A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf
.
Adaptation Algorithm and Theory Based on Generalized Discrepancy
2015
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15
We present a new algorithm for domain adaptation improving upon the discrepancy minimization algorithm (DM), which was previously shown to outperform a number of popular algorithms designed for this task. Unlike most previous approaches adopted for domain adaptation, our algorithm does not consist of a fixed reweighting of the losses over the training sample. Instead, it uses a reweighting that depends on the hypothesis considered and is based on the minimization of a new measure of generalized
doi:10.1145/2783258.2783368
dblp:conf/kdd/CortesMM15
fatcat:uiuvopyfxzcpdodtbtdp4q2vdm