Combining model-based and instance-based learning for first order regression

Kurt Driessens, Saso Dzeroski

Abstract: The introduction of relational reinforcement learning and the RRL algorithm gave rise to the development of several first order regression algorithms. So far, these algorithms have employed either a model-based approach or an instance-based approach. As a consequence, they suffer from the typical drawbacks of model-based learning such as coarse function approximation or those of lazy learning such as high computational intensity.In this paper we develop a new regression algorithm that combines the strong points of both approaches and tries to avoid the normally inherent draw-backs. By combining model-based and instance-based learning, we produce an incremental first order regression algorithm that is both computationally efficient and produces better predictions earlier in the learning experiment.