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Semi-supervised Support Vector Machines - A Genetic Algorithm Approach

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9948))

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Abstract

Semi-supervised learning combines both labeled and unlabeled examples in order to find better future predictions. Semi-supervised support vector machines (SSSVM) present a non-convex optimization problem. In this paper a genetic algorithm is used to optimize the non-convex error - GSSSVM. It is experimented with multiple datasets and the performance of the genetic algorithm is compared to its supervised equivalent and shows very good results. A tailor-made modification of the genetic algorithm is also proposed which uses less unlabeled examples – the closest neighbors of the labeled instances.

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Author information

Authors and Affiliations

  1. Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria

    Gergana Lazarova

Authors
  1. Gergana Lazarova
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Correspondence to Gergana Lazarova .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

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Lazarova, G. (2016). Semi-supervised Support Vector Machines - A Genetic Algorithm Approach. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_28

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  • DOI: https://doi.org/10.1007/978-3-319-46672-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46671-2

  • Online ISBN: 978-3-319-46672-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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