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Neural Networks for Pattern Recognition (Advanced Texts in Econometrics) Paperback

ISBN-13: 978-0198538646 ISBN-10: 0198538642 Edition: 1st

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Product Details

  • Series: Advanced Texts in Econometrics
  • Paperback: 504 pages
  • Publisher: Oxford University Press, USA; 1 edition (January 18, 1996)
  • Language: English
  • ISBN-10: 0198538642
  • ISBN-13: 978-0198538646
  • Product Dimensions: 9.2 x 6.2 x 1.1 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.7 out of 5 stars  See all reviews (24 customer reviews)
  • Amazon Best Sellers Rank: #238,163 in Books (See Top 100 in Books)

Editorial Reviews

Amazon.com Review

This book provides a solid statistical foundation for neural networks from a pattern recognition perspective. The focus is on the types of neural nets that are most widely used in practical applications, such as the multi-layer perceptron and radial basis function networks. Rather than trying to cover many different types of neural networks, Bishop thoroughly covers topics such as density estimation, error functions, parameter optimization algorithms, data pre-processing, and Bayesian methods. All topics are organized well and all mathematical foundations are explained before being applied to neural networks. The text is suitable for a graduate or advanced undergraduate level course on neural networks or for practitioners interested in applying neural networks to real-world problems. The reader is assumed to have the level of math knowledge necessary for an undergraduate science degree.

Review


"Should be in the library of any student, teacher, or researcher with a keen interest in modern statistical methods, a large volume of meaningful data to analyze (including simulations), and a fast workstation with good numerical and graphical capabilities."--Journal of the American Statistical Association


"....should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science."--Computer Journal


"An excellent and rigorous treatment of a number of neural network architectures."--Journal of Mathematical Psychology


"Its sequential organization and end-of-chapter exercises make it an ideal mental gymnasium. The author has eschewed biological metaphor and sweeping statements in favour of welcome mathematical rigour."--Scientific Computing World


"A first-class book for the researcher in statistical pattern recognition."--Times Higher Education Supplement


"Although there has been a plethora of books on neural networks published in the last five years, none has really addressed the subject with the necessary mathematical rigour. Professor Bishop's book is the first textbook to provide a clear and comprehensive treatment of the mathematical principles underlying the main types of artificial neural networks."--Dr. L. Tarassenko and Professor J.M. Brady, Department of Engineering Science, University of Oxford


"There has been an acute need for authoritative textbooks in neural networks that explain the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability theory. There have been many attempts to provide such a text, but until now, none has succeeded. This is a serious attempt at providing such an ideal textbook. By concentrating on pattern recognition aspects of neural works, the author is able to treat many important topics in much greater depth. The most important contribution of the book is the solid statistical pattern recognition approach, a sign of increasing maturity in the field."--Mathematical Reviews


"The following keywords concisely indicate the contents: artificial neural networks, statistical pattern recognition, probability density estimation, single-layer networks, multi-layer perception, radial basis functions, error functions, parameter optimization algorithms, Bayesian techniques, etc. The book is aimed at researchers and practitioners. It can also be used as the primary text in a course for graduate students (129 graded exercises!)."--Industrial Mathematics



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Customer Reviews

4.7 out of 5 stars
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This book has the fundamentals covered very well.
tbeavers
This was a text book in my master's program, and I heard from students with a weak math background that they found it extremely challenging.
Andrew M. Olney
Do not be put off by the title: this book is more about pattern recognition than neural networks.
Peter J. Kootsookos

Most Helpful Customer Reviews

115 of 121 people found the following review helpful By Peter Norvig on June 9, 2000
Format: Paperback
This book came out at about the same time as Ripley's, which has almost the same title, but in reverse. At the time, I liked Ripley's better, because it covered more things that were totally new to me. Then a friend said he had chosen Bishop for a course he was teaching, and I went back and reconsidered the two books. I soon found that my friend was right: Bishop's book is better laid out for a course in that it starts at the beginning (well, not quite the beginning--you do need to be fairly sophisticated mathematically) and works up, while Ripley's is more a collection of insights all at the same level; confusing to learn from. Bishop is able to cover both theoretical and practical aspects well. There certainly are topics that aren't covered, but the ones that are there fit together nicely, are accurate and up to date, and are easy to understand. It has migrated from my bookcase to my desk, where it now stays, and I reach for it often.

To the reviewer who said "I was looking forward to a detailed insight into neural networks in this book. Instead, almost every page is plastered up with sigma notation", that's like saying about a book on music theory "Instead, almost every page is plastered with black-and-white ovals (some with sticks on the edge)." Or to the reviewer who complains this book is limited to the mathematical side of neural nets, that's like complaining about a cookbook on beef being limited to the carnivore side. If you want a non-technical overview, you can get that elsewhere (e.g. Michael Arbib's Handbook of Brain Theory and Neural Networks or Andy Clark's Connectionism in Context or Fausett's Fundamentals of Neural Networks), but if you want understanding of the techniques, you have to understand the math. Otherwise, there's no beef.
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46 of 46 people found the following review helpful By Andrew M. Olney on June 6, 2002
Format: Paperback
When I came across this book, I had already read several on the subject, including Beale & Jackson (lightweight) and Haykin (middleweight)
For a reader unafraid of basic statistics and linear algebra, this is an excellent beginning book. For the math wary, I would say read a math-lite conceptual book first. This was a text book in my master's program, and I heard from students with a weak math background that they found it extremely challenging.
Bishop rightly emphasizes the statistical foundations of feedforward networks. This is a large subject in and of itself, and he covers it well. It provides an extremely solid foundation.
Neural dynamics via recurrence, Hopfield Nets, and many other topics outside or on the edges of feedforward networks are not covered.
I find many NN books are poorly written, imprecise, and have little content. This is one of the best books I have read on the subject.
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26 of 26 people found the following review helpful By A Customer on July 8, 1999
Format: Paperback
Rarely do I encounter a book of such technical quality that also is a pleasure to read. Bishop moves through sometimes difficult topics in a clear, well-motivated style that is appropriate as both an introduction and a desktop reference on neural nets. Definitely on the "A list."
Bishop chose to not include discussions on a number of topics that might have diluted his focus on pattern recognition (for example, Hebbian learning and neural net approaches to principal components analysis). I think that these choices greatly strengthened the integrity of his presentation.
I would love to see an updated edition with a discussion of recent results in statistical learning theory, kernel methods and support vector machines.
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19 of 19 people found the following review helpful By Peter J. Kootsookos on August 8, 2000
Format: Paperback Verified Purchase
Do not be put off by the title: this book is more about pattern recognition than neural networks. Of course it covers neural networks, but the central aim of the book is to investigate statistical approaches to the problem of pattern recognition.
An excellent companion to "Duda & Hart".
As other reviewers have said: you will need a reasonable maths or stats background to get the most out of this book.
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13 of 13 people found the following review helpful By A Customer on June 20, 1999
Format: Paperback
I'd like to agree with previous reviewers. Note that you will need a good mathematical background (especially in statistics) to understand the content. However, the book is completely thorough in developing all the key concepts and really tries to give you insight into the meaning behind the equations. It's style is that of an undergraduate level textbook, but a very well written one. To use neural nets effectively, I think you need to have at least one book like this.
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10 of 10 people found the following review helpful By A Customer on June 8, 1996
Format: Paperback
Bishop cuts through the hype surrounding neural networks, and
shows how they relate to standard techniques
in statistical pattern recognition. He concentrates on feedforward
and radial basis function networks, which are the ones used most
widely in practice. This book is about as mathematical as
Hertz, Krogh and Palmer ("An Introduction to the Theory of Neural
Computation", 1991), but is probably easier to read, and is
certainly of more use to the practitioner. A real gem!
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10 of 11 people found the following review helpful By A Customer on November 7, 1997
Format: Paperback
This is a terrific book if you want to understand why neural nets work, and how to make them work. As advertised, it really goes into practical issues like preprocessing and generalization, which are easy to do halfheartedly, but are complex issues if you really want to get the best results. If I had to have only one book on neural nets, this would be it, no contest.
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