Rule learning by searching on adapted nets
LiMin Fu · National Conference on Artificial Intelligence · 1991
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.