Generating rules with predicates, terms and variables from the pruned neural networks

DOI10.1016/j.neunet.2009.02.001zbMath1335.68207OpenAlexW1995604850WikidataQ51849251 ScholiaQ51849251MaRDI QIDQ280332

Richi Nayak

Publication date: 10 May 2016

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://eprints.qut.edu.au/30070/1/c30070.pdf

zbMATH Keywords

pruningrule extractionfirst-order rulespropositional rules

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Knowledge representation (68T30)

Related Items

Comprehensibility maximization and humanly comprehensible representations

Cites Work

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• Neural network explanation using inversion
• Generalized subsumption and its applications to induction and redundancy
• Is it worth generating rules from neural network ensembles?
• A Penalty-Function Approach for Pruning Feedforward Neural Networks
• Extracting Rules from Neural Networks by Pruning and Hidden-Unit Splitting

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