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Neural Networks for Pattern Recognition (Anglais) Relié – 1 décembre 1995

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--Ce texte fait référence à l'édition Broché.
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Revue de presse

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) --Ce texte fait référence à l'édition Broché .

Présentation de l'éditeur

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. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks. --Ce texte fait référence à l'édition Broché .

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Par Un client le 3 juin 2003
Format: Broché
Ce livre est vraiment très complet, depuis l'introduction aux techniques de 'pattern recognition', en passant par 3 types différents de réseaux de neurones, jusq'aux réseaux bayesien et autres fonctions
de calcul d'erreur, l'auteur dévoile progressivement les differentes possibilité des réseaux de neurones.
Malheureusement, cet ouvrage ne s'adresse pas à toute personne aussi motivé qu'il soit.
De très bonne connaissances en maths sont obligatoires à la bonne compréhension du livre.
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Commentaires client les plus utiles sur Amazon.com (beta)

Amazon.com: HASH(0x90602f48) étoiles sur 5 27 commentaires
135 internautes sur 141 ont trouvé ce commentaire utile 
HASH(0x9099a6f0) étoiles sur 5 Grows on You 9 juin 2000
Par Peter Norvig - Publié sur Amazon.com
Format: Broché
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 internautes sur 49 ont trouvé ce commentaire utile 
HASH(0x9099a720) étoiles sur 5 An excellent book 6 juin 2002
Par Andrew M. Olney - Publié sur Amazon.com
Format: Broché
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 internautes sur 26 ont trouvé ce commentaire utile 
HASH(0x9046933c) étoiles sur 5 Extraordinarily well written and comprehensive 8 juillet 1999
Par Un client - Publié sur Amazon.com
Format: Broché
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 internautes sur 20 ont trouvé ce commentaire utile 
HASH(0x90602d80) étoiles sur 5 An excellent introduction to pattern recognition 8 août 2000
Par Amazon Customer - Publié sur Amazon.com
Format: Broché Achat vérifié
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 internautes sur 14 ont trouvé ce commentaire utile 
HASH(0x9060aa08) étoiles sur 5 Excellent technical reference and tutorial 21 juin 1999
Par Un client - Publié sur Amazon.com
Format: Broché
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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