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.
Proximal support vector machine classifiers
2001616 citationsGlenn Fung, O. L. Mangasarianprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Glenn Fung'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 Glenn Fung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Glenn Fung more than expected).
This network shows the impact of papers produced by Glenn Fung. 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 Glenn Fung. The network helps show where Glenn Fung may publish in the future.
Co-authorship network of co-authors of Glenn Fung
This figure shows the co-authorship network connecting the top 25 collaborators of Glenn Fung.
A scholar is included among the top collaborators of Glenn Fung 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 Glenn Fung. Glenn Fung is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Fung, Glenn, et al.. (2020). Using Optimal Embeddings to Learn New Intents with Few Examples: An Application in the Insurance Domain.. Knowledge Discovery and Data Mining.2 indexed citations
2.
You, Qian, et al.. (2020). Rationale-based Human-in-the-Loop via Supervised Attention.. Knowledge Discovery and Data Mining.1 indexed citations
Yan, Yan, et al.. (2012). Active Learning from Multiple Knowledge Sources. International Conference on Artificial Intelligence and Statistics. 1350–1357.27 indexed citations
8.
Farooq, Faisal, et al.. (2012). Building Hospital-Specific Readmission Risk Prediction Models for Heart Failure, Acute Myocardial Infarction and Pneumonia patients.. AMIA.2 indexed citations
9.
Yan, Yan, Rómer Rosales, Glenn Fung, & Jennifer Dy. (2010). Modeling multiple annotator expertise in the semi-supervised learning scenario. Uncertainty in Artificial Intelligence. 674–682.20 indexed citations
10.
Yan, Yan, Rómer Rosales, Glenn Fung, et al.. (2010). Modeling annotator expertise: Learning when everybody knows a bit of something. International Conference on Artificial Intelligence and Statistics. 932–939.113 indexed citations
11.
Masaeli, Mahdokht, Yan Yan, Ying Cui, Glenn Fung, & Jennifer Dy. (2010). Convex Principal Feature Selection. 619–628.32 indexed citations
12.
Masaeli, Mahdokht, Jennifer Dy, & Glenn Fung. (2010). From Transformation-Based Dimensionality Reduction to Feature Selection. 751–758.90 indexed citations
Fung, Glenn, Rómer Rosales, & R. Bharat Rao. (2007). Feature selection and kernel design via linear programming. International Joint Conference on Artificial Intelligence. 786–791.6 indexed citations
16.
Fung, Glenn, Sriram Krishnan, Rómer Rosales, et al.. (2007). Automated heart wall motion abnormality detection from ultrasound images using Bayesian networks. International Joint Conference on Artificial Intelligence. 519–525.33 indexed citations
Fung, Glenn, O. L. Mangasarian, & Jude Shavlik. (2002). Knowledge-Based Support Vector Machine Classifiers. Neural Information Processing Systems. 15. 537–544.101 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.