• DocumentCode
    285254
  • Title

    Push-and-pull for piecewise linear machine training

  • Author

    Knoll, Travis Dean ; Lo, James Ting-Ho

  • Author_Institution
    Dept. of Math. & Stat., Maryland Univ., Baltimore, MD, USA
  • Volume
    3
  • fYear
    1992
  • fDate
    7-11 Jun 1992
  • Firstpage
    573
  • Abstract
    A piecewise linear machine (PLM) is capable of pattern classification. A simple and robust training method for the PLM called push-and-pull training is presented. This method avoids the difficulties encountered by the learning vector quantization (LVQ) methods of T. Kohonen. The machine was capable of finding optimal solution efficiency for problems with either separated or overlapping category regions
  • Keywords
    learning (artificial intelligence); pattern recognition; pattern classification; piecewise linear machine training; push-and-pull training; training method; Aggregates; Mathematics; Mirrors; Piecewise linear techniques; Probability density function; Prototypes; Robustness; Statistics; Vector quantization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1992. IJCNN., International Joint Conference on
  • Conference_Location
    Baltimore, MD
  • Print_ISBN
    0-7803-0559-0
  • Type

    conf

  • DOI
    10.1109/IJCNN.1992.227113
  • Filename
    227113
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=285254