Backpropagation: Theory, Architectures, and Applications

ISBN-13: 9780805812596 / Angielski / Miękka / 1995 / 576 str.

Chauvin; Yves Chauvin; David E. Rumelhart

Opis

Composed of three sections, this book presents the training algorithm for neural networks: backpropagation. The first presents the theory and principles; the second a number of network architectures; and the third shows how these can be applied to a number of different fields.

Spis treści

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.

O autorze

Yves Chauvin, David E. Rumelhart