Self bounding learning algorithms (1998)
| Venue: | In Proceedings of the Eleventh Annual Conference on Computational Learning Theory |
| Citations: | 17 - 0 self |
BibTeX
@INPROCEEDINGS{Freund98selfbounding,
author = {Yoav Freund},
title = {Self bounding learning algorithms},
booktitle = {In Proceedings of the Eleventh Annual Conference on Computational Learning Theory},
year = {1998},
pages = {247--258},
publisher = {Morgan Kaufmann}
}
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Abstract
Most of the work which attempts to give bounds on the generalization error of the hypothesis generated by a learning algorithm is based on methods from the theory of uniform convergence. These bounds are a-priori bounds that hold for any distribution of examples and are calculated before any data is observed. In this paper we propose a different approach for bounding the generalization error after the data has been observed. A self-bounding learning algorithm is an algorithm which, in addition to the hypothesis that it outputs, outputs a reliable upper bound on the generalization error of this hypothesis. We first explore the idea in the statistical query learning framework of Kearns [10]. After that we give an explicit self bounding algorithm for learning algorithms that are based on local search. 1
