Marc Sebban
Impact in
- Artificial Intelligence top 5%
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Domain Adaptation and Few-Shot Learning
- Machine Learning and Algorithms
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- Face and Expression Recognition
Papers in
-
- Machine Learning and Data Classification 12
- Imbalanced Data Classification Techniques 10
- Machine Learning and Algorithms 9
- Domain Adaptation and Few-Shot Learning 6
- Algorithms and Data Compression 6
- Co-authors
- Richard NockAmaury HabrardAurélien BelletÉlisa FromontNalin RastogiChristophe SolaIgor MokrousovJosé Oncina
- Journals
- Pattern Recognition Letters (6 papers)Machine Learning (5 papers)Pattern Recognition (5 papers)IEEE Transactions on Nuclear Science (4 papers)Information Sciences (1 paper)
- Partner nations
- FranceSpainSwitzerland
In The Last Decade
Marc Sebban
53 papers receiving 716 citations
Peers
Comparison fields: 5 of 125
- Artificial Intelligence 393
- Computer Vision and Pattern Recognition 202
- Biochemistry 52
- Signal Processing 63
- Hematology 59
Countries citing papers authored by Marc Sebban
This map shows the geographic impact of Marc Sebban'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 Marc Sebban with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc Sebban more than expected).
Fields of papers citing papers by Marc Sebban
This network shows the impact of papers produced by Marc Sebban. 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 Marc Sebban. The network helps show where Marc Sebban may publish in the future.
Co-authors
The 25 scholars most cited alongside Marc Sebban, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2024 | 5 | |
| 3 | 2023 | 13 | |
| 4 | 2023 | 3 | |
| 5 | 2023 | 6 | |
| 6 | 2022 | 5 | |
| 7 | 2022 | 3 | |
| 8 | 2022 | 0 | |
| 9 | 2021 | 4 | |
| 10 | A survey on domain adaptation theory | 2020 | 10 |
| 11 | 2020 | 13 | |
| 12 | Non-Linear Gradient Boosting for Class-Imbalance Learning | 2018 | 2 |
| 13 | 2012 | 22 | |
| 14 | Learning stochastic tree edit distance | 2006 | 0 |
| 15 | On state merging in grammatical inference: a statistical approach for dealing with noisy data | 2003 | 4 |
| 16 | Stopping criterion for boosting based data reduction techniques: from binary to multiclass problem | 2003 | 24 |
| 17 | 2002 | 65 | |
| 18 | Boosting Neighborhood-Based Classifiers | 2001 | 3 |
| 19 | Instance Pruning as an Information Preserving Problem | 2000 | 11 |
| 20 | Impact of learning set quality and size on decision tree performances. | 2000 | 31 |
About Marc Sebban
Marc Sebban is a scholar working on Computational Mathematics, Artificial Intelligence, Acoustics and Ultrasonics, Computer Vision and Pattern Recognition and Health Information Management, having authored 55 papers that have together received 763 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (12 papers), Imbalanced Data Classification Techniques (10 papers), Machine Learning and Algorithms (9 papers), Data Mining Algorithms and Applications (8 papers), Face and Expression Recognition (6 papers), Domain Adaptation and Few-Shot Learning (6 papers), Algorithms and Data Compression (6 papers) and Multimodal Machine Learning Applications (5 papers). The work is most often cited by research in Artificial Intelligence (393 citations), Computer Vision and Pattern Recognition (202 citations), Biochemistry (52 citations), Signal Processing (63 citations) and Hematology (59 citations). Marc Sebban has collaborated with scholars based in France, Spain and Switzerland. Frequent co-authors include Richard Nock, Amaury Habrard, Aurélien Bellet, Élisa Fromont, Nalin Rastogi, Christophe Sola, Igor Mokrousov, José Oncina, Ricco Rakotomalala and Stéphane Lallich. Their work appears in journals such as Pattern Recognition Letters, Machine Learning, Pattern Recognition, IEEE Transactions on Nuclear Science and Information Sciences.
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.