Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/99486
Title: Complexity of learning according to two models of a drifting environment
Authors: Long, Philip M. 
Issue Date: 1998
Citation: Long, 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.
Abstract: The 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.
Source Title: Proceedings of the Annual ACM Conference on Computational Learning Theory
URI: http://scholarbank.nus.edu.sg/handle/10635/99486
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