default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
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.
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2024-12-12 20:47 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
