PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An Online Support Vector Machine for Abnormal Events Detection
Manuel Davy, Frederic Desobry, Arthur Gretton and Christian Doncarli
Signal Processing 2002. ISSN 0165-1684


The ability to detect online abnormal events in signals is essential in many realworld 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 estimation 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, descriptorbased approach that does not require an explicit descriptors statistical model, based on Support Vector novelty detection. A sequential optimization algorithm is introduced. Theoretical considerations as well as simulations on real signals demonstrate its practical efficiency.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1705
Deposited By:Arthur Gretton
Deposited On:28 November 2005