the recession of the global economy induces the coming of a new era of low interest-rates, which resulted in the stock market as an alternative investment channel for investors. the diversity and complication of domain knowledge existing in the stock market enhance its importance for developing a decision support system, which can gather real-time pricing information for supporting decision-making in financial investment. We tackle these challenges by proposing an integrated solution on the basis of K-chart analysis and the over-whelming self-organizing map neural networks. We not only endeavor to improve the accuracy of uncovering trading signals, but also to maximize the profits of trading. the resulting decision model can help investment decision-makers of national stable funds make the most profitable decisions. in addition, financial experts can benefit from the ability of verifying or refining their tacit investment knowledge offered by the uncovered knowledge.",
}

@InProceedings{inspek472_bibuniq_1103,
  author =       "Merenyi E. and Farrand W. H. and Tracadas P.",
  editor =       "P. K. Srimani",
  title =        "Mapping surface materials on Mars from Mars Pathfinder spectral images with {HYPEREYE}",
  booktitle =    "Proceedings. ITCC 2004. International Conference on Information Technology:-Coding and Computing. 2004: 607--14 Vol. 2",
  pages =        "1710",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "A comprehensive mapping of spectral variations is presented for one octant of the SuperPan data set of the Imager for Mars Pathfinder. Both left and right eye images are analyzed, simultaneously utilizing all respective spectral bands for each. We achieve fine discrimination of over 20 surface units using a self-organizing map. Agreements with earlier analyses are very good where comparison is available. in spite of the separate analysis of the left and right eye data, which cover different spectral windows, many classes show similar spatial distribution in both images. the {SOM} clustering produced a refinement within the unit formerly labeled as {"}black rock{"}, discovered previously undiscussed units that may be coatings on rocks, and presented some disagreements with existing units. the clustering tools are part of HYPEREYE, a research software developed with NASA/OSSA AISRP support.",
}

@InCollection{Oja03_bibuniq_1777,
  author =       "Merja Oja and Panu Somervuo and Samuel Kaski and Teuvo Kohonen",
  title =        "Clustering of human endogenous retrovirus sequences with median self-organizing map",
  booktitle =    "Proceedings of {WSOM}'03, Workshop on Self-Organizing Maps",
  pages =        "134--139",
  publisher =    "Kyushu Institute of Technology",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  note =         "(Proceedings on CD-ROM)",
}

@Article{inspek383_bibuniq_1031,
  author =       "Meyer-Base A. and Lange O. and Wismuller A. and Ritter H.",
  title =        "Model-free functional {MRI} analysis using topographic independent component analysis",
  journal =      "International Journal of Neural Systems. Aug. 2004; 14(4): 217--28",
  pages =        "",
  year =         "2004",
  publisher =    "World Scientific",
  abstract =     "Data-driven {fMRI} analysis techniques include independent component analysis ({ICA}) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies {K}ohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the {ICA} model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. in this contribution, we can show that when applied to {fMRI} analysis it outperforms FastICA.",
}

@InProceedings{inspek818_bibuniq_630,
  author =       "Mezghani N. and Mitiche A. and Cheriet M.",
  title =        "On-line recognition of handwritten Arabic characters using a {K}ohonen neural network",
  booktitle =    "Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition",
  pages =        "490--495",
  year =         "2002",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Neural networks have been applied to various pattern classification and recognition problems for their learning ability, discrimination power and generalization ability the neural network most referenced in the pattern recognition literature are the multi-layer perceptron, the {K}ohonen associative memory and the Capenter-Grossberg ART network. the {K}ohonen memory runs an unsupervised clustering algorithm. It is easily trained and has attractive properties such as topological ordering and good generalization. in this study an on-line system for the recognition of handwriting Arabic characters using a {K}ohonen network is investigated. the input of the neural network is a feature vector of elliptic Fourier coefficients extracted from the handwritten dynamic representation. Experimental results show that the network successfully recognizes both clearly and roughly written characters with good performance.",
}

@InProceedings{inspek289_bibuniq_945,
  author =       "Mezghani N. and Mitiche A. and Cheriet M.",
  title =        "A new representation of character shape and its use in on-line character recognition by a self organizing map",
  booktitle =    "2004 International Conference on Image Processing {ICIP}",
  pages =        "2123--2126",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The purpose of this study is to investigate a new representation of shape and its use in handwritten on-line character recognition by a {K}ohonen associative memory. This representation is based on the empirical distribution of features such as tangents, and tangent differences at regularly spaced points along the character signal. Recognition is carried out by a {K}ohonen neural network trained using the representation. in addition to the traditional Hudidean distance, functions such as the Kullback Leibler divergence and the Hellinger distance are investigated to evaluate similarity of feature vectors because these functions provide measures of distance between distributions. We report on extensive experiments using a database of on-line Arabic characters produced without constraints by a large number of writers. Comparative results show the representation relevance and the superior performance of the scheme.",
}

@Article{inspek58_bibuniq_1171,
  author =       "Mezghani N. and Mitiche A. and Cheriet M.",
  title =        "A new representation of shape and its use for high performance in online Arabic character recognition by an associative memory",
  journal =      "International Journal on Document Analysis and Recognition. Sept. 2005; 7(4): 201--10",
  pages =        "",
  year =         "2005",
  publisher =    "Springer-Verlag",
  abstract =     "The purpose of this study is to investigate a new representation of shape and its use in handwritten online character recognition by a {K}ohonen associative memory. This representation is based on the empirical distribution of features such as tangents and tangent differences at regularly spaced points along the character signal. Recognition is carried out by a {K}ohonen neural network trained using the representation. in addition to the Euclidean distance traditionally used in the {K}ohonen training algorithm to measure the similarities among feature vectors, we also investigate the Kullback-Leibler divergence and the Hellinger distance, functions that measure distance between distributions. Furthermore, we perform operations (pruning and filtering) on the trained memory to improve its classification potency. We report on extensive experiments using a database of online Arabic characters produced without constraints by a large number of writers. Comparative results show the pertinence of the representation and the superior performance of the scheme.",
}

@InProceedings{inspek516_bibuniq_500,
  author =       "Michihata M. and Miyoshi T. and Masuyama H.",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "Limited scope learning for self-organizing map and its applications",
  booktitle =    "{ICONIP}'02. Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        "2542--2545",
  volume =       "5",
  year =         "2002",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "Self-Organizing Map ({SOM}) is a kind of neural network that teams without supervision. in this paper, we propos {"}Limited Scope Learning{"} on {SOM}. This technique is able to get the feature map that is shown by distributed expression, i. e., more than one winner is selected out of the whole map at the learning time. in the case that the troubled nodes exist on the map, the degree of node fault tolerance will be improved by using this method rather than the conventional technique.",
}

@InProceedings{inspek612_bibuniq_519,
  author =       "Michihata M. and Miyoshi T. and Masuyama H.",
  editor =       "N. {Damiani, E. ; Howlett, R. J. ; Jain, L. C. ; Ichalkaranje}",
  title =        "Self-organizing map with limited scope learning",
  booktitle =    "Knowledge Based Intelligent Information Engineering Systems and Allied Technologies {KES} 2002",
  pages =        "1296--1300",
  volume =       "2",
  year =         "2002",
  publisher =    "IOS Press, Amsterdam, Netherlands",
  abstract =     "Self-organizing map ({SOM}) is a kind of neural networks that learns without supervision. We propose {"}limited scope learning{"} on {SOM}. This technique is able to get the feature map that is shown by distributed expression, i. e., at the learning time, more than one winners are selected out of the whole map. in the case that the troubled nodes exist on the map, the degree of node fault tolerance will be improved by using this method rather than the conventional technique.",
}

@MastersThesis{Sulkava2003_bibuniq_1789,
  author =       "Mika Sulkava",
  title =        "Identifying spatial and temporal profiles from forest nutrition data",
  school =       "Helsinki University of Technology",
  year =         "2003",
  month =        "May",
}

@InProceedings{Sulkava2003b_bibuniq_1785,
  author =       "Mika Sulkava and Jaakko Hollm{\'e}n",
  title =        "Finding Profiles of Forest Nutrition by Clustering of the Self-Organizing Map",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "243--248",
  year =         "2003",
  address =      "Hibikino, Kitakyushu, Japan",
  month =        "September",
}

@Misc{webform_1173_bibuniq_4193,
  author =       "Mikaela Klami",
  title =        "Unsupervised discovery of morphs in children's stories and their use in Self-Organizing Map -based analysis",
  howpublished = "Master's thesis",
  pages =        "",
  note =         "",
  year =         "2005",
}

@InProceedings{inspek285_bibuniq_712,
  author =       "Miller P. and Inoue A.",
  editor =       "E. L. Walker",
  title =        "Collaborative intrusion detection system",
  booktitle =    "NAFIPS'2003. 22nd International Conference of the North American Fuzzy Information Processing Society NAFIPS Proceedings 519--24",
  pages =        "544",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper presents an intrusion detection system consisting of multiple intelligent agents. Each agent uses a self-organizing map ({SOM}) in order to detect intrusive activities on a computer network. A blackboard mechanism is used for the aggregation of results generated from such agents (i. e. a group decision). in addition, this system is capable of reinforcement learning with the reinforcement signal generated within the blackboard and then distributed over all agents which are involved in the group decision making. Systems with various configurations of agents are evaluated for criteria such as speed, accuracy, and consistency. the results indicate an increase in classification accuracy as well as in its constancy as more sensors are incorporated. Currently this system is primarily tested on the data set for KDD Cup '99.",
}

@InProceedings{inspek404_bibuniq_1050,
  author =       "Ming-Lun-Fang {Yan-ling-Han, Yun-Chen, Shou-qi-Cao, Zhi-xiong-Ying}",
  title =        "The diagnostic reasoning based on fuzzy self-organizing neural network and its application",
  booktitle =    "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",
  pages =        "",
  volume =       "4",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "By means of the maturational fuzzy theory and the self-organizing map ({SOM}) neural network which is prepotent in the way of information mapping and self-organizing characteristics, this paper combines the fuzzy theory with {SOM} neural network having the feature of pinpoint nonlinearity, and applies it to the field of fault diagnosis for large-scale electro-mechanical equipments. the network structure and theory of learning of {SOM} are exhausted in detail, and the diagnostic principle and the implementation strategy of fault reasoning based on fuzzy self-organizing map (FSOM) neural network are researched. Finally, we verify the correctness and the practicability of this method by the instance during the procedure of diagnostic reasoning.",
}

@InProceedings{inspek286_bibuniq_713,
  author =       "Ming-Wei-Yu {Sheng-Chai-Chi, Wei-Ling-Peng, Pei-Tsang-Wu}",
  editor =       "E. L. Walker",
  title =        "The study on the relationship among technical indicators and the development of stock index prediction system",
  booktitle =    "NAFIPS'2003. 22nd International Conference of the North American Fuzzy Information Processing Society NAFIPS Proceedings 291--6",
  pages =        "544",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The purpose of this research is to study the relationship of changes between the stock indicators and stock index in order to understand how the trend of stock index change is under the complex influence among the stock technical indicators. the proposed methodology, first of all, applies the self-organizing map ({SOM}) neural network to cluster the similar indicators into groups based on their similarity of moving curve within a certain period of time. To investigate the relationship between the stock index and the technical indicators within any of the groups, the fuzzy neural network (FNN) technique is employed to search for the rules about their relationships. To evaluate the performance of the SOM, the grey relationship analysis was used for the verification of how similar of the indicators which was clustered into a group. According to the results, it is clear that the capability of the {SOM} in clustering is confirmed. To further improve the predication accuracy, this research selected some key indicators from each of the groups as the inputs of neural network and the results completes a much better prediction accuracy than all of the previous networks.",
}

@InProceedings{inspek305_bibuniq_958,
  author =       "Minglu-Li Tianbai-Qian",
  editor =       "Y. {Wei, D. ; Wang, H. ; Peng, Z. ; Kara, A. ; He}",
  title =        "Multispectral {MR} images segmentation using {SOM} network",
  booktitle =    "Proceedings. The Fourth International Conference on Computer and Information Technology",
  pages =        "155--158",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "The precise segmentation of magnetic resonance images (MRI) is an important subject in both medical and computer science communities. With MRI's property of multi-spectrum, we use the information from its PD-, T1-, and T2-weighted images, mapping them into a multi-dimensional intensity space and getting its vector gradient. Through the improvement of the step function, an unsupervised self-organizing map ({SOM}) neural network is trained dynamically. To improve the effectiveness of segmentation, we develop a semi-supervised training scheme at the edge of image in multi-dimensional intensity space.",
}

@InProceedings{inspek7_bibuniq_1137,
  author =       "Mironenko A. and Akhmetshin A. M. and Akhmetshina L. G.",
  title =        "Efficient segmentation of geophysical field images on basis of independent component analysis",
  booktitle =    "IGARSS 2005. {IEEE} International Geoscience and Remote Sensing Symposium. 2005: 4 pp.",
  pages =        "CD--ROM",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In the paper, we consider a new method of geophysical fields image analysis and sensitive segmentation. the method has a high sensitivity in comparison with other well known techniques of geophysical field image analysis and consists of the following main steps: expand the informational features using zero-space imaging method, processing by independent component analysis, data fusion based on {K}ohonen's self-organizing map.",
}

@InProceedings{inspek492_bibuniq_496,
  author =       "Miyazawa K. and Hakkarainen J. and Parkkinen J. and Jaaskelainen T.",
  title =        "Ordering of color spectra for digital image enhancement",
  booktitle =    "Proceedings of {ICIS}'02: International Congress of Imaging Science 2002, Tokyo",
  pages =        "492--293",
  year =         "2002",
  publisher =    "Soc. Photographic Sci. \& Technol. Japan, Tokyo, Japan",
  abstract =     "In this paper we study the ordering of color spectra especially for filtering multispectral images. Two methods are used: a self-organizing map ({SOM}) and a maximum reflectance based order (MRBO). We study how these methods structure the color space, especially what kind of order of color spectra can be determined. Three overlapping subsets selected from reflectance spectra of 1269 Munsell color chips, and 218 reflectance spectra of natural color samples are used for testing. the capability of the {SOM} and the MRBO to cluster and order of the spectra is determined. Our results indicate that both methods can be used for analysing the structure of the spectral color space and for ordering colors based on different criteria. the MRBO works only with fixed hue and chroma and with fixed hue and value. the {SOM}. however, orders also arbitrary set of spectra in a reasonable way. We also show an example of the use of {SOM} in rank order filtering of multispectral image of a natural scene.",
}

@Article{inspek250_bibuniq_927,
  author =       "Mlakar P.",
  title =        "Analysis of ambient {SO}/sub 2/ concentrations and winds in the complex surroundings of a thermal power plant",
  journal =      "Nuovo Cimento C. Nov. Dec. 2004; 27C(6): 595--609",
  pages =        "",
  year =         "2004",
  publisher =    "Soc. Italiana Fis",
  abstract =     "SO/sub 2/ pollution is still a significant problem in Slovenia, especially around large thermal power plants (TPPs), like the one at Sostanj. the Sostanj TPP is the exclusive source of SO/sub 2/ in the area and is therefore a perfect example for air pollution studies. in order to understand air pollution around the Sostanj TPP in detail, some analyses of emissions and ambient concentrations of SO/sub 2/ at six automated monitoring stations in the surroundings of the TPP were made. the data base from 1991 to 1993 was used when there were no desulfurisation plants in operation. Statistical analyses of the influence of the emissions from the three TPP stacks at different, measuring points were made. the analyses prove that the smallest stack (100 m) mainly pollutes villages and towns near the TPP within a radius of a few kilometres. the medium stack's (150 m) influence is noticed at shorter as well as at longer distances up to more than ten kilometres. the highest stack (230 m) pollutes mainly at longer distances, where the plume reaches the higher hills. Detailed analyses of ambient SO/sub 2/ concentrations were made. They show the temporal and spatial distribution of different classes of SO/sub 2/ concentrations from very low to alarming values. These analyses show that pollution patterns at a particular station remain the same if observed on a yearly basis, but can vary very much if observed on a monthly basis, mainly because of different weather patterns. Therefore the winds in the basin (as the most important feature influencing air pollution dispersion) were further analysed in detail to find clusters of similar patterns. For cluster analysis of ground-level winds patterns in the basin around the Sostanj Thermal Power Plant, the {K}ohonen neural network and Leaders' method were used. Furthermore, the dependence of ambient SO/sub 2/ concentrations on the clusters obtained was analysed. the results proved that effective cluster analysis can be a useful tool for compressing a huge wind data base in order to find the correlation between winds and pollutant concentrations. the analyses made provide a better insight into air pollution over complex terrain.",
}

@InProceedings{inspek449_bibuniq_1090,
  author =       "Morajda J.",
  editor =       "L. A. {Rutkowski, L. ; Siekmann, J. ; Tadeusiewicz, R. ; Zadeh}",
  title =        "Special cluster analysis and basic feature estimation with a modification of self-organizing map",
  booktitle =    "Artificial Intelligence and Soft Computing ICAISC 2004. Proceedings Lecture Notes in Artificial Intelligence",
  pages =        "646--651",
  year =         "2004",
  publisher =    "Springer-Verlag, Berlin, Germany",
  abstract =     "The paper describes a proposed method of modification of self-organizing map and its application to special cluster analysis (delivering the information about selected essential feature, which values derive from an ordered set) and also to estimation of the selected (basic) feature for newly occurring patterns. the utilization of this technique in the issue of real estate appraisal has been described. the visualizations of a cluster map, selected estimation maps and numerical results for this problem have been presented.",
}

@InProceedings{inspek657_bibuniq_856,
  author =       "Morra L. and Lamberti F. and Demartini C.",
  title =        "A neural network approach to unsupervised segmentation of single-channel {MR} images",
  booktitle =    "Conference Proceedings. 1st International {IEEE} {EMBS} Conference on Neural Engineering",
  pages =        "515--518",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "A novel neural network-based technique for segmentation of single-channel magnetic resonance images is presented. the segmentation of single-channel magnetic resonance images is a daunting task due to the relatively little information available at each pixel site. the proposed algorithm is based on unsupervised clustering by means of a {K}ohonen Self-Organizing Map: unsupervised segmentation algorithms are highly desirable in order to eliminate intra- and interobserver variability. Particular attention has been devoted to the choice of suitable features, in order to ensure an accurate and reliable segmentation: in particular, a feature set extracted from the neighborhood of each pixel has been evaluated. the proposed technique has been tested on simulated magnetic resonance images to assess its stability against the presence of noise and intensity inhomogeneities. Moreover, it has been tested on real magnetic resonance images of both volunteers and brain tumor patients. the preliminary results presented make the proposed technique a promising alternative for the segmentation of single-channel magnetic resonance images and encourage further investigation.",
}

@Article{inspek122_bibuniq_1225,
  author =       "Moshou D. and Bravo C. and Oberti R. and West J. and Bodria L. and McCartney A. and Ramon H.",
  title =        "Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using {K}ohonen maps",
  journal =      "Real Time Imaging. April 2005; 11(2): 75--83",
  pages =        "",
  year =         "2005",
  publisher =    "Academic Press",
  abstract =     "The objective of this research was to develop a ground-based real-time remote sensing system for detecting diseases in arable crops under field conditions and in an early stage of disease development, before it can visibly be detected. This was achieved through sensor fusion of hyper-spectral reflection information between 450 and 900nm and fluorescence imaging. the work reported here used yellow rust (Puccinia striiformis) disease of winter wheat as a model system for testing the featured technologies. Hyper-spectral reflection images of healthy and infected plants were taken with an imaging spectrograph under field circumstances and ambient lighting conditions. Multi-spectral fluorescence images were taken simultaneously on the same plants using UV-blue excitation. Through comparison of the 550 and 690nm fluorescence images, it was possible to detect disease presence. the fraction of pixels in one image, recognized as diseased, was set as the final fluorescence disease variable called the lesion index (LI). A spectral reflection method, based on only three wavebands, was developed that could discriminate disease from healthy with an overall error of about 11. 3\%. the method based on fluorescence was less accurate with an overall discrimination error of about 16. 5\%. However, fusing the measurements from the two approaches together allowed overall disease from healthy discrimination of 94. 5\% by using QDA. Data fusion was also performed using a Self-Organizing Map ({SOM}) neural network which decreased the overall classification error to 1\%. the possible implementation of the {SOM}-based disease classifier for rapid retraining in the field is discussed. Further, the real-time aspects of the acquisition and processing of spectral and fluorescence images are discussed. With the proposed adaptations the multi-sensor fusion disease detection system can be applied in the real-time detection of plant disease in the field. [All rights reserved Elsevier].",
}

@Article{inspek221_bibuniq_1314,
  author =       "Moshou D. and Hostens I. and Papaioannou G. and Ramon H.",
  title =        "Dynamic muscle fatigue detection using self-organizing maps",
  journal =      "Applied Soft Computing. July 2005; 5(4): 391--8",
  pages =        "",
  year =         "2005",
  publisher =    "Elsevier",
  abstract =     "Wavelets are used for the processing of signals that are non-stationary and time varying. the electromyogram (EMG) contains transient signals related to muscle activity. Wavelet coefficients are proposed as features for identifying muscle fatigue. By observing the approximation coefficients it is shown that their amplitude follows closely the muscle fatigue development. the proposed method for detecting fatigue is automated by using neural networks. the self-organizing map ({SOM}) has been used to visualize the variation of the approximation wavelet coefficients and aid the detection of muscle fatigue. the results show that a 2{D} {SOM} separates EMG signatures from fresh and fatigued muscles, thus providing a visualization of the onset of fatigue over time. the map is able to detect if muscles have recovered temporarily. the system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific. [All rights reserved Elsevier].",
}

@Article{inspek569_bibuniq_780,
  author =       "Moshou D. and Wahlen S. and Strasser R. and Schenk A. and Ramon H.",
  title =        "Apple mealiness detection using fluorescence and self-organising maps",
  journal =      "Computers and Electronics in Agriculture. Oct. 2003; 40(1--3): 103--14",
  pages =        "",
  year =         "2003",
  publisher =    "Elsevier",
  abstract =     "The chlorophyll fluorescence kinetics of 'Jonagold' and 'Cox' apples, stored under different conditions to induce mealiness, were measured. Three different storage conditions were considered causing three mealiness levels: not mealy, moderately and strongly mealy. Also destructive measurements of the texture (firmness, hardness, juice content and soluble solids content) were done. Classification into different mealiness levels based on the fluorescence measurements was more performant than a classification based on the destructive measurements. To estimate the mealiness level in a nondestructive way from the fluorescence features, a number of different classifiers were constructed. Quadratic discriminants and supervised and unsupervised neural networks were tested and compared. the self-organising map gives promising results when compared with the multilayer perceptrons and quadratic discriminant analysis. the different advantages of the constructed classifiers suggest that fluorescence can be used in an automatic sorting line to assess certain types of mealiness.",
}

@InProceedings{katsuya03_bibuniq_4280,
  author =       "Mototsugu Katsuya and Kouichi Mitsunaga and MeiHong Zheng and Osamu Hoshino",
  title =        "Improvement in Cognitive Performance of a Neuronal Network by Neuromodulation",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{Mu00a_bibuniq_4057,
  author =       "Mu Chun Su and Yu Xiang Zhao and J. Lee",
  title =        "{SOM}-based optimization",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks 781-6",
  year =         "2004",
}

@InProceedings{bansal03_bibuniq_4312,
  author =       "Mukti Bansal and C. M. Markan",
  title =        "Floating gate 'Time staggered {WTA}' for feature selectivity",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@Article{inspek468_bibuniq_729,
  author =       "Mäenpää T. and Turtinen M. and Pietikäinen M.",
  title =        "Real-time surface inspection by texture",
  journal =      "Real Time Imaging",
  pages =        "289--96",
  year =         "2003",
  month =        "October",
  volume =       "9",
  number =       "5",
  publisher =    "Academic Press",
  abstract =     "Here a real-time surface inspection method based on texture features is introduced. the proposed approach is based on the local binary pattern (LBP) texture operator and the self-organizing map ({SOM}). A very fast software implementation of the LBP operator is presented. the {SOM} is used as a powerful classifier and visualizer. the efficiency of the method is empirically evaluated in two different problems including textures from the Outex database and from a paper inspection problem.",
}

@InProceedings{Ampazis04a_bibuniq_1466,
  author =       "N. Ampazis and H. Iakovaki",
  title =        "Cross-language information retrieval using latent semantic indexing and self-organizing maps",
  booktitle =    "{IEEE} International-Joint Conference on Neural Networks {IEEE} 751-5",
  year =         "2004",
  volume =       "",
  pages =        "",
  abstract =     "The present paper describes a method of fully automated cross-language information retrieval, which does not require any query translation. Namely, monolingual queries retrieve documents from a multilingual collection, which includes items from the query's source language. This is achieved by a method, which combines the construction of a multilingual semantic space using latent semantic indexing (LSI) and the clustering ability of self-organizing maps ({SOM}) for the generation of multilingual semantic categories. Tests on an English-Greek corpus reveal the effectiveness and robustness of the proposed method.",
}

@Article{ampazis04a_bibuniq_256,
  author =       "N. Ampazis and S. J. Perantonis",
  title =        "{Lsisom} - {A} latent semantic indexing approach to Self-Organizing Maps of document collections",
  journal =      "Neural Processing Letters",
  year =         "2004",
  volume =       "19",
  number =       "2",
  month =        "April",
  pages =        "157--173",
}

@InProceedings{Ampazis02a_bibuniq_1657,
  author =       "N. Ampazis and S. J. Perantonis",
  title =        "Evaluation of dimensionality reduction techniques for {SOM} clustering of textual data",
  booktitle =    "Proceedings of the Second-Iasted International Conference. Artificial Intelligence and Applications. 2002: 216-21",
  year =         "2002",
  volume =       "",
  pages =        "",
  abstract =     "The dimensionality of the data vectors which represent textual document collections, encoded according to the Vector Space Model (VSM), is usually very high, and this results in burdensome computations for the majority of clustering algorithms. It is therefore beneficial to reduce the dimensionality of the data vectors before the application of any clustering algorithm which is based on the computation of distances in the original data space. Two of the most frequently used dimensionality reduction methods are Principal Component Analysis ({PCA}) (or Latent Semantic Indexing - LSI) and Random Projection (RP). A third dimensionality reduction method can be constructed as a two-step approach where firstly a random projection to a lower dimension is applied to the initial corpus which is then followed by LSI (RP/LSI). However, empirical results for all these methods are sparse, especially for the evaluation of the effects that these data representation techniques have on the ability of the Self Organizing Map ({SOM}) algorithm to semantically cluster textual data. in this paper we use a well defined measure for comparing the similarity of different maps trained with the three different representations of the original textual data set, and we illustrate that RP and RP/LSI produce maps whose quality is equivalent to that of maps trained with the computationally expensive LSI representations.",
}

@Article{aras03a_bibuniq_335,
  author =       "N. Aras and I. K. Altinel and J. Oommen",
  title =        "A {K}ohonen-like decomposition method for the Euclidean traveling salesman problem - Knies Decompose",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2003",
  volume =       "14",
  number =       "4",
  month =        "July",
  pages =        "869--890",
}

@Article{Aras03a_bibuniq_1661,
  author =       "N. Aras and I. K. Altinel and J. Oommen",
  title =        "A {K}ohonen-like decomposition method for the Euclidean traveling salesman problem",
  journal =      "{IEEE} Transactions on Neural Networks. July 2003; 14(4): 869-90",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "In addition to the classical heuristic algorithms of operations research, there have also been several approaches based on artificial neural networks for solving the traveling salesman problem. Their efficiency, however, decreases as the problem size (number of cities) increases. A technique to reduce the complexity of a large-scale traveling salesman problem (TSP) instance is to decompose or partition it into smaller subproblems. We introduce an all-neural decomposition heuristic that is based on a recent self-organizing map called KNIES, which has been successfully implemented for solving both the Euclidean traveling salesman problem and the Euclidean Hamiltonian path problem. Our solution for the Euclidean TSP proceeds by solving the Euclidean HPP for the subproblems, and then patching these solutions together. No such all-neural solution has ever been reported.",
}

@Article{arous03a_bibuniq_370,
  author =       "N. Arous and N. Ellouze",
  title =        "Cooperative supervised and unsupervised learning algorithm for phoneme recognition in continuous speech and speaker-independent context",
  journal =      "Neurocomputing",
  year =         "2003",
  volume =       "51",
  month =        "April",
  pages =        "225--235",
}

@InProceedings{Arous02a_bibuniq_1565,
  author =       "N. Arous and N. Ellouze",
  title =        "Phoneme classification accuracy improvements by means of new variants of unsupervised learning neural networks",
  booktitle =    "6th World Multiconference on Systemics, Cybernetics and Informatics. Proceedings. 2002: 298-303 vol. 9",
  year =         "2002",
  volume =       "9",
  pages =        "",
  abstract =     "Speech recognition is a difficult problem due to the inability of current systems to cope with connected speech. Neural networks are able to learn some aspects of this task. An unsupervised learning scheme like self organizing map ({SOM}) can be used to both classify and order speech sounds. {SOM} provides means of reducing the inherent dimensionality of speech data. in this paper, we propose new variants of {SOM} in order to improve phoneme classification accuracy. the proposed {SOM} variants show good robustness and high vowel classification rates in the context of phonetically near vowels.",
}

@InProceedings{Karayiannis00a_bibuniq_4078,
  author =       "N. B. Karayiannis and A. Mukherjee and J. R. Glover and P. Y. Ktonas and J. D. Jr. Frost and R. A. Hrachovy and E. M. Mizrahi",
  title =        "Quantifying and visualizing uncertainty in {EEG} data of neonatal seizures",
  booktitle =    "Conference Proceedings. 26th-Annual International Conference of the {IEEE} Engineering in Medicine and Biology-Society Vol. 1",
  year =         "2004",
}

@Article{hsieh05a_bibuniq_129,
  author =       "N. C. Hsieh",
  title =        "Hybrid mining approach in the design of credit scoring models",
  journal =      "Expert Systems With Applications",
  year =         "2005",
  volume =       "28",
  number =       "4",
  month =        "May",
  pages =        "655--665",
}

@Article{hsieh04a_bibuniq_192,
  author =       "N. C. Hsieh",
  title =        "An integrated data mining and behavioral scoring model for analyzing bank customers",
  journal =      "Expert Systems With Applications",
  year =         "2004",
  volume =       "27",
  number =       "4",
  month =        "November",
  pages =        "623--633",
}

@Article{yeo05a_bibuniq_56,
  author =       "N. C. Yeo and K. H. Lee and Y. V. Venkatesh and S. H. Ong",
  title =        "Colour image segmentation using the self-organizing map and adaptive resonance theory",
  journal =      "Image and Vision Computing",
  year =         "2005",
  volume =       "23",
  number =       "12",
  month =        "November 1",
  pages =        "1060--1079",
}

@InProceedings{chen04a_bibuniq_136,
  author =       "N. Chen",
  title =        "Fuzzy classification using self-organizing map and learning vector quantization",
  booktitle =    "Data Mining and Knowledge Management, Lecture Notes in Artificial Intelligence",
  year =         "2004",
  pages =        "697--708",
}

@Article{silulwane02a_bibuniq_488,
  author =       "N. F. Silulwane and A. J. Richardson and F. A. Shillington and B. A. Mitchell-Innes",
  title =        "Identification and classification of vertical chlorophyll patterns in the Benguela upwelling system and Angola-Benguela front using an artificial neural network",
  journal =      "South African Journal of Marine Science-Suid-Afrikaanse Tydskrif VIR Seewetenskap",
  year =         "2002",
  volume =       "42",
  number =       "1",
  month =        "January-February",
  pages =        "36--45",
}

@Article{fankhauser05a_bibuniq_122,
  author =       "N. Fankhauser and P. Maser",
  title =        "Identification of {GPI} anchor attachment signals by a {K}ohonen self-organizing map",
  journal =      "Bioinformatics",
  year =         "2005",
  volume =       "21",
  number =       "9",
  month =        "May 1",
  pages =        "1846--1852",
}

@InProceedings{Homma00a_bibuniq_3988,
  author =       "N. Homma and M. M. Gupta and M. Yoshizawa and K. Abe",
  title =        "Self-organizing neural networks by dynamic and spatial changing weights",
  booktitle =    "Fourth International-Symposium on Uncertainty-Modeling and Analysis. Isuma-2003. 2003: 129-34",
  year =         "2003",
}

@Article{iizuka05a_bibuniq_141,
  author =       "N. Iizuka and M. Oka and H. Yamada-Okabe and N. Mori and T. Tamesa and T. Okada and N. Takemoto and K. Sakamoto and K. Hamada and H. Ishitsuka and T. Miyamoto and S. Uchimura and Y. Hamamoto",
  title =        "Self-organizing-map-based molecular signature representing the development of hepatocellular carcinoma",
  journal =      "Febs Letters",
  year =         "2005",
  volume =       "579",
  number =       "5",
  month =        "February 14",
  pages =        "1089--1100",
}

@Article{Kamimoto00b_bibuniq_4031,
  author =       "N. Kamimoto and Y. Yamada and M. Kitamura and K. Nishikawa",
  title =        "Pattern classification of spectra of vibration tests in a wrist by motor-operated electric tolls using {SOM}",
  journal =      "Transactions of the-Institute of Electrical-Engineers of Japan, Part-C. Aug. 2004; 124-C(8): 1613-18",
  year =         "2004",
}

@Article{kubota05a_bibuniq_114,
  author =       "N. Kubota",
  title =        "Computational intelligence for structured learning of a partner robot based on imitation",
  journal =      "Information Sciences",
  year =         "2005",
  volume =       "171",
  number =       "4",
  month =        "May 12",
  pages =        "403--429",
}

@Article{laitinen02a_bibuniq_476,
  author =       "N. Laitinen and J. Rantanen and S. Laine and O. Antikainen and E. Rasanen and S. Airaksinen and J. Yliruusi",
  title =        "Visualization of particle size and shape distributions using self-organizing maps",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  year =         "2002",
  volume =       "62",
  number =       "1",
  month =        "April 28",
  pages =        "47--60",
}

@Article{li03a_bibuniq_351,
  author =       "N. Li and Y. F. Li",
  title =        "Feature encoding for unsupervised segmentation of color images",
  journal =      "{IEEE} Transactions on Systems Man and Cybernetics Part B-Cybernetics",
  year =         "2003",
  volume =       "33",
  number =       "3",
  month =        "June",
  pages =        "438--447",
}

@Article{rahim02a_bibuniq_412,
  author =       "N. M. S. Rahim and T. Yahagi",
  title =        "Image coding using an improved feature map finite-state vector quantization",
  journal =      "Ieice Transactions on Fundamentals of Electronics Communications and Computer Sciences",
  year =         "2002",
  volume =       "E85A",
  number =       "11",
  month =        "November",
  pages =        "2453--2458",
}

@Article{pal05a_bibuniq_55,
  author =       "N. R. Pal and A. Laha and J. Das",
  title =        "Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images (vol 26, pg 2219, 2005)",
  journal =      "International Journal of Remote Sensing",
  year =         "2005",
  volume =       "26",
  number =       "17",
  month =        "September 10",
  pages =        "3875--3875",
}

@Article{pal03a_bibuniq_271,
  author =       "N. R. Pal and S. Pal and J. Das and K. Majumdar",
  title =        "{Sofm-MLP}: {A} hybrid neural network for atmospheric temperature prediction",
  journal =      "{IEEE} Transactions on Geoscience and Remote Sensing",
  year =         "2003",
  volume =       "41",
  number =       "12",
  month =        "December",
  pages =        "2783--2791",
}

@Article{sudha03a_bibuniq_358,
  author =       "N. Sudha and T. Srikanthan and B. Mailachalam",
  title =        "A {VLSI} architecture for 3-{D} self-organizing map based color quantization and its {FPGA} implementation",
  journal =      "Journal of Systems Architecture",
  year =         "2003",
  volume =       "48",
  number =       "11-12",
  month =        "April",
  pages =        "337--352",
}

@InProceedings{tsuruta03a_bibuniq_292,
  author =       "N. Tsuruta and H. Iuchi and A. El Sagheer and T. El Tobley",
  title =        "Self-organizing feature maps for {HMM} based lip-reading",
  booktitle =    "Knowledge-Based Intellignet Information and Engineering Systems, Pt. 2, Proceedings, Lecture Notes in Artificial Intelligence",
  year =         "2003",
  pages =        "877--888",
}

@Article{zhao05a_bibuniq_145,
  author =       "N. Zhao and W. Ai and Z. Shao and B. Zhu and S. Brosse and J. Chang",
  title =        "Microsatellites assessment of {C}hinese sturgeon (Acipenser sinensis Gray) genetic variability",
  journal =      "Journal of Applied Ichthyology",
  year =         "2005",
  volume =       "21",
  number =       "1",
  month =        "February",
  pages =        "7--13",
}

@Article{inspek844_bibuniq_653,
  author =       "Nabhani F. and Shaw T.",
  title =        "Performance analysis and optimisation of shape recognition and classification using {ANN}",
  journal =      "Robotics and Computer Integrated Manufacturing. June Aug. 2002; 18(3--4): 177--85",
  pages =        "",
  year =         "2002",
  publisher =    "Elsevier",
  abstract =     "A number of important problems have to be addressed to sustain growth in the area of industrial machine vision. Artificial neural networks (ANNs) coupled with machine vision systems offer a new methodology for solving difficult computational problems. the paper investigates several novel uses of machine vision and ANNs in the processing of single camera multipositional images for 2{D} and 3{D} object recognition and classification. Many industrial applications of machine vision allow objects to be identified and classified by their boundary contour or silhouette. the composite signature generated using vectors obtained from the generation of multicentroidal positions and the boundary pixels can be re-sampled to form a suitable input vector for an ANN. Three different ANN topologies have been implemented: the multilayer perceptron (MLP), a learning vector quantisation network ({LVQ}) and hybrid self-organising map ({SOM}). This method of representing industrial components has been used to compare the ANN architectures when implemented as classifiers based on shape and dimensional tolerance. A number of shortcomings with this methodology have been highlighted.",
}

@InProceedings{inspek256_bibuniq_928,
  author =       "Naenna T. and Bress R. A. and Embrechts M. J.",
  title =        "A modified {K}ohonen network for {DNA} splice junction classification",
  booktitle =    "TENCON 2004. 2004 {IEEE} Region 10 Conference {IEEE} 2",
  pages =        "4 vol. (2729)",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "This paper describes an application of {K}ohonen network, self-organizing maps (SOMs), for exon/intron classification in {DNA} using windowed splice junction data. Splice junctions are groups of nucleotides that serve as boundaries between sections of {DNA} that code for genetic material and sections that do not. Genes are often interrupted by sections of noncoding {DNA} sequences. the data used for this study is human {DNA} data taken from the National Center for Bioinformatics Information (http://www. ncbi. nih. gov/). the {DNA} dataset contains 1, 424 {DNA} sequences with 128 descriptors for each sequence. SOMs were used to classify each {DNA} sequence into three categories that are sequences that transition from gene (exon) to nongene (intron), nongene (intron) to gene (exon), and no transition categories where the two-basepair code for the splice junction was coincidental. the multidimensional sequences are clustered into a two-dimensional space that was graphically displayed for data exploration and classification. Visual and graphical capabilities of SOMs are applied to classify the {DNA} dataset. the topographic properties of SOMs preserve similar sequences close to each other on the output map. Clusters of the dataset are determined and labeled based on the classes of the output neuron in the cluster. the highest frequency classes mapped on the output neuron are labeled as the classes of the output neurons.",
}

@InProceedings{inspek74_bibuniq_1185,
  author =       "Naftel A. and Khalid S.",
  title =        "Motion clustering using spatiotemporal approximations",
  booktitle =    "Proceedings of the Ninth {IASTED} International Conference on Internet and Multimedia Systems and Applications",
  pages =        "207--212",
  year =         "2005",
  publisher =    "ACTA Press, Anaheim, CA, USA",
  abstract =     "In this paper a new technique is proposed for the clustering and classification of spatio-temporal object trajectories extracted from video motion clips. the trajectories are represented as motion time series and modelled using Chebyshev polynomial approximations. Trajectory clustering is then performed to discover patterns of similar object motion. the coefficients of the basis functions are used as an input feature vector to a self-organising map which can learn similarities between object trajectories in an unsupervised manner. It is shown that applying machine learning techniques in the Chebyshev parameter subspace leads to significant performance gains over previous approaches that encode trajectories as point-based flow (PBF) vectors. Experiments using the PETS'04 tracking dataset demonstrate the effectiveness of clustering in the parameter subspace and improvements in overall classification accuracy in comparison with PBF vector encoding. We also show how this technique can be further extended to the detection of anomalous motion paths. Applications to motion data mining and event detection in video surveillance systems are envisaged.",
}

@InProceedings{inspek601_bibuniq_516,
  author =       "Nagao T. and Mitsukura Y. and Fukumi M. and Akamatsu N.",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "Drift ice recognition using remote sensing data by neural networks",
  booktitle =    "{ICONIP}'02. Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        "645--649",
  volume =       "2",
  year =         "2002",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "In recent years, observation of a wide variety in the Earth's surface can be done by improvement of remote sensing technology. the purpose of the paper is to recognize a drift ice as thick ice, thin ice, and sea using synthetic aperture radar (SAR) images. the recognition of the drift ice is achieved by using neural networks (NN). the neural network applies two methods, a BP trained neural network and a self-organizing map. Training data are image features extracted from SAR images. There are three methods for extracting the features: Fourier transform, high-order autocorrelation function (HACF), and image features based on a run length method. We carry out a comparative experiment, and demonstrate their effectiveness by means of computer simulation.",
}

@InProceedings{inspek720_bibuniq_548,
  author =       "Nagao T. and Mitsukura Y. and Fukumi M. and Akamatsu N.",
  title =        "Recognition of drift ice using synthetic aperture radar images",
  booktitle =    "{SICE} 2002. Proceedings of the 41st {SICE} Annual Conference",
  pages =        "",
  volume =       "3",
  year =         "2002",
  publisher =    "Soc. Instrument \& Control Eng. (SICE), Tokyo, Japan",
  abstract =     "In recent years, observation of a wide variety in the Earth surface can be done by improvement of the remote sensing technology. the purpose in this paper is to recognize a drift ice using synthetic aperture radar (SAR) images. the recognition of the drift ice is achieved by using neural networks. the neural networks used include: a BP trained neural network and a self-organizing map. the training data are image features extracted from SAR images. the two methods used of extracting the features are: Fourier transform and high-order autocorrelation function. Furthermore, false colors are given to the SAR image. Features are extracted from that image and are recognized by the neural networks.",
}

@Article{inspek63_bibuniq_1175,
  author =       "Nagao T. and Mitsukura Y. and Fukumi M. and Akamatsu N.",
  title =        "Drift ice classification using {SAR} image data by a self organizing neural network",
  journal =      "Transactions of the Institute of Electrical Engineers of Japan, Part C. 2005; 125 C(5): 800--6",
  pages =        "",
  year =         "2005",
  publisher =    "Inst. Electr. Eng. Japan",
  abstract =     "This paper proposes a segmentation method of SAR (synthetic aperture radar) images which uses a {SOM} (self-organizing map). SAR images are obtained by observation using microwave sensor. They are segmented into the drift ice (thick, thin), and sea regions manually, and then features are extracted from partitioned data. However they are not necessarily effective for neural network learning because they can include incorrectly segmented data. Therefore, in particular, a multi-step {SOM} is used as a learning method to improve reliability of teacher data, and carries out classification. This process enable us to fix all mistook data and segment the SAR data using just data. the validity of this method was demonstrated by computer simulations using the actual SAR images.",
}

@InProceedings{inspek552_bibuniq_769,
  author =       "Nahavandi S. {Li-Pan, Hong-Zheng}",
  title =        "The application of rough set and {K}ohonen network to feature selection for object extraction",
  booktitle =    "Proceedings of the 2003 International Conference on Machine Learning and Cybernetics",
  pages =        "",
  volume =       "2",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. the paper describes an application of rough sets method to feature selection and reduction in texture images recognition. the proposed methods include continuous data discretization based on {K}ohonen neural network and maximum covariance, and rough set algorithms for feature selection and reduction. the experiments on trees extraction from aerial images show that the methods presented in this paper are practical and effective.",
}

@InProceedings{inspek494_bibuniq_498,
  author =       "Nakauchi S. and Hayasaka T. Niitsuma and Usui S.",
  title =        "Color world by nonhuman color vision revealed by adaptive color scaling with self-organizing map",
  booktitle =    "Proceedings of {ICIS}'02: International Congress of Imaging Science 2002, Tokyo",
  pages =        "393--394",
  year =         "2002",
  publisher =    "Soc. Photographic Sci. \& Technol. Japan, Tokyo, Japan",
  abstract =     "We previously proposed a visualization method for multispectral color images: spectral data are converted to a set of RGB values in which the topographical relation of the spectra is preserved. the present paper utilized this method for visualizing the pseudo-color-image captured by nonhuman color vision system. Also, statistical analysis of flowers' reflectance spectra is performed.",
}

@InProceedings{Nattkemper2005-TSR_bibuniq_4119,
  author =       "Nattkemper Tim W.",
  title =        "The {SOM} reef - {A} new metaphoric visualization approach for self organizing maps",
  booktitle =    "Proc. ~of {WSOM} 2005, 5th Workshop On Self-Organizing Maps",
  year =         "2005",
  editor =       "M. Cottrell",
  address =      "Paris, France",
}

@Article{inspek66_bibuniq_1177,
  author =       "Nechaeva O. I.",
  title =        "Comparative analysis of neural network clustering algorithms for symbol sequences",
  journal =      "Optoelectronics, Instrumentation and Data Processing. ; : 53--64; Original: Avtometriya-. 2005; 41(1): 57--70",
  pages =        "",
  year =         "2005",
  publisher =    "Allerton Press",
  abstract =     "Algorithms for symbol sequence clustering with the use of Levenshtein's distance, which are based on the k-means method and the {K}ohonen neural network, are considered. A description and a comparative characteristic in time and accuracy of two heuristic algorithms for finding the kernel in clusters, namely, the algorithm for choosing the kernel from a cluster and the algorithm for signal counting, are presented. the possibility of applying the k-means method with transfer to frequency dictionaries is assessed for clustering various types of symbol sequences. the clustering algorithms for Euclidean space vectors by the k-means and {K}ohonen neural network methods are compared in time and quality.",
}

@InProceedings{inspek633_bibuniq_833,
  author =       "Neocleous C. C. and Schizas C. N.",
  title =        "Neural networks in comparing {USN} and Wageningen {B}-Series marine propellers",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "",
  volume =       "1",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The USN-series of experimental data on marine propeller performance (Denny et al, 1989) were compared with fitted Wageningen B-series data. A {K}ohonen network has been used to attempt finding non-obvious similarities between the two data sets. the USN-series has been tested under cavitating conditions, while the available B-series not. A non-linear fit of the USN-series, including information on cavitation number sigma, has been developed and compared with a neural network function approximation. Using the {K}ohonen classification results, the non-linear regression was re-applied with slightly improved results. in overall, the feedforward neural network architecture mapping gave the best fit both in a statistical correlation measure and in the maximum percentage deviation measure.",
}

@InProceedings{inspek609_bibuniq_811,
  author =       "Ngan A. and Thiria S. and Badran F. and Yaccoub M. and Moulin C. and Crepon M.",
  title =        "Clustering and classification based on expert knowledge propagation using probabilistic self-organizing map({PRSOM}): application to the classification of satellite ocean color {TOA} observations",
  booktitle =    "{CIMSA}'03. 2003 {IEEE} International Symposium on Computational Intelligence for Measurement Systems and Applications",
  pages =        "146--148",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The {K}ohonen map with PRSOM was used to analyze a time sequence of SeaWifs images These images were observed on the Mediterranean Sea from the 6/sup th/ of August to the 12/sup th/. We processed the normalized reflectance of full TOA spectrum by first using a 20*20 {K}ohonen map and then we aggregated these 400 classes into 50 classes by using the PRSOM algorithm. the classifier was trained on a one year (1999) Mediterranean SeaWifs images using a temporal homogeneity. (2 images/month). We have developed an automatic labeling procedure based on the SeaWifs LUT used to invert the TOA signal. the labeling procedure allows us to identify four different aerosol type (Coastal, Maritime, Tropospheric Dust, Oceanic) and their corresponding optical thickness. We clearly see an important event of Saharian dust coming from the Sahara, crossing the Mediterranean Sea and invading North of the Mediterranean. A meteorological map taken the 10/sup th/ of August shows a strong South West wind supporting the above interpretation. This study clearly shows the possibility to use the above algorithm for automatically classify the aerosols at the Top of the Atmosphere.",
}

@InProceedings{inspek371_bibuniq_1019,
  author =       "Nishida S. and Ishii K. and Ura T.",
  title =        "Adaptive learning to environment using Self-Organizing Map and its application for underwater vehicles",
  booktitle =    "Proceedings of the 2004 International Symposium on Underwater Technology",
  pages =        "223--228",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Autonomous underwater vehicles (AUVs) have great advantages for activities in deep sea, and expected as the attractive tool. However, AUVs have various problems which should be solved. in this paper, the Self-Organizing Map ({SOM}) is applied as the clustering method for the navigation system. the {SOM} is known as one of the effective methods to extract the principle feature from many parameters and decrease the dimension of parameters. Through the competitive learning algorithms, the obtained map is tuned to express specific features of the input signals. We have been investigating the possibility of navigation system based on {SOM} through simulations are experiments with an AUV called {"}Twin-Burger{"}. the learning algorithm of usual {SOM} is unsupervised learning. However, supervised learning algorithms should be introduced because the relationship between distances information and desirable behavior of the robot, that is, the relationship from inputs to outputs should be acquired and learned. in this paper, a supervised learning algorithm is introduced into {SOM} and a method to adapt the local map to its environment by learning and evaluating the trajectory of robot is proposed. in the proposed method, the {"}initial map{"} is made static and digital value as teaching data. in order to include more information of environment in the initial map, the trajectories of robot are evaluated, and the evaluation is utilized in the learning process. This method enables the map to have both the effect of dynamics of robot and environmental information. the efficiency of the method is investigated through the simulations and experiments.",
}

@InProceedings{inspek196_bibuniq_916,
  author =       "Nishio H. and Altaf-Ul-Amin M. and Nakamura Y. and Kurokawa K. and Sinbo Y. and Abe T. and Kinouchi M. and Ikemura T. and Kobayashi K. and Ogasawara N. and Kanaya S.",
  editor =       "A. {Callaos, N. ; Horimoto, K. ; Chen, J. ; Kit-Sze-Chan}",
  title =        "Gene classification based on expression profile using {BL}-{SOM}: suitability assessment of multivariate gene expression data to spherical and plain {SOM} by {N}-measure",
  booktitle =    "The 8th World Multi Conference on Systemics, Cybernetics and Informatics",
  pages =        "189--192",
  volume =       "7",
  year =         "2004",
  publisher =    "IIIS, Orlando, FL, USA",
  abstract =     "Expression profile data for all the genes in a genome can be obtained by transcriptome experiments such as GeneChip and cDNA array systems. Classification of genes in high resolution based on expression profiles may become a key to understand the integrated system in a cell. To attain this purpose, we conduct butch-learning self-organizing map (BL-SOM) for classifying genes based on expression profiles. in the present study, we consider two-types of SOMs based on configuration of units. One of them has planar representation space called {"}plain SOM{"} and the other has spherical space called {"}spherical SOM{"}. in general, the profile vectors of gene expression are normalized to unity in length in order to focus not on the absolute quantity of expression but the similarity of the direction. in the present study, we propose a measure for the suitability of {SOM} to a given data set and confirm that the spherical {SOM} is actually suitable for the data on gene expression. Then, we examine biological meanings of gene classification of Bacillus subtilis.",
}

@InProceedings{inspek880_bibuniq_677,
  author =       "Niskanen M. and Kauppinen H. and Silven O.",
  title =        "Real-time aspects of {SOM}-based visual surface inspection",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering",
  pages =        "123--134",
  volume =       "4664",
  year =         "2002",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "We have developed a self-organizing map ({SOM})-based approach for training and classification in visual surface inspection applications. the approach combines the advantages of non-supervised and supervised training and offers an intuitive visual user interface. the training is less sensitive to human errors, since labeling of large amounts of individual training samples is not necessary. in the classification, the user interface allows on-line control of class boundaries. Earlier experiments show that our approach gives good results in wood inspection. in this paper, we evaluate its real time capability. When quite simple features are used, the bottleneck in real time inspection is the nearest {SOM} code vector search during the classification phase. in experiments, we compare acceleration techniques that are Suitable for high dimensional nearest neighbor search typical for the method. We show that even simple acceleration techniques can improve the speed considerably, and the {SOM} approach can be used in real time with a standard PC.",
}

@InProceedings{inspek528_bibuniq_756,
  author =       "Niskanen M. and Silven O.",
  title =        "Comparison of dimensionality reduction methods for wood surface inspection",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering",
  pages =        "178--188",
  volume =       "5132",
  year =         "2003",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "Dimensionality reduction methods for visualization map the original high-dimensional data typically into two dimensions. Mapping preserves the important information of the data, and in order to be useful, fulfils the needs of a human observer. We have proposed a self-organizing map ({SOM})-based approach for visual surface inspection. the method provides the advantages of unsupervised learning and an intuitive user interface that allows one to very easily set and tune the class boundaries based on observations made on visualization, for example, to adapt to changing conditions or material. There are, however, some problems with a SOM. It does not address the true distances between data, and it has a tendency to ignore rare samples in the training set at the expense of more accurate representation of common samples. in this paper, some alternative methods for a {SOM} are evaluated. These methods, {PCA}, MDS, LLE, ISOMAP, and GTM, are used to reduce dimensionality in order to visualize the data. Their principal differences are discussed and performances quantitatively evaluated in a few special classification cases, such as in wood inspection using centile features. For the test material experimented with, {SOM} and GTM outperform the others when classification performance is considered. For data mining kinds of applications, ISOMAP and LLE appear to be more promising methods.",
}

@InProceedings{inspek429_bibuniq_1073,
  author =       "Nojima Y. and Kubota N. and Kojima F.",
  title =        "Trajectory generation and accumulation for partner robots based on structured learning",
  booktitle =    "Proceedings of the 2004 Congress on Evolutionary Computation",
  pages =        "",
  volume =       "2",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The aim of this paper is to develop partner robots that can obtain and accumulate human-friendly behaviors. To realize it, we use a concept of structured learning which emphasizes the importance of an interactive learning of several modules through interaction with its environment. in a proposed method, a robot obtains hand-to-hand behavior by using an interactive evolutionary computation based on human evaluations estimated by fuzzy state-value functions. Moreover, a self-organizing map is used for clustering human hand positions. A state-value function and a knowledge database are assigned to each clustered positions. Furthermore, the best trajectory is stored in the knowledge database to reuse it in the same situation. Some experimental results show the effectiveness of the proposed method.",
}

@InProceedings{shigei03_bibuniq_4267,
  author =       "Noritaka SHIGEI and Hiromi MIYAJIMA",
  title =        "Preliminary Considerations on Self-Organizing Neural Networks and Module Placement for Gate Array",
  booktitle =    "Proceedings of the Workshop on Self-Organizing Maps ({WSOM}'03)",
  pages =        "",
  year =         "2003",
  address =      "Kitakyushu, Japan",
  month =        "September",
}

@InProceedings{inspek415_bibuniq_1059,
  author =       "Novak D. and Kordik P. and Macas M. and Vyhnalek M. and Brzezny R. and Lhotska L.",
  title =        "School children dyslexia analysis using self organizing maps",
  booktitle =    "Conference Proceedings. 26th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society",
  pages =        "",
  volume =       "1",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "The main goal of the study is an unsupervised classification of school children dyslexia. Eye movements of 49 subjects were measured using videooculographic technique (VOG) during two non-reading and one reading tasks. A feature selection was performed obtaining data set consisting of 26 features. Next an inductive modelling technique was applied to data set resulting in extraction of six features which were used as the input to self-organizing map ({SOM}). Three clusters were finally formed by the {SOM} proving that the proposed methodology is suitable for automatic dyslexia analysis.",
}

@Article{postolache05a_bibuniq_159,
  author =       "O. A. Postolache and PMBS Girao and J. M. D. Pereira and H. M. G. Ramos",
  title =        "Self-organizing maps application in a remote water quality monitoring system",
  journal =      "{IEEE} Transactions on Instrumentation and Measurement",
  year =         "2005",
  volume =       "54",
  number =       "1",
  month =        "February",
  pages =        "322--329",
}

@Article{carpinteiro04a_bibuniq_143,
  author =       "O. A. S. Carpinteiro and A. J. R. Reis and A. P. A. da Silva",
  title =        "A hierarchical neural model in short-term load forecasting",
  journal =      "Applied Soft Computing",
  year =         "2004",
  volume =       "4",
  number =       "4",
  month =        "September",
  pages =        "405--412",
}

@Article{Beckonert03a_bibuniq_1673,
  author =       "O. Beckonert and J. Monnerjahn and U. Bonk and D. Leibfritz",
  title =        "Visualizing metabolic changes in breast-cancer tissue using /sup 1/{H}-{NMR} spectroscopy and self-organizing maps",
  journal =      "{NMR} in Biomedicine",
  year =         "2003",
  volume =       "16",
  number =       "1",
  month =        "February",
  pages =        "1--11",
  abstract =     "In-vitro {NMR} spectroscopic examinations of tissue extracts can be combined with appropriate pattern-recognition and visualization techniques in order to monitor characteristic metabolic differences between tissue classes. in the present study, such techniques are applied to a set of 88 breast-tissue samples with the intention of identifying typical differences between various tissue classes. the set contains 49 breast-tumor samples of various tumor grades and 39 samples of healthy tissue. the metabolite compositions of the tissue extracts were investigated using a dual extraction technique and high-resolution /sup 1/H-{NMR} spectroscopy. the spectra of the hydrophilic and the lipophilic compounds were assigned to three groups according to different malignancy grades of the respective tissue samples. the group characteristics were analyzed using the k-nearest-neighbor method and self-organizing-map visualizations. the results show an increase of UDP-hexose, phosphocholine and phosphoethanolamine concentrations according to the tumor grade. Higher concentrations of taurine were detected in the malignant samples. Myo-inositol and glucose content were elevated in control samples compared with malignant tissue. Both compounds also characterized different subgroups in the pool of unaffected tissue samples depending upon fat content or fibrosis. Several lipid metabolites showed a characteristic elevation with high malignancy.",
}

@Article{beckonert03a_bibuniq_387,
  author =       "O. Beckonert and K. Monnerjahn and U. Bonk and D. Leibfritz",
  title =        "Visualizing metabolic changes in breast-cancer tissue using {H}-1-{NMR} spectroscopy and self-organizing maps",
  journal =      "NMR in Biomedicine",
  year =         "2003",
  volume =       "16",
  number =       "1",
  month =        "February",
  pages =        "1--11",
}

@InProceedings{fontenla-romero02a_bibuniq_4955,
  author =       "O. Fontenla-Romero and A. Alonso-Betanzos and E. Castillo and J. C. Principe and B. Guijarro-Berdinas",
  title =        "Local modeling using self-organizing maps and single layer neural networks",
  booktitle =    "Artificial Neural Networks - {ICANN} 2002, Lecture Notes in Computer Science",
  year =         "2002",
  pages =        "945--950",
  abstract =     "",
}

@Article{tekbas04a_bibuniq_204,
  author =       "O. H. Tekbas and N. Serinken and O. Ureten",
  title =        "An experimental performance evaluation of a novel radio-transmitter identification system under diverse environmental conditions",
  journal =      "Canadian Journal of Electrical and Computer Engineering-Revue Canadienne DE Genie Electrique ET Informatique",
  year =         "2004",
  volume =       "29",
  number =       "3",
  month =        "July",
  pages =        "203--209",
}

@Article{kohonen05a_bibuniq_63,
  author =       "O. Kohonen and T. Jaaskelainen and M. Hauta-Kasari and J. Parkkinen and K. Miyazawa",
  title =        "Organizing spectral image database using Self-Organizing Maps",
  journal =      "Journal of Imaging Science and Technology",
  year =         "2005",
  volume =       "49",
  number =       "4",
  month =        "July-August",
  pages =        "431--441",
}

@Article{kontkanen02a_bibuniq_408,
  author =       "O. Kontkanen and P. Törönen and M. A. Lakso and G. Wong and E. Castrén",
  title =        "Antipsychotic drug treatment induces differential gene expression in the rat cortex",
  journal =      "Journal of Neurochemistry",
  year =         "2002",
  volume =       "83",
  number =       "5",
  month =        "December",
  pages =        "1043--1053",
}

@Article{al-jarrah02a_bibuniq_405,
  author =       "O. M. Al-Jarrah and O. Q. Bani-Melhem",
  title =        "Building maps for mobile robot navigation using fuzzy classification of ultrasonic range data",
  journal =      "Journal of Intelligent \& Fuzzy Systems",
  year =         "2002",
  volume =       "24",
  number =       "12",
  month =        "December",
  pages =        "1289--1303",
}

@InProceedings{nechaeva05a_bibuniq_49,
  author =       "O. Nechaeva",
  title =        "Neural network approach for parallel construction of adaptive meshes",
  booktitle =    "Parallel Computing Technologies, Lecture Notes in Computer Science",
  year =         "2005",
  pages =        "713--722",
}

@Article{Simula02a_bibuniq_1759,
  author =       "O. Simula and J. Hollmen and E. Alhoniemi",
  title =        "Models from data: analysis of industrial processes and telecommunication systems",
  journal =      "Automazione e Strumentazione",
  year =         "2002",
  volume =       "50",
  number =       "2",
  month =        "February",
  pages =        "107--113",
  abstract =     "Modeling of systems is often based on the knowledge of the physical phenomena in the system. When physical knowledge is not available, or when modeling is too difficult due to nonlinearities, operational measurement data from the process may be used to build laws concerning the behavior of such a system. in this paper, we review some data-driven methods that have been successfully applied in industrial settings and in telecommunications. in particular, we present a neural network model-self-organizing map-that has been applied in various projects.",
}

@InProceedings{inspek427_bibuniq_1071,
  author =       "Obayashi S. and Sasaki D.",
  title =        "Multi-objective optimization for aerodynamic designs by using {ARMOGA}s",
  booktitle =    "Proceedings. Seventh International Conference on High Performance Computing and Grid in Asia Pacific Region",
  pages =        "396--403",
  year =         "2004",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Global trade-offs for aerodynamic design of supersonic transport (SST) have been investigated by multiobjective evolutionary algorithms (MOEAs). the objectives are to reduce both drag and sonic boom to make next-generation SST more feasible. Adaptive range multiobjective genetic algorithms (ARMOGAs) are utilized for the efficient search. the trade-offs are analysed by self-organizing map ({SOM}), which provides a topology preserving mapping from the high dimensional space to two dimensions. ARMOGAs and {SOM} can be good design tools to conduct aerodynamic design optimizations and analyse the results.",
}

@Article{inspek925_bibuniq_698,
  author =       "Obu-Cann K. and Fujimura K. and Tokutaka H. and Ohkita M. and Inui M. and Yamada S.",
  title =        "Data mining with self-organising maps ({SOM}) and minimal spanning tree ({MST})",
  journal =      "International Journal of Knowledge Based Intelligent Engineering Systems. Jan. 2002; 6(1): 40--7",
  pages =        "",
  year =         "2002",
  publisher =    "Univ. Brighton",
  abstract =     "data mining or exploration is part of a large area of recent research in artificial intelligence and information processing and management otherwise known as knowledge discovery in database (KDD). the main aim here is to identify new information or knowledge from database in which the dimensionality or amount of data is so large that it is beyond human comprehension. Self-organising map and minimal spanning tree are used to analyse power transformer database from one of the electric energy providers in Japan. Evaluation of the clusters generated by {SOM} is usually done by human eye. Due to its qualitative nature, the evaluator may either overestimate or underestimate the number of clusters formed on the map. With this approach, the exact number of clusters generated by the map cannot be confirmed because of the misinterpretation of the grey level expression. This paper looks at clustering with minimal spanning tree (MST).",
}

@InProceedings{inspek536_bibuniq_503,
  author =       "Ohtsuka A. and Kamiura N. and Isokawa T. and Matsui N.",
  editor =       "X. {Wang, L. ; Rajapakse, J. C. ; Fukushima, K. ; Lee, S-Y. ; Yao}",
  title =        "On detection of confused blood samples using self-organizing maps and genetic algorithm",
  booktitle =    "{ICONIP}'02. Proceedings of the 9th International Conference on Neural Information Processing. Computational Intelligence for the {E}-Age",
  pages =        "2233--2238",
  volume =       "5",
  year =         "2002",
  publisher =    "Nanyang Technol. Univ, Singapore",
  abstract =     "A {SOM} (self-organizing map)-based detection of confusion of blood test data referred to as CBC (complete blood count) data is proposed. Firstly, the method based on only {SOM} is shown. the learning data applied to {SOM}s are generated by subtracting the immediately anterior CBC data of subjects from the present CBC data. All the neurons in the second layer of {SOM} trained by applying the above learning data are roughly divided into the following two clusters: a cluster with neurons reacting to regular input data, and a cluster reacting to irregular input data which are generated by subtraction between confused CBC data. So, if the firing neuron belongs to the latter cluster, it is presumed that the confusion arises among CBC data of some subjects. Next, a method based on both {SOM} and GA (genetic algorithm) is shown. With the exception of selecting some elements, which instruct the weights to be updated in the second layer of CBC data by means of GA, the learning and the detection strategy adopted by this method are similar to those by the firstly proposed method. Experimental results on detecting the confusion, which arises among CBC data of 750 subjects, show that the second proposed method produces the second layer which achieves the high accuracy of detection especially when the input data, not to be employed during the learning, are applied.",
}

@Article{inspek76_bibuniq_1187,
  author =       "Ohtsuka A. and Kamiura N. and Isokawa T. and Minamide N. and Okamoto M. and Koeda N. and Matsui N.",
  title =        "A self-organizing map approach for detecting confusion between blood",
  journal =      "Transactions of the Society of Instrument and Control Engineers. 2005; 41(7): 587--95",
  pages =        "",
  year =         "2005",
  publisher =    "Soc. Instrum. \& Control Eng",
  abstract =     "Self-organizing map-based methods for the detection of confusion between blood test data are presented. Learning data for the self-organizing map ({SOM}) is generated by subtracting each element of complete blood count (CBC) data of the immediately previous patient's results from that of the current results. the neurons in the well-trained {SOM} are roughly divided into two clusters: one with neurons reacting to regular input; data, and the other with neurons reacting to irregular input data generated by subtraction between confused CBC data. If a winner neuron belongs to the latter cluster, it is presumed that confusion has arises between the CBC data of different patients. in addition, a genetic algorithm is adopted to eliminate redundant elements in the CBC data, which have an unfavorable influence on the judgment of confusion. Experimental results show that the proposed methods achieve high accuracy of detection even when the input data irrelevant to the learning of maps is applied to them.",
}

@InProceedings{inspek366_bibuniq_1014,
  author =       "Oja M. and Sperber G. and Blomberg J. and Kaski S.",
  title =        "Grouping and visualizing human endogenous retroviruses by bootstrapping median self-organizing maps",
  booktitle =    "Proceedings of the 2004 {IEEE} Symposium on Computational Intelligence in Bioinformatics and Computational Biology",
  pages =        "95--101",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "About eight percent of the human genome consists of human endogenous retrovirus sequences. Human endogenous retroviruses (HERV) are remains from ancient infections by retroviruses. the HERVs are mutated and deficient, but they still may give rise to transcripts or may affect the expression of human genes. the HERVs stem from several kinds of retroviruses., the possible current functioning of the HERV sequences may reflect the origin of the HERVs. Hence, the classification of the diverse HERV sequences is a natural starting point when investigating the effect of HERVs in humans. the current HERV taxonomy is incomplete: some sequences cannot be assigned to any class and the classification is ambiguous for others. A median self-organizing map ({SOM}), a {SOM} for data about pairwise distances between samples, can be used to group all the HERVs found in the human genome. It visualizes the collection of 3661 HERV sequences found by the RetroTector system, on a two-dimensional display that represents similarity relationships between individual sequences, as well as cluster structures and similarities of clusters. the SOM, as any dimensionality reduction method, necessarily has to make compromises when representing the data. in this work we extend the visualizations by bootstrap-based estimates on which parts of the visualization are reliable and which not, and use the {SOM} to find potentially new HERV groups.",
}

@Article{inspek104_bibuniq_1209,
  author =       "Okada Y. and Sahara T. and Mitsubayashi H. and Ohgiya S. and Nagashima T.",
  title =        "Knowledge-assisted recognition of cluster boundaries in gene expression data",
  journal =      "Artificial Intelligence in Medicine. 2005; 35(1--2): 171--83",
  pages =        "",
  year =         "2005",
  publisher =    "Elsevier",
  abstract =     "DNA microarray technology has made it possible to determine the expression levels of thousands of genes in parallel under multiple experimental conditions. Genome-wide analyses using {DNA} microarrays make a great contribution to the exploration of the dynamic state of genetic networks, and further lead to the development of new disease diagnosis technologies. An important step in the analysis of gene expression data is to classify genes with similar expression patterns into the same groups. To this end, hierarchical clustering algorithms have been widely used. Major advantages of hierarchical clustering algorithms are that investigators do not need to specify the number of clusters in advance and results are presented visually in the form of a dendrogram. However, since traditional hierarchical clustering methods simply provide results on the statistical characteristics of expression data, biological interpretations of the resulting clusters are not easy, and it requires laborious tasks to unveil hidden biological processes regulated by members in the clusters. Therefore, it has been a very difficult routine for experts. Here, we propose a novel algorithm in which cluster boundaries are determined by referring to functional annotations stored in genome databases. the algorithm first performs hierarchical clustering of gene expression profiles. Then, the cluster boundaries are determined by the Variance Inflation Factor among the Gene Function Vectors, which represents distributions of gene functions in each cluster. Our algorithm automatically specifies a cutoff that leads to functionally independent agglomerations of genes on the dendrogram derived from similarities among gene expression patterns. Finally, each cluster is annotated according to dominant gene functions within the respective cluster. in this paper, we apply our algorithm to two gene expression datasets related to cell cycle and cold stress response in budding yeast Saccharomyces cerevisiae. As a result, we show that the algorithm enables us to recognize cluster boundaries characterizing fundamental biological processes such as the Early G1, Late G1, S, G2 and M phases in cell cycles, and also provides novel annotation information that has not been obtained by traditional hierarchical clustering methods. in addition, using formal cluster validity indices, high validity of our algorithm is verified by the comparison through other popular clustering algorithms, K-means, self-organizing map and AutoClass. [All rights reserved Elsevier].",
}

@InProceedings{inspek400_bibuniq_1046,
  author =       "Oktem V. and Jouny I.",
  title =        "Automatic detection of malignant tumors in mammograms",
  booktitle =    "Conference Proceedings. 26th Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society",
  pages =        "",
  volume =       "3",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Detection of malignant tumors at an early stage is an important first step in diagnosis of the cancerous regions in mammograms. Although many detection schemes have been presented, they are still not adequate to safely eliminate all risks. in this paper, we propose classification schemes of unknown test mammograms using fractal analysis and spatial moments distributions as image processing techniques. Two classifiers will be used in conjunction with these techniques: a backpropagation neural network and a self-organizing map. Investigation of the histograms of the spatial moments at low orders shows that discrete image spatial moments cannot distinguish between benign and malignant mammograms. the two-stage backpropagation neural network and the one-stage self-organizing map both give detection rates of 70\% and low false positive rates. With further preprocessing and optimization, the performance of these classifiers may be further improved.",
}

@Article{inspek683_bibuniq_880,
  author =       "Olmez T. and Dokur Z.",
  title =        "Application of In{P} neural network to {ECG} beat classification",
  journal =      "Neural Computing \& Applications. 2003; 11(3--4): 144--55",
  pages =        "",
  year =         "2003",
  publisher =    "Springer-Verlag",
  abstract =     "The paper presents an application of a hybrid neural network structure to the classification of the electrocardiogram (ECG) beats. Three different feature extraction methods are comparatively examined: the discrete cosine transform, wavelet transform and direct method. Classification performances, training times and the numbers of nodes of the {K}ohonen network, restricted Coulomb energy network and the hybrid neural network are presented. To increase the classification performance and to decrease the number of nodes, the hybrid neural network is trained by genetic algorithms (GAs). Ten different types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success rate of 98\% by using the hybrid neural network structure and discrete cosine transform together.",
}

@InProceedings{inspek623_bibuniq_824,
  author =       "Omatu S. Bingchen-Wang and Abe T.",
  title =        "Failure analysis of transmission devices using self-organizing map",
  booktitle =    "Proceedings of the International Joint Conference on Neural Networks",
  pages =        "",
  volume =       "2",
  year =         "2003",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper, a general failure analysis method of transmission devices is proposed. First, we record the acoustic signals of good and no good transmission devices in operation. Then we decompose the acoustic signals by using wavelet transform. From all the components of signal, we select the significant component, which corresponds to the specified failure, based on the reconstructed signal, while setting the lower level wavelet approximation to zero. Next, we compute the frequency characteristic of the significant component and use the self-organizing map ({SOM}) to classify the specified no good products from the good and other no good products. By comparing the difference between the groups of specified no good products and good products, we can estimate the cause of trouble. Furthermore, while an unknown product is inputted to the {SOM} network, we can determine whether the inputted product has the specified failure. the experimental results show that the proposed method can perform the failure analysis of transmission devices successfully.",
}

@Article{inspek684_bibuniq_524,
  author =       "Oreski S. and Zupan J. and Glavic P.",
  title =        "Artificial neural network classification of phase equilibrium methods part 2",
  journal =      "Chemical and Biochemical Engineering Quarterly. 2002; 16(2): 41--57",
  pages =        "",
  year =         "2002",
  publisher =    "Croatian Soc. Chem. Eng",
  abstract =     "A further study of the neural network application for predicting appropriate methods of phase equilibrium on the basis of known physical properties is presented. {K}ohonen neural networks are used to classify objects into none, one or more possible classes. the classes in the study represent possible methods of phase equilibrium. the trained neural network estimates the reliability of its predictions - the adequacy of individual methods of phase equilibrium for further efficient chemical process design and simulation. the analysis of the preliminary, less accurate results confirms the hypothesis to use {K}ohonen networks for classification of the classes as a correct one. Therefore, the {K}ohonen network architecture yielding the best separation of clusters was chosen for further analysis. It has been adapted and the training continued until the conflicting situations were resolved. Out of the several {K}ohonen networks trained the best one was analyzed. the maps of individual physical properties and the probability maps were obtained for each specific phase equilibrium. the correlation among maps is shown.",
}

@Article{inspek130_bibuniq_1233,
  author =       "Osaki T. and Ooshima N. and Saitoh T. and Konishi R.",
  title =        "Discrimination of herb species using self organizing map",
  journal =      "Transactions of the Society of Instrument and Control Engineers. 2005; 41(5): 383--7",
  pages =        "",
  year =         "2005",
  publisher =    "Soc. Instrum. \& Control Eng",
  abstract =     "Method of quartz crystal oscillator of odor sensors is drawing public attention recently. in this paper, we modulated gas flow used a magnetic valve in order to improve the rate of discrimination. Furthermore, we change into urethane resin the film applied to a quartz crystal oscillator, and also report the result which used and discriminated the triangular wave in gas flow modulation. We tried discrimination of five herb species (linden, chamomile, rose hips, lavender and lemon balm) using the BP (back propagation) method and {SOM} (self-organizing map). the BP method must use the neural network of three hierarchy, in order to discriminate five herb species. However, this method is complicated. Then, the improvement of discrimination method was done by using SOM, which is simple one, compared with the BP method. the obtained discrimination result is 96. 7\% and the complexity of discernment has improved.",
}

@InProceedings{inspek440_bibuniq_1083,
  author =       "Oshiro N. and Kurata K.",
  editor =       "H. {Sugisaka, M. ; Tanaka}",
  title =        "Information separation of position and direction of a robot by two interacting self-organizing maps",
  booktitle =    "Ninth International Symposium on Artificial Life and Robotics {AROB} 9th'04. 2004: 71--4 Vol. 1",
  pages =        "799",
  year =         "2004",
  publisher =    "Oita Univ, Oita, Japan",
  abstract =     "In this paper, we proposed a model to self-organize a map for a robot navigation by using visual information of itself. the robot is assumed to have visual sensors around it. the recognition model is based on {K}ohonen's {SOM} (self-organizing map), which was proposed as a model of self-organization of a cortex. Ordinary {SOM} consists of a two-dimensional array of neuron-like feature detector units. We want to extract the information of direction and position separately from visual input, which is a function of the two information factors. Our model consists of two layers. the first layer is for directional information and consists of units arranged in a circular array, and the second layer for position information and consists of a two-dimensional array. the units in the second layer accept inputs from all the units in the first layer through plastic inhibitory synapses. It would be shown by computer simulation that the units in the first layer develop direction sensitivity and lose position sensitivity through the training, while in the second layer, the units develop position sensitivity and lose direction sensitivity.",
}

@Article{inspek152_bibuniq_1255,
  author =       "Oshiro N. and Kurata K.",
  title =        "Separating visual information into position and direction by two inhibitory connected {SOM}s",
  journal =      "Artificial Life and Robotics. 2005; 9(2): 86--9",
  pages =        "",
  year =         "2005",
  publisher =    "Springer-Verlag",
  abstract =     "In this article, we propose a model to self-organize a map for robot navigation using its own visual information. the robot is assumed to have visual sensors around it. the recognition model is based on {K}ohonen's self-organizing map ({SOM}), which was proposed as a model of the self-organization of a cortex. An ordinary {SOM} consists of a two-dimensional array of neuron-like feature detector units. We want to extract the direction and position information separately from the visual input, which is a function of the two information factors. Our model consists of two layers. the first layer is for directional information, and consists of units arranged in a circular array, and the second layer is for position information, and consists of a two-dimensional array. the units in the second layer accept inputs from all the units in the first layer through plastic inhibitory synapses. It would be shown by computer simulation that the units in the first layer develop direction sensitivity and lose position sensitivity through training, while in the second layer, the units develop position sensitivity and lose direction sensitivity.",
}

@InProceedings{kohonen05amklc_bibuniq_5,
  author =       "Oskar {K}ohonen and Sakari Katajam{\"{a}}ki and Timo Honkela",
  title =        "In Search for Volta: Statistical Analysis of Word Patterns in Shakespeare's Sonnets",
  booktitle =    "Proceedings of AMKLC'05, International Symposium on Adaptive Models of Knowledge, Language and Cognition",
  pages =        "44--47",
  year =         "2005",
}

@Article{inspek847_bibuniq_655,
  author =       "Osowski S. and Do-Dinh-Nghia",
  title =        "Fourier and wavelet descriptors for shape recognition using neural networks-a comparative study",
  journal =      "Pattern Recognition. Sept. 2002; 35(9): 1949--57",
  pages =        "",
  year =         "2002",
  publisher =    "Elsevier",
  abstract =     "This paper presents the application of three different types of neural networks to the 2{D} pattern recognition on the basis of its shape. They include the multilayer perceptron (MLP), {K}ohonen self-organizing network and hybrid structure composed of the self-organizing layer and the MLP subnetwork connected in cascade. the recognition is based on the features extracted from the Fourier and wavelet transformations of the data, describing the shape of the pattern. Application of different neural network structures associated with different preprocessing of the data results in different accuracy of recognition and classification. the numerical experiments performed for the recognition of simulated shapes of the airplanes have shown the superiority of the wavelet preprocessing associated with the self-organizing neural network structure. the integration of the individual classifiers based on the weighted summation of the signals from the neural networks has been proposed and checked in numerical experiments.",
}

@InProceedings{inspek500_bibuniq_740,
  author =       "Otto T. D. and Meyer-Base A. and Hurdal M. and Sumners D. and Auer D. and Wismuller A.",
  title =        "Model-free functional {MRI} analysis using cluster-based methods",
  booktitle =    "Proceedings of the {SPIE} the International Society for Optical Engineering. 2003; 5103: 17--24",
  pages =        "",
  year =         "2003",
  publisher =    "{SPIE} Int. Soc. Opt. Eng",
  abstract =     "Conventional model-based or statistical analysis methods for functional MRI ({fMRI}) are easy to implement, and are effective in analyzing data with simple paradigms. However, they are not applicable in situations in which patterns of neural response are complicated and when {fMRI} response is unknown. in this paper the {"}neural gas{"} network is adapted and rigorously studied for analyzing {fMRI} data. the algorithm supports spatial connectivity aiding in the identification of activation sites in functional brain imaging. A comparison of this new method with {K}ohonen's self-organizing map and with a minimal free energy vector quantizer is done in a systematic {fMRI} study showing comparative quantitative evaluations. the most important findings in this paper are: (1) the {"}neural gas{"} network outperforms the other two methods in terms of detecting small activation areas, and (2) computed reference function several that the {"}neural gas{"} network outperforms the other two methods. the applicability of the new algorithm is demonstrated on experimental data.",
}

@Article{estevez05a_bibuniq_61,
  author =       "P. A. Estevez and C. J. Figueroa and K. Saito",
  title =        "Cross-entropy embedding of high-dimensional data using the neural gas model",
  journal =      "Neural Networks",
  year =         "2005",
  volume =       "18",
  number =       "5-6",
  month =        "June-July",
  pages =        "727--737",
}

@Article{chang05a_bibuniq_119,
  author =       "P. C. Chang and C. Y. Lai",
  title =        "A hybrid system combining self-organizing maps with case-based reasoning in wholesaler's new-release book forecasting",
  journal =      "Expert Systems With Applications",
  year =         "2005",
  volume =       "29",
  number =       "1",
  month =        "July",
  pages =        "183--192",
}

@Article{chamarthy04a_bibuniq_268,
  author =       "P. Chamarthy and R. J. Stanley and G. Cizek and R. Long and S. Antani and G. Thoma",
  title =        "Image analysis techniques for characterizing disc space narrowing in cervical vertebrae interfaces",
  journal =      "Computerized Medical Imaging and Graphics",
  year =         "2004",
  volume =       "28",
  number =       "1-2",
  month =        "January-March",
  pages =        "39--50",
}

@Article{xu05a_bibuniq_106,
  author =       "P. F. Xu and C. H. Chang and A. Paplinski",
  title =        "Self-organizing topological tree for online vector quantization and data clustering",
  journal =      "{IEEE} Transactions on Systems Man and Cybernetics Part B-Cybernetics",
  year =         "2005",
  volume =       "35",
  number =       "3",
  month =        "June",
  pages =        "515--526",
}

@Article{toivanen03a_bibuniq_303,
  author =       "P. J. Toivanen and J. Ansamaki and J. P. S. Parkkinen and J. Mielikainen",
  title =        "Edge detection in multispectral images using the self-organizing map",
  journal =      "Pattern Recognition Letters",
  year =         "2003",
  volume =       "24",
  number =       "16",
  month =        "December",
  pages =        "2987--2994",
}

@Article{kang03a_bibuniq_400,
  author =       "P. Kang and D. Birtwhistle",
  title =        "Condition assessment of power transformer onload tap changers using wavelet analysis and self-organizing map: Field evaluation",
  journal =      "{IEEE} Transactions on Power Delivery",
  year =         "2003",
  volume =       "18",
  number =       "1",
  month =        "January",
  pages =        "78--84",
}

@Article{kauraniemi04a_bibuniq_273,
  author =       "P. Kauraniemi and S. Hautaniemi and R. Autio and J. Astola and O. Monni and A. Elkahloun and A. Kallioniemi",
  title =        "Effects of Herceptin treatment on global gene expression patterns in {HER2}-amplified and nonamplified breast cancer cell lines",
  journal =      "Oncogene",
  year =         "2004",
  volume =       "23",
  number =       "4",
  month =        "January 29",
  pages =        "1010--1013",
}

@InProceedings{lehtimaki05a_bibuniq_83,
  author =       "P. Lehtimaki and K. Raivio",
  title =        "A {SOM} based approach for visualization of {GSM} network performance data",
  booktitle =    "Innovations in Applied Artificial Intelligence, Lecture Notes in Artificial Intelligence",
  year =         "2005",
  pages =        "75--83",
}

@InBook{c6_bibuniq_1795,
  author =       "P. Letrémy M. Cottrell",
  title =        "Connectionist Approaches in Economics and Management Sciences",
  chapter =      "Working times in atypical forms of employment: the special case of part-time work",
  publisher =    "Kluwer",
  year =         "2003",
  pages =        "111--129",
}

@InProceedings{c9_bibuniq_1798,
  author =       "P. Letrémy M. Cottrell",
  title =        "Analyzing surveys using the {K}ohonen algorithm",
  booktitle =    "Proc. {ESANN} 2003",
  pages =        "85--92",
  editor =       "M. Verleysen",
  address =      "Bruxelles, Belgium",
}

@InProceedings{c11_bibuniq_1800,
  author =       "P. Letrémy {M. Cottrell, S. Ibbou}",
  title =        "Traitement des données manquantes au moyen de l'algorithme de {K}ohonen",
  booktitle =    "Actes de la Dixième Conférence {ACSEG2003}",
  year =         "2003",
  address =      "Nantes, France",
}

@Article{li04b_bibuniq_175,
  author =       "P. Li and I. Farkas and B. MacWhinney",
  title =        "Early lexical development in a self-organizing neural network",
  journal =      "Neural Networks",
  year =         "2004",
  volume =       "17",
  number =       "8-9",
  month =        "October-November",
  pages =        "1345--1362",
}

@Article{Lingras00b_bibuniq_3923,
  author =       "P. Lingras and M. Hogo and M. Snorek",
  title =        "Interval set clustering of Web users using modified {K}ohonen self-organizing maps based on the properties of rough sets",
  journal =      "Web Intelligence and Agent-Systems. 2004; 2(3): 217-25",
  year =         "2004",
}

@Article{Lingras00a_bibuniq_3901,
  author =       "P. Lingras and Rui Yan and M. Hogo",
  title =        "Evolutionary, neural, and statistical approaches to interval clustering for Web mining",
  journal =      "Journal of Intelligent-Systems. 2004; 13(4): 329-50",
  year =         "2004",
}

@Article{Mautner00a_bibuniq_4072,
  author =       "P. Mautner and T. Marsalek and O. Rohlik and V. Matousek",
  title =        "Using of {ART}-2 and {SOM} for signature verification",
  journal =      "Wseas Transactions on Electronics. July 2004; 1(3): 488-93",
  year =         "2004",
}

@Article{muneesawang02a_bibuniq_447,
  author =       "P. Muneesawang and L. Guan",
  title =        "Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2002",
  volume =       "13",
  number =       "4",
  month =        "July",
  pages =        "821--834",
}

@Article{email_sungatan_bibuniq_1801,
  author =       "P. N. Suganthan",
  title =        "Shape indexing using self-organising maps",
  journal =      "{IEEE} Transactions on Neural Networks",
  year =         "2002",
  volume =       "13",
  number =       "4",
  pages =        "835--840",
  month =        "July",
}

@Article{ozdzynski02a_bibuniq_457,
  author =       "P. Ozdzynski and A. Lin and M. Liljeholm and J. Beatty",
  title =        "A parallel general implementation of {K}ohonen's self-organizing map algorithm: performance and scalability",
  journal =      "Neurocomputing",
  year =         "2002",
  volume =       "44",
  month =        "June",
  pages =        "567--571",
}

@Article{Jha03a_bibuniq_1497,
  author =       "P. P. Jha and J. Glassey and G. A. Montague and P. Mohan",
  title =        "Product cost management structures: a review and neural network modelling",
  journal =      "Australian-Journal of Information-Systems. Sept. 2003; 11(1): 76-90",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "We review the growth of approaches in product costing and draw synergies with information management and resource planning systems, to investigate potential application of state of the art modelling techniques of neural networks. Increasing demands on costing systems to serve multiple decision-making objectives, have made it essential to use better techniques for analysis of available data. This need is highlighted. the approach of neural networks, which have several analogous facets to complement and aid the information demands of modem product costing, enterprise resource planning (ERP) structures and the dominant-computing environment (for information management in the object oriented paradigm) form the domain for investigation. Simulated data is used in neural network applications across activities that consume resources and deliver products, to generate information for monitoring and control decisions. the results in application for feature extraction and variation detection and their implications are presented.",
}

@Article{lin02a_bibuniq_396,
  author =       "P. P. Lin and K. Jules",
  title =        "An intelligent system for monitoring the microgravity environment quality on-board the international space station",
  journal =      "{IEEE} Transactions on Instrumentation and Measurement",
  year =         "2002",
  volume =       "51",
  number =       "5",
  month =        "October",
  pages =        "1002--1009",
}

@InProceedings{Povalej00a_bibuniq_3962,
  author =       "P. Povalej and M. Lenic and G. Stiglic and T. Welzer and P. Kokol",
  title =        "Improving classification accuracy using cellular automata",
  booktitle =    "Knowledge-Based Intelligent-Information and Engineering-Systems. 8th International Conference, KES-2004. Proceedings Lecture Notes in Artificial Intelligence Vol. 3214. 2004: 1025-31 Vol. 2",
  year =         "2004",
}

@Article{c4_bibuniq_1793,
  author =       "P. Rousset {M. Cottrell, S. Ibbou, P. Letrémy}",
  title =        "Cartes organisées pour l'analyse exploratoire des données et la visualisation",
  journal =      "Journal de la Société Française de Statistique",
  year =         "2003",
  volume =       "144",
  number =       "4",
  pages =        "67--106",
}

@Article{schneider03a_bibuniq_301,
  author =       "P. Schneider and G. Schneider",
  title =        "Collection of bioactive reference compounds for focused library design",
  journal =      "{QSAR} \& Combinatorial Science",
  year =         "2003",
  volume =       "22",
  number =       "7",
  month =        "October",
  pages =        "713--718",
}

@Article{somervuo03a_bibuniq_355,
  author =       "P. Somervuo",
  title =        "Speech dimensionality analysis on hypercubical self-organizing maps",
  journal =      "Neural Processing Letters",
  year =         "2003",
  volume =       "17",
  number =       "2",
  month =        "April",
  pages =        "125--136",
}

@Article{Tino03a_bibuniq_1622,
  author =       "P. Tino and G. Polcicova",
  title =        "Topographic organization of user preference patterns in collaborative filtering",
  journal =      "Neural-Network-World. 2003; 13(3): 311-24",
  year =         "2003",
  volume =       "",
  pages =        "",
  abstract =     "We introduce topographic versions of two latent class models for collaborative filtering. Topographic organization of latent classes makes orientation in rating/preference patterns captured by the latent classes easier and more systematic. Furthermore, since we deal with probabilistic models of the data, we can readily use tools from probability and information theories to interpret and visualize information extracted by the model. We apply our models to a large collection of user ratings for films.",
}

@Article{toiviainen03a_bibuniq_332,
  author =       "P. Toiviainen and C. L. Krumhansl",
  title =        "Measuring and modeling real-time responses to music: the dynamics of tonality induction",
  journal =      "Perception",
  year =         "2003",
  volume =       "73",
  number =       "13",
  month =        "August 15",
  pages =        "1705--1719",
}

@InProceedings{inspek748_bibuniq_570,
  author =       "Padoan A. C. Jr. and Araujo A. F. R. and de-A-Barreto G.",
  title =        "Dynamic modeling of robotic trajectories using the parametrized {SOM}",
  booktitle =    "Proceedings 7th Brazilian Symposium on Neural Networks",
  pages =        "",
  year =         "2002",
  publisher =    "IEEE Comput. Soc, Los Alamitos, CA, USA",
  abstract =     "Planning and control of robotic trajectories is an important and open issue. This paper uses an unsupervised neural network model to construct the dynamical modelling of trajectories. A neural network with a short term memory mechanism, was designed to provide the associated joint angles when it receives as input the present and some past states of the robot spatial position. the model uses the self-organizing map ({SOM}) to approximate the mapping using just some states of the trajectory.",
}

@InProceedings{inspek243_bibuniq_1332,
  author =       "Paiva A. R. C. and Principe J. C. and Sanchez J. C.",
  title =        "Compression of spike data using the self-organizing map",
  booktitle =    "2nd International {IEEE}/EMBS Conference on Neural Engineering",
  pages =        "233--236",
  year =         "2005",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "Motivated by current attempts to use wireless in brain-machine interfaces (BMIs), this paper presents a method for the compression of spike data. Supported by vector quantization (VQ) theory, we use a 1-dimensional self-organizing map ({SOM}) to quantize vectors of input samples. the indices are entropy coded to further reduce the necessary bandwidth, taking advantage of the non-uniform frequency of firing of the {SOM} processing elements (PEs). the complexity of the use of the {SOM} is also considered and addressed. After training several SOMs, the method was simulated with real data achieving compression ratios as high as 185. 7:1, i. e. a bitrate of 862 bits-per-second-per-channel, assuming sampling at 20 kHz with 8 bits-per-sample (bps).",
}

@InProceedings{inspek374_bibuniq_1022,
  author =       "Pakkanen J. and Iivarinen J.",
  title =        "A novel self-organizing neural network for defect image classification",
  booktitle =    "2004 {IEEE} International Joint Conference on Neural Networks",
  pages =        "2553--2556",
  volume =       "4",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this paper a novel self-organizing neural network called the evolving tree is applied to classification of defect images. the evolving tree resembles the self-organizing map ({SOM}) but it has several advantages over the SOM. Experiments present a comparison between a normal SOM, a supervised SOM, and the evolving tree algorithm for classification of defect images that are taken from a real web inspection system. the MPEG-7 standard feature descriptors are applied. the results show that the evolving tree provides better classification accuracies and reduced computational costs over the normal SOMs.",
}

@Article{inspek310_bibuniq_962,
  author =       "Pakkanen J. and Iivarinen J. and Oja E.",
  title =        "The evolving tree - a novel self-organizing network for data analysis",
  journal =      "Neural Processing Letters. Dec. 2004; 20(3): 199--211",
  pages =        "",
  year =         "2004",
  publisher =    "Kluwer Academic Publishers",
  abstract =     "The self-organizing map ({SOM}) is one of the best known and most popular neural network-based data analysis tools. Many variants of the {SOM} have been proposed, like the neural gas by Martinetz and Schulten, the growing cell structures by Fritzke, and the tree-structured {SOM} by Koikkalainen and Oja. the purpose of such variants is either to make a more flexible topology, suitable for complex data analysis problems or to reduce the computational requirements of the SOM, especially the time-consuming search for the best-matching unit in large maps. We propose here a new variant called the evolving tree which tries to combine both of these advantages. the nodes are arranged in a tree topology that is allowed to grow when any given branch receives a lot of hits from the training vectors. the search for the best matching unit and its neighbors is conducted along the tree and is therefore very efficient. A comparison experiment with high dimensional real world data shows that the performance of the proposed method is better than some classical variants of SOM.",
}

@Article{inspek561_bibuniq_773,
  author =       "Pampalk E.",
  title =        "Islands of Music: Analysis, Organization and Visualization of Music Archives",
  journal =      "OEGAI Journal-. Dec. 2003; 22(4): 20--3",
  pages =        "",
  year =         "2003",
  publisher =    "Osterreichische Gesellschaft fur Artificial Intelligence",
  abstract =     "Islands of Music are a graphical user interface to music collections based on a metaphor of geographic maps. the thesis deals with the challenges involved in the automatic creation of such interfaces given only raw music data (e. g. MP3s) without any further information such as to which genres the pieces of music belong. the main challenge is to teach machines how to listen to music, i. e. how to calculate the perceived similarity of two pieces of music. An approach based on psychoacoustic models is presented which focuses on the dynamic properties of music. Using a neural network algorithm, namely the self-organizing map, the music collection is organized and using a novel visualization technique the map of islands is created. Furthermore, methods to automatically find descriptions for the mountains and hills are demonstrated.",
}

@Article{inspek287_bibuniq_944,
  author =       "Pampalk E. and Dixon S. and Widmer G.",
  title =        "Exploring music collections by browsing different views",
  journal =      "Computer Music Journal. Summer 2004; 28(2): 49--62",
  pages =        "",
  year =         "2004",
  publisher =    "MIT Press",
  abstract =     "Music similarity as such might appear to be a rather simple concept. in this article, we present a new approach to combining information extracted from audio with meta-information such as artist or genre. in particular, we extract spectrum and periodicity histograms to roughly describe timbre and rhythm, respectively. For each of these aspects of similarity, the collection is organized using a self-organizing map (SOM; {K}ohonen 1982, 2001). the {SOM} arranges the pieces of music on a map such that similar pieces are located near each other. We use smoothed data histograms to visualize the cluster structure and to create an {"}islands of music{"} metaphor where groups of similar pieces are visualized as islands. We integrate a third type of organization that is not derived from audio analysis. This could be based on meta-data such as artist or genre information, or it could be any arbitrary user-defined organization. We align these three different views and interpolate between them using Aligned SOMs (Pampalk et al. 2003b). the user is able to browse the collection and interactively explore different aspects by gradually changing focus from one view to another. We demonstrate our approach on a small music collection. in this article, we present the spectrum and periodicity histograms used to calculate similarities from the respective viewpoints.",
}

@Article{inspek265_bibuniq_931,
  author =       "Pampalk E. and Widmer G. and Chan A.",
  title =        "A new approach to hierarchical clustering and structuring of data with self-organizing maps",
  journal =      "Intelligent Data Analysis. 2004; 8(2): 131--49",
  pages =        "",
  year =         "2004",
  publisher =    "IOS Press",
  abstract =     "The self-organizing map ({SOM}) is a powerful tool for exploratory data analysis, which has been employed in a wide range of data mining applications. We present a novel approach to reveal the inherent hierarchical structure of data using multiple SOMs together with heuristics, which optimize the stability. in particular, we address shortcomings of the growing hierarchical self-organizing map (GHSOM) regarding the decision which areas in the hierarchical structure need to be represented by a finer granularity and which areas do not. We introduce the tension and mapping ratio extension to exploit specific characteristics of the {SOM} based on the topology preservation. As a main result, in contrast to the GHSOM, the inherent hierarchical structure of the data is revealed without requiring the user to define a threshold parameter, which controls the map sizes of the individual SOMs. We evaluate our approach using data from real-world data mining projects in the music domain.",
}

@Article{Pan00b_bibuniq_3999,
  author =       "Pan Zhi Song and Chen Song Can and Zhang Dao Qiang",
  title =        "Generalized grey {SOM} and their performance evaluations",
  journal =      "Chinese-Journal of Computers. April 2004; 27(4): 530-4",
  year =         "2004",
}

@Article{Pan00a_bibuniq_3917,
  author =       "Pan Zhi song and Chen Song can and Zhang Dao qiang",
  title =        "A kernel-based {SOM} classification in input space",
  journal =      "Acta-Electronica-Sinica. Feb. 2004; 32(2): 227-31",
  year =         "2004",
}

@InProceedings{inspek339_bibuniq_989,
  author =       "Panazio C. M. and de-F-Attux R. R.",
  editor =       "S. {Barros, A., ; Principe, J. ; Larsen, J. ; Adali, T. ; Douglas}",
  title =        "A 4/sup {N}/-{QAM} adaptive decision device to mitigate {I}/{Q} imbalance and impairments caused by time-varying flat fading channels",
  booktitle =    "Machine Learning for Signal Processing XIV. Proceedings of the 2004 {IEEE} Signal Processing Society Workshop",
  pages =        "665--674",
  year =         "2004",
  publisher =    "IEEE, Piscataway, NJ, USA",
  abstract =     "In this work, we propose an adaptive decision device based on a {K}ohonen network that can automatically generate the classes associated with each symbol of a 4/sup n/-QAM in the presence of non-linearities caused by the I/Q imbalance and additive Gaussian white noise, being also capable of compensating phase and gain variations produced by a time-varying flat-fading channel. Our proposal can achieve optimality in the maximum-likelihood sense with a small computational cost. Furthermore, due to the tracking ability inherent to the devised scheme, there is no need for an automatic gain controller or a phase-locked loop.",
}

@Article{inspek276_bibuniq_1348,
  author =       "Panek J. J. and Jezierska A. and Vracko M.",
  title =        "Kohonen network study of aromatic compounds based on electronic and nonelectronic structure descriptors",
  journal =      "Journal of Chemical Information and Modeling. March April 2005; 45(2): 264--72",
  pages =        "",
  year =         "2005",
  publisher =    "ACS",
  abstract =     "Atoms in molecules (AIM) and electron localization function (ELF) methodologies were applied to describe the electronic structure of 88 aromatic compounds. the analyzed database contains molecules substituted by nucleophilic and electrophilic groups which are responsible for electron density distribution in the molecule and further for its reactivity. Radial distribution function (RDF), weighted holistic invariant molecular (WHIM), thr