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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.
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Add Neural Network Learning: Theoretical Foundations, This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research, Neural Network Learning: Theoretical Foundations to the inventory that you are selling on WonderClubX
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Add Neural Network Learning: Theoretical Foundations, This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research, Neural Network Learning: Theoretical Foundations to your collection on WonderClub |