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COLT
2001
Springer

On Learning Monotone DNF under Product Distributions

10 years 3 months ago
On Learning Monotone DNF under Product Distributions
We show that the class of monotone 2O( √ 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(log2 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. ∗ This research was performed while the author was at Harvard University supported by NSF Grant CCR-98-77049 and by an NSF Mathematical Sciences Postdoctoral Research Fellowship. 1
Rocco A. Servedio
Added 28 Jul 2010
Updated 28 Jul 2010
Type Conference
Year 2001
Where COLT
Authors Rocco A. Servedio
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