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

By: Contributor(s): Material type: TextTextSeries: Springer series in statisticsPublication details: New York, NY : Springer, 2017.Edition: 2nd edDescription: xxii, 745 p. : ill. (some col.) ; 25 cmISBN:
  • 9780387848570
Subject(s): DDC classification:
  • 006.3/1 22
LOC classification:
  • Q325.75 .H37 2009
Contents:
Overview of supervised learning -- Linear methods for regression -- Linear methods for classification -- Basic expansions and regularization -- Kernel smoothing methods -- Mode;s assessment and selection -- Model assessment and selection -- Additive models,trees , and related methods --Boosting and additive trees -- Natural networks -- Support vector machines and flexible discriminants -- Prototype methods and nearest- neighbors Unsupervised learning -- Random forests -- Ensemble learning -- Undirected graphic models - High- dimensional problems : p>>n
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode
Books Books Zetech Library - TRC General Stacks Non-fiction Q325.75 .H37 2017 (Browse shelf(Opens below)) C2 Available Z010870
Books Books Zetech Library - TRC General Stacks Non-fiction Q325.75 .H37 2017 (Browse shelf(Opens below)) C1 Available Z009609

Includes bibliographical references (p. [699]-727) and indexes.

Overview of supervised learning -- Linear methods for regression -- Linear methods for classification -- Basic expansions and regularization -- Kernel smoothing methods -- Mode;s assessment and selection -- Model assessment and selection -- Additive models,trees , and related methods --Boosting and additive trees -- Natural networks -- Support vector machines and flexible discriminants -- Prototype methods and nearest- neighbors Unsupervised learning -- Random forests -- Ensemble learning -- Undirected graphic models - High- dimensional problems : p>>n

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