Backpropagation
Theory, Architectures, and Applications
Published on 1. February 1995
Book
Paperback/Softback
576 pages
978-0-8058-1259-6 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Philadelphia
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 31 mm
Weight
820 gr
ISBN-13
978-0-8058-1259-6 (9780805812596)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
02/2013
Psychology Press Ltd
€138.99
Available for download
E-Book
02/2013
Psychology Press Ltd
€138.99
Available for download
Book
02/1995
Psychology Press
€234.00
Shipment within 10-20 days
Persons
Yves Chauvin, David E. Rumelhart
Content
Contents: D.E. Rumelhart, R. Durbin, R. Golden, Y. Chauvin, Backpropagation: The Basic Theory. A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, K.J. Lang, Phoneme Recognition Using Time-Delay Neural Networks. C. Schley, Y. Chauvin, V. Henkle, Automated Aircraft Flare and Touchdown Control Using Neural Networks. F.J. Pineda, Recurrent Backpropagation Networks. M.C. Mozer, A Focused Backpropagation Algorithm for Temporal Pattern Recognition. D.H. Nguyen, B. Widrow, Nonlinear Control with Neural Networks. M.I. Jordan, D.E. Rumelhart, Forward Models: Supervised Learning with a Distal Teacher. S.J. Hanson, Backpropagation: Some Comments and Variations. A. Cleeremans, D. Servan-Schreiber, J.L. McClelland, Graded State Machines: The Representation of Temporal Contingencies in Feedback Networks. S. Becker, G.E. Hinton, Spatial Coherence as an Internal Teacher for a Neural Network. J.R. Bachrach, M.C. Mozer, Connectionist Modeling and Control of Finite State Systems Given Partial State Information. P. Baldi, Y. Chauvin, K. Hornik, Backpropagation and Unsupervised Learning in Linear Networks. R.J. Williams, D. Zipser, Gradient-Based Learning Algorithms for Recurrent Networks and Their Computational Complexity. P. Baldi, Y. Chauvin, When Neural Networks Play Sherlock Homes. P. Baldi, Gradient Descent Learning Algorithms: A Unified Perspective.