Tools

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003 OCoLC;

005 20141212050059.0;

008 090130s2009 nyua b 001 0 eng ;

020 a| 9780387848570;

020 a| 0387848576;

029 1 a| OHXb| har080114565;

029 1 a| AU@b| 000043975105;

029 1 a| NLGGCb| 318598388;

029 1 a| NZ1b| 12859116;

029 1 a| NZ1b| 12879163;

035 a| (Sirsi) o300478243;

035 a| (OCoLC)300478243;

049 a| EREE;

050 4 a| Q325.75b| .H37 2009;

082 04 a| 006.3'1 222| 22;

100 1 a| Hastie, Trevor.=| ^A313058;

245 14 a| The elements of statistical learning :b| data mining, inference, and prediction /c| Trevor Hastie, Robert Tibshirani, Jerome Friedman.;

250 a| 2nd ed.;

300 a| xxii, 745 pages :b| illustrations ;c| 24 cm.;

336 a| text2| rdacontent;

337 a| unmediated2| rdamedia;

338 a| volume2| rdacarrier;

490 1 a| Springer series in statistics;

504 a| Includes bibliographical references and indexes.;

505 0 a| 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.;

520 1 a| "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."--BOOK JACKET.;

650 0 a| Supervised learning (Machine learning)=| ^A719462;

650 7 a| Estatística computacional.2| larpcal;

650 7 a| Estatística.2| larpcal;

650 7 a| Mineração de dados.2| larpcal;

650 7 a| Inferência estatística.2| larpcal;

700 1 a| Tibshirani, Robert.=| ^A329964;

700 1 a| Friedman, J. H.q| (Jerome H.)=| ^A364345;

856 41 3| e-printu| http://www-stat.stanford.edu/~tibs/ElemStatLearn/;

830 0 a| Springer series in statistics.=| ^A236188;

994 a| C0b| ERE;

938 a| YBP Library Servicesb| YANKn| 2961327;

938 a| Coutts Information Servicesb| COUTn| 9269713c| 89.95 USD;

938 a| Blackwell Book Serviceb| BBUSn| R0768656c| $89.95;

938 a| Otto Harrassowitzb| HARRn| har080114565;

596 a| 1;

998 a| 2191580;

999 a| Q325.75 .H37 2009w| LCc| 1i| 30372017287985d| 4/27/2026e| 1/29/2026f| 8/20/2025g| 5l| JGESm| JOYNERn| 26r| Ys| Yt| JGESBKu| 5/3/2010x| BOOKz| JSTACKSo| .STAFF. jmpl;

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

Author/creator	Hastie, Trevor
Other author	Tibshirani, Robert.
Other author	Friedman, J. H. (Jerome H.)
Format	Book
Edition	2nd ed.
Publication Info	New York : Springer, ©2009.
Description	xxii, 745 pages : illustrations ; 24 cm.
Supplemental Content	e-print
Subjects	Supervised learning (Machine learning)

Series	Springer series in statistics Springer series in statistics. ^A236188
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.
Review	"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."--BOOK JACKET.
Bibliography note	Includes bibliographical references and indexes.
ISBN	9780387848570
ISBN	0387848576

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

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