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In this paper, we explore noise-tolerant learning of classifiers. We formulate the problem as follows. We assume that there is an training set that is noise free. The actual training set given to the learning algorithm is obtained from this ideal data set by corrupting the class label of each example. The probability that the class label of an example is corrupted is a function...
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