Learning with imperfectly labeled patterns

摘要

The problem of learning in pattern recognition using imperfectly labeled patterns is considered. Using a probabilistic model for the mislabeling of the training patterns, the author discusses performance of the Bayes and nearest neighbor classifiers with imperfect labels. Schemes are presented for training the classifier using both parametric and nonparametric techniques. Methods are developed for the correction of imperfect labels. To gain an understanding of the learning process, the author derives expressions for success probability as a function of training time for a one-dimensional increment error correction classifier with imperfect labels. Furthermore, feature selection with imperfectly labeled patterns is considered.