TWSVR: Regression via Twin Support Vector Machine

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• 作者：Reshma Khemchandani^a ; ^{reshma.khemchandani@sau.ac.in" class="auth_mail" title="E-mail the corresponding author} ; Keshav Goyal^b ; ^{gkeshav91@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Suresh Chandra^b ; ^{chandras@maths.iitd.ac.in" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Machine Learning ; Support Vector Machines ; Support vector regression ; Twin Support Vector Machines
• 刊名：Neural Networks
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：74
• 期：Complete
• 页码：14-21
• 全文大小：621 K

文摘

Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets.