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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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Contributor : Rémi Flamary Connect in order to contact the contributor
Submitted on : Friday, September 23, 2011 - 4:27:21 PM
Last modification on : Wednesday, March 2, 2022 - 10:10:07 AM
Long-term archiving on: : Tuesday, November 13, 2012 - 2:25:33 PM


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  • HAL Id : hal-00626168, version 1


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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