SBL-PM-M: A system for partial memory learning

Abstract

Partial Memory Learning (PML) is a machine learning paradigm in which only a subset of cases generated from an original training set is used for classification. This paper concerns a new method for partial memory learning. The SBL-PM-M method is a completely new model. We evaluate the performance of the new algorithm on several real-world datasets and compare it to a few other PML systems and to the base classifier.