Backpropagation

Theory, Architectures, and Applications

Yves Chauvin, David E. Rumelhart

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Hardback

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English

Psychology Press
01 February 1995

Computer architecture & logic design; Neural networks & fuzzy systems

Series: Developments in Connectionist Theory Series

Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section presents the theory and principles behind backpropagation as seen from different perspectives such as statistics, machine learning, and dynamical systems. The second presents a number of network architectures that may be designed to match the general concepts of Parallel Distributed Processing with backpropagation learning. Finally, the third section shows how these principles can be applied to a number of different fields related to the cognitive sciences, including control, speech recognition, robotics, image processing, and cognitive psychology. The volume is designed to provide both a solid theoretical foundation and a set of examples that show the versatility of the concepts. Useful to experts in the field, it should also be most helpful to students seeking to understand the basic principles of connectionist learning and to engineers wanting to add neural networks in general -- and backpropagation in particular -- to their set of problem-solving methods.

Edited by: Yves Chauvin, David E. Rumelhart
Imprint: Psychology Press
Country of Publication: United States
Dimensions: Height: 229mm, Width: 152mm, Spine: 41mm
Weight: 1.088kg
ISBN: 9780805812589
ISBN 10: 080581258X
Series: Developments in Connectionist Theory Series
Pages: 574
Publication Date: 01 February 1995
Audience: College/higher education , Professional and scholarly , Professional & Vocational , A / AS level , Further / Higher Education
Format: Hardback
Publisher's Status: Active

Yves Chauvin, David E. Rumelhart