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

By: Hastie, Trevor
Contributor(s): Tibshirani, Robert | Friedman, J. H
Material type: Computer fileComputer filePublisher: New York, : Springer, 2009Edition: Second editionDescription: 1 online resource : illustrationsISBN: 9780387848587 (E-book)Subject(s): MACHINE LEARNING | STATISTICS -- METHODOLOGY | DATA MINING | BIOINFORMATICS | INFERENCE | FORECASTING | COMPUTATIONAL INTELLIGENCELOC classification: Q 325.5 | H37E 2009Online resources: View Full-text
Contents:
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 -- Unsupervised Learning -- Random Forests -- Ensemble Learning -- Undirected Graphical Models -- High-Dimensional Problems: p ≫ N
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Item type Current location Collection Shelving location Call number Status Date due Barcode Item holds Course reserves
E-Book E-Book SPU Library, Bangkhen (Main campus)
Electronic Resources On Display Q 325.5 H37E 2009 (Browse shelf) Available 9780387848587

เทคโนโลยีบิ๊กดาต้า (BIG DATA TECHNOLOGY) ภาคการศึกษาที่ 2

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

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 -- Unsupervised Learning -- Random Forests -- Ensemble Learning -- Undirected Graphical Models -- High-Dimensional Problems: p ≫ N

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