Lizhong Ding
- Artificial Intelligence top 10%
- Molecular Biology
- Computer Vision and Pattern Recognition top 10%
- Computational Mechanics
- Cancer Research
- Topics
- Sparse and Compressive Sensing Techniques (10 papers)Face and Expression Recognition (8 papers)Machine Learning and ELM (6 papers)
- Journals
- BioinformaticsIEEE Transactions on Pattern Analysis and Machine IntelligenceBMC Bioinformatics
- Partner nations
- ChinaSaudi ArabiaUnited States
In The Last Decade
Lizhong Ding
33 papers receiving 545 citations
Peers
Comparison fields: 5 of 119
- Artificial Intelligence 196
- Molecular Biology 191
- Computer Vision and Pattern Recognition 92
- Computational Mechanics 55
- Cancer Research 52
Countries citing papers authored by Lizhong Ding
This map shows the geographic impact of Lizhong Ding'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 Lizhong Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lizhong Ding more than expected).
Fields of papers citing papers by Lizhong Ding
This network shows the impact of papers produced by Lizhong Ding. 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 Lizhong Ding. The network helps show where Lizhong Ding may publish in the future.
Co-authorship network of co-authors of Lizhong Ding
This figure shows the co-authorship network connecting the top 25 collaborators of Lizhong Ding. A scholar is included among the top collaborators of Lizhong Ding based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lizhong Ding. Lizhong Ding is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 17 | |
| 4 | 3 | |
| 5 | Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test | 2 |
| 6 | 7 | |
| 7 | 250 | |
| 8 | 5 | |
| 9 | Multi-Class Learning: From Theory to Algorithm | 27 |
| 10 | A new Carya cathayensis cultivar 'Zhelinshan 3'. | 1 |
| 11 | 3 | |
| 12 | 26 | |
| 13 | 40 | |
| 14 | 8 | |
| 15 | 7 | |
| 16 | 1 | |
| 17 | 1 | |
| 18 | Approximate Model Selection for Large Scale LSSVM | 19 |
| 19 | 1 | |
| 20 | Occurrence regularity of Carya cathayensis canker disease and its control. | 4 |
About Lizhong Ding
Lizhong Ding is a scholar working on Computational Mathematics, Business and International Management and Computational Mechanics, having authored 36 papers that have together received 564 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (10 papers), Face and Expression Recognition (8 papers) and Machine Learning and ELM (6 papers). The work is most often cited by research in Computational Mathematics (13 citations), Artificial Intelligence (196 citations) and Computer Vision and Pattern Recognition (92 citations). Lizhong Ding has collaborated with scholars based in China, Saudi Arabia and United States. Frequent co-authors include Xin Gao, Yu Li, Zhongxiao Li, Yijie Pan, Chao Huang, Shizhong Liao, Yongsheng Bai, Weiping Wang, Yong Liu and Rong Yin. Their work appears in journals such as Bioinformatics, IEEE Transactions on Pattern Analysis and Machine Intelligence and BMC Bioinformatics.
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.