000			03080cam a22003255a 4500
999			_c51411 _d52958
003			ISURa
008			081106s2009 nyua b 001 0 eng
020			_a9780387848570
020			_a9780387848587
041			_aEnglish
082	0	0	_a006.3122 _2HAS
100	1		_aHastie, Trevor _972279
245	1	4	_aThe elements of statistical learning _bdata mining, inference, and prediction
250			_a2nd ed.
260			_aNew York, NY : _bSpringer, _c2009.
300			_axxii, 745 p. : _c25 cm.
440			_aSpringer series in statistics. _972280
500			_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
520			_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. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates."--Publisher's description.
650		0	_aMachine learning. _969133
650		0	_aStatistics _xMethodology. _972281
650		0	_aData mining. _929634
650		0	_aBioinformatics. _94946
650		0	_aInference. _972282
650		0	_aForecasting. _9376
650		0	_aComputational intelligence. _91589
700	1		_aTibshirani, Robert _972283
700	1		_aFriedman, J. H. _972284
942			_2ddc _cLN