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
Link To Document :
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