Michael Kearns ATT Bell Laboratories Ming Li University of Waterloo  Home/Search
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Abstract: In this paper we study an extension of the distribution-free model of learning introduced by Valiant [23] (also known as the probably approximately correct or PAC model) that allows the presence of malicious errors in the examples given to a learning algorithm. Such errors are generated by an adversary with unbounded computational power and access to the entire history of the learning algorithm's computation. Thus, we study a worst-case model of errors. (Update)
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BibTeX entry: (Update)
Michael Kearns and Ming Li. Learning in the presence of malicious errors. SIAM Journal on Computing, 22(4):807--837, August 1993. http://citeseer.ist.psu.edu/kearns93learning.html More
@inproceedings{ kearns88learning,
author = "Michael Kearns and Ming Li",
title = "Learning in the presence of malicious errors",
pages = "267--280",
year = "1988",
url = "citeseer.ist.psu.edu/kearns93learning.html" }
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149 Information Processing Letters (context) - Blumer, Ehrenfeucht et al. - 1987
142 Learning from noisy examples (context) - Angluin, Laird - 1988 ACM DBLP
139 A greedy heuristic for the set covering problem (context) - Chvatal - 1979
132 Fast probabilistic algorithms for Hamiltonian circuits and m.. (context) - Angluin, Valiant - 1979 ACM DBLP
94 Learning in the presence of malicious errors - Kearns, Li - 1988 ACM DBLP
81 Equivalence of models for polynomial learnability (context) - Haussler, Kearns et al. ACM DBLP
78 the learnability of Boolean formulae - Kearns, Li et al. - 1987
36 A polynomial-time algorithm for learning k-variable pattern .. (context) - Kearns, Pitt
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