Pak-Ming Cheung
Impact in
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- Face and Expression Recognition
- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
- Artificial Intelligence top 2%
- Text and Document Classification Technologies
- Machine Learning and ELM
- Anomaly Detection Techniques and Applications
- Machine Learning and Data Classification
- Machine Learning and Algorithms
Papers in
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- Advanced Image and Video Retrieval Techniques 3
- Image Retrieval and Classification Techniques 3
- Human Pose and Action Recognition 2
- Video Surveillance and Tracking Methods 1
- Face and Expression Recognition 1
- Journals
- Journal of Machine Learning Research (1 paper)Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. (1 paper)Rare & Special e-Zone (The Hong Kong University of Science and Technology) (3 papers)
- Partner nations
- Hong Kong
In The Last Decade
Pak-Ming Cheung
5 papers receiving 768 citations
Hit Papers
Peers
Comparison fields: 5 of 96
- Computer Vision and Pattern Recognition 456
- Artificial Intelligence 527
- Signal Processing 71
- Media Technology 48
- Control and Systems Engineering 90
Countries citing papers authored by Pak-Ming Cheung
This map shows the geographic impact of Pak-Ming Cheung's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pak-Ming Cheung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pak-Ming Cheung more than expected).
Fields of papers citing papers by Pak-Ming Cheung
This network shows the impact of papers produced by Pak-Ming Cheung. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pak-Ming Cheung. The network helps show where Pak-Ming Cheung may publish in the future.
Co-authors
The 2 scholars most cited alongside Pak-Ming Cheung, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Core Vector Machines: Fast SVM Training on Very Large Data Sets Hit paper breakdown → | 2005 | 657 |
| 2 | 2006 | 64 | |
| 3 | Marginalized multi-instance kernels | 2007 | 43 |
| 4 | 2006 | 32 | |
| 5 | Very Large SVM Training using Core Vector Machines. | 2005 | 23 |
About Pak-Ming Cheung
Pak-Ming Cheung is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Molecular Biology, Media Technology and Analytical Chemistry, having authored 5 papers that have together received 819 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (3 papers), Image Retrieval and Classification Techniques (3 papers), Human Pose and Action Recognition (2 papers), Video Surveillance and Tracking Methods (1 paper), Remote-Sensing Image Classification (1 paper), Face and Expression Recognition (1 paper), Machine Learning in Bioinformatics (1 paper) and Spectroscopy and Chemometric Analyses (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (456 citations), Artificial Intelligence (527 citations), Signal Processing (71 citations), Media Technology (48 citations) and Control and Systems Engineering (90 citations). Pak-Ming Cheung has collaborated with scholars based in Hong Kong. Frequent co-authors include James T. Kwok and Ivor W. Tsang. Their work appears in journals such as Journal of Machine Learning Research, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. and Rare & Special e-Zone (The Hong Kong University of Science and Technology).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.