Brussels, Belgium, April 19-20-21
Content of the proceedings
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Self-organisation
Models I
Signal processing and chaos
Biological models
Special session on the Elena-Nerves2 ESPRIT Basic Research project
Theory of learning systems
Biological vision
Models II
Classification and control
Invited paper
Radial-basis functions
Function approximation
Multi-layer perceptrons
Self-organisation
ES1995-15
Self-organisation, metastable states and the ODE method in the Kohonen neural network
J.A. Flanagan, M. Hasler
Self-organisation, metastable states and the ODE method in the Kohonen neural network
J.A. Flanagan, M. Hasler
ES1995-26
About the Kohonen algorithm: strong or weak self-organization?
J.-C. Fort, G. Pagès
About the Kohonen algorithm: strong or weak self-organization?
J.-C. Fort, G. Pagès
ES1995-79
Topological interpolation in SOM by affine transformations
J. Göppert, W. Rosenstiel
Topological interpolation in SOM by affine transformations
J. Göppert, W. Rosenstiel
ES1995-97
Dynamic Neural Clustering
K. Moscinska
Dynamic Neural Clustering
K. Moscinska
ES1995-109
Multiple correspondence analysis of a crosstabulations matrix using the Kohonen algorithm
S. Ibbou, M. Cottrell
Multiple correspondence analysis of a crosstabulations matrix using the Kohonen algorithm
S. Ibbou, M. Cottrell
Models I
ES1995-63
Identification of the human arm kinetics using dynamic recurrent neural networks
J.-P. Draye, G. Cheron, M. Bourgeois, D. Pavisic, G. Libert
Identification of the human arm kinetics using dynamic recurrent neural networks
J.-P. Draye, G. Cheron, M. Bourgeois, D. Pavisic, G. Libert
ES1995-3
Simplified cascade-correlation learning
M. Lehtokangas, J. Saarinen, K. Kaski
Simplified cascade-correlation learning
M. Lehtokangas, J. Saarinen, K. Kaski
ES1995-59
Active noise control with dynamic recurrent neural networks
D. Pavisic, L. Blondel, J.-P. Draye, G. Libert, P. Chapelle
Active noise control with dynamic recurrent neural networks
D. Pavisic, L. Blondel, J.-P. Draye, G. Libert, P. Chapelle
ES1995-94
Cascade learning for FIR-TDNNs
M. Diepenhorst, J.A.G. Nijhuis, L. Spaanenburg
Cascade learning for FIR-TDNNs
M. Diepenhorst, J.A.G. Nijhuis, L. Spaanenburg
Signal processing and chaos
ES1995-61
Adaptive signal processing with unidirectional Hebbian adaptation laws
J. Dehaene, J. Vandewalle
Adaptive signal processing with unidirectional Hebbian adaptation laws
J. Dehaene, J. Vandewalle
ES1995-86
MAP decomposition of a mixture of AR signal using multilayer perceptrons
C. Couvreur
MAP decomposition of a mixture of AR signal using multilayer perceptrons
C. Couvreur
ES1995-24
XOR and backpropagation learning: in and out of the chaos?
K. Bertels, L. Neuberg, S. Vassiliadis, G. Pechanek
XOR and backpropagation learning: in and out of the chaos?
K. Bertels, L. Neuberg, S. Vassiliadis, G. Pechanek
ES1995-73
Analog Brownian weight movement for learning of artificial neural networks
M.R. Belli, M. Conti, C. Turchetti
Analog Brownian weight movement for learning of artificial neural networks
M.R. Belli, M. Conti, C. Turchetti
Biological models
ES1995-31
Spatial summation in simple cells: computational and experimental results
F. Wörgötter, E. Nelle, B. Li, L. Wang, Y.-C. Diao
Spatial summation in simple cells: computational and experimental results
F. Wörgötter, E. Nelle, B. Li, L. Wang, Y.-C. Diao
ES1995-58
Activity-dependent neurite outgrowth in a simple network model including excitation and inhibition
C. van Oss, A. van Ooyen
Activity-dependent neurite outgrowth in a simple network model including excitation and inhibition
C. van Oss, A. van Ooyen
ES1995-64
Predicting spike train responses of neuron models
S. Joeken, H. Schwegler
Predicting spike train responses of neuron models
S. Joeken, H. Schwegler
ES1995-75
A distribution-based model of the dynamics of neural networks in the cerebral cortex
A. Terao, M. Akamatsu, J. Seal
A distribution-based model of the dynamics of neural networks in the cerebral cortex
A. Terao, M. Akamatsu, J. Seal
ES1995-110
Some new results on the coding of pheromone intensity in an olfactory sensory neuron
A. Vermeulen, J.-P. Rospars, P. Lansky, H.C. Tuckwell
Some new results on the coding of pheromone intensity in an olfactory sensory neuron
A. Vermeulen, J.-P. Rospars, P. Lansky, H.C. Tuckwell
Special session on the Elena-Nerves2 ESPRIT Basic Research project
ES1995-500
Invited paper: Supervised classification: a probabilistic approach
P. Comon
Invited paper: Supervised classification: a probabilistic approach
P. Comon
ES1995-501
Invited paper: Pruning methods: a review
C. Jutten, O. Fambon
Invited paper: Pruning methods: a review
C. Jutten, O. Fambon
ES1995-4
A deterministic method for establishing the initial conditions in the RCE algorithm
J.M. Moreno, F.X. Vazquez, F. Castillo, J. Madrenas, J. Cabestany
A deterministic method for establishing the initial conditions in the RCE algorithm
J.M. Moreno, F.X. Vazquez, F. Castillo, J. Madrenas, J. Cabestany
ES1995-103
Pruning kernel density estimators
O. Fambon, C. Jutten
Pruning kernel density estimators
O. Fambon, C. Jutten
ES1995-96
Suboptimal Bayesian classification by vector quantization with small clusters
J.L. Voz, M. Verleysen, P. Thissen, J.D. Legat
Suboptimal Bayesian classification by vector quantization with small clusters
J.L. Voz, M. Verleysen, P. Thissen, J.D. Legat
Theory of learning systems
ES1995-20
Knowledge and generalisation in simple learning systems
D. Barber, D. Saad
Knowledge and generalisation in simple learning systems
D. Barber, D. Saad
ES1995-38
Control of complexity in learning with perturbed inputs
Y. Grandvalet, S. Canu, S. Boucheron
Control of complexity in learning with perturbed inputs
Y. Grandvalet, S. Canu, S. Boucheron
ES1995-101
An episodic knowledge base for object understanding
U.-D. Braumann, H.-J. Boehme, H.-M. Gross
An episodic knowledge base for object understanding
U.-D. Braumann, H.-J. Boehme, H.-M. Gross
ES1995-21
Neurosymbolic integration: unified versus hybrid approaches
M. Hilario, Y. Lallement, F. Alexandre
Neurosymbolic integration: unified versus hybrid approaches
M. Hilario, Y. Lallement, F. Alexandre
Biological vision
ES1995-29
Improving object recognition by using a visual latency mechanism
R. Opara, F. Wörgörter
Improving object recognition by using a visual latency mechanism
R. Opara, F. Wörgörter
ES1995-42
On the function of the retinal bipolar cell in early vision
S. Ohshima, T. Yagi, Y. Funahashi
On the function of the retinal bipolar cell in early vision
S. Ohshima, T. Yagi, Y. Funahashi
ES1995-108
Sustained and transient amacrine cell circuits underlying the receptive fields of ganglion cells in the vertebrate retina
G. Maguire
Sustained and transient amacrine cell circuits underlying the receptive fields of ganglion cells in the vertebrate retina
G. Maguire
ES1995-30
Latency-reduction in antagonistic visual channels as the result of corticofugal feedback
J. Köhn, F. Wörgötter
Latency-reduction in antagonistic visual channels as the result of corticofugal feedback
J. Köhn, F. Wörgötter
Models II
ES1995-40
On threshold circuit depth
A. Albrecht
On threshold circuit depth
A. Albrecht
ES1995-48
Minimum entropy queries for linear students learning nonlinear rules
P. Sollich
Minimum entropy queries for linear students learning nonlinear rules
P. Sollich
ES1995-74
An asymmetric associative memory model based on relaxation labeling processes
M. Pelillo, A.M. Fanelli
An asymmetric associative memory model based on relaxation labeling processes
M. Pelillo, A.M. Fanelli
ES1995-88
Invariant measure for an infinite neural network
T.S. Turova
Invariant measure for an infinite neural network
T.S. Turova
ES1995-107
Growing adaptive neural networks with graph grammars
S.M. Lucas
Growing adaptive neural networks with graph grammars
S.M. Lucas
ES1995-41
Constructing feed-forward neural networks for binary classification tasks
C. Campbell, C. Perez Vincente
Constructing feed-forward neural networks for binary classification tasks
C. Campbell, C. Perez Vincente
Classification and control
ES1995-18
Improvement of EEG classification with a subject-specific feature selection
M. Pregenzer, G. Pfurtscheller, C. Andrew
Improvement of EEG classification with a subject-specific feature selection
M. Pregenzer, G. Pfurtscheller, C. Andrew
ES1995-9
Neural networks for invariant pattern recognition
J. Wood, J. Shawe-Taylor
Neural networks for invariant pattern recognition
J. Wood, J. Shawe-Taylor
ES1995-66
Derivation of a new criterion function based on an information measure for improving piecewise linear separation incremental algorithms
J. Cuguero, J. Madrenas, J.M. Moreno, J. Cabestany
Derivation of a new criterion function based on an information measure for improving piecewise linear separation incremental algorithms
J. Cuguero, J. Madrenas, J.M. Moreno, J. Cabestany
ES1995-47
Neural network based one-step ahead control and its stability
Y. Tan, A.R. Van Cauwenberghe
Neural network based one-step ahead control and its stability
Y. Tan, A.R. Van Cauwenberghe
ES1995-50
NLq theory: unifications in the theory of neural networks, systems and control
J. Suykens, B. De Moor, J. Vandewalle
NLq theory: unifications in the theory of neural networks, systems and control
J. Suykens, B. De Moor, J. Vandewalle
Invited paper
ES1995-502
Learning of cognitive maps from sequences of views
H.P. Mallot
Learning of cognitive maps from sequences of views
H.P. Mallot
Radial-basis functions
ES1995-36
Trimming the inputs of RBF networks
C. Andrew, M. Kubat, G. Pfurtscheller
Trimming the inputs of RBF networks
C. Andrew, M. Kubat, G. Pfurtscheller
ES1995-69
Learning the appropriate representation paradigm by circular processing units
S. Ridella, S. Rovetta, R. Zunino
Learning the appropriate representation paradigm by circular processing units
S. Ridella, S. Rovetta, R. Zunino
ES1995-105
Radial basis functions in the Fourier domain
M. Orr
Radial basis functions in the Fourier domain
M. Orr
Function approximation
ES1995-10
Function approximation by localized basis function neural network
M. Kokol, I. Grabec
Function approximation by localized basis function neural network
M. Kokol, I. Grabec
ES1995-25
Functional approximation by perceptrons: a new approach
J.-G. Attali, G. Pagès
Functional approximation by perceptrons: a new approach
J.-G. Attali, G. Pagès
ES1995-35
Approximation of functions by Gaussian RBF networks with bouded number of hidden units
V. Kurkova
Approximation of functions by Gaussian RBF networks with bouded number of hidden units
V. Kurkova
ES1995-83
Neural network piecewise linear preprocessing for time-series prediction
T.W.S. Chow, C.T. Leung
Neural network piecewise linear preprocessing for time-series prediction
T.W.S. Chow, C.T. Leung
ES1995-51
An upper estimate of the error of approximation of continuous multivariable functions by KBF networks
K. Hlavackova
An upper estimate of the error of approximation of continuous multivariable functions by KBF networks
K. Hlavackova
Multi-layer perceptrons
ES1995-5
Multi-sigmoidal units and neural networks
J.A. Drakopoulos
Multi-sigmoidal units and neural networks
J.A. Drakopoulos
ES1995-7
Performance analysis of a MLP weight initialization algorithm
M. Karouia, R. Lengellé, T. Denoeux
Performance analysis of a MLP weight initialization algorithm
M. Karouia, R. Lengellé, T. Denoeux
ES1995-13
Alternative output representation schemes affect learning and generalization of back-propagation ANNs; a decision support application
P.K. Psomas, G.D. Hilakos, C.F. Christoyannis, N.K. Uzunoglu
Alternative output representation schemes affect learning and generalization of back-propagation ANNs; a decision support application
P.K. Psomas, G.D. Hilakos, C.F. Christoyannis, N.K. Uzunoglu
ES1995-17
A new training algorithm for feedforward neural networks
B.K. Verma, J.J. Mulawka
A new training algorithm for feedforward neural networks
B.K. Verma, J.J. Mulawka
ES1995-111
An evolutive architecture coupled with optimal perceptron learning for classification
J.-M. Torres Moreno, P. Peretto, M. B. Gordon
An evolutive architecture coupled with optimal perceptron learning for classification
J.-M. Torres Moreno, P. Peretto, M. B. Gordon