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|Title:||Improved bounds on the sample complexity of learning||Authors:||Li, Yi
Long, Philip M.
|Issue Date:||2000||Citation:||Li, Yi,Long, Philip M.,Srinivasan, Aravind (2000). Improved bounds on the sample complexity of learning. Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms : 309-318. ScholarBank@NUS Repository.||Abstract:||We present two improved bounds on the sample complexity of learning. First, we present a new general upper bound on the number of examples required to estimate all of the expectations of a set of random variables uniformly well. The quality of the estimates is measured using a variant of the relative error proposed by Haussler and Pollard. We also show that our bound is within a constant factor of the best possible. Our upper bound implies improved bounds on the sample complexity of learning according to Haussler's decision theoretic model. Next, we prove a lower bound on the sample complexity for learning according to the prediction model that is optimal to within a factor of 1+o(1).||Source Title:||Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms||URI:||http://scholarbank.nus.edu.sg/handle/10635/43158|
|Appears in Collections:||Staff Publications|
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