Abstract
We give an algorithm that with high probability properly learns random monotone DNF with t(n) terms of length ≈ logt(n) under the uniform distribution on the Boolean cube {0,1}n. For any function t(n) ≤ poly(n) the algorithm runs in time poly(n,1/ε) and with high probability outputs an ε-accurate monotone DNF hypothesis. This is the first algorithm that can learn monotone DNF of arbitrary polynomial size in a reasonable average-case model of learning from random examples only.KeywordsBoolean FunctionFull VersionRandom DrawTerm LengthBoolean CubeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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