title: TWSVR: Regression via Twin Support Vector Machine.
abstract: 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.
authors.affiliation: Department of Computer Science, Faculty of Mathematics and Computer Science, South Asian University, Delhi, India. Electronic address: reshma.khemchandani@sau.ac.in.
authors.forename: Reshma
authors.initials: R
authors.lastname: Khemchandani
authors.affiliation: Department of Mathematics, Indian Institute of Technology, Delhi, India. Electronic address: gkeshav91@gmail.com.
authors.forename: Keshav
authors.initials: K
authors.lastname: Goyal
authors.affiliation: Department of Mathematics, Indian Institute of Technology, Delhi, India. Electronic address: chandras@maths.iitd.ac.in.
authors.forename: Suresh
authors.initials: S
authors.lastname: Chandra
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data; D000465:Algorithms; D016208:Databases, Factual; D003920:Diabetes Mellitus / Q000453:epidemiology; D006801:Humans; D000069550:Machine Learning; D009049:Motorcycles / Q000706:statistics & numerical data; D012044:Regression Analysis; D060388:Support Vector Machine
keywords: Machine Learning; Support Vector Machines; Support vector regression; Twin Support Vector Machines