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

Bayes classifier cannot be learned from noisy responses with unknown noise rates