Combining Symbolic and Neural Learning to Revise Probabilistic Theories (1992)
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.
In Proceedings of the ML92 Workshop on Integrated Learning in Real Domains, Aberdeen, Scotland, July 1992.

Jeff Mahoney Ph.D. Alumni mahoney [at] firstadvisors com
Raymond J. Mooney Faculty mooney [at] cs utexas edu