Combining Connectionist and Symbolic Learning to Reene Certainty-factor Rule Bases
نویسنده
چکیده
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 combined approach generally performs better than previous methods.
منابع مشابه
Appears in The Intl Symposium on Integrating Knowledge and Neural Heuristics ( ISIKNH - 94 )
This paper describes Rapture | a system for revising probabilistic rule bases that converts symbolic rules into a connectionist network, which is then trained via connectionist techniques. It uses a modiied version of backpropagation to reene the certainty factors of the rule base, and uses ID3's information-gain heuristic (Quinlan, 1986) to add new rules. Work is currently under way for nding ...
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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 Refine Certainty Factor Rule Bases
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متن کاملCombining Neural and Symbolic Learning to Revise Probabilistic Rule Bases
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 ...
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Neural networks, despite their empirically-proven abilities, have been little used for the reenement of existing knowledge because this task requires a three-step process. First, knowledge must be inserted into a neural network. Second, the network must be reened. Third, the reened knowledge must be extracted from the network. We have previously described a method for the rst step of this proce...
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