ESANN 2000: Bruges, Belgium

Data and signal analysis

• Mark Girolami:
  A generative model for sparse discrete binary data with non-uniform categorical priors. 1-6
  
• Michael Dittenbach, Dieter Merkl, Andreas Rauber:
  Using Growing hierarchical self-organizing maps for document classification. 7-12
  
• John Aldo Lee, Amaury Lendasse, Nicolas Donckers, Michel Verleysen:
  A robust non-linear projection method. 13-20
  
• Jordi Solé i Casals, Anisse Taleb, Christian Jutten:
  Parametric approach to blind deconvolution of nonlinear channels. 21-26
  

Support Vector Machines

• Colin Campbell:
  Algorithmic approaches to training Support Vector Machines: a survey. 27-36
  
• Johan A. K. Suykens, Lukas Lukas, Joos Vandewalle:
  Sparse least squares Support Vector Machine classifiers. 37-42
  
• Dominique Martinez, Gilles Millerioux:
  Support Vector Committee Machines. 43-48
  
• Ralf Herbrich, Thore Graepel, Colin Campbell:
  Robust Bayes Point Machines. 49-54
  

Model selection and evaluation

• Joaquín Pizarro Junquera, Elisa Guerrero Vázquez, Pedro L. Galindo:
  A statistical model selection strategy applied to neural networks. 55-60
  
• Riadh Kallel, Marie Cottrell, Vincent Vigneron:
  Bootstrap for neural model selection. 61-68
  
• Masashi Sugiyama, Hidemitsu Ogawa:
  A new information criterion for the selection of subspace models. 69-74
  
• Georgios Papadopoulos, Peter J. Edwards, Alan F. Murray:
  Confidence estimation methods for neural networks : a practical comparison. 75-80
  

Artificial neural networks and robotics

• Richard J. Duro, José Santos Reyes, José Antonio Becerra, Francisco Bellas, José Luis Crespo:
  Using higher order synapses and nodes to improve sensing capabilities of mobile robots. 81-88
  
• Elsa Fernandez, Imanol Echave, Manuel Graña:
  Competitive neural networks for robust computation of the optical flow. 89-94
  
• Francesco Panerai, Giorgio Metta, Giulio Sandini:
  Learning VOR-like stabilization reflexes in robots. 95-102
  
• Roberto Iglesias, Manuel Fernández Delgado, Senén Barro:
  Learning of perceptual states in the design of an adaptive wall-following behavior. 103-108
  

ANN models and learning I

• Wen-Jyi Hwang, Chien-Min Ou, Shi-Chiang Liao, Ching-Fung Chine:
  Fuzzy entropy-constrained competitive learning algorithm. 109-116
  
• Gianluigi Rech:
  Specification, estimation and evaluation of single hidden-layer feedforward autoregressive artificial neural network models. 117-122
  
• Lu Wei, Jagath C. Rajapakse:
  A neural network for undercomplete independent component analysis. 123-128
  
• Aleksandar Lazarevic, Dragoljub Pokrajac, Zoran Obradovic:
  Distributed clustering and local regression for knowledge discovery in multiple spatial databases. 129-134
  
• Frank Heister, Gregor Schock:
  Nonlinear, statistical data-analysis for the optimal construction of neural-network inputs with the concept of a mutual information. 439-444
  
• Mercedes Fernández-Redondo, Carlos Hernández-Espinosa:
  Influence of weight-decay training in input selection methods. 135-140
  
• Peter J. Edwards, Alan F. Murray:
  Committee formation for reliable and accurate neural prediction in industry. 141-146
  

Non-linear dynamics and control

• Toru Ohira:
  Toward encryption with neural network analogy. 147-152
  
• Ping Jiang, Rolf Unbehauen:
  Iterative learning neural network control for nonlinear system trajectory tracking. 153-158
  
• Piero A. Gili, Manuela Battipede:
  A comparative design of a MIMO neural adaptive rate damping for a nonlinear helicopter model. 159-164
  

Neural networks in medicine

• Thomas Villmann:
  Neural networks approaches in medicine - a review of actual developments. 165-176
  
• Tim W. Nattkemper, Heiko Wersing, Walter Schubert, Helge Ritter:
  A neural network architecture for automatic segmentation of fluorescence micrographs. 177-182
  
• Guojun Bao, Jagath C. Rajapakse:
  Boundary based movement correction of functional MR data using a genetic algorithm. 183-188
  
• Axel Wismüller, Frank Vietze, Dominik R. Dersch, Klaus Hahn, Helge Ritter:
  A neural network approach to adaptive pattern analysis - the deformable feature map. 189-194
  
• Armando Bazzani, Alessandro Bevilacqua, Dante Bollini, Rosa Brancaccio, Renato Campanini, Nico Lanconelli, Alessandro Riccardi, Davide Romani, Gianluca Zamboni:
  Automatic detection of clustered microcalcifications in digital mammograms using an SVM classifier. 195-200
  
• Rüdiger W. Brause, F. Friedrich:
  A neuro-fuzzy approach as medical diagnostic interface. 201-206
  

ANN models and learning II

• John A. Bullinaria, Patricia M. Riddell:
  Regularization in oculomotor control. 207-212
  
• Barbara Hammer:
  Limitations of hybrid systems. 213-218
  
• Elio D. Di Claudio, Raffaele Parisi, Gianni Orlandi:
  Discriminative learning for neural decision feedback equalizers. 219-226
  
• M. A. Torres, M. E. Pardo, J. M. Pupo, Luciano Boquete, Rafael Barea, Luis Miguel Bergasa:
  Neurocontrol of a binary distillation column. 227-232
  
• Rafael Barea, Luciano Boquete, Manuel Mazo, Elena López Guillén, Luis Miguel Bergasa:
  E.O.G. guidance of a weelchair using spiking neural networks. 233-238
  

Self-organizing maps for data analysis

• Jouko Lampinen, Timo Kostiainen:
  Self-Organizing Maps in data analysis - notes on overfitting and overinterpretation. 239-244
  
• Eric de Bodt, Marie Cottrell:
  Bootstrapping Self-Organizing Maps to assess the statistical significance of local proximity. 245-254
  
• Arnulfo P. Azcarraga:
  Evaluating SOMs using order metrics. 255-260
  
• John A. Flanagan:
  Self-Organisation in the SOM with a finite number of possible inputs. 261-266
  
• Mustapha Lebbah, Fouad Badran, Sylvie Thiria:
  Topological map for binary data. 267-272
  
• Markus Varsta, Jukka Heikkonen, Jouko Lampinen:
  Analytical comparison of the Temporal Kohonen Map and the Recurrent Self Organizing Map. 273-280
  

Recurrent networks

• Jochen J. Steil:
  Local input-output stability of recurrent networks with time-varying weights. 281-286
  
• Zalán Heszberger, József Bíró, Edith Halász:
  An optimization neural network model with time-dependent and lossy dynamics. 287-292
  
• Romuald Boné, Michel Crucianu, Jean Pierre Asselin de Beauville:
  An algorithm for the addition of time-delayed connections to recurrent neural networks. 293-298
  

Time series prediction

• Johan A. K. Suykens, Joos Vandewalle:
  The K.U.Leuven competition data: a challenge for advanced neural network techniques. 299-304
  
• James McNames:
  Local model optimization for time series prediction. 305-310
  
• Gianluca Bontempi, Mauro Birattari:
  A multi-steap ahead prediction method based on local dynamic properties. 311-316
  
• Ulrich Parlitz, Christian Merkwirth:
  Nonlinear prediction of spatio-temporal time series. 317-322
  
• Maurits D. Out, Walter A. Kosters:
  A Bayesian approach to combined neural networks forecasting. 323-328
  
• Amaury Lendasse, John Aldo Lee, Vincent Wertz, Michel Verleysen:
  Time series forecasting using CCA and Kohonen maps - application to electricity consumption. 329-334
  
• Anna Lombardi, Antonio Vicino:
  Financial predictions based on bootstrap-neural networks. 335-340
  
• Skander Soltani:
  On the use of the wavelet decomposition for time series prediction. 341-346
  
• Luis Monzón Benítez, Ademar Ferreira, Diana I. Pedreira Iparraguirre:
  Chaotic time series prediction using the Kohonen algorithm. 347-352
  
• Patrick Rousset:
  Curve forecast with the SOM algorithm: using a tool to follow the time on a Kohonen map. 353-358
  

ANN models and learning III

• Thomas Voegtlin:
  Learning principal components in a contextual space. 359-364
  
• Valentina Colla, Leonardo Maria Reyneri, Mirko Sgarbi:
  Training activation function in parametric classification. 365-370
  
• Jun-ichi Inoue:
  Quantum iterative algorithm for image reconstruction problems. 371-376
  
• Sven Buchholz, Gerald Sommer:
  Quaternionic spinor MLP. 377-382
  
• Bernard Girau:
  Simplified neural architectures for symmetric boolean functions. 383-388
  

Artificial neural networks for energy management systems

• Gonzalo Joya Caparrós:
  Connectionist solutions for energy management systems. 389-400
  
• Christian Rehtanz, Carsten Leder:
  Stability assessment of electric power systems using growing neural gas and self-organizing maps. 401-406
  
• José Nuno Fidalgo, João Abel Peças Lopes:
  Load forecasting dealing with medium voltage network reconfiguration. 407-412
  
• Elena Savelieva, Alexey Kravetski, Sergey Chernov, Vasiliy V. Demyanov, Vadim Timonin, Rafael V. Arutyunyan, Leonid Aleksandrovich Bolshov, Mikhail F. Kanevski:
  Application of MLP and stochastic simulations for electricity load forecasting in Russia. 413-418
  

Learning in biological and artificial systems

• Sander M. Bohte, Joost N. Kok, Johannes A. La Poutré:
  SpikeProp: backpropagation for networks of spiking neurons. 419-424
  
• Gaetano Liborio Aiello, Paul Bach-y-Rita:
  Nonsynaptically connected neural nets. 425-430
  
• Winfried A. Fellenz, John G. Taylor:
  Establishing retinotopy by lateral-inhibition type homogeneous neural fields. 431-436
  

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