Raphaël Féraud

18 papers receiving 277 citations

Peers

Raphaël Féraud
Comparison fields: 5 of 84
  • Management Science and Operations Research 91
  • Health Informatics 7
  • Artificial Intelligence 140
  • Computer Vision and Pattern Recognition 82
  • Signal Processing 18
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Countries citing papers authored by Raphaël Féraud

Since Specialization
Citations

This map shows the geographic impact of Raphaël Féraud'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 Raphaël Féraud with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raphaël Féraud more than expected).

Fields of papers citing papers by Raphaël Féraud

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Raphaël Féraud. 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 Raphaël Féraud. The network helps show where Raphaël Féraud may publish in the future.

Co-authors

The 19 scholars most cited alongside Raphaël Féraud, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Raphaël Féraud Line = papers co-authored together Raphaël Féraud links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002103
2 200243
3 201639
4 201724
5 200817
6 201516
7
Random Forest for the Contextual Bandit Problem
201613
8 200211
9 20028
10
Ensemble and Modular Approaches for Face Detection: A Comparison
19976
11 20214
12
A stochastic bandit algorithm for scratch games
20123
13 20233
14 20172
15 20132
16
Traitement du signal audio-visuel et visiophone personne libre
19971
17 20231
18 20231
19 20171
20
Driven Forward Features Selection: a comparative study on Neural Networks
20080

About Raphaël Féraud

Raphaël Féraud is a scholar working on Artificial Intelligence, Management Science and Operations Research, Computer Vision and Pattern Recognition, Computer Networks and Communications and Signal Processing, having authored 22 papers that have together received 298 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (13 papers), Reinforcement Learning in Robotics (7 papers), Machine Learning and Algorithms (5 papers), Face and Expression Recognition (4 papers), Data Stream Mining Techniques (4 papers), Optimization and Search Problems (3 papers), Video Surveillance and Tracking Methods (3 papers) and Face recognition and analysis (3 papers). The work is most often cited by research in Management Science and Operations Research (91 citations), Health Informatics (7 citations), Artificial Intelligence (140 citations), Computer Vision and Pattern Recognition (82 citations) and Signal Processing (18 citations). Raphaël Féraud has collaborated with scholars based in France, United States and Canada. Frequent co-authors include Fabrice Clérot, Djallel Bouneffouf, Olivier Bernier, Michel Collobert, J.E. Viallet, Odalric-Ambrym Maillard, Vincent Lemaire, Tanguy Urvoy, Y. Mahieux and Vincent Lemaire. Their work appears in journals such as Ad Hoc Networks, Neural Networks, Machine Learning, Neurocomputing and International Journal of Data Science and Analytics.

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.

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