文章基本信息

标题：Optimal attribute-efficient learning of disjunction, parity, and threshold functions
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作者：Ryuhei Uehara ; Kensei Tsuchida ; Ingo Wegener 等
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：Decision trees are a very general computation model. Here the problem is to identify a Boolean function f out of a given set of Boolean functions F by asking for the value of f at adaptively chosen inputs. For classes F consisting of functions which may be obtained from one function g on n inputs by replacing arbitrary n−k inputs by given constants this problem is known as attribute-efficient learning with k essential attributes. Results on general classes of functions are known. More precise and often optimal results are presented for the cases where g is one of the functions disjunction, parity or threshold.