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eBook Neural Networks for Pattern Recognition download

by Christopher M. Bishop

eBook Neural Networks for Pattern Recognition download ISBN: 0198538499
Author: Christopher M. Bishop
Publisher: Oxford University Press (January 18, 1996)
Language: English
Pages: 504
ePub: 1837 kb
Fb2: 1398 kb
Rating: 4.9
Other formats: mobi mbr txt doc
Category: Technologies
Subcategory: Computer Science

Christopher M. Bishop. Dr. Bishop is a world-renowned expert in this field, but his book didn't work for me. Despite the title, it covers the more general topic of classification, not just Neural Networks.

Christopher M.

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. 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. L. Tarassenko and Professor . Brady, Department of Engineering Science, University of Oxford.

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 modelling 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.

This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition.

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From the perspective of pattern recognition, neural networks can be regarded as an extension of the many conventional techniques which have been developed over several decades. Indeed, this book includes discussions of several concepts in conventional statistical pattern recognition which I regard as essential for a clear understanding of neural networks. More extensive treatments of these topics can be found in the many texts on statistical pattern recognition, including Duda and Hart (1973), Hand (1981), Devijver and Kifctler (1982), and Fiikunaga (1990).

Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them. 1. Data Mining: Know It All. Bishop, Geoffrey Hinton.

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Comments: (7)
Nicanagy
Great introduction to simple neural networks. It only covers feed-forward networks and not recurrent networks, so you don't get a full feel for the breadth of the neural networks field. Being from the early 1990's, it also doesn't cover any of the more recent advances in deep learning, which is a hot, and fascinating field. That being said, by focusing only on feedforward networks, the book has more time to develop the background (the first chapter is a basics statistics overview) and theory for why neural networks are powerful. It then builds up neural networks (from single layer networks to multilayer networks), and finally covers more practical aspects of using neural networks, such as training algorithms, choosing error functions, and how to use neural networks in a Bayesian setting. Its treatment of training algorithms (especially back propagation and the hessian equivalent of back propagation) is especially succinct and enlightening.

Overall, it's a great introduction to neural networks, and will allow you to dive into more modern treatments of neural networks, such as deep learning.
Castiel
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.
Oghmaghma
Mr Bishop's book is very well written and contains a lot of useful information on neural networks. It is outlined well and progresses in a logical form. If, however, you are looking for a book that gives discussions with concrete examples of neural networks applications or set ups, you will be sorely disappointed. The mathematical treatment is universally generalized with very few specific concrete examples shown. Even the exercises will not serve you well. The term 'graded' is used; however, that simply referes to the description of difficulty. There are no answers to these exercises, so unless you have a teacher or are already firmly familiar with the material, you will not know if you have completed them correctly or not. Even worse, the exercises are in general not written to reinforce concepts in the chapter, but in most cases extend the chapter material into new regions.

In summary, this book should only be purchased by someone already familiar with neural networks and their mathematical basis. Anyone else will be wasting their money.
SadLendy
This book is of the type I would call a monologue. It sounds like it is not intended to teach or explain, but rather to speak to someone who already knows. The author discuss the issues which the reader has no idea about, without even trying to give an explanation. A couple of general formulas, which can be put in any scientific book. Pictures are few, and explanations are short and hard to understand without prior knowledge. Maybe this is a good book for specialists to review their knowledge, but not as an introduction.
Beazezius
This book has the fundamentals covered very well. It was written awhile ago, but the concepts and models are still heavily implemented to date. I used this in a grad class and thus far have been exceptionally impressed with the quality and usefulness!
Delagamand
Classic work in the NN field.

Seller provided perfect like-new copy promptly.
AnnyMars
A book that worth reading. A very good reference. I used it for a class and also for future study.
Not good for rookies.