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

By:

Hastie, Trevor

Contributor(s):

Material type: Text

TextLanguage: English Publication details: New York, NY : Springer, c2009.Edition: 2nd edDescription: xxii, 745 p. : ill. (some col.) ; H.B. 25 cmISBN:

9780387848570 (hardcover : alk. paper)
9780387848587 (electronic)

Subject(s):

DDC classification:

R 006.31 TRE(ELE)Ed2

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Item type	Current library	Collection	Call number	Vol info	Copy number	Status	Date due	Barcode
REFERENCE STATISTICS	St. Xavier's University, Kolkata Reference Section	Reference	R 006.31 TRE(ELE)Ed2 (Browse shelf(Opens below))	S.X.U.K	10608	Not For Loan		US10608
STATISTICS	St. Xavier's University, Kolkata Lending Section		006.31 TRE(ELE)Ed2 (Browse shelf(Opens below))	S.X.U.K	10609	Checked out	01/22/2025	S10609
STATISTICS	St. Xavier's University, Kolkata Lending Section		006.31 TRE(ELE)Ed2.C1 (Browse shelf(Opens below))	S.X.U.K	10610	Available		S10610

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Introduction
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 1-8
Overview of Supervised Learning
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 9-41
Linear Methods for Regression
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 43-99
Linear Methods for Classification
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 101-137
Basis Expansions and Regularization
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 139-189
Kernel Smoothing Methods
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 191-218
Model Assessment and Selection
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 219-259
Model Inference and Averaging
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 261-294
Additive Models, Trees, and Related Methods
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 295-336
Boosting and Additive Trees
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 337-387
Neural Networks
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 389-416
Support Vector Machines and Flexible Discriminants
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 417-458
Prototype Methods and Nearest-Neighbors
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 459-483
Unsupervised Learning
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 485-585
Random Forests
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 587-604
Ensemble Learning
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 605-624
Undirected Graphical Models
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 625-648
High-Dimensional Problems: p N
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Pages 649-698
Back Matter

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