Pre-selection of working set for SVM decomposition algorithm

Xulei Yang, Qing Song, Sheng Liu

Abstract: The decomposition algorithm is currently one of the major methods for solving support vector machines (SVM) training problems. The most important issue of this method is the selection of working set, which greatly affects the speed of the decomposition algorithm. In this paper, we propose a novel method for pre-selection of the working set for bound-constrained SVM formulation, which aims to make the training process more efficient. The pre-selection method is implemented based on fuzzy clustering technique in the high dimensional feature space using kernel methods. The effectiveness of the proposed method is supported by experimental results.