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

By:

Hastie, Trevor, 1953- [VerfasserIn]

Contributor(s):

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Springer series in statisticsPublisher: New York, NY : Springer, [2009]Copyright date: © 2009Edition: Second editionDescription: 1 Online-Ressource (xxii, 745 Seiten) : IllustrationenISBN:

0387848584
9780387848587

Subject(s):

Additional physical formats: 0387848576 | 9780387848570 | Erscheint auch als: The elements of statistical learning. Druck-Ausgabe Second edition. New York, NY : Springer, 2009. xxii, 745 SeitenMSC: MSC: *62-01 | 62C99 | 68T05 | 68T99RVK: RVK: CM 4000 | QH 231 | SK 840 | ST 530 | SK 830LOC classification:

Q325.75

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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."--JacketSummary: 1. Introduction2. 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.PPN: PPN: 802948154Package identifier: Produktsigel: ZDB-4-NLEBK

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