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 Harris Drucker
Since
Specialization
Citations
This map shows the geographic impact of Harris Drucker'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 Harris Drucker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Harris Drucker more than expected).
This network shows the impact of papers produced by Harris Drucker. 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 Harris Drucker. The network helps show where Harris Drucker may publish in the future.
Co-authorship network of co-authors of Harris Drucker
This figure shows the co-authorship network connecting the top 25 collaborators of Harris Drucker.
A scholar is included among the top collaborators of Harris Drucker 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 Harris Drucker. Harris Drucker is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Drucker, Harris, et al.. (2001). Relevance Feedback using Support Vector Machines. International Conference on Machine Learning. 122–129.33 indexed citations
8.
Drucker, Harris, Donghui Wu, & Vladimir Vapnik. (1999). Support vector machines for spam categorization. IEEE Transactions on Neural Networks. 10(5). 1048–1054.992 indexed citations breakdown →
9.
Drucker, Harris. (1997). Fast committee machines for regression and classification. Knowledge Discovery and Data Mining. 159–162.3 indexed citations
10.
Drucker, Harris. (1997). Improving Regressors using Boosting Techniques. International Conference on Machine Learning. 107–115.436 indexed citations
11.
Drucker, Harris, Christopher J. C. Burges, Linda Kaufman, Alex Smola, & Vladimir Vapnik. (1996). Support Vector Regression Machines. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 9. 155–161.3109 indexed citations breakdown →
Cortes, Corinna, et al.. (1995). Capacity and complexity control in predicting the spread between borrowing and lending interest rates. Knowledge Discovery and Data Mining. 51–56.2 indexed citations
Drucker, Harris, Corinna Cortes, L. D. Jackel, Yann LeCun, & Vladimir Vapnik. (1994). Boosting and Other Ensemble Methods. Neural Computation. 6(6). 1289–1301.218 indexed citations
16.
Drucker, Harris, Robert E. Schapire, & Patrice Simard. (1993). BOOSTING PERFORMANCE IN NEURAL NETWORKS. International Journal of Pattern Recognition and Artificial Intelligence. 7(4). 705–719.128 indexed citations
17.
Drucker, Harris, Robert E. Schapire, & Patrice Simard. (1992). Improving Performance in Neural Networks Using a Boosting Algorithm. Neural Information Processing Systems. 5. 42–49.101 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.