ESANN 2008: Bruges, Belgium

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
  
• Guillaume Jouffroy:
  Design of Oscillatory Recurrent Neural Network Controllers with Gradient based Algorithms. 7-12
  
• Duy Nguyen-Tuong, Jan Peters, Matthias Seeger, Bernhard Schölkopf:
  Learning Inverse Dynamics: a Comparison. 13-18
  
• Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters:
  Model-Based Reinforcement Learning with Continuous States and Actions. 19-24
  

Feature selection, imputation and projection

• Emil Eirola, Elia Liitiäinen, Amaury Lendasse, Francesco Corona, Michel Verleysen:
  Using the Delta Test for Variable Selection. 25-30
  
• Marc Strickert, Frank-Michael Schleif, Thomas Villmann:
  Metric adaptation for supervised attribute rating. 31-36
  
• 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
  
• Victor Onclinx, Vincent Wertz, Michel Verleysen:
  Nonlinear data projection on a sphere with controlled trade-off between trustworthiness and continuity. 43-48
  
• John Aldo Lee, Michel Verleysen:
  Rank-based quality assessment of nonlinear dimensionality reduction. 49-54
  

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
  
• Ben Van Calster, Dirk Timmerman, Antonia C. Testa, Lil Valentin, Sabine Van Huffel:
  Multi-class classification of ovarian tumors. 65-70
  
• 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
  
• Félix Fernando González-Navarro, Lluís A. Belanche Muñoz:
  Feature Selection in Proton Magnetic Resonance Spectroscopy for Brain Tumor Classification. 77-82
  
• Annalisa Barla, Sofia Mosci, Lorenzo Rosasco, Alessandro Verri:
  A method for robust variable selection with significance assessment. 83-88
  
• Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
  Survival SVM: a practical scalable algorithm. 89-94
  
• Enrique Romero, Margarida Julià-Sapé, Alfredo Vellido:
  DSS-oriented exploration of a multi-centre magnetic resonance spectroscopy brain tumour dataset through visualization. 95-100
  
• 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
  

Learning I

• Lee Calcraft, Rod Adams, Weiliang Chen, Neil Davey:
  Using graph-theoretic measures to predict the performance of associative memory models. 107-112
  
• Rama Murthy Garimella, Praveen Dasigi:
  A novel autoassociative memory on the complex hypercubic lattice. 113-118
  
• Zöhre Kara Kayikci, Günther Palm:
  Word recognition and incremental learning based on neural associative memories and hidden Markov models. 119-124
  
• Huaien Gao, Rudolf Sollacher:
  Conditional prediction of time series using spiral recurrent neural network. 125-130
  
• Alexander Groß, Jan Friedland, Friedhelm Schwenker:
  Learning to play Tetris applying reinforcement learning methods. 131-136
  
• Benoît Frénay, Marco Saerens:
  QL2, a simple reinforcement learning scheme for two-player zero-sum Markov games. 137-142
  
• Alexander Hans, Daniel Schneegaß, Anton Maximilian Schäfer, Steffen Udluft:
  Safe exploration for reinforcement learning. 143-148
  
• Verena Heidrich-Meisner, Christian Igel:
  Similarities and differences between policy gradient methods and evolution strategies. 149-154
  
• Leo Galway, Darryl Charles, Michaela M. Black:
  Improvement in Game Agent Control Using State-Action Value Scaling. 155-160
  
• Victor Uc Cetina:
  Multilayer Perceptrons with Radial Basis Functions as Value Functions in Reinforcement Learning. 161-166
  
• Jarkko Tikka, Jaakko Hollmén:
  Selection of important input variables for RBF network using partial derivatives. 167-172
  
• Michele La Rocca, Cira Perna:
  A multiple testing procedure for input variable selection in neural networks. 173-178
  
• Alexander Gepperth, Jannik Fritsch, Christian Goerick:
  Computationally Efficient Neural Field Dynamics. 179-184
  

Biological systems and biologically-inspired networks

• Thomas Burwick:
  The gamma cycle and its role in the formation of assemblies. 185-190
  
• Conrad Attard, Andreas Alexander Albrecht:
  The impact of axon wiring costs on small neuronal networks. 191-196
  
• Hooman Shayani, Peter J. Bentley, Andrew M. Tyrrell:
  An FPGA-based model suitable for evolution and development of spiking neural networks. 197-202
  

Clustering and vector quantization

• Markus Hagenbuchner, Alessandro Sperduti, Ah Chung Tsoi:
  Self-Organizing Maps for cyclic and unbounded graphs. 203-208
  
• Hanane Azzag, Mustapha Lebbah:
  Clustering of Self-Organizing Map. 209-214
  
• Lutz Herrmann, Alfred Ultsch:
  Explaining Ant-Based Clustering on the basis of Self-Organizing Maps. 215-220
  
• Aree Witoelar, Anarta Ghosh, Michael Biehl:
  Phase transitions in Vector Quantization. 221-226
  
• Nikolai Alex, Barbara Hammer:
  Parallelizing single patch pass clustering. 227-232
  
• Kai Labusch, Erhardt Barth, Thomas Martinetz:
  Learning Data Representations with Sparse Coding Neural Gas. 233-238
  

Methodology and standards for data analysis with machine learning tools

• Damien François:
  Methodology and standards for data analysis with machine learning tools. 239-246
  
• Yoan Miche, Patrick Bas, Christian Jutten, Olli Simula, Amaury Lendasse:
  A Methodology for Building Regression Models using Extreme Learning Machine: OP-ELM. 247-252
  
• Christiane Lemke, Bogdan Gabrys:
  Do we need experts for time series forecasting?. 253-258
  
• Michaël Aupetit:
  Homogeneous bipartition based on multidimensional ranking. 259-264
  
• Tuomas Alhonnoro, Miki Sirola:
  Feature selection on process fault detection and visualization.
  
• Elena Roglia, Rossella Cancelliere, Rosa Meo:
  Classification of chestnuts with feature selection by noise resilient classifiers. 271-276
  
• Alexei Pozdnoukhov, Mikhail F. Kanevski:
  GeoKernels: modeling of spatial data on geomanifolds. 277-282
  

Learning II

• Cesar García-Osorio, Nicolás García-Pedrajas:
  Constructing ensembles of classifiers using linear projections based on misclassified instances. 283-288
  
• 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
  
• David Martínez-Rego, Oscar Fontenla-Romero, Amparo Alonso-Betanzos:
  A Method for Time Series Prediction using a Combination of Linear Models. 295-300
  
• Sebastian Nusser, Clemens Otte, Werner Hauptmann:
  Interpretable ensembles of local models for safety-related applications. 301-306
  
• 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
  
• Fazia Bellal, Khalid Benabdeslem, Alexandre Aussem:
  SOM based clustering with instance-level constraints. 313-318
  
• Alexander Denecke, Heiko Wersing, Jochen J. Steil, Edgar Körner:
  Robust object segmentation by adaptive metrics in Generalized LVQ. 319-324
  
• Alexander Hasenfuss, Barbara Hammer, Tina Geweniger, Thomas Villmann:
  Magnification Control in Relational Neural Gas. 325-330
  
• Marta Kolasa, Rafal Dlugosz:
  Parallel asynchronous neighborhood mechanism for WTM Kohonen network implemented in CMOS technology. 331-336
  
• Tomasz Talaska, Rafal Dlugosz:
  Initialization mechanism in Kohonen neural network implemented in CMOS technology. 337-342
  
• Narayanan Ramanan, Sonia Khatchadourian, Jean-Christophe Prévotet, Lounis Kessal:
  Neural network hardware architecture for pattern recognition in the HESS2 project. 343-348
  
• Massimo De Gregorio:
  Active and reactive use of virtual neural sensors. 349-354
  
• 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
  
• Claudio Castellanos Sánchez:
  Neuromimetic motion indicator for visual perception. 361-366
  

Neural Networks for Computational Neuroscience

• David Meunier, Hélène Paugam-Moisy:
  Neural networks for computational neuroscience. 367-378
  
• Frédéric Henry, Emmanuel Daucé:
  Emergence of stimulus-specific synchronous response through STDP in recurrent neural networks. 379-384
  
• Sylvain Chevallier, Philippe Tarroux:
  Visual focus with spiking neurons. 385-389
  
• Andreas Herzog, Karsten Kube, Bernd Michaelis, Ana D. de Lima, Thomas B. Voigt:
  Simulation of a recurrent neurointerface with sparse electrical connections. 391-396
  
• Dominik Brugger, Sergejus Butovas, Martin Bogdan, Cornelius Schwarz, Wolfgang Rosenstiel:
  Direct and inverse solution for a stimulus adaptation problem using SVR. 397-402
  
• Jean Marc Salotti:
  Computational model for amygdala neural networks. 403-408
  

Kernel methods

• Pierre Mahé, Nicola Cancedda:
  Factored sequence kernels. 409-414
  
• Karina Zapien Arreola, Thomas Gärtner, Gilles Gasso, Stéphane Canu:
  Regularization path for Ranking SVM. 415-420
  
• Álvaro Barbero Jiménez, Jorge López Lázaro, José R. Dorronsoro:
  An accelerated MDM algorithm for SVM training. 421-426
  
• Thorsten Suttorp, Christian Igel:
  Approximation of Gaussian process regression models after training. 427-432
  

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
  
• Fabrice Rossi, Nathalie Villa:
  Consistency of Derivative Based Functional Classifiers on Sampled Data. 445-450
  
• Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl:
  Generalized matrix learning vector quantizer for the analysis of spectral data. 451-456
  
• Amaury Lendasse, Francesco Corona:
  Linear Projection based on Noise Variance Estimation - Application to Spectral Data. 457-462
  
• Caroline Bernard-Michel, Sylvain Douté, Laurent Gardes, Stéphane Girard:
  Inverting hyperspectral images with Gaussian Regularized Sliced Inverse Regression. 463-468
  

Learning III

• Shigeo Abe:
  Comparison of sparse least squares support vector regressors trained in primal and dual. 469-474
  
• Tobias Glasmachers:
  On related violating pairs for working set selection in SMO algorithms. 475-480
  
• 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
  
• Laura Diosan, Alexandrina Rogozan, Jean-Pierre Pécuchet:
  Automatic alignment of medical vs. general terminologies. 487-492
  
• Zhang Yuecheng, Andriy Mnih, Geoffrey E. Hinton:
  Improving a statistical language model by modulating the effects of context words. 493-498
  
• Soufiane El Jelali, Abdelouahid Lyhyaoui, Aníbal R. Figueiras-Vidal:
  An emphasized target smoothing procedure to improve MLP classifiers performance. 499-504
  
• Mauricio Cerda, Bernard Girau:
  A neural model with feedback for robust disambiguation of motion. 505-510
  
• 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
  
• 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
  
• Wolfram Schenck, Dennis Sinder, Ralf Möller:
  Combining neural networks and optimization techniques for visuokinesthetic prediction and motor planning. 523-528
  
• Edouard Leclercq, Souleiman Ould el Medhi, Dimitri Lefebvre:
  Petri nets design based on neural networks. 529-534
  
• Ludwig Lausser, Friedhelm Schwenker, Günther Palm:
  Detecting zebra crossings utilizing AdaBoost. 535-540
  

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