000			03355nam a2200337 i 4500
003			MIUC
005			20200219142835.0
008			170908s2009 nyua 001 | eng
020			_a9780387848570
040			_aMIUC _beng _cMIUC
082	0		_a519.5
100	1		_93158 _aHastie, Trevor
245	1	4	_aThe elements of statistical learning : _bdata mining, inference, and prediction / _cTrevor Hastie, Robert Tibshirani, Jerome Friedman.
250			_a2nd ed.
260			_aNew York : _bSpringer, _c2009.
300			_a745 p. : _bill. b&w and col. ; _c25 cm.
336			_2rdacontent _atext
490	1		_aSpringer series in statistics, _x01727397 ; _v692
504			_aIncludes bibliographical references (p. [699]-727) and indexes.
505	0		_aCh. 1. Introduction -- Ch. 2. Overview of supervised learning -- Ch. 3. Linear method for regression -- Ch. 4. Linear methods for classification -- Ch. 5. Basis expansions and regularization -- Ch. 6 Kernel smoothing methods -- Ch. 7. Model assessment and selection -- Ch. 8. Model inference and averaging -- Ch. 9. Additive model, trees and related methods -- Ch. 10. Boosting and additive trees -- Ch. 11. Neural networks -- Ch. 12. Support vector machines and flexible discriminants -- Ch. 13. Prototype methods and nearest-neighbors -- Ch. 14. Unsupervised learning -- Ch. 15. Random forests -- Ch. 16. Ensemble learning -- Ch. 17. Undirected graphical models -- Ch. 18. High-dimensional problems: p >> N.
520			_aDuring 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.
650		0	_9555 _aStatistics
650		0	_93159 _aMathematical statistics
650		0	_9794 _aData mining
650		0	_93160 _aBioinformatics
650		0	_93161 _aComputational intelligence
700	1		_4aut _93162 _aTibshirani, Robert
700	1		_4aut _93163 _aFriedman, J. H. _q(Jerome H.)
830		0	_93164 _aSpringer texts in statistics _v692
942			_2ddc _cBK