Holdings: An introduction to statistical learning

An introduction to statistical learning: with applications in R

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Bibliographic Details
Main Author:	James, Gareth (Author)
Format:	Electronic eBook
Language:	English
Published:	New York, NY [u.a.] Springer 2013
Series:	Springer texts in statistics 103
Subjects:	Statistik R > Programm Maschinelles Lernen Einführung
Online Access:	FFW01 Volltext Inhaltsverzeichnis Abstract
Physical Description:	1 Online-Ressource
ISBN:	9781461471370 9781461471387
DOI:	10.1007/978-1-4614-7138-7

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adam_text	AN INTRODUCTION TO STATISTICAL LEARNING / JAMES, GARETH : 2013 TABLE OF CONTENTS / INHALTSVERZEICHNIS 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 DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT. AN INTRODUCTION TO STATISTICAL LEARNING / JAMES, GARETH : 2013 ABSTRACT / INHALTSTEXT 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 DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
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subject_GND	(DE-588)4056995-0 (DE-588)4705956-4 (DE-588)4193754-5 (DE-588)4151278-9
title	An introduction to statistical learning with applications in R
title_auth	An introduction to statistical learning with applications in R
title_exact_search	An introduction to statistical learning with applications in R
title_full	An introduction to statistical learning with applications in R Gareth James ...
title_fullStr	An introduction to statistical learning with applications in R Gareth James ...
title_full_unstemmed	An introduction to statistical learning with applications in R Gareth James ...
title_short	An introduction to statistical learning
title_sort	an introduction to statistical learning with applications in r
title_sub	with applications in R
topic	Statistik (DE-588)4056995-0 gnd R Programm (DE-588)4705956-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd
topic_facet	Statistik R Programm Maschinelles Lernen Einführung
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