Backpropagation : Theory, Architectures, and Applications by Chauvin, Rumelhart

Description: Backpropagation Theory, Architectures, and Applicationsby Yves Chauvin, David E. Rumelhart ISBN-13: 9780805812596 ISBN-10: 0805812598 Publisher: Lawrence Erlbaum Associates Binding: Paperback Publication Year: 1995 Edition: First Condition: Very Good – name written on first page About: “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.” Contents: 1 Backpropagation: The Basic Theory 2 Phoneme Recognition Using Time-Delay Neural Networks 3 Automated Aircraft Flare and Touchdown Control Using Neural Networks 4 Recurrent Backpropagation Networks 5 A Focused Backpropagation Algorithm for Temporal Pattern Recognition 6 Nonlinear Control with Neural Networks 7 Forward Models: Supervised Learning with a Distal Teacher 8 Backpropagation: Some Comments and Variations 9 Graded State Machines: The Representation of Temporal Contingencies in Feedback Networks 10 Spatial Coherence as an Internal Teacher for a Neural Network 11 Connectionist Modeling and Control of Finite State Systems Given Partial State Information 12 Backpropagation and Unsupervised Learning in Linear Networks 13 Gradient-Based Learning Algorithms for Recurrent Networks and Their Computational Complexity 14 When Neural Networks Play Sherlock Holmes 15 Gradient Descent Learning Algorithms: A Unified Perspective mySku 6749

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