The elements of statistical learning : data mining, inference, and prediction /
Material type:
TextSeries: 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
- computer
- online resource
- 9780387848587
| Item type | Current library | Collection | Call number | URL | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|---|
eBook
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Online | Information Technology | Link to resource | Available | E1000042 |
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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