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|Title:||Improved bounds on the sample complexity of learning|
|Authors:||Li, Y. |
Empirical process theory
|Citation:||Li, Y., Long, P.M., Srinivasan, A. (2001). Improved bounds on the sample complexity of learning. Journal of Computer and System Sciences 62 (3) : 516-527. ScholarBank@NUS Repository. https://doi.org/10.1006/jcss.2000.1741|
|Abstract:||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.|
|Source Title:||Journal of Computer and System Sciences|
|Appears in Collections:||Staff Publications|
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