Complexity theoretic limitations on learning DNF's
Machine Learning
2014-11-05 v2 Computational Complexity
Abstract
Using the recently developed framework of [Daniely et al, 2014], we show that under a natural assumption on the complexity of refuting random K-SAT formulas, learning DNF formulas is hard. Furthermore, the same assumption implies the hardness of learning intersections of halfspaces, agnostically learning conjunctions, as well as virtually all (distribution free) learning problems that were previously shown hard (under complexity assumptions).
Cite
@article{arxiv.1404.3378,
title = {Complexity theoretic limitations on learning DNF's},
author = {Amit Daniely and Shai Shalev-Shwatz},
journal= {arXiv preprint arXiv:1404.3378},
year = {2014}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1311.2272