Probabilistic Incremental Rule Learning.
Gregory D. Weber · 2003
This paper describes PISCES 1.2E, a system for incremental learning of probabilistic rules. PISCES is efficiently incremental in the sense that both its processing time per instance and its memory usage are independent of the number of training instances. Classification accuracy alone does not provide a sufficient measure of performance for probabilistic classifiers.