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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© 1997 Springer-Verlag Berlin Heidelberg
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Uehara, R., Tsuchida, K., Wegener, I. (1997). Optimal attribute-efficient learning of disjunction, parity, and threshold functions. In: Ben-David, S. (eds) Computational Learning Theory. EuroCOLT 1997. Lecture Notes in Computer Science, vol 1208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62685-9_15
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DOI: https://doi.org/10.1007/3-540-62685-9_15
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