Title :
Learning and representing concepts with graded structure
Author_Institution :
Dept. of Comput. Sci., Utah State Univ., Logan, UT, USA
Abstract :
The author presents a novel method for representing and learning concepts with graded structure. The method uses a hybrid concept representation that combines symbolic and numeric representations. In learning a concept, the method builds a general concept description for representing common cases of the concept. Such a description is in the form of decision rules, interpreted by a weighted distance measure, and numerical thresholds. The method has been implemented in the system FCLS (flexible concept learning system) and tested on a variety of problems
Keywords :
knowledge acquisition; knowledge representation; learning (artificial intelligence); learning systems; FCLS; decision rules; flexible concept learning system; graded structure; hybrid concept representation; learning; numeric representations; numerical thresholds; weighted distance measure; Blood pressure; Computer science; Contracts; Decision trees; Diseases; Fuzzy sets; Learning systems; System testing; Weight measurement;
Conference_Titel :
Artificial Intelligence for Applications, 1992., Proceedings of the Eighth Conference on
Conference_Location :
Monterey, CA
Print_ISBN :
0-8186-2690-9
DOI :
10.1109/CAIA.1992.200033
