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A Non-singular Twin Support Vector Machine

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Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 891))

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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.

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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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Authors and Affiliations

  1. School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Wu Qing, Qi Shaowei, Zhang Haoyi, Jing Rongrong & Miao Jianchen

Authors
  1. Wu Qing
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  2. Qi Shaowei
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  3. Zhang Haoyi
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  4. Jing Rongrong
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  5. Miao Jianchen
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Corresponding author

Correspondence to Wu Qing .

Editor information

Editors and Affiliations

  1. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Pavel Krömer

  2. School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, China

    Hong Zhang

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Yongquan Liang

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

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Qing, W., Shaowei, Q., Haoyi, Z., Rongrong, J., Jianchen, M. (2019). A Non-singular Twin Support Vector Machine. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_87

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