- Tapa dura: 745 páginas
- Editor: Springer; Edición: 2nd ed. 2009, Corr. 9th printing 2017 (19 de mayo de 2017)
- Colección: Springer Series in Statistics
- Idioma: Inglés
- ISBN-10: 0387848576
- ISBN-13: 978-0387848570
- Valoración media de los clientes: 2 opiniones de clientes
- Clasificación en los más vendidos de Amazon: nº538 en Libros en idiomas extranjeros (Ver el Top 100 en Libros en idiomas extranjeros)
- Ver el Índice completo
Compara Precios en Amazon
+ EUR 2,99 de gastos de envío
+ EUR 2,99 de gastos de envío
+ EUR 7,98 de gastos de envío
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) (Inglés) Tapa dura – 19 may 2017
|Nuevo desde||Usado desde|
Comprados juntos habitualmente
Los clientes que compraron este producto también compraron
Descripción del producto
From the reviews:
"Like the first edition, the current one is a welcome edition to researchers and academicians equally…. Almost all of the chapters are revised.… The Material is nicely reorganized and repackaged, with the general layout being the same as that of the first edition.… If you bought the first edition, I suggest that you buy the second editon for maximum effect, and if you haven’t, then I still strongly recommend you have this book at your desk. Is it a good investment, statistically speaking!" (Book Review Editor, Technometrics, August 2009, VOL. 51, NO. 3)
From the reviews of the second edition:
"This second edition pays tribute to the many developments in recent years in this field, and new material was added to several existing chapters as well as four new chapters … were included. … These additions make this book worthwhile to obtain … . In general this is a well written book which gives a good overview on statistical learning and can be recommended to everyone interested in this field. The book is so comprehensive that it offers material for several courses." (Klaus Nordhausen, International Statistical Review, Vol. 77 (3), 2009)
“The second edition … features about 200 pages of substantial new additions in the form of four new chapters, as well as various complements to existing chapters. … the book may also be of interest to a theoretically inclined reader looking for an entry point to the area and wanting to get an initial understanding of which mathematical issues are relevant in relation to practice. … this is a welcome update to an already fine book, which will surely reinforce its status as a reference.” (Gilles Blanchard, Mathematical Reviews, Issue 2012 d)
“The book would be ideal for statistics graduate students … . This book really is the standard in the field, referenced in most papers and books on the subject, and it is easy to see why. The book is very well written, with informative graphics on almost every other page. It looks great and inviting. You can flip the book open to any page, read a sentence or two and be hooked for the next hour or so.” (Peter Rabinovitch, The Mathematical Association of America, May, 2012)
Reseña del editor
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.Ver Descripción del producto
No es necesario ningún dispositivo Kindle. Descárgate una de las apps de Kindle gratuitas para comenzar a leer libros Kindle en tu smartphone, tablet u ordenador.
Obtén la app gratuita:
Detalles del producto
Si eres el vendedor de este producto, ¿te gustaría sugerir ciertos cambios a través del servicio de atención al vendedor?
¿Qué otros productos compran los clientes tras ver este producto?
Opiniones de clientes
Principales opiniones de clientes
Ha surgido un problema al filtrar las opiniones justo en este momento. Vuelva a intentarlo en otro momento.
Muy interesante y ligero de leer. Todo perfectamente explicado
Opiniones de clientes más útiles en Amazon.com
The Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective. This means you get good derivations of popular methods such as support vector machines, random forests, and graphical models; but each is developed only after the appropriate (and wrongly considered less sexy) statistical framework has already been derived (linear models, kernel smoothing, ensembles, and so on).
In addition to having excellent and correct mathematical derivations of important algorithms The Elements of Statistical Learning is fairly unique in that it actually uses the math to accomplish big things. My favorite examples come from Chapter 3 "Linear Methods for Regression." The standard treatments of these methods depend heavily on respectful memorization of regurgitation of original iterative procedure definitions of the various regression methods. In such a standard formulation two regression methods are different if they have superficially different steps or if different citation/priority histories. The Elements of Statistical Learning instead derives the stopping conditions of each method and considers methods the same if they generate the same solution (regardless of how they claim they do it) and compares consequences and results of different methods. This hard use of isomorphism allows amazing results such as Figure 3.15 (which shows how Least Angle Regression differs from Lasso regression, not just in algorithm description or history: but by picking different models from the same data) and section 3.5.2 (which can separate Partial Least Squares' design CLAIM of fixing the x-dominance found in principle components analysis from how effective it actually is as fixing such problems).
The biggest issue is who is the book for? This is a mathy book emphasizing deep understanding over mere implementation. Unlike some lesser machine learning books the math is not there for appearances or mere intimidating typesetting: it is there to allow the authors to organize many methods into a smaller number of consistent themes. So I would say the book is for researchers and machine algorithm developers. If you have a specific issue that is making inference difficult you may find the solution in this book. This is good for researchers but probably off-putting for tinkers (as this book likely has methods superior to their current favorite new idea). The interested student will also benefit from this book, the derivations are done well so you learn a lot by working through them.
Finally- don't buy the kindle version, but the print book. This book is satisfying deep reading and you will want the advantages of the printed page (and Amazon's issues in conversion are certainly not the authors' fault).
I have a PhD in Math and more particularly stochastic processes and everytime I open this book I can't quite understand what the content is.
I've read other Machine Learning books with the same level of math and abstraction and did not encounter this difficulty. 'Boosting' by Schapire and Freund for instance.
The flow of the text is hard to follow. A lot of Jargon is used assuming the reader is already familiar with the notions exposed.
I think I will end up making sense of the text only after I've been working in the field for many years.
1. The book uses bolded text to refer to matrices - this text is not in bold on the Kindle (iPad version) which makes it extremely hard to follow the notation.
2. There is a table reference that is "table error" instead of a figure number.
3. Instead of the symbol for real numbers the Kindle book displays IR.
4. The equations and mathematical symbols haven't been typeset using LaTeX; instead they are low resolution scanned images presumably taken from the paper version of the book. These are readable but fuzzy - not what you expect from a $70 ebook.
The authors are the real thing (Tibshirani is the one behind the LASSO regularization technique).
Uses some mathematical statistics without the burdens of measure theory and avoids the obvious but complicated proofs.
I own two copies of this edition, one for the office, one for my house, and the authors generously provide the PDF for travelers like me.
In addition the bound copy, I have the PDF from Hastie's site on my kindle, but for a reference as good as this one, having the bound version is absolutely worth the money. When I'm reading a math text, I tend to have one finger in a previous section, so I can refer back to definitions when I need to, and one finger in the index, which just isn't possible with the PDF / Kindle edition.