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Boosting for Regression Transfer.
David Pardoe and Peter
Stone.
In Proceedings of the 27th International Conference on Machine Learning (ICML), June 2010.
Some
of the data used in the experiments.
[PDF]600.5kB [postscript]1.7MB
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.
@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>.},
}
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