ESANN 1995: Brussels, Belgium

Self-organisation

• John A. Flanagan, Martin Hasler:
  Self-organisation, metastable states and the ODE method in the Kohonen neural network.
  
• Jean-Claude Fort, Gilles Pagès:
  About the Kohonen algorithm: strong or weak self-organization?
  
• Josef Göppert, Wolfgang Rosenstiel:
  Topological interpolation in SOM by affine transformations.
  
• Katarzyna Moscinska:
  Dynamic Neural Clustering.
  
• Smaïl Ibbou, Marie Cottrell:
  Multiple correspondence analysis of a crosstabulations matrix using the Kohonen algorithm.
  

Models I

• Jean-Philippe Draye, Guy Cheron, Marc Bourgeois, Davor Pavisic, Gaetan Libert:
  Identification of the human arm kinetics using dynamic recurrent neural networks.
  
• Mikko Lehtokangas, Jukka Saarinen, Kimmo Kaski:
  Simplified cascade-correlation learning.
  
• Davor Pavisic, Laurent Blondel, Jean-Philippe Draye, Gaetan Libert, Pierre Chapelle:
  Active noise control with dynamic recurrent neural networks.
  
• M. Diepenhorst, J. A. G. Nijhuis, Lambert Spaanenburg:
  Cascade learning for FIR-TDNNs.
  

Signal processing and chaos

• Jeroen Dehaene, Joos Vandewalle:
  Adaptive signal processing with unidirectional Hebbian adaptation laws.
  
• Christophe Couvreur:
  MAP decomposition of a mixture of AR signal using multilayer perceptrons.
  
• Koen Bertels, Luc Neuberg, Stamatis Vassiliadis, D. G. Pechanek:
  XOR and backpropagation learning: in and out of the chaos?
  
• M. R. Belli, Massimo Conti, Claudio Turchetti:
  Analog Brownian weight movement for learning of artificial neural networks.
  

Biological models

• Florentin Wörgötter, Eckart Nelle, Bing Li, Lei Wang, Yun-Chen Diao:
  Spatial summation in simple cells: computational and experimental results.
  
• Catelijne van Oss, Arjen van Ooyen:
  Activity-dependent neurite outgrowth in a simple network model including excitation and inhibition.
  
• Stephan Joeken, Helmut Schwegler:
  Predicting spike train responses of neuron models.
  
• A. Terao, M. Akamatsu, J. Seal:
  A distribution-based model of the dynamics of neural networks in the cerebral cortex.
  
• Arthur Vermeulen, Jean-Pierre Rospars, Petr Lánský, Henry C. Tuckwell:
  Some new results on the coding of pheromone intensity in an olfactory sensory neuron.
  

Special session on the Elena-Nerves2 ESPRIT Basic Research project

• Pierre Comon:
  Invited paper: Supervised classification: a probabilistic approach.
  
• Christian Jutten, Olivier Fambon:
  Invited paper: Pruning methods: a review.
  
• Juan Manuel Moreno, F. X. Vazquez, F. Castillo, Jordi Madrenas, Joan Cabestany:
  A deterministic method for establishing the initial conditions in the RCE algorithm.
  
• Olivier Fambon, Christian Jutten:
  Pruning kernel density estimators.
  
• Jean-Luc Voz, Michel Verleysen, Philippe Thissen, Jean-Didier Legat:
  Suboptimal Bayesian classification by vector quantization with small clusters.
  

Theory of learning systems

• David Barber, David Saad:
  Knowledge and generalisation in simple learning systems.
  
• Yves Grandvalet, Stéphane Canu, Stéphane Boucheron:
  Control of complexity in learning with perturbed inputs.
  
• Ulf-Dietrich Braumann, Hans-Joachim Böhme, Horst-Michael Gross:
  An episodic knowledge base for object understanding.
  
• Melanie Hilario, Yannick Lallement, Frédéric Alexandre:
  Neurosymbolic integration: unified versus hybrid approaches.
  

Biological vision

• Ralf Opara, Florentin Wörgötter:
  Improving object recognition by using a visual latency mechanism.
  
• Shigemichi Ohshima, Tetsuya Yagi, Yasuyuki Funahashi:
  On the function of the retinal bipolar cell in early vision.
  
• Greg Maguire:
  Sustained and transient amacrine cell circuits underlying the receptive fields of ganglion cells in the vertebrate retina.
  
• J. Köhn, Florentin Wörgötter:
  Latency-reduction in antagonistic visual channels as the result of corticofugal feedback.
  

Models II

• Andreas Alexander Albrecht:
  On threshold circuit depth.
  
• Peter Sollich:
  Minimum entropy queries for linear students learning nonlinear rules.
  
• Marcello Pelillo, Anna Maria Fanelli:
  An asymmetric associative memory model based on relaxation labeling processes.
  
• Tatyana S. Turova:
  Invariant measure for an infinite neural network.
  
• Simon M. Lucas:
  Growing adaptive neural networks with graph grammars.
  
• Colin Campbell, Conrado J. Pérez Vicente:
  Constructing feed-forward neural networks for binary classification tasks.
  

Classification and control

• Martin Pregenzer, Gert Pfurtscheller, C. Andrew:
  Improvement of EEG classification with a subject-specific feature selection.
  
• Jeffrey Wood, John Shawe-Taylor:
  Neural networks for invariant pattern recognition.
  
• Josep Cugueró, Jordi Madrenas, Juan Manuel Moreno, Joan Cabestany:
  Derivation of a new criterion function based on an information measure for improving piecewise linear separation incremental algorithms.
  
• Yonghong Tan, Achiel R. Van Cauwenberghe:
  Neural network based one-step ahead control and its stability.
  
• Johan A. K. Suykens, Bart De Moor, Joos Vandewalle:
  NLq theory: unifications in the theory of neural networks, systems and control.
  

Invited paper

• Hanspeter A. Mallot:
  Learning of cognitive maps from sequences of views.
  

Radial-basis functions

• C. Andrew, Miroslav Kubat, Gert Pfurtscheller:
  Trimming the inputs of RBF networks.
  
• Sandro Ridella, Stefano Rovetta, Rodolfo Zunino:
  Learning the appropriate representation paradigm by circular processing units.
  
• Mark J. L. Orr:
  Radial basis functions in the Fourier domain.
  

Function approximation

• Miran Kokol, Igor Grabec:
  Function approximation by localized basis function neural network.
  
• Jean-Gabriel Attali, Gilles Pagès:
  Functional approximation by perceptrons: a new approach.
  
• Vera Kurková:
  Approximation of functions by Gaussian RBF networks with bouded number of hidden units.
  
• Tommy W. S. Chow, Chi-Tat Leung:
  Neural network piecewise linear preprocessing for time-series prediction.
  
• Katerina Hlavácková-Schindler:
  An upper estimate of the error of approximation of continuous multivariable functions by KBF networks.
  

Multi-layer perceptrons

• John A. Drakopoulos:
  Multi-sigmoidal units and neural networks.
  
• Mohamed Karouia, Régis Lengellé, Thierry Denoeux:
  Performance analysis of a MLP weight initialization algorithm.
  
• Panagiotis K. Psomas, Gregory D. Hilakos, Costas F. Christoyannis, Nikolaos K. Uzunoglu:
  Alternative output representation schemes affect learning and generalization of back-propagation ANNs; a decision support application.
  
• Brijesh Verma, Jan J. Mulawka:
  A new training algorithm for feedforward neural networks.
  
• Juan Manuel Torres Moreno, Pierre Peretto, Mirta B. Gordon:
  An evolutive architecture coupled with optimal perceptron learning for classification.
  

Last update Thu May 24 04:17:19 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page