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Neural smithing : supervised learning in feedforward artificial neural networks

Author: Russell D Reed; Robert J Marks
Publisher: Cambridge, Mass. : MIT Press, ©1999.
Series: Bradford book.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
"Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptions (MLP).  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Reed, Russell D.
Neural smithing.
Cambridge, Mass. : MIT Press, ©1999
(DLC) 98013416
(OCoLC)38468402
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Russell D Reed; Robert J Marks
ISBN: 0585078386 9780585078380 9780262282215 0262282216
OCLC Number: 42856076
Notes: "A Bradford book."
Description: 1 online resource (viii, 346 pages) : illustrations
Contents: 1. Introduction --
2. Supervised learning --
3. Single-layer networks --
4. MLP representational capabilities --
5. Back-propagation --
6. Learning rate and momentum --
7. Weight-initialization techniques --
8. The error surface --
9. Faster variations of back-propagation --
10. Classical optimization techniques --
11. Genetic algorithms and neural networks --
12. Constructive methods --
13. Pruning algorithms --
14. Factors influencing generalization --
15. Generalization prediction and assessment --
16. Heuristics for improving generalization --
17. Effects of training with noisy inputs.
Series Title: Bradford book.
Responsibility: Russell D. Reed and Robert J. Marks II.

Abstract:

This text presents an extensive and practical overview of almost every aspect of MLP (multilayer perceptrons) methodology, progressing from an initial discussion of what MLPs are and how they might  Read more...

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