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dc.contributor.authorGavaldà Mestre, Ricard
dc.contributor.authorWatanabe, Osamu
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-12-07T10:32:46Z
dc.date.available2016-12-07T10:32:46Z
dc.date.issued2001-11
dc.identifier.citationGavaldà, R., Watanabe, O. "Sequential sampling algorithms: unified analysis and lower bounds". 2001.
dc.identifier.urihttp://hdl.handle.net/2117/97833
dc.description.abstractSequential sampling algorithms have recently attracted interest as a way to design scalable algorithms for Data mining and KDD processes. In this paper, we identify an elementary sequential sampling task (estimation from examples), from which one can derive many other tasks appearing in practice. We present a generic algorithm to solve this task and an analysis of its correctness and running time that is simpler and more intuitive than those existing in the literature. For two specific tasks, frequency and advantage estimation, we derive lower bounds on running time in addition to the general upper bounds.
dc.format.extent19 p.
dc.language.isoeng
dc.relation.ispartofseriesLSI-01-50-R
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.otherRandom sampling
dc.subject.otherSequential sampling
dc.subject.otherAdaptive sampling
dc.subject.otherCherno bounds
dc.subject.otherData mining
dc.titleSequential sampling algorithms: unified analysis and lower bounds
dc.typeExternal research report
dc.rights.accessOpen Access
local.identifier.drac1893850
dc.description.versionPostprint (published version)
local.citation.authorGavaldà, R.; Watanabe, O.


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