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017	7	0	_a10.1007/978-0-387-84858-7 _2DOI
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200	1		_a˜The œelements of statistical learning _edata mining, inference, and prediction _fTrevor Hastie, Robert Tibshirani, Jerome Friedman
205			_aSecond edition
214		0	_aNew York, NY _cSpringer New York
214		2	_aCham _cSpringer Nature _d[20..]
225	0		_aSpringer Series in Statistics _x2197-568X
320			_aBibliogr. p. [699]-727 de l'édition imprimée Index
327	1		_aOverview of Supervised Learning _aLinear Methods for Regression _aLinear Methods for Classification _aBasis Expansions and Regularization _aKernel Smoothing Methods _aModel Assessment and Selection _aModel Inference and Averaging _aAdditive Models, Trees, and Related Methods _aBoosting and Additive Trees _aNeural Networks _aSupport Vector Machines and Flexible Discriminants _aPrototype Methods and Nearest-Neighbors _aUnsupervised Learning _aRandom Forests _aEnsemble Learning _aUndirected Graphical Models _aHigh-Dimensional Problems: p ? N
330			_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. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting
371	0		_aAccès en ligne pour les établissements français bénéficiaires des licences nationales
371	0		_aAccès soumis à abonnement pour tout autre établissement
371	1		_aConditions particulières de réutilisation pour les bénéficiaires des licences nationales _chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017
410			_0161243738 _tSpringer series in statistics (Internet) _x2197-568X
452			_0159033705 _t˜The œelements of statistical learning _odata mining, inference, and prediction _fTrevor Hastie, Robert Tibshirani, Jerome Friedman _e2nd edition, corrected at 5th printing _p1 vol. (XXII- 745 p.) _sSpringer series in statistics
452			_t˜The œElements of Statistical Learning _bTexte imprimé _y9780387848846
452			_t˜The œElements of Statistical Learning _bTexte imprimé _y9780387848570
452			_t˜The œElements of Statistical Learning _bTexte imprimé _y9781071621226
606			_3028627008 _aStatistiques _2rameau
606			_3035198222 _aExploration de données _2rameau
606			_3027940373 _aApprentissage automatique _2rameau
606			_3027234541 _aIntelligence artificielle _2rameau
606	2		_aArtificial Intelligence (incl. Robotics) _2lc
606	2		_aStatistical Theory and Methods _2lc
606	2		_aComputational Biology/Bioinformatics _2lc
606	2		_aComputer Appl. in Life Sciences _2lc
606	2		_aStatistics for Engineering, Physics, Computer Science, Chemistry & Geosciences _2lc
606	2		_aData Mining and Knowledge Discovery _2lc
606			_aComputer science _2lc
606			_aMathematical statistics _2lc
606			_aDistribution (Probability theory. _2lc
606			_aBiology _xData processing. _2lc
606			_aComputational biology _2lc
606			_aProbabilities _2lc
606			_aStatistics _2lc
606			_aArtificial intelligence _2lc
606			_aData mining _2lc
606			_aBioinformatics _2lc
606			_aBioinformatics _2lc
615			_a@Mathematics and Statistics _n11649 _2Springer
676			_a006.3 _v23
680			_aQ334-342
680			_aTJ210.2-211.495
686			_a68Q87 _c2000 _2msc
686			_a62B15 _c2000 _2msc
700		1	_3059538473 _aHastie _bTrevor J. _f1953-.... _cmathématicien _4070
701		1	_3032895321 _aTibshirani _bRobert John _f1956-.... _cbiostatisticien _4070
701		1	_3063807998 _aFriedman _bJerome H. _f1939-.... _4070
702		1	_3032895321 _aTibshirani _bRobert John _f1956-.... _cbiostatisticien _4070
702		1	_3063807998 _aFriedman _bJerome H. _f1939-.... _4070
856	4		_qPDF _uhttps://doi.org/10.1007/978-0-387-84858-7 _zAccès sur la plateforme de l'éditeur
856	4		_uhttps://revue-sommaire.istex.fr/ark:/67375/8Q1-1RXNF8M1-C _zAccès sur la plateforme Istex