Elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani & Jerome Friedman.
Material type:
TextLanguage: English Series: Springer series in statistics: Publisher: New York : Springer, 2009Edition: 2nd edn, corrected 7th printingDescription: xxii, 745 p. : color illustrationsISBN: 9780387848570; 0387848576Subject(s): Supervised learning (Machine learning) | Electronic data processing | Statistics | Biology -- Data processing | Computational biology | Mathematics -- Data processing | Data mining | Computer Science -- IT | COMPUTERS -- Database Management -- Data Mining | Supervised learning (Machine learning) | Supervised learning (Machine learning) | Electronic data processing | Statistics | Biology -- Data processing | Computational biology | Mathematics -- Data processing | Data mining | Machine-learning | Datamining | Prognoses | Estatistica computacional | Estatistica | Mineracao de dados | Inferencia estatistica | Maschinelles Lernen | Statistik | Maschinelles Lernen | Statistik | Statistics as Topic | Computational Biology | Mathematical Computing | Data MiningGenre/Form: Electronic books.Additional physical formats: Print version:: Elements of statistical learning.DDC classification: 519.5 Online resources: ebrary | EBSCOhost | MyiLibrary | SpringerLink | Detailed table of contents | MyiLibrary, Table of contents | Item type | Current location | Call number | Status | Date due | Barcode |
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Reference
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GKVK Library | 519.5 HAS (Browse shelf) | Not for loan (Restricted Access) | 143404 |
Second edition corrected at 7th printing in 2013.
Includes bibliographical references (pages 699-727) and indexes.
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.
"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.
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