Open Access
Issue
JNWPU
Volume 39, Number 5, October 2021
Page(s) 1122 - 1129
DOI https://doi.org/10.1051/jnwpu/20213951122
Published online 14 December 2021
  1. Wang Yaoli, Wang Lipo, Yang Fangjun, et al. Advantages of direct input-to-output connections in neural networks: the Elman network for stock index forecasting[J]. Information Sciences, 2021, 547 : 1066–1079. [Article] [Google Scholar]
  2. Wang Yaoli, Liu Xiaohui, Li Maozhen, et al. Deep convolution and correlated manifold embedded distribution alignment for forest fire smoke prediction[J]. Computing and Informatics, 2020, 39(1/2)318–339 [Google Scholar]
  3. Karlweiss T K, Wang D. A survey of transfer learning[J]. Journal of Big Data, 2016, 3(9) : 1–40 [Google Scholar]
  4. Fernando B, Habrard A, Sebban M, et al. Unsupervised visual domain adaptation using subspace alignment[C]//Proceedings of the IEEE International Conference on Computer Vision, 2013: 2960–2967 [Google Scholar]
  5. Sun Baochen, Saenko Kate. Subspace distribution alignment for unsupervised domain[C]//Proceedings of BMVC, 2015 [Google Scholar]
  6. Sun Baochen, Feng Jiashi, Saenko Kate. Return of frustratingly dasy domain adaptation[C]//Proceedings of 30th AAAI Conference on Artificial Intelligence, 2016 [Google Scholar]
  7. Ghifary M, Balduzzi D, Kleijn W B, et al. Scater component analysis: a unified framework for domain adaptation and domain generalization[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2017, 39(7) : 1414–1430. [Article] [Google Scholar]
  8. Gopalan R, Li R, Chellappa R. Domain adaptation for object recognition: an unsupervised approach[C]//Proceedings of the IEEE International Conference on Computer Vision, 2011 [Google Scholar]
  9. Gong Boqing, Shi Yuan, Sha Fei, et al. Geodesic flow kernel for unsupervised domain adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012 [Google Scholar]
  10. Baktashmotlagh Mahsa, Harandi Mehrtash, Salzmann Mathieu. Distribution-matching embedding for visual domain adaptation[J]. Journal of Machine Learning Research, 2016, 17 : 3760–3789 [Google Scholar]
  11. Baktashmotlagh M, Harandi M T, Lovell B C, et al. Domain adaptation on the statistical manifold[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014 [Google Scholar]
  12. Gong M, Zhang K, Liu T, et al. Domain adaptation with conditional transferable components[C]//Proceedings of International Conference on Machine Learning, 2016 [Google Scholar]
  13. Pan S J, Tsang I W, Kwok J T, et al. Domain adaptation via transfer component analysis[J]. IEEE Trans on Neural Networks, 2011, 22(2) : 199–210. [Article] [Google Scholar]
  14. Dorri F, Ghodsi A. Adapting component analysis[C]//Proceedings of the IEEE 12th International Conference on Data Mining, 2012: 846–851 [Google Scholar]
  15. Duan L, Tsang I W, Xu D. Domain transfer multiple kernel learning[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2012, 34(3) : 465–479. [Article] [Google Scholar]
  16. Zellinger W, Grubinger T, Lughofer E, et al. Central moment discrepancy (CMD) for domain-invariant representation learning[C]//Proceedings of the International Conference on Learning Representations, 2017 [Google Scholar]
  17. Long M, Wang J, Ding G, et al. Transfer feature learning with joint distribution adaptation[C]//IEEE International Conference on Computer Vision, 2013 [Google Scholar]
  18. Wang Jindong, Chen Yiqiang, Hao Shuji, et al. Balanced distribution adaptation for transfer learning[C]//Proceedings of the IEEE International Conference on Data Mining, 2017: 1129–1134 [Google Scholar]
  19. Wang Jindong, Feng Wenjie, Chen Yiqiang, et al. Visual domain adaptation with manifold embedded distribution alignment[C]//Proceedings of the 26th ACM International Conference on Multimedia, 2018 [Google Scholar]
  20. Bu Fengju, Mohammad Samadi Gharajeh. Intelligent and vision-based fire detection systems: a survey[J]. Image and Vision Computing, 2019, 91(2) : 103803 [Google Scholar]
  21. He Xiaofei, Niyogi Partha. Locality preserving projections[C]//Proceedings of the 16th International Conference on Neural Information Processing Systems, 2003: 153–160 [Google Scholar]
  22. Pei Zhongyi, Cao Zhangjie, Long Mingsheng, et al. Multi-adversarial domain adaptation[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2018 [Google Scholar]

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