Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4970178 | Pattern Recognition Letters | 2017 | 8 Pages |
Abstract
This paper introduces a non-parametric test to decide whether to transfer data from a source domain to a target domain to improve the generalization performance of predictive models on the target domain. The test is based on the conformal prediction framework: it statistically tests whether the target and source data are generated from the same distribution under the exchangeability assumption. The experiments show that the test is capable of outperforming existing methods when it decides on instance transfer.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Shuang Zhou, Evgueni Smirnov, Gijs Schoenmakers, Kurt Driessens, Ralf Peeters,