• DocumentCode
    286746
  • Title

    Valid generalization in radial basis function networks and modified Kanerva models

  • Author

    Holden, S.B.

  • Author_Institution
    Cambridge Univ., UK
  • fYear
    1993
  • fDate
    25-27 May 1993
  • Firstpage
    100
  • Lastpage
    104
  • 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;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Artificial Neural Networks, 1993., Third International Conference on
  • Conference_Location
    Brighton
  • Print_ISBN
    0-85296-573-7
  • Type

    conf

  • Filename
    263248
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=286746