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	Barcode
STATISTICS	St. Xavier's University, Kolkata Lending Section		006.31 TRE(ELE)Ed2.C2 (Browse shelf(Opens below))		14178	Available	S14178
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	Available	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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