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.
An introduction to kernel-based learning algorithms
20012.5k citationsK. Müller, Mika Sirén et al.IEEE Transactions on Neural Networksprofile →
Fisher discriminant analysis with kernels
20031.8k citationsMika Sirén, Gunnar Rätsch et al.Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)profile →
Input space versus feature space in kernel-based methods
1999823 citationsBernhard Schölkopf, Mika Sirén et al.IEEE Transactions on Neural Networksprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Mika Sirén'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 Mika Sirén with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mika Sirén more than expected).
This network shows the impact of papers produced by Mika Sirén. 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 Mika Sirén. The network helps show where Mika Sirén may publish in the future.
Co-authorship network of co-authors of Mika Sirén
This figure shows the co-authorship network connecting the top 25 collaborators of Mika Sirén.
A scholar is included among the top collaborators of Mika Sirén 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 Mika Sirén. Mika Sirén is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Müller, K., Mika Sirén, Gunnar Rätsch, Koji Tsuda, & Bernhard Schölkopf. (2001). An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks. 12(2). 181–201.2495 indexed citations breakdown →
10.
Sirén, Mika, Bernhard Schölkopf, & AJ Smola. (2001). An Improved Training Algorithm for Kernel Fisher Discriminants. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 98–104.53 indexed citations
Schölkopf, Bernhard, Mika Sirén, Christopher J. C. Burges, et al.. (1999). Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks. 10(5). 1000–1017.823 indexed citations breakdown →
13.
Smola, AJ, Mika Sirén, & Bernhard Schölkopf. (1998). Quantization Functionals and Regularized Principal Manifolds. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft).4 indexed citations
Heikinheimo, Liisa, et al.. (1995). Metal-ceramic joints for aggressive environments. 120–125.
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.