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

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Date

July 6, 2010

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

arXiv

Organization

Cornell University


Keywords

Computer Science - Machine Learning

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Filtering Filtration

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


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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.

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