Learning Declarative Bias (Learning Constraints)
In these papers, we present an approach to learning constraints
from results of previous machine learning sessions. The learned
constraints can be applied as declarative bias to constrain the
space of hypotheses in related learning tasks from the same or
similar domains. We empirically evaluated the approach in the
context of learning models of dynamic systems from time-series
data and process-based knowledge (i.e., process-based modeling).
Publications
- Bridewell W and Todorovski L (2007) Learning declarative bias. Proceedings of the Seventeenth International Conference on Inductive Logic Programming, 63-77. Draft PDF.
- Bridewell W, Borrett S, and Todorovski L (2007) Extracting constraints for process modeling. Proceedings of the Fourth International Conference on Knowledge Capture, 87-94. Draft PDF.
- Bridewell W and Todorovski L (2010) The induction and transfer of declarative bias. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. To appear. Draft PDF.