An introduction to statistical learning: with applications in R
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY [u.a.]
Springer
2013
|
| Schriftenreihe: | Springer texts in statistics
103 |
| Schlagworte: | |
| Online-Zugang: | FFW01 Volltext Inhaltsverzeichnis Abstract |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9781461471370 9781461471387 |
| DOI: | 10.1007/978-1-4614-7138-7 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804150655985647616 |
|---|---|
| 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.
|
| any_adam_object | 1 |
| author | James, Gareth |
| author_GND | (DE-588)1038457327 |
| author_facet | James, Gareth |
| author_role | aut |
| author_sort | James, Gareth |
| author_variant | g j gj |
| building | Verbundindex |
| bvnumber | BV041219513 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA ebook ZDB-30-PQE |
| ctrlnum | (OCoLC)855544217 (DE-599)DNB1036416917 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Medizin |
| doi_str_mv | 10.1007/978-1-4614-7138-7 |
| format | Electronic eBook |
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| 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 |
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| topic_facet | Statistik R Programm Maschinelles Lernen Einführung |
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