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ORCIDZheng Fang 0013
Person information
- affiliation: Zhejiang University, Department of Information Science and Electronic Engineering, China
Other persons with the same name
- Zheng Fang — disambiguation page
- Zheng Fang 0001
![[0000-0003-3887-3141]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0003-3887-3141")
— Northeastern University, Faculty of Robot Science and Engineering, Shenyang, China (and 1 more) - Zheng Fang 0002 — Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
- Zheng Fang 0003 — Sichuan University, Chengdu, Sichuan, China
- Zheng Fang 0004 — University of Massachusetts, Department of Computer Science, Lowell, MA, USA
- Zheng Fang 0005 — Shanghai Jiao Tong University, School of Electronic, Information and Electrical Engineering, China
- Zheng Fang 0006 — East China University of Science and Technology, School of Mechacnial and Power Engineering, Shanghai, China
- Zheng Fang 0007 — Peking University, School of of Intelligence Science and Technology, Beijing, China
- Zheng Fang 0008 — Nanyang Technological University, School of Computer Science and Engineering, Singapore
- Zheng Fang 0009 — University of California, Department of Computer Science, Davis, USA
- Zheng Fang 0010 — Nanjing University of Aeronautics and Astronautics, Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, China
- Zheng Fang 0011 — University of Warwick, Department of Computer Science, Coventry, UK
- Zheng Fang 0012 — Army Engineering University of PLA, Institute of Command and Control Engineering, Nanjing, China
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2010 – 2019
- 2014
[c4]Zheng Fang, Zhongfei Zhang:
Cross Domain Shared Subspace Learning for Unsupervised Transfer Classification. ICPR 2014: 3927-3932- 2013
- [c3]Zheng Fang, Zhongfei (Mark) Zhang:
Discriminative feature selection for multi-view cross-domain learning. CIKM 2013: 1321-1330 - [c2]Zheng Fang, Zhongfei Zhang:
Discriminative Transfer Learning on Manifold. SDM 2013: 539-547 - 2012
- [c1]Zheng Fang, Zhongfei (Mark) Zhang:
Simultaneously Combining Multi-view Multi-label Learning with Maximum Margin Classification. ICDM 2012: 864-869
Coauthor Index
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