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|Title:||Complexity of learning according to two models of a drifting environment||Authors:||Long, P.M.||Issue Date:||1999||Citation:||Long, P.M. (1999). Complexity of learning according to two models of a drifting environment. Machine Learning 37 (3) : 337-354. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1007666507971||Abstract:||We show that a cε3/VC dim(F) bound on the rate of drift of the distribution generating the examples is sufficient for agnostic learning to relative accuracy ε, where c>0 is a constant; this matches a known necessary condition to within a constant factor. We establish a cε2/VC dim (F) sufficient condition for the realizable case, also matching a known necessary condition to within a constant factor . We provide a relatively simple proof of a bound of O(1/ε2(VC dim (F)+log 1/δ)) on the sample complexity of agnostic learning in a fixed environment.||Source Title:||Machine Learning||URI:||http://scholarbank.nus.edu.sg/handle/10635/39144||ISSN:||08856125||DOI:||10.1023/A:1007666507971|
|Appears in Collections:||Staff Publications|
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