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dc.contributor.advisorRivest, Ronald L.en_US
dc.contributor.authorAslam, Javed Alexanderen_US
dc.date.accessioned2023-03-29T15:25:00Z
dc.date.available2023-03-29T15:25:00Z
dc.date.issued1995-02
dc.identifier.urihttps://hdl.handle.net/1721.1/149801
dc.description.abstractWe 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 highlyen_US
dc.relation.ispartofseriesMIT-LCS-TR-657
dc.titleNoise Tolerant Algorithms for Learning and Searchingen_US


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