Backpropagation (eBook, PDF)

Theory, Architectures, and Applications
Redaktion: Chauvin, Yves; Rumelhart, David E.

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 576
• Erscheinungstermin: 1. Februar 2013
• Englisch
• ISBN-13: 9781134775743
• Artikelnr.: 38402946

Autorenporträt

Yves Chauvin, David E. Rumelhart

Inhaltsangabe

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.