Customer Reviews

Neural Networks for Pattern Recognition

5 star:		(18)
4 star:		(1)
3 star:		(2)
2 star:		(0)
1 star:		(0)

 

 

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.

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.

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.

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.

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.

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!

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.

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.

This is definitely the NN bible for beginners. I used it first in 1996 just after it came out and I still use it for reference. Reading some of the other reviews I saw that some people think there is too much math in the book -- that is not true -- the well explained math in the book is necessary to make the topic extremely clear.
Now 6 years later it would be nice to have a second, extended edition covering other successful NN related areas like recurrent NNs, PPCA, ICA, etc., also maybe some online adaptation techniques using Bishop's gift of being able to explain in simple words & math.

This book is the best treatment of the subject. To really understand the content, it's necessary prior knowledge of probability theory, but not in depth. It is well illustrated and, more important, the topics are explained in manner logic and sweep. This work don't contains everything, but it's cool because it's readable and sufficient rigorous.