The Elements of Statistical Learning
The Elements of Statistical Learning

The Elements of Statistical Learning
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Springer Verlag

UPC:
978038795284

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$89.95

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The Elements of Statistical Learning Specs:
Product NameThe Elements of Statistical Learning
ManufacturerSpringer Verlag
Product Number MPN0387952845
Retail Price $89.95
EAN-1409780387952840
UPC978038795284
EAN-139780387952840
Specifications 
TitleThe Elements of Statistical Learning
ISBN0387952845
Author(s)T. Hastie, R. Tibshirani, J. H. Friedman
Release Date09 August, 2001, 2003-07-30, 2001-08-09
FormatHardcover
Num of Pages552
Num. of Items1
EAN9780387952840
EditionCorrected
Dimensions9.3 x 6.4 x 1.2 in.
Weight0.5 lbs.
Deal first added on:26-January-2004

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Latest 6 Reviews
Here is what people are saying about the The Elements of Statistical Learning
4 Star Rating  "authoritative textbook for data mining"2009-06-22
- Reviewed By User: A20DXHZ8PSN643
This edition adds some essential features for supervised statistical learning, such as supervised principle components, which I find fairly useful.
 
1 Star Rating  "Not the best textbook for a class"2009-04-14
- Reviewed By stf481982
I used this book for my stats course. While I do enjoy reading some of the parts of the book, I have to say that I am rather dissappointed with the presentation in the book.

1. This book assumes that you already have some background and quite a bit of familiarity with the subject

2. While it contains many topics, most materials are only "presented" rather than "clearly explained". And so, while it may be good as a reference book, at least for me, this definitely shouldn't be your main resource when first studying the subject.

3. Definitely the authors are expert on the field and I just hope they would come up with a much better revision of the book

4. One nice feature of the book ... it contains pretty picture! Unfortunately, just like the old saying, "a picture contains a thousand words". Thats exactly what happens here. Some of the pictures are hard to understand.

It may or may not be fair to give this book 1 star (I might update my rating in the future). But the simple truth is that I am not impressed when I first read the book. It surely falls below my expectation from such a highly acclaimed book.


 
4 Star Rating  "Has the most post-its of any book on my shelf"2009-04-04
- Reviewed By User: AAG19DPW2P2X4
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.
 
5 Star Rating  "my big brown book of statistic learning tools"2009-03-22
- Reviewed By seanmatthews
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]).
 
2 Star Rating  "Not recommended as a text book"2009-03-01
- Reviewed By User: A3PP2OYXEEEQO4
I had to use this book as the text book for a Machine Learning course at MIT and it was not very understantable. There are much better Machine Learning text books.
 
5 Star Rating  "Good Book!"2008-09-22
- Reviewed By User: A4X1CLCT2Y935
The book is really helpful and was being delivered to me in a timely fashion.
 
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