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Article Dans Une Revue Signal Processing Année : 2006

An Online Support Vector Machine for Abnormal Events Detection

Manuel Davy
  • Fonction : Auteur
Frederic Desobry
  • Fonction : Auteur
Arthur Gretton
  • Fonction : Auteur
Christian Doncarli
  • Fonction : Auteur

Résumé

The ability to detect online abnormal events in signals is essential in many real- world Signal Processing applications. Previous algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. Corresponding implementation relies on maximum likelihood or on Bayes estima- tion theory with generally excellent performance. However, there are numerous cases where a robust and tractable model cannot be obtained, and model-free approaches need to be considered. In this paper, we investigate a machine learning, descriptor- based approach that does not require an explicit descriptors statistical model, based on Support Vector novelty detection. A sequential optimization algorithm is intro- duced. Theoretical considerations as well as simulations on real signals demonstrate its practical efficiency.

Dates et versions

inria-00120256 , version 1 (28-05-2007)

Identifiants

Citer

Manuel Davy, Frederic Desobry, Arthur Gretton, Christian Doncarli. An Online Support Vector Machine for Abnormal Events Detection. Signal Processing, 2006, 86 (8), pp.2009-2025. ⟨10.1016/j.sigpro.2005.09.027⟩. ⟨inria-00120256⟩
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