Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/99486
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dc.titleComplexity of learning according to two models of a drifting environment
dc.contributor.authorLong, Philip M.
dc.date.accessioned2014-10-27T06:04:37Z
dc.date.available2014-10-27T06:04:37Z
dc.date.issued1998
dc.identifier.citationLong, Philip M. (1998). Complexity of learning according to two models of a drifting environment. Proceedings of the Annual ACM Conference on Computational Learning Theory : 116-125. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/99486
dc.description.abstractThe problem of learning functions from some set X to {0, 1} using two models of a drifting environment is studied. It is shown that a bound on the rate of drift of the distribution generating the examples is sufficient for learning to relative accuracy; this matches a known necessary condition to within a constant factor. A sufficient condition is established for the realizable case, also matching a known necessary condition to within a constant factor. A relatively simple proof of a bound of on the sample complexity of agnostic learning in a fixed environment is presented.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentINFORMATION SYSTEMS & COMPUTER SCIENCE
dc.description.sourcetitleProceedings of the Annual ACM Conference on Computational Learning Theory
dc.description.page116-125
dc.description.coden215
dc.identifier.isiutNOT_IN_WOS
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