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ESANN 1996: Bruges, Belgium
- 4th European Symposium on Artificial Neural Networks, ESANN 1996, Bruges, Belgium, April 24-26, 1996, Proceedings. 1996
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. - Nigel 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, 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 A. 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 Avilés-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 E. E. 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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