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APPROX
2005
Springer

On Learning Random DNF Formulas Under the Uniform Distribution

14 years 4 months ago
On Learning Random DNF Formulas Under the Uniform Distribution
Abstract: We study the average-case learnability of DNF formulas in the model of learning from uniformly distributed random examples. We define a natural model of random monotone DNF formulas and give an efficient algorithm which with high probability can learn, for any fixed constant γ > 0, a random t-term monotone DNF for any t = O(n2−γ). We also define a model of random non-monotone DNF and give an efficient algorithm which with high probability can learn a random t-term DNF for any t = O(n3/2−γ). These are the first known algorithms that can learn a broad class of polynomial-size DNF in a reasonable average-case model of learning from random examples.
Jeffrey C. Jackson, Rocco A. Servedio
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where APPROX
Authors Jeffrey C. Jackson, Rocco A. Servedio
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