Syntactic learning by induction from examples and experiments

Patrick T. Reed; Robert L. Cannon; Gautam Biswas; James C. Bezdek; Christopher G. St. C. Kendall

doi:10.1117/12.21114

1 January 1990 Syntactic learning by induction from examples and experiments

Patrick T. Reed, Robert L. Cannon, Gautam Biswas, James C. Bezdek, Christopher G. St. C. Kendall

Proceedings Volume 1293, Applications of Artificial Intelligence VIII; (1990) https://doi.org/10.1117/12.21114
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

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

Citation Download Citation

Patrick T. Reed, Robert L. Cannon, Gautam Biswas, James C. Bezdek, and Christopher G. St. C. Kendall "Syntactic learning by induction from examples and experiments", Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); https://doi.org/10.1117/12.21114

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