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  An Online Support Vector Machine for Abnormal Events Detection

Davy, M., Desodry, F., Gretton, A., & Doncarli, C. (2006). An Online Support Vector Machine for Abnormal Events Detection. Signal Processing, 86(8), 2009-2025. doi:10.1016/j.sigpro.2005.09.027.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D081-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-835D-0
Genre: Journal Article

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Davy, M, Author
Desodry, F, Author
Gretton, A1, 2, Author              
Doncarli, C, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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 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, descriptor-based 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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 Dates: 2006-08
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1016/j.sigpro.2005.09.027
BibTex Citekey: 3589
 Degree: -

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Title: Signal Processing
Source Genre: Journal
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Publ. Info: New York, NY : Elsevier
Pages: - Volume / Issue: 86 (8) Sequence Number: - Start / End Page: 2009 - 2025 Identifier: ISSN: 0165-1684
CoNE: https://pure.mpg.de/cone/journals/resource/954925481601