Combining Neural and Symbolic Learning to Revise Probabilistic Rule Bases
نویسندگان
چکیده
This paper describes RAPTURE a system for revising probabilistic knowledge bases that combines neural and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a MYCIN-style rule base and uses ID3's information gain heuristic to add new rules. Results on refining two actual expert knowledge bases demonstrate that this combined approach performs better than previous methods.
منابع مشابه
on Architectures for Integrating Neural and Symbolic Processing ) Combining
This paper describes Rapture | a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. Rapture uses a modi ed version of backpropagation to re ne the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on re ning three actual expert knowledge bases demonstrate that this combi...
متن کاملCombining Connectionist and Symbolic Learning to Reene Certainty-factor Rule Bases
This paper describes Rapture | a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. Rapture uses a modiied version of backpropagation to reene the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on reening three actual expert knowledge bases demonstrate that this combi...
متن کاملAppears in Proceedings of the 1992 Machine Learning Workshop on Integrated Learning in Real Domains , Aberdeen , Scotland , July , 1992 Combining
This paper describes Rapture | a system for revising probabilistic theories that combines symbolic and neural-network learning methods. Rapture uses a modi ed version of backpropagation to re ne 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...
متن کاملAppears in Proceedings of The 11 th International Conference on Machine Learning ( ML - 94 )
This paper compares two methods for reen-ing uncertain knowledge bases using propo-sitional certainty-factor rules. The rst method, implemented in the Rapture system , employs neural-network training to re-ne the certainties of existing rules but uses a symbolic technique to add new rules. The second method, based on the one used in the Kbann system, initially adds a complete set of potential n...
متن کاملProceedings of the Eleventh International Workshop on Machine Learning
This paper compares two methods for reen-ing uncertain knowledge bases using propo-sitional certainty-factor rules. The rst method, implemented in the Rapture system , employs neural-network training to re-ne the certainties of existing rules but uses a symbolic technique to add new rules. The second method, based on the one used in the Kbann system, initially adds a complete set of potential n...
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