# Benoît Frénay

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^{1}, Benoît Frénay## Timeline

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Book In proceedings Article PhD thesis Other## Links

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## Bibliography

2017

Label-noise-tolerant classification for streaming data.

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2016

Reinforced Extreme Learning Machines for Fast Robust Regression in the Presence of Outliers.

IEEE Trans. Cybernetics, 2016

Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment.

CoRR, 2016

2015

Special issue on advances in learning with label noise.

Neurocomputing, 2015

Feature ranking in changing environments where new features are introduced.

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

2014

Classification in the Presence of Label Noise: A Survey.

IEEE Trans. Neural Netw. Learning Syst., 2014

Pointwise probability reinforcements for robust statistical inference.

Neural Networks, 2014

Estimating mutual information for feature selection in the presence of label noise.

Computational Statistics & Data Analysis, 2014

Automatic correction of SVM for drifted data classification.

Proceedings of the 14èmes Journées Francophones Extraction et Gestion des Connaissances, 2014

A comprehensive introduction to label noise.

Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

Valid interpretation of feature relevance for linear data mappings.

Proceedings of the 2014 IEEE Symposium on Computational Intelligence and Data Mining, 2014

2013

Uncertainty and label noise in machine learning.

PhD thesis, 2013

Is mutual information adequate for feature selection in regression?

Neural Networks, 2013

Feature selection for nonlinear models with extreme learning machines.

Neurocomputing, 2013

Theoretical and empirical study on the potential inadequacy of mutual information for feature selection in classification.

Neurocomputing, 2013

Risk Estimation and Feature Selection.

Proceedings of the 21st European Symposium on Artificial Neural Networks, 2013

2012

On the Potential Inadequacy of Mutual Information for Feature Selection.

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011

Parameter-insensitive kernel in extreme learning for non-linear support vector regression.

Neurocomputing, 2011

Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs.

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

2010

Using SVMs with randomised feature spaces: an extreme learning approach.

Proceedings of the ESANN 2010, 2010

2009

2S

_{2}, a simple reinforcement learning scheme for two-player zero-sum Markov games.
Neurocomputing, 2009

Improving the transition modelling in hidden Markov models for ECG segmentation.

Proceedings of the ESANN 2009, 2009

2008

QL2, a simple reinforcement learning scheme for two-player zero-sum Markov games.

Proceedings of the ESANN 2008, 2008