The Elements of Statistical Learning und über 1,5 Millionen weitere Bücher verfügbar für Amazon Kindle. Erfahren Sie mehr
EUR 73,00
  • Statt: EUR 74,85
  • Sie sparen: EUR 1,85 (2%)
  • Alle Preisangaben inkl. MwSt.
Nur noch 2 auf Lager (mehr ist unterwegs).
Verkauf und Versand durch Amazon.
Geschenkverpackung verfügbar.
Menge:1
The Elements of Statistic... ist in Ihrem Einkaufwagen hinzugefügt worden
Ihren Artikel jetzt
eintauschen und
EUR 26,25 Gutschein erhalten.
Möchten Sie verkaufen?
Zur Rückseite klappen Zur Vorderseite klappen
Anhören Wird wiedergegeben... Angehalten   Sie hören eine Probe der Audible-Audioausgabe.
Weitere Informationen
Alle 2 Bilder anzeigen

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) (Englisch) Gebundene Ausgabe – 23. Dezember 2011


Alle 2 Formate und Ausgaben anzeigen Andere Formate und Ausgaben ausblenden
Amazon-Preis Neu ab Gebraucht ab
Kindle Edition
"Bitte wiederholen"
Gebundene Ausgabe
"Bitte wiederholen"
EUR 73,00
EUR 69,96 EUR 75,15
44 neu ab EUR 69,96 5 gebraucht ab EUR 75,15

Hinweise und Aktionen

  • Beim Kauf von Produkten ab 40 EUR erhalten Sie eine E-Mail mit einem 10 EUR Gutscheincode, einlösbar auf ausgewählte Premium-Beauty-Produkte. Diese Aktion gilt nur für Produkte mit Verkauf und Versand durch Amazon.de. Für weitere Informationen zur Aktion bitte hier klicken.

  • Sparpaket: 3 Hörbücher für 33 EUR: Entdecken Sie unsere vielseitige Auswahl an reduzierten Hörbüchern und erhalten Sie 3 Hörbücher Ihrer Wahl für 33 EUR. Klicken Sie hier, um direkt zur Aktion zu gelangen.


Wird oft zusammen gekauft

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) + An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) + Applied Predictive Modeling
Preis für alle drei: EUR 208,60

Die ausgewählten Artikel zusammen kaufen
Jeder kann Kindle Bücher lesen — selbst ohne ein Kindle-Gerät — mit der KOSTENFREIEN Kindle App für Smartphones, Tablets und Computer.


Produktinformation

  • Gebundene Ausgabe: 745 Seiten
  • Verlag: Springer; Auflage: 2nd ed. 2009. Corr. 7th printing 2013 (23. Dezember 2011)
  • Sprache: Englisch
  • ISBN-10: 0387848576
  • ISBN-13: 978-0387848570
  • Größe und/oder Gewicht: 23,4 x 15,7 x 3,8 cm
  • Durchschnittliche Kundenbewertung: 4.2 von 5 Sternen  Alle Rezensionen anzeigen (4 Kundenrezensionen)
  • Amazon Bestseller-Rang: Nr. 39.725 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
  • Komplettes Inhaltsverzeichnis ansehen

Produktbeschreibungen

Pressestimmen

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)

Buchrückseite

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas 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 color 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 and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.

Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.


Welche anderen Artikel kaufen Kunden, nachdem sie diesen Artikel angesehen haben?


In diesem Buch (Mehr dazu)
Ausgewählte Seiten ansehen
Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis
Hier reinlesen und suchen:

Kundenrezensionen

4.2 von 5 Sternen
Sagen Sie Ihre Meinung zu diesem Artikel

Die hilfreichsten Kundenrezensionen

3 von 3 Kunden fanden die folgende Rezension hilfreich Von J. Stephan am 6. Juli 2012
Format: Gebundene Ausgabe
Ich verwende das Buch gelegentlich als "Nachschlagwerk" v.a. wegen seiner Vollstaendigkeit. Die Einfuehrungskapitel sind nicht (sehr) hilfreich. Zum Lernen wuerde ich eindeutig Bishop und/oder Mackay empfehlen.
1 Kommentar War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback. Wenn diese Rezension unangemessen ist, informieren Sie uns bitte darüber.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Von hape am 3. Juli 2014
Format: Gebundene Ausgabe Verifizierter Kauf
Der Hastie ist ein solides Lehrbuch. Er ist sehr gut lesbar und angenehm gestaltet. Insbesondere Format und Typographie sind schöner Latex Satz ohne zu viel Eingriffen von Seiten des Verlages und von daher ist das Buch eine Augenweide.

Ich habe den (Frequentistischen) Hastie als Ergänzung zum Bayesianischen Bishop gekauft und nutze ihn meist ebenso, als Ergänzung. Hastie wählt den konservativen, frequentistischen Ansatz und ist damit näher an der "klassischen" kanonischen Statistik.

Leider finde ich oft die frequentistischen Ansätze an zentraler Stelle nicht motiviert im Hastie. Das erschwert das Nachvollziehen erheblich. Ein ausführliches Verständnis stellt sich dann eher am Ende eines Abschnitts ein, wenn man sieht wohin man mit dem kaum erläuterten Ansatz gekommen ist. Dafür werden die Ergebnisse oft schön erkläutert und erklärt, leider eben fehlt die Motivation am Anfang der Abschnitte oft.

An einigen Stellen ist der Hastie wesentlich verständlicher als der Bishop was auch am frequentistischen Ansatz liegt. Wo Bishop Verteilungen betrachtet, sind die Modelle im Hastie oft etwas simpler und dafür wesentlich leichter durchzurechnen.

Natürlich enthält der Hastie auch viele Algorithmen die Bishop nicht bespricht und andersrum.

Fazit. Der Hastie ist eine gute Einführung.
Kommentar War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback. Wenn diese Rezension unangemessen ist, informieren Sie uns bitte darüber.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Format: Gebundene Ausgabe
The Elements of Statistical Learning (ESL) ist ein Standardwerk für Maschine Learning. Die Autoren sind sehr renommierte Frequentists und sind seit Jahrzehnten Statistik-Professoren an der Stanford University. Im Buch ist dies sehr deutlich zu sehen. Das Buch geht sehr tief in die Materie und erfordert Konzentration und Zeit. Es wird alles behandelt und bewiesen.

Die Mathematikvorkenntnisse sind deswegen relativ hoch. Deswegen wird dieses Buch sehr häufig für ML-Vorlesungen auf Master-Niveau weltweit benutzt und dies zu Recht. Allerdings bleibt ESL in der Theorie und man findet so gut wie keine Anwendungsbeispiele, die man direkt einsetzen kann. Obwohl am Ende jedes Kapitels, Übungen und Fragen zu finden, sind, gibt es leider keine Lösungen. Deswegen ist zum Selbstlernen bedingt geeignet.

Aus den oben genannten Gründen werden viele Leser dem kleinen Bruder „An Introduction to Statistical Learning“ vorziehen. Man kann beide Bücher erwerben und ISL als Hauptliteratur haben, während man ESL als Nachschlagwerk behält.
Kommentar War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback. Wenn diese Rezension unangemessen ist, informieren Sie uns bitte darüber.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen
Von Ein Kunde am 3. März 2015
Format: Gebundene Ausgabe Verifizierter Kauf
das viel abdeckt. Leider in manchen Dingen etwas zu unkonkret, man würde sich teilweise etwas mehr, an anderen Stellen etwas weniger Details, wünschen.
Kommentar War diese Rezension für Sie hilfreich? Ja Nein Feedback senden...
Vielen Dank für Ihr Feedback. Wenn diese Rezension unangemessen ist, informieren Sie uns bitte darüber.
Wir konnten Ihre Stimmabgabe leider nicht speichern. Bitte erneut versuchen

Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)

Amazon.com: 45 Rezensionen
41 von 45 Kunden fanden die folgende Rezension hilfreich
excellent overview, especially for outsiders, ties the field together conceptually 13. April 2011
Von Matthew Grosso - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
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.
46 von 52 Kunden fanden die folgende Rezension hilfreich
Has the most post-its of any book on my shelf 4. April 2009
Von Craig Garvin - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe
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.
13 von 13 Kunden fanden die folgende Rezension hilfreich
Actually does something (huge) with the math 17. Mai 2014
Von John Mount - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
I have been using The Elements of Statistical Learning for years, so it is finally time to try and review it.

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).
98 von 125 Kunden fanden die folgende Rezension hilfreich
Useful research summary; a disaster otherwise 17. Februar 2010
Von SP, ML, Stats - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
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.
14 von 16 Kunden fanden die folgende Rezension hilfreich
my big brown book of statistic learning tools 22. März 2009
Von S. Matthews - Veröffentlicht auf Amazon.com
Format: Gebundene Ausgabe Verifizierter Kauf
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]).
Waren diese Rezensionen hilfreich? Wir wollen von Ihnen hören.