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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"--Jacket
Member of
Cataloging source
NUI
http://library.link/vocab/creatorName
Hastie, Trevor
Illustrations
illustrations
Index
index present
LC call number
Q325.75
LC item number
.H37 2009
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
  • Computational Biology
  • Data Mining
  • Supervised learning (Machine learning)
  • Electronic data processing
  • Statistics
  • Biology
  • Computational biology
  • Mathematics
  • Data mining
  • Biology
  • Computational biology
  • Data mining
  • Electronic data processing
  • Mathematics
  • Statistics
  • Supervised learning (Machine learning)
Label
The elements of statistical learning : data mining, inference, and prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman
Instantiates
Publication
Bibliography note
Includes bibliographical references (p. [699]-727) and indexes
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
Dimensions
24 cm
Edition
2nd ed
Extent
xxii, 745 p.
Isbn
9780387848570
Lccn
2008941148
Other physical details
ill. (some col.)
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 (p. [699]-727) and indexes
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
Dimensions
24 cm
Edition
2nd ed
Extent
xxii, 745 p.
Isbn
9780387848570
Lccn
2008941148
Other physical details
ill. (some col.)
System control number
(OCoLC)300478243

Library Locations

    • Biddle Law LibraryBorrow it
      3400 Chestnut Street, Philadelphia, Pennsylvania, 19104, US
      39.954941 -75.193362
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