• DocumentCode
    315249
  • Title

    An improved expand-and-truncate learning

  • Author

    Yamamoto, Atsushi ; Saito, Toshimichi

  • Author_Institution
    Dept. of Electron. & Electr. Eng., Hosei Univ., Tokyo, Japan
  • Volume
    2
  • fYear
    1997
  • fDate
    9-12 Jun 1997
  • Firstpage
    1111
  • Abstract
    This paper proposes a novel learning algorithm that can realize any binary-to-binary mapping by using three-layer binary neural networks. The algorithm includes an improved expand-and-truncate learning routine that can reduce the number of the hidden neurons by conventional methods. Also, the output layer parameters can be given by simple analytic formulae
  • Keywords
    learning (artificial intelligence); multilayer perceptrons; binary-to-binary mapping; expand-and-truncate learning; hidden neurons; three-layer binary neural networks; Equations; Gravity; Hypercubes; Neural networks; Neurons;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks,1997., International Conference on
  • Conference_Location
    Houston, TX
  • Print_ISBN
    0-7803-4122-8
  • Type

    conf

  • DOI
    10.1109/ICNN.1997.616185
  • Filename
    616185
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=315249