Mark van Heeswijk

969 citations
14 papers · 649 indexed · h-index 11
Topics
Machine Learning and ELM (11 papers)Neural Networks and Applications (5 papers)MicroRNA in disease regulation (3 papers)
Partner nations
FinlandSpainChina

In The Last Decade

Mark van Heeswijk

14 papers receiving 632 citations

Peers

Mark van Heeswijk
Comparison fields: 5 of 75
  • Artificial Intelligence 543
  • Electrical and Electronic Engineering 180
  • Computer Vision and Pattern Recognition 156
  • Molecular Biology 59
  • Cancer Research 56
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Zuo Bai Singapore
Chamara Kasun Liyanaarachchi Lekamalage Singapore
Qingping Lin Singapore
Gwang-Hoon Park South Korea
Anton Akusok Finland
Khamron Sunat Thailand
Guoqiang Li China
Nanqing Dong China
Mark van Heeswijk relative to Zuo Bai Singapore Zuo Bai's profile →
Citations per field
00.5×1.7×
Zuo Bai · 1×
Citations per year

Countries citing papers authored by Mark van Heeswijk

Since Specialization
Citations

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

Fields of papers citing papers by Mark van Heeswijk

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark van Heeswijk. 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 Mark van Heeswijk. The network helps show where Mark van Heeswijk may publish in the future.

Co-authorship network of co-authors of Mark van Heeswijk

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

All Works

14 of 14 papers shown
#WorkIndexed citations
1 4
2 1
3 10
4
Advances in Extreme Learning Machines
7
5 31
6 10
7 46
8 42
9 32
10 60
11 90
12 178
13 127
14
Solving Large Regression Problems using an Ensemble of GPU-accelerated ELMs
11

About Mark van Heeswijk

Mark van Heeswijk is a scholar working on Artificial Intelligence, Cancer Research and Statistics, Probability and Uncertainty, having authored 14 papers that have together received 649 indexed citations. Recurring topics across this work include Machine Learning and ELM (11 papers), Neural Networks and Applications (5 papers) and MicroRNA in disease regulation (3 papers). The work is most often cited by research in Artificial Intelligence (543 citations), Computer Vision and Pattern Recognition (156 citations) and Computational Mathematics (4 citations). Mark van Heeswijk has collaborated with scholars based in Finland, Spain and China. Frequent co-authors include Yoan Miché, Amaury Lendasse, Patrick Bas, Olli Simula, Erkki Oja, Qi Yu, Éric Séverin, Rui Nian, Emil Eirola and Michel Verleysen. Their work appears in journals such as Neurocomputing, Entropy and Cognitive Computation.

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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