Complementary Discrimination Learning with Decision Lists

This paper describes the integration of a learning mechanism called complementary discrimination learning with a knowledge representation schema called decision lists. There are two main results of such an integration. One is an eecient representation for complementary concepts that is crucial for complementary discrimination style learning. The other is the rst behaviorally incremental algorithm, called CDL2, for learning decision lists. Theoretical analysis and experiments in several domains have shown that CDL2 is more eecient than many existing symbolic or neural network learning algorithms, and can learn multiple concepts from noisy and inconsistent data.