Title The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman

Edition Second edition, corrected 7th printing Published New York : Springer, [2009] ©2009

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Description 1 online resource (xxii, 745 pages) : color illustrations Series Springer series in statistics, 0172-7397 Springer series in statistics. 0172-7397 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 Notes Second edition corrected at 7th printing in 2013 Bibliography Includes bibliographical references (pages 699-727) and indexes Notes Online resource and print version record. SpringerLink (viewed September 29, 2014) Subject Supervised learning (Machine learning) Electronic data processing. Statistics. Biology -- Data processing. Computational biology. Mathematics -- Data processing. Data mining. Bioinformatics. Statistics as Topic Computational Biology Mathematical Computing Data Mining statistics. COMPUTERS -- Database Management -- Data Mining. Supervised learning (Machine learning) Bioinformatics Supervised learning (Machine learning) Electronic data processing Statistics Biology -- Data processing Computational biology Mathematics -- Data processing Data mining Maschinelles Lernen Statistik Machine-learning. Datamining. Prognoses. Estatística computacional. Estatística. Mineração de dados. Inferência estatística. Form Electronic book Author Tibshirani, Robert, author Friedman, J. H. (Jerome H.), author ISBN 9780387848587 0387848584 9781282126749 1282126741

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