Abstract
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.
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Kůrková, V., Kainen, P.C. (2011). Kernel Networks with Fixed and Variable Widths. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_2
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DOI: https://doi.org/10.1007/978-3-642-20282-7_2
Publisher Name: Springer, Berlin, Heidelberg
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