000			02004cam a22003855a 4500
001			15512507
003			ZET-ke
005			20230724053539.0
008			081106s2009 nyua b 001 0 eng
010			_a 2008941148
020			_a9780387848570
040			_aDLC _cDLC _dZET-ke _beng
050	0	0	_aQ325.75 _b.H37 2009
082	0	0	_a006.3/1 _222
100	1		_aHastie, Trevor. _eAuthor
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, NY : _bSpringer, _c2017.
300			_axxii, 745 p. : _bill. (some col.) ; _c25 cm.
490	0		_aSpringer series in statistics, _x0172-7397
504			_aIncludes bibliographical references (p. [699]-727) and indexes.
505			_aOverview of supervised learning -- Linear methods for regression -- Linear methods for classification -- Basic expansions and regularization -- Kernel smoothing methods -- Mode;s assessment and selection -- Model assessment and selection -- Additive models,trees , and related methods --Boosting and additive trees -- Natural networks -- Support vector machines and flexible discriminants -- Prototype methods and nearest- neighbors Unsupervised learning -- Random forests -- Ensemble learning -- Undirected graphic models - High- dimensional problems : p>>n
650		0	_aMachine learning.
650		0	_aStatistics _xMethodology.
650		0	_aData mining. _9201
650		0	_aBioinformatics.
650		0	_aInference.
650		0	_aForecasting.
650		0	_aComputational intelligence.
700	1		_aTibshirani, Robert.
700	1		_aFriedman, J. H. _q(Jerome H.)
906			_a7 _bcbc _corignew _d2 _eepcn _f20 _gy-gencatlg
942			_2lcc _cBK _hQ325.75 _i.H37 2017 _kQ325.75 _m.H37 2017
999			_c4943 _d4943