In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses or patterns characterizing the input data. If one can assume that training data contain no noise, then ...

Research on a new system implementing the AQ learning methodology, called AQ20, is briefly described, and illustrated by initial results from an experimental version. Like its predecessors, AQ20 is a multi-purpose learning ...

Natural induction and conceptual clustering are two methodologies pioneered by the GMU Machine Learning and Inference Laboratory for discovering conceptual relationships in data, and presenting them in the forms easy for ...