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 of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
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