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Viser: The Elements of Statistical Learning - Data Mining, Inference, and Prediction

The Elements of Statistical Learning, 2. udgave

The Elements of Statistical Learning Vital Source e-bog

Trevor Hastie, Robert Tibshirani og Jerome Friedman
(2009)
Springer Nature
792,00 kr.
Leveres umiddelbart efter køb
The Elements of Statistical Learning, 2. udgave

The Elements of Statistical Learning Vital Source e-bog

Trevor Hastie, Robert Tibshirani og Jerome Friedman
(2009)
Springer Nature
853,00 kr.
Leveres umiddelbart efter køb
The Elements of Statistical Learning, 2. udgave

The Elements of Statistical Learning Vital Source e-bog

Trevor Hastie, Robert Tibshirani og Jerome Friedman
(2009)
Springer Nature
853,00 kr.
Leveres umiddelbart efter køb
The Elements of Statistical Learning - Data Mining, Inference, and Prediction, 2. udgave

The Elements of Statistical Learning

Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, Jerome Friedman og J. H. Friedman
(2017)
Sprog: Engelsk
Springer New York
555,00 kr. 499,50 kr.
På lager, Bestil nu og få den leveret
om ca. 2 hverdage
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Detaljer om varen

  • 2. Udgave
  • Vital Source leje e-bog 180 dage
  • Udgiver: Springer Nature (August 2009)
  • Forfattere: Trevor Hastie, Robert Tibshirani og Jerome Friedman
  • ISBN: 9780387848587R180
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Licens varighed:
Online udgaven er tilgængelig: 180 dage fra købsdato.
Offline udgaven er tilgængelig: 180 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 75 sider kan printes ad gangen
Copy: højest 75 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 2. Udgave
  • Vital Source leje e-bog 365 dage
  • Udgiver: Springer Nature (August 2009)
  • Forfattere: Trevor Hastie, Robert Tibshirani og Jerome Friedman
  • ISBN: 9780387848587R365
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Licens varighed:
Online udgaven er tilgængelig: 365 dage fra købsdato.
Offline udgaven er tilgængelig: 365 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 75 sider kan printes ad gangen
Copy: højest 75 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 2. Udgave
  • Vital Source E-book
  • Udgiver: Springer Nature (August 2009)
  • Forfattere: Trevor Hastie, Robert Tibshirani og Jerome Friedman
  • ISBN: 9780387848587
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Licens varighed:
Online udgaven er tilgængelig: 365 dage fra købsdato.
Offline udgaven er tilgængelig: ubegrænset dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 75 sider kan printes ad gangen
Copy: højest 75 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 2. Udgave
  • Hardback
  • Udgiver: Springer New York (April 2017)
  • Forfattere: Trevor Hastie, Robert Tibshirani, Jerome Friedman og J. H. Friedman
  • ISBN: 9780387848570

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.

This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.

Introduction
- Overview of supervised learning. - Linear methods for regression. - Linear methods for classification. - Basis expansions and regularization. - Kernel smoothing methods. - Model assessment and selection. - Model inference and averaging. - Additive models, trees, and related methods. - Boosting and additive trees. - Neural networks. - Support vector machines and flexible discriminants. - Prototype methods and nearest-neighbors. - Unsupervised learning.
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