An effective algorithm for inverse problem of SVM based on MM algorithm
Jie Zhu, Run-Ya Li, Shu-Fang Wu, Song Ji, Man Li · 2009
This paper investigates an effective algorithm for inverse problem of support vector machines. The inverse problem is how to split a given dataset into two clusters such that the margin between the two clusters attains maximum. However the training time for inverse problem of SVM is incredible. Clustering is a feasible way to simplify the process of it, but it is difficult to estimate the number of the clusters. In this paper, we design a margin-merging cluster algorithm to solve this problem. We compare our approach with the k-means solution in terms of accuracy loss and training time. Simulations show that the proposed algorithm can solve it efficiently.