Using SVMs Method to Detect Abrupt Change

To detect the change-points in signal data is an important practical problem. The classical method to solve this problem is using the statistical algorithms which are based on Bayesian theory. The efficiency of these methods always depends on the character of the given data. In this paper, we introd...

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Bibliographic Details
Main Authors: Yi-Zhange Guan, Zhi-Feng Hao
Format: Conference Proceeding
Language:English
Subjects:
Bayesian methods
Change detection algorithms
Change-point
Cybernetics
Educational institutions
Gaussian distribution
Intrusion detection
Machine learning
Signal detection
Support vector machine
Support vector machine classification
Support vector machines
Testing
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Summary:To detect the change-points in signal data is an important practical problem. The classical method to solve this problem is using the statistical algorithms which are based on Bayesian theory. The efficiency of these methods always depends on the character of the given data. In this paper, we introduce support vector machine method to detect the abrupt change on signal data. The experience shows that the idea is effective, and it does not limit to the character of the distribution.
ISSN:2160-133X
DOI:10.1109/ICMLC.2007.4370717