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Selecting from an infinite set of features in SVM

Abstract : Dealing with the continuous parameters of a feature extraction method has always been a difficult task that is usually solved by cross-validation. In this paper, we propose an active set algorithm for selecting automatically these parameters in a SVM classification context. Our experiments on texture recognition and BCI signal classification show that optimizing the feature parameters in a continuous space while learning the decision function yields to better performances than using fixed parameters obtained from a grid sampling.
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https://hal.archives-ouvertes.fr/hal-00626168
Contributor : Rémi Flamary <>
Submitted on : Friday, September 23, 2011 - 4:27:21 PM
Last modification on : Friday, November 1, 2019 - 4:46:06 PM
Long-term archiving on: : Tuesday, November 13, 2012 - 2:25:33 PM

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  • HAL Id : hal-00626168, version 1

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Rémi Flamary, Florian Yger, Alain Rakotomamonjy. Selecting from an infinite set of features in SVM. European Symposium on Artificial Neural Networks, Apr 2011, Bruges, Belgium. ⟨hal-00626168⟩

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