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.
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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)About the Author:
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.
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Descrizione libro Springer, 2011. Soft cover. Condizione libro: New. Condizione sovraccoperta: New. International Edition. **INTERNATIONAL EDITION** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments go through via USPS/UPS/DHL with tracking numbers. Great professional textbook selling experience and expedite shipping service. Codice libro della libreria ABE-14952918404
Descrizione libro U.S.A.: Springer, 2011. Soft cover. Condizione libro: New. Condizione sovraccoperta: New. International Edition. Low price guarantee! The book is the brand new international edition textbook with the different ISBN and cover design. The book main content black/white printed in full English as same as the corresponding original US edition. Fast shipments will sent out by DHL/UPS or standard post mail with tracking numbers in one to two working days after the orders confirmed. Codice libro della libreria ABE-1473071536726
Descrizione libro U.S.A.: Springer, 2011. Hardcover. Condizione libro: New. Condizione sovraccoperta: New. 2nd Edition. BRAND NEW!.NO CD ROM & NO ACCESS CODE. BOOK SHIP ON SAME DAY!. Codice libro della libreria ABE-1476786816940
Descrizione libro Condizione libro: New. Codice libro della libreria 6089443-n
Descrizione libro Condizione libro: New. New. US edition. Perfect condition. Ship by express service to USA, Canada, Australia, France, Italy, UK, Germany and Netherland. Customer satisfaction our priority. Codice libro della libreria ABE-190516-24977
Descrizione libro Condizione libro: Brand New. New. US edition. Customer Satisfaction guaranteed!!. Codice libro della libreria SHAK24977
Descrizione libro Condizione libro: Brand New. Ships from multiple locations. FedEx or DHL 4-6 business days delivery to your doorstep. Codice libro della libreria 20JULY16APP-13058
Descrizione libro Springer-Verlag New York Inc. 2009-02-09, New York, NY, 2009. hardback. Condizione libro: New. Codice libro della libreria 9780387848570
Descrizione libro Springer, 2011. Condizione libro: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: Introduction.- 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. Codice libro della libreria ABE_book_new_0387848576
Descrizione libro Condizione libro: Brand New. Brand New Original US Edition, Perfect Condition. Printed in English. Excellent Quality, Service and customer satisfaction guaranteed!. Codice libro della libreria AIND-93303