In Proceedings of the ICML Workshop on Structural Knowledge Transfer for Machine Learning, Pittsburgh, PA, July 2006.
Paper ID: 189
Category: Transfer Learning, Statistical Relational Learning
We propose a new algorithm for transfer learning of Markov Logic Network (MLN) structure. An important aspect of our approach is that it first diagnoses the provided source MLN and then focuses on re-learning only the incorrect portions. Experiments in a pair of synthetic domains demonstrate that this strategy significantly decreases the search space and speeds up learning while maintaining a level of accuracy comparable to that of the current best algorithm.
