• DocumentCode
    2336839
  • Title

    Quasi-pseudo-metric of measurable classifiers

  • Author

    Chen, Shao-Bai ; Tian, Sen-ping ; Mao, Zong-yuan

  • Author_Institution
    Coll. of Autom. & Eng., South China Univ. of Technol., Guangzhou, China
  • Volume
    7
  • fYear
    2005
  • fDate
    18-21 Aug. 2005
  • Firstpage
    4340
  • Abstract
    This paper is concerned with the quasi-pseudo-metrics of measurable classifiers in pattern recognition problems. A quasi-pseudo-metric of measurable classifiers in probability spaces is proposed, which is an average expense between classifiers. The optimal classifiers in subspace and their estimations are investigated and two important examples are discussed in the quasi-pseudo-metric space.
  • Keywords
    Bayes methods; pattern classification; probability; measurable classifier quasipseudometric; optimal classifier; pattern recognition; probability space; Application software; Automation; Computer applications; Computer errors; Educational institutions; Electronic mail; Extraterrestrial measurements; Loss measurement; Pattern recognition; Space technology; Pattern recognition; measurable classifier; quasi-pseudo-metric;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
  • Conference_Location
    Guangzhou, China
  • Print_ISBN
    0-7803-9091-1
  • Type

    conf

  • DOI
    10.1109/ICMLC.2005.1527701
  • Filename
    1527701
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2336839