Learning with mislabeled training samples using stochastic approximation

For the problem of parameter learning in pattern recognition, the convergence of stochastic approximation-based learning algorithms have been investigated for the situation in which mislabeled training samples are present. In the cases considered, it is found that estimates converge to nontrue values in the presence of labeling errors. The general m-class N-feature pattern recognition problem is considered. A possible solution to the problem is also discussed. Some simulation results are provided to support the conclusions drawn.