Gauthier Doquire
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Cognitive Neuroscience top 10%
- Molecular Biology
- Information Systems top 10%
- Co-authors
- Michel VerleysenGaël de LannoyD. FrançoisBenoît FrénayAmaury LendasseEmil EirolaDamien François
- Topics
- Face and Expression Recognition (11 papers)Neural Networks and Applications (8 papers)Advanced Statistical Methods and Models (3 papers)
In The Last Decade
Gauthier Doquire
16 papers receiving 594 citations
Peers
Comparison fields: 5 of 112
- Artificial Intelligence 276
- Computer Vision and Pattern Recognition 170
- Cognitive Neuroscience 162
- Molecular Biology 74
- Information Systems 45
Countries citing papers authored by Gauthier Doquire
This map shows the geographic impact of Gauthier Doquire'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 Gauthier Doquire with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gauthier Doquire more than expected).
Fields of papers citing papers by Gauthier Doquire
This network shows the impact of papers produced by Gauthier Doquire. 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 Gauthier Doquire. The network helps show where Gauthier Doquire may publish in the future.
Co-authorship network of co-authors of Gauthier Doquire
This figure shows the co-authorship network connecting the top 25 collaborators of Gauthier Doquire. A scholar is included among the top collaborators of Gauthier Doquire 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 Gauthier Doquire. Gauthier Doquire is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Risk Estimation and Feature Selection | 1 |
| 2 | 63 | |
| 3 | 62 | |
| 4 | 24 | |
| 5 | 37 | |
| 6 | 27 | |
| 7 | 120 | |
| 8 | On the Potential Inadequacy of Mutual Information for Feature Selection | 5 |
| 9 | On the Potential Inadequacy of Mutual Information for Feature Selection: Proceedings of the 20th International Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012) | 2 |
| 10 | 55 | |
| 11 | 25 | |
| 12 | Mutual information based feature selection for mixed data | 4 |
| 13 | Feature selection for supervised inter-patient heart beat classification | 1 |
| 14 | Mutual information for feature selection with missing data | 1 |
| 15 | 13 | |
| 16 | 170 |
About Gauthier Doquire
Gauthier Doquire is a scholar working on Computer Vision and Pattern Recognition, Statistics and Probability and Artificial Intelligence, having authored 16 papers that have together received 610 indexed citations. Recurring topics across this work include Face and Expression Recognition (11 papers), Neural Networks and Applications (8 papers) and Advanced Statistical Methods and Models (3 papers). The work is most often cited by research in Artificial Intelligence (276 citations), Cognitive Neuroscience (162 citations) and Computer Vision and Pattern Recognition (170 citations). Gauthier Doquire has collaborated with scholars based in Belgium, Finland and Spain. Frequent co-authors include Michel Verleysen, Gaël de Lannoy, D. François, Benoît Frénay, Amaury Lendasse, Emil Eirola and Damien François. Their work appears in journals such as Information Sciences, Neurocomputing and Neural Networks.
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.