Combining Connectionist and Symbolic Learning to Refine Certainty Factor Rule Bases

摘要: Abstract 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 rule base it ID3's information-gain heuristic add new rules. Results on refining three actual expert demonstrate this combined approach generally performs better than previous