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Paper:

TR96-061 | 27th November 1996 00:00

Optimal attribute-efficient learning of disjunction, parity, and threshold functions

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TR96-061
Authors: Ryuhei Uehara, Kensei Tsuchida, Ingo Wegener
Publication: 28th November 1996 11:10
Downloads: 852
Keywords: 


Abstract:

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.



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