A simple decomposition method for support vector machines

276Citations
Citations of this article
141Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for bound-constrained SVM formulations we demonstrate that the working set selection is not a trivial task. Then from the experimental analysis we propose a simple selection of the working set which leads to faster convergences for difficult cases. Numerical experiments on different types of problems are conducted to demonstrate the viability of the proposed method.

Cite

CITATION STYLE

APA

Hsu, C. W., & Lin, C. J. (2002). A simple decomposition method for support vector machines. Machine Learning, 46(1–3), 291–314. https://doi.org/10.1023/A:1012427100071

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free