INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
9781461471387
NATIONAL BIBLIOGRAPHY NUMBER
Country Code
IR
Number
EN-50069
LANGUAGE OF THE ITEM
.Language of Text, Soundtrack etc
انگلیسی
COUNTRY OF PUBLICATION OR PRODUCTlON
Country of publication
IR
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
An Introduction to Statistical Learnin
General Material Designation
[Book]
Other Title Information
:with Applications in R
First Statement of Responsibility
/ by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
New York, NY
Name of Publisher, Distributor, etc.
: Springer New York :Imprint: Springer,
Date of Publication, Distribution, etc.
, 2013.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
XIV, 426 p. 150 illus., 146 illus. in color., online resource.
SERIES
Series Title
(Springer Texts in Statistics,1431-875X
Volume Designation
; 103)
NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
Text of Note
Print
CONTENTS NOTE
Text of Note
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. 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.
Text of Note
Introduction -- Statistical Learning -- Linear Regression -- Classification -- Resampling Methods -- Linear Model Selection and Regularization -- Moving Beyond Linearity -- Tree-Based Methods -- Support Vector Machines -- Unsupervised Learning -- Index.?╗╣
SERIES
Title
Springer Texts in Statistics,1431-875X
Volume Number
103
TOPICAL NAME USED AS SUBJECT
Statistics
Mathematical statistics
Electronic books
LIBRARY OF CONGRESS CLASSIFICATION
Class number
E-BOOK
PERSONAL NAME - PRIMARY RESPONSIBILITY
James, Gareth.
PERSONAL NAME - SECONDARY RESPONSIBILITY
Witten, Daniela
Hastie, Trevor
Tibshirani, Robert
SpringerLink (Online service)
ORIGINATING SOURCE
Country
ایران
ELECTRONIC LOCATION AND ACCESS
Host name
9781461471370.pdf
Access number
عادی
Compression information
عادی
Date and Hour of Consultation and Access
9781461471370.pdf
Electronic Format Type
متن
old catalog
p
BL
1
a
Y
