An Error Correcting Procedure for Learning with an Imperfect Teacher

Supervised learning in pattern recognition problems takes place through the use of a set of labeled sample patterns, the labels being provided by a "teacher." In most of the procedures for learning with a teacher, it is commonly assumed that the teacher is perfect, i. e., the labels of the sample patterns are always correct. However, there are many circumstances in which the patterns used for learning are occasionally mislabeled. A procedure for learning with an imperfect teacher who occasionally mislabels some of the learning patterns is developed. The proposed error correction scheme is based on a nonparametric learning scheme. The error correction scheme questions and attempts to correct the labels provided by the imperfect teacher using a threshold in the correction scheme. The use of threshold facilitates control over the amount of correction and provides a simple method for combining the knowledge acquired by the learning scheme with that provided by the teacher. Expressions for the threshold are derived, and the properties of the proposed error correction scheme are discussed. Through computer simulations the performance of the proposed error correction scheme is compared with that of an identical learning scheme without error correction.