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Viser: An Introduction to Statistical Learning - With Applications in R

An Introduction to Statistical Learning
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An Introduction to Statistical Learning Vital Source e-bog

Gareth James, Daniela Witten, Trevor Hastie og Robert Tibshirani
(2013)
Springer Nature
428,00 kr.
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An Introduction to Statistical Learning, 1. udgave

An Introduction to Statistical Learning Vital Source e-bog

Gareth James, Daniela Witten, Trevor Hastie og Robert Tibshirani
(2013)
Springer Nature
471,00 kr.
Leveres umiddelbart efter køb
An Introduction to Statistical Learning

An Introduction to Statistical Learning Vital Source e-bog

Gareth James, Daniela Witten, Trevor Hastie og Robert Tibshirani
(2013)
Springer Nature
435,00 kr.
Leveres umiddelbart efter køb
An Introduction to Statistical Learning, 1. udgave

An Introduction to Statistical Learning Vital Source e-bog

Gareth James, Daniela Witten, Trevor Hastie og Robert Tibshirani
(2013)
Springer Nature
492,00 kr.
Leveres umiddelbart efter køb
An Introduction to Statistical Learning - With Applications in R, 1. udgave

An Introduction to Statistical Learning

With Applications in R
Gareth James, Trevor Hastie, Robert Tibshirani og Daniela Witten
(2017)
Sprog: Engelsk
Springer New York
499,00 kr.
Denne bog er blevet erstattet af en nyere udgave.

Detaljer om varen

  • Vital Source searchable e-book (Fixed pages)
  • Udgiver: Springer Nature (Juni 2013)
  • Forfattere: Gareth James, Daniela Witten, Trevor Hastie og Robert Tibshirani
  • ISBN: 9781461471387
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.
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Bookshelf online: 5 år fra købsdato.
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Detaljer om varen

  • 1. Udgave
  • Vital Source 180 day rentals (fixed pages)
  • Udgiver: Springer Nature (Juni 2013)
  • Forfattere: Gareth James, Daniela Witten, Trevor Hastie og Robert Tibshirani
  • ISBN: 9781461471387R180
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.
Licens varighed:
Bookshelf online: 180 dage fra købsdato.
Bookshelf appen: 180 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: -1 sider kan printes ad gangen
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Detaljer om varen

  • Vital Source 120 day rentals (fixed pages)
  • Udgiver: Springer Nature (Juni 2013)
  • Forfattere: Gareth James, Daniela Witten, Trevor Hastie og Robert Tibshirani
  • ISBN: 9781461471387R120
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.
Licens varighed:
Bookshelf online: 120 dage fra købsdato.
Bookshelf appen: 120 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
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Detaljer om varen

  • 1. Udgave
  • Vital Source 365 day rentals (fixed pages)
  • Udgiver: Springer Nature (Juni 2013)
  • Forfattere: Gareth James, Daniela Witten, Trevor Hastie og Robert Tibshirani
  • ISBN: 9781461471387R365
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.
Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: 5 år fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: -1 sider kan printes ad gangen
Copy: højest -1 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 1. Udgave
  • Hardback: 430 sider
  • Udgiver: Springer New York (September 2017)
  • Forfattere: Gareth James, Trevor Hastie, Robert Tibshirani og Daniela Witten
  • ISBN: 9781461471370

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.

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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