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Research Output 2007 2019

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Article
2019

BIR: A Method for Selecting the Best Interpretable Multidimensional Scaling Rotation using External Variables

Marion, R., Bibal, A. & Frenay, B., 4 Feb 2019, In : Neurocomputing. 342, p. 83-96 14 p.

Research output: Contribution to journalArticle

File
Ecology
Invariance
Psychology
2015

Reinforced extreme learning machines for fast robust regression in the presence of outliers

Frenay, B. & Verleysen, M., 17 Dec 2015, In : IEEE Transactions on Cybernetics. PP, 99, p. 3351-3363 7358117.

Research output: Contribution to journalArticle

Learning systems
Neurons
Reinforcement
Experiments
2014

Classification in the presence of label noise: A survey

Frénay, B. & Verleysen, M., 2014, In : IEEE Transactions on Neural Networks and Learning Systems. 25, 5, p. 845-869 25 p., 6685834.

Research output: Contribution to journalArticle

Open Access
File
Labels
Taxonomies
Design of experiments

Estimating Mutual information for feature selection in the presence of label noise

Frénay, B., Doquire, G. & Verleysen, M., Mar 2014, In : Computational Statistics and Data Analysis. 71, p. 832-848 17 p.

Research output: Contribution to journalArticle

Mutual Information
Feature Selection
Feature extraction
Labels
Classification Problems

Pointwise probability reinforcements for robust statistical inference

Frénay, B. & Verleysen, M., 2014, In : Neural Networks. 50, p. 124-141 18 p.

Research output: Contribution to journalArticle

File
Reinforcement
Observation
Learning systems
Experiments
2013

Feature selection for nonlinear models with extreme learning machines

Frénay, B., van Heeswijk, M., Miche, Y., Verleysen, M. & Lendasse, A., 2013, In : Neurocomputing. 102, p. 111-124 14 p.

Research output: Contribution to journalArticle

Nonlinear Dynamics
Learning systems
Feature extraction
Statistics
Machine Learning

Is mutual information adequate for feature selection in regression?

Frénay, B., Doquire, G. & Verleysen, M., Dec 2013, In : Neural Networks. 48, p. 1-7 7 p.

Research output: Contribution to journalArticle

Feature extraction
Learning systems
Machine Learning

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

Frénay, B., Doquire, G. & Verleysen, M., 18 Jul 2013, In : Neurocomputing. 112, p. 64-78 15 p.

Research output: Contribution to journalArticle

File
Patient Selection
Feature extraction
Theoretical Models
Heuristics
Experiments
2011

Ensembles of local linear models for bankruptcy analysis and prediction

Kainulainen, L., Miche, Y., Eirola, E., Yu, Q., Frenay, B., Séverin, E. & Lendasse, A., 2011, In : Case Studies in Business, Industry and Government Statistics. 4, 2, p. 116–133

Research output: Contribution to journalArticle

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

Frénay, B. & Verleysen, M., Sep 2011, In : Neurocomputing. 74, 16, p. 2526-2531 6 p.

Research output: Contribution to journalArticle

Learning
Computational complexity
Experiments
2010

Clustering patterns of urban built-up areas with curves of fractal scaling behaviour

Thomas, I., Frankhauser, P., Frenay, B., Verleysen, M. & Samos-Matisse, S. M., 2010, In : Environment and Planning B: Planning & Design. 37, 5, p. 942-954 13 p.

Research output: Contribution to journalArticle

Fractal dimension
scaling
Fractals
Textures
Planning
2008

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

Frénay, B. & Saerens, M., 2008, In : Neurocomputing. 72, 7-9, p. 1494-1507 14 p.

Research output: Contribution to journalArticle

Reinforcement learning
Learning
Ashes
Boidae
Learning algorithms