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Multiple birth support vector machine for multi-class classification

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Abstract

For multi-class classification problem, a novel algorithm, called as multiple birth support vector machine (MBSVM), is proposed, which can be considered as an extension of twin support vector machine. Our MBSVM has been compared with the several typical support vector machines. From theoretical point of view, it has been shown that its computational complexity is remarkably low, especially when the class number K is large. Based on our MBSVM, the dual problems of MBSVM are equivalent to symmetric mixed linear complementarity problems to which successive overrelaxation (SOR) can be directly applied. We establish our SOR algorithm for MBSVM. The SOR algorithm handles one data point at a time, so it can process large dataset that need no reside in memory. From practical point of view, its accuracy has been validated by the preliminary numerical experiments.

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Authors and Affiliations

  1. College of Mathematics and Systems Science, Xinjiang University, Urumqi, 830046, People’s Republic of China

    Zhi-Xia Yang

  2. Zhijiang College, Zhejiang University of Technology, Hangzhou, 310024, People’s Republic of China

    Yuan-Hai Shao

  3. Academy of Mathematics and Systems Science, China Academy of Sciences, Beijing, 100190, People’s Republic of China

    Xiang-Sun Zhang

Authors
  1. Zhi-Xia Yang
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  2. Yuan-Hai Shao
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  3. Xiang-Sun Zhang
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Corresponding author

Correspondence to Zhi-Xia Yang.

Additional information

This work is supported by the National Natural Science Foundation of China (No.11161045) and Zhejiang Provincial Natural Science Foundation of China (No. LQ12A01020).

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Yang, ZX., Shao, YH. & Zhang, XS. Multiple birth support vector machine for multi-class classification. Neural Comput & Applic 22 (Suppl 1), 153–161 (2013). https://doi.org/10.1007/s00521-012-1108-x

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