Support Vectors Classification and Incremental Learning

摘要：

According to whether the slack variable of the support vector is equal to zero, the support vector is divided into two categories, one is linear separable support vector and the other is non-linear separable support vector, in this paper. Using linear separable support vector set instead of support vector set in the incremental learning, Simple ISVM 1 (Simple Incremental Support Vector Machine Algorithm) is proposed. Because the linear separable support vectors are far less than support vectors, the speed of Simple ISVM J is fast than SVM-Inc.l. But the accuracy is slightly worse than SVM-Inc. For improving the accuracy of Simple ISVM 1, generalized linear separable support vector set is used to replace linear separable support vector set in incremental learning. The Simple ISVM 2(Simple Incremental Support Vector Machine 2) is proposed. The generalized support vector is the support vector whose slack variable is less than a positive constant. Set a proper threshold, the accuracy of Simple ISVM 2 can be no less than SVM-Inc. 1 and the speed is fast than SVM-Inc. Empirical results show that the linear separable support vector set(or generalized separable support vector set) is the minimum subset which can approximately represent the historical set in the incremental learning, which is smaller than the support vector set.

关键词： component SVM Incremental learning slack variable KTT conditions Simple ISVM

作者: Fa Zhu Ning Ye Sheng Xu Xiaojun Gu

作者单位: School of Information technology Nanjing Forestry University Nanjing, China

会议类型: 国际会议

会议名称: 2011 6th Joint International Information Technology and Artificial Intelligence Conference(2011年第六届IEEE联合国际信息技术与人工智能会议 IEEE ITAIC 2011)

会议地点: 重庆

会议语种:英文

页码: 206-210

在线出版日期: 2011-08-20（万方平台首次上网日期，不代表论文的发表时间）