Lecture notes in computer science (1 paper)The MIT Press eBooks (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (1 paper)IEEE Intelligent Systems and their Applications (1 paper)
An improved training algorithm for support vector machines2002 · 799 citations
What are hit papers?
Paper score: A paper's citations measured against the top-1% bar of its own field and year: the square root of citations ÷ bar, so 1 means right at the bar and 2 means four times its citations. The bar blends the citation counts that enter the top 1% of the paper's subfields in its year (weighted 89.5%), of those subfields over all years (0.5%) and of its year across all fields (10%); a subfield and year with fewer than 400 papers takes its year's bar. Papers from 2026 are not scored yet: in an unfinished year, when a paper appeared matters more than how it is cited.
Hit paper: A paper cited at least 1.5× the top-1% bar of its field and year: a paper score of at least √1.5.
2002An improved training algorithm for support vector machines
2002Training support vector machines: an application to face detection
2002Pedestrian detection using wavelet templates
1998IEEE Intelligent Systems and their Applications
1997DSpace@MIT (Massachusetts Institute of Technology)
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).
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-authors
The 10 scholars most cited alongside E. Osuna, linked wherever they
have co-authored with each other. Click a name or a connecting line to browse the papers
they share.
Border = papers with E. OsunaLine = papers co-authored togetherE. Osuna links everyone, so they are left out of the graph.
Lecture notes in computer science·E. Osuna, Osberth De Castro
2002
19
About E. Osuna
E. Osuna is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Media Technology and Statistical and Nonlinear Physics, having authored 8 papers that have together received 9.6k indexed citations. Recurring topics across this work include Face and Expression Recognition (4 papers), Neural Networks and Applications (3 papers), Machine Learning and Algorithms (2 papers), Sparse and Compressive Sensing Techniques (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Image Retrieval and Classification Techniques (1 paper), Text and Document Classification Technologies (1 paper) and Complex Systems and Time Series Analysis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (3.7k citations), Artificial Intelligence (3.5k citations), Signal Processing (953 citations), Media Technology (521 citations) and Control and Systems Engineering (817 citations). E. Osuna has collaborated with scholars based in United States, Australia and Germany. Frequent co-authors include Federico Girosi, Robert M. Freund, Marti A. Hearst, Susan Dumais, Bernhard Schölkopf, John C. Platt, Sayan Mukherjee, Michael B. Oren, Chrysovalantis Papageorgiou and Tomaso Poggio. Their work appears in journals such as Lecture notes in computer science, The MIT Press eBooks, DSpace@MIT (Massachusetts Institute of Technology) and IEEE Intelligent Systems and their Applications.
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.