计算机工程与应用2017，Vol.53Issue(4)：64-69,6.DOI:10.3778/j.issn.1002-8331.1607-0111
间隔值辅助的SMO算法改进研究
Margin value assisted sequential minimal optimization improvement
摘要
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.关键词
支持向量机/顺序最小优化/间隔/交叉验证Key words
support vector machine/sequential minimal optimization/margin/cross-validation分类
信息技术与安全科学引用本文复制引用
郑奇,段会川,孙海涛..间隔值辅助的SMO算法改进研究[J].计算机工程与应用,2017,53(4):64-69,6.基金项目
国家自然科学基金(No.61572299). （No.61572299）
