An introduction to statistical learning : (Record no. 46349)

MARC details
000 -LEADER
fixed length control field 03969cam a2200481Ii 4500
003 - CONTROL NUMBER IDENTIFIER
control field OCoLC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251211143651.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210801s2021 nyua ob 001 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781071614181
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781071614204
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9781071614174
041 ## - LANGUAGE CODE
Language English
049 ## - LOCAL HOLDINGS (OCLC)
Holding library MAIN
072 #7 - SUBJECT CATEGORY CODE
Subject category code MAT029000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.5
Edition number 23
Item number JAM
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name James, Gareth
245 13 - TITLE STATEMENT
Title An introduction to statistical learning :
Remainder of title with applications in R /
Statement of responsibility, etc Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
250 ## - EDITION STATEMENT
Edition statement Second edition.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Boston :
Name of publisher, distributor, etc Springer,
Date of publication, distribution, etc 2022.
300 ## - PHYSICAL DESCRIPTION
Extent 607p. :
Other physical details illustrations (black and white, and colour) ;
Dimensions 24 cm.
500 ## - GENERAL NOTE
General note Previous edition: New York: Springer, 2013.
505 0# - FORMATTED CONTENTS NOTE
Contents 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 ## - SUMMARY, ETC.
Summary, etc 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 naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical statistics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical models.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element R (Computer program language)
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Witten, Daniela,
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Hastie, Trevor,
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tibshirani, Robert,
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://ezproxy.lib.gla.ac.uk/login?url=https://link.springer.com/10.1007/978-1-0716-1418-1">https://ezproxy.lib.gla.ac.uk/login?url=https://link.springer.com/10.1007/978-1-0716-1418-1</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type General Books
100 1# - MAIN ENTRY--PERSONAL NAME
Fuller form of name (Gareth Michael),
Relator term author.
490 1# - SERIES STATEMENT
Series statement Springer texts in statistics.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index.
506 ## - RESTRICTIONS ON ACCESS NOTE
Terms governing access Access restricted to subscribing institutions.
700 1# - ADDED ENTRY--PERSONAL NAME
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Relator term author.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
Main entry heading James, Gareth (Gareth Michael).
Title Introduction to statistical learning.
Edition Second edition.
Place, publisher, and date of publication Boston : Springer, 2021
International Standard Book Number 9781071614174
Record control number (OCoLC)1242740707.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Springer texts in statistics.
856 40 - ELECTRONIC LOCATION AND ACCESS
Public note Connect to resource
907 ## - LOCAL DATA ELEMENT G, LDG (RLIN)
a .b37980452
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Location Shelving location Date of Cataloging Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Non-fiction CUTN Central Library CUTN Central Library Sciences 11/12/2025   519.5 JAM 54642 11/12/2025 11/12/2025 General Books