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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xin Yan & Yanqin Bai & Shu-Cherng Fang & Jian Luo, 2016. "A kernel-free quadratic surface support vector machine for semi-supervised learning," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(7), pages 1001-1011, July.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:7:p:1001-1011
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    Citations

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    Cited by:

    1. Jun Sun & Wentao Qu, 2022. "DCA for Sparse Quadratic Kernel-Free Least Squares Semi-Supervised Support Vector Machine," Mathematics, MDPI, vol. 10(15), pages 1-17, August.
    2. Gao, Zheming & Fang, Shu-Cherng & Luo, Jian & Medhin, Negash, 2021. "A kernel-free double well potential support vector machine with applications," European Journal of Operational Research, Elsevier, vol. 290(1), pages 248-262.

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