Introduction To The Theory Of Neural Computation
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A01=Anders S. Krogh
A01=John A. Hertz
A01=Richard G. Palmer
Adaptive Resonance Theory
Anders Krogh
associative memory models
Author_Anders S. Krogh
Author_John A. Hertz
Author_Richard G. Palmer
Binary Threshold Units
Boltzmann Machine
Category=UYQN
competitive learning algorithms
computational neuroscience
Counter-propagation Networks
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eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
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Feed Forward Networks
Generalization Ability
Gradient Descent
Hebb Rule
Hebbian learning theory
hidden
Hidden Units
Hopfield Model
Hopfield Network
input
Input Output Pairs
Input Pattern
John Hertz
Kohonen Feature Mapping
Linearly Independent
neural computation for scientific research
output
Output Unit
pattern
perceptron
Receptive Fields
Recurrent Back Propagation
recurrent neural networks
Richard G. Palmer
simple
Simple Perceptron
statistical mechanics applications
Terminal Attractors
Travelling Salesman Problem
unit
units
Unsupervised Learning
vector
Vice Versa
weight
Weight Vector
Weighted Matching Problem
Product details
- ISBN 9780201515602
- Weight: 408g
- Dimensions: 152 x 229mm
- Publication Date: 24 Jun 1991
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Paperback
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Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
John A Hertz
