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001 978-1-4614-7138-7
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005 20140220082828.0
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008 130625s2013 xxu| s |||| 0|eng d
020 _a9781461471387
_9978-1-4614-7138-7
024 7 _a10.1007/978-1-4614-7138-7
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aJames, Gareth.
_eauthor.
245 1 3 _aAn Introduction to Statistical Learning
_h[electronic resource] :
_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.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Texts in Statistics,
_x1431-875X ;
_v103
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.
650 0 _aMathematical statistics.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aStatistics and Computing/Statistics Programs.
650 2 4 _aTheoretical, Mathematical and Computational Physics.
650 2 4 _aStatistics, general.
700 1 _aWitten, Daniela.
_eauthor.
700 1 _aHastie, Trevor.
_eauthor.
700 1 _aTibshirani, Robert.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461471370
830 0 _aSpringer Texts in Statistics,
_x1431-875X ;
_v103
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-7138-7
912 _aZDB-2-SMA
999 _c95854
_d95854