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

Benoît Frénay, Michel Verleysen

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


Support vector regression (SVR) is a state-of-the-art method for regression which uses the εsensitive loss and produces sparse models. However, non-linear SVRs are difficult to tune because of the additional kernel parameter. In this paper, a new parameter-insensitive kernel inspired from extreme learning is used for non-linear SVR. Hence, the practitioner has only two meta-parameters to optimise. The proposed approach reduces significantly the computational complexity yet experiments show that it yields performances that are very close from the state-of-the-art. Unlike previous works which rely on Monte-Carlo approximation to estimate the kernel, this work also shows that the proposed kernel has an analytic form which is computationally easier to evaluate. © 2011 Elsevier B.V.

langue originaleAnglais
Pages (de - à)2526-2531
Nombre de pages6
Numéro de publication16
Les DOIs
Etat de la publicationPublié - sept. 2011
Modification externeOui

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