Here is a detailed list of the changes in this edition:
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 Regularization | 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 |
Some further notes: