Implicitly supervised fuzzy pattern recognition

Author

Hirota, Kaoru ; Pedrycz, Witold

Author_Institution

Dept. of Control Syst. Eng., Hosei Univ., Tokyo, Japan

fYear

1994

fDate

18-21 Dec 1994

Firstpage

65

Lastpage

69

Abstract

We introduce a new model of fuzzy pattern recognition where data available about class membership are given implicitly rather than explicitly. While the explicit classification training set conveys complete details about class membership, the implicit format of classification lends itself to more synthetic forms of classification outcomes (such as those expressed in terms of similarities between some pairs of patterns). The relevant architectures are proposed along with the pertinent learning schemes

Keywords

fuzzy logic; learning (artificial intelligence); pattern classification; pattern recognition; class membership; explicit classification training set; implicit format of classification; implicitly supervised fuzzy pattern recognition; learning schemes; Control system synthesis; Data engineering; Fuzzy control; Fuzzy sets; Fuzzy systems; Pattern recognition; Supervised learning; Systems engineering and theory; Taxonomy; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society Biannual Conference, 1994. Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic,

Conference_Location

San Antonio, TX

Print_ISBN

0-7803-2125-1

Type

conf

DOI

10.1109/IJCF.1994.375149

Filename

375149