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General Kernel Optimization Model Based on Kernel Fisher Criterion

  • Bo Chen
  • Hongwei Liu
  • Zheng Bao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

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.

Keywords

Classification Performance Kernel Optimization Combination Coefficient Wisconsin Breast Cancer Pima Indian Diabetes 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 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)CrossRefGoogle Scholar
  2. 2.
    Amari, S., Wu, S.: Improving support vector machine classifiers by modifying kernel functions. Neural Networks 12(6), 783–789 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bo Chen
    • 1
  • Hongwei Liu
    • 1
  • Zheng Bao
    • 1
  1. 1.National Lab of Radar Signal ProcessingXidian UniversityShaanxiP.R. China

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