000			04319cam a2200613 i 4500
001			u11871
003			SA-PMU
005			20210418124049.0
008			160120t20152013nyua 001 0 eng c
040			_aOCC _beng _erda _cOCC _dOSU _dOCLCF _dUSU _dTSC
020			_a9781461471370
020			_a1461471370
035			_a(OCoLC)935355844
042			_apcc
050		4	_aQA276 _b.I58 2015
060		4	_aQA276 _b.I58 2015
082	0		_a519.5 _223
245	0	3	_aAn introduction to statistical learning : _bwith applications in R / _cGareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
246	3	0	_aStatistical learning
250			_a[Corrected at 6th printing 2015].
264		1	_aNew York : _bSpringer : _bSpringer Science+Business Media, _c2015.
264		4	_c©2013
300			_axiv, 426 pages : _billustrations (chiefly color) ; _c25 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aSpringer texts in statistics, _x1431-875X ; _v103
500			_aIncludes index.
505	0		_aIntroduction -- Statistical learning -- Linear regression -- Classification -- Resampling methods -- Linear model selection and regularization -- Moving beyond linearity -- Tree-based methods -- Support vector machines -- Unsupervised learning.
520			_a"An 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. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. Provides tools for Statistical Learning that are essential for practitioners in science, industry and other fields. Analyses and methods are presented in R. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering. Extensive use of color graphics assist the reader"--Publisher description.
650		0	_aMathematical statistics.
650		0	_aMathematical models.
650		0	_aMathematical statistics _vProblems, exercises, etc.
650		0	_aMathematical models _vProblems, exercises, etc.
650		0	_aR (Computer program language)
650		0	_aStatistics.
650		7	_aMathematical models. _2fast _0(OCoLC)fst01012085
650		7	_aMathematical statistics. _2fast _0(OCoLC)fst01012127
650		7	_aR (Computer program language) _2fast _0(OCoLC)fst01086207
650		7	_aStatistics. _2fast _0(OCoLC)fst01132103
650	1	2	_aModels, Statistical.
650	1	2	_aStatistics as Topic.
655		7	_aProblems and exercises. _2fast _0(OCoLC)fst01423783
700	1		_aJames, Gareth _q(Gareth Michael), _eauthor.
700	1		_aWitten, Daniela, _eauthor.
700	1		_aHastie, Trevor, _eauthor.
700	1		_aTibshirani, Robert, _eauthor.
830		0	_aSpringer texts in statistics.
029	1		_aAU@ _b000057908748
942			_cBOOK
994			_aZ0 _bSUPMU
948			_hNO HOLDINGS IN SUPMU - 36 OTHER HOLDINGS
596			_a1 2
999			_c6018 _d6018