|
|
|
|
| LEADER |
12398cam a2201429 4500 |
| 001 |
183-1765220424 |
| 003 |
DE-627 |
| 005 |
20231005091549.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
210802s2021 xxu|||||o 00| ||eng c |
| 020 |
|
|
|a 9781071614181
|9 978-1-07-161418-1
|
| 024 |
7 |
|
|a 10.1007/978-1-0716-1418-1
|2 doi
|
| 035 |
|
|
|a (DE-627)1765220424
|
| 035 |
|
|
|a (DE-599)KEP067400531
|
| 035 |
|
|
|a (DE-He213)978-1-0716-1418-1
|
| 035 |
|
|
|a (EBP)067400531
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rda
|
| 041 |
|
|
|a eng
|
| 044 |
|
|
|c XD-US
|
| 072 |
|
7 |
|a PBT
|2 bicssc
|
| 072 |
|
7 |
|a MAT029000
|2 bisacsh
|
| 084 |
|
|
|a SK 830
|q SEPA
|2 rvk
|0 (DE-625)rvk/143259:
|
| 084 |
|
|
|a SK 840
|q SEPA
|2 rvk
|0 (DE-625)rvk/143261:
|
| 084 |
|
|
|a ST 601
|q SEPA
|2 rvk
|0 (DE-625)rvk/143682:
|
| 084 |
|
|
|a QH 231
|q SEPA
|2 rvk
|0 (DE-625)rvk/141546:
|
| 084 |
|
|
|a ST 250
|q SEPA
|2 rvk
|0 (DE-625)rvk/143626:
|
| 084 |
|
|
|a QH 740
|q SEPA
|2 rvk
|0 (DE-625)rvk/141614:
|
| 084 |
|
|
|a QH 233
|q SEPA
|2 rvk
|0 (DE-625)rvk/141548:
|
| 084 |
|
|
|a 31.73
|2 bkl
|
| 084 |
|
|
|a 54.72
|2 bkl
|
| 100 |
1 |
|
|a James, Gareth
|e VerfasserIn
|0 (DE-588)1038457327
|0 (DE-627)75743861X
|0 (DE-576)392417332
|4 aut
|
| 245 |
1 |
3 |
|a An introduction to statistical learning
|b with applications in R
|c Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
|
| 250 |
|
|
|a Second edition
|
| 264 |
|
1 |
|a New York, NY
|b Springer
|c 2021
|
| 300 |
|
|
|a 1 Online-Ressource(xv, 607 Seiten)
|b Illustrationen, Diagramme
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a Computermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a Online-Ressource
|b cr
|2 rdacarrier
|
| 490 |
0 |
|
|a Springer texts in statistics
|
| 490 |
0 |
|
|a Springer eBook Collection
|
| 520 |
|
|
|a 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 |
|
|
|a 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 naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
|
| 650 |
|
0 |
|a Statistics .
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
4 |
|a Mathematical models
|
| 650 |
|
4 |
|a Mathematical statistics
|
| 650 |
|
4 |
|a R (Computer program language)
|
| 650 |
|
4 |
|a Statistics
|
| 655 |
|
7 |
|a Lehrbuch
|0 (DE-588)4123623-3
|0 (DE-627)104270187
|0 (DE-576)209561262
|2 gnd-content
|
| 655 |
|
7 |
|a Einführung
|0 (DE-588)4151278-9
|0 (DE-627)104450460
|0 (DE-576)209786884
|2 gnd-content
|
| 689 |
0 |
0 |
|D s
|0 (DE-588)4056995-0
|0 (DE-627)106152955
|0 (DE-576)209119799
|a Statistik
|2 gnd
|
| 689 |
0 |
1 |
|D s
|0 (DE-588)4705956-4
|0 (DE-627)356147487
|0 (DE-576)215406362
|a R
|g Programm
|2 gnd
|
| 689 |
0 |
|
|5 DE-101
|
| 689 |
1 |
0 |
|D s
|0 (DE-588)4056995-0
|0 (DE-627)106152955
|0 (DE-576)209119799
|a Statistik
|2 gnd
|
| 689 |
1 |
1 |
|D s
|0 (DE-588)4193754-5
|0 (DE-627)105224782
|0 (DE-576)21008944X
|a Maschinelles Lernen
|2 gnd
|
| 689 |
1 |
2 |
|D s
|0 (DE-588)4705956-4
|0 (DE-627)356147487
|0 (DE-576)215406362
|a R
|g Programm
|2 gnd
|
| 689 |
1 |
|
|5 (DE-627)
|
| 689 |
2 |
0 |
|D s
|0 (DE-588)4129903-6
|0 (DE-627)105706523
|0 (DE-576)209614412
|a Regressionsanalyse
|2 gnd
|
| 689 |
2 |
1 |
|D s
|0 (DE-588)4288033-6
|0 (DE-627)10428367X
|0 (DE-576)210820004
|a Resampling
|2 gnd
|
| 689 |
2 |
2 |
|D s
|0 (DE-588)4134827-8
|0 (DE-627)104790636
|0 (DE-576)209655577
|a Lineares Modell
|2 gnd
|
| 689 |
2 |
3 |
|D s
|0 (DE-588)4347788-4
|0 (DE-627)156895994
|0 (DE-576)211488801
|a Entscheidungsbaum
|2 gnd
|
| 689 |
2 |
4 |
|D s
|0 (DE-588)4505517-8
|0 (DE-627)245346708
|0 (DE-576)213125757
|a Support-Vektor-Maschine
|2 gnd
|
| 689 |
2 |
5 |
|D s
|0 (DE-588)4070044-6
|0 (DE-627)106101536
|0 (DE-576)209179082
|a Cluster-Analyse
|2 gnd
|
| 689 |
2 |
|
|5 (DE-627)
|
| 700 |
1 |
|
|a Witten, Daniela
|e VerfasserIn
|0 (DE-588)108120849X
|0 (DE-627)845688642
|0 (DE-576)454037805
|4 aut
|
| 700 |
1 |
|
|a Hastie, Trevor
|d 1953-
|e VerfasserIn
|0 (DE-588)172128242
|0 (DE-627)697041832
|0 (DE-576)167899988
|4 aut
|
| 700 |
1 |
|
|a Tibshirani, Robert
|d 1956-
|e VerfasserIn
|0 (DE-588)172417740
|0 (DE-627)697358836
|0 (DE-576)167899996
|4 aut
|
| 776 |
1 |
|
|z 9781071614174
|
| 776 |
1 |
|
|z 9781071614198
|
| 776 |
1 |
|
|z 9781071614204
|
| 776 |
0 |
8 |
|i Erscheint auch als
|n Druck-Ausgabe
|a James, Gareth
|t An introduction to statistical learning
|b Second edition
|d New York, NY : Springer, 2021
|h xv, 607 Seiten
|w (DE-627)1765978874
|z 9781071614174
|z 9781071614204
|
| 776 |
0 |
8 |
|i Erscheint auch als
|n Druck-Ausgabe
|z 9781071614174
|
| 776 |
0 |
8 |
|i Erscheint auch als
|n Druck-Ausgabe
|z 9781071614198
|
| 776 |
0 |
8 |
|i Erscheint auch als
|n Druck-Ausgabe
|z 9781071614204
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-0716-1418-1
|m X:SPRINGER
|x Resolving-System
|z lizenzpflichtig
|
| 856 |
4 |
2 |
|u https://swbplus.bsz-bw.de/bsz1765220424cov.jpg
|m V:DE-576
|m X:SPRINGER
|q image/jpeg
|v 20220808105702
|3 Cover
|
| 912 |
|
|
|a ZDB-2-SEB
|b 2021
|
| 912 |
|
|
|a ZDB-2-SMA
|b 2021
|
| 912 |
|
|
|a ZDB-2-SXMS
|b 2021
|
| 924 |
1 |
|
|a 3962714154
|b DE-84
|9 84
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 4326557974
|b DE-46
|9 46
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3962684409
|b DE-18
|9 18
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|k http://emedien.sub.uni-hamburg.de/han/SpringerEbooks/doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3962707301
|b DE-830
|9 830
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 401280555X
|b DE-705
|9 705
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 4365191659
|b DE-28
|9 28
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|l R
|
| 924 |
1 |
|
|a 3962786058
|b DE-3
|9 3
|c GBV
|d d
|g ebook
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3962692509
|b DE-89
|9 89
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3962737170
|b DE-Ma9
|9 Ma 9
|c GBV
|d d
|g eBook Springer
|k https://doi.org/10.1007/978-1-0716-1418-1
|k https://han.med.uni-magdeburg.de/han/SPR-eBook-MathematicsandStatistics-einzeln/doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3962747850
|b DE-Ma14
|9 Ma 14
|c GBV
|d d
|g eBook Springer
|k https://doi.org/10.1007/978-1-0716-1418-1
|k https://han.med.uni-magdeburg.de/han/SPR-eBook-MathematicsandStatistics-einzeln/doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3962732772
|b DE-Luen4
|9 Lün 4
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3963312173
|b DE-715
|9 715
|c GBV
|d d
|k http://49gbv-uob-primo.hosted.exlibrisgroup.com/openurl/49GBV_UOB/UOB_services_page?u.ignore_date_coverage=true&rft.mms_id=991015429071203501
|l Springer ebook collection / Mathematics and Statistics 2021 (Kauf)
|
| 924 |
1 |
|
|a 396275900X
|b DE-700
|9 700
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3962566953
|b DE-Sra5
|9 Sra 5
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 4415991068
|b DE-527
|9 527
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3962768084
|b DE-517
|9 517
|c GBV
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 4460928604
|b DE-21
|9 21
|c BSZ
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|l Zugang für die Universität Tübingen
|
| 924 |
1 |
|
|a 3959007329
|b DE-25
|9 25
|c BSZ
|d d
|k https://www.redi-bw.de/start/unifr/EBooks-springer/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 4117419382
|b DE-291
|9 291
|c BSZ
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3959115083
|b DE-14
|9 14
|c BSZ
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3959115091
|b DE-352E
|9 352E
|c BSZ
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3959115105
|b DE-180
|9 180
|c BSZ
|d d
|
| 924 |
1 |
|
|a 3959968353
|b DE-180
|9 180
|c BSZ
|d d
|k http://primo-49man.hosted.exlibrisgroup.com/openurl/MAN/MAN_UB_service_page?u.ignore_date_coverage=true&rft.mms_id=9918867306902561
|
| 924 |
1 |
|
|a 3959007337
|b DE-16
|9 16
|c BSZ
|d b
|e n
|e p
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3959115113
|b DE-90
|9 90
|c BSZ
|d b
|e p
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3959115121
|b DE-90
|9 90
|c BSZ
|d b
|e p
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 395911513X
|b DE-93
|9 93
|c BSZ
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3959115148
|b DE-576
|9 576
|c BSZ
|d d
|
| 924 |
1 |
|
|a 3959115156
|b DE-Ch1
|9 Ch 1
|c BSZ
|d d
|k https://doi.org/10.1007/978-1-0716-1418-1
|
| 924 |
1 |
|
|a 3959007345
|b DE-105
|9 105
|c BSZ
|d d
|
| 924 |
1 |
|
|a 3959115164
|b DE-Frei3c
|9 Frei 3c
|c BSZ
|d d
|g Springer eBook Collection Mathematics and Statistics 2021
|k https://doi.org/10.1007/978-1-0716-1418-1
|l Click here to access online.
|l For local users only.
|
| 924 |
1 |
|
|a 4211350315
|b DE-943
|9 943
|c BSZ
|d d
|g eBook Springer
|k https://doi.org/10.1007/978-1-0716-1418-1
|l Zum Online-Dokument
|l Nur aus dem Campusnetz erreichbar
|
| 936 |
r |
v |
|a SK 830
|b Statistische Entscheidungstheorie
|k Mathematik
|k Monografien
|k Wahrscheinlichkeitstheorie
|k Statistische Entscheidungstheorie
|0 (DE-627)1270877526
|0 (DE-625)rvk/143259:
|0 (DE-576)200877526
|
| 936 |
r |
v |
|a SK 840
|b Spezielle statistische Verfahren
|k Mathematik
|k Monografien
|k Wahrscheinlichkeitstheorie
|k Spezielle statistische Verfahren
|0 (DE-627)1271481863
|0 (DE-625)rvk/143261:
|0 (DE-576)201481863
|
| 936 |
r |
v |
|a ST 601
|b Einzelne Systeme (alphabetisch)
|k Informatik
|k Monografien
|k Einzelne Anwendungen der Datenverarbeitung
|k Datenverarbeitung in Anwendungsgebieten
|k Mathematik, Statistik
|k Einzelne Systeme (alphabetisch)
|0 (DE-627)127114283X
|0 (DE-625)rvk/143682:
|0 (DE-576)20114283X
|
| 936 |
r |
v |
|a QH 231
|b Einführende Lehrbücher
|k Wirtschaftswissenschaften
|k Mathematik. Statistik. Ökonometrie. Unternehmensforschung
|k Statistik
|k Theoretische Statistik
|k Einführende Lehrbücher
|0 (DE-627)1271479206
|0 (DE-625)rvk/141546:
|0 (DE-576)201479206
|
| 936 |
r |
v |
|a ST 250
|b Einzelne Programmiersprachen (A-Z)
|k Informatik
|k Monografien
|k Software und -entwicklung
|k Programmiersprachen
|k Einzelne Programmiersprachen (A-Z)
|0 (DE-627)1270877445
|0 (DE-625)rvk/143626:
|0 (DE-576)200877445
|
| 936 |
r |
v |
|a QH 740
|b Lernprozesse, Lerntheorien
|k Wirtschaftswissenschaften
|k Mathematik. Statistik. Ökonometrie. Unternehmensforschung
|k Wirtschaftsinformatik. Datenverarbeitung.
|k Systemtheorie
|k Lernprozesse, Lerntheorien
|0 (DE-627)1271739615
|0 (DE-625)rvk/141614:
|0 (DE-576)201739615
|
| 936 |
r |
v |
|a QH 233
|b Häufigkeitsverteilungen. Stichprobenverteilungen. Schätztheorie. Testtheorie. Statistische Entscheidungstheorie
|k Wirtschaftswissenschaften
|k Mathematik. Statistik. Ökonometrie. Unternehmensforschung
|k Statistik
|k Theoretische Statistik
|k Häufigkeitsverteilungen. Stichprobenverteilungen. Schätztheorie. Testtheorie. Statistische Entscheidungstheorie
|0 (DE-627)127149115X
|0 (DE-625)rvk/141548:
|0 (DE-576)20149115X
|
| 936 |
b |
k |
|a 31.73
|j Mathematische Statistik
|q SEPA
|0 (DE-627)106418998
|
| 936 |
b |
k |
|a 54.72
|j Künstliche Intelligenz
|q SEPA
|0 (DE-627)10641240X
|
| 951 |
|
|
|a BO
|
| 980 |
|
|
|a 1765220424
|b 183
|c sid-183-col-kxpbbi
|
SOLR
| _version_ |
1795392258706505728 |
| author |
James, Gareth, Witten, Daniela, Hastie, Trevor, Tibshirani, Robert |
| author_facet |
James, Gareth, Witten, Daniela, Hastie, Trevor, Tibshirani, Robert |
| author_role |
aut, aut, aut, aut |
| author_sort |
James, Gareth |
| author_variant |
g j gj, d w dw, t h th, r t rt |
| building |
Library A |
| collection |
ZDB-2-SEB, ZDB-2-SMA, ZDB-2-SXMS, sid-183-col-kxpbbi |
| 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., 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 naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility. |
| ctrlnum |
(DE-627)1765220424, (DE-599)KEP067400531, (DE-He213)978-1-0716-1418-1, (EBP)067400531 |
| doi_str_mv |
10.1007/978-1-0716-1418-1 |
| edition |
Second edition |
| facet_912a |
ZDB-2-SEB, ZDB-2-SMA, ZDB-2-SXMS |
| facet_avail |
Online |
| facet_local_del330 |
Statistik, R, Maschinelles Lernen, Regressionsanalyse, Resampling, Lineares Modell, Entscheidungsbaum, Support-Vektor-Maschine, Cluster-Analyse |
| finc_class_facet |
Mathematik, Informatik, Wirtschaftswissenschaften |
| fincclass_txtF_mv |
science-mathematics, science-computerscience, economics |
| format |
eBook |
| format_access_txtF_mv |
Book, E-Book |
| format_de105 |
Ebook |
| format_de14 |
Book, E-Book |
| format_de15 |
Book, E-Book |
| format_del152 |
Buch |
| format_detail_txtF_mv |
text-online-monograph-independent |
| format_dezi4 |
e-Book |
| format_finc |
Book, E-Book |
| format_legacy |
ElectronicBook |
| format_legacy_nrw |
Book, E-Book |
| format_nrw |
Book, E-Book |
| format_strict_txtF_mv |
E-Book |
| genre |
Lehrbuch (DE-588)4123623-3 (DE-627)104270187 (DE-576)209561262 gnd-content, Einführung (DE-588)4151278-9 (DE-627)104450460 (DE-576)209786884 gnd-content |
| genre_facet |
Lehrbuch, Einführung |
| geogr_code |
not assigned |
| geogr_code_person |
not assigned |
| id |
183-1765220424 |
| illustrated |
Not Illustrated |
| imprint |
New York, NY, Springer, 2021 |
| imprint_str_mv |
New York, NY: Springer, 2021 |
| institution |
FID-BBI-DE-23 |
| is_hierarchy_id |
|
| is_hierarchy_title |
|
| isbn |
9781071614181 |
| isbn_isn_mv |
9781071614174, 9781071614198, 9781071614204 |
| language |
English |
| last_indexed |
2024-04-04T08:31:24.414Z |
| marc024a_ct_mv |
10.1007/978-1-0716-1418-1 |
| marc_error |
[geogr_code]Unable to make public java.lang.AbstractStringBuilder java.lang.AbstractStringBuilder.append(java.lang.String) accessible: module java.base does not "opens java.lang" to unnamed module @289001a2 |
| match_str |
james2021anintroductiontostatisticallearningwithapplicationsinr |
| mega_collection |
K10plus Verbundkatalog |
| physical |
1 Online-Ressource(xv, 607 Seiten); Illustrationen, Diagramme |
| publishDate |
2021 |
| publishDateSort |
2021 |
| publishPlace |
New York, NY |
| publisher |
Springer |
| record_format |
marcfinc |
| record_id |
1765220424 |
| recordtype |
marcfinc |
| rvk_facet |
SK 830, SK 840, ST 601, QH 231, ST 250, QH 740, QH 233 |
| rvk_label |
Mathematik, Monografien, Wahrscheinlichkeitstheorie, Statistische Entscheidungstheorie, Spezielle statistische Verfahren, Informatik, Einzelne Anwendungen der Datenverarbeitung, Datenverarbeitung in Anwendungsgebieten, Mathematik, Statistik, Einzelne Systeme (alphabetisch), Wirtschaftswissenschaften, Mathematik. Statistik. Ökonometrie. Unternehmensforschung, Statistik, Theoretische Statistik, Einführende Lehrbücher, Software und -entwicklung, Programmiersprachen, Einzelne Programmiersprachen (A-Z), Wirtschaftsinformatik. Datenverarbeitung., Systemtheorie, Lernprozesse, Lerntheorien, Häufigkeitsverteilungen. Stichprobenverteilungen. Schätztheorie. Testtheorie. Statistische Entscheidungstheorie |
| rvk_path |
QH 720 - QH 740, ST, QH 230 - QH 239, SK 800, SQ - SU, ST 240 - ST 250, ST 600, ST 601, SA - SP, ST 600 - ST 690, Q, QH 740, QH 500 - QH 740, ST 320 - ST 690, QH, QH 233, SK, QH 231, QH 200 - QH 254, SK 840, SK 830, ST 230 - ST 285, ST 250 |
| rvk_path_str_mv |
QH 720 - QH 740, ST, QH 230 - QH 239, SK 800, SQ - SU, ST 240 - ST 250, ST 600, ST 601, SA - SP, ST 600 - ST 690, Q, QH 740, QH 500 - QH 740, ST 320 - ST 690, QH, QH 233, SK, QH 231, QH 200 - QH 254, SK 840, SK 830, ST 230 - ST 285, ST 250 |
| series2 |
Springer texts in statistics, Springer eBook Collection |
| source_id |
183 |
| spelling |
James, Gareth VerfasserIn (DE-588)1038457327 (DE-627)75743861X (DE-576)392417332 aut, An introduction to statistical learning with applications in R Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Second edition, New York, NY Springer 2021, 1 Online-Ressource(xv, 607 Seiten) Illustrationen, Diagramme, Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Springer texts in statistics, Springer eBook Collection, 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., 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 naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility., Statistics ., Artificial intelligence., Mathematical models, Mathematical statistics, R (Computer program language), Statistics, Lehrbuch (DE-588)4123623-3 (DE-627)104270187 (DE-576)209561262 gnd-content, Einführung (DE-588)4151278-9 (DE-627)104450460 (DE-576)209786884 gnd-content, s (DE-588)4056995-0 (DE-627)106152955 (DE-576)209119799 Statistik gnd, s (DE-588)4705956-4 (DE-627)356147487 (DE-576)215406362 R Programm gnd, DE-101, s (DE-588)4193754-5 (DE-627)105224782 (DE-576)21008944X Maschinelles Lernen gnd, (DE-627), s (DE-588)4129903-6 (DE-627)105706523 (DE-576)209614412 Regressionsanalyse gnd, s (DE-588)4288033-6 (DE-627)10428367X (DE-576)210820004 Resampling gnd, s (DE-588)4134827-8 (DE-627)104790636 (DE-576)209655577 Lineares Modell gnd, s (DE-588)4347788-4 (DE-627)156895994 (DE-576)211488801 Entscheidungsbaum gnd, s (DE-588)4505517-8 (DE-627)245346708 (DE-576)213125757 Support-Vektor-Maschine gnd, s (DE-588)4070044-6 (DE-627)106101536 (DE-576)209179082 Cluster-Analyse gnd, Witten, Daniela VerfasserIn (DE-588)108120849X (DE-627)845688642 (DE-576)454037805 aut, Hastie, Trevor 1953- VerfasserIn (DE-588)172128242 (DE-627)697041832 (DE-576)167899988 aut, Tibshirani, Robert 1956- VerfasserIn (DE-588)172417740 (DE-627)697358836 (DE-576)167899996 aut, 9781071614174, 9781071614198, 9781071614204, Erscheint auch als Druck-Ausgabe James, Gareth An introduction to statistical learning Second edition New York, NY : Springer, 2021 xv, 607 Seiten (DE-627)1765978874 9781071614174 9781071614204, Erscheint auch als Druck-Ausgabe 9781071614174, Erscheint auch als Druck-Ausgabe 9781071614198, Erscheint auch als Druck-Ausgabe 9781071614204, https://doi.org/10.1007/978-1-0716-1418-1 X:SPRINGER Resolving-System lizenzpflichtig, https://swbplus.bsz-bw.de/bsz1765220424cov.jpg V:DE-576 X:SPRINGER image/jpeg 20220808105702 Cover |
| spellingShingle |
James, Gareth, Witten, Daniela, Hastie, Trevor, Tibshirani, Robert, An introduction to statistical learning: with applications in R, 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., 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 naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility., Statistics ., Artificial intelligence., Mathematical models, Mathematical statistics, R (Computer program language), Statistics, Lehrbuch, Einführung, Statistik, R Programm, Maschinelles Lernen, Regressionsanalyse, Resampling, Lineares Modell, Entscheidungsbaum, Support-Vektor-Maschine, Cluster-Analyse |
| title |
An introduction to statistical learning: with applications in R |
| title_auth |
An introduction to statistical learning with applications in R |
| title_full |
An introduction to statistical learning with applications in R Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani |
| title_fullStr |
An introduction to statistical learning with applications in R Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani |
| title_full_unstemmed |
An introduction to statistical learning with applications in R Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani |
| title_short |
An introduction to statistical learning |
| title_sort |
an introduction to statistical learning with applications in r |
| title_sub |
with applications in R |
| title_unstemmed |
An introduction to statistical learning: with applications in R |
| topic |
Statistics ., Artificial intelligence., Mathematical models, Mathematical statistics, R (Computer program language), Statistics, Lehrbuch, Einführung, Statistik, R Programm, Maschinelles Lernen, Regressionsanalyse, Resampling, Lineares Modell, Entscheidungsbaum, Support-Vektor-Maschine, Cluster-Analyse |
| topic_facet |
Statistics ., Artificial intelligence., Mathematical models, Mathematical statistics, R (Computer program language), Statistics, Lehrbuch, Einführung, Statistik, R, Maschinelles Lernen, Regressionsanalyse, Resampling, Lineares Modell, Entscheidungsbaum, Support-Vektor-Maschine, Cluster-Analyse |
| url |
https://doi.org/10.1007/978-1-0716-1418-1, https://swbplus.bsz-bw.de/bsz1765220424cov.jpg |
| work_keys_str_mv |
AT jamesgareth anintroductiontostatisticallearningwithapplicationsinr, AT wittendaniela anintroductiontostatisticallearningwithapplicationsinr, AT hastietrevor anintroductiontostatisticallearningwithapplicationsinr, AT tibshiranirobert anintroductiontostatisticallearningwithapplicationsinr, AT jamesgareth introductiontostatisticallearningwithapplicationsinr, AT wittendaniela introductiontostatisticallearningwithapplicationsinr, AT hastietrevor introductiontostatisticallearningwithapplicationsinr, AT tibshiranirobert introductiontostatisticallearningwithapplicationsinr |