## On learning monotone DNF under product distributions (2001)

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Venue: | In Proceedings of the Fourteenth Annual Conference on Computational Learning Theory |

Citations: | 32 - 15 self |

### BibTeX

@INPROCEEDINGS{Servedio01onlearning,

author = {Rocco A. Servedio},

title = {On learning monotone DNF under product distributions},

booktitle = {In Proceedings of the Fourteenth Annual Conference on Computational Learning Theory},

year = {2001},

pages = {473--489}

}

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### Abstract

We show that the class of monotone 2 O( √ log n)-term DNF formulae can be PAC learned in polynomial time under the uniform distribution from random examples only. This is an exponential improvement over the best previous polynomial-time algorithms in this model, which could learn monotone o(log 2 n)-term DNF. We also show that various classes of small constant-depth circuits which compute monotone functions are PAC learnable in polynomial time under the uniform distribution. All of our results extend to learning under any constant-bounded product distribution.