Fulop, Sean and Chater, Nick (2013) Why formal learning theory matters for cognitive science. Topics in Cognitive Science, Volume 5 (Number 1). pp. 3-12. doi:10.1111/tops.12004 ISSN 1756-8757.

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Abstract

This article reviews a number of different areas in the foundations of formal learning theory. After outlining the general framework for formal models of learning, the Bayesian approach to learning is summarized. This leads to a discussion of Solomonoff's Universal Prior Distribution for Bayesian learning. Gold's model of identification in the limit is also outlined. We next discuss a number of aspects of learning theory raised in contributed papers, related to both computational and representational complexity. The article concludes with a description of how semi-supervised learning can be applied to the study of cognitive learning models. Throughout this overview, the specific points raised by our contributing authors are connected to the models and methods under review.

Item Type:	Journal Article
Divisions:	Faculty of Social Sciences > Warwick Business School > Behavioural Science Faculty of Social Sciences > Warwick Business School
Journal or Publication Title:	Topics in Cognitive Science
Publisher:	Wiley-Blackwell Publishing, Inc.
ISSN:	1756-8757
Official Date:	January 2013
Dates:	Date Event January 2013 Published
Volume:	Volume 5
Number:	Number 1
Page Range:	pp. 3-12
DOI:	10.1111/tops.12004
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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