1. A method for classifying data in a system with limited memory, comprising the steps of: determining a training data set; acquiring a size of a memory for storing data; applying exemplar learning (EL) procedures to the training data set to produce an exemplar data set adapted to the size of the memory, wherein the EL procedure is selected form a group consisting of an entropy based exemplar learning (EBEL) procedure and an advanced broadband enabled learning (ABEL) procedure; acquiring data to be classified; and classifying the data using the exemplar data set.