Combining Connectionist and Symbolic Learning to Refine Certainty Factor Rule Bases
J. Jeffrey Mahoney, Raymond J. Mooney · Connection Science · 1993
This paper describes RAPTURE—a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on refining three actual expert rule bases demonstrate that this combined approach generally performs better than previous methods.