ESANN 1999: Bruges, Belgium

Dynamical systems

• Frank Pasemann:
  Synchronizing chaotic neuromodules. 1-6
  
• Emmanuel Daucé, Olivier Moynot, Olivier Pinaud, Manuel Samuelides, Bernard Doyon:
  Mean-field equations reveal synchronization in a 2-populations neural network model. 7-12
  

Self-organization

• Stefan Schünemann, Bernd Michaelis:
  A hierarchical self-organizing feature map for analysis of not well separable clusters of different feature density. 13-18
  
• Eric de Bodt, Marie Cottrell, Michel Verleysen:
  Using the Kohonen algorithm for quick initialization of Simple Competitive Learning algorithm. 19-26
  

Special session: Adaptive computation of data structures

• Marco Gori:
  Learning in structured domains. 27-32
  
• Barbara Hammer:
  Approximation capabilities of folding networks. 33-38
  
• Andreas Küchler:
  Tree-recursive computation of gradient information for structures. 39-44
  
• Christoph Goller:
  Learning search-control heuristics for automated deduction systems with folding architecture networks. 45-50
  
• Fabrizio Costa, Paolo Frasconi, Giovanni Soda:
  A topological transformation for hidden recursive modelsarchitecture networks. 51-56
  
• Mikel L. Forcada, Antonio M. Corbí-Bellot, Marco Gori, Marco Maggini:
  Neural learning of approximate simple regular languages. 57-62
  
• Markus Hagenbuchner, Ah Chung Tsoi:
  A benchmark for testing adaptive systems on structured data. 63-68
  

Methodology

• Peter J. Edwards, Alan F. Murray, Georgios Papadopoulos, A. Robin Wallace, John Barnard:
  The application of neural networks to the paper-making industry. 69-74
  
• Juan Martínez-Cabeza-de-Vaca-Alajarín, Luis-Manuel Tomás-Balibrea:
  Marble slabs quality classification system using texture recognition and neural networks methodology. 75-80
  
• Andrea Corradini, Hans-Joachim Böhme, Horst-Michael Gross:
  Visual-based posture recognition using hybrid neural networks. 81-86
  
• Bart Bakker, Tom Heskes:
  Model clustering by deterministic annealing. 87-92
  

Special session: Remote sensing spectral image analysis

• Erzsébet Merényi:
  The challenges in spectral image analysis: an introduction, and review of ANN approaches. 93-98
  
• Neil Pendock:
  A simple associative neural network for producing spatially homogenous spectral abundance interpretations of hyperspectral imagery. 99-104
  
• Jörg Bruske, Erzsébet Merényi:
  Estimating the intrinsic dimensionality of hyperspectral images. 105-110
  
• Thomas Villmann:
  Benefits and limits of the self-organizing map and its variants in the area of satellite remote sensoring processing. 111-116
  
• Donald MacDonald, Stephen McGlinchey, John Kawala, Colin Fyfe:
  Comparison of Kohonen, scale-invariant and GTM self-organising maps for interpretation of spectral data. 117-122
  

ANN models and learning I

• Terry Windeatt, Reza Ghaderi:
  AdaBoost and neural networks. 123-128
  
• Francesca Acerra, Yves Burnod, Scania de Schonen:
  Modeling face recognition learning in early infant development. 129-134
  
• John Carney, Padraig Cunningham:
  The NeuralBAG algorithm: optimizing generalization performance in bagged neural networks. 135-140
  
• Valentina Colla, Leonardo Maria Reyneri, Mirko Sgarbi:
  Neuro-wavelet parametric characterization of hardness profiles. 141-146
  
• Shin Mizutani, Katsunori Shimohara:
  Heterogeneity enhanced order in a chaotic neural network. 147-152
  
• Christophe Lecerf:
  Tackling the stability/plasticity dilemma with double loop dynamic systems. 153-158
  

Biological models and inspiration

• John A. Bullinaria, Patricia M. Riddell, Simon K. Rushton:
  Regularization in oculomotor adaptation. 159-164
  
• Heiko Neumann, Wolfgang Sepp:
  Recurrent V1-V2 interaction for early visual information processing. 165-170
  
• Katrin Suder, Florentin Wörgötter, Thomas Wennekers:
  Neural field description of state-dependent receptive field changes in the visual cortex. 171-176
  

Special session: Support Vector Machines

• James Tin-Yau Kwok:
  Integrating the evidence framework and the support vector machine. 177-182
  
• Koji Tsuda:
  Support vector classifier with asymetric kernel function. 183-188
  
• Nello Cristianini, Colin Campbell, John Shawe-Taylor:
  A multiplicative updating algorithm for training support vector machine. 189-194
  
• Rodrigo Fernández, Emmanuel Viennet:
  Face identification using support vector machines. 195-200
  
• Arnaud Buhot, Mirta B. Gordon:
  Statistical mechanics of support vector machine. 201-206
  
• Pierre M. L. Drezet, Robert F. Harrison:
  An efficient formulation of sparsity controlled support vector regression. 207-212
  
• Davide Mattera, Francesco Palmieri, Simon Haykin:
  Generalized support vector machines. 213-218
  
• Jason Weston, Chris Watkins:
  Support vector machines for multi-class pattern recognition. 219-224
  
• Massimiliano Pontil, Ryan M. Rifkin, Theodoros Evgeniou:
  From regression to classification in support vector machines. 225-230
  
• Michèle Sebag:
  From first order logic to Nd: a data driven reformulation. 231-236
  
• Christian Wöhler, Ulrich Kressel, Jürgen Schürmann, Joachim K. Anlauf:
  Dimensionality reduction by local processing. 237-244
  
• Thilo-Thomas Frieß, Robert F. Harrison:
  A kernel based adaline. 245-250
  
• David M. J. Tax, Robert P. W. Duin:
  Data domain description using support vectors. 251-256
  
• N. Barabino, M. Pallavicini, A. Petrolini, Massimiliano Pontil, Alessandro Verri:
  Support vector machines vs multi-layer perceptrons in particle identification. 257-262
  

ANN models and learning II

• Hervé Frezza-Buet, Frédéric Alexandre:
  Specialization with cortical models: An application to causality learning. 263-268
  
• Arnaud Ribert, Abdel Ennaji, Yves Lecourtier:
  Generalisation capabilities of a distributed neural classifier. 269-274
  
• Simone Fiori, Francesco Piazza:
  A comparison of three PCA neural techniques. 275-280
  
• Donald MacDonald, Darryl Charles, Colin Fyfe:
  Neural networks which identify composite factors. 281-288
  
• Kamal R. Al-Rawi, Consuelo Gonzalo, Agueda Arquero:
  Supervised Art-II: a new neural network architecture, with quicker learning algorithm, for learning and classifying multivaled input patterns. 289-294
  

Classification

• Heiko Wersing, Helge Ritter:
  Feature binding and relaxation labeling with the competitive layer model. 295-300
  
• Christian Wöhler, Jürgen Schürmann, Joachim K. Anlauf:
  Segmentation-free detection of overtaking vehicles with a two-stage time-delay neural network classifier. 301-306
  
• Roelof K. Brouwer:
  An integer recurrent artificial neural network for classifying feature vectors. 307-312
  
• L. B. Jack, Asoke K. Nandi:
  Feature selection for ANNs using genetic algorithms in condition monitoring. 313-318
  

Special session: Information extraction using unsupervised neural networks

• Colin Fyfe:
  Trends in Unsupervised Learning. 319-326
  
• Arnaud Buhot, Mirta B. Gordon:
  Detection of two Gaussian clusters. 327-332
  
• Francesco Palmieri, Alessandra Budillon, Davide Mattera:
  Independent component analysis for mixture densities. 333-338
  
• Nicolas Donckers, Amaury Lendasse, Vincent Wertz, Michel Verleysen:
  Extraction of intrinsic dimension using CCA - Application to blind sources separation. 339-344
  
• Darryl Charles, Colin Fyfe:
  Noise to extract independent causes. 345-350
  
• Jan van den Berg, Martijn J. Schuemie:
  Information retrieval systems using an associative conceptual space. 351-356
  
• Arnaud Revel, Philippe Gaussier, Jean-Paul Banquet:
  Taking inspiration from the Hippocampus can help solving robotics problems. 357-362
  

ANN models and learning III

• Valentina Colla, Leonardo Maria Reyneri, Mirko Sgarbi:
  Orthogonal least square algorithm applied to the initialization of multi-layer perceptrons. 363-369
  
• Jochen J. Steil, Helge Ritter:
  Maximisation of stability ranges for recurrent neural networks subject to on-line adaptation. 370-374
  
• Ramón P. Ñeco, Mikel L. Forcada, Rafael C. Carrasco, M. Ángeles Valdés-Muñoz:
  Encoding of sequential translators in discrete-time recurrent neural nets. 375-380
  
• Antoine Fache, Olivier Dubois, Alain Billat:
  On the invertibility of the RBF model in a predictive control strategy. 381-386
  
• Anton M. Sirota, Alexander A. Frolov, Dusan Húsek:
  Nonlinear factorization in sparsely encoded Hopfield-like neural networks. 387-392
  
• Bruno Crespi, Ignazio Lazzizzera:
  Storage capacity and dynamics of nonmonotonic networks. 393-398
  
• Fabien Belloir, Antoine Fache, Alain Billat:
  A general approach to construct RBF net-based classifier. 399-404
  
• Stefan Liehr, Klaus Pawelzik, Jens Kohlmorgen, Steven Lemm, Klaus-Robert Müller:
  Hidden Markov gating for prediction of change points in switching dynamical systems. 405-410
  
• Christian W. Eurich, Thorsten Conradi, Helmut Schwegler:
  Critical and non-critical avalanche behavior in networks of integrate-and-fire neurons. 411-416
  

Special session: Spiking neurons

• Thomas Natschläger, Wolfgang Maass:
  Fast analog computation in networks of spiking neurons using unreliable synapses. 417-422
  
• Philipp Häfliger:
  Learning a temporal code. 423-428
  
• Michael Schmitt:
  VC dimension bounds for networks of spiking neurons. 429-434
  
• Stefan D. Wilke, Christian W. Eurich:
  What does a neuron talk about? 435-440
  

Temporal series

• Jean-Marc Boite, Christophe Ris:
  Development of a French speech recognizer using a hybrid HMM/MLP system. 441-446
  
• Yves Moreau, Ellen Lerouge, Herman Verrelst, Joos Vandewalle, Christof Störmann, Peter Burge:
  A hybrid system for fraud detection in mobile communications. 447-454
  
• Joseph Rynkiewicz:
  Hybrid HMM/MLP models for times series prediction. 455-462
  

Last update Thu May 24 04:17:18 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page