A kernel-free quadratic surface support vector machine for semi-supervised learning

Xin Yan, Yanqin Bai, Shu-Cherng Fang and Jian Luo
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Xin Yan: Shanghai University, Shanghai, China
Yanqin Bai: Shanghai University, Shanghai, China
Shu-Cherng Fang: North Carolina State University, Raleigh, USA
Jian Luo: Dongbei University of Finance and Economics, Dalian, China

Journal of the Operational Research Society, 2016, vol. 67, issue 7, 1001-1011

Abstract: In this paper, we propose a kernel-free semi-supervised quadratic surface support vector machine model for binary classification. The model is formulated as a mixed-integer programming problem, which is equivalent to a non-convex optimization problem with absolute-value constraints. Using the relaxation techniques, we derive a semi-definite programming problem for semi-supervised learning. By solving this problem, the proposed model is tested on some artificial and public benchmark data sets. Preliminary computational results indicate that the proposed method outperforms some existing well-known methods for solving semi-supervised support vector machine with a Gaussian kernel in terms of classification accuracy.

Date: 2016
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