Neural Networks for Pattern Recognition (Anglais) Broché – 23 novembre 1995
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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)
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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. Lire la première page
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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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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.
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.
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