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Noise Tolerant Algorithms for Learning and Searching

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Author(s)
Aslam, Javed Alexander
Advisor(s)
Rivest, Ronald L.
Date Issued
February 1995
Series/Report no.
MIT-LCS-TR-657
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
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