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A Non-singular Twin Support Vector Machine

  • Wu QingEmail author
  • Qi Shaowei
  • Zhang Haoyi
  • Jing Rongrong
  • Miao Jianchen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

Due to high efficiency, twin support vector machine (TWSVM) is suitable for large-scale classification problems. However, there is a singularity in solving the quadratic programming problems (QPPs). In order to overcome it, a new method to solve the QPPs is proposed in this paper, named non-singular twin support vector machine (NSTWSVM). We introduce a nonzero term to the result of the problem. Compared to the TWSVM, it does not need extra parameters. In addition, the successive overrelaxation technique is adopted to solve the QPPs in the NSTWSVM algorithm to speed up the training procedure. Experimental results show the effectiveness of the proposed method in both computation time and accuracy.

Keywords

Twin support vector machine Singularity Classification 

Notes

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China under Grants (61472307, 51405387), the Key Research Project of Shaanxi Province (2018GY-018) and the Foundation of Education Department of Shaanxi Province (17JK0713).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Wu Qing
    • 1
    Email author
  • Qi Shaowei
    • 1
  • Zhang Haoyi
    • 1
  • Jing Rongrong
    • 1
  • Miao Jianchen
    • 1
  1. 1.School of AutomationXi’an University of Posts and TelecommunicationsXi’anChina

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