Relation between feasibility of learning and Hoeffding's Inequality.
Hello.
I understand that learning is picking out a function from a candidate set of functions that most closely resembles the target function. The feasibility of learning would be related to how close this resemblance is.
I understand also that Hoeffding's Inequality is an upper bound to the probability that the insample error rate deviates significantly from the real error rate. In the end, this upper bound simply implies that given a large enough sample, estimating the real error rate is feasible.
Is there any misconception in anything here so far?
So my question is: what does the Hoeffding inequality say about the feasibility of learning? Shouldn't it be feasibility of verifying hypothesis?
