Rule learning by searching on adapted nets

Authors:
LiMin Fu
Affiliations:
Dept. of Computer and Information Sciences, University of Florida, Gainesville, Florida
Venue:
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Year:
1991

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Abstract

If the back propagation network can produce an inference structure with high and robust performance, then it is sensible to extract rules from it. The KT algonthm is a novel algonthm for generating rules from an adapted net efficiently. The algorithm is able to deal with both single-layer and multi-layer networks, and can learn both confirming and disconfirming rules. Empirically, the algorithm is demonstrated in the domain of wind shear detection by infrared sensors with success.