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Online abnormal events detection with online support vector machine  

Park, Hye-Jung (Daegu University)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.2, 2011 , pp. 197-206 More about this Journal
Abstract
The ability to detect online abnormal events in signals is essential in many real-world signal processing applications. In order to detect abnormal events, previously known algorithms require an explicit signal statistical model, and interpret abnormal events as statistical model abrupt changes. In general, maximum likelihood and Bayesian estimation theory to estimate well as detection methods have been used. However, the above-mentioned methods for robust and tractable model, it is not easy to estimate. More freedom to estimate how the model is needed. In this paper, we investigate a machine learning, descriptor-based approach that does not require a explicit descriptors statistical model, based on support vector machines are known to be robust statistical models and a sequential optimal algorithm online support vector machine is introduced.
Keywords
Abnormal event detection; online support vector machine; support vector machine;
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Times Cited By KSCI : 4  (Citation Analysis)
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