Using SVMs with randomised feature spaces: an extreme learning approach: Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning (ESANN 2010)

Benoît Frénay, Michel Verleysen

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférenceArticle dans les actes d'une conférence/un colloque

Résumé

Extreme learning machines are fast models which almost compare to standard SVMs in terms of accuracy, but are much faster. However, they optimise a sum of squared errors whereas SVMs are maximum-margin classifiers. This paper proposes to merge both approaches by defining a new kernel. This kernel is computed by the first layer of an extreme learning machine and used to train a SVM. Experiments show that this new kernel compares to the standard RBF kernel in terms of accuracy and is faster. Indeed, experiments show that the number of neurons of the ELM behind the randomised kernel does not need to be tuned and can be set to a sufficient value without altering the accuracy significantly.
langue originaleAnglais
titreProceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Pages315-320
Nombre de pages6
Etat de la publicationPublié - 2010
Modification externeOui
Evénement18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010 - Bruges, Belgique
Durée: 28 avr. 201030 avr. 2010

Une conférence

Une conférence18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010
PaysBelgique
La villeBruges
période28/04/1030/04/10

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  • Contient cette citation

    Frénay, B., & Verleysen, M. (2010). Using SVMs with randomised feature spaces: an extreme learning approach: Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning (ESANN 2010). Dans Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 (p. 315-320)