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ORCIDESANN 2011: Bruges, Belgium
- 19th European Symposium on Artificial Neural Networks, ESANN 2011, Bruges, Belgium, April 27-29, 2011, Proceedings. 2011
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, José C. Príncipe:
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 David Martín-Guerrero, 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-Villena, Pablo Escandell-Montero, Emilio Soria-Olivas, José David Martín-Guerrero, Juan Gómez, Joan Vila-Francés:
Growing Hierarchical Sectors on Sectors. - Vicente Buendia-Ramon, Emilio Soria-Olivas, José David Martín-Guerrero, Pablo Escandell-Montero, José M. Martínez-Villena:
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
- Gaël 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 Kabán:
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 David Martín-Guerrero, 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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