Backpropagation : Theory, Architectures, and Applications

Yves Chauvin

ISBN 10: 0805812598 / ISBN 13: 9780805812596

Published by Psychology Press, 1995

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9780805812596

Bibliographic Details

• Title: Backpropagation : Theory, Architectures, and Applications
• Publisher: Psychology Press
• Publication Date: 1995
• Language: English
• ISBN 10: 0805812598
• ISBN 13: 9780805812596
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 31D millimeters 229 millimeters width by 152 millimeters height by 31 millimeters depth
• Weight: 820 grams

About this title

Synopsis

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.

About the Author

Yves Chauvin, David E. Rumelhart