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.

• Type: Conference
• Year: 2005
• Where: APPROX
• Authors: Jeffrey C. Jackson, Rocco A. Servedio
• Keywords: Algorithms, APPROX 2005, DNF Formulas, Monotone Dnf, Random Monotone Dnf