1. Introduction
Part I. Pattern Recognition with Binary-output Neural Networks: 2. The pattern recognition problem
3. The growth function and VC-dimension
4. General upper bounds on sample complexity
5. General lower bounds
6. The VC-dimension of linear threshold networks
7. Bounding the VC-dimension using geometric techniques
8. VC-dimension bounds for neural networks
Part II. Pattern Recognition with Real-output Neural Networks: 9. Classification with real values
10. Covering numbers and uniform convergence
11. The pseudo-dimension and fat-shattering dimension
12. Bounding covering numbers with dimensions
13. The sample complexity of classification learning
14. The dimensions of neural networks
15. Model selection
Part III. Learning Real-Valued Functions: 16. Learning classes of real functions
17. Uniform convergence results for real function classes
18. Bounding covering numbers
19. The sample complexity of learning function classes
20. Convex classes
21. Other learning problems
Part IV. Algorithmics: 22. Efficient learning
23. Learning as optimisation
24. The Boolean perceptron
25. Hardness results for feed-forward networks
26. Constructive learning algorithms for two-layered networks.
