A Simple Decomposition Method for Support Vector Machines

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.

• Type: Journal
• Year: 2002
• Where: ML
• Authors: Chih-Wei Hsu, Chih-Jen Lin
• Keywords: Decomposition Methods, Machine Learning, ML 2002, Selection, Support Vector Machines