Optimal Boundary SVM Incremental Learning Algorithm

摘要：

　　Support vectors(SVs) cant be selected completely in support vector machine(SVM) incremental,resulting incremental learning process cant be sustained.In order to solve this problem,the article proposes optimal boundary SVM incremental learning algorithm.Based on in-depth analysis of the trend of the classification surface and make use of the KKT conditions,selecting the border of the vectors include the support vectors to participate SVM incremental learning.The experiment shows that the algorithm can be completely covered the support vectors and have the identical result with the classic support vector machine,it also saves lots of time.Therefore it can provide the conditions for future large sample classification and incremental learning sustainability.

关键词： supportt vector mechine increamental learning KKT condition

作者: Jian Cao Shiyu Sun Xiusheng Duan

作者单位: Mechanic Engineering College Department of Optics and Electronics Engineering Shijiazhuang, China

会议类型: 国际会议

会议名称: 2013 2nd International Symposium on Computer,Communication,Control and Automation(ISCCCA-13)(2013年第二届计算机、通信与自动化国际会议)

会议地点: 太原

会议语种:英文

页码: 132-135

在线出版日期: 2013-04-06（万方平台首次上网日期，不代表论文的发表时间）