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Abstract: this paper, we study the extension of Valiant's learning model [25] 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, 15, 24, 11],... (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/article/kearns93efficient.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/article/kearns93efficient.html" }
Citations (may not include all citations):493 Communications of the ACM (context) - Valiant, of et al. - 1984
465 Learnability and the VapnikChervonenkis dimension (context) - Blumer, Ehrenfeucht et al. - 1989
454 the uniform convergence of relative frequencies of events to.. (context) - Vapnik, Ya et al. - 1971
221 Perceptrons: An Introduction to Computational Geometry (context) - Minsky, Papert - 1988
215 Learning decision lists - Rivest - 1987 ACM DBLP
151 A general lower bound on the number of examples needed for l.. (context) - Ehrenfeucht, Haussler et al. - 1988 ACM DBLP
149 Quantifying inductive bias: AI learning algorithms and Valia.. (context) - Haussler - 1988 ACM DBLP
144 Computational limitations on learning from examples (context) - Pitt, Valiant - 1988 ACM DBLP
142 Learning from noisy examples (context) - Angluin, Laird - 1988 ACM DBLP
115 Efficient distribution-free learning of probabilistic concep.. - Kearns, Schapire - 1990 ACM DBLP
94 Learning in the presence of malicious errors - Kearns, Li - 1988 ACM DBLP
84 Learning disjunctions of conjunctions (context) - Valiant - 1985
78 the learnability of Boolean formulae - Kearns, Li et al. - 1987
66 Constant depth circuits (context) - Linial, Mansour et al. - 1989
58 Statistical mechanics of learning from examples (context) - Seung, Sompolinsky et al. - 1992
52 The Computational Complexity of Machine Learning (context) - Kearns - 1990 ACM
52 Learning from Good and Bad Data (context) - Laird - 1988 ACM
40 Types of noise in data for concept learning (context) - Sloan - 1988
36 A polynomial-time algorithm for learning k-variable pattern .. (context) - Kearns, Pitt - 1989
27 Learning integer lattices (context) - Helmbold, Sloan et al. - 1992 ACM DBLP
22 DNF formulas on product distributions (context) - Hancock, Mansour - 1991
19 Improved learning of AC 0 functions - Furst, Jackson et al. - 1991
12 Learning probabilistic read-once formulas on product distrib.. (context) - Schapire - 1991 ACM DBLP
11 The transition to perfect generalization in perceptrons (context) - Baum, Lyuu - 1991
8 The Design and Analysis of Efficient Learning Algorithms (context) - Schapire - 1992
4 Research Report IIAS-RR-9122E (context) - Sakakibara, of et al. - 1991
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