(Enter summary)
Abstract: this paper, we study the extension of Valiant's learning model [32] in which
the positive or negative classification label provided with each random example
may be corrupted by random noise. This extension was first examined in the
learning theory literature by Angluin and Laird [1], who formalized the simplest
type of white label noise and then sought algorithms tolerating the highest
possible rate of noise. In addition to being the subject of a number of theoretical
studies [1, 22, 31, 17],... (Update)
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BibTeX entry: (Update)
Kearns, M. (1993). Efficient noise-tolerant learning from statistical queries. In Proceedings of the Twenty-Fifth Annual ACM Symposium on Theory of Computing, pages 392--401. http://citeseer.ist.psu.edu/kearns98efficient.html More
@inproceedings{ kearns93efficient,
author = "Michael Kearns",
title = "Efficient noise-tolerant learning from statistical queries",
pages = "392--401",
year = "1993",
url = "citeseer.ist.psu.edu/kearns98efficient.html" }
Citations (may not include all citations):
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