Machine Learning Chapter 7. Computational Learning Theory Tom M. Mitchell. - ppt download

3 Computational Learning Theory (2/2)  What general laws constrain inductive learning?  We seek theory to relate: –Probability of successful learning –Number of training examples –Complexity of hypothesis space –Accuracy to which target concept is approximated –Manner in which training examples presented
(maximum over all possible c  C, and all possible training sequences)  Definition: Let C be an arbitrary non-empty concept class. The optimal mistake bound for C, denoted Opt(C), is the minimum over all possible learning algorithms A of M A (C)..

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