A method that combines inductive learning with exemplar-based learning

Author

Zhang, Jianping

Author_Institution

Dept. of Comput. Sci., Utah State Univ., Logan, UT, USA

fYear

1990

fDate

6-9 Nov 1990

Firstpage

31

Lastpage

37

Abstract

A learning approach that combines inductive learning with exemplar-based learning is described. In the method, a concept is represented by two parts: a generalized abstract description and a set of exemplars (exceptions). Generalized descriptions represent the principles of concepts, whereas exemplars represent the exceptional or rare cases. The method is an alternative for solving the problem of small disjuncts and for representing concepts with imprecise and irregular boundaries. The method for combining inductive learning and exemplar-based learning has been implemented in the flexible concept learning system. Experiments showed that the combined method has comparable performance to that of AQ16 and ASSISTANT in three natural domains

Keywords

knowledge representation; learning systems; AQ16; ASSISTANT; exemplar-based learning; flexible concept learning system; generalized abstract description; inductive learning; irregular boundaries; learning approach; natural domains; rare cases; small disjuncts; Algorithm design and analysis; Computer science; Decision trees; Humans; Indexing; Learning systems; Logic; Machine learning; Vocabulary;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools for Artificial Intelligence, 1990.,Proceedings of the 2nd International IEEE Conference on

Conference_Location

Herndon, VA

Print_ISBN

0-8186-2084-6

Type

conf

DOI

10.1109/TAI.1990.130306

Filename

130306