Proceedings of the 1992 Machine Learning Workshop on Integrated Learning in Real Domains, Aberdeen, Scotland, July 1992.
Paper ID: 14
Category: Theory and Knowledge Refinedment, Uncertain Reasoning, Neural-Network Learning
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.
