An unsupervised dual-network connectionist model of rule emergence in category learning

Cowell, Rosemary A. and French, Robert M. (2007) An unsupervised dual-network connectionist model of rule emergence in category learning. In: Proceedings of the European Cognitive Science Conference 2007. (Full text available)

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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: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Computational Intelligence Group
Depositing User: Mark Wheadon
Date Deposited: 29 Mar 2010 12:11
Last Modified: 12 Jun 2014 10:57
Resource URI: (The current URI for this page, for reference purposes)
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