Learning Monotone Decision Trees in Polynomial Time.
R. O'Donnell and R. Servedio.
SIAM Journal on Computing 37(3), 2007, pp. 827--844.
Preliminary version in Eighteenth Annual Conference on Computational Complexity (CCC), 2006, pp. 213--225.


We give an algorithm that learns any monotone Boolean function $\fisafunc$ to any constant accuracy, under the uniform distribution, in time polynomial in $n$ and in the decision tree size of $f.$ This is the first algorithm that can learn arbitrary monotone Boolean functions to high accuracy, using random examples only, in time polynomial in a reasonable measure of the complexity of $f.$ A key ingredient of the result is a new bound showing that the average sensitivity of any monotone function computed by a decision tree of size $s$ must be at most $\sqrt{\log s}$.

We generalize the basic inequality and learning result described above in various ways; specifically, to partition size (a stronger complexity measure than decision tree size), $p$-biased measures over the Boolean cube (rather than just the uniform distribution), and real-valued (rather than just Boolean-valued) functions.

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