• DocumentCode
    295980
  • Title

    On optimal radial basis function nets and nonlinear function estimates

  • Author

    Krzyzak, Adam

  • Author_Institution
    Dept. of Comput. Sci., Concordia Univ., Montreal, Que.
  • Volume
    1
  • fYear
    1995
  • fDate
    Nov/Dec 1995
  • Firstpage
    265
  • Abstract
    Radial basis function (RBF) networks with one hidden layer are considered. Using the connections between RBF nets and the kernel regression estimates (KRE) upper bounds on L2 errors of RBF nets are derived and optimized with respect to the radial functions. Analytical expressions the optimal radial functions are given and the optimal rates of convergence in the class smooth functions are derived
  • Keywords
    estimation theory; feedforward neural nets; learning (artificial intelligence); statistical analysis; kernel regression estimates; nonlinear function estimates; optimal radial basis function nets; optimal rates of convergence; smooth functions; Computational Intelligence Society; Computer errors; Computer science; Convergence; Kernel; Neural networks; Radial basis function networks; Regression analysis; Tail; Upper bound;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1995. Proceedings., IEEE International Conference on
  • Conference_Location
    Perth, WA
  • Print_ISBN
    0-7803-2768-3
  • Type

    conf

  • DOI
    10.1109/ICNN.1995.488106
  • Filename
    488106
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=295980