000			03473nam a22004817a 4500
001			sulb0079947
003			BD-SySUS
005			20241007182806.0
007			cr nn 008mamaa
008			130625s2013 xxu| s |||| 0|eng d
020			_a9781461471387
024	7		_a10.1007/978-1-4614-7138-7 _2doi
040			_aBD-SySUS _cBD-SySUS
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223 _bJAI
100	1		_aJames, Gareth. _eauthor. _967913
245	1	3	_aAn Introduction to Statistical Learning _bwith Applications in R / _cby Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aXIV, 426 p. 150 illus., 146 illus. in color.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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 -- Index.
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	_aStatistics. _967914
650		0	_aArtificial intelligence. _967915
650	1	4	_aStatistics. _967916
650	2	4	_aStatistical Theory and Methods. _967917
650	2	4	_aStatistics and Computing/Statistics Programs. _967918
650	2	4	_aArtificial Intelligence (incl. Robotics). _967919
650	2	4	_aStatistics, general. _967920
700	1		_aWitten, Daniela. _eauthor. _967921
700	1		_aHastie, Trevor. _eauthor. _967922
700	1		_aTibshirani, Robert. _eauthor. _967923
710	2		_aSpringerLink (Online service) _967924
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461471370
942			_2ddc _cBK
999			_c86446 _d86446