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A Polynomial Smooth Support Vector Machine for Classification

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Advanced Data Mining and Applications (ADMA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3584))

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Abstract

A new polynomial smooth method for solving the support vector machine (SVM) is presented in this paper. It is called the polynomial smooth support vector machine (PSSVM). BFGS method and Newton-Armijo method are applied to solve the PSSVM. Numerical experiments confirm that PSSVM is more effective than SVM.

Supported by the Youth Key Foundations of Univ. of Electronic Science and Technology of China (Jx04042) and NCET of China.

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Author information

Authors and Affiliations

  1. School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, P. R. China

    YuBo Yuan & TingZhu Huang

Authors
  1. YuBo Yuan
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  2. TingZhu Huang
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, Queensland, Australia

    Xue Li

  2. The State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430072, Wuhan, China

    Shuliang Wang

  3. School of ITEE, The Univ of Queensland, St. Lucia, 4072, QLD, Australia

    Zhao Yang Dong

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© 2005 Springer-Verlag Berlin Heidelberg

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Yuan, Y., Huang, T. (2005). A Polynomial Smooth Support Vector Machine for Classification. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_19

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  • DOI: https://doi.org/10.1007/11527503_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27894-8

  • Online ISBN: 978-3-540-31877-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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