DocumentCode
1936064
Title
Using SVMs Method to Detect Abrupt Change
Author
Guan, Yi-Zhange ; Hao, Zhi-Feng
Author_Institution
South China Univ. of Technol., Guangzhou
Volume
6
fYear
2007
fDate
19-22 Aug. 2007
Firstpage
3298
Lastpage
3301
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/ICMLC.2007.4370717
Filename
4370717
Link To Document