Benoît Frénay

According to our database1, Benoît Frénay authored at least 30 papers between 2008 and 2018.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Article 
PhD thesis 
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Links

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Bibliography

2018
Smoothness Bias in Relevance Estimators for Feature Selection in Regression.
Proceedings of the Artificial Intelligence Applications and Innovations, 2018

Information visualisation and machine learning: latest trends towards convergence.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

clustering with decision trees: divisive and agglomerative approach.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Finding the most interpretable MDS rotation for sparse linear models based on external features.
Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

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

Information visualisation and machine learning: characteristics, convergence and perspective.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Interpretability of machine learning models and representations: an introduction.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 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

Survival Analysis with Cox Regression and Random Non-linear Projections.
Proceedings of the 23rd European Symposium on Artificial 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
2S2, 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


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