65 results on '"Bartlett, Peter L."'
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2. Learning Theory
3. A Learning-Based Approach to Reactive Security
4. A Regularization Approach to Metrical Task Systems
5. A Unifying View of Multiple Kernel Learning
6. Optimal Online Prediction in Adversarial Environments
7. Sample complexity of policy search with known dynamics
8. Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds
9. Bounded Parameter Markov Decision Processes with Average Reward Criterion
10. On the Consistency of Multiclass Classification Methods
11. Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results
12. Local Complexities for Empirical Risk Minimization
13. An Introduction to Reinforcement Learning Theory: Value Function Methods
14. Localized Rademacher Complexities
15. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
16. Efficient neural network learning
17. Perceptron Learning with Reasonable Distributions of Examples
18. Investigating the Distribution Assumptions in the Pac Learning Model
19. A Learning-Based Approach to Reactive Security.
20. A Unifying View of Multiple Kernel Learning.
21. Bounded Parameter Markov Decision Processes with Average Reward Criterion.
22. AdaBoost is Consistent.
23. On the Consistency of Multiclass Classification Methods.
24. Chapter III: Boosting: 12: Functional Gradient Techniques for Combining Hypotheses.
25. Subject index.
26. Bibliography.
27. Useful Results.
28. Constructive Learning Algorithms for Two-Layer Networks.
29. The Boolean Perceptron.
30. Hardness Results for Feed-Forward Networks.
31. Learning as Optimization.
32. Efficient Learning.
33. Other Learning Problems.
34. Convex Classes.
35. Sample Complexity of Learning Real Function Classes.
36. Learning Classes of Real Functions.
37. Bounding Covering Numbers.
38. Uniform Convergence Results for Real Function Classes.
39. Model Selection.
40. The Dimensions of Neural Networks.
41. The Sample Complexity of Classification Learning.
42. Bounding Covering Numbers with Dimensions.
43. The Pseudo-Dimension and Fat-Shattering Dimension.
44. Classification with Real-Valued Functions.
45. Covering Numbers and Uniform Convergence.
46. Vapnik-Chervonenkis Dimension Bounds for Neural Networks.
47. Bounding the VC-Dimension using Geometric Techniques.
48. The VC-Dimension of Linear Threshold Networks.
49. General Lower Bounds on Sample Complexity.
50. General Upper Bounds on Sample Complexity.
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