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 Biddle Law Library University of Pennsylvania Law School.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 Biddle Law Library University of Pennsylvania Law School.
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"Jacket
 Language
 eng
 Edition
 2nd ed
 Extent
 xxii, 745 p.
 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
 9780387848570
 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
 Subject

 Computational biology
 Data Mining
 Data mining
 Data mining
 Electronic data processing
 Electronic data processing
 Mathematics  Data processing
 Mathematics  Data processing
 Statistics
 Statistics
 Supervised learning (Machine learning)
 Supervised learning (Machine learning)
 Biology  Data processing
 Biology  Data processing
 Computational Biology
 Computational biology
 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
 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
 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 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
 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
 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 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
 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
Subject
 Computational biology
 Data Mining
 Data mining
 Data mining
 Electronic data processing
 Electronic data processing
 Mathematics  Data processing
 Mathematics  Data processing
 Statistics
 Statistics
 Supervised learning (Machine learning)
 Supervised learning (Machine learning)
 Biology  Data processing
 Biology  Data processing
 Computational Biology
 Computational biology
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