E. Osuna

12.4k total citations · 4 hit papers
5 papers, 7.6k citations indexed

About

E. Osuna is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, E. Osuna has authored 5 papers receiving a total of 7.6k indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 1 paper in Information Systems. Recurrent topics in E. Osuna's work include Neural Networks and Applications (2 papers), Machine Learning and Algorithms (2 papers) and Face and Expression Recognition (2 papers). E. Osuna is often cited by papers focused on Neural Networks and Applications (2 papers), Machine Learning and Algorithms (2 papers) and Face and Expression Recognition (2 papers). E. Osuna collaborates with scholars based in United States, Australia and Germany. E. Osuna's co-authors include Federico Girosi, Susan Dumais, Bernhard Schölkopf, Marti A. Hearst, John Platt, Robert M. Freund, Sayan Mukherjee, Tomaso Poggio, C. Papageorgiou and Michael B. Oren and has published in prestigious journals such as IEEE Intelligent Systems and their Applications.

In The Last Decade

E. Osuna

5 papers receiving 7.0k citations

Hit Papers

align trajectories

What are hit papers?

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.

Support vector machines

1998 4.4k citations Marti A. Hearst, Susan Dumais et al. IEEE Intelligent Systems and their Applications profile →
Training support vector machines: an application to face detection

2002 1.7k citations E. Osuna, Robert M. Freund et al. profile →
An improved training algorithm for support vector machines

2002 688 citations E. Osuna, Robert M. Freund et al. profile →
Pedestrian detection using wavelet templates

2002 433 citations Michael B. Oren, C. Papageorgiou et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
E. Osuna United States	5	2.9k	2.7k	720	650	536	5	7.6k
Aristidis Likas Greece	34	2.1k 0.7×	2.8k 1.0×	799 1.1×	410 0.6×	545 1.0×	153	7.1k
Michel Verleysen Belgium	44	2.5k 0.9×	4.1k 1.5×	1.0k 1.4×	522 0.8×	649 1.2×	287	8.7k
Shigeo Abe Japan	31	2.1k 0.7×	3.0k 1.1×	641 0.9×	1.1k 1.7×	1.1k 2.1×	198	7.4k
Pierre-Antoine Manzagol Canada	7	3.1k 1.1×	3.7k 1.4×	1.2k 1.6×	741 1.1×	607 1.1×	9	8.1k
P. Simard United States	25	2.4k 0.8×	3.5k 1.3×	893 1.2×	594 0.9×	1.1k 2.0×	56	9.4k
Jianchang Mao United States	19	3.2k 1.1×	3.7k 1.4×	1.1k 1.6×	710 1.1×	809 1.5×	43	9.9k
Mika Sirén Germany	12	2.8k 1.0×	2.4k 0.9×	776 1.1×	664 1.0×	267 0.5×	17	5.9k
Mehdi Mirza Canada	5	3.9k 1.4×	3.1k 1.2×	719 1.0×	532 0.8×	514 1.0×	5	9.2k
Vinod Nair India	14	3.9k 1.4×	3.2k 1.2×	962 1.3×	427 0.7×	754 1.4×	23	9.8k
BengioYoshua	9	1.8k 0.6×	3.7k 1.4×	705 1.0×	401 0.6×	434 0.8×	10	6.8k

Countries citing papers authored by E. Osuna

Since Specialization

Citations

This map shows the geographic impact of E. Osuna'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 E. Osuna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E. Osuna more than expected).

Fields of papers citing papers by E. Osuna

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by E. Osuna. 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 E. Osuna. The network helps show where E. Osuna may publish in the future.

Co-authorship network of co-authors of E. Osuna

This figure shows the co-authorship network connecting the top 25 collaborators of E. Osuna. A scholar is included among the top collaborators of E. Osuna 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 E. Osuna. E. Osuna is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

5 of 5 papers shown

1.

Osuna, E., Robert M. Freund, & Federico Girosi. (2002). An improved training algorithm for support vector machines. 276–285. 688 indexed citations breakdown →

2.

Oren, Michael B., et al.. (2002). Pedestrian detection using wavelet templates. 193–199. 433 indexed citations breakdown →

3.

Mukherjee, Sayan, E. Osuna, & Federico Girosi. (2002). Nonlinear prediction of chaotic time series using support vector machines. 511–520. 383 indexed citations

4.

Osuna, E., Robert M. Freund, & Federico Girosi. (2002). Training support vector machines: an application to face detection. 130–136. 1664 indexed citations breakdown →

5.

Hearst, Marti A., Susan Dumais, E. Osuna, John Platt, & Bernhard Schölkopf. (1998). Support vector machines. IEEE Intelligent Systems and their Applications. 13(4). 18–28. 4392 indexed citations breakdown →

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.

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