Combining Model-Based and Instance-Based Learning for First Order Regression
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.
Cite
Text
Driessens and Dzeroski. "Combining Model-Based and Instance-Based Learning for First Order Regression." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102376Markdown
[Driessens and Dzeroski. "Combining Model-Based and Instance-Based Learning for First Order Regression." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/driessens2005icml-combining/) doi:10.1145/1102351.1102376BibTeX
@inproceedings{driessens2005icml-combining,
title = {{Combining Model-Based and Instance-Based Learning for First Order Regression}},
author = {Driessens, Kurt and Dzeroski, Saso},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {193-200},
doi = {10.1145/1102351.1102376},
url = {https://mlanthology.org/icml/2005/driessens2005icml-combining/}
}