Use of fuzzy feature vectors and neural networks for case retrieval in case based systems

Author

Main, Julie ; Dillon, Tharam S. ; Khosla, Rajiv

Author_Institution

Dept. of Comput. Sci. & Comput. Eng., La Trobe Univ., Bundoora, Vic., Australia

fYear

1996

fDate

19-22 Jun 1996

Firstpage

438

Lastpage

443

Abstract

Case-based reasoning is a subset of artificial intelligence and expert systems, and is a powerful mechanism for developing systems that can learn from and adapt past experiences to solve current problems. One of the main tasks involved in the design of case-based systems is determining the features that make up a case and finding a way to index these cases in a case-base for efficient and correct retrieval. This paper looks at how the use of fuzzy feature vectors and neural networks can improve the indexing and retrieval steps in case-based systems

Keywords

case-based reasoning; deductive databases; fuzzy logic; indexing; information retrieval; learning (artificial intelligence); neural nets; problem solving; vectors; adaptation; artificial intelligence; case indexing; case retrieval; case-based reasoning; expert systems; fuzzy feature vectors; learning; neural networks; past experiences; problem solving; Artificial intelligence; Computer aided software engineering; Computer science; Fuzzy neural networks; Fuzzy systems; Indexing; Intelligent networks; Intelligent systems; Laboratories; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American

Conference_Location

Berkeley, CA

Print_ISBN

0-7803-3225-3

Type

conf

DOI

10.1109/NAFIPS.1996.534774

Filename

534774