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|Title:||On the sample complexity of learning functions with bounded variation||Authors:||Long, Philip M.||Issue Date:||1998||Citation:||Long, Philip M. (1998). On the sample complexity of learning functions with bounded variation. Proceedings of the Annual ACM Conference on Computational Learning Theory : 126-133. ScholarBank@NUS Repository.||Abstract:||We show that the class FBV of [0, 1]-valued functions with total variation at most 1 can be agnostically learned with respect to the absolute loss in polynomial time from O (1/ε2 log 1/δ) examples, matching a known lower bound to within a constant factor. We establish a bound of O (1/m) on the expected error of a polynomial-time algorithm for learning FBV in the prediction model, also matching a known lower bound to within a constant factor. Applying a known algorithm transformation to our prediction algorithm, we obtain a polynomial-time PAC learning algorithm for FBV with a sample complexity bound of O (1/ε log 1/δ); this also matches a known lower bound to within a constant factor.||Source Title:||Proceedings of the Annual ACM Conference on Computational Learning Theory||URI:||http://scholarbank.nus.edu.sg/handle/10635/99572|
|Appears in Collections:||Staff Publications|
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