Integrating Priors into Domain Adaptation Based on Evidence Theory

Author:

Lv Ying1ORCID,Ma Jianpeng2ORCID,Zhang Yiqiu1ORCID,Xu Gang2ORCID

Affiliation:

1. Shanghai Artificial Intelligence Laboratory, Shanghai, China

2. Shanghai Artificial Intelligence Laboratory & Fudan University, Shanghai, China

Publisher

ACM

Reference24 articles.

1. Yoshua Bengio . 2012 . Deep learning of representations for unsupervised and transfer learning . In Proceedings of ICML workshop on unsupervised and transfer learning. 17--36 . Yoshua Bengio. 2012. Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML workshop on unsupervised and transfer learning. 17--36.

2. John Blitzer , Mark Dredze , and Fernando Pereira . 2007. Biographies , bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification . In Pro-ceedings of the 45th annual meeting of the association of computational linguistics. 440--447. John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Pro-ceedings of the 45th annual meeting of the association of computational linguistics. 440--447.

3. Minmin Chen Kilian Q Weinberger and John Blitzer. 2011. Co-training for domain adaptation. In Advances in neural information processing systems. 2456--2464. Minmin Chen Kilian Q Weinberger and John Blitzer. 2011. Co-training for domain adaptation. In Advances in neural information processing systems. 2456--2464.

4. Nicolas Courty , Rémi Flamary , Devis Tuia , and Alain Rakotomamonjy . 2016. Optimal transport for domain adaptation . IEEE transactions on pattern analysis and machine intelligence 39, 9 ( 2016 ), 1853--1865. Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy. 2016. Optimal transport for domain adaptation. IEEE transactions on pattern analysis and machine intelligence 39, 9 (2016), 1853--1865.

5. Boosting for transfer learning

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