Generating predicate rules from neural networks
Nayak, Richi (2005) Generating predicate rules from neural networks. Lecture Notes in Computer Science, 3578, pp. 234-241.
Artificial neural networks (ANN) have demonstrated good predictive performance in a wide variety of practical problems. However, due to poor comprehensibility of the learned ANN, and the inability to represent explanation structures, ANNs are not considered sufficient for the general representation of knowledge. This paper details a methodology that represents the knowledge of a trained network in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner.
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|Item Type:||Journal Article|
|Additional Information:||Intelligent Data Engineering and Automated Learning - IDEAL 2005: 6th International Conference, Brisbane, Australia, July 6-8, 2005. Proceedings|
|Keywords:||neural networks, data mining, rule extraction|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2005 Springer-Verlag Berlin Heidelberg|
|Copyright Statement:||This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via SpringerLink. http://www.springer.de/comp/lncs/ Lecture Notes in Computer Science|
|Deposited On:||06 Jun 2005 00:00|
|Last Modified:||12 Aug 2013 05:17|
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