Connection Science, 5 (1993), pp. 339-364. (Special issue on Architectures for Integrating Neural and Symbolic Processing)
Paper ID: 23
Category: Theory and Knowledge Refinedment, Uncertain Reasoning, Neural-Network Learning
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 Mycin-style rule base and it uses ID3's information gain heuristic to add new rules. Results on refining three actual expert knowledge bases demonstrate that this combined approach generally performs better than previous methods.
