Valid generalization in radial basis function networks and modified Kanerva models

The Vapnik-Chervonenkis (VC) dimension has in recent years been successfully applied to the analysis of generalization in artificial neural networks of various types. The author presents an investigation of the VC dimension of radial basis function networks and of a related quantity, called the growth function, of modified Kanerva models.<
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