國立虎尾科技大學 |

An Introduction to Statistical Learning = with Applications in R /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	An Introduction to Statistical Learning/ by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
Reminder of title:	with Applications in R /
Author:	James, Gareth.
other author:	Witten, Daniela.
Description:	XV, 607 p. 191 illus., 182 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistics . -
Online resource:	https://doi.org/10.1007/978-1-0716-1418-1
ISBN:	9781071614181

An Introduction to Statistical Learning = with Applications in R /
James, Gareth.

An Introduction to Statistical Learningwith Applications in R /[electronic resource] :by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. - 2nd ed. 2021. - XV, 607 p. 191 illus., 182 illus. in color.online resource. - Springer Texts in Statistics,2197-4136. - Springer Texts in Statistics,.

Preface -- 1 Introduction -- 2 Statistical Learning -- 3 Linear Regression -- 4 Classification -- 5 Resampling Methods -- 6 Linear Model Selection and Regularization -- 7 Moving Beyond Linearity -- 8 Tree-Based Methods -- 9 Support Vector Machines -- 10 Deep Learning -- 11 Survival Analysis and Censored Data -- 12 Unsupervised Learning -- 13 Multiple Testing -- Index.

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, deep learning, survival analysis, multiple testing, 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. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

ISBN: 9781071614181

Standard No.: 10.1007/978-1-0716-1418-1doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

An Introduction to Statistical Learning = with Applications in R /
LDR:03713nam a22004095i 4500 001 1050765
003 DE-He213
005 20210811115030.0
007 cr nn 008mamaa
008 220103s2021 xxu| s |||| 0|eng d
020 $a 9781071614181 $9 978-1-0716-1418-1
024 7 $a 10.1007/978-1-0716-1418-1 $2 doi
035 $a 978-1-0716-1418-1
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
100 1 $a James, Gareth. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1077322
245 1 3 $a An Introduction to Statistical Learning $h [electronic resource] : $b with Applications in R / $c by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
250 $a 2nd ed. 2021.
264 1 $a New York, NY : $b Springer US : $b Imprint: Springer, $c 2021.
300 $a XV, 607 p. 191 illus., 182 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Texts in Statistics, $x 2197-4136
505 0 $a Preface -- 1 Introduction -- 2 Statistical Learning -- 3 Linear Regression -- 4 Classification -- 5 Resampling Methods -- 6 Linear Model Selection and Regularization -- 7 Moving Beyond Linearity -- 8 Tree-Based Methods -- 9 Support Vector Machines -- 10 Deep Learning -- 11 Survival Analysis and Censored Data -- 12 Unsupervised Learning -- 13 Multiple Testing -- Index.
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, deep learning, survival analysis, multiple testing, 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. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
650 0 $a Statistics . $3 1253516
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Statistics and Computing/Statistics Programs. $3 669775
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Statistics, general. $3 671463
700 1 $a Witten, Daniela. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1355152
700 1 $a Hastie, Trevor. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 898195
700 1 $a Tibshirani, Robert. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 898196
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781071614174
776 0 8 $i Printed edition: $z 9781071614198
776 0 8 $i Printed edition: $z 9781071614204
830 0 $a Springer Texts in Statistics, $x 1431-875X $3 1257998
856 4 0 $u https://doi.org/10.1007/978-1-0716-1418-1
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)