Property Testing
Current Research and Surveys
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Beschreibung
Property Testing is the study of super-fast (randomized) algorithms for approximate decision making. These algorithms are given direct access to items of a huge data set, and determine, whether this data set has some predetermined (global) property or is far from having this property. Remarkably, this approximate decision is made by accessing a small portion of the data set. This state-of-the-art survey presents a collection of extended abstracts and surveys of leading researchers in property testing and related areas; it reflects the program of a mini-workshop on property testing that took place in January 2010 at the Institute for Computer Science (ITCS), Tsinghua University, Beijing, China. The volume contains two editor's introductions, 10 survey papers and 18 extended abstracts.
Introduction to property testing Survey by leading researchers in the field Subject of intensive research in the last decades
Autor*in
Oded Goldreich
Themen in »Property Testing«
VLDB algorithms clustering coding theory combinatorics complexity computational geometry computational learning theory decision making graph theory huge data set learning statistics vertex cover algorithm analysis and problem complexity