An Improved Sequential Minimal Optimization Algorithm
一种改进的序贯最小优化算法

At present sequential minimal optimization (SMO) algorithm is a very efficient method for training support vector machines (SVM). However, the training speed of SMO is very slow for the large-scale datasets. Analyzing the varieties of the objective function in SMO iterations, we propose a novel improved SMO algorithm in this paper, where the changed value of the objective function is taken as the termination condition. Experiments on several benchmark datasets have been done and the results show that the training time of the proposed algorithm is reduced greatly, especially for the large-scale problems.