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Abstract: We show that the class of monotone 2 O( p log n) -term DNF formulae can be PAC learned in polynomial time under the uniform distribution. This is an exponential improvement over previous algorithms in this model, which could learn monotone o(log 2 n)-term DNF, and is the first efficient algorithm for monotone (log n) !(1) -term DNF in any nontrivial model of learning from random examples. Our result extends to any constant-bounded product distribution. Supported in part by an NSF... (Update)
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BibTeX entry: (Update)
R. Servedio. On learning monotone DNF under product distributions. In Proceedings of the Fourteenth Annual Conference on Computational Learning Theory, pages 473-489, 2001. http://citeseer.ist.psu.edu/servedio01learning.html More
@inproceedings{ servedio01learning,
author = "Rocco A. Servedio",
title = "On Learning Monotone {DNF} under Product Distributions",
booktitle = "14th Annual Conference on Computational Learning Theory, {COLT} 2001 and 5th {E}uropean Conference on Computational Learning Theory, {EuroCOLT} 2001, Amsterdam, The Netherlands, July 2001, Proceedings",
volume = "2111",
publisher = "Springer, Berlin",
pages = "558--573",
year = "2001",
url = "citeseer.ist.psu.edu/servedio01learning.html" }
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