• DocumentCode
    3020765
  • Title

    Modified stochastic approximation to enhance unsupervised learning

  • Author

    Schwartz, S.C. ; Katopis, A.

  • Author_Institution
    Princeton University, Princeton, New Jersey
  • fYear
    1977
  • fDate
    7-9 Dec. 1977
  • Firstpage
    1067
  • Lastpage
    1069
  • Abstract
    By simple modifications of a decision-directed learning procedure, the regression curves of multidimensional stochastic approximation can be rotated further apart, leading to enhanced convergence properties. Results of a Monte Carlo simulation for a binary hypotheses testing problem are given which illustrates this faster convergence.
  • Keywords
    Computer science; Convergence; Equations; Gaussian noise; Jacobian matrices; Multidimensional systems; Pattern recognition; Stochastic processes; Testing; Unsupervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications, 1977 IEEE Conference on
  • Conference_Location
    New Orleans, LA, USA
  • Type

    conf

  • DOI
    10.1109/CDC.1977.271728
  • Filename
    4045998
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3020765