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The elements of statistical learning : data mining, inference, and prediction /

By: Contributor(s): Material type: TextTextSeries: Springer series in statisticsPublisher: New York : Springer Science+Business Media, LLC., Springer Nature, [2009]Copyright date: ©2009Edition: Second editionDescription: 1 online resource (xxii, 745 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780387848587
Subject(s): Genre/Form: Online resources:
Contents:
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Item type Current library Collection Call number URL Status Date due Barcode Item holds
eBook eBook Online Information Technology Link to resource Available E1000042
Total holds: 0

Includes bibliographical references and indexes.

Introduction -- Overview of Supervised Learning -- Linear Methods for Regression -- Linear Methods for Classification -- Basis Expansions and Regularization -- Kernel Smoothing Methods -- Model Assessment and Selection -- Model Inference and Averaging -- Additive Models, Trees, and Related Methods -- Boosting and Additive Trees -- Neural Networks -- Support Vector Machines and Flexible Discriminants --Prototype Methods and Nearest-Neighbors -- Prototype Methods and Nearest-Neighbors -- Random Forests -- Random Forests -- Random Forests -- High-Dimensional Problems: p N -- References -- Index.

Available on-campus and off-campus.

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