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.
Countries citing papers authored by Miroslav Kubát
Since
Specialization
Citations
This map shows the geographic impact of Miroslav Kubát'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 Miroslav Kubát with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Miroslav Kubát more than expected).
This network shows the impact of papers produced by Miroslav Kubát. 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 Miroslav Kubát. The network helps show where Miroslav Kubát may publish in the future.
Co-authorship network of co-authors of Miroslav Kubát
This figure shows the co-authorship network connecting the top 25 collaborators of Miroslav Kubát.
A scholar is included among the top collaborators of Miroslav Kubát 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 Miroslav Kubát. Miroslav Kubát is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Premaratne, Kamal, et al.. (2013). MeMO : Membership-Based Minority Oversampling for Class Balancing.. 44–56.1 indexed citations
5.
Kubát, Miroslav, et al.. (2007). Time spent on a web page is sufficient to infer a user's interest. 41–46.17 indexed citations
6.
Kubát, Miroslav, et al.. (2003). Association Mining in Gradually Changing Domains. The Florida AI Research Society. 366–370.3 indexed citations
7.
Kubát, Miroslav, et al.. (2002). Modifying Upstart for Use in Multiclass Numerical Domains. The Florida AI Research Society. 339–343.1 indexed citations
8.
Kubát, Miroslav. (2001). Should machines learn how to play games. Nova Science Publishers, Inc. eBooks. 1–10.
9.
Kubát, Miroslav, et al.. (2001). Using the Genetic Algorithm to Reduce the Size of a Nearest-Neighbor Classifier and to Select Relevant Attributes. International Conference on Machine Learning. 449–456.5 indexed citations
10.
Fürnkranz, Johannes & Miroslav Kubát. (2001). Machines that learn to play games. Nova Science Publishers, Inc. eBooks.36 indexed citations
Kubát, Miroslav. (2000). Recycling decision trees in numeric domains. Informatica (slovenia). 24(2). 195–204.1 indexed citations
13.
Kubát, Miroslav, et al.. (2000). Voting Nearest-Neighbor Subclassifiers. International Conference on Machine Learning. 503–510.15 indexed citations
14.
Kubát, Miroslav, et al.. (1999). Initializing RBF-networks with small subsets of training examples. National Conference on Artificial Intelligence. 188–193.2 indexed citations
15.
Kubát, Miroslav, Robert C. Holte, & Stan Matwin. (1998). Machine Learning for the Detection of Oil Spills in Satellite Radar Images. Machine Learning. 30(2-3). 195–215.858 indexed citations breakdown →
Kubát, Miroslav. (1996). Second Tier for Decision Trees.. International Conference on Machine Learning. 293–301.9 indexed citations
18.
Kubát, Miroslav, et al.. (1995). Trimming the inputs of RBF networks.. The European Symposium on Artificial Neural Networks.2 indexed citations
19.
Kubát, Miroslav & Gerhard Widmer. (1995). Adapting to Drift in Continuous Domains.10 indexed citations
20.
Widmer, Gerhard & Miroslav Kubát. (1992). Learning flexible concepts from streams of examples: FLORA2. European Conference on Artificial Intelligence. 463–467.15 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.