ESANN 2011: Bruges, Belgium

Information theory related learning

• Thomas Villmann, José C. Príncipe, Andrzej Cichocki:
  Information theory related learning.
  
• Tina Geweniger, Marika Kästner, Thomas Villmann:
  Optimization of Parametrized Divergences in Fuzzy c-Means.
  
• Petra Schneider, Tina Geweniger, Frank-Michael Schleif, Michael Biehl, Thomas Villmann:
  Multivariate class labeling in Robust Soft LVQ.
  
• Verónica Bolón-Canedo, Sohan Seth, Noelia Sánchez-Maroño, Amparo Alonso-Betanzos, Jose C. Principe:
  Statistical dependence measure for feature selection in microarray datasets.
  
• Kerstin Bunte, Frank-Michael Schleif, Sven Haase, Thomas Villmann:
  Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization.
  

Self-organizing maps and recurrent networks

• Markus Hagenbuchner, Giovanni Da San Martino, Ah Chung Tsoi, Alessandro Sperduti:
  Sparsity Issues in Self-Organizing-Maps for Structures.
  
• Jan Faigl, Jan Macák:
  Multi-Goal Path Planning Using Self-Organizing Map with Navigation Functions.
  
• Miguel A. Atencia Ruiz, Gonzalo Joya Caparrós:
  Statistical properties of the 'Hopfield estimator' of dynamical systems.
  
• Ali Rodan, Peter Tiño:
  Negatively Correlated Echo State Networks.
  
• René Felix Reinhart, Jochen J. Steil:
  Reservoir regularization stabilizes learning of Echo State Networks with output feedback.
  

Semi-supervised learning

• Xilan Tian, Gilles Gasso, Stéphane Canu:
  A Multi-kernel Framework for Inductive Semi-supervised Learning.
  
• Martin Schels, Patrick Schillinger, Friedhelm Schwenker:
  Training of multiple classifier systems utilizing partially labeled sequential data sets.
  

Computational Intelligence in Life Sciences

• Udo Seiffert, Frank-Michael Schleif, Dietlind Zühlke:
  Recent trends in computational intelligence in life sciences.
  
• Federico Montesino-Pouzols, Amaury Lendasse:
  Adaptive Kernel Smoothing Regression for Spatio-Temporal Environmental Datasets.
  
• Marika Kästner, Barbara Hammer, Michael Biehl, Thomas Villmann:
  Generalized functional relevance learning vector quantization.
  
• Xibin Zhu, Barbara Hammer:
  Patch Affinity Propagation.
  
• Marc Strickert, Björn Labitzke, Andreas Kolb, Thomas Villmann:
  Multispectral image characterization by partial generalized covariance.
  

Learning I

• Sarah Jarvis, Stefan Rotter, Ulrich Egert:
  Increased robustness and intermittent dynamics in structured Reservoir Networks with feedback.
  
• Mohamed Oubbati:
  Anticipating Rewards in Continuous Time and Space with Echo State Networks and Actor-Critic Design.
  
• Denise Gorse:
  Application of stochastic recurrent reinforcement learning to index trading.
  
• Christopher J. Gatti, Jonathan D. Linton, Mark J. Embrechts:
  A brief tutorial on reinforcement learning: The game of Chung Toi.
  
• Ayres Roberto Araújo Barcelos, Rita Maria da Silva Julia, Rivalino Matias Júnior:
  D-VisionDraughts: a draughts player neural network that learns by reinforcement in a high performance environment.
  
• Enrique Pelayo, Carlos Orrite-Uruñuela, J. David Buldain Pérez:
  SO-VAT: Self-Organizing Visual Assessment of cluster Tendency for large data sets.
  
• Jean Marc Salotti:
  New conditioning model for robots.
  
• Pornchai Khlaeo-om, Sasikanchana Yenaeng, Sunya Pasuk, Supachai Aroonpun, Sompun Aumpawan:
  Stability of Neural Network Control for Uncertain Sampled-Data Systems.
  
• Mariacarla Staffa, Silvia Rossi, Massimo De Gregorio, Ernesto Burattini:
  Thresholds tuning of a neuro-symbolic net controlling a behavior-based robotic system.
  
• Alexander Hans, Steffen Udluft:
  Ensemble Usage for More Reliable Policy Identification in Reinforcement Learning.
  
• Rafal Dlugosz, Marta Kolasa, Witold Pedrycz:
  Fisherman learning algorithm of the SOM realized in the CMOS technology.
  
• Atsushi Hashimoto, Haruo Hosoya:
  Abstract category learning.
  
• Siamak Mehrkanoon, Li Jiang, Carlos Alzate, Johan A. K. Suykens:
  Symbolic computing of LS-SVM based models.
  
• Jorge López Lázaro, Kris De Brabanter, José R. Dorronsoro, Johan A. K. Suykens:
  Sparse LS-SVMs with L0 - norm minimization.
  
• Haydemar Núñez, Luis González Abril, Cecilio Angulo:
  A post-processing strategy for SVM learning from unbalanced data.
  
• Douglas de O. Cardoso, Priscila M. V. Lima, Massimo De Gregorio, João Gama, Felipe M. G. França:
  Clustering data streams with weightless neural networks.
  
• Li Yao, Amaury Lendasse, Francesco Corona:
  Locating Anomalies Using Bayesian Factorizations and Masks.
  
• Hans-Georg Zimmermann, Alexey Minin, Victoria Kusherbaeva:
  Comparison of the Complex Valued and Real Valued Neural Networks Trained with Gradient Descent and Random Search Algorithms.
  

Seeing is believing: The importance of visualization in real-world machine learning applications

• Alfredo Vellido, José D. Martín, Fabrice Rossi, Paulo J. G. Lisboa:
  Seeing is believing: The importance of visualization in real-world machine learning applications.
  
• Stéphan Clémençon, Héctor de Arazoza, Fabrice Rossi, Viet-Chi Tran:
  Hierarchical clustering for graph visualization.
  
• Alfredo Vellido, Martha Ivón Cárdenas, Iván Olier, Xavier Rovira, Jesús Giraldo:
  A probabilistic approach to the visual exploration of G Protein-Coupled Receptor sequences.
  
• José M. Martínez, Pablo Escandell-Montero, Emilio Soria-Olivas, José D. Martín, Juan Gómez, Joan Vila-Francés:
  Growing Hierarchical Sectors on Sectors.
  
• Vicente Buendia-Ramon, Emilio Soria-Olivas, José D. Martín, Pablo Escandell-Montero, José M. Martínez:
  Analysis of a Reinforcement Learning algorithm using Self-Organizing Maps.
  

Learning theory

• Joseph Rynkiewicz:
  General bound of overfitting for MLP regression models.
  
• Davide Anguita, Alessandro Ghio, Luca Oneto, Sandro Ridella:
  Maximal Discrepancy vs. Rademacher Complexity for error estimation.
  

Feature selection and dimensionality reduction

• Gael de Lannoy, Damien François, Michel Verleysen:
  Class-Specific Feature Selection for One-Against-All Multiclass SVMs.
  
• Gauthier Doquire, Michel Verleysen:
  Mutual information for feature selection with missing data.
  
• Charles Bouveyron, Camille Brunet:
  Probabilistic Fisher discriminant analysis.
  
• Kerstin Bunte, Michael Biehl, Barbara Hammer:
  Supervised dimension reduction mappings.
  

Learning of causal relations

• John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl:
  Learning of causal relations.
  
• Jan Lemeire, Stijn Meganck, Francesco Cartella, Tingting Liu, Alexander R. Statnikov:
  Inferring the causal decomposition under the presence of deterministic relations.
  
• Angelos P. Armen, Ioannis Tsamardinos:
  A unified approach to estimation and control of the False Discovery Rate in Bayesian network skeleton identification.
  
• Tom Claassen, Tom Heskes:
  A structure independent algorithm for causal discovery.
  
• Ernest Mwebaze, John A. Quinn, Michael Biehl:
  Causal relevance learning for robust classification under interventions.
  
• Giorgos Borboudakis, Sofia Triantafilou, Vincenzo Lagani, Ioannis Tsamardinos:
  A constraint-based approach to incorporate prior knowledge in causal models.
  

Learning II

• Rémi Flamary, Florian Yger, Alain Rakotomamonjy:
  Selecting from an infinite set of features in SVM.
  
• Gauthier Doquire, Michel Verleysen:
  Mutual information based feature selection for mixed data.
  
• Artur J. Ferreira, Mário A. T. Figueiredo:
  Unsupervised feature selection for sparse data.
  
• Jakramate Bootkrajang, Ata Kaban:
  Multi-class classification in the presence of labelling errors.
  
• Michiel Van Dyck, Herbert Peremans:
  Principal component analysis for unsupervised calibration of bio-inspired airflow array sensors.
  
• Eli Parviainen:
  Effects of sparseness and randomness of pairwise distance matrix on t-SNE results.
  
• Jérôme Lapuyade-Lahorgue, Ali Mohammad-Djafari:
  Nearest neighbors and correlation dimension for dimensionality estimation. Application to factor analysis of real biological time series data.
  
• Rubén Suárez, Rocío García-Durán, Fernando Fernández:
  A Similarity Function with Local Feature Weighting for Structured Data.
  
• Claudio Gallicchio, Alessio Micheli:
  Exploiting vertices states in GraphESN by weighted nearest neighbor.
  
• Héctor Ruiz, Ian H. Jarman, José D. Martín, Paulo J. G. Lisboa:
  The role of Fisher information in primary data space for neighbourhood mapping.
  
• Fabrice Rossi, Matthieu Durut:
  Communication Challenges in Cloud K-means.
  
• Lazhar Labiod, Younès Bennani:
  A Spectral Based Clustering Algorithm for Categorical Data with Maximum Modularity.
  
• Lucie Daubigney, Olivier Pietquin:
  Single-trial P300 detection with Kalman filtering and SVMs.
  
• Carina Walter, Gabriele Cierniak, Peter Gerjets, Wolfgang Rosenstiel, Martin Bogdan:
  Classifying mental states with machine learning algorithms using alpha activity decline.
  
• Karim Youssef, Bastien Breteau, Sylvain Argentieri, Jean-Luc Zarader, Zefeng Wang:
  Approaches for Automatic Speaker Recognition in a Binaural Humanoid Context.
  
• Georg Hinselmann, Lars Rosenbaum, Andreas Jahn, Andreas Zell:
  Fast Data Mining with Sparse Chemical Graph Fingerprints by Estimating the Probability of Unique Patterns.
  
• Denis Schulze, Sven Wachsmuth, Katharina J. Rohlfing:
  Automatic Enhancement of Correspondence Detection in an Object Tracking System.
  

Sequence and time processing

• Michail Maniadakis, Marc Wittmann, Panos E. Trahanias:
  Time Experiencing by Robotic Agents.
  
• Mathieu Dubois, Hervé Guillaume, Emmanuelle Frenoux, Philippe Tarroux:
  Visual place recognition using Bayesian filtering with Markov chains.
  
• Francis Maes, Julien Becker, Louis Wehenkel:
  Iterative multi-task sequence labeling for predicting structural properties of proteins.
  
• Nisrine Jrad, Marco Congedo:
  Identification of sparse spatio-temporal features in Evoked Response Potentials.
  
• Yann Soullard, Thierry Artières:
  Hybrid HMM and HCRF model for sequence classification.
  
• DaeEun Kim:
  A Neural Filter for Electrolocation in Weakly Electric Fish.
  

Optimization and learning

• Verena Heidrich-Meisner, Christian Igel:
  Non-linearly increasing resampling in racing algorithms.
  
• Diego Peteiro-Barral, Bertha Guijarro-Berdiñas, Beatriz Pérez-Sánchez, Oscar Fontenla-Romero:
  A distributed learning algorithm based on two-layer artificial neural networks and genetic algorithms.
  

Deep Learning

• Ludovic Arnold, Sébastien Rebecchi, Sylvain Chevallier, Hélène Paugam-Moisy:
  An Introduction to Deep Learning.
  
• Alex Krizhevsky, Geoffrey E. Hinton:
  Using very deep autoencoders for content-based image retrieval.
  
• Asja Fischer, Christian Igel:
  Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives.
  
• Florian Yger, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy:
  A supervised strategy for deep kernel machine.
  

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