The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman. [electronic resource]
Material type:![Computer file](/opac-tmpl/lib/famfamfam/CF.png)
- 9780387848587 (E-book)
- Q 325.5 H37E 2009
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
Item type | Current library | Collection | Shelving location | Call number | Status | Date due | Barcode | Item holds | Course reserves | |
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SPU Library, Bangkok (Main Campus) | Electronic Resources | On Display | Q 325.5 H37E 2009 (Browse shelf(Opens below)) | Available | 9780387848587 |
Total holds: 0
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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