Please note that the figures are provided courtesy of MIT Press, Bernhard Schölkopf, and Alex Smola. You may use them in your talks and for teaching, provided that you include a reference to the book.
Preface
1 A Tutorial Introduction
2 Kernels
3 Risk and Loss Functions
4 Regularization
5 Elements of Statistical Learning Theory
6 Optimization
7 Pattern Recognition
8 Single-Class Problems: Quantile Estimation and Novelty Detection
9 Regression Estimation
10 Implementation
11 Incorporating Invariances
12 Learning Theory Revisited
13 Designing Kernels
14 Kernel Feature Extraction
15 Kernel Fisher Discriminant
16 Bayesian Kernel Methods
17 Regularized Principal Manifolds
18 Pre-Images and Reduced Set Methods
A Addenda