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1. Let be a hypothesis class of binary classifiers. Show that if is agnostic PAC learnable, then His PAC learnable as well. Furthermore, if is a successful agnostic PAC learner for H, then is also a successful PAC learner for H.

2. (*) The Bayes optimal predictor: Show that for every probability distribution D, the Bayes optimal predictor fD is optimal, in the sense that for every classifier from to {0,1}, LDfD) ≤ LD(g).

3. (*) We say that a learning algorithm A is better than B with respect to some probability distribution, D, if

LD(A(S))≤ LD(B(S)) for all samples ∈ (×{0,1})m.We say that a learning algorithm A is better than B, if it is better than with respect to all probability distributions over ×{0,1}.

 

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