• DocumentCode
    3376394
  • Title

    The minimum entropy network

  • Author

    Brause, Rüdiger W.

  • Author_Institution
    J.W. Goethe-Univ., Frankfurt, Germany
  • fYear
    1992
  • fDate
    10-13 Nov 1992
  • Firstpage
    85
  • Lastpage
    92
  • Abstract
    It is shown that, using as basic building block a linear neuron with an anti-Hebb rule and restricted weights, an asymmetric network which computes the eigenvectors in the ascending order of their corresponding eigenvalues can be built. The conditions for their convergence are obtained and demonstrated by simulations
  • Keywords
    Hebbian learning; eigenvalues and eigenfunctions; entropy; neural nets; anti-Hebb rule; asymmetric network; convergence; eigenvalues; eigenvectors; linear neuron; minimum entropy network; restricted weights; Autocorrelation; Clouds; Eigenvalues and eigenfunctions; Entropy; Mean square error methods; Neural networks; Neurons; Pattern recognition; Prototypes; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 1992. TAI '92, Proceedings., Fourth International Conference on
  • Conference_Location
    Arlington, VA
  • Print_ISBN
    0-8186-2905-3
  • Type

    conf

  • DOI
    10.1109/TAI.1992.246369
  • Filename
    246369
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3376394