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A SVM Method Trained by Improved Particle Swarm Optimization for Image Classification

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Pattern Recognition (CCPR 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 483))

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Abstract

As an important classification method, SVM has been widely used in different fields. But it is still a problem how to choose the favorable parameters of SVM. For optimizing the parameters and increasing the accuracy of SVM, this paper proposed an improved quantum behaved particle swarm algorithm based on a mutation operator (MQPSO). The new operator is used for enhancing the global search ability of particle. We test SVM based on MPSO method on solving the problem of image classification. Result shows our algorithm is quite stable and gets higher accuracy.

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Author information

Authors and Affiliations

  1. College of Automation, Nanjing University of Posts and Telecommunications, Jiang Su, 210023, China

    Qifeng Qian, Hao Gao & Baoyun Wang

Authors
  1. Qifeng Qian
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  2. Hao Gao
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  3. Baoyun Wang
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Editor information

Editors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, 410082, Changsha, P.R. China

    Shutao Li

  2. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  3. College of electrical and information engineering, Hunan University, 410082, Changsha, P.R. China

    Yaonan Wang

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Qian, Q., Gao, H., Wang, B. (2014). A SVM Method Trained by Improved Particle Swarm Optimization for Image Classification. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_27

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  • DOI: https://doi.org/10.1007/978-3-662-45646-0_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45645-3

  • Online ISBN: 978-3-662-45646-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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