000			02100cam a22003615i 4500
003			EG-CaNGU
005			20241009125007.0
008			230829s2009 ny a fob 001 0 eng d
020			_a9780387848587 _q(online resource)
040			_aDLC _cDLC _dDLC _dEG-CaNGU _beng _erda
100	1		_aHastie, Trevor, _eauthor. _95542
245	1	4	_aThe elements of statistical learning : _bdata mining, inference, and prediction /
250			_aSecond edition.
264		1	_aNew York : _bSpringer Science+Business Media, LLC., Springer Nature, _c[2009]
264		4	_c©2009
300			_a1 online resource (xxii, 745 pages) : _billustrations.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	0		_aSpringer series in statistics, _x2197-568X
504			_aIncludes bibliographical references and indexes.
505	0	0	_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 -- Prototype Methods and Nearest-Neighbors -- Random Forests -- Random Forests -- Random Forests -- High-Dimensional Problems: p N -- References -- Index.
506			_aAvailable on-campus and off-campus.
650		7	_aMachine learning. _2NGU-sh _93897
650		7	_aStatistics _xmethodology. _2NGU-sh _95543
650		7	_aData mining. _2NGU-sh _93604
650		7	_aBioinformatics. _2NGU-sh _971
650		7	_aComputational intelligence. _2NGU-sh _95544
655		7	_aElectronic books _2NGU-sh _91203
700	1		_aTibshirani, Robert, _eauthor. _95545
700	1		_aFriedman, J. H., _q(Jerome H.), _eauthor. _95546
856	4	0	_aOnline resource. _uhttps://link.springer.com/book/10.1007/978-0-387-84858-7 _zOnline resource.
999			_c1931 _d1931