In this paper we explore the problem of noise tolerant learning of
classifiers. We formulate the problem as follows. We assume that there is an
${\bf unobservable}$ training set which 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 where the probability that the class
label on an example is corrupted is a function of the feature vector of the
example. This would account for almost all kinds of noisy data one may
encounter in practice.