Filtrage vaste marge pour l'étiquetage séquentiel à noyaux de signaux

Rémi Flamary (1) , Benjamin Labbé (1) , Alain Rakotomamonjy (1)

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes

We address in this paper the problem of multi-channel signal sequence labeling. In particular, we consider the problem where the signals are contaminated by noise or may present some dephasing with respect to their labels. For that, we propose to jointly learn a SVM sample classifier with a temporal filtering of the channels. This will lead to a large margin filtering that is adapted to the specificity of each channel (noise and time-lag). We derive algorithms to solve the optimization problem and we discuss different filter regularizations for automated scaling or selection of channels. Our approach is tested on a non-linear toy example and on a BCI dataset. Results show that the classification performance on these problems can be improved by learning a large margin filtering.

Mots clés: SVM; Étiquetage séquentiel; Filtrage; vaste marge

Domaines: Apprentissage [cs.LG]

Rémi Flamary, Benjamin Labbé, Alain Rakotomamonjy. Filtrage vaste marge pour l'étiquetage séquentiel à noyaux de signaux. Conférence Francophone sur l'Apprentissage Automatique, May 2010, Clermont Ferrand, France. 12p. ⟨hal-00497296⟩