Title :
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
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;
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
DOI :
10.1109/NAFIPS.1996.534774
