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An overview on twin support vector machines

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Abstract

Twin support vector machines (TWSVM) is based on the idea of proximal SVM based on generalized eigenvalues (GEPSVM), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/4 of standard SVM. In addition to keeping the superior characteristics of GEPSVM, the classification performance of TWSVM significantly outperforms that of GEPSVM. However, the stand-alone method requires the solution of two smaller quadratic programming problems. This paper mainly reviews the research progress of TWSVM. Firstly, it analyzes the basic theory and the algorithm thought of TWSVM, then tracking describes the research progress of TWSVM including the learning model and specific applications in recent years, finally points out the research and development prospects.

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Authors and Affiliations

  1. School of Computer Science and Technolog, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding, Junzhao Yu, Bingjuan Qi & Huajuan Huang

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Science, Beijing, 100190, China

    Shifei Ding

  3. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Shifei Ding

Authors
  1. Shifei Ding
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  2. Junzhao Yu
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  3. Bingjuan Qi
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  4. Huajuan Huang
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Correspondence to Shifei Ding.

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Ding, S., Yu, J., Qi, B. et al. An overview on twin support vector machines. Artif Intell Rev 42, 245–252 (2014). https://doi.org/10.1007/s10462-012-9336-0

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