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An Introduction to Statistical Learning : with Applications in R
Gareth. James, Daniela. Witten, Trevor. Hastie, Robert. Tibshirani, SpringerLink (Online service)
ISBN 9781461471387, DOI 10.1007/978-1-4614-7138-7
utgivning
New York, NY : Springer New York; Imprint: Springer, 2013XIV, 426 p. 150 illus., 146 illus. in color.
Seriemedlemskap
Springer Texts in Statistics · ISSN 1431-875X · Springer Texts in Statistics ; 103by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
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Ej skönlitteraturämne
MAT029000, Statistics., Mathematical statistics., Statistics., Statistical Theory and Methods., Statistics and Computing/Statistics Programs., Theoretical, Mathematical and Computational Physics., Statistics, general.Annat bärarformat
An Introduction to Statistical Learning · ISBN 9781461471370 (Printed edition:)Sammanfattning
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.
Innehållsförteckning
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.
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Medverkan och funktion
Gareth. James, Daniela. Witten, Trevor. Hastie, Robert. Tibshirani, SpringerLink (Online service)klassifikation
519.5 (DDK-klassifikation)Identifikator
ISBN 9781461471387, DOI 10.1007/978-1-4614-7138-7Indirekt identifierad av
ISBN 9781461471370 · Printhar titel
An Introduction to Statistical Learning : with Applications in Rupphovsuppgift
by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshiraniutgivning
New York, NY : Springer New York; Imprint: Springer, 2013omfång
XIV, 426 p. 150 illus., 146 illus. in color.Relaterad beskrivning eller innehåll
Table of Contents / AbstractsMått
nÖvriga fysiska detaljer
digital.klassifikation
QA276-280 (LC-klassifikation)Annat bärarformat
An Introduction to Statistical Learning · ISBN 9781461471370 (Printed edition:)Seriemedlemskap
Springer Texts in Statistics · ISSN 1431-875X · Springer Texts in Statistics ; 103Digital karakteristika
008färginnehåll
nkontrollnummer
14557777Resursens ID / Permalänk: https://libris.kb.se/wf7cvdj741w0khp#it
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