1. The learning methodology;
1.1 Supervised Learning
1.2 Learning and Generalisation
1.3 Inproving Generalisation
1.4 Attractions and Drawbacks of Learning
1.5 Support Vector Machines for Learning
1.6 Exercises
1.7 Further Reading and Advanced Topics
2. Linear learning machines;
2.1 Linear Classification
2.2 Linear Regression
2.3 Dual Representation of Linear Machines
2.4 Exercises
2.5 Further Reading and Advanced Topics
3. Kernel-induced feature spaces;
3.1 Learning in Feature Space
3.2 The Implicit Mapping into Feature Space
3.3 Making Kernels
3.4 Working in Feature Space
3.5 Kernels and Gaussian Processes
3.6 Exercises
3.7 Further Reading and Advanced Topics
4. Generalisation theory;
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5. Optimisation theory;
6. Support vector machines;
7. Implementation techniques;
8. Applications of support vector machines;
Pseudocode for the SMO algorithm;
Background Mathematics;
Index.