• DocumentCode
    3060457
  • Title

    Direct calculation of predictions for K29/K29*

  • Author

    Burford, Brian

  • Author_Institution
    Univ. of London, Egham
  • fYear
    2007
  • fDate
    13-15 Dec. 2007
  • Firstpage
    458
  • Lastpage
    463
  • Abstract
    Implementation of the K29 learning algorithm presents the problem of finding a root of an often non-invertible function. Numerical methods giving approximations to these roots have small numerical inaccuracies. These inaccuracies, despite possibly seeming negligible, can accumulate quickly when applied to K29. The main mathematical result of this paper presents a simple but novel derivation of two formulae, which directly calculate predictions for the K29 (and the regularised version K29*) learning algorithms. We present comparisons between this new implementation and the numerical method through empirical investigation.
  • Keywords
    approximation theory; learning (artificial intelligence); K29 learning algorithm; K29* learning algorithm; approximations; noninvertible function; numerical inaccuracy; Application software; Computer science; Machine learning; Machine learning algorithms; Protocols; Terminology; Tin; Upper bound;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Applications, 2007. ICMLA 2007. Sixth International Conference on
  • Conference_Location
    Cincinnati, OH
  • Print_ISBN
    978-0-7695-3069-7
  • Type

    conf

  • DOI
    10.1109/ICMLA.2007.56
  • Filename
    4457272