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
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;
Conference_Titel :
Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of
Conference_Location :
Tunis
DOI :
10.1109/SOCPAR.2014.7007988
