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Título:	Sequential sampling algorithms: unified analysis and lower bounds
Autor/a:	Gavaldà Mestre, Ricard; Watanabe, Osamu
Otros autores:	Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
Abstract:	Sequential 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.
Materia(s):	-Àrees temàtiques de la UPC::Informàtica -Random sampling -Sequential sampling -Adaptive sampling -Cherno bounds -Data mining
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Tipo de documento:	Artículo - Versión publicada Informe
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