-norm Twin Support Vector Machine-based Regression. (2nd November 2017)

• Record Type: Journal Article
• Authors: Rastogi (nee Khemchandani), Reshma; Anand, Pritam; Chandra, Suresh
• Is Part Of: Optimization. Volume 66:Number 11(2017)
• Page Start: 1895
• Page End: 1911
• Publication Date: 2017-11-02
• Subjects: Support Vector Machine -- Regression -- Twin Support Vector Machine -- Twin Support Vector Regression -- -norm; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2017.1364739
• Languages: English
• ISSNs: 0233-1934

Abstract

Abstract: This paper presents two variants of -norm Twin Support Vector Machine-based Regression ( -norm TWSVR) model. The proposed methods are robust, efficient and own better generalization ability. The first method, termed as -norm TWSVR via QPP, results into the solution of a pair of QPPs. -norm TWSVR via QPP does not require the inversion of the kernel matrices during the learning process which makes it suitable for the large-scale problems. The second method, termed as -norm TWSVR via LPP, results into the solution of a pair of linear programs. The solution vectors of the -norm TWSVR via LPP is sparse which increases its prediction speed significantly. The experimental results on several artificial and UCI benchmark data-sets show that the use of -norm distances enables the proposed methods to perform better than the existing methods.