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2010

Anomaly Detection Through a Bayesian Support Vector Machine

8 years 5 months ago
Anomaly Detection Through a Bayesian Support Vector Machine
This paper investigates the use of a one-class support vector machine algorithm to detect the onset of system anomalies, and trend output classification probabilities, as a way to monitor the health of a system. In the absence of "unhealthy" (negative class) information, a marginal kernel density estimate of the "healthy" (positive class) distribution is used to construct an estimate of the negative class. The output of the one-class support vector classifier is calibrated to posterior probabilities by fitting a logistic distribution to the support vector predictor model in an effort to manage false alarms.
Vasilis A. Sotiris, Peter W. Tse, Michael Pecht
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TR
Authors Vasilis A. Sotiris, Peter W. Tse, Michael Pecht
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