The Optimal Sample Complexity of PAC Learning
Machine Learning
2016-09-13 v4 Machine Learning
Abstract
This work establishes a new upper bound on the number of samples sufficient for PAC learning in the realizable case. The bound matches known lower bounds up to numerical constant factors. This solves a long-standing open problem on the sample complexity of PAC learning. The technique and analysis build on a recent breakthrough by Hans Simon.
Cite
@article{arxiv.1507.00473,
title = {The Optimal Sample Complexity of PAC Learning},
author = {Steve Hanneke},
journal= {arXiv preprint arXiv:1507.00473},
year = {2016}
}