• DocumentCode
    296024
  • Title

    Learning rate and outlier analysis of linear learning algorithms

  • Author

    Yin, Hongfeng ; Klasa, Stan

  • Author_Institution
    Dept. of Comput. Sci., Concordia Univ., Montreal, Que., Canada
  • Volume
    5
  • fYear
    1995
  • fDate
    Nov/Dec 1995
  • Firstpage
    2835
  • Abstract
    The learning rate is analyzed for linear learning algorithms in this paper. In the presence of outliers, the robustness of several linear learning algorithms is given and it is shown that an absolute criterion based learning algorithm is more robust than the corresponding quadratic criterion based learning algorithm
  • Keywords
    convergence; learning (artificial intelligence); neural nets; pattern recognition; statistical analysis; learning rate; linear learning algorithms; outlier analysis; robustness; Algorithm design and analysis; Approximation algorithms; Computer science; Convergence; Difference equations; Differential equations; Neural networks; Principal component analysis; Robustness; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1995. Proceedings., IEEE International Conference on
  • Conference_Location
    Perth, WA
  • Print_ISBN
    0-7803-2768-3
  • Type

    conf

  • DOI
    10.1109/ICNN.1995.488183
  • Filename
    488183
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=296024