Fuzzy Support Vector Regression for Function Approximation With Noises

摘要：

Fuzzy support vector machine (FSVM) have been very successful in pattern recognition problems with outliers or noises. FSVM enhances the SVM in reducing the effect of noises in data points. In this paper, we introduce FSVM to regression problems for function approximation with noises. We apply a fuzzy membership to each input point of SVR and reformulate SVR into fuzzy SVR (FSVR) such that different input points can make different contributions to the learning of decision function.

关键词： FSVM regression SVR SVM

作者: Rui Zhang Xian-bao Duan Lei Han

作者单位: School of Sciences Shandong University of Technology, Zibo, P.R.China School of Sciences and School of Automation and Information Engineering XT an University of Technolo Shandong Vocational College of Aluminum, Zibo, P.R.China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 14-17

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）