Domain Adaptation with Few Labeled Source Samples by Graph Regularization-Reference-Cited by-同舟云学术

Domain Adaptation with Few Labeled Source Samples by Graph Regularization

Published:2019-07-09 Issue:1 Volume:51 Page:23-39
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Li Jinfeng,Liu Weifeng^ORCID,Zhou Yicong,Tao Dapeng,Nie Liqiang

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

http://link.springer.com/content/pdf/10.1007/s11063-019-10075-z.pdf

Reference47 articles.

1. Long M, Wang J, Ding G, Shen D, Yang Q (2014) Transfer learning with graph co-regularization. IEEE Trans Knowl Data Eng 26(7):1805–1818

2. Pan S, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

3. Xie X, Sun S, Chen H, Qian J (2018) Domain adaptation with twin support vector machines. Neural Process Lett 48(2):1213–1226

4. Li Y, Gong M, Tian X, Liu T, Tao D (2018) Domain generalization via conditional invariant representation. In: AAAI conference on artificial intelligence, pp 3579–3587

5. Gong M, Zhang K, Liu T, Tao D, Glymour C, Scholkopf B (2016) Domain adaptation with conditional transferable components. In: Proceedings of the 33rd international conference on international conference on machine learning (ICML), pp 2839–2848

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