- Paperback: 504 pages
- Publisher: Clarendon Press (Jun 1 1994)
- Language: English
- ISBN-10: 0198538642
- ISBN-13: 978-0198538646
- Product Dimensions: 23.4 x 15.8 x 2.7 cm
- Shipping Weight: 748 g
- Average Customer Review: 4.9 out of 5 stars See all reviews (18 customer reviews)
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Amazon Bestsellers Rank:
#277,916 in Books (See Top 100 in Books)
- #17 in Books > Computers & Internet > Programming > Algorithms > Fuzzy Logic
- #18 in Books > Computers & Internet > Programming > Algorithms > Pattern Recognition
- #26 in Books > Computers & Internet > Computer Science > Artificial Intelligence > Human Vision & Language Systems
- See Complete Table of Contents
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Product Details |
Product Description
From Amazon
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.' Timer 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
`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.' Timer 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
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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
Explore More
Concordance
Browse Sample PagesConcordance
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Most helpful customer reviews
5.0 out of 5 stars
An excellent book,
By
This review is from: Neural Networks for Pattern Recognition (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.
5.0 out of 5 stars
Sheer pleasure.,
By statW "sfbasksg" (CA United States) - See all my reviews
This review is from: Neural Networks for Pattern Recognition (Paperback)
If you want a very good, intermediate introduction to pattern classification this book must be on your bookshelf. It even does a very nice job explaining the EM algorithm in a few pages! Basic calculus is all you need to understand the book. A must read.
5.0 out of 5 stars
It makes a difficult topic easy to understand,
By Robert Goodman (NY, USA) - See all my reviews
This review is from: Neural Networks for Pattern Recognition (Paperback)
The theories of NN and PR are quite difficult to understand. But this book makes them much easier. The author can explain the concepts without using too much formula. If other authors could follow his step then the life is much easier!
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