Error tolerance based support vector machine for regression

Guoqi Li, Changyun Wen, Guang-Bin Huang, Yan Chen

Abstract: Most existing online algorithms in support vector machines (SVM) can only grow support vectors. This paper proposes an online error tolerance based support vector machine (ET-SVM) which not only grows but also prunes support vectors. Similar to least square support vector machines (LS-SVM), ET-SVM converts the original quadratic program (QP) in standard SVM into a group of easily solved linear equations. Different from LS-SVM, ET-SVM remains support vectors sparse and realizes a compact structure. Thus, ET-SVM can significantly reduce computational time while ensuring satisfactory learning accuracy. Simulation results verify the effectiveness of the newly proposed algorithm.