文章基本信息

标题：An Efficient Twin Projection Support Vector Machine for Regression
本地全文：下载
作者：Xinyu Ouyang ; Nannan Zhao ; Chuang Gao 等
期刊名称：Engineering Letters
印刷版ISSN：1816-093X
电子版ISSN：1816-0948
出版年度：2019
卷号：27
期号：1
页码：103-107
出版社：Newswood Ltd
摘要：Taking motivation from ɛ-insensitive twin supportvector regression (ɛ-TSVR) and the projection idea, this paperproposes a novel ɛ-twin projection support vector regressionmodels, called ɛ-TPSVR. The proposed ɛ-TPSVR, which isbased on ɛ-TSVR, determines the regression function through apair of nonparallel hyperplanes solved by two smaller sizedquadratic programming problems. Different from ɛ-TSVR, aprojection axis is sought for each optimization problem ofɛ-TPSVR such that the variance of the projected points isminimized. Therefore, the empirical correlation coefficientbetween each hyperplane and the projected inputs can beoptimized. The experimental results indicate that the proposedɛ-TPSVR obtains the better prediction performance than TSVRand ɛ-TSVR methods that were widely adopted.
关键词：Support vector machine (SVM); Regression;analysis; Projection algorithms; Benchmark testing; ɛ;twin;support vector regression (ɛ;TSVR)