Please use this identifier to cite or link to this item:
|Title:||Improved bounds on the sample complexity of learning||Authors:||Li, Y.
Empirical process theory
|Issue Date:||2001||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||URI:||http://scholarbank.nus.edu.sg/handle/10635/43067||ISSN:||00220000||DOI:||10.1006/jcss.2000.1741|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 6, 2019
WEB OF SCIENCETM
checked on Nov 28, 2019
checked on Dec 2, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.