• DocumentCode
    2344003
  • Title

    Neural network approximation and estimation of functions

  • Author

    Cheang, Gerald H L

  • Author_Institution
    Dept. of Stat., Yale Univ., New Haven, CT, USA
  • fYear
    1994
  • fDate
    27-29 Oct 1994
  • Firstpage
    59
  • Abstract
    Approximation and estimation bounds were obtained by Barron (see Proc. of the 7th Yale workshop on adaptive and learning systems, 1992, IEEE Transactions on Information Theory, vol.39, pp.930-944, 1993 and Machine Learning, vol.14, p.113-143, 1994) for function estimation by single hidden-layer neural nets. This paper highlights the extension of his results to the two hidden-layer case. The bounds derived for the two hidden-layer case depend on the number of nodes T1 and T2 in each hidden-layer, and also on the sample size N. It is seen from our bounds that in some cases, an exponentially large number of nodes, and hence parameters, is not required
  • Keywords
    approximation theory; estimation theory; feedforward neural nets; function approximation; functional analysis; multilayer perceptrons; estimation bounds; function estimation; hidden-layer neural nets; neural network approximation; nodes; Approximation error; Ellipsoids; Neural networks; Probability distribution; Statistics; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
  • Conference_Location
    Alexandria, VA
  • Print_ISBN
    0-7803-2761-6
  • Type

    conf

  • DOI
    10.1109/WITS.1994.513888
  • Filename
    513888
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2344003