Deep Transfer Learning for Cross-domain Activity Recognition-Reference-Cited by-同舟云学术

Deep Transfer Learning for Cross-domain Activity Recognition

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 3rd International Conference on Crowd Science and Engineering - ICCSE'18
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Author:

Wang Jindong¹,Zheng Vincent W.²,Chen Yiqiang¹,Huang Meiyu³

Affiliation:

1. Institute of Computing Technology, CAS, Beijing, China

2. Advanced Digital Sciences Center, Singapore

3. Qian Xuesen Laboratory of Space Technology, CAST, Beijing, China

Publisher

ACM Press

Reference48 articles.

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2. Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. 2007. Analysis of representations for domain adaptation. In Advances in neural information processing systems. 137--144.

3. Himanshu S Bhatt, Arun Rajkumar, and Shourya Roy. 2016. Multi-Source Iterative Adaptation for Cross-Domain Classification.. In IJCAI. 3691--3697.

4. John Blitzer, Ryan McDonald, and Fernando Pereira. 2006. Domain adaptation with structural correspondence learning. In Proceedings of the 2006 conference on empirical methods in natural language processing. Association for Computational Linguistics, 120--128.

5. Karsten M Borgwardt, Arthur Gretton, Malte J Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alex J Smola. 2006. Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22, 14 (2006), e49--e57.

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