Nonparallel support vector machine with large margin distribution for pattern classification

Abstract The large margin distribution machine (LDM) combines the working principle of support vector machine (SVM) and the margin distribution to directly improve the algorithm's generalization. The margin distribution can be expressed with the margin mean and margin variance. It has been proved to be an efficient algorithm for binary classification. Inspired by the LDM, a novel classifier termed as LMD-NPSVM is proposed to improve the generalization performance of the nonparallel support vector machine (NPSVM) in this paper. Firstly, to meet the structure of NPSVM, the large margin distribution is reconstructed. Then, the linear LMD-NPSVM is built by introducing the reconstructed margin distribution into NPSVM. In addition, the linear case is extended to the nonlinear case with a kernel trick. All experiments show that our LMD-NPSVM is superior to the state-of-the-art algorithms in generalization performance.