Neural Networks
A Comprehensive Foundation: United States Edition
2nd Edition
Published on 7. August 1998
Book
Hardback
842 pages
978-0-13-273350-2 (ISBN)
Description
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.
Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.
Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.
More details
Edition
2nd edition
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
College/higher education
Dimensions
Height: 244 mm
Width: 181 mm
Thickness: 35 mm
Weight
1382 gr
ISBN-13
978-0-13-273350-2 (9780132733502)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Simon Haykin
Neural Networks and Learning Machines
Book
03/2009
3rd Edition
Pearson
€290.49
Shipment within 15-20 days
Previous edition
Book
01/1994
Macmillan USA
€54.61
Article exhausted; check for reprint
Content
1. Introduction.
2. Learning Processes.
3. Single-Layer Perceptrons.
4. Multilayer Perceptrons.
5. Radial-Basis Function Networks.
6. Support Vector Machines.
7. Committee Machines.
8. Principal Components Analysis.
9. Self-Organizing Maps.
10. Information-Theoretic Models.
11. Stochastic Machines & Their Approximates Rooted in Statistical Mechanics.
12. Neurodynamic Programming.
13. Temporal Processing Using Feedforward Networks.
14. Neurodynamics.
15. Dynamically Driven Recurrent Networks.
Epilogue.
Bibliography.
Index.
2. Learning Processes.
3. Single-Layer Perceptrons.
4. Multilayer Perceptrons.
5. Radial-Basis Function Networks.
6. Support Vector Machines.
7. Committee Machines.
8. Principal Components Analysis.
9. Self-Organizing Maps.
10. Information-Theoretic Models.
11. Stochastic Machines & Their Approximates Rooted in Statistical Mechanics.
12. Neurodynamic Programming.
13. Temporal Processing Using Feedforward Networks.
14. Neurodynamics.
15. Dynamically Driven Recurrent Networks.
Epilogue.
Bibliography.
Index.