Neural Networks and Learning Machines, Volume 10

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Prentice Hall, 2009 - Computers - 906 pages
13 Reviews

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.

Neural Networks and Learning Machines, Third Edition is 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. This is ideal for professional engineers and research scientists.

Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/

Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

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This is an excellent book with lastest adcances fully reflected.

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Contents

Introduction
1
Models of a Neuron
10
Feedback
18
Copyright

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About the author (2009)

SIMON HAYKIN, PhD, is Distinguished University Professor in the Department of Electrical and Computer Engineering at McMaster University. He has pioneered signal-processing techniques and systems for radar and communication applications, and authored several acclaimed textbooks. Dr. Haykin has received numerous awards for his research including Honorary Doctor of Technical Sciences from ETH Zurich, Switzerland, and the first International Union of Radio Science Henry Booker Gold Medal.

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