000			03456cam a22004937i 4500
001			17673262
005			20240319104111.0
008			130326t20132013nyua 001 0 eng d
010			_a 2013936251
020			_a9781461471370 (acidfree paper)
020			_a1461471370 (acidfree paper)
020			_z9781461471387 (eBook)
020			_z1461471389 (eBook)
035			_a(OCoLC)ocn828488009
040			_aBTCTA _beng _cBTCTA _dYDXCP _dOHX _erda _dVTU _dIQU _dCDX _dSINIE _dDLC
042			_alccopycat
050	0	0	_aQA276 _b.I585 2013
072		7	_aQA _2lcco
082	0	4	_a519.5 _223
245	0	3	_aAn introduction to statistical learning _b: with applications in R _c/ Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
246	3	0	_aStatistical learning
264		1	_aNew York : _bSpringer, _c[2013]
264		4	_c©2013
300			_axvi, 426 pages : _billustrations (some color) ; _c24 cm.
490	1		_aSpringer texts in statistics, _x1431-875X ; _v103
500			_aIncludes index.
505			_aStatistical Learning — Linear Regression — Classification — Resampling Methods — Linear Model Selection and Regularization — Moving Beyond Linearity — Tree-Based Methods — Support Vector Machines — 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. 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.
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.
653			_aR software _adata mining _ainference
653			_astatistical learning
700	1		_aJames, Gareth, _eauthor.
700	1		_aWitten, Daniela, _eauthor.
700	1		_aHastie, Trevor, _eauthor.
700	1		_aTibshirani, Robert, _eauthor.
830		0	_aSpringer texts in statistics ; _v103.
942			_2lcc
999			_c3677 _d3677
041			_aEnglish