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Neural Networks for Pattern Recognition (Advanced Texts in Econometrics) Paperback – 23 Nov 1995


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Product details

  • Paperback: 504 pages
  • Publisher: Clarendon Press (23 Nov. 1995)
  • Language: English
  • ISBN-10: 0198538642
  • ISBN-13: 978-0198538646
  • Product Dimensions: 23.3 x 2.8 x 15.7 cm
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: 109,470 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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Amazon 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, Christopher Bishop thoroughly covers topics such as density estimation, error functions, parameter optimisation algorithms, data pre-processing and Bayesian methods. All topics are organised 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 mathematical knowledge necessary for an undergraduate science degree. --Jake Bond

Review

excellent... Bishop is able to achieve a level of depth on these topics which is unparalleled in other neural-net texts.... clear and concise mathematical analysis. Bishop's text [] picks up where Duda and Hart left off, and, luckily does so with the same level of clarity and elegance. Neural Networks for Pattern Recognition is an excellent read, and represents a real contribution to the neural-net community. IEEE Transactions on Neural Networks, May 1997

this is an excellent book in the specialised area of statistical pattern recognition with statistical neural nets ... a good starting point for new students in those laboratories where research into statistico-neural pattern recognition is being done ... The examples for the reader at the end of this and every chapter are well chosen and will ensure sales as a course textbook ... this is a first-class book for the researcher in statistical pattern recognition. (Times Higher)

Bishop leads the way through a forest of mathematical minutiae. Readers will emerge with a rigorous statistical grounding in the theory of how to construct and train neural networks in pattern recognition. New Scientist

[Bishop] has written a textbook, introducing techniques, relating them to the theory, and explaining their pitfalls. Moreover, a large set of exercises makes it attractive for the teacher to use the book.... should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science. The Computer Journal, Volume 39, No. 6, 1996

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 neural network introduction placed in a pattern recognition context. ...He has written a textbook, introducing techniques, relating them to the theory and explaining their pitfalls. Moreover, a large set of exercises makes it attractive for the teacher to use the book ... should be warmly welcomed by the neural network and pattern recognition communities. (Robert P. W. Duin, IAPR Newsletter Vol. 19 No. 2 April 1997)

This outstanding book contributes remarkably to a better statistical understanding of artificial neural networks. The superior quality of this book is that it presents a comprehensive self-contained survey of feed-forward networks from the point of view of statistical pattern recognition. (Zbl.Math 868)

Inside This Book (Learn More)
First Sentence
The term pattern recognition encompasses a wide range of information processing problems of great practical significance, from speech recognition and the classification of handwritten characters, to fault detection in machinery and medical diagnosis. Read the first page
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Most Helpful Customer Reviews

26 of 28 people found the following review helpful By A Customer on 30 Aug. 1999
Format: Paperback
Bishop's book is the current bible on Neural Computing. It is superbly written and presented, and the subject material carefully selected. The ideas of neural computing are motivated from a statistical pattern recognition point of view, though the reader is not expected to have a strong foundation in probability theory - just a basic appreciation is enough to begin with. The book has enormous (though not excessive) breadth, and covers practically every aspect of tradiational neural networks, from theoretical aspects motivated by probability theory, to practical concerns about optimisation and learning, and finally to a more advanced treatement on Bayesian methods. Above all, Bishop's writing is lucid and clear, and although some of the topics are conceptually intricate, they are always readable and accessible. Buy this book if you have anything to do with neural networks!
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12 of 16 people found the following review helpful By Christos Dimitrakakis on 18 Jun. 2004
Format: Paperback
This is good and quite clear introduction to the field that tries to give the reader an intuitive overview to the neural networks and pattern recognition in general.
This is a good book if you are interested in a conversationalist overview to neural networks. There are sufficient formulas to implement the algorithms, so it is good as a list of commonly used neural architectures and how they work, in a single easy-to-access place.
However, the book is quite short and hurriedly goes through many different techniques and algorithms, giving you a brief snapshot of each one. Nice pictures abound and explanations, but the understanding that one may obtained from this book will be only superficial. Since the book does not discuss the foundations behind each technique, most of them appear disjoint and unrelated.
Actually, the lack of detail and mathematical rigour can be confusing. The need to explain concepts intuitively is hardly an excuse, since there exist other books that manage to achieve clarity, easy of understanding and mathematical rigour, while they develop concepts with sufficient generality for the student to fully grasp the relation between various methods.
From my own viewpoint, supervised neural network learning is just a special case of optimisation (the quantity to be optimised is the neural network parameter) under statistical uncertainty (the cost function to be minimised is only partially defined by a set of data and needs to be estimated).
Thus, in addition to this book I also recommend taking a look at Bertseka's "Constrained optimization and Lagrange multiplier methods" and his newer "Nonlinear Pogramming" book. His "Neuro-Dynamic programming" book covers a lot more than just neural networks for pattern recognition.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 26 reviews
126 of 132 people found the following review helpful
Grows on You 9 Jun. 2000
By Peter Norvig - Published on Amazon.com
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.
49 of 49 people found the following review helpful
An excellent book 6 Jun. 2002
By Andrew M. Olney - Published on Amazon.com
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.
26 of 26 people found the following review helpful
Extraordinarily well written and comprehensive 8 July 1999
By A Customer - Published on Amazon.com
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.
20 of 20 people found the following review helpful
An excellent introduction to pattern recognition 8 Aug. 2000
By Amazon Customer - Published on Amazon.com
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.
14 of 14 people found the following review helpful
Excellent technical reference and tutorial 21 Jun. 1999
By A Customer - Published on Amazon.com
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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