Largest Source Subset Selection for Instance Transfer
Abstract
Instance-transfer learning has emerged as a promising learning framework to boost performance of prediction models on newly-arrived tasks. The success of the framework depends on the relevance of the source data to the target data. This paper proposes a new approach to source data selection for instance-transfer learning. The approach is capable of selecting the largest subset S^* of the source data which relevance to the target data is statistically guaranteed to be the highest among any superset of S^*. The approach is formally described and theoretically justified. Experimental results on real-world data sets demonstrate that the approach outperforms existing instance selection methods.
Cite
Text
Zhou et al. "Largest Source Subset Selection for Instance Transfer." Proceedings of The 7th Asian Conference on Machine Learning, 2015.Markdown
[Zhou et al. "Largest Source Subset Selection for Instance Transfer." Proceedings of The 7th Asian Conference on Machine Learning, 2015.](https://mlanthology.org/acml/2015/zhou2015acml-largest/)BibTeX
@inproceedings{zhou2015acml-largest,
title = {{Largest Source Subset Selection for Instance Transfer}},
author = {Zhou, Shuang and Schoenmakers, Gijs and Smirnov, Evgueni and Peeters, Ralf and Driessens, Kurt and Chen, Siqi},
booktitle = {Proceedings of The 7th Asian Conference on Machine Learning},
year = {2015},
pages = {423-438},
volume = {45},
url = {https://mlanthology.org/acml/2015/zhou2015acml-largest/}
}