A KERNEL-BASED WEIGHT-SETTING METHOD IN ROBUST WEIGHTED LEAST SQUARES SUPPORT VECTOR REGRESSION

摘要：

By combining the basic idea of weighted least squares support vector machines (WLS-SVM) and fuzzy support vector machines (FSVM), a weight-setting strategy based on 2-norm distance and neighborhood density (WLS-SVM Ⅰ) is presented in this paper. Then the relationship between the 2-norm distance and RBF kernel is revealed. Consequently, an equivalent weight setting strategy (WLS-SVM Ⅱ) using information from RBF kernel is put forward. Numerical experiments show both the 2-norm distance-based strategy and the Kernel-based strategy produce robust LS-SVM estimators of noisy data. And when satisfying some conditions,WLS-SVM Ⅰ can be substituted by WLS- SVM Ⅱ, which may provide an efficiency-enhancing strategy for online LS-SVM.

关键词： Support vector machine (Weighted) least squares regression robust

作者: WEN WEN ZHI-FENG HAO ZHUANG-FENG SHAO XIAO-WEI YANG

作者单位: College of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641 School of Mathematical Science, South China University of Technology, Guangzhou, 510641, China School of Mathematical Science, South China University of Technology, Guangzhou, 510641, China;Facul

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 4206-4211

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）