@inbook {13455,
	title = {Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer},
	booktitle = {Machine Learning and Knowledge Discovery in DatabasesMachine Learning and Knowledge Discovery in Databases},
	series = {Lecture Notes in Computer Science},
	volume = {5211},
	year = {2008},
	month = {2008///},
	pages = {317 - 332},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	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.},
	isbn = {978-3-540-87478-2},
	url = {http://dx.doi.org/10.1007/978-3-540-87479-9_39},
	author = {Eaton,Eric and desJardins, Marie and Lane,Terran},
	editor = {Daelemans,Walter and Goethals,Bart and Morik,Katharina}
}
