### Margin value assisted sequential minimal optimization improvement

ZHEGN Qi1，2, DUAN Huichuan1，2, SUN Haitao3

1. 1.School of Information Science & Engineering, Shandong Normal University, Jinan 250014, China
2.Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Shandong Normal University, Jinan 250014, China
3.The Laboratory and Equipment Management Sector, Shandong Normal University, Jinan 250014, China
• Online:2017-02-15 Published:2017-05-11

### 间隔值辅助的SMO算法改进研究

1. 1.山东师范大学 信息科学与工程学院，济南 250014
2.山东师范大学 山东省分布式计算机软件新技术重点实验室，济南 250014
3.山东师范大学 实验室与设备管理处，济南 250014

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.