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.
Multisurface proximal support vector machine classification via generalized eigenvalues
2006620 citationsO. L. Mangasarian, Edward W. WildIEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Citations per year, relative to Edward W. Wild Edward W. Wild (= 1×)
peers
Pak-Ming Cheung
Countries citing papers authored by Edward W. Wild
Since
Specialization
Citations
This map shows the geographic impact of Edward W. Wild'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 Edward W. Wild with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward W. Wild more than expected).
This network shows the impact of papers produced by Edward W. Wild. 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 Edward W. Wild. The network helps show where Edward W. Wild may publish in the future.
Co-authorship network of co-authors of Edward W. Wild
This figure shows the co-authorship network connecting the top 25 collaborators of Edward W. Wild.
A scholar is included among the top collaborators of Edward W. Wild 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 Edward W. Wild. Edward W. Wild is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Mangasarian, O. L., Edward W. Wild, & Glenn Fung. (2009). Proximal Knowledge‐based Classification. Statistical Analysis and Data Mining The ASA Data Science Journal. 1(4). 215–222.5 indexed citations
4.
Mangasarian, O. L. & Edward W. Wild. (2008). Privacy-Preserving Classification of Horizontally Partitioned Data via Random Kernels.. 473–479.33 indexed citations
5.
Mangasarian, O. L. & Edward W. Wild. (2008). Nonlinear Knowledge-Based Classification. IEEE Transactions on Neural Networks. 19(10). 1826–1832.32 indexed citations
Mangasarian, O. L. & Edward W. Wild. (2008). Optimization-based machine learning and data mining.1 indexed citations
8.
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
Mangasarian, O. L. & Edward W. Wild. (2006). Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(1). 69–74.620 indexed citations breakdown →
11.
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
12.
Mangasarian, O. L., Jude Shavlik, & Edward W. Wild. (2004). Knowledge-Based Kernel Approximation. Journal of Machine Learning Research. 5. 1127–1141.54 indexed citations
13.
Wild, Edward W. & O. L. Mangasarian. (2004). Feature Selection in k-Median Clustering. Minds at UW (University of Wisconsin).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.