Han Stiekema

580 citations
4 papers · 83 indexed · h-index 3
Topics
Advanced Image and Video Retrieval Techniques (1 paper)Sparse and Compressive Sensing Techniques (1 paper)Metabolomics and Mass Spectrometry Studies (1 paper)
Journals
Journal of Surgical OncologyIEEE Transactions on Neural NetworksUniversity of Groningen research database (University of Groningen / Centre for Information Technology)

In The Last Decade

Han Stiekema

4 papers receiving 79 citations

Peers

Han Stiekema
Comparison fields: 5 of 36
  • Artificial Intelligence 42
  • Computer Vision and Pattern Recognition 34
  • Molecular Biology 10
  • Surgery 9
  • Cancer Research 9
Replace Yonghui Wu with:
Yonghui Wu China
Hanwen Liang Canada
Xiaomei Li China
Orestis Plevrakis United States
Francesco Visin United Kingdom
Yifeng Geng China
Muhammad Aminu United States
Byungseok Roh Germany
Yanpeng Zhao China
Zhiqiang Shen China
Han Stiekema relative to Yonghui Wu China Yonghui Wu's profile →
Citations per field
00.5×
Yonghui Wu · 1×
Citations per year

Countries citing papers authored by Han Stiekema

Since Specialization
Citations

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

Fields of papers citing papers by Han Stiekema

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Han Stiekema

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

All Works

4 of 4 papers shown
#WorkIndexed citations
1
Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors
20
2 52
3
1 Regularization in Matrix Relevance Learning
1
4 10

About Han Stiekema

Han Stiekema is a scholar working on Cancer Research, Endocrinology, Diabetes and Metabolism and Pathology and Forensic Medicine, having authored 4 papers that have together received 83 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (1 paper), Sparse and Compressive Sensing Techniques (1 paper) and Metabolomics and Mass Spectrometry Studies (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (34 citations), Artificial Intelligence (42 citations) and Media Technology (7 citations). Han Stiekema has collaborated with scholars based in Netherlands, Germany and United Kingdom. Frequent co-authors include Michael Biehl, Petra Schneider, Barbara Hammer, Kerstin Bunte, Thomas Villmann, David J. Smith, Beverly Hughes, Cedric Shackleton, Wiebke Arlt and Angela E. Taylor. Their work appears in journals such as Journal of Surgical Oncology, IEEE Transactions on Neural Networks and University of Groningen research database (University of Groningen / Centre for Information Technology).

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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