DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Sang Chan | ko |
dc.contributor.author | Lam Monica-S | ko |
dc.contributor.author | Gupta Amit | ko |
dc.date.accessioned | 2013-03-03T08:53:55Z | - |
dc.date.available | 2013-03-03T08:53:55Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1995 | - |
dc.identifier.citation | 한국전문가시스템학회지, v.1, no.2, pp.57 - 72 | - |
dc.identifier.issn | 1229-0262 | - |
dc.identifier.uri | http://hdl.handle.net/10203/78055 | - |
dc.description.abstract | This paper presents a rule extraction algorithm RE to acquire explicit rules from trained neural networks. The validity of extracted rules has been confirmed using 6 different data sets. Based on experimental results, we conclude that extracted rules from RE predict more accurately and robustly than neural networks themselves and rules obtained from an inductive learning algorithm do. Rule extraction algorithm for neural networks are important for incorporating knowledge obtained from trained networks into knowledge based systems. In lieu of this, the proposed RE algorithm contributes to the trend toward developing hybrid and versatile knowledge-based system including expert systems and knowledge-based decision su, pp.rt systems. | - |
dc.language | English | - |
dc.publisher | 한국지능정보시스템학회 | - |
dc.title | Rule Extraction from Neural Networks: Enhancing the Explanation Capability | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.citation.volume | 1 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 57 | - |
dc.citation.endingpage | 72 | - |
dc.citation.publicationname | 한국전문가시스템학회지 | - |
dc.contributor.nonIdAuthor | Lam Monica-S | - |
dc.contributor.nonIdAuthor | Gupta Amit | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.