关键词: 支持向量机, 顺序最小优化, 间隔, 交叉验证

Abstract: Sequential Minimal Optimization algorithm is one of the best method to solve Support Vector Machine now. Its efficiency directly affects the training efficiency of SVM. For this reason, the paper summarizes the law of margin value changing with the number of iterations by a certain amount of empirical test. This law shows that the changes is in hinge function form. It has a start of a rapid descent and a horizon field which has no changes on margin value after a short period of slowly changing period. Therefore, the paper provides and realizes the algorithm which tracks the rate of margin?value’s changing along with the number of iterations and stops the training when entering the horizontal region. Contrast experiment and k-CV shows that this improved algorithm can improve efficiency by 45% at least and keep the predictive ability at the same time.

Key words: support vector machine, sequential minimal optimization, margin, cross-validation