imusti The Elements Of Statistical Learning: Data Mining, Inference, And Prediction, Second Edition (springer Series In Statistics) - Hardcover

Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome

Hardcover

ISBN 10: 0387848576 ISBN 13: 9780387848570

Verlag: Springer-Verlag New York Inc., 2009

Inhaltsangabe

<P>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.</P> <P>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.</P>

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Críticas

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.

Über diese Auflage

• Verlag: Springer-Verlag New York Inc.
• Erscheinungsdatum: 2009
• ISBN 10: 0387848576
• ISBN 13: 9780387848570
• Einband: Tapa dura
• Auflage: 2
• Anzahl der Seiten: 745