Online Learning and Resource-Bounded Dimension: Winnow Yields New Lower Bounds for Hard Sets
Computational Complexity
2007-05-23 v1 Machine Learning
Abstract
We establish a relationship between the online mistake-bound model of learning and resource-bounded dimension. This connection is combined with the Winnow algorithm to obtain new results about the density of hard sets under adaptive reductions. This improves previous work of Fu (1995) and Lutz and Zhao (2000), and solves one of Lutz and Mayordomo's "Twelve Problems in Resource-Bounded Measure" (1999).
Cite
@article{arxiv.cs/0512053,
title = {Online Learning and Resource-Bounded Dimension: Winnow Yields New Lower Bounds for Hard Sets},
author = {John M. Hitchcock},
journal= {arXiv preprint arXiv:cs/0512053},
year = {2007}
}