Decidability of Sample Complexity of PAC Learning in finite setting
Machine Learning
2020-02-27 v1 Logic in Computer Science
Machine Learning
Abstract
In this short note we observe that the sample complexity of PAC machine learning of various concepts, including learning the maximum (EMX), can be exactly determined when the support of the probability measures considered as models satisfies an a-priori bound. This result contrasts with the recently discovered undecidability of EMX within ZFC for finitely supported probabilities (with no a priori bound). Unfortunately, the decision procedure is at present, at least doubly exponential in the number of points times the uniform bound on the support size.
Cite
@article{arxiv.2002.11519,
title = {Decidability of Sample Complexity of PAC Learning in finite setting},
author = {Alberto Gandolfi},
journal= {arXiv preprint arXiv:2002.11519},
year = {2020}
}