English
资源说明：Chapter What’s new
1. Introduction
2. Overview of Supervised Learning
3. Linear Methods for Regression LAR algorithm and generalizations
of the lasso
4. Linear Methods for Classification Lasso path for logistic regression
5. Basis Expansions and Regulariza-tion
Additional illustrations of RKHS
6. Kernel Smoothing Methods
7. Model Assessment and Selection Strengths and pitfalls of cross-validation
8. Model Inference and Averaging
9. Additive Models, Trees, and
Related Methods
10. Boosting and Additive Trees New example from ecology; some
material split off to Chapter 16.
11. Neural Networks Bayesian neural nets and the NIPS
2003 challenge
12. Support Vector Machines and
Flexible Discriminants
Path algorithm for SVM classifier
13. Prototype Methods and
Nearest-Neighbors
14. Unsupervised Learning Spectral clustering, kernel PCA,
sparse PCA, non-negative matrix
factorization archetypal analysis,
nonlinear dimension reduction,
Google page rank algorithm, a
direct approach to ICA
15. Random Forests New
16. Ensemble Learning New
17. Undirected Graphical Models New
18. High-Dimensional Problems New
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