André Elisseeff

6.5k total citations
25 papers, 1.9k citations indexed

About

André Elisseeff is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, André Elisseeff has authored 25 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 6 papers in Molecular Biology and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in André Elisseeff's work include Machine Learning and Algorithms (8 papers), Neural Networks and Applications (7 papers) and Face and Expression Recognition (6 papers). André Elisseeff is often cited by papers focused on Machine Learning and Algorithms (8 papers), Neural Networks and Applications (7 papers) and Face and Expression Recognition (6 papers). André Elisseeff collaborates with scholars based in Germany, France and United States. André Elisseeff's co-authors include Jason Weston, Bernhard Schölkopf, Asa Ben‐Hur, Isabelle Guyon, Jean‐Philippe Pellet, Gianluca Antonini, Massimiliano Pontil, Theodoros Evgeniou, Christina S. Leslie and William Stafford Noble and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Bioinformatics and European Journal of Operational Research.

In The Last Decade

André Elisseeff

25 papers receiving 1.8k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
André Elisseeff Germany 15 981 496 474 163 158 25 1.9k
Ethem Alpaydın Türkiye 24 1.6k 1.6× 291 0.6× 1.3k 2.7× 293 1.8× 146 0.9× 80 3.1k
Zili Zhang China 27 868 0.9× 215 0.4× 381 0.8× 119 0.7× 54 0.3× 119 2.0k
Colin Fyfe United Kingdom 22 1.1k 1.1× 94 0.2× 494 1.0× 393 2.4× 56 0.4× 142 1.8k
Simon Shiu Hong Kong 19 785 0.8× 242 0.5× 671 1.4× 181 1.1× 131 0.8× 94 1.8k
C.A. Murthy India 25 1.6k 1.6× 241 0.5× 1.4k 2.9× 252 1.5× 54 0.3× 104 3.1k
Francesco Camastra Italy 19 900 0.9× 123 0.2× 779 1.6× 223 1.4× 68 0.4× 39 1.8k
Michael Collins United States 15 1.3k 1.3× 117 0.2× 391 0.8× 126 0.8× 108 0.7× 27 1.7k
Rie Johnson United States 10 1.8k 1.8× 128 0.3× 301 0.6× 113 0.7× 421 2.7× 12 2.3k
Liping Jing China 24 1.5k 1.5× 123 0.2× 806 1.7× 167 1.0× 59 0.4× 137 2.2k
Heitor Silvério Lopes Brazil 21 1.6k 1.6× 348 0.7× 405 0.9× 109 0.7× 17 0.1× 148 2.5k

Countries citing papers authored by André Elisseeff

Since Specialization
Citations

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

Fields of papers citing papers by André Elisseeff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of André Elisseeff

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

All Works

20 of 20 papers shown
1.
Elisseeff, André, et al.. (2010). Causal networks for risk and compliance: Methodology and application. IBM Journal of Research and Development. 54(3). 6:1–6:12. 2 indexed citations
2.
Pellet, Jean‐Philippe & André Elisseeff. (2008). Using Markov Blankets for Causal Structure Learning. Journal of Machine Learning Research. 9(43). 1295–1342. 130 indexed citations
3.
Pellet, Jean‐Philippe & André Elisseeff. (2008). Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets. 21. 1249–1256. 12 indexed citations
4.
Elisseeff, André, et al.. (2007). The 2005 ISMS Practice Prize Winner—Customer Equity and Lifetime Management (CELM) Finnair Case Study. Marketing Science. 26(4). 553–565. 27 indexed citations
5.
Elisseeff, André, Theodoros Evgeniou, & Massimiliano Pontil. (2005). Stability of Randomized Learning Algorithms. Journal of Machine Learning Research. 6(3). 55–79. 50 indexed citations
6.
Weston, Jason, Christina S. Leslie, Eugene Ie, et al.. (2005). Semi-supervised protein classification using cluster kernels. Bioinformatics. 21(15). 3241–3247. 147 indexed citations
7.
Weston, Jason, André Elisseeff, Dengyong Zhou, Christina S. Leslie, & William Stafford Noble. (2004). Protein ranking: From local to global structure in the protein similarity network. Proceedings of the National Academy of Sciences. 101(17). 6559–6563. 94 indexed citations
8.
Weston, Jason, et al.. (2003). Use of the zero norm with linear models and kernel methods. Journal of Machine Learning Research. 3. 1439–1461. 496 indexed citations
9.
Weston, Jason, Fernando Pérez‐Cruz, Olivier Bousquet, et al.. (2003). Feature selection and transduction forprediction of molecular bioactivity for drug design. Bioinformatics. 19(6). 764–771. 74 indexed citations
10.
Guermeur, Yann, Gianluca Pollastri, André Elisseeff, et al.. (2003). Combining protein secondary structure prediction models with ensemble methods of optimal complexity. Neurocomputing. 56. 305–327. 20 indexed citations
11.
Elisseeff, André, et al.. (2002). A simple algorithm for learning stable machines. European Conference on Artificial Intelligence. 70(1-2). 513–517. 11 indexed citations
12.
Weston, Jason, Olivier Chapelle, Vladimir Vapnik, André Elisseeff, & Bernhard Schölkopf. (2002). Kernel Dependency Estimation. MPG.PuRe (Max Planck Society). 15. 897–904. 84 indexed citations
13.
Ben‐Hur, Asa, André Elisseeff, & Isabelle Guyon. (2001). A stability based method for discovering structure in clustered data. PubMed. 6–17. 364 indexed citations
14.
Weston, Jason, et al.. (2001). KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug Design-Binding to Thrombin. MPG.PuRe (Max Planck Society). 9 indexed citations
15.
Paugam‐Moisy, Hélène, André Elisseeff, & Yann Guermeur. (2000). Generalization performance of multiclass discriminant models. 177–182 vol.4. 4 indexed citations
16.
Bousquet, Olivier & André Elisseeff. (2000). Algorithmic Stability and Generalization Performance. MPG.PuRe (Max Planck Society). 13. 196–202. 43 indexed citations
17.
Elisseeff, André, et al.. (2000). Margin Error and Generalization Capabilities of Multi-Class Discriminant Systems. HAL (Le Centre pour la Communication Scientifique Directe). 6 indexed citations
18.
Elisseeff, André, et al.. (2000). A new multi-class SVM based on a uniform convergence result. 183–188 vol.4. 32 indexed citations
19.
Elisseeff, André & Hélène Paugam‐Moisy. (1999). JNN, a randomized algorithm for training multilayer networks in polynomial time. Neurocomputing. 29(1-3). 3–24. 6 indexed citations
20.
Elisseeff, André & Hélène Paugam‐Moisy. (1996). Size of Multilayer Networks for Exact Learning: Analytic Approach. OpenGrey (Institut de l'Information Scientifique et Technique). 9. 162–168. 27 indexed citations

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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