By Topic

A Data Fusion Approach Based on Parallel Support Vector Machine

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

3 Author(s)
Yun Luo ; Sch. of Comput. Sci. & Technol., SouthWest Univ. of Sci. & Technol., Mianyang, China ; Yuanzhi Wang ; Min Sun

Support vector machine has some advantages, such as simple structure and good generalization, which is one implementation in statistical learning theory. SVM offers a kind of effective way for the data fusion problem of little sample, non-linear and high dimension. In this paper, mobile agents are applied to data fusion system. The model and the study method of data fusion system are improved. An approach of data fusion based on SVM is proposed. The experiment results show that this hierarchical and parallel SVM training algorithm is efficient to deal with large-scale classification problems and has more satisfying accuracy in classification precision.

Published in:

Artificial Intelligence, 2009. JCAI '09. International Joint Conference on

Date of Conference:

25-26 April 2009