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Online Support Vector Machine: A Survey

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Harmony Search Algorithm

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 382))

Abstract

Support Vector Machine (SVM) is one of the fastest growing methods of machine learning due to its good generalization ability and good convergence performance; it has been successfully applied in various fields, such as text classification, statistics, pattern recognition, and image processing. However, for real-time data collection systems, the traditional SVM methods could not perform well. In particular, they cannot well cope with the increasing new samples. In this paper, we give a survey on online SVM. Firstly, the description of SVM is introduced, then the brief summary of online SVM is given, and finally the research and development of online SVM are presented.

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Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Power Station Automation Technology, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Xujun Zhou, Xianxia Zhang & Bing Wang

Authors
  1. Xujun Zhou
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  2. Xianxia Zhang
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  3. Bing Wang
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Corresponding authors

Correspondence to Xujun Zhou or Xianxia Zhang .

Editor information

Editors and Affiliations

  1. Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  2. Energy IT, Gachon University, Seongnam-Si, Korea (Republic of)

    Zong Woo Geem

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© 2016 Springer-Verlag Berlin Heidelberg

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Zhou, X., Zhang, X., Wang, B. (2016). Online Support Vector Machine: A Survey. In: Kim, J., Geem, Z. (eds) Harmony Search Algorithm. Advances in Intelligent Systems and Computing, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47926-1_26

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  • DOI: https://doi.org/10.1007/978-3-662-47926-1_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47925-4

  • Online ISBN: 978-3-662-47926-1

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