An Online Support Vector Machine for Abnormal Events Detection

Manuel Davy 1 Frederic Desobry Arthur Gretton Christian Doncarli
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : 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.
Type de document :
Article dans une revue
Signal Processing, Elsevier, 2006, 86 (8), pp.2009-2025
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https://hal.inria.fr/inria-00120256
Contributeur : Manuel Loth <>
Soumis le : lundi 28 mai 2007 - 19:36:22
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13

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  • HAL Id : inria-00120256, version 1

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Manuel Davy, Frederic Desobry, Arthur Gretton, Christian Doncarli. An Online Support Vector Machine for Abnormal Events Detection. Signal Processing, Elsevier, 2006, 86 (8), pp.2009-2025. 〈inria-00120256〉

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