By Topic

Application of critical support vector machine to time series prediction

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
$33 $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

3 Author(s)
T. Raicharoen ; Dept. of Math., Chulalongkorn Univ., Bangkok, Thailand ; C. Lursinsap ; P. Sanguanbhokai

This paper proposes a novel technique to approximate functions for time series and signal processing using the special type of neural network, called Critical Support Vector Machine (CSVM). CSVM is a combination of the Support Vector Machine, the Nearest Neighbor Algorithm and the Perceptron. The CSVM has been shown to be an effective method for classification problems. In this work, we generalize CSVM so that it can be used for the application of time series prediction. The experiment on the chaotic Mackey-Glass time series significantly verifies the performance of our algorithm.

Published in:

Circuits and Systems, 2003. ISCAS '03. Proceedings of the 2003 International Symposium on  (Volume:5 )

Date of Conference:

25-28 May 2003