The goal of transfer learning is to improve the learning of a new target concept given knowledge of related source concept(s). We introduce the first boosting-based algorithms for transfer learning that apply to regression tasks. First, we describe two existing classification transfer algorithms, ExpBoost and TrAdaBoost, and show how they can be modified for regression. We then introduce extensions of these algorithms that improve performance significantly on controlled experiments in a wide range of test domains.

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In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), June 2010.

@InProceedings{ICML10-pardoe, title={Boosting for Regression Transfer}, author={David Pardoe and Peter Stone}, booktitle={Proceedings of the 27th International Conference on Machine Learning (ICML 2010)}, month={June}, url="http://www.cs.utexas.edu/users/ai-lab?pardoe:icml10", year={2010} }