LMSVCR: novel effective method of semi-supervised multi-classification

Zijie Dong, Yimo Qin, Bin Zou, Jie Xu, Yuan Yan Tang

Abstract: The previously known works studying the learning performance of multi-classification algorithm are usually based on supervised samples, but large amount of data generated in real-life is usually unlabeled. This paper introduces a novel Laplacian multi-classification support vector classification and regression (LMSVCR) algorithm for the case of semi-supervised learning. We first establish the fast learning rate of LMSVCR algorithm with semi-supervised multi-classification samples, and prove that LMSVCR algorithm with semi-supervised multi-classification samples is consistent. We show the numerical investigation on the learning performance of LMSVCR algorithm. The experimental studies indicate that the proposed LMSVCR algorithm has better learning performance in terms of prediction accuracy, sampling and training total time than other semi-supervised multi-classification algorithms.