Countries citing papers authored by Marc Strickert
Since
Specialization
Citations
This map shows the geographic impact of Marc Strickert'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 Marc Strickert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc Strickert more than expected).
This network shows the impact of papers produced by Marc Strickert. 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 Marc Strickert. The network helps show where Marc Strickert may publish in the future.
Co-authorship network of co-authors of Marc Strickert
This figure shows the co-authorship network connecting the top 25 collaborators of Marc Strickert.
A scholar is included among the top collaborators of Marc Strickert 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 Marc Strickert. Marc Strickert is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tehrani, Ali Fallah, Marc Strickert, & Eyke Hüllermeier. (2014). The Choquet Kernel for Monotone Data. The European Symposium on Artificial Neural Networks.1 indexed citations
5.
Strickert, Marc, et al.. (2013). A sparse kernelized matrix learning vector quantization model for human activity recognition.. The European Symposium on Artificial Neural Networks.22 indexed citations
6.
Strickert, Marc & Michael Seifert. (2012). Posterior regularization and attribute assessment of under-determined linear mappings.. The European Symposium on Artificial Neural Networks.1 indexed citations
7.
Strickert, Marc, et al.. (2011). Multispectral image characterization by partial generalized covariance.. The European Symposium on Artificial Neural Networks.2 indexed citations
Strickert, Marc, Axel J. Soto, & Gustavo E. Vázquez. (2010). Adaptive matrix distances aiming at optimum regression subspaces.. The European Symposium on Artificial Neural Networks.4 indexed citations
Seifert, Michael, Jens Keilwagen, Marc Strickert, & Ivo Große. (2008). Utilizing Promoter Pair Orientations for HMM-based Analysis of ChIP-chip Data.. 116–127.1 indexed citations
14.
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
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.
Hammer, Barbara, et al.. (2005). Relevance learning for mental disease classification. PUB – Publications at Bielefeld University (Bielefeld University). 139–144.1 indexed citations
17.
Hammer, Barbara, et al.. (2005). Self-Organizing Maps for Time Series. PUB – Publications at Bielefeld University (Bielefeld University). 115–122.15 indexed citations
18.
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
19.
Strickert, Marc & Barbara Hammer. (2004). Self-organizing context learning. PUB – Publications at Bielefeld University (Bielefeld University). 39–44.8 indexed citations
20.
Strickert, Marc & Barbara Hammer. (2003). Unsupervised recursive sequence processing. PUB – Publications at Bielefeld University (Bielefeld University). 27–32.9 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.