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首页> 外文期刊>LIPIcs : Leibniz International Proceedings in Informatics >Error-Tolerant Non-Adaptive Learning of a Hidden Hypergraph
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Error-Tolerant Non-Adaptive Learning of a Hidden Hypergraph

机译:隐藏超图的容错非自适应学习

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We consider the problem of learning the hypergraph using edge-detecting queries. In this model, the learner is allowed to query whether a set of vertices includes an edge from a hidden hypergraph. Except a few, all previous algorithms assume that a query's result is always correct. In this paper we study the problem of learning a hypergraph where alpha -fraction of the queries are incorrect. The main contribution of this paper is generalizing the well-known structure CFF (Cover Free Family) to be Dense (we will call it DCFF - Dense Cover Free Family) while presenting three different constructions for DCFF. Later, we use these constructions wisely to give a polynomial time non-adaptive learning algorithm for a hypergraph problem with at most alpha-fracion incorrect queries. The hypergraph problem is also known as both monotone DNF learning problem, and complexes group testing problem.
机译:我们考虑使用边缘检测查询来学习超图的问题。在此模型中,允许学习者查询一组顶点是否包含来自隐藏超图的边。除少数例外,所有以前的算法均假定查询结果始终正确。在本文中,我们研究了在查询的alpha分数不正确的情况下学习超图的问题。本文的主要贡献是将众所周知的CFF(无封面家庭)结构概括为Dense(我们将其称为DCFF-Dense Cover Free Family),同时介绍了DCFF的三种不同结构。后来,我们明智地使用这些构造来为最多具有alpha分数不正确查询的超图问题提供多项式时间非自适应学习算法。超图问题也被称为单调DNF学习问题和复杂群测试问题。

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