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Anomaly Detection Through a Bayesian Support Vector Machine


Abstract:

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...Show More

Abstract:

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.
Published in: IEEE Transactions on Reliability ( Volume: 59, Issue: 2, June 2010)
Page(s): 277 - 286
Date of Publication: 01 June 2010

ISSN Information:


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