Cowell, Rosemary A. and French, Robert M. (2007) An unsupervised dual-network connectionist model of rule emergence in category learning. In: Vosniadou, Stella and Kayser, Daniel and Protopapas, Athanassios, eds. Proceedings of the European Cognitive Science Conference 2007. Taylor and Francis. ISBN 978-1-84169-696-6. (KAR id:24034)
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Abstract
We develop an unsupervised dual-network connectionist model of category learning in which rules gradually emerge from a standard Kohonen network. The architecture is based on the interaction of a statistical-learning (Kohonen) network and a competitive-learning rule network. The rules that emerge in the rule network are weightings of individual features according to their importance for categorisation. Once the combined system has learned a particular rule, it de-emphasizes those features that are not sufficient for categorisation, thus allowing correct classification of novel, but atypical, stimuli, for which a standard Kohonen network fails. We explain the principles and architectural details of the model and show how it works correctly for stimuli that are misclassified by a standard Kohonen network.
Item Type: | Book section |
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Subjects: | Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Mark Wheadon |
Date Deposited: | 29 Mar 2010 12:11 UTC |
Last Modified: | 05 Nov 2024 10:03 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/24034 (The current URI for this page, for reference purposes) |
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