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The Resource The elements of statistical learning : data mining, inference, and prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman

The elements of statistical learning : data mining, inference, and prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman

Label
The elements of statistical learning : data mining, inference, and prediction
Title
The elements of statistical learning
Title remainder
data mining, inference, and prediction
Statement of responsibility
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Creator
Contributor
Author
Subject
Language
eng
Summary
"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"--
Member of
Assigning source
Provided by Publisher
Cataloging source
NUI
http://library.link/vocab/creatorName
Hastie, Trevor
Dewey number
519.5
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Tibshirani, Robert
  • Friedman, J. H.
Series statement
Springer series in statistics,
http://library.link/vocab/subjectName
  • Supervised learning (Machine learning)
  • Electronic data processing
  • Statistics
  • Biology
  • Bioinformatics
  • Mathematics
  • Data mining
Label
The elements of statistical learning : data mining, inference, and prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman
Instantiates
Publication
Bibliography note
Includes bibliographical references (pages 699-727) and indexes
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier.
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Contents
  • 5.
  • Basis expansions and regularization
  • 6.
  • Kernel smoothing methods
  • 7.
  • Model assessment and selection
  • 8.
  • Model inference and averaging
  • 9.
  • Additive models, trees, and related methods
  • 1.
  • 10.
  • Boosting and additive trees
  • 11.
  • Neural networks
  • 12.
  • Support vector machines and flexible discriminants
  • 13.
  • Prototype methods and nearest-neighbors
  • 14.
  • Unsupervised learning
  • Introduction
  • 15.
  • Random forests
  • 16.
  • Ensemble learning
  • 17.
  • Undirected graphical models
  • 18.
  • High-dimensional problems: p N
  • 2.
  • Overview of supervised learning
  • 3.
  • Linear methods for regression
  • 4.
  • Linear methods for classification
Control code
FIEb1781408x
Dimensions
24 cm.
Edition
2nd edition.
Extent
xxii, 745 pages
Isbn
9780387848846
Media category
unmediated
Media MARC source
rdamedia.
Media type code
  • n
Other physical details
illustrations (some color)
System control number
(OCoLC)300478243
Label
The elements of statistical learning : data mining, inference, and prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman
Publication
Bibliography note
Includes bibliographical references (pages 699-727) and indexes
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier.
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent.
Contents
  • 5.
  • Basis expansions and regularization
  • 6.
  • Kernel smoothing methods
  • 7.
  • Model assessment and selection
  • 8.
  • Model inference and averaging
  • 9.
  • Additive models, trees, and related methods
  • 1.
  • 10.
  • Boosting and additive trees
  • 11.
  • Neural networks
  • 12.
  • Support vector machines and flexible discriminants
  • 13.
  • Prototype methods and nearest-neighbors
  • 14.
  • Unsupervised learning
  • Introduction
  • 15.
  • Random forests
  • 16.
  • Ensemble learning
  • 17.
  • Undirected graphical models
  • 18.
  • High-dimensional problems: p N
  • 2.
  • Overview of supervised learning
  • 3.
  • Linear methods for regression
  • 4.
  • Linear methods for classification
Control code
FIEb1781408x
Dimensions
24 cm.
Edition
2nd edition.
Extent
xxii, 745 pages
Isbn
9780387848846
Media category
unmediated
Media MARC source
rdamedia.
Media type code
  • n
Other physical details
illustrations (some color)
System control number
(OCoLC)300478243

Library Locations

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