• DocumentCode
    2619756
  • Title

    On approximations of functions by depth-two neural networks

  • Author

    Venkatesh, Santosh S.

  • Author_Institution
    Dept. of Electr. Eng., Pennsylvania Univ., Philadelphia, PA, USA
  • fYear
    1994
  • fDate
    27 Jun-1 Jul 1994
  • Firstpage
    216
  • Abstract
    The simple Pythagorean notion of orthogonal projections is used to show that depth-two sigmoidal neural networks can approximate any square-integrable function with compact support in Rn with arbitrarily small integrated squared-error
  • Keywords
    approximation theory; error analysis; function approximation; neural nets; Pythagorean notion; approximations; depth-two sigmoidal neural networks; integrated squared-error; orthogonal projections; square-integrable function; Convergence; Frequency locked loops; Function approximation; Hilbert space; Hypercubes; Multi-layer neural network; Neural networks; Neurons; Terminology; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory, 1994. Proceedings., 1994 IEEE International Symposium on
  • Conference_Location
    Trondheim
  • Print_ISBN
    0-7803-2015-8
  • Type

    conf

  • DOI
    10.1109/ISIT.1994.394752
  • Filename
    394752
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2619756