Backpropagation
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Adjoint Methods
Adjoint System
Automated Fingerprint Recognition Systems
Average Time Complexity
Back Propagation
Category=UYF
Category=UYQN
cognitive modeling
Context Layer
Context Units
Data Base
descent
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Error Gradient
Fingerprint Images
Forward Model
gradient
Gradient Based Learning Algorithms
Gradient Computation
gradient descent
Gradient Descent Learning
Gradient Descent Learning Algorithms
hidden
Hidden Stage
Hidden Unit
layer
machine learning theory
network
networks
neural
Neural Network Controller
neural network learning algorithms in engineering
output
Output Stage
parallel distributed processing
Receptive Field
recurrent
Recurrent Back Propagation
Recurrent Network
recurrent neural networks
Squashing Function
Teacher Forcing
temporal sequence analysis
units
Product details
- ISBN 9780805812596
- Weight: 1070g
- Dimensions: 152 x 229mm
- Publication Date: 01 Feb 1995
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Paperback
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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.
Yves Chauvin, David E. Rumelhart
