Neural Networks for Pattern Recognition | 
 enlarge | Author: Christopher M. Bishop
Publisher: Oxford University Press, USA
Category: Book
List Price: $92.95
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Rating:  20 reviews
Sales Rank: 73068
Media: Paperback
Edition: 1
Pages: 504
Number Of Items: 1
Shipping Weight (lbs): 1.7
Dimensions (in): 9 x 6.1 x 1.1
ISBN: 0198538642
Dewey Decimal Number: 006.4
EAN: 9780198538646
Publication Date: January 18, 1996
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Condition: Perfect condition.
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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.
Product Description
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
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| Customer Reviews: Read 15 more reviews...
Grows on You June 9, 2000
Peter Norvig (Palo Alto, CA USA)
51 out of 54 found this review helpful
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 palstered 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, but if you want understanding of the techniques, you have to understand the math. Otherwise, there's no beef.
An excellent book June 6, 2002
Andrew M. Olney (Memphis, Tennessee United States)
32 out of 32 found this review helpful
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.
An excellent introduction to pattern recognition August 8, 2000
Peter J. Kootsookos (West Hartford, CT, USA)
19 out of 19 found this review helpful
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.
Very formal and well presented May 19, 2001
Steven Burns (-)
1 out of 1 found this review helpful
Although this book is not for beginners, you can use it as a startup text as long as you can understand the math behind it. The contents are beautifully presented and with the expected detail and formalism of such a great book. As a software developer I also use other books that are more algorithm-centered, but this is the one I look for when I want to read a formal exposure.
fine technical exposition December 13, 2000
De Paoli Andrea (Rome Italy)
3 out of 4 found this review helpful
I found the clarity of the math and technical aspects of pattern exposition to be extremely high. The more math, in particular statistics, one has the better, but still does an excellent job in explaining some of the basic concepts for those who have not had sufficient exposure to them.Certainly fundamental and I would consider a valid university text.
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