@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 Fei