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.
This map shows the geographic impact of Ron Kohavi'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 Ron Kohavi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ron Kohavi more than expected).
This network shows the impact of papers produced by Ron Kohavi. 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 Ron Kohavi. The network helps show where Ron Kohavi may publish in the future.
Co-authorship network of co-authors of Ron Kohavi
This figure shows the co-authorship network connecting the top 25 collaborators of Ron Kohavi.
A scholar is included among the top collaborators of Ron Kohavi 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 Ron Kohavi. Ron Kohavi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kohavi, Ron & Stefan Thomke. (2017). The Surprising Power of Online Experiments.61 indexed citations
3.
Kim, Won Yong, Ron Kohavi, Johannes Gehrke, & William DuMouchel. (2004). Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, USA, August 22-25, 2004. Knowledge Discovery and Data Mining.18 indexed citations
4.
Kohavi, Ron, et al.. (2002). WEBKDD 2001 -- mining web log data across all customers touch points : Third International Workshop, San Francisco, CA, USA, August 26, 2001 : revised papers. Springer eBooks.2 indexed citations
5.
Becker, Barry, Ron Kohavi, & Dan Sommerfield. (2001). Visualizing the simple Baysian classifier. Morgan Kaufmann Publishers Inc. eBooks. 237–249.4 indexed citations
Kohavi, Ron, et al.. (1998). Targeting business users with decision table classifiers. Knowledge Discovery and Data Mining. 249–253.33 indexed citations
10.
Kohavi, Ron, et al.. (1997). Improving Simple Bayes.46 indexed citations
11.
Kohavi, Ron, et al.. (1997). Option Decision Trees with Majority Votes. International Conference on Machine Learning. 161–169.57 indexed citations
12.
Kohavi, Ron, et al.. (1997). Data Mining Using MLC a Machine Learning Library in C. International Journal of Artificial Intelligence Tools. 6. 537–566.167 indexed citations
13.
Kohavi, Ron & David H. Wolpert. (1996). Bias plus variance decomposition for zero-one loss functions. International Conference on Machine Learning. 275–283.386 indexed citations
14.
Kohavi, Ron & Mehran Sahami. (1996). Error-based and entropy-based discretization of continuous features. Knowledge Discovery and Data Mining. 114–119.169 indexed citations
Kohavi, Ron, et al.. (1995). Oblivious decision trees graphs and top down pruning. International Joint Conference on Artificial Intelligence. 1071–1077.47 indexed citations
Kohavi, Ron, et al.. (1994). Useful Feature Subsets and Rough Set Reducts.22 indexed citations
20.
Kohavi, Ron. (1994). Bottom-up induction of oblivious read-once decision graphs: strengths and limitations. National Conference on Artificial Intelligence. 613–618.25 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.