Title :
Valid generalization in radial basis function networks and modified Kanerva models
Author_Institution :
Cambridge Univ., UK
Abstract :
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
Keywords :
generalisation (artificial intelligence); neural nets; Vapnik-Chervonenkis dimension; generalization; growth function; modified Kanerva models; neural networks; radial basis function networks;
Conference_Titel :
Artificial Neural Networks, 1993., Third International Conference on
Conference_Location :
Brighton
Print_ISBN :
0-85296-573-7
