Effect of wrong samples on the convergence of learning processes

Abstract

For the problem of parameter learning in pattern recognition, when there is a possibility of training samples being mislabeled, the authors have investigated the convergence of stochastic-approximation-based learning algorithms. 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.