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

Benoît Frenay, Michel Verleysen

Résultats de recherche: Contribution à un journal/une revueArticleRevue par des pairs

Résumé

Extreme learning machines (ELMs) are fast methods that obtain state-of-the-art results in regression. However, they are not robust to outliers and their meta-parameter (i.e., the number of neurons for standard ELMs and the regularization constant of output weights for L2-regularized ELMs) selection is biased by such instances. This paper proposes a new robust inference algorithm for ELMs which is based on the pointwise probability reinforcement methodology. Experiments show that the proposed approach produces results which are comparable to the state of the art, while being often faster.

langue originaleAnglais
Numéro d'article7358117
Pages (de - à) 3351-3363
journalIEEE Transactions on Cybernetics
VolumePP
Numéro de publication99
Les DOIs
Etat de la publicationPublié - 17 déc. 2015

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