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Abstract: We consider the problem of developing robust algorithms which cope with noisy data. In the Probably Approximately Correct model of machine learning, we develop a general technique which allows nearly all PAC learning algorithms to be converted into highly efficient PAC learning algorithms which tolerate noise. In the field of combinatorial algorithms, we develop techniques for constructing search algorithms which tolerate linearly bounded errors and probabilistic errors. (Update)
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BibTeX entry: (Update)
@techreport{ aslam95noise,
author = "J. A Aslam",
title = "Noise Tolerant Algorithms for Learning and Searching",
number = "MIT/LCS/TR-657",
pages = "127",
year = "1995",
url = "citeseer.ist.psu.edu/article/aslam95noise.html" }
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3 Searching with a non-constant number of lies (context) - Frazier - 1990
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