Michael Biehl

10.7k total citations · 1 hit paper
254 papers, 4.5k citations indexed

About

Michael Biehl is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Michael Biehl has authored 254 papers receiving a total of 4.5k indexed citations (citations by other indexed papers that have themselves been cited), including 116 papers in Artificial Intelligence, 55 papers in Computer Vision and Pattern Recognition and 20 papers in Signal Processing. Recurrent topics in Michael Biehl's work include Neural Networks and Applications (95 papers), Face and Expression Recognition (28 papers) and Image Retrieval and Classification Techniques (16 papers). Michael Biehl is often cited by papers focused on Neural Networks and Applications (95 papers), Face and Expression Recognition (28 papers) and Image Retrieval and Classification Techniques (16 papers). Michael Biehl collaborates with scholars based in Germany, Netherlands and United States. Michael Biehl's co-authors include Barbara Hammer, Petra Schneider, Thomas Villmann, Kerstin Bunte, Nicolai Petkov, Albrecht Rau, Timothy L. H. Watkin, Joachim K. Anlauf, Marcel F. Jonkman and Ioannis Giotis and has published in prestigious journals such as Physical Review Letters, Nucleic Acids Research and Circulation.

In The Last Decade

Michael Biehl

249 papers receiving 4.3k citations

Hit Papers

align trajectories

What are hit papers?

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.

Survey of feature selection and extraction techniques for stock market prediction

2023 98 citations Michael Biehl, Nicolai Petkov et al. Financial Innovation profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael Biehl Germany	35	2.0k	837	415	396	340	254	4.5k
Vladimir Makarenkov Canada	31	1.3k 0.6×	427 0.5×	165 0.4×	152 0.4×	110 0.3×	83	4.0k
Jinbo Bi United States	34	1.9k 0.9×	1.6k 1.9×	101 0.2×	257 0.6×	258 0.8×	150	4.7k
Sungroh Yoon South Korea	40	1.5k 0.8×	1.0k 1.2×	269 0.6×	371 0.9×	89 0.3×	234	7.2k
Carsten Peterson Sweden	49	1.5k 0.7×	535 0.6×	566 1.4×	242 0.6×	345 1.0×	157	10.1k
Volker Röth Switzerland	36	1.2k 0.6×	670 0.8×	63 0.2×	486 1.2×	79 0.2×	133	4.4k
Masanori Koyama Japan	10	2.5k 1.2×	1.9k 2.3×	63 0.2×	348 0.9×	136 0.4×	32	6.3k
Mark Goadrich United States	6	1.6k 0.8×	580 0.7×	73 0.2×	289 0.7×	143 0.4×	14	4.1k
Ming Yuan United States	38	2.9k 1.4×	1.3k 1.6×	58 0.1×	786 2.0×	256 0.8×	174	10.6k
Takuya Akiba Japan	14	1.2k 0.6×	603 0.7×	55 0.1×	372 0.9×	385 1.1×	25	4.1k
Gábor J. Székely Hungary	28	849 0.4×	654 0.8×	55 0.1×	153 0.4×	160 0.5×	113	4.3k

Countries citing papers authored by Michael Biehl

Since Specialization

Citations

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

Fields of papers citing papers by Michael Biehl

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Biehl

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

All Works

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20 of 20 papers shown

Carli, Giulia, Remco J. Renken, Tamara Shiner, et al.. (2025). IRMA: Machine learning-based harmonization of $$^{18}$$F-FDG PET brain scans in multi-center studies. European Journal of Nuclear Medicine and Molecular Imaging. 52(8). 2941–2958.

Biehl, Michael, et al.. (2025). Hermite polynomials facilitating on-line learning analysis of layered neural networks with arbitrary activation function. Neurocomputing. 655. 131328–131328.

Biehl, Michael, et al.. (2025). Phase transition analysis for shallow neural networks with arbitrary activation functions. Physica A Statistical Mechanics and its Applications. 660. 130356–130356. 3 indexed citations

Ghosh, Sreejita, Elizabeth S. Baranowski, Michael Biehl, et al.. (2025). Interpretable modelling and visualization of biomedical data. Neurocomputing. 626. 129405–129405.

Stouwe, A.M. Madelein van der, et al.. (2025). Explainable machine learning for movement disorders - Classification of tremor and myoclonus. Computers in Biology and Medicine. 192(Pt B). 110180–110180. 1 indexed citations

Biehl, Michael, et al.. (2024). On-line Learning Dynamics in Layered Neural Networks with Arbitrary Activation Functions. University of Groningen research database (University of Groningen / Centre for Information Technology). 437–442. 1 indexed citations

Meles, Sanne K., Remco J. Renken, Gert-Jan de Vries, et al.. (2024). Subspace corrected relevance learning with application in neuroimaging. Artificial Intelligence in Medicine. 149. 102786–102786. 6 indexed citations

Biehl, Michael. (2023). The Shallow and the Deep: A biased introduction to neural networks and old school machine learning. University of Groningen research database (University of Groningen / Centre for Information Technology). 5 indexed citations

Biehl, Michael, et al.. (2023). Survey of feature selection and extraction techniques for stock market prediction. Financial Innovation. 9(1). 26–26. 98 indexed citations breakdown →

10.

Meles, Sanne K., Remco J. Renken, Fransje E. Reesink, et al.. (2022). FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder. Computer Methods and Programs in Biomedicine. 225. 107042–107042. 18 indexed citations

11.

Meles, Sanne K., Gert-Jan de Vries, Remco J. Renken, et al.. (2020). An application of generalized matrix learning vector quantization in neuroimaging. Computer Methods and Programs in Biomedicine. 197. 105708–105708. 10 indexed citations

12.

Panda, Anshuman, Anupama Yadav, Huwate Yeerna, et al.. (2020). Tissue- and development-stage–specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples. Nucleic Acids Research. 48(13). 7079–7098. 19 indexed citations

13.

Hinder, Fabian, et al.. (2020). Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information. Neurocomputing. 416. 266–279. 4 indexed citations

14.

Biehl, Michael, et al.. (2019). On-line learning dynamics of ReLU neural networks using statistical physics techniques. University of Groningen research database (University of Groningen / Centre for Information Technology). 1 indexed citations

15.

Biehl, Michael, Sanne K. Meles, Remco J. Renken, et al.. (2016). Differentiating Early and Late Stage Parkinson’s Disease Patients from Healthy Controls. University of Groningen research database (University of Groningen / Centre for Information Technology). 3(6). 33–43. 1 indexed citations

16.

Mokbel, Bassam, Wouter Lueks, Andrej Gisbrecht, Michael Biehl, & Barbara Hammer. (2012). Visualizing the quality of dimensionality reduction. University of Groningen research database (University of Groningen / Centre for Information Technology). 1 indexed citations

17.

Bunte, Kerstin, Michael Biehl, & Barbara Hammer. (2011). Dimensionality reduction mappings. University of Groningen research database (University of Groningen / Centre for Information Technology). 349–356. 7 indexed citations

18.

Bunte, Kerstin, Barbara Hammer, Thomas Villmann, Michael Biehl, & Axel Wismüller. (2011). Neighbor embedding XOM for dimension reduction and visualization. Neurocomputing. 74(9). 1340–1350. 45 indexed citations

19.

Schneider, Petra, Michael Biehl, & Barbara Hammer. (2008). Matrix Adaptation in Discriminative Vector Quantization. PUB – Publications at Bielefeld University (Bielefeld University). 1 indexed citations

20.

Biehl, Michael, Anarta Ghosh, & Barbara Hammer. (2005). The dynamics of Learning Vector Quantization. University of Groningen research database (University of Groningen / Centre for Information Technology). 13–18. 3 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.

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