Two smooth support vector machines for $$\varepsilon $$ ε -insensitive regression

Weizhe Gu (), Wei-Po Chen (), Chun-Hsu Ko (), Yuh-Jye Lee () and Jein-Shan Chen ()
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Weizhe Gu: Tianjin University
Wei-Po Chen: National Taiwan Normal University
Chun-Hsu Ko: I-Shou University
Yuh-Jye Lee: National Chiao Tung University
Jein-Shan Chen: National Taiwan Normal University

Computational Optimization and Applications, 2018, vol. 70, issue 1, No 6, 199 pages

Abstract: Abstract In this paper, we propose two new smooth support vector machines for $$\varepsilon $$ ε -insensitive regression. According to these two smooth support vector machines, we construct two systems of smooth equations based on two novel families of smoothing functions, from which we seek the solution to $$\varepsilon $$ ε -support vector regression ( $$\varepsilon $$ ε -SVR). More specifically, using the proposed smoothing functions, we employ the smoothing Newton method to solve the systems of smooth equations. The algorithm is shown to be globally and quadratically convergent without any additional conditions. Numerical comparisons among different values of parameter are also reported.

Keywords: Support vector machine; $$\varepsilon $$ ε -insensitive loss; $$\varepsilon $$ ε -smooth support vector regression; Smoothing Newton algorithm (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10589-017-9975-9

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