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Descriptions du produit

The dramatic growth in practical applications for machine learning over the years has been accompanied by many important developments in the underlying algorithms and techniques. This textbook reflects these developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning.


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Détails sur le produit

  • Relié: 760 pages
  • Editeur : Springer-Verlag New York Inc.; Édition : 1st ed. 2006. Corr. 2nd printing 2011 (17 août 2006)
  • Collection : Information Science and Statistics
  • Langue : Anglais
  • ISBN-10: 0387310738
  • ISBN-13: 978-0387310732
  • Dimensions du produit: 23,9 x 18,3 x 4,3 cm
  • Moyenne des commentaires client : 5.0 étoiles sur 5  Voir tous les commentaires (3 commentaires client)
  • Classement des meilleures ventes d'Amazon: 24.458 en Livres anglais et étrangers (Voir les 100 premiers en Livres anglais et étrangers)
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Couverture | Copyright | Table des matières | Extrait | Index | Quatrième de couverture
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17 internautes sur 17 ont trouvé ce commentaire utile  Par M. GELGON sur 24 janvier 2007
Format: Relié
Excellent à tous points de vue

- progressivité dans l'introduction des concepts, tant au niveau "macro" (chapitres) que "micro" (suite des idées dans un paragraphe)

- choix des questions couvertes et cohérence de l'ensemble

- illustrations, schémas

- l' "objet" livre est parfait.

Bishop reste une des lectures fondamentales pour mettre en ordre

les idées dans sa tête et comme modèle quant à la manière de les exprimer.

Pour un thésard en reconnaissance de forme/multimédia ce livre est une clé

pour la réussite (apprendre, mais aussi se motiver).

Vaut largement le coup même si vous avez [Bishop95] car largement amélioré

(completé ).

Apres 3 mois, déjà largement couvert de tâches de café...

Marc, Univ Nantes.
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Format: Relié Achat vérifié
Pattern Recognition And Machine Learning was written for researchers, engineers, students interested in machine learning and Bayesian approaches. It contains exercises and is really well written, which make it almost "self-contained" for everyone who wants to learn about recent machine learning methods.
I love this book, and i really consider it as a must have, if you're into machine learning !
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0 internautes sur 2 ont trouvé ce commentaire utile  Par J-claude Rabusseau sur 21 avril 2013
Format: Relié Achat vérifié
Il s'agissait d'un cadeau et ce livre touche à un domaine très particulier où je ne comprends pas grand chose, donc je ne peux me prononcer sur le contenu mais la personne qui l'a reçu en cadeau était ravie.
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146 internautes sur 153 ont trouvé ce commentaire utile 
Great Insights, but a hard read 16 juin 2007
Par Sidhant - Publié sur Amazon.com
Format: Relié Achat vérifié
This new book by Chris Bishop covers most areas of pattern recognition quite exhaustively. The author is an expert, this is evidenced by the excellent insights he gives into the complex math behind the machine learning algorithms. I have worked for quite some time with neural networks and have had coursework in linear algebra, probability and regression analysis, and found some of the stuff in the book quite illuminating.

But that said, I must point out that the book is very math heavy. Inspite of my considerable background in the area of neural networks and statistics, I still was struggling with the equations. This is certainly not the book that can teach one things from the ground up, and thats why I would give it only 3 stars. I am new to kernels, and I am finding the relevant chapters difficult and confusing. This book wont be very useful if all you want to do is write machine learning code. The intended audience for this book I guess are PhD students/researchers who are working with the math related aspects of machine learning. Undergraduates or people with little exposure to machine learning will have a hard time with this book. But that said, time spent in struggling with the contents of this book will certainly pay-off, not instantly though.
79 internautes sur 84 ont trouvé ce commentaire utile 
concentrates too much on the easy stuff 9 juillet 2008
Par _claudia_ - Publié sur Amazon.com
Format: Relié
The book is worth a look, but after some of 5 star reviews i read here, it was quite a disappointment. Yes, the book covers a lot of ground. Yes, the book has lots of nice pictures and easy examples, but that is exactly the problem. There are lots and lots of simple examples to explain the most basic concepts, but when it gets complicated the book often sounds as if the text was taken out of a mathematics book. For example: the basics of probability theory are introduced for over 5 pages with the example of "two coloured boxes each containing fruit". Nothing wrong with that. Then the chapter continues with probability densities which are covered within 2 pages and contain sentences like "Under a nonlinear change of variable, a probability density transforms differently from a simple function, due to the Jacobian factor". There is no mentioning how a simple function exactly transforms, what a Jacobian factor actually is and why we would be interested in a nonlinear change. Surely, some of the introductory pages could have been thrown out to explain in depth the more difficult issues. Unfortunately, this is not the only time, where easy concepts get a lot of attention and the truly important complex concepts are skimmed over. All in all, still worth a read, though do not expect too much.
330 internautes sur 368 ont trouvé ce commentaire utile 
Thorough but vastly unclear 28 février 2007
Par dc - Publié sur Amazon.com
Format: Relié Achat vérifié
I can appreciate others who might think that this is a great book.... but I am a student using it and I have some very different opinions of it.

First, although Mr. Bishop is clearly an expert in Machine Learning, he is also obviously a HUGE fan of Bayesian Statistics. The title of the book is misleading as it makes no mention of Bayes at all but EVERY CHAPTER ends with how all of the chapter's contents are combined in a Bayes method. That's not bad it's just not clear from the title. The title should be appended with "... using Bayesian Methods"

Second, while it is certainly a textbook, the author clearly has an understanding of the material that seems to undermine his ability to explain it. Though there are mentions of examples there are, in fact, none. There are many graphics and tiny, trivial indicators, but I can't help to think that every single one of the concepts in the book would have benefited from even a single application. There aren't any. I am lead to believe that if you are already aware of many of the methods and techniques that this would be an excellent reference or refresher. As a student starting out I almost always have no idea what his intentions are.

To make matter worse, he occasionally uses symbols that are flat-out confusing. Why would you use PI for anything other than Pi or Product? He does. Why use little k, Capital K, and Greek Letter Kappa (a K!) in a series of explanations. He does. He even references articles that he has written... in 2008!!

Every chapter seems to be an exercise to see how many equations he can stuff in it. There are 300 in Chapter 2 alone. Over and over and over again I have the feeling that he is trying to TELL me how to ride a bicycle when it would have been so much easier to at least let me see the view from behind the handle bars with my feet on the pedals. Chapter five on Neural Nets, for example, is abysmally over-complicated. Would you hand someone a dictionary and ask them to write a poem? ("Hey, all the words you need are in here!") Of course not.

Third, the book mentions that there is a lot of information available on the web site. The only info available on his website is a brief overview of the text, a detailed overview of the text (that's not a typo.... he has both), an example chapter, links to where the book can be purchased, and (actually, quite useful for creating slides) an archive of all of the figures available in the book. There are no answers to problems or explorations of any part of the material. The upcoming book might be amazing and exactly what I am looking for but it could be months away and another $50 or so to purchase it. Hardly ideal. How about putting some of that MatLab code on your site? *Something* to crystalize the concepts!

Finally, while the intro indicates this might be a good book for Computer Scientists it would actually make more sense to call it a Math book. More specifically a Statistics book. There are no methods, no algorithms, no bits of pseudo-code, and (again) no applications are in the text. Even examples that actually used hard numbers and/or elements from a real problem and explained would be much appreciated.

Maybe I am being a little critical and perhaps I want for too much but in my mind if you are writing a book with the goal of TEACHING a subject, it would be in your interest to make things clear and illustrative. Instead, the book feels more like a combination of "I am smart. Just read this!" and a reference text.
44 internautes sur 50 ont trouvé ce commentaire utile 
The book should change its title 25 septembre 2007
Par John E - Publié sur Amazon.com
Format: Relié Achat vérifié
This book (PRML) should be re-titled as "PRML: a bayesian approach". Yes, bayesian approach is very useful for machine learning, and sometimes the final goal of learning is to maximize some sort of posterior probability. However, if the author is such a huge fun of bayes statistics, please tell perspective readers in a clear way. Emphasize bayes aspects too much really hurt the quality of this book as a general-purpose textbook of machine learning.

For a better textbook of machine learning, I recommend:
1) The elements of statistical learning (perhaps this book a little hard for beginner in this field -- but as least better than PRML -- you can compare their chapters about linear regression to see which one is better).
2) Pattern classification (focus on classification, not regression. Also not very easy -- anyway, machine learning is not an easy field ^_^).
3) Machine Learning (a little old, but great for beginner.)

These three book also mention bayesian statistics, but in a proper way. If you have some experience in machine learning and have engineering-level math background, just choose the 1) or 2). If you are completely a beginner, first take a glance on 3), and then go to 1) or 2).

Finally, if you want a book that discusses machine learning purely from bayesian perspective, PRML is good.
22 internautes sur 25 ont trouvé ce commentaire utile 
A very nice theoretical book, but not useful for the practitioner 9 juillet 2010
Par stefan - Publié sur Amazon.com
Format: Relié
This books reviews the personal illuminations of the author about the fields of Pattern Recognition and Machine Learning. As such it is quite interesting, but only if you have a deep understanding of the field already and want to see a new view on the field. In an interview on Microsoft's Channel 9 (the author works for Microsoft Research, Cambridge, England), the author mentions that this book provides a unifying view of the field with recent break-throughs. The unification is through Bayesian approach. For those that don't know, Bayesian means lots of math and integrals, lots of computation and "you should believe me, this is the right model, because it's Bayesian". From what I've seen Bayesian methods and graphical models seem to work best for images. In practice one is faced with much more acute problems than finding the right values for the hyper-parameters. Topics like feature selection, data imbalance are not discussed.
My feeling is that this book is not appropriate for practitioners in Machine learning and Pattern Recognition. It does not offer any statistical intuition why methods work and how to reason about problems. It's very math heavy, but in my opinion the more math-heavy a machine learning algorithm, the worst it is in practice. Statistical intuition and under what conditions a method works are absent.
I like the book of Hastie, Tibshirani, Friedman: "The elements of statistical learning" that offers much more intuition(discussion), less useless math (from practical point of view) and more experiments. Friedman has produced one of the best-working machine learning algorithms to-date: gradient-boosted decision trees, by putting together four components proven to work in practice (boosting, decision-trees, bagging, and linear combination of week classifiers). Hastie, Tibshirani, Friedman have took lifetimes to think what works and how models related to each other. Graphical models, kernel methods, neural networks: that stuff is only good if you want to write papers, but not solve problems.
Again, a very nice theoretical book, but not useful for the practitioner. This books should be called "My personal unifying theory of Machine Learning and Pattern Recognition using the Bayesian Approach".
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