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Quadratic Kernel-Free Least Square Twin Support Vector Machine for Binary Classification Problems

  • Qian-Qian Gao
  • Yan-Qin BaiEmail author
  • Ya-Ru Zhan
Article
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Abstract

In this paper, a new quadratic kernel-free least square twin support vector machine (QLSTSVM) is proposed for binary classification problems. The advantage of QLSTSVM is that there is no need to select the kernel function and related parameters for nonlinear classification problems. After using consensus technique, we adopt alternating direction method of multipliers to solve the reformulated consensus QLSTSVM directly. To reduce CPU time, the Karush-Kuhn-Tucker (KKT) conditions is also used to solve the QLSTSVM. The performance of QLSTSVM is tested on two artificial datasets and several University of California Irvine (UCI) benchmark datasets. Numerical results indicate that the QLSTSVM may outperform several existing methods for solving twin support vector machine with Gaussian kernel in terms of the classification accuracy and operation time.

Keywords

Twin support vector machine Quadratic kernel-free Least square Binary classification 

Mathematics Subject Classification

68T99 90C20 

Notes

Acknowledgements

We are very grateful to the editor and the anonymous reviewers for their helpful and valuable comments of this paper.

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Copyright information

© Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of MathematicsShanghai UniversityShanghaiChina

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