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  • 标题:Property Testing and its connection to Learning and Approximation
  • 本地全文:下载
  • 作者:Oded Goldreich, Dana Ron
  • 期刊名称:Electronic Colloquium on Computational Complexity
  • 印刷版ISSN:1433-8092
  • 出版年度:1996
  • 卷号:1996
  • 出版社:Universität Trier, Lehrstuhl für Theoretische Computer-Forschung
  • 摘要:In this paper, we consider the question of determining whether a function f has property P or is \e-far from any function with property P. The property testing algorithm is given a sample of the value of f on instances drawn according to some distribution. In some cases, it is also allowed to query f on instances of its choice. We study this question for different properties and establish some connection to problems in learning theory and approximation. In particular we focus our attention on testing graph properties. Given access to a graph G in the form of being able to query whether an edge exists or not between a pair of vertices, we devise algorithms to test whether the underlying graph has properties such as being bipartite, k-colorable, or having a -clique (clique of density w.r.t the vertex set). Our graph property testing algorithms are probabilistic and make assertions which are correct with high probability, utilizing only a constant number of queries into the graph. Moreover, the property testing algorithms can be used to efficiently (i.e., in time linear in the number of vertices) construct partitions of the graph which correspond to the property being tested, if it holds for the input graph. For k-colorability this sheds new light on the problem of approximatly coloring a k-colorable graph
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