By Topic

Support vector machines based on hyper-ball clustering

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

2 Author(s)
Ying-Hua He ; School of Computer Science and Technology, Tianjin University, China ; Kun-Long Zhang

In this paper, in order to reduce the support vectors on a large scale data set, we train support vector machines which utilize the hyper-spheres as the training samples. By representing adjacent samples of the same class as hyper-spheres, the boundary location can be controlled both by the center and radius of the hyper-spheres. We demonstrate that the optimization problem in this condition can be solved easily only by revising initial conditions of sequential minimal optimization (SMO) algorithm. Compared with previous algorithms on several data sets, the proposed algorithm is quite competitive in both the computational efficiency and the classification accuracy.

Published in:

2008 International Conference on Machine Learning and Cybernetics  (Volume:2 )

Date of Conference:

12-15 July 2008