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All issues Volume 38 / No 4 (August 2020) JNWPU, 38 4 (2020) 828-837 References
Open Access
Issue
JNWPU
Volume 38, Number 4, August 2020
Page(s) 828 - 837
DOI https://doi.org/10.1051/jnwpu/20203840828
Published online 06 October 2020
  1. PAN S J, YANG Q. A Survey on Transfer Learning[J]. IEEE Trans on Knowledge and Data Engineering, 2010, 22 (10): 1345– 1359 [Article] [CrossRef] [Google Scholar]
  2. SO Y J, NAMHYUN A, YUNSOO L, et al. Transfer Learning-Based Vehicle Classification[C]//2018 International SoC Design Conference, 2018 [Google Scholar]
  3. SHI Q, ZHANG Y, LIU X, et al. Regularised Transfer Learning for Hyperspectral Image Classification[J]. IET Computer Vision, 2019, 13 (2): 188– 193 [Article] [CrossRef] [Google Scholar]
  4. HUANG Z, CAO Y, WANG T. Transfer Learning with Efficient Convolutional Neural Networks for Fruit Recognition[C]//IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, 2019 [Google Scholar]
  5. ZHENG W, ZONG Y, ZHOU X, et al. Cross-Domain Color Facial Expression Recognition Using Transductive Transfer Subspace Learning[J]. IEEE Trans on Affective Computing, 2016, 9 (1): 21– 37 [Article] [CrossRef] [Google Scholar]
  6. AMORNPAN P, PRAISAN P. Face Recognition Using Transferred Deep Learning for Feature Extraction[C]//2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, 2019 [Google Scholar]
  7. CHEN L. Assertion Detection in Clinical Natural Language Processing: a Knowledge-Poor Machine Learning Approach[C]//2019 IEEE 2nd International Conference on Information and Computer Technologies, 2019 [Google Scholar]
  8. IOAN C S. Integrating Deep Learning for NLP in Romanian Psychology[C]//2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2018 [Google Scholar]
  9. VAN O A, IKRAM M A, VERNOOIJ M W, et al. Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols[J]. IEEE Trans on Medical Imaging, 2015, 34 (5): 1018– 1030 [Article] [CrossRef] [Google Scholar]
  10. ANNEGREET V O, ACHTERBERG H C, VERNOOIJ M W, et al. Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning[J]. IEEE Trans on Medical Imaging, 2018, 38 (1): 213– 224 [Article] [Google Scholar]
  11. DAI W, YANG Q, XUE G R, et al. Boosting for Transfer Learning[C]//International Conference on Machine Learning, 2007 [Google Scholar]
  12. HUANG J, SMOLA A J, GRETTON A, et al. Correcting Sample Selection Bias by Unlabeled Data[C]//International Conference on Neural Information Processing Systems, 2006 [Google Scholar]
  13. TAN B, SONG Y, ZHONG E, et al. Transitive Transfer Learning[C]//The 21th ACM SIGKDD International Conference, 2015 [Google Scholar]
  14. CHEN M, WEINBERGER K Q, BLITZER J C. Co-Training for Domain Adaptation[J]. Advances in Data Analysis & Classification, 2011, 8 (4): 1– 23 [Google Scholar]
  15. JIANG J, ZHAI C. Instance Weighting for Domain Adaptation in NLP[C]//The 45th Annual Meeting of the Association of Computational Linguistics, 2007 [Google Scholar]
  16. JIANG J. A Literature Survey on Domain Adaptation of Statistical Classifiers[J]. British Journal of Psychiatry, 2008, 131 (7): 83– 89 [Google Scholar]
  17. LONG M, WANG J, DING G, et al. Transfer Joint Matching for Unsupervised Domain Adaptation[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014 [Google Scholar]
  18. 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] [CrossRef] [Google Scholar]
  19. 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]
  20. WANG J, CHEN Y, FENG W, et al. Transfer Learning with Dynamic Distribution Adaptation[J]. ACM Trans on Intelligent Systems and Technology, 2019, 1 (1): 1– 25 [Article] [CrossRef] [Google Scholar]
  21. GRAUMAN K. Geodesic Flow Kernel for Unsupervised Domain Adaptation[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012 [Google Scholar]
  22. JHUO I H, LIU D, LEE D T, et al. Robust Visual Domain Adaptation with Low-Rank Reconstruction[C]//Computer Vision and Pattern Recognition, 2013 [Google Scholar]
  23. WANG J, CHEN Y, HU L, et al. Stratified Transfer Learning for Cross-Domain Activity Recognition[C]//IEEE International Conference on Pervasive Computing and Communications, 2018 [Google Scholar]
  24. GRETTON A, BORGWARDT K M, RASCH M, et al. A Kernel Method for the Two Sample Problem[C]//Neural Information Processing Systems, 2008 [Google Scholar]
  25. SMOLA A, GRETTON A, SONG L, et al. a Hilbert Space Embedding for Distributions[C]//International Conference on Algorithmic Learning Theory, 2007 [Google Scholar]
  26. ZHONG E, ZHANG K, REN J, et al. Cross Domain Distribution Adaptation via Kernel Mapping[C]//ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2009 [Google Scholar]
  27. QUANZ B, HUAN J, MISHRA M. Knowledge Transfer with Low-Quality Data:A Feature Extraction Issue[J]. IEEE Trans on Knowledge and Data Engineering, 2012, 24 (10): 1789– 1802 [Article] [CrossRef] [Google Scholar]
  28. GEORGE C, ROGER L B. Statistical Inference[M]. 2nd edition. Beijing: China Machine Press, 2002 [Google Scholar]
  29. LIU Jianwei, SUN Zhengkang, LUO Xionglin. Review and Research Development on Domain Adaptation Learning[J]. Acta Automatica Sinica, 2014, 40 (8): 1576– 1600 [Article] [Google Scholar]
  30. SHUANG L, SHIJI S, GAO H, et al. Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation[J]. IEEE Trans on Image Processing, 2018, 27 (9): 4260– 4273 [Article] [CrossRef] [Google Scholar]
  31. PENG J, SUN W, MA L, et al. Discriminative Transfer Joint Matching for Domain Adaptation in Hyperspectral Image Classification[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16 (6): 972– 976 [Article] [CrossRef] [Google Scholar]

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