ESANN 1996: Bruges, Belgium

Learning and generalization I

• Nicolas Pican:
  Synaptic efficiency modulations for context integration: The meta ODWE architecture.
  
• Colin McCormack:
  Using a Meta Neural Network for RPROP parameter adaptation.
  
• Christian Goerick, Werner von Seelen:
  On unlearnable problems -or- A model for premature saturation in backpropagation learning.
  
• Hubert A. B. te Braake, H. J. L. van Can, Gerrit van Straten, Henk B. Verbruggen:
  Regulated Activation Weights Neural Network (RAWN).
  
• Jörg Bruske, Ingo Ahrns, Gerald Sommer:
  Praticing Q-learning.
  

Recurrent models

• Jean-Philippe Draye, Davor Pavisic, Guy Cheron, Gaetan Libert:
  Adaptative time constants improve the dynamic features of recurrent neural networks.
  
• Myriam Mokhtari, Herman Akdag:
  An adaptive technique for pattern recognition by the random neural network.
  
• Davor Pavisic, Jean-Philippe Draye, Roberto Teran, Gustavo Calderon, Guy Cheron, Gaetan Libert:
  Negative initial weights improve learning in recurrent neural networks.
  

Fuzzy neural networks

• Adelmo Luis Cechin, Ulrich Epperlein, Wolfgang Rosenstiel, Bernhard Koppenhoefer:
  The extraction of Sugeno fuzzy rules from neural networks.
  
• Selwyn Piramuthu:
  Neural versus neurofuzzy systems for credit approval.
  

Invited paper I

• Bernd Fritzke:
  Growing self-organizing networks - Why ?
  

Self-organizing maps

• Monika Köhle, Dieter Merkl:
  Identification of gait patterns with self-organizing maps based on ground reaction force.
  
• Stefan Schünemann, Bernd Michaelis:
  A self-organizing map for analysis of high-dimensional feature spaces with clusters of highly differing feature density.
  
• Jean-Claude Fort, Gilles Pagès:
  Quantization vs Organization in the Kohonen S.O.M.
  
• Wlodzislaw Duch, Antoine Naud:
  On global self-organizing maps.
  
• Damien Lamberton, Gilles Pagès:
  On the critical points of the 1-dimensional competitive learning vector quantization algorithm.
  
• Marie Cottrell, Eric de Bodt:
  A Kohonen map representation to avoid misleading interpretations.
  

Incremental learning

• Karim Mohraz, Peter Protzel:
  FlexNet - A flexible neural network construction algorithm.
  
• Christian Scheier:
  Incremental category learning in a real world artifact using growing dynamic cell structures.
  
• Stefan Wermter, Manuela Meurer:
  Towards constructive and destructive dynamic network configuration.
  
• Rachida Chentouf, Christian Jutten:
  Combining sigmoids and radial basis functions in evolutive neural architectures.
  

Invited paper II

• Fabrizio Mura, Nicolas Martin, Nicolas H. Franceschini:
  Biologically inspired eye movements for visually guided navigation of mobile robots.
  

Classification

• G. Qiu:
  A novel two-layer neural network classifier.
  
• N. R. Ball:
  Representation of obstacles in a neural network based classifier system.
  
• Malti Patel:
  Investigating lexical access using neural nets.
  

Mathematical aspects of neural networks

• Katerina Hlavácková-Schindler, Vera Kurková:
  Rates of approximation of real-valued boolean functions by neural networks.
  
• Eddy Mayoraz:
  Bounds on the degree of high order binary perceptrons.
  
• Andreu Català Mallofré, Joseph Aguilar-Martin, Bernardo Morcego Seix, Núria Piera Carreté:
  A fast Bayesian algorithm for Boolean functions synthesis by means of perceptron networks.
  
• Hui Wang, David Bell:
  Accomodating relevance in neural networks.
  

Natural and artificial vision

• Enno Littmann, Heiko Neumann, Luiz Pessoa:
  Neural model for visual contrast detection.
  
• A. de la Hera, Manuel Graña, Alicia D'Anjou, F. Xabier Albizuri:
  Application of high-order Boltzmann machines in OCR.
  
• Greg Maguire, Simon X. Yang:
  Simulation of an inner plexiform layer neural circuit in vertebrate retina leads to sustained and transient excitation.
  
• Christof Born:
  Analysis of visual information by receptive field dynamics.
  
• H. A. K. Mastebroek:
  Neurotransmitterdynamics in a model of a movement detecting visual system.
  

Neural networks and statistics

• Wlodzimierz Kasprzak, Andrzej Cichocki:
  Recurrent least square learning for quasi-parallel principal component analysis.
  
• Anne Guérin-Dugué, Carlos Avilez-Cruz, Patricia Palagi:
  Interpreting data through neural and statistical tools.
  
• A. Varfis, L. Corleto:
  Error rate estimation via cross-validation and learning curve theory.
  
• Mikko Lehtokangas, Petri Korpisaari, Kimmo Kaski:
  Maximum covariance method for weight initialization of multilayer perceptron network.
  

Invited paper III

• Juha Karhunen:
  Neural approaches to independent component analysis and source separation.
  

Learning and generalization II

• Joost N. Kok, Elena Marchiori, Massimo Marchiori, Claudio Rossi:
  Constraining of weights using regularities.
  
• E. Schaeffer, P. Bourret, S. Montrozier:
  Regularization and neural computation: application to aerial images analysis.
  
• Wojtek Kowalczyk:
  An algorithm for training multilayer networks on non-numerical data.
  
• Jey Ngole:
  A correlation-based network for real-time processing.
  
• Simon M. Lucas:
  Evolving neural network learning behaviours with set-based chromosomes.
  

Prediction

• Tommy W. S. Chow, Siu-Yeung Cho:
  Neural network application: rainfall forecasting system in Hong Kong.
  
• Yves Moreau, Joos Vandewalle:
  Prediction of dynamical systems with composition networks.
  
• Radu Dogaru, A. T. Murgan, Cristina Comaniciu:
  Fast signal recognition and detection using ART1 neural networks and nonlinear preprocessing units based on time delay embeddings.
  
• Arnfried Ossen, Stefan M. Rüger:
  An analysis of the metric structure of the weight space of feedforward networks and its application to time series modeling and prediction.
  
• Rienk S. Venema, Alexander Ypma, J. A. G. Nijhuis, Lambert Spaanenburg:
  Time series prediction using neural networks and its application to artificial human walking.
  

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