Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer
Abstract
In this paper, we propose a novel graph-based method for knowledge transfer. We model the transfer relationships between source tasks by embedding the set of learned source models in a graph using transferability as the metric. Transfer to a new problem proceeds by mapping the problem into the graph, then learning a function on this graph that automatically determines the parameters to transfer to the new learning task. This method is analogous to inductive transfer along a manifold that captures the transfer relationships between the tasks. We demonstrate improved transfer performance using this method against existing approaches in several real-world domains.
Cite
Text
Eaton et al. "Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87479-9_39Markdown
[Eaton et al. "Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/eaton2008ecmlpkdd-modeling/) doi:10.1007/978-3-540-87479-9_39BibTeX
@inproceedings{eaton2008ecmlpkdd-modeling,
title = {{Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer}},
author = {Eaton, Eric and desJardins, Marie and Lane, Terran},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {317-332},
doi = {10.1007/978-3-540-87479-9_39},
url = {https://mlanthology.org/ecmlpkdd/2008/eaton2008ecmlpkdd-modeling/}
}