Bild 1 von 5
Galerie
Bild 1 von 5
Ähnlichen Artikel verkaufen?
The Elements of Statistical Learning: Data Mining, Inference, and Prediction...
US $26,89
Ca.CHF 21,41
Artikelzustand:
- Sofort-KaufenThe Elements of Statistical Learning: Data Mining, Inference, and Prediction...
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
Versand:
US $6,72 (ca. CHF 5,35) USPS Media MailTM.
Standort: Altamont, Tennessee, USA
Lieferung:
Lieferung zwischen Fr, 21. Nov und Di, 25. Nov nach 94104 bei heutigem Zahlungseingang
Rücknahme:
Keine Rücknahme.
Zahlungen:
Sicher einkaufen
Info zum Artikel
Der Verkäufer ist für dieses Angebot verantwortlich.
eBay-Artikelnr.:317228185870
Artikelmerkmale
- Artikelzustand
- Release Year
- 2009
- Book Title
- The Elements of Statistical Learning: Data Mining, Inference, ...
- ISBN
- 9780387848570
Über dieses Produkt
Product Identifiers
Publisher
Springer New York
ISBN-10
0387848576
ISBN-13
9780387848570
eBay Product ID (ePID)
69737567
Product Key Features
Number of Pages
Xxii, 745 Pages
Publication Name
Elements of Statistical Learning : Data Mining, Inference, and Prediction
Language
English
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining
Publication Year
2009
Type
Textbook
Subject Area
Mathematics, Computers
Series
Springer Series in Statistics Ser.
Format
Hardcover
Dimensions
Item Height
1.5 in
Item Weight
51.2 Oz
Item Length
9.4 in
Item Width
6.5 in
Additional Product Features
Edition Number
2
Intended Audience
Scholarly & Professional
LCCN
2008-941148
Reviews
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), 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), 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)
Dewey Edition
22
TitleLeading
The
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
Overview of Supervised Learning.- Linear Methods for Regression.- Linear Methods for Classification.- Basis Expansions and Regularization.- Kernel Smoothing Methods.- Model Assessment and Selection.- Model Inference and Averaging.- Additive Models, Trees, and Related Methods.- Boosting and Additive Trees.- Neural Networks.- Support Vector Machines and Flexible Discriminants.- Prototype Methods and Nearest-Neighbors.- Unsupervised Learning.- Random Forests.- Ensemble Learning.- Undirected Graphical Models.- High-Dimensional Problems: p ? N.
Synopsis
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., 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 & 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., 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., 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 & 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.
LC Classification Number
Q334-342
Artikelbeschreibung des Verkäufers
Info zu diesem Verkäufer
eajoh-8791
100% positive Bewertungen•452 Artikel verkauft
Angemeldet als privater VerkäuferDaher finden verbraucherschützende Vorschriften, die sich aus dem EU-Verbraucherrecht ergeben, keine Anwendung. Der eBay-Käuferschutz gilt dennoch für die meisten Käufe.
Verkäuferbewertungen (198)
- l***d (1321)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufWonderful seller! Item as described, was fairly priced, well packaged, and arrived promptly. Communication was wonderful as well. Thanks East! Happy doing business with East.
- n***4 (9)- Bewertung vom Käufer.Letztes JahrBestätigter KaufBook was shipped fast, was packed well to prevent any damage during shipping, arrived in the exact condition described and looks exactly as shown in the photos in the listing. When I made this purchase I had several options from other sellers with listings of the same book who used stock photos so actual condition of the books could not be easily determined. It was the provided photos of the actual book I was buying that made me opt to go with this seller and I'm glad I did.Indian Depredations in Texas by J. W. Wilbarger (1985, Hardcover) (Nr. 315894335831)
- d***u (51)- Bewertung vom Käufer.Letzte 6 MonateBestätigter KaufDVD came exactly as described. With good packaging and prompt shipping. Always appreciate a seller who posts more detailed pictures about they item they sell. Thank you!Naruto Shippuden Uncut Set 23 (DVD) (Nr. 315891194668)
Noch mehr entdecken:
- Masters of the Universe Jugendliche Hörbücher und Hörspiele,
- Erwachsene Masters of the Universe Hörbücher und Hörspiele,
- Masters of the Universe Jugendliche Hörbücher und Hörspiele,
- Erwachsene Masters of the Universe Hörbücher und Hörspiele,
- Masters-of-the-Universe - Europa-Editions Hörbücher und Hörspiele,
- Ungekürzte Masters of the Universe Buchreihe Hörbücher und Hörspiele,
- Masters of the Universe Buchreihe Hörbücher und Hörspiele mit Kinder- & Jugendliteratur,
- Masters of the Universe Buchreihe Hörbücher und Hörspiele mit Kinder- & Jugendliteratur,
- Bücher Sachbuch Fühlbare Elemente,
- Deutsche Bücher Fühlbare Elemente Belletristik