This map shows the geographic impact of Clint Scovel'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 Clint Scovel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Clint Scovel more than expected).
This network shows the impact of papers produced by Clint Scovel. 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 Clint Scovel. The network helps show where Clint Scovel may publish in the future.
Co-authorship network of co-authors of Clint Scovel
This figure shows the co-authorship network connecting the top 25 collaborators of Clint Scovel.
A scholar is included among the top collaborators of Clint Scovel 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 Clint Scovel. Clint Scovel is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Steinwart, Ingo, Don Hush, & Clint Scovel. (2009). Optimal Rates for Regularized Least Squares Regression.. Conference on Learning Theory.106 indexed citations
6.
Steinwart, Ingo, Don Hush, & Clint Scovel. (2009). Training SVMs without offset. University of North Texas Digital Library (University of North Texas).35 indexed citations
Steinwart, Ingo, Don Hush, & Clint Scovel. (2008). Learning from dependent observations. Journal of Multivariate Analysis. 100(1). 175–194.74 indexed citations
9.
Scovel, Clint, D. Hush, & Ingo Steinwart. (2007). Approximate Duality. Journal of Optimization Theory and Applications. 135(3). 429–443.5 indexed citations
10.
Hush, Don, Patrick J. Kelly, Clint Scovel, & Ingo Steinwart. (2006). QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. Journal of Machine Learning Research. 7(26). 733–769.44 indexed citations
11.
Steinwart, Ingo, Don Hush, & Clint Scovel. (2005). A Classification Framework for Anomaly Detection. Journal of Machine Learning Research. 6(8). 211–232.171 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.