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
Resource Information
The item The elements of statistical learning : data mining, inference, and prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.This item is available to borrow from 1 library branch.
Resource Information
The item The elements of statistical learning : data mining, inference, and prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.
This item is available to borrow from 1 library branch.
 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"
 Language
 eng
 Edition
 2nd edition.
 Extent
 xxii, 745 pages
 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 nearestneighbors
 14.
 Unsupervised learning
 Introduction
 15.
 Random forests
 16.
 Ensemble learning
 17.
 Undirected graphical models
 18.
 Highdimensional problems: p N
 2.
 Overview of supervised learning
 3.
 Linear methods for regression
 4.
 Linear methods for classification
 Isbn
 9780387848846
 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
 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"
 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
 Bibliography note
 Includes bibliographical references (pages 699727) 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 nearestneighbors
 14.
 Unsupervised learning
 Introduction
 15.
 Random forests
 16.
 Ensemble learning
 17.
 Undirected graphical models
 18.
 Highdimensional 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
 Bibliography note
 Includes bibliographical references (pages 699727) 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 nearestneighbors
 14.
 Unsupervised learning
 Introduction
 15.
 Random forests
 16.
 Ensemble learning
 17.
 Undirected graphical models
 18.
 Highdimensional 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
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.library.eui.eu/portal/Theelementsofstatisticallearningdata/kEo8I70Gn90/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/Theelementsofstatisticallearningdata/kEo8I70Gn90/">The elements of statistical learning : data mining, inference, and prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>