By Topic

Using SVMs Method to Detect Abrupt Change

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yi-Zhange Guan ; South China Univ. of Technol., Guangzhou ; Zhi-Feng Hao

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.

Published in:

Machine Learning and Cybernetics, 2007 International Conference on  (Volume:6 )

Date of Conference:

19-22 Aug. 2007