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In Order to LearnHow the sequence of topics influences learning$
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Frank E. Ritter, Josef Nerb, Erno Lehtinen, and Timothy O'Shea

Print publication date: 2007

Print ISBN-13: 9780195178845

Published to Oxford Scholarship Online: April 2010

DOI: 10.1093/acprof:oso/9780195178845.001.0001

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Order Out of Chaos: Order in Neural Networks

Order Out of Chaos: Order in Neural Networks

Chapter:
(p.71) Chapter 5 Order Out of Chaos: Order in Neural Networks
Source:
In Order to Learn
Author(s):

Peter C. R. Lane

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195178845.003.0005

This chapter provides details about order effects in neural networks, a commonly used modeling approach. It examines two networks in detail, the Adaptive Resonance Theory (ART) architecture and Jeff Elman's recurrent networks. The ART model shows that about a 25% difference in recognition rates can arise from using different orders. Elman's recurrent network shows that, with the wrong order, a task might not even be learnable. The chapter also discusses why these effects arise, which is important for understanding the impact and claims of computational models.

Keywords:   order effects, neural networks, Adaptive Resonance Theory, recurrent networks, Jeff Elman

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