This paper describes RAPTURE --- a system for revising probabilistic theories that combines symbolic and neural-network 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 two real-world domains demonstrate that this combined approach performs as well or better than previous methods.

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In Proceedings of the ML92 Workshop on Integrated Learning in Real Domains, Aberdeen, Scotland, July 1992.

@InProceedings{mahoney:mlw92, title={Combining Symbolic and Neural Learning to Revise Probabilistic Theories}, author={J. Jeffrey Mahoney and Raymond J. Mooney}, booktitle={Proceedings of the ML92 Workshop on Integrated Learning in Real Domains}, month={July}, address={Aberdeen, Scotland}, url="http://www.cs.utexas.edu/users/ai-lab?mahoney:mlw92", year={1992} }