SØG - mellem flere end 8 millioner bøger:

Søg på: Titel, forfatter, forlag - gerne i kombination.
Eller blot på isbn, hvis du kender dette.

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
561,00 kr. 504,90 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
863,00 kr. 776,70 kr.
Leveres umiddelbart efter køb
The Elements of Statistical Learning - Data Mining, Inference, and Prediction, 2. udgave
Studiepris!

The Elements of Statistical Learning

Data Mining, Inference, and Prediction
Trevor Hastie, Robert Tibshirani, Jerome Friedman og J. H. Friedman
(2017)
Sprog: Engelsk
Springer
555,00 kr. 499,50 kr.
ikke på lager, Bestil nu og få den leveret
om ca. 15 hverdage
  • Klik for at bedømme:
  • 3.0/6 (13 bedømmelser)

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 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 (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.

About Springer

Science, technology and medicine play an important role in our daily lives. With Springer at the top, these subjects are quite at home in our publishing houses.

 

Some of the most renowned scientists in the world are our authors. Alongside Springer, with offices in many of the world’s centers of knowledge, the Science / Technology / Medicine (STM) publishers Apress, BioMed Central, Humana, Key Curriculum Press, Spektrum Akademischer Verlag and others enjoy excellent professional reputations.

 

All in all, some 2,000 academic journals and around 6,500 new books are published each year – in Berlin, Heidelberg, Dordrecht, Vienna, Paris, London, Milan, Moscow, New York, Beijing, Tokyo and New Delhi. 90 percent of our publications appear on the market in English.