자료유형  

 단행본

 

160411234444

ISBN  

9781461471370 : \111340

DDC  

519.5-21

청구기호  

519 J27a

저자명  

James, Gareth

서명/저자  

An introduction to statistical learning : with applications in R / by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

발행사항  

New York : Springer, 2013

형태사항  

xvi, 426 p : ill. (some color) ; 24cm

총서명  

Springer texts in statistics = 1431-875X ; 103

주기사항  

교수 신청도서

주기사항  

이준표 교수 신청도서, 2016

주기사항  

예술과 과학

주기사항  

Art & Technology

서지주기  

Includes index

초록/해제  

초록: 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, and more. Color graphics and real-world examples are used to illustrate the methods presented.

일반주제명  

Mathematical statistics

일반주제명  

Mathematical models

일반주제명  

Mathematical statistics Problems, exercises, etc

일반주제명  

Mathematical models Problems, exercises, etc

일반주제명  

R (Computer program language)

일반주제명  

Statistics

키워드  

통계

기타저자  

Witten, Daniela

기타저자  

Hastie, Trevor

기타저자  

Tibshirani, Robert

가격  

\100206

Control Number  

sacl:102708

책소개  

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.