• DocumentCode
    1796103
  • Title

    Experimental analysis of crisp similarity and distance measures

  • Author

    Baccour, Leila ; John, Robert I.

  • Author_Institution
    Res. Groups in Intell. Machines, Univ. of Sfax, Sfax, Tunisia
  • fYear
    2014
  • fDate
    11-14 Aug. 2014
  • Firstpage
    96
  • Lastpage
    100
  • Abstract
    Distance measures are a requirement in many classification systems. Euclidean distance is the most commonly used in these systems. However, there exists a huge number of distance and similarity measures. In this paper we present an experimental analysis of crisp distance and similarity measures applied to shapes classification and Arabic sentences recognition.
  • Keywords
    handwritten character recognition; image classification; image matching; natural language processing; shape recognition; Arabic sentence recognition; Euclidean distance; classification systems; crisp similarity; distance measures; shape classification; Euclidean distance; Indexes; Measurement uncertainty; Shape; Shape measurement; Vectors; Similarity measures; classification of Arabic sentences; classification of shapes; crisp sets; distance measures;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of
  • Conference_Location
    Tunis
  • Type

    conf

  • DOI
    10.1109/SOCPAR.2014.7007988
  • Filename
    7007988
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1796103