Selecting from an infinite set of features in SVM

Rémi Flamary; Florian Yger; Alain Rakotomamonjy

Selecting from an infinite set of features in SVM

Rémi Flamary
, Florian Yger
, Alain Rakotomamonjy

Normandie Université

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

4 Citations (Scopus)

Résumé

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.

langue originale	Anglais
titre	Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Pages	327-332
Nombre de pages	6
état	Publié - 1 déc. 2010
Modification externe	Oui

Série de publications

Nom	ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Contient cette citation

Flamary, R., Yger, F., & Rakotomamonjy, A. (2010). Selecting from an infinite set of features in SVM. Dans Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010 (p. 327-332). (ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Flamary, Rémi ; Yger, Florian ; Rakotomamonjy, Alain. / Selecting from an infinite set of features in SVM. Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. p. 327-332 (ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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title = "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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Flamary, R, Yger, F & Rakotomamonjy, A 2010, Selecting from an infinite set of features in SVM. Dans Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, p. 327-332.

Selecting from an infinite set of features in SVM. / Flamary, Rémi; Yger, Florian; Rakotomamonjy, Alain.
Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. p. 327-332 (ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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AU - Yger, Florian

AU - Rakotomamonjy, Alain

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AB - 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.

M3 - Conference contribution

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Flamary R, Yger F, Rakotomamonjy A. Selecting from an infinite set of features in SVM. Dans Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010. 2010. p. 327-332. (ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Selecting from an infinite set of features in SVM

Résumé

Série de publications

Empreinte digitale

Contient cette citation