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EBOOK
Author Hastie, Trevor,
Title The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman.
Imprint New York : Springer, [2009]
2009
Edition Second edition, corrected 7th printing.

LOCATION CALL # STATUS MESSAGE
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LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
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 OHIOLINK OPEN TEXTBOOK LIBRARY    ONLINE  
View online
Author Hastie, Trevor,
Series Springer series in statistics, 0172-7397
Springer series in statistics. 0172-7397
Subject Supervised learning (Machine learning)
Electronic data processing.
Statistics.
Biology -- Data processing.
Computational biology.
Mathematics -- Data processing.
Data mining.
Statistics as Topic.
Computational Biology.
Mathematical Computing.
Data Mining.
Alt Name Tibshirani, Robert,
Friedman, J. H. (Jerome H.),
Description 1 online resource (xxii, 745 pages) : color illustrations
Edition Second edition, corrected 7th printing.
Note Second edition corrected at 7th printing in 2013.
Bibliography Note Includes bibliographical references (pages 699-727) and indexes.
Contents 1. Introduction -- 2. Overview of supervised learning -- 3. Linear methods for regression -- 4. Linear methods for classification -- 5. Basis expansions and regularization -- 6. Kernel smoothing methods -- 7. Model assessment and selection -- 8. Model inference and averaging -- 9. Additive models, trees, and related methods -- 10. Boosting and additive trees -- 11. Neural networks -- 12. Support vector machines and flexible discriminants -- 13. Prototype methods and nearest-neighbors -- 14. Unsupervised learning -- 15. Random forests -- 16. Ensemble learning -- 17. Undirected graphical models -- 18. High-dimensional problems: p>> N.
Summary "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."--Jacket.
Note Online resource and print version record. SpringerLink (viewed September 29, 2014).
ISBN 9780387848587 (electronic bk.)
0387848584 (electronic bk.)
9781282126749 (electronic bk.)
1282126741 (electronic bk.)
9780387848570 (print)
0387848576 (print)
9780387848846 (paperback)
0387848843 (paperback)
ISBN/ISSN 9786612126741
OCLC # 405547558
Additional Format Print version: Hastie, Trevor. Elements of statistical learning. 2nd ed. New York : Springer, 2009 9780387848570 0387848576 (OCoLC)300478243



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