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

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Pages315-320
Number of pages6
Publication statusPublished - 2010
Externally publishedYes
Event18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010 - Bruges, Belgium
Duration: 28 Apr 201030 Apr 2010

Conference

Conference18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2010
CountryBelgium
CityBruges
Period28/04/1030/04/10

Keywords

  • ICTEAM:MLAI

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  • Cite this

    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). In Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 (pp. 315-320)