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APPROX
2008
Springer

Learning Random Monotone DNF

10 years 19 days ago
Learning Random Monotone DNF
We give an algorithm that with high probability properly learns random monotone DNF with t(n) terms of length log t(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/ ) and with high probability outputs an -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.
Jeffrey C. Jackson, Homin K. Lee, Rocco A. Servedi
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where APPROX
Authors Jeffrey C. Jackson, Homin K. Lee, Rocco A. Servedio, Andrew Wan
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