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Combining Connectionist and Symbolic Learning to Refine Certainty Factor Rule Bases

 

作者: J.JEFFREY MAHONEY,   RAYMONDJ. MOONEY,  

 

期刊: Connection Science  （Taylor Available online 1993）
卷期: Volume 5, issue 3-4  

页码: 339-364

 

ISSN:0954-0091

 

年代: 1993

 

DOI:10.1080/09540099308915704

 

出版商: Taylor & Francis Group

 

关键词: Backpropagation;certainty factors;rule base revision.

 

数据来源: Taylor

 

摘要:

This paper describes RAPTURE—a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. RAPTURE uses a modified version of backpropagation to refine the certainty factors of a probabilistic rule base and it uses ID3's information-gain heuristic to add new rules. Results on refining three actual expert rule bases demonstrate that this combined approach generally performs better than previous methods.

 

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