Fast support vector machine training via three-term conjugate-like SMO algorithm

Abstract

Support vector machine (SVM) is an important class of methods in pattern recognition, and the sequential minimal optimization (SMO) algorithm is one of the most popular methods for training SVM at present. Based on the conjugate sequential minimal optimization algorithm (CSMO), we propose a novel three-term conjugate-like sequential minimal optimization algorithm (TCSMO) for classification and regression tasks. Compared with the CSMO, although the three-term conjugate-like SMO slightly increases the amount of arithmetic operations in each iteration, it significantly reduces the number of iterations required to converge to the specified accuracy and shortens the training time of the SVM. Additionally, we give a convergence proof of the three-term conjugate-like SMO algorithm and four new conjugate parameters. Numerical experiments show that the three-term conjugate-like SMO algorithm performs better numerically in both classification and regression tasks.