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The Elements of Statistical Learning: Data Mining, Inference, and Prediction [Anglais] [Relié]

Trevor Hastie , Robert Tibshirani , Jerome Friedman
5.0 étoiles sur 5  Voir tous les commentaires (1 commentaire client)
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Descriptions du produit

The Elements of Statistical Learning This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world. Full description

Détails sur le produit

  • Relié: 767 pages
  • Editeur : Springer-Verlag New York Inc.; Édition : 5e (9 février 2009)
  • Collection : Springer Series in Statistics
  • Langue : Anglais
  • ISBN-10: 0387848576
  • ISBN-13: 978-0387848570
  • Dimensions du produit: 23,4 x 15,7 x 3,8 cm
  • Moyenne des commentaires client : 5.0 étoiles sur 5  Voir tous les commentaires (1 commentaire client)
  • Classement des meilleures ventes d'Amazon: 23.662 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
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0 internautes sur 4 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Satisfaite 21 décembre 2012
Format:Relié|Achat vérifié
Je suis satisfaite du délai de réception toujours très rapide, de la qualité de l'emballage bien protecteur. Je ne peux donner de jugement sur le contenu qui est très spécifique. Ce livre est un cadeau destiné à un étudiant en master de mathématiques et je n'ai pas le niveau - loin s'en faut - pour juger de la qualité du texte, sans parler des formules mathématiques que je trouve fort jolies sur le plan esthétique faute d'en comprendre le sens :)))
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Amazon.com: 4.0 étoiles sur 5  36 commentaires
28 internautes sur 31 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 excellent overview, especially for outsiders, ties the field together conceptually 13 avril 2011
Par Matthew Grosso - Publié sur Amazon.com
Format:Relié|Achat vérifié
This review is written from the perspective of a programmer who has sometimes had the chance to choose, hire, and work with algorithms and the mathematician/statisticians that love them in order to get things done for startup companies. I don't know if this review will be as helpful to professional mathematicians, statisticians, or computer scientists.

The good news is, this is pretty much the most important book you are going to read in the space. It will tie everything together for you in a way that I haven't seen any other book attempt. The bad news is you're going to have to work for it. If you just need to use a tool for a single task this book won't be worth it; think of it as a way to train yourself in the fundamentals of the space, but don't expect a recipe book. Get something in the "using R" series for that.

When it came out in 2001 my sense of machine learning was of a jumbled set of recipes that tended to work in some cases. This book showed me how the statistical concepts of bias, variance, smoothing and complexity cut across both fields of traditional statistics and inference and the machine learning algorithms made possible by cheaper cpus. Chapters 2-5 are worth the price of the book by themselves for their overview of learning, linear methods, and how those methods can be adopted for non-linear basis functions.

The hard parts:

First, don't bother reading this book if you aren't willing to learn at least the basics of linear algebra first. Skim the second and third chapters to get a sense for how rusty
your linear algebra is and then come back when you're ready.

Second, you really really want to use the SQRRR technique with this book. Having that glimpse of where you are going really helps guide you're understanding when you dig in for real.

Third, I wish I had known of R when I first read this; I recommend using it along with some sample data sets to follow along with the text so the concepts become skills not just
abstract relationships to forget. It would probably be worth the extra time, and I wish I had known to do that then.

Fourth, if you are reading this on your own time while making a living, don't expect to finish the book in a month or two.
37 internautes sur 43 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Has the most post-its of any book on my shelf 4 avril 2009
Par Craig Garvin - Publié sur Amazon.com
Format:Relié
This is one of the best books in a difficult field to survey and summarize. Like 'Pattern Recognition', 'Statistical Learning' is an umbrella term for a broad range of techniques of varying complexity, rigor and acceptance by practitioners in the field. The audience for such a text ranges from the user requiring a code library to the mathematician seeking proof of every statement. I sit somewhere in the middle, but more towards the mathematical end. I subscribe to the traditional statistician's view of Machine Learning. It is a term invented in order to avoid having to prove theorems and dodge the rigors of 'real' statistics. However, I strongly support such a course of action. There is an immense need for Machine Learning algorithms, whether they have actual properties or not, and an equal need for books to introduce these topics to people like myself who have a strong mathematical background, but have not been exposed to these techniques.

Hastie & Tibshirani has the most post-it's of any book on my shelf. When my company built an custom multivariate statistical library for our targeted product, we largely followed Hastie & Tibshirani's taxonomy. Their overview of support vector machines is excellent, and I found little of value to me in dedicated volumes like Cristianini & Shawe-Taylor that wasn't covered in Hastie & Tibshirani. Hastie & Tibshirani is another book with excellent visual aides. In addition to some great 2-D representations of complex multidimensional spaces, I thought the 'car going up hill' icon was a very useful cue that the level was going up a notch.

Having praised this book, I can't argue with any of the negative reviews. There is no right answer of where to start or what to cover. This book will be too mathematical for some, insufficiently rigorous for others, but was just right for me. It will offer too much of a hodge-podge of techniques, miss someone's favorite, or offer just the right balance. In the end, it was the best one for me, so if you're like me (someone with a very solid math base, not a mathematician, who appreciates rigor, but isn't married to it, and who is looking to self-start on this topic.) you'll like it.
87 internautes sur 107 ont trouvé ce commentaire utile 
1.0 étoiles sur 5 Useful research summary; a disaster otherwise 17 février 2010
Par SP, ML, Stats - Publié sur Amazon.com
Format:Relié|Achat vérifié
I have three texts in machine learning (Duda et. al, Bishop, and this one), and I can unequivocally say that, in my judgement, if you're looking to learn the key concepts of machine learning, this one is by far the worst of the three. Quite simply, it reads almost as a research monologue, only with less explanation and far less coherence. There's little/no attempt to demystify concepts to the newcomer, and the exposition is all over the map. There simply isn't a clear, coherent path that the authors set out to go on in writing a given chapter of this text; it's as if they tried to squeeze every bit of information of the most recent results into the chapter, with little regard to what such a decision might do to the overall readability of the text and the newcomer's understanding. To people who might disagree with me on this point, I'd recommend reading a chapter in Bishop's text and comparing it to similar content in this one, and I think you'll at least better appreciate my viewpoint, if not agree with it.

So you might be wondering, why do I even own the text given my opinion? Well, two reasons: (1) it cost 25 dollars through Springer and a contract they have with my university (definitely look into this before buying on Amazon!), and (2) if you actually already know the concepts, it is quite useful as a summary of what's out there. So to those who understand the basics of machine learning, and also have exposure to greedy algorithms, convex optimization, wavelets, and some other often-utilized methods in the text, this makes for a pretty good reference.

The authors are definitely very well-known researchers in the field, who in particular have written some good papers on a variety of machine learning topics (l1-norm penalized regression, analysis of boosting, to name just two), and thus this book naturally will attract some buzz. It may be very useful to someone like myself who is already familiar with much of what's in the book, or someone who is an expert in the field and just uses it as a quick reference. As a pedagogical tool, however, I think it's pretty much a disaster, and feel compelled to write this as to prevent the typical buyer -- who undoubtedly is buying it to learn and not to use as a reference -- from wasting a lot of money on the wrong text.
13 internautes sur 15 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 my big brown book of statistic learning tools 22 mars 2009
Par S. Matthews - Publié sur Amazon.com
Format:Relié|Achat vérifié
This is a quite interesting, and extremely useful book, but it is wearing to read in large chunks. The problem, if you want to call it that, is that it is essentially a 700 page catalogue of clever hacks in statistical learning. From a technical point of view it is well-ehough structured, but there is not the slightest trace of an overarching philosophy. And if you don't actually have a philosophical perspective in place before you start, the read you face might well be an even harder grind. Be warned.

Some of the reviews here complain that there is too much math. I don't think that is an issue. If you have decent intuitions in geometry, linear algebra, probability and information theory, then you should be able to cruise through and/or browse in a fairly relaxed way. If you don't have those intuitions, then you are attempting to read the wrong book.

There were a couple of things that I expected (things I happen to know a bit about), but that were missing. On the unsupervised learning side, the discussion of Gaussian mixture clustering was, I thought, a bit short and superficial, and did not bring out the combination of theoretical and practical power that the method offers. On the supervised learning side, I was surprised that a book that dedicates so much time to linear regression finds no room for a discussion of Gaussian process regression as far as I could see (the nearest point of approach is the use of Gaussian radial basis functions [oops: having written that, I immediately came across a brief discussion (S5.8.1) of, essentially, GP regression - though with no reference to standard literature]).
8 internautes sur 9 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Excellent but assumes considerable background 23 janvier 2010
Par M. D. HEALY - Publié sur Amazon.com
Format:Relié|Achat vérifié
This should certainly not be the first statistics book you read, or even the second or third book, but when you are ready for it then you should absolutely read it. But be prepared to read it very slowly and digest each page. Its greatest strength is that it shows how much of modern statistics comes down to a few fundamental issues: bias, variance, model complexity, and the curse of dimensionality. There is no free lunch in statistics, methods that claim to avoid these tradeoffs only do so by adding more assumptions about the structure of your data. If your data match the assumptions of such methods, you gain statistical power, but if your data don't match the assumptions then you lose.

By looking closely at the assumptions, the book shows how many contemporary methods that look different are fundamentally similar under the hood.

And in my own work I have adopted their use of open circles for the points in scatterplots. These circles are easier to see than tiny solid dots, but overlapping symbols don't cover each other the way large filled symbols do.
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