Brieuc Conan‐Guez

550 citations
13 papers · 272 indexed · h-index 7
Topics
Neural Networks and Applications (11 papers)Face and Expression Recognition (5 papers)Blind Source Separation Techniques (4 papers)
Partner nations
FranceBelgium

In The Last Decade

Brieuc Conan‐Guez

11 papers receiving 245 citations

Peers

Brieuc Conan‐Guez
Comparison fields: 5 of 72
  • Artificial Intelligence 163
  • Computer Vision and Pattern Recognition 74
  • Signal Processing 55
  • Analytical Chemistry 33
  • Molecular Biology 32
Replace Nathalie Villa with:
Nathalie Villa France
Wen Wen China
Ahlame Douzal-Chouakria France
Ismo Kärkkäinen Finland
Shingo Tomita Japan
T. Ravi India
Hyunsoo Kim South Korea
R.J. Alcock United Kingdom
Facundo Bromberg Argentina
Rafael A. Rodríguez‐Gómez Spain
Brieuc Conan‐Guez relative to Nathalie Villa France Nathalie Villa's profile →
Citations per field
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Nathalie Villa · 1×
Citations per year

Countries citing papers authored by Brieuc Conan‐Guez

Since Specialization
Citations

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

Fields of papers citing papers by Brieuc Conan‐Guez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brieuc Conan‐Guez. 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 Brieuc Conan‐Guez. The network helps show where Brieuc Conan‐Guez may publish in the future.

Co-authorship network of co-authors of Brieuc Conan‐Guez

This figure shows the co-authorship network connecting the top 25 collaborators of Brieuc Conan‐Guez. A scholar is included among the top collaborators of Brieuc Conan‐Guez 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 Brieuc Conan‐Guez. Brieuc Conan‐Guez is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
#WorkIndexed citations
1
Un modèle semi-paramétrique neuronal pour la régression et la discrimination sur données fonctionnelles
0
2 28
3 0
4 81
5
A Fast Algorithm for the Self-Organizing Map on Dissimilarity Data
7
6
Clustering Functional Data with the SOM algorithm
43
7
Functional Radial Basis Function Network (FRBFN)
2
8
Functional radial basis function networks.
2
9
Functional preprocessing for multilayer perceptrons.
1
10 73
11
Self-organizing maps and symbolic data
4
12 24
13
Theoretical properties of functional Multi Layer Perceptrons.
7

About Brieuc Conan‐Guez

Brieuc Conan‐Guez is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 13 papers that have together received 272 indexed citations. Recurring topics across this work include Neural Networks and Applications (11 papers), Face and Expression Recognition (5 papers) and Blind Source Separation Techniques (4 papers). The work is most often cited by research in Signal Processing (55 citations), Artificial Intelligence (163 citations) and Statistics and Probability (31 citations). Brieuc Conan‐Guez has collaborated with scholars based in France and Belgium. Frequent co-authors include Fabrice Rossi, Michel Verleysen and François Fleuret. Their work appears in journals such as Neurocomputing, Neural Networks and Comptes Rendus Mathématique.

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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