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Neural Networks: A Comprehensive Foundation
 
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Neural Networks: A Comprehensive Foundation (Hardcover)

by Simon Haykin (Author)
4.2 out of 5 stars  See all reviews (4 customer reviews)

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Product details

  • Hardcover: 842 pages
  • Publisher: Prentice Hall; 2 edition (7 Aug 1998)
  • Language English
  • ISBN-10: 0132733501
  • ISBN-13: 978-0132733502
  • Product Dimensions: 23.6 x 18.2 x 3.8 cm
  • Average Customer Review: 4.2 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon.co.uk Sales Rank: 705,045 in Books (See Bestsellers in Books)

    Popular in these categories:

    #68 in  Books > Computing & Internet > Computer Science > Algorithms > Fuzzy Logic
    #95 in  Books > Computing & Internet > Computer Science > Artificial Intelligence > Neural Networks

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  • See Complete Table of Contents

Product Description

Product 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.

From the Back Cover

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.

NEW TO THIS EDITION

  • NEW—New chapters now cover such areas as:
    • Support vector machines.
    • Reinforcement learning/neurodynamic programming.
    • Dynamically driven recurrent networks.
    • NEW-End—of-chapter problems revised, improved and expanded in number.

    FEATURES

    • Extensive, state-of-the-art coverage exposes the reader to the many facets of neural networks and helps them appreciate the technology's capabilities and potential applications.
    • Detailed analysis of back-propagation learning and multi-layer perceptrons.
    • Explores the intricacies of the learning process—an essential component for understanding neural networks.
    • Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics.
    • Integrates computer experiments throughout, giving the opportunity to see how neural networks are designed and perform in practice.
    • Reinforces key concepts with chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary.
    • Includes a detailed and extensive bibliography for easy reference.
    • Computer-oriented experiments distributed throughout the book
    • Uses Matlab SE version 5.

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Customer Reviews

4 Reviews
5 star:
 (3)
4 star:    (0)
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2 star:
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Average Customer Review
4.2 out of 5 stars (4 customer reviews)
 
 
 
 
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Most Helpful Customer Reviews

 
10 of 11 people found the following review helpful:
2.0 out of 5 stars Don't use for self study. Often unclear, contains errors., 4 April 2005
By A Customer
This review is from: Neural Networks: A Comprehensive Foundation (International Edition) (Paperback)
This book is fairly mathematically demanding, and when you are tackling such a book you have to rely on the text being accurate. Quite early on, in equation 2.62, there is an error that wasted me two days as I tried to figure out exactly what it was I had missed. I ended up having to refer to the article the material was based on, Geman et al.
From that point on, the clarity of the explanation degraded to the point that I was taking so long understanding it (and with my faith in the accuracy of the information now severely impaired), that I gave up and bought "Introduction to the Theory of Neural Computation", recommended in the comp.ai.neural-nets faq, instead.
The material was not so bad up to that point, and perhaps if you have a tutor to help you when you get stuck it is worth using, but for self-study I would not recommend it. If like me you really want to fully understand the material you will end up wasting a lot of time. If not, there are clearer, simpler books available.
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6 of 9 people found the following review helpful:
5.0 out of 5 stars Comprehensive introduction to Neural Networks, 10 April 2001
By A Customer
This review is from: Neural Networks: A Comprehensive Foundation (International Edition) (Paperback)
This is a mammoth of a book. Each chapter introduces and developes a particular model of neural network. Really useful for related undergraduate university courses. It's taught me all I know!!
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3 of 5 people found the following review helpful:
5.0 out of 5 stars Informative and masterfully written., 14 Sep 1998
By A Customer
A wonderfully well written, insightful, treatment of artificial neural networks. Beginning from the basics, the author sets forth both a technological and historical perspective for the understanding this multidisiplinary subject area. The book is written from a practical engineering perspective and comprehensively spans the entire discipline of modern neural network theory. A+
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Most Recent Customer Reviews

5.0 out of 5 stars Great theoretical support book
Far from being an in-depth search for neural networks' learning habilities, this book has a wide range of machine learning models' theoretical background. Read more
Published on 11 Sep 2003 by Pedro Rodrigues

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