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"Should be in the library of any student, teacher, or researcher with a keen interest in modern statistical methods, a large volume of meaningful data to analyze (including simulations), and a fast workstation with good numerical and graphical capabilities."--Journal of the American StatisticalAssociation
.."..should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science."--Computer Journal
"An excellent and rigorous treatment of a number of neural network architectures."--Journal of Mathematical Psychology
"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 first-class book for the researcher in statistical pattern recognition."--Times Higher Education Supplement
"Although there
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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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Customer Reviews
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. Advanced readers that are also interested in optimal stochastic control and reinforcement learning will find it useful.
All in all, recommended for people that simply want to implement some neural network algorithms or for people that want a quick introduction. It is advisable, however, to keep a couple of books on estimation theory and on optimisation theory as an aid to deeper understanding.
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.
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.
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.
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