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General Kernel Optimization Model Based on Kernel Fisher Criterion

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Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4221))

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Abstract

In this paper a general kernel optimization model based on kernel Fisher criterion (GKOM) is presented. Via a data-dependent kernel function and maximizing the kernel Fisher criterion, the combination coefficients of different kernels can be learned adaptive to the input data. Finally positive empirical results on benchmark datasets are reported.

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References

  1. Xiong, H.L., Swamy, M.N.S., Ahmad, M.O.: Optimizing the kernel in the empirical feature space. IEEE Trans. Neural Networks 16(2), 460–474 (2005)

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Author information

Authors and Affiliations

  1. National Lab of Radar Signal Processing, Xidian University, Xi’an, Shaanxi, 710071, P.R. China

    Bo Chen, Hongwei Liu & Zheng Bao

Authors
  1. Bo Chen
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  2. Hongwei Liu
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  3. Zheng Bao
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Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., 710071, Xi’an, P.R. China

    Xin-bo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Chen, B., Liu, H., Bao, Z. (2006). General Kernel Optimization Model Based on Kernel Fisher Criterion. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_24

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  • DOI: https://doi.org/10.1007/11881070_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45901-9

  • Online ISBN: 978-3-540-45902-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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