What's New in this Edition

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:

Our first edition was unfriendly to colorblind readers; in particular, we tended to favor red/ green contrasts which are particularly troublesome. We have changed the color palette in this edition to a large extent, replacing the above with an orange/ blue contrast.

We have changed the name of Chapter 6 from ``Kernel Methods'' to ``Kernel Smoothing Methods'', to avoid confusion with the machine-learning kernel method that is discussed in the context of support vector machines (Chapter 11) and more generally in Chapters 5 and 14.

In the first edition, the discussion of error-rate estimation in Chapter 7 was sloppy, as we did not clearly differentiate the notions of conditional error rates (conditional on the training set) and unconditional rates. We have fixed this in the new edition.

Chapters 15 and 16 follow naturally from Chapter 10, and the chapters are probably best read in that order.

In Chapter 17, we have not attempted a comprehensive treatment of graphical models, and discuss only undirected models and some new methods for their estimation. Due to a lack of space, we have specifically omitted coverage of directed graphical models.

Chapter 18 explores the p greater than N problem, which is learning in high-dimensional feature spaces. These problems arise in many areas, including genomic and proteomic studies, and document classification.