Kernel Networks with Fixed and Variable Widths

  • Věra Kůrková
  • Paul C. Kainen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)


The role of width in kernel models and radial-basis function networks is investigated with a special emphasis on the Gaussian case. Quantitative bounds are given on kernel-based regularization showing the effect of changing the width. These bounds are shown to be d-th powers of width ratios, and so they are exponential in the dimension of input data.


Kernel models Gaussian kernel networks Minimization of error functionals Regularization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Věra Kůrková
    • 1
  • Paul C. Kainen
    • 2
  1. 1.Institute of Computer ScienceAcademy of Sciences of the Czech RepublicPrague 8Czech Republic
  2. 2.Department of MathematicsGeorgetown UniversityWashingtonUSA

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