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.
