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2. Introduction Consistency of learning processes To explain when a learning machine that minimizes empirical risk can achieve a small value of actual risk (to generalize) and when it can not. Equivalently, to describe necessary and sufficient conditions for the consistency of learning processes that minimize the empirical risk. It is extremely important to use concepts that describe necessary and sufficient conditions for consistency. This guarantees that the constructed theory is general and cannot be improved from the conceptual point of view. 2

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4. The classical definition of consistency Let Q(z,α l ) be a function that minimizes the empirical risk functional for a given set of i.i.d. observations z1,…, z l. 4

5. The classical definition of consistency The ERM principle is consistent for the set of functions Q(z, α),αЄΛ, and for the p.d.f. F (z) if the following two sequences converge in probability to the same limit Equation (1) asserts that the values of achieved risks converge to the best possible. Equation (2) asserts that one can estimate on the basis of the values of empirical risk the minimal possible value of the risk. 5

6. The classical definition of consistency 6

7. Goal To obtain conditions of consistency for the ERM method in terms of general characteristics of the set of functions and the probability measure. This is an impossible task because the classical definition of consistency includes cases of trivial consistency. 7

8. Trivial consistency 8

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10. ERM consistency In order to create a theory of consistency of the ERM method depending only on the general properties (capacity) of the set of functions, a consistency definition excluding trivial consistency cases is needed. This is done by non-trivial (strict) consistency definition. 10

11. Non-trivial consistency 11

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13. Key theorem of learning theory 13

14. Consistency of the ERM principle 14

15. Consistency of the ERM principle In contrast to the so-called uniform two-sided convergence defined by the equation 15

16. Consistency of the ML Method 16

17. Consistency of the ML Method 17

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19. Empirical process 19

20. Consistency of an empirical process The necessary and sufficient conditions for an empirical process to converge in probability to zero imply that the equality holds true. 20

21. Law of large numbers and its generalization 21

22. Law of large numbers and its generalization 22

23. Entropy 23

24. Entropy of the set of indicator functions (Diversity) 24

25. Entropy of the set of indicator functions (Diversity) 25

26. Entropy of the set of indicator functions (Random entropy) 26

27. Entropy of the set of real functions (Diversity) 27

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29. VC Entropy of the set of real functions 29

30. VC Entropy of the set of real functions (Random entropy and VC entropy) 30

31. Conditions for uniform two-sided convergence 31

32. Conditions for uniform two-sided convergence (Corollary) 32

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34. Uniform one-sided convergence Uniform two-sided convergence can be described as which includes uniform one-sided convergence, and it's sufficient condition for ERM consistency. But for consistency of ERM method, the second condition on the left-hand side of (9) can be violated. 34

35. Conditions for uniform one-sided convergence 35

36. Conditions for uniform one-sided convergence 36

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38. Models of reasoning Deductive Moving from general to particular. The ideal approach is to obtain corollaries (consequences) using a system of axioms and inference rules. Guarantees that true consequences are obtained from true premises. Inductive Moving from particular to general. Formation of general judgments from particular assertions. Judgments obtained from particular assertions are not always true. 38

39. Demarcation problem Proposed by Kant, it is a central question of inductive theory. Demarcation problem What is the difference between the cases with a justified inductive step and those for which the inductive step is not justified? Is there a formal way to distinguish between true theories and false theories? 39

40. Example Assume that meteorology is a true theory and astrology is a false one. What is the formal difference between them? The complexity of the models? The predictive ability of their models? Their use of mathematics? The level of formality of inference? None of the above gives a clear advantage to either of these theories. 40

41. Criterion for demarcation Suggested by Popper (1930), a necessary condition for justifiability of a theory is the feasibility of its falsification. By falsification, Popper means the existence of a collection of particular assertions that cannot be explained by the given theory although they fall into its domain. If the given theory can be falsified it satisfies the necessary conditions of a scientific theory. 41

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43. Nature of the ERM principle What happens if the condition of one-side convergence (theorem 11) is not valid? Why is the ERM method not consistent is this case? Answer: if uniform two-sided convergence does not take place, then the method of minimizing the empirical risk is non-falsifiable. 43

44. Complete (Popper's) non- falsifiability 44

45. Complete (Popper's) non- falsifiability 45

46. Complete (Popper's) non- falsifiability 46

47. Partial non-falsifiability 47

48. Partial non-falsifiability 48

49. Potential non-falsifiability (Definition) 49

50. Potential non-falsifiability (Graphical representation) A potentially non-falsifiable learning machine 50

51. Potential non-falsifiability (Generalization) 51

52. Potential non-falsifiability 52

53. Refrences Vapnik, Vladimir,”The Nature of Statistical Learning Theory”, 2000 Miguel A. Veganzones, “Consistency and bounds on the rate of convergence for ERM methods” 53

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