Selecting from an infinite set of features in SVM - Archive ouverte HAL Access content directly
Conference Papers Year : 2011

Selecting from an infinite set of features in SVM


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.
Fichier principal
Vignette du fichier
ESANN2011.pdf (109.95 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00626168 , version 1 (23-09-2011)


  • 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⟩
94 View
86 Download


Gmail Facebook Twitter LinkedIn More