Filtrage vaste marge pour l'\'etiquetage s\'equentiel \`a noyaux de signaux



July 6, 2010

  • handle:  10670/1.bu76yj
  • Conf\'erence Francophone sur l'Apprentissage Automatique, Clermont Ferrand : France (2010)



Cornell University


Computer Science - Machine Learning

Similar subjects En

Filtering Filtration

Cite this document

Rémi Flamary et al., « Filtrage vaste marge pour l'\'etiquetage s\'equentiel \`a noyaux de signaux », arXiv, ID : 10670/1.bu76yj


Share / Export

Abstract 0

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.

From the same authors

On the same subjects

Similar documents

Within the same disciplines