This map shows the geographic impact of Emil Eirola'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 Emil Eirola with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emil Eirola more than expected).
This network shows the impact of papers produced by Emil Eirola. 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 Emil Eirola. The network helps show where Emil Eirola may publish in the future.
Co-authorship network of co-authors of Emil Eirola
This figure shows the co-authorship network connecting the top 25 collaborators of Emil Eirola.
A scholar is included among the top collaborators of Emil Eirola 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 Emil Eirola. Emil Eirola 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.
Eirola, Emil, et al.. (2019). Rhythmicity of health information behaviour.. 71. 773–788.1 indexed citations
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
Eirola, Emil, et al.. (2015). 14th IEEE international conference on Trust, Security, and Privacy in Computing and Communications (TrustCom), Helsinki, Finland, August 20-22, 2015.
Eirola, Emil, Amaury Lendasse, Francesco Corona, & Michel Verleysen. (2014). The delta test: The 1-NN estimator as a feature selection criterion. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 4214–4222.3 indexed citations
14.
Eirola, Emil. (2014). Machine learning methods for incomplete data and variable selection. Aaltodoc (Aalto University).1 indexed citations
Yu, Qi, et al.. (2010). Ensembles of Locally Linear Models: Application to Bankruptcy Prediction.. 280–286.1 indexed citations
19.
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
20.
Eirola, Emil, et al.. (2008). Using the Delta Test for Variable Selection. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 25–30.29 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.