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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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Davy, M, Autor
Desodry, F, Autor
Gretton, A1, 2, Autor           
Doncarli, C, Autor
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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 Zusammenfassung: 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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 Datum: 2006-08
 Publikationsstatus: Erschienen
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 Identifikatoren: DOI: 10.1016/j.sigpro.2005.09.027
BibTex Citekey: 3589
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Titel: Signal Processing
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: New York, NY : Elsevier
Seiten: - Band / Heft: 86 (8) Artikelnummer: - Start- / Endseite: 2009 - 2025 Identifikator: ISSN: 0165-1684
CoNE: https://pure.mpg.de/cone/journals/resource/954925481601