• DocumentCode
    767761
  • Title

    Controlling selectivity in nonstandard pattern recognition algorithms

  • Author

    Carreté, Nuria Piera ; Aguilar-Martin, Joseph

  • Author_Institution
    CNRS, Toulouse, France
  • Volume
    21
  • Issue
    1
  • fYear
    1991
  • Firstpage
    71
  • Lastpage
    82
  • Abstract
    A type of aggregation operator, referred to as mixed connectives, is used to summarize the information about objects to be classified (supplied by the descriptors). Because mixed connectives depend on a parameter, this leads to the concept of families for these operators. Therefore, given such a family, it is possible to associate different classifications with the same data set, depending on the value chosen for the parameter. The manner in which the algorithm selectivity classifications are compared is illustrated by an example involving a quantitative data basis
  • Keywords
    aggregation; pattern recognition; aggregation operator; classifications; mixed connectives; nonstandard pattern recognition algorithms; quantitative data basis; selectivity; Chaos; Classification algorithms; Clustering algorithms; Context modeling; Data analysis; Data mining; Entropy; Machine learning; Pattern analysis; Pattern recognition;
  • fLanguage
    English
  • Journal_Title
    Systems, Man and Cybernetics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9472
  • Type

    jour

  • DOI
    10.1109/21.101138
  • Filename
    101138