Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
Top-down induction of first-order logical decision trees
Artificial Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
A polynomial time computable metric between point sets
Acta Informatica
Relational Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Integrating Guidance into Relational Reinforcement Learning
Machine Learning
Non-parametric policy gradients: a unified treatment of propositional and relational domains
Proceedings of the 25th international conference on Machine learning
Top-Down Induction of Relational Model Trees in Multi-instance Learning
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
A Learning Classifier System Approach to Relational Reinforcement Learning
Learning Classifier Systems
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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.