1st Edition
Backpropagation Theory, Architectures, and Applications
576 Pages
by
Psychology Press
574 Pages
by
Psychology Press
576 Pages
by
Psychology Press
Also available as eBook on:
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... Read more
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.
Biography
Yves Chauvin, David E. Rumelhart
