The elements of statistical learning : data mining, inference, and prediction /
- Second edition.
- 1 online resource (xxii, 745 pages) : illustrations.
- Springer series in statistics, 2197-568X .
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.
9780387848587
Machine learning. Statistics--methodology. Data mining. Bioinformatics. Computational intelligence.