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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Communication dans un congrès
European Symposium on Artificial Neural Networks, Apr 2011, Bruges, Belgium. 2011
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https://hal.archives-ouvertes.fr/hal-00626168
Contributeur : Rémi Flamary <>
Soumis le : vendredi 23 septembre 2011 - 16:27:21
Dernière modification le : vendredi 23 septembre 2011 - 16:31:49
Document(s) archivé(s) le : mardi 13 novembre 2012 - 14:25:33

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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. 2011. <hal-00626168>

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