Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A review: Deep learning for medical image segmentation using multi-modality fusion
2019417 citationsSu Ruan, Stéphane Canu et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Stéphane Canu'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 Stéphane Canu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stéphane Canu more than expected).
This network shows the impact of papers produced by Stéphane Canu. 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 Stéphane Canu. The network helps show where Stéphane Canu may publish in the future.
Co-authorship network of co-authors of Stéphane Canu
This figure shows the co-authorship network connecting the top 25 collaborators of Stéphane Canu.
A scholar is included among the top collaborators of Stéphane Canu 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 Stéphane Canu. Stéphane Canu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Canu, Stéphane, et al.. (2013). AIC and Cp as estimators of loss for spherically symmetric distributions. arXiv (Cornell University).3 indexed citations
12.
Gasso, Gilles, et al.. (2011). A Multi-kernel Framework for Inductive Semi-supervised Learning.. The European Symposium on Artificial Neural Networks.2 indexed citations
13.
Grandvalet, Yves, Alain Rakotomamonjy, Joseph Keshet, & Stéphane Canu. (2008). Support Vector Machines with a Reject Option. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 21. 537–544.54 indexed citations
14.
Gärtner, Thomas, et al.. (2008). Regularization path for Ranking SVM.. The European Symposium on Artificial Neural Networks. 415–420.4 indexed citations
15.
Gasso, Gilles, et al.. (2007). Regularization Paths for nu -SVM and nu -SVR.. 486–496.1 indexed citations
16.
Canu, Stéphane, et al.. (2007). Comments on the Core Vector Machines: Fast SVM Training on Very Large Data Sets. Journal of Machine Learning Research. 8(11). 291–301.22 indexed citations
17.
Gasso, Gilles, et al.. (2007). Estimation of Tangent Planes for Neighborhood Graph Correction. The European Symposium on Artificial Neural Networks. 397–402.2 indexed citations
18.
Canu, Stéphane & Alexander J. Smola. (2005). Kernel methods and the exponential family. ANU Open Research (Australian National University). 447–454.4 indexed citations
19.
Grandvalet, Yves & Stéphane Canu. (2002). Adaptive Scaling for Feature Selection in SVMs. Neural Information Processing Systems. 15. 569–576.98 indexed citations
20.
Grandvalet, Yves & Stéphane Canu. (1998). Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage. Neural Information Processing Systems. 11. 445–451.36 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.