Combining connectionist and symbolic learning to refine certainty-factor rule-bases
| Author(s) : | Raymond J. Mooney J. Jeffrey Mahoney, |
| Publisher : | N/A |
| Publication Date : | 1993 |
| ISSN : | N/A |
| Abstract : | 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 knowledge bases demonstrate that this combined approach generally performs better than previous methods. 1, |