About: Kernel Networks with Fixed and Variable Widths     Goto   Sponge   NotDistinct   Permalink

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  • 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.
  • 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. (en)
Title
  • Kernel Networks with Fixed and Variable Widths
  • Kernel Networks with Fixed and Variable Widths (en)
skos:prefLabel
  • Kernel Networks with Fixed and Variable Widths
  • Kernel Networks with Fixed and Variable Widths (en)
skos:notation
  • RIV/67985807:_____/11:00357302!RIV12-AV0-67985807
http://linked.open...avai/riv/aktivita
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  • P(ME10023), P(OC10047), Z(AV0Z10300504)
http://linked.open...vai/riv/dodaniDat
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  • 207357
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  • RIV/67985807:_____/11:00357302
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  • kernel models; Gaussian kernel networks; minimization of error functionals; regularization (en)
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  • [BE996C707A6D]
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  • Ljubljana
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  • Heidelberg
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  • Adaptive and Natural Computing Algorithms. Part I
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  • Kůrková, Věra
  • Kainen, P. C.
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  • 000302389300002
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number of pages
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  • 10.1007/978-3-642-20282-7_2
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  • Springer-Verlag
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  • 978-3-642-20281-0
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