• DocumentCode
    2727995
  • Title

    Nonparametric classification using radial basis function nets and empirical risk minimization

  • Author

    Krzyzak, Adam ; Linder, Tamas ; Lugosi, Gabor

  • Author_Institution
    Dept. of Comput. Sci., Concordia Univ., Montreal, Que., Canada
  • fYear
    1995
  • fDate
    17-22 Sep 1995
  • Firstpage
    258
  • Abstract
    We study convergence properties of radial basis function (RBF) networks in nonparametric classification for a large class of basis functions with parameters of RBF nets learned through empirical risk minimization. In the classification (pattern recognition) problem, based upon the observation of a random vector X∈Rd, one has to guess the value of a corresponding label Y, where Y is a random variable taking its values from (-1,1)
  • Keywords
    convergence of numerical methods; feedforward neural nets; learning (artificial intelligence); minimisation; pattern recognition; basis functions; convergence properties; empirical risk minimization; nonparametric classification; pattern recognition; radial basis function nets; radial basis function networks; random variable; random vector; Approximation error; Convergence; Error probability; Kernel; Pattern recognition; Q measurement; Radial basis function networks; Random variables; Risk management; Virtual colonoscopy;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory, 1995. Proceedings., 1995 IEEE International Symposium on
  • Conference_Location
    Whistler, BC
  • Print_ISBN
    0-7803-2453-6
  • Type

    conf

  • DOI
    10.1109/ISIT.1995.535773
  • Filename
    535773
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2727995