000			02693nam a22003017a 4500
999			_c51405 _d52952
003			ISURa
008			190524b xxu||||| |||| 00| 0 eng d
020			_a9781461471370
020			_a9781461471387
041			_aEnglish
082			_a519.5 _bANI
100			_aJames, Gareth _972254
245			_aAn introduction to statistical learning _bwith applications in R
260			_aNew York, NY _b Springer, _c2013.
300			_aXiv, 426 p. _bill. _c24 cm.
440			_aSpringer texts in statistics, 103. _972255
500			_a Introduction -- Statistical 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			_aMathematical statistics. _920830
650			_a R (Computer program language) _925134
650			_aStatistics as Topic. _972256
650			_aMathematical models _95773
650			_aStatistics _95598
700			_aTrevor, Hastie _972257
700			_aRobert, Tibshirani _972258
700			_aGareth, James _972259
942			_2ddc _cLN