Title :
Using SVMs Method to Detect Abrupt Change
Author :
Guan, Yi-Zhange ; Hao, Zhi-Feng
Author_Institution :
South China Univ. of Technol., Guangzhou
Abstract :
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.
Keywords :
Bayes methods; signal detection; support vector machines; Bayesian theory; SVMS method; signal abrupt change detection; statistical algorithm; support vector machine method; Bayesian methods; Change detection algorithms; Cybernetics; Educational institutions; Gaussian distribution; Intrusion detection; Machine learning; Support vector machine classification; Support vector machines; Testing; Change-point; Signal detection; Support vector machine;
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
DOI :
10.1109/ICMLC.2007.4370717
