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International Conference on Intelligent Computing

ICIC 2012: Intelligent Computing Technology pp 103–111Cite as

SVM Regularizer Models on RKHS vs. on R m

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7389))

Abstract

There are two types of regularizer for SVM. The most popular one is that the classification function is norm-regularized on a Reproduced Kernel Hilbert Space(RKHS), and another important model is generalized support vector machine(GSVM), in which the coefficients of the classification function is norm-regularized on a Euclidean space R m. In this paper, we analyze the difference between them on computing stability, computational complexity and the efficiency of the Newton-type algorithms. Many typical loss functions are considered. The results show that the model of GSVM has more advantages than the other model. Some experiments support our analysis.

This work is supported by NNSFC under Grant No. 61072144, 61179040, 61173089 and 11101322.

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Author information

Authors and Affiliations

  1. Foundation Department, Xian Eurasia University, P.R. China, 701165

    Yinli Dong

  2. School of Science, Xidian University, P.R. China, 710071

    Shuisheng Zhou

Authors
  1. Yinli Dong
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  2. Shuisheng Zhou
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    Changjun Jiang

  3. Electrical and Electronics Department, Polytechnic of Bari, Via Orabona, 4, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  4. Faculty of Engineering, District University Francisco José de Caldas, Cra. 7a No. 40-53, Fifth Floor, Bogotá, Colombia

    Juan Carlos Figueroa

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© 2012 Springer-Verlag Berlin Heidelberg

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Dong, Y., Zhou, S. (2012). SVM Regularizer Models on RKHS vs. on R m . In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_14

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  • DOI: https://doi.org/10.1007/978-3-642-31588-6_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31587-9

  • Online ISBN: 978-3-642-31588-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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