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.

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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.