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.
Popular Ensemble Methods: An Empirical Study
19991.9k citationsDavid W. Opitz, Richard Maclinprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Richard Maclin
Since
Specialization
Citations
This map shows the geographic impact of Richard Maclin'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 Richard Maclin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Maclin more than expected).
This network shows the impact of papers produced by Richard Maclin. 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 Richard Maclin. The network helps show where Richard Maclin may publish in the future.
Co-authorship network of co-authors of Richard Maclin
This figure shows the co-authorship network connecting the top 25 collaborators of Richard Maclin.
A scholar is included among the top collaborators of Richard Maclin 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 Richard Maclin. Richard Maclin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kunapuli, Gautam, Richard Maclin, & Jude Shavlik. (2011). Advice Refinement in Knowledge-Based SVMs. Neural Information Processing Systems. 24. 1728–1736.1 indexed citations
3.
Natarajan, Sriraam, Gautam Kunapuli, Richard Maclin, et al.. (2010). Learning from Human Teachers: Issues and Challenges for ILP in Bootstrap Learning.1 indexed citations
4.
Crouch, Carolyn J., et al.. (2009). Automatic Detection of RWIS Sensor Malfunctions (Phase I). University of Minnesota Digital Conservancy (University of Minnesota).
5.
Torrey, Lisa, Trevor Walker, Richard Maclin, & Jude Shavlik. (2008). Advice Taking and Transfer Learning: Naturally Inspired Extensions to Reinforcement Learning.. National Conference on Artificial Intelligence. 103–110.1 indexed citations
6.
Maclin, Richard, Edward W. Wild, Jude Shavlik, Lisa Torrey, & Trevor Walker. (2007). Refining rules incorporated into knowledge-based support vector learners via successive linear programming. National Conference on Artificial Intelligence. 584–589.6 indexed citations
7.
Maclin, Richard, Jude Shavlik, Trevor Walker, & Lisa Torrey. (2006). A simple and effective method for incorporating advice into kernel methods. National Conference on Artificial Intelligence. 427–432.8 indexed citations
8.
Maclin, Richard, Jude Shavlik, Lisa Torrey, Trevor Walker, & Edward W. Wild. (2005). Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression. National Conference on Artificial Intelligence. 819–824.61 indexed citations
9.
Joshi, Mahesh, Ted Pedersen, & Richard Maclin. (2005). A comparative study of support vector machines applied to the supervised word sense disambiguation problem in the medical domain. 3449–3468.28 indexed citations
10.
Maclin, Richard. (1998). Boosting classifiers regionally. National Conference on Artificial Intelligence. 700–705.13 indexed citations
11.
Asker, Lars & Richard Maclin. (1997). Ensembles as a sequence of classifiers. International Joint Conference on Artificial Intelligence. 2. 860–865.17 indexed citations
Maclin, Richard & David W. Opitz. (1997). An empirical evaluation of bagging and boosting. National Conference on Artificial Intelligence. 546–551.176 indexed citations
Maclin, Richard. (1996). Learning from instruction and experience: methods for incorporating procedural domain theories into knowledge-based neural networks. Minds at UW (University of Wisconsin).7 indexed citations
Maclin, Richard & Jude Shavlik. (1995). Combining the predictions of multiple classifiers: using competitive learning to initialize neural networks. International Joint Conference on Artificial Intelligence. 524–530.74 indexed citations
18.
Maclin, Richard & Jude Shavlik. (1994). Incorporating advice into agents that learn from reinforcements. Minds at UW (University of Wisconsin). 694–699.25 indexed citations
19.
Maclin, Richard & Jude Shavlik. (1994). Refining algorithms with knowledge-based neural networks: improving the Chou-Fasman algorithm for protein folding. Conference on Learning Theory. 249–286.7 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.