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Generating predicate rules from neural networks

By Richi Nayak

Abstract

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

Topics: 080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, neural networks, data mining, rule extraction
Publisher: Springer Berlin Heidelberg
Year: 2005
DOI identifier: 10.1007/11508069_31
OAI identifier: oai:eprints.qut.edu.au:1471

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