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Proceedings Paper

Syntactic learning by induction from examples and experiments
Author(s): Patrick T. Reed; Robert L. Cannon; Gautam Biswas; James C. Bezdek; Christopher G. St. C. Kendall
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Paper Abstract

A variety of problems must be overcome for a system that learns from examples to be useful. Such problems include reducing the dependency on the order of presented examples; reducing the number of examples required to learn a concept; pruning the generalization space; handling both conjunctive and disjunctive concept descriptions; and dealing with noisy training instances. This paper presents a system that effectively deals with many of these problems in a real-world domain by actively participating in the example selection process

Paper Details

Date Published: 1 January 1990
PDF: 9 pages
Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); doi: 10.1117/12.21114
Show Author Affiliations
Patrick T. Reed, Univ. of South Carolina (United States)
Robert L. Cannon, Univ. of South Carolina (United States)
Gautam Biswas, Vanderbilt Univ. (United States)
James C. Bezdek, Univ. of West Florida (United States)
Christopher G. St. C. Kendall, Univ. of South Carolina (United States)


Published in SPIE Proceedings Vol. 1293:
Applications of Artificial Intelligence VIII
Mohan M. Trivedi, Editor(s)

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