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.
OP-ELM: Optimally Pruned Extreme Learning Machine
2009596 citationsYoan Miché, Patrick Bas et al.IEEE Transactions on Neural Networksprofile →
Prototyping a Digital Twin for Real Time Remote Control Over Mobile Networks: Application of Remote Surgery
This map shows the geographic impact of Yoan Miché'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 Yoan Miché with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yoan Miché more than expected).
This network shows the impact of papers produced by Yoan Miché. 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 Yoan Miché. The network helps show where Yoan Miché may publish in the future.
Co-authorship network of co-authors of Yoan Miché
This figure shows the co-authorship network connecting the top 25 collaborators of Yoan Miché.
A scholar is included among the top collaborators of Yoan Miché 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 Yoan Miché. Yoan Miché is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Akusok, Anton, Emil Eirola, Yoan Miché, Andrey Gritsenko, & Amaury Lendasse. (2017). Advanced query strategies for Active Learning with Extreme Learning Machines.. The European Symposium on Artificial Neural Networks.1 indexed citations
Yu, Qi, et al.. (2010). Ensembles of Locally Linear Models: Application to Bankruptcy Prediction.. 280–286.1 indexed citations
14.
Miché, Yoan, Benjamin Schrauwen, & Amaury Lendasse. (2010). Machine Learning Techniques based on Random Projections.. The European Symposium on Artificial Neural Networks.11 indexed citations
15.
Miché, Yoan, Emil Eirola, Patrick Bas, et al.. (2010). Ensemble Modeling with a Constrained Linear System of Leave-One-Out Outputs. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)).9 indexed citations
16.
Heeswijk, Mark van, Yoan Miché, Erkki Oja, & Amaury Lendasse. (2010). Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs. The European Symposium on Artificial Neural Networks. 309–314.11 indexed citations
Miché, Yoan & Amaury Lendasse. (2009). A faster model selection criterion for OP-ELM and OP-KNN: Hannan-Quinn criterion. The European Symposium on Artificial Neural Networks.12 indexed citations
Miché, Yoan, Patrick Bas, Christian Jutten, Olli Simula, & Amaury Lendasse. (2008). A methodology for Building Regression Models using Extreme Learning Machine: OP-ELM. The European Symposium on Artificial Neural Networks. 247–252.48 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.