SVM Learning transformation matrix 的几篇paper

最新推荐文章于 2022-05-13 12:22:14 发布

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最新推荐文章于 2022-05-13 12:22:14 发布

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分类专栏：乐在科研中

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本文深入探讨了在监督学习和半监督学习环境下，特征增强技术在异构域适应中的应用，详细介绍了由WenLi、Lixin Duan、Dong Xu和Ivor W.H. Tsang共同发表的论文《Learning with Augmented Features for Supervised and Semi-supervised Heterogeneous Domain Adaptation》。同时，文章回顾了Lixin Duan、Dong Xu和Ivor W. Tsang于2012年在国际机器学习会议上提出的《Learning with Augmented Features for Heterogeneous Domain Adaptation》。研究指出通过特征增强技术，可以有效提升在不同领域间迁移学习的效果。

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Wen Li, Lixin Duan, Dong Xu, and Ivor W.H. Tsang, “Learning with Augmented Features for Supervised and Semi-supervised Heterogeneous Domain Adaptation,” IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI ). [Preprint][Appendix][codes]
Lixin Duan, Dong Xu, and Ivor W. Tsang, “Learning with Augmented Features for Heterogeneous Domain Adaptation,” in Proceedings of International Conference on Machine Learning (ICML), pp. 711–718, 2012. [pdf] [bib]
http://www4.comp.polyu.edu.hk/~cslzhang/papers.htm
P. Zhu, L. Zhang, W. Zuo, and D. Zhang, “From Point to Set: Extend the Learning of Distance Metrics,” In ICCV 2013. (paper) (code) (We extended the metric learning from point-to-point to point-to-set and set-to-set!)