This map shows the geographic impact of Udo Seiffert'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 Udo Seiffert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Udo Seiffert more than expected).
This network shows the impact of papers produced by Udo Seiffert. 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 Udo Seiffert. The network helps show where Udo Seiffert may publish in the future.
Co-authorship network of co-authors of Udo Seiffert
This figure shows the co-authorship network connecting the top 25 collaborators of Udo Seiffert.
A scholar is included among the top collaborators of Udo Seiffert 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 Udo Seiffert. Udo Seiffert is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Backhaus, Andreas, et al.. (2019). Transfer Learning for transferring machine-learning based models among hyperspectral sensors.. The European Symposium on Artificial Neural Networks.1 indexed citations
Ihlow, Alexander & Udo Seiffert. (2014). Automating microscope colour image analysis using the Expectation Maximisation algorithm. Common Library Network (Der Gemeinsame Bibliotheksverbund).1 indexed citations
9.
Villmann, Thomas, et al.. (2013). Processing Hyperspectral Data in Machine Learning.. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft).2 indexed citations
10.
Backhaus, Andreas, P. Ashok, Bavishna B. Praveen, Kishan Dholakia, & Udo Seiffert. (2012). Classifying Scotch Whisky from near-infrared Raman spectra with a Radial Basis Function Network with Relevance Learning. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 411–416.11 indexed citations
11.
Backhaus, Andreas, et al.. (2012). Hardware accelerated real time classification of hyperspectral imaging data for coffee sorting. PUB – Publications at Bielefeld University (Bielefeld University). 632.4 indexed citations
12.
Villmann, Thomas, Erzsébet Merényi, & Udo Seiffert. (2008). Machine learning approches and pattern recognition for spectral data.. The European Symposium on Artificial Neural Networks. 433–444.7 indexed citations
13.
Villmann, Thomas, Marc Strickert, C. Bayan Bruss, Frank-Michael Schleif, & Udo Seiffert. (2007). Visualization of Fuzzy Information in Fuzzy-Classification for Image Segmentation using MDS. The European Symposium on Artificial Neural Networks. 103–108.8 indexed citations
14.
Bruss, C. Bayan, et al.. (2006). Fuzzy Image Segmentation with Fuzzy Labelled Neural Gas. PUB – Publications at Bielefeld University (Bielefeld University). 563–568.6 indexed citations
15.
Strickert, Marc, Nese Sreenivasulu, & Udo Seiffert. (2006). Sanger-driven MDSLocalize - a comparative study for genomic data.. The European Symposium on Artificial Neural Networks. 265–270.2 indexed citations
16.
Seiffert, Udo, Barbara Hammer, Samuel Kaski, & Thomas Villmann. (2006). Neural Networks and Machine Learning in Bioinformatics - Theory and Applications. PUB – Publications at Bielefeld University (Bielefeld University). 521–532.12 indexed citations
17.
Strickert, Marc, Nese Sreenivasulu, Winfriede Weschke, Udo Seiffert, & Thomas Villmann. (2005). Generalized Relevance LVQ with Correlation Measures for Biological Data. The European Symposium on Artificial Neural Networks. 331–338.2 indexed citations
18.
Villmann, Thomas, Udo Seiffert, & Axel Wismüller. (2004). Theory and applications of neural maps.. The European Symposium on Artificial Neural Networks. 25–38.4 indexed citations
19.
Seiffert, Udo. (2001). Multiple Layer Perceptron training using genetic algorithms.. The European Symposium on Artificial Neural Networks. 159–164.64 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.