Learning random monotone DNF

5Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

Abstract

We give an algorithm that with high probability properly learns random monotone DNF with t(n) terms of length ≈logt(n) under the uniform distribution on the Boolean cube {0,1}n. For any function t(n)≤poly(n) the algorithm runs in time poly(n,1/ε) and with high probability outputs an ε-accurate monotone DNF hypothesis. This is the first algorithm that can learn monotone DNF of arbitrary polynomial size in a reasonable average-case model of learning from random examples only. Our approach relies on the discovery and application of new Fourier properties of monotone functions which may be of independent interest. © 2010 Elsevier B.V. All rights reserved.

Cite

CITATION STYLE

APA

Jackson, J. C., Lee, H. K., Servedio, R. A., & Wan, A. (2011). Learning random monotone DNF. Discrete Applied Mathematics, 159(5), 259–271. https://doi.org/10.1016/j.dam.2010.08.022

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free