Noise Tolerant Algorithms for Learning and Searching

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