000			01870nmm a22003737a 4500
003			SPU
005			20210629161032.0
008			210517b2009 nyu|||||o|||| 00| 0 eng d
020			_a9780387848587 (E-book)
040			_aSPU
049			_amain
050	0	0	_aQ 325.5 _bH37E 2009
100			_aHastie, Trevor _9238058
245	1	4	_aThe elements of statistical learning : _bdata mining, inference, and prediction / _cTrevor Hastie, Robert Tibshirani, Jerome Friedman. _h[electronic resource]
250			_aSecond edition
260			_aNew York, : _bSpringer, _c2009
300			_a1 online resource : _billustrations
449			_a140501
504			_aIncludes bibliographical references (p. [699]-727) and index
505			_aIntroduction -- Overview of Supervised Learning -- Linear Methods for Regression -- Linear Methods for Classification -- Basis Expansions and Regularization -- Kernel Smoothing Methods -- Model Assessment and Selection -- Model Inference and Averaging -- Additive Models, Trees, and Related Methods -- Boosting and Additive Trees -- Neural Networks -- Support Vector Machines and Flexible Discriminants -- Prototype Methods and Nearest-Neighbors -- Unsupervised Learning -- Random Forests -- Ensemble Learning -- Undirected Graphical Models -- High-Dimensional Problems: p ≫ N
650		0	_aMACHINE LEARNING _954523
650		0	_aSTATISTICS _xMETHODOLOGY _9168993
650		0	_aDATA MINING _954308
650		0	_aBIOINFORMATICS _957219
650		0	_aINFERENCE _9217618
650		0	_aFORECASTING _943905
650		0	_aCOMPUTATIONAL INTELLIGENCE _969461
700			_aTibshirani, Robert _9238059
700			_aFriedman, J. H. _9242530
850			_aSPU
856			_uhttps://drive.google.com/file/d/1cS9-MrVnAYrSKjEGrGQRSd1fqT0uW1J2/view?usp=sharing _yView Full-text
910			_aLibrary _bSpringer _c170521
942			_2lcc _cEBK
999			_c201764