Laiwan Chan
Impact in
- Signal Processing top 5%
- Blind Source Separation Techniques
- Artificial Intelligence top 5%
- Neural Networks and Applications
- Bayesian Modeling and Causal Inference
Papers in
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- Neural Networks and Applications 23
- Bayesian Modeling and Causal Inference 11
- Neural Networks and Reservoir Computing 4
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- Blind Source Separation Techniques 18
- Time Series Analysis and Forecasting 4
- Co-authors
- Chi-Sing Leung (10 shared papers)Kun Zhang (8 shared papers)Li Teng (3 shared papers)Irwin King (5 shared papers)Haiqin Yang (3 shared papers)Michael R. Lyu (2 shared papers)Kaizhu Huang (2 shared papers)John Sum (7 shared papers)
In The Last Decade
Laiwan Chan
56 papers receiving 622 citations
Peers
Comparison fields: 5 of 74
- Signal Processing 128
- Artificial Intelligence 364
- Computer Vision and Pattern Recognition 130
- Analytical Chemistry 46
- Control and Systems Engineering 94
Countries citing papers authored by Laiwan Chan
This map shows the geographic impact of Laiwan Chan'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 Laiwan Chan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laiwan Chan more than expected).
Fields of papers citing papers by Laiwan Chan
This network shows the impact of papers produced by Laiwan Chan. 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 Laiwan Chan. The network helps show where Laiwan Chan may publish in the future.
Co-authors
The 25 scholars most cited alongside Laiwan Chan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | The Minimum Error Minimax Probability Machine | 2004 | 84 |
| 2 | 2003 | 56 | |
| 3 | 1999 | 39 | |
| 4 | 2007 | 39 | |
| 5 | 2009 | 38 | |
| 6 | 2005 | 27 | |
| 7 | Domain Generalization via Multidomain Discriminant Analysis. | 2019 | 21 |
| 8 | 1997 | 18 | |
| 9 | 1998 | 18 | |
| 10 | Biased Minimax Probability Machine for Medical Diagnosis. | 2004 | 17 |
| 11 | 2004 | 17 | |
| 12 | Minimal Nonlinear Distortion Principle for Nonlinear Independent Component Analysis | 2008 | 16 |
| 13 | 1997 | 16 | |
| 14 | 2006 | 16 | |
| 15 | 1998 | 15 | |
| 16 | 2013 | 14 | |
| 17 | 2006 | 14 | |
| 18 | 1995 | 13 | |
| 19 | 2003 | 13 | |
| 20 | 2010 | 13 |
About Laiwan Chan
Laiwan Chan is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Analytical Chemistry and Molecular Biology, having authored 59 papers that have together received 652 indexed citations. Recurring topics across this work include Neural Networks and Applications (23 papers), Blind Source Separation Techniques (18 papers), Bayesian Modeling and Causal Inference (11 papers), Face and Expression Recognition (8 papers), Spectroscopy and Chemometric Analyses (8 papers), Gene expression and cancer classification (5 papers), Neural Networks and Reservoir Computing (4 papers) and Time Series Analysis and Forecasting (4 papers). The work is most often cited by research in Signal Processing (128 citations), Artificial Intelligence (364 citations), Computer Vision and Pattern Recognition (130 citations), Analytical Chemistry (46 citations) and Control and Systems Engineering (94 citations). Laiwan Chan has collaborated with scholars based in Hong Kong, Australia and Germany. Frequent co-authors include Chi-Sing Leung, Kun Zhang, Li Teng, Irwin King, Haiqin Yang, Michael R. Lyu, Kaizhu Huang, John Sum, Zhitang Chen and Kun Zhang. Their work appears in journals such as Neural Computation, Neural Networks, ACM Transactions on Intelligent Systems and Technology, Journal of Machine Learning Research and IEEE Signal Processing Letters.
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.