• DocumentCode
    2612577
  • Title

    The generalized AdaTron algorithm

  • Author

    Nachbar, P. ; Nossek, J.A. ; Strobl, J.

  • Author_Institution
    Inst. for Network Theory & Circuit Design, Tech. Univ. of Munich, Germany
  • fYear
    1993
  • fDate
    3-6 May 1993
  • Firstpage
    2152
  • Abstract
    The AdaTron algorithm can find the most insensitive weights of a perceptron or attractor network with respect to the usual Euclidean norm. The authors define various notions of robustness for q-norms as appropriate for perceptrons or attractor networks. By extending the so-called AdaTron theorem, they are able to generalize the AdaTron algorithm, which then finds the most insensitive weights with respect to an arbitrary q-norm
  • Keywords
    learning (artificial intelligence); pattern recognition; perceptrons; Euclidean norm; attractor network; generalized AdaTron algorithm; most insensitive weights; perceptron; q-norms; robustness; Circuit synthesis; Electronic mail; Neural networks; Neurons; Quadratic programming; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuits and Systems, 1993., ISCAS '93, 1993 IEEE International Symposium on
  • Conference_Location
    Chicago, IL
  • Print_ISBN
    0-7803-1281-3
  • Type

    conf

  • DOI
    10.1109/ISCAS.1993.394184
  • Filename
    394184
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2612577