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Publication search results
found 20 matches
- 2021
- Alexandra Degeest, Benoît Frénay
, Michel Verleysen
:
Reading grid for feature selection relevance criteria in regression. Pattern Recognit. Lett. 148: 92-99 (2021) - 2019
- Alexandra Degeest, Michel Verleysen
, Benoît Frénay
:
Comparison Between Filter Criteria for Feature Selection in Regression. ICANN (2) 2019: 59-71 - Alexandra Degeest, Michel Verleysen
, Benoît Frénay
:
About Filter Criteria for Feature Selection in Regression. IWANN (2) 2019: 579-590 - 2018
- Alexandra Degeest, Michel Verleysen
, Benoît Frénay
:
Smoothness Bias in Relevance Estimators for Feature Selection in Regression. AIAI 2018: 285-294 - 2016
- Benoît Frénay
, Michel Verleysen
:
Reinforced Extreme Learning Machines for Fast Robust Regression in the Presence of Outliers. IEEE Trans. Cybern. 46(12): 3351-3363 (2016) - 2015
- Alexandra Degeest, Michel Verleysen
, Benoît Frénay
:
Feature ranking in changing environments where new features are introduced. IJCNN 2015: 1-8 - 2014
- Benoît Frénay
, Gauthier Doquire, Michel Verleysen
:
Estimating mutual information for feature selection in the presence of label noise. Comput. Stat. Data Anal. 71: 832-848 (2014) - Benoît Frénay
, Michel Verleysen
:
Pointwise probability reinforcements for robust statistical inference. Neural Networks 50: 124-141 (2014) - Benoît Frénay
, Michel Verleysen
:
Classification in the Presence of Label Noise: A Survey. IEEE Trans. Neural Networks Learn. Syst. 25(5): 845-869 (2014) - Alexandra Degeest, Benoît Frénay, Michel Verleysen:
Automatic correction of SVM for drifted data classification. EGC 2014: 311-316 - 2013
- Benoît Frénay
, Gauthier Doquire, Michel Verleysen
:
Theoretical and empirical study on the potential inadequacy of mutual information for feature selection in classification. Neurocomputing 112: 64-78 (2013) - Benoît Frénay
, Mark van Heeswijk, Yoan Miche
, Michel Verleysen
, Amaury Lendasse
:
Feature selection for nonlinear models with extreme learning machines. Neurocomputing 102: 111-124 (2013) - Benoît Frénay
, Gauthier Doquire, Michel Verleysen
:
Is mutual information adequate for feature selection in regression? Neural Networks 48: 1-7 (2013) - Gauthier Doquire, Benoît Frénay, Michel Verleysen:
Risk Estimation and Feature Selection. ESANN 2013 - 2012
- Benoît Frénay, Gauthier Doquire, Michel Verleysen:
On the Potential Inadequacy of Mutual Information for Feature Selection. ESANN 2012 - 2011
- Benoît Frénay
, Michel Verleysen
:
Parameter-insensitive kernel in extreme learning for non-linear support vector regression. Neurocomputing 74(16): 2526-2531 (2011) - Benoît Frénay
, Gaël de Lannoy, Michel Verleysen
:
Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs. ECML/PKDD (1) 2011: 455-470 - 2010
- Benoît Frénay, Michel Verleysen:
Using SVMs with randomised feature spaces: an extreme learning approach. ESANN 2010 - 2009
- Michiel Schaap, Coert Metz, Theo van Walsum
, Alina G. van der Giessen, Annick C. Weustink, Nico Mollet, Christian Bauer, Hrvoje Bogunovic
, Carlos Castro-Gonzalez
, Xiang Deng, Engin Dikici, Thomas O'Donnell, Michel Frenay, Ola Friman, Marcela Hernández Hoyos, Pieter H. Kitslaar, Karl Krissian, Caroline Kühnel, Miguel A. Luengo-Oroz
, Maciej Orkisz
, Örjan Smedby
, Martin Styner
, Andrzej Szymczak, Hüseyin Tek, Chunliang Wang, Simon K. Warfield
, Sebastian Zambal, Yong Zhang, Gabriel P. Krestin, Wiro J. Niessen
:
Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Medical Image Anal. 13(5): 701-714 (2009) - Benoît Frénay, Gaël de Lannoy, Michel Verleysen:
Improving the transition modelling in hidden Markov models for ECG segmentation. ESANN 2009
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