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Multiclass Lagrangian support vector machine

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Abstract

A support vector machine (SVM) has been developed for two-class problems, although its application to multiclass problems is not straightforward. This paper proposes a new Lagrangian SVM (LSVM) for application to multiclass problems. The multiclass Lagrangian SVM is formulated as a single optimization problem considering all the classes together, and a training method tailored to the multiclass problem is presented. A multiclass output representation matrix is defined to simplify the optimization formulation and associated training method. The proposed method is applied to some benchmark datasets in repository, and its effectiveness is demonstrated via simulation.

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Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (2011-0005274).

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Yonsei University, C613, Sinchon-dong, Seodaemun-gu, Seoul, 120-749, Korea

    Jae Pil Hwang, Baehoon Choi & Euntai Kim

  2. Korea Electronics Technology Institute, #68 Yatap-dong, Bundang-gu, 463-816, Korea

    In Wha Hong

Authors
  1. Jae Pil Hwang
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  2. Baehoon Choi
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  3. In Wha Hong
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  4. Euntai Kim
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Correspondence to Euntai Kim.

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Hwang, J.P., Choi, B., Hong, I.W. et al. Multiclass Lagrangian support vector machine. Neural Comput & Applic 22, 703–710 (2013). https://doi.org/10.1007/s00521-011-0755-7

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  • DOI: https://doi.org/10.1007/s00521-011-0755-7

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