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@InProceedings{ICML10-pardoe,
	author="David Pardoe and Peter Stone",
	title="Boosting for Regression Transfer",
	booktitle="Proceedings of the 27th International Conference on Machine Learning (ICML)",
	month="June",
	year="2010",
	abstract={
		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.
	},
	wwwnote={Some of the <a href="http://www.cs.utexas.edu/~TacTex/transfer_data.html">data used in the experiments</a>.},
}