  Training a classifier with noisy labels typically requires the learner to
specify the distribution of label noise, which is often unknown in practice.
Although there have been some recent attempts to relax that requirement, we
show that the Bayes decision rule is unidentified in most classification
problems with noisy labels. This suggests it is generally not possible to
bypass/relax the requirement. In the special cases in which the Bayes decision
rule is identified, we develop a simple algorithm to learn the Bayes decision
rule, that does not require knowledge of the noise distribution.
