A fast SVM training algorithm based on the set segmentation and k-means clustering

摘要

At present, studies on training algorithms for support vector machines (SVM) are important issues in the field of machine learning. It is a challenging task to improve the efficiency of the algorithm without reducing the generalization performance of SVM. To face this challenge, a new SVM training algorithm based on the set segmentation and k -means clustering is presented in this paper. The new idea is to divide all the original training data into many subsets, followed by clustering each subset using k -means clustering and finally train SVM using the new data set obtained from clustering centroids. Considering that the decomposition algorithm such as SVM~(light) is one of the major methods for solving support vector machines, the SVM~(light) is used in our experiments. Simulations on different types of problems show that the proposed method can solve efficiently not only large linear classification problems but also large nonlinear ones.