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Scaling the Kernel Function to Improve Performance of the Support Vector Machine

  • Conference paper
Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3496))

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Abstract

The present study investigates a geometrical method for optimizing the kernel function of a support vector machine. The method is an improvement of the one proposed in [4,5]. It consists of using prior knowledge obtained from conventional SVM training to conformally rescale the initial kernel function, so that the separation between two classes of data is effectively enlarged. It turns out that the new algorithm works efficiently, has few free parameters, consumes very low computational cost, and overcomes the susceptibility of the original method.

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References

  1. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  2. Scholkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)

    Google Scholar 

  3. Burges, C.: Geometry and Invariance in Kernel Based Method. In: Scholkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods, pp. 89–116. MIT Press, Cambridge (1999)

    Google Scholar 

  4. Amari, S., Wu, S.: Improving Support Vector Machine Classifiers by Modifying Kernel Functions. Neural Networks 12, 783–789 (1999)

    Article  Google Scholar 

  5. Wu, S., Amari, S.: Conformal Transformation of Kernel Functions: A Data- Dependent Way to Improve Support Vector Machine Classifiers. Neural Processing Letters 15, 59–67 (2001)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Informatics, University of Sussex, UK

    Peter Williams, Sheng Li, Jianfeng Feng & Si Wu

Authors
  1. Peter Williams
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  2. Sheng Li
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  3. Jianfeng Feng
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  4. Si Wu
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiaofeng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Williams, P., Li, S., Feng, J., Wu, S. (2005). Scaling the Kernel Function to Improve Performance of the Support Vector Machine. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_133

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  • DOI: https://doi.org/10.1007/11427391_133

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25912-1

  • Online ISBN: 978-3-540-32065-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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