000			01891nam a2200301 i 4500
003			MIUC
005			20200219142858.0
008			170905s2013 nyua 001 | eng
020			_a9781461471370
040			_aMIUC _beng _cMIUC
082	0		_a519.5
245	0	3	_aAn introduction to statistical learning : _bwith applications in R / _cGareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
260			_aNew York : _bSpringer, _c2013.
300			_axvi, 426 p. : _bill. b&w and col. _c24 cm.
336			_2rdacontent _atext
490	1		_aSpringer texts in statistics, _x1431875X ; _v417
500			_aIncludes index.
505	0		_aCh. 1 Introduction -- Ch. 2 Statistical learning -- Ch. 3 Linear regression -- Ch. 4 Classification -- Ch. 5 Resampling methods -- Ch. 6 Linear model selection and regularization -- Ch. 7 Moving beyond linearity -- Ch. 8 Tree-based methods -- Ch. 9 Support vector machines -- Ch. 10 Unsupervised learning.
520			_aAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.
650		0	_93159 _aMathematical statistics
650		0	_9555 _aStatistics
700	1		_4aut _93173 _aJames, Gareth _q(Gareth Michael)
700	1		_4aut _93174 _aWitten, Daniela
700	1		_4aut _93158 _aHastie, Trevor
700	1		_4aut _93162 _aTibshirani, Robert
830		0	_93164 _aSpringer texts in statistics _v417
942			_2ddc _cBK