文献类型：会议论文

英文题名：Weighted Least Squares Twin Support Vector Machine For Regression With Noise

作者：Li, Juntao[1];Jing, Junchang[1];Cao, Yimin[1];Xiao, Huimin[2]

第一作者：Li, Juntao

通讯作者：Li, JT[1]

机构：[1]Henan Normal Univ, Sch Math & Informat Sci, Xinxiang 453007, Peoples R China;[2]Henan Univ Econ & Law, Coll Comp & Informat Engn, Zhengzhou 450002, Henan, Peoples R China

第一机构：Henan Normal Univ, Sch Math & Informat Sci, Xinxiang 453007, Peoples R China

通讯机构：[1]corresponding author), Henan Normal Univ, Sch Math & Informat Sci, Xinxiang 453007, Peoples R China.

会议论文集：36th Chinese Control Conference (CCC)

会议日期：JUL 26-28, 2017

会议地点：Dalian, PEOPLES R CHINA

语种：英文

外文关键词：Twin support vector regression; regression with noise; weighted mechanism

摘要：Twin support vector regression and its extensions have been widely applied in machine learning and data mining. However, most of them can not achieve the satisfactory performances when the noise is involved. To this end, this paper presents a weighted least squares twin support vector regression (WLSTSVR) which can reduce the influence of the noise on prediction accuracy by using the information of the responses of the samples. Furthermore, both offline and online learning algorithms are developed. The experimental results on artificial and benchmark datasets with noise indicate that the both online and offline learning WLSTSVR achieve better prediction accuracy and agreement between estimations and real- values compared with least squares twin support vector regression.