Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
532527 | Pattern Recognition | 2002 | 10 Pages |
Abstract
Based on the analysis of the conclusions in the statistical learning theory, especially the VC dimension of linear functions, linear programming support vector machines (or SVMs) are presented including linear programming linear and nonlinear SVMs. In linear programming SVMs, in order to improve the speed of the training time, the bound of the VC dimension is loosened properly. Simulation results for both artificial and real data show that the generalization performance of our method is a good approximation of SVMs and the computation complex is largely reduced by our method.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Weida Zhou, Li Zhang, Licheng Jiao,