• DocumentCode
    1713908
  • Title

    Selecting parameter values for mahalanobis distance fuzzy classifiers

  • Author

    Deer, Peter ; Eklund, Peter

  • Author_Institution
    Knowledge, Visualization & Ordering Lab., Griffith Univ., Gold Coast, Qld., Australia
  • Volume
    2
  • fYear
    2001
  • Firstpage
    541
  • Abstract
    The fuzzy c-means clustering algorithm, and a related supervised classifier, require the a priori selection of a weighting parameter called the fuzzy exponent. This paper investigates suitable values of this fuzzy exponent using the criterion that fuzzy set memberships reflect class proportions in the mixed pixels of a remotely sensed image.
  • Keywords
    fuzzy set theory; image classification; remote sensing; fuzzy c-means clustering; fuzzy exponent; fuzzy image classification; fuzzy set theory; pattern recognition; remote sensing; supervised classifier; weighting parameter; Australia; Clustering algorithms; Covariance matrix; Fuzzy control; Fuzzy sets; Gold; Laboratories; Pattern recognition; Pixel; Visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2001. The 10th IEEE International Conference on
  • Print_ISBN
    0-7803-7293-X
  • Type

    conf

  • DOI
    10.1109/FUZZ.2001.1009011
  • Filename
    1009011
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1713908