ESANN 2008:
Bruges,
Belgium
ESANN 2008, 16th European Symposium on Artificial Neural Networks, Bruges, Belgium, April 23-25, 2008, Proceedings.
2008 BibTeX
Dynamical and recurrent systems,
control
- Xavier Dutoit, Benjamin Schrauwen, Jan M. Van Campenhout, Dirk Stroobandt, Hendrik Van Brussel, Marnix Nuttin:
Pruning and Regularisation in Reservoir Computing: a First Insight.
1-6
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- Guillaume Jouffroy:
Design of Oscillatory Recurrent Neural Network Controllers with Gradient based Algorithms.
7-12
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- Duy Nguyen-Tuong, Jan Peters, Matthias Seeger, Bernhard Schölkopf:
Learning Inverse Dynamics: a Comparison.
13-18
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- Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters:
Model-Based Reinforcement Learning with Continuous States and Actions.
19-24
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Feature selection,
imputation and projection
- Emil Eirola, Elia Liitiäinen, Amaury Lendasse, Francesco Corona, Michel Verleysen:
Using the Delta Test for Variable Selection.
25-30
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- Marc Strickert, Frank-Michael Schleif, Thomas Villmann:
Metric adaptation for supervised attribute rating.
31-36
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- Pedro J. García-Laencina, José-Luis Sancho-Gómez, Aníbal R. Figueiras-Vidal, Michel Verleysen:
K-nearest neighbours based on mutual information for incomplete data classification.
37-42
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- Victor Onclinx, Vincent Wertz, Michel Verleysen:
Nonlinear data projection on a sphere with controlled trade-off between trustworthiness and continuity.
43-48
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- John Aldo Lee, Michel Verleysen:
Rank-based quality assessment of nonlinear dimensionality reduction.
49-54
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Machine learning methods in cancer research
- Alfredo Vellido, Elia Biganzoli, Paulo J. G. Lisboa:
Machine learning in cancer research: implications for personalised medicine.
55-64
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- Ben Van Calster, Dirk Timmerman, Antonia C. Testa, Lil Valentin, Sabine Van Huffel:
Multi-class classification of ovarian tumors.
65-70
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- René Natowicz, Antônio de Pádua Braga, Roberto Incitti, Euler G. Horta, Roman Rouzier, Thiago S. Rodrigues, Marcelo Azevedo Costa:
A new method of DNA probes selection and its use with multi-objective neural network for predicting the outcome of breast cancer preoperative chemotherapy.
71-76
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- Félix Fernando González-Navarro, Lluís A. Belanche Muñoz:
Feature Selection in Proton Magnetic Resonance Spectroscopy for Brain Tumor Classification.
77-82
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- Annalisa Barla, Sofia Mosci, Lorenzo Rosasco, Alessandro Verri:
A method for robust variable selection with significance assessment.
83-88
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- Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
Survival SVM: a practical scalable algorithm.
89-94
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- Enrique Romero, Margarida Julià-Sapé, Alfredo Vellido:
DSS-oriented exploration of a multi-centre magnetic resonance spectroscopy brain tumour dataset through visualization.
95-100
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- Sergio Rodrigues de Morais, Alexandre Aussem, Marilys Corbex:
Handling almost-deterministic relationships in constraint-based Bayesian network discovery : Application to cancer risk factor identification.
101-106
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Learning I
- Lee Calcraft, Rod Adams, Weiliang Chen, Neil Davey:
Using graph-theoretic measures to predict the performance of associative memory models.
107-112
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- Rama Murthy Garimella, Praveen Dasigi:
A novel autoassociative memory on the complex hypercubic lattice.
113-118
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- Zöhre Kara Kayikci, Günther Palm:
Word recognition and incremental learning based on neural associative memories and hidden Markov models.
119-124
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- Huaien Gao, Rudolf Sollacher:
Conditional prediction of time series using spiral recurrent neural network.
125-130
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- Alexander Groß, Jan Friedland, Friedhelm Schwenker:
Learning to play Tetris applying reinforcement learning methods.
131-136
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- Benoît Frénay, Marco Saerens:
QL2, a simple reinforcement learning scheme for two-player zero-sum Markov games.
137-142
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- Alexander Hans, Daniel Schneegaß, Anton Maximilian Schäfer, Steffen Udluft:
Safe exploration for reinforcement learning.
143-148
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- Verena Heidrich-Meisner, Christian Igel:
Similarities and differences between policy gradient methods and evolution strategies.
149-154
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- Leo Galway, Darryl Charles, Michaela M. Black:
Improvement in Game Agent Control Using State-Action Value Scaling.
155-160
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- Victor Uc Cetina:
Multilayer Perceptrons with Radial Basis Functions as Value Functions in Reinforcement Learning.
161-166
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- Jarkko Tikka, Jaakko Hollmén:
Selection of important input variables for RBF network using partial derivatives.
167-172
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- Michele La Rocca, Cira Perna:
A multiple testing procedure for input variable selection in neural networks.
173-178
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- Alexander Gepperth, Jannik Fritsch, Christian Goerick:
Computationally Efficient Neural Field Dynamics.
179-184
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Biological systems and biologically-inspired networks
Clustering and vector quantization
Methodology and standards for data analysis with machine learning tools
Learning II
- Cesar García-Osorio, Nicolás García-Pedrajas:
Constructing ensembles of classifiers using linear projections based on misclassified instances.
283-288
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- Bertha Guijarro-Berdiñas, Oscar Fontenla-Romero, Beatriz Pérez-Sánchez, Amparo Alonso-Betanzos:
A Regularized Learning Method for Neural Networks Based on Sensitivity Analysis.
289-294
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- David Martínez-Rego, Oscar Fontenla-Romero, Amparo Alonso-Betanzos:
A Method for Time Series Prediction using a Combination of Linear Models.
295-300
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- Sebastian Nusser, Clemens Otte, Werner Hauptmann:
Interpretable ensembles of local models for safety-related applications.
301-306
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- Mohamed Farouk Abdel Hady, Günther Palm, Friedhelm Schwenker:
Multi-View Forests of Tree-Structured Radial Basis Function Networks Based on Dempster-Shafer Evidence Theory.
307-312
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- Fazia Bellal, Khalid Benabdeslem, Alexandre Aussem:
SOM based clustering with instance-level constraints.
313-318
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- Alexander Denecke, Heiko Wersing, Jochen J. Steil, Edgar Körner:
Robust object segmentation by adaptive metrics in Generalized LVQ.
319-324
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- Alexander Hasenfuss, Barbara Hammer, Tina Geweniger, Thomas Villmann:
Magnification Control in Relational Neural Gas.
325-330
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- Marta Kolasa, Rafal Dlugosz:
Parallel asynchronous neighborhood mechanism for WTM Kohonen network implemented in CMOS technology.
331-336
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- Tomasz Talaska, Rafal Dlugosz:
Initialization mechanism in Kohonen neural network implemented in CMOS technology.
337-342
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- Narayanan Ramanan, Sonia Khatchadourian, Jean-Christophe Prévotet, Lounis Kessal:
Neural network hardware architecture for pattern recognition in the HESS2 project.
343-348
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- Massimo De Gregorio:
Active and reactive use of virtual neural sensors.
349-354
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- Carlo Casarino, Gaetano Liborio Aiello, Davide Valenti, Bernardo Spagnolo:
Noise influence on correlated activities in a modular neuronal network: from synapses to functional connectivity.
355-360
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- Claudio Castellanos Sánchez:
Neuromimetic motion indicator for visual perception.
361-366
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Neural Networks for Computational Neuroscience
- David Meunier, Hélène Paugam-Moisy:
Neural networks for computational neuroscience.
367-378
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- Frédéric Henry, Emmanuel Daucé:
Emergence of stimulus-specific synchronous response through STDP in recurrent neural networks.
379-384
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- Sylvain Chevallier, Philippe Tarroux:
Visual focus with spiking neurons.
385-389
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- Andreas Herzog, Karsten Kube, Bernd Michaelis, Ana D. de Lima, Thomas B. Voigt:
Simulation of a recurrent neurointerface with sparse electrical connections.
391-396
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- Dominik Brugger, Sergejus Butovas, Martin Bogdan, Cornelius Schwarz, Wolfgang Rosenstiel:
Direct and inverse solution for a stimulus adaptation problem using SVR.
397-402
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- Jean Marc Salotti:
Computational model for amygdala neural networks.
403-408
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Kernel methods
Machine Learning Approaches and Pattern Recognition for Spectral Data
- Thomas Villmann, Erzsébet Merényi, Udo Seiffert:
Machine learning approches and pattern recognition for spectral data.
433-444
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- Fabrice Rossi, Nathalie Villa:
Consistency of Derivative Based Functional Classifiers on Sampled Data.
445-450
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- Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl:
Generalized matrix learning vector quantizer for the analysis of spectral data.
451-456
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- Amaury Lendasse, Francesco Corona:
Linear Projection based on Noise Variance Estimation - Application to Spectral Data.
457-462
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- Caroline Bernard-Michel, Sylvain Douté, Laurent Gardes, Stephane Girard:
Inverting hyperspectral images with Gaussian Regularized Sliced Inverse Regression.
463-468
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Learning III
- Shigeo Abe:
Comparison of sparse least squares support vector regressors trained in primal and dual.
469-474
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- Tobias Glasmachers:
On related violating pairs for working set selection in SMO algorithms.
475-480
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- Rene te Boekhorst, Irina I. Abnizova, Lorenz Wernisch:
Discrimination of regulatory DNA by SVM on the basis of over- and under-represented motifs.
481-486
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- Laura Diosan, Alexandrina Rogozan, Jean-Pierre Pécuchet:
Automatic alignment of medical vs. general terminologies.
487-492
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- Zhang Yuecheng, Andriy Mnih, Geoffrey E. Hinton:
Improving a statistical language model by modulating the effects of context words.
493-498
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- Soufiane El Jelali, Abdelouahid Lyhyaoui, Aníbal R. Figueiras-Vidal:
An emphasized target smoothing procedure to improve MLP classifiers performance.
499-504
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- Mauricio Cerda, Bernard Girau:
A neural model with feedback for robust disambiguation of motion.
505-510
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- Carlos Spínola, Carlos J. Gálvez-Fernández, José Muñoz-Pérez, José M. Bonelo, Javier Ferrer, Julio Vizoso:
Multilayer perceptron to model the decarburization process in stainless steel production.
511-516
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- Guido Vagliasindi, Paolo Arena, Luigi Fortuna, Andrea Murari, Giuseppe Mazzitelli, Antonio Gallo, Umberto Vagliasindi:
An automatic identifier of Confinement Regimes at JET combining Fuzzy Logic and Classification Trees.
517-522
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- Wolfram Schenck, Dennis Sinder, Ralf Möller:
Combining neural networks and optimization techniques for visuokinesthetic prediction and motor planning.
523-528
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- Edouard Leclercq, Souleiman Ould el Medhi, Dimitri Lefebvre:
Petri nets design based on neural networks.
529-534
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- Ludwig Lausser, Friedhelm Schwenker, Günther Palm:
Detecting zebra crossings utilizing AdaBoost.
535-540
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Copyright © Sat May 16 23:10:40 2009
by Michael Ley (ley@uni-trier.de)
