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Testing exchangeability for transfer decision

delete2017-03-01
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OA
AI
S
Shuang Zhou *
E
Evgueni Smirnov
G
Gijs Schoenmakers
K
Kurt Driessens
R
Ralf Peeters
DOI:10.1016/j.patrec.2016.12.021delete
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Abstract

Abstract

En 中文
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. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Instance-transfer learning
Conformity prediction framework
Exchangeability test
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Journal

Pattern Recognition Letters cover
Pattern Recognition Letters
IF:
3.3
Papers:
7.9K
Citations:
1.6W

Organization

M
Maastricht University
Scholars:
3.1W
Papers: 2.8W
Citations: 277