Abstract: A classification system or classifying images of solder joints has a two-sided classifier to which query cases are presented. It can operate in an environment without task-specific counter-concept cases (“bad examples”) because it filters a library of general counter-concept cases to provide a refined set of counter-concept cases. The filtering is performed by a one-sided classifier, which uses a base of task-specific concept cases to perform the filtration.

Abstract: A classification system automatically generates an indication that a training feature vector case is a pollutant based on a mis-labelled reference image. It excludes the case from the set, builds a k-nearest neighbour confidence classifier, and then classifies the case using this classifier. The case may be marked as suspect if its classification does not match what is determined, or if they do match, if the confidence level is below a threshold. The system can automatically remove or re-label all suspect cases.