Ulf Brefeld
Impact in
- Artificial Intelligence top 1%
- Anomaly Detection Techniques and Applications
- Text and Document Classification Technologies
- Machine Learning and Data Classification
- Domain Adaptation and Few-Shot Learning
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- Face and Expression Recognition
Papers in
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- Anomaly Detection Techniques and Applications 13
- Machine Learning and Data Classification 8
- Topic Modeling 5
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- Face and Expression Recognition 6
- Video Analysis and Summarization 5
- Co-authors
- Tobias Scheffer (10 shared papers)Konrad Rieck (4 shared papers)Michael Kloft (1 shared paper)Marius Kloft (5 shared papers)Alexander Zien (3 shared papers)Klaus‐Robert Müller (4 shared papers)Pavel Laskov (2 shared papers)Sören Sonnenburg (2 shared papers)
In The Last Decade
Ulf Brefeld
60 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 117
- Artificial Intelligence 882
- Computer Vision and Pattern Recognition 380
- Signal Processing 188
- Orthopedics and Sports Medicine 96
- Computer Networks and Communications 231
Countries citing papers authored by Ulf Brefeld
This map shows the geographic impact of Ulf Brefeld'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 Ulf Brefeld with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ulf Brefeld more than expected).
Fields of papers citing papers by Ulf Brefeld
This network shows the impact of papers produced by Ulf Brefeld. 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 Ulf Brefeld. The network helps show where Ulf Brefeld may publish in the future.
Co-authors
The 25 scholars most cited alongside Ulf Brefeld, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 64 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 231 | |
| 2 | Efficient and Accurate Lp-Norm Multiple Kernel Learning | 2009 | 151 |
| 3 | 2004 | 133 | |
| 4 | 2006 | 116 | |
| 5 | 2009 | 49 | |
| 6 | 2006 | 47 | |
| 7 | AUC Maximizing Support Vector Learning | 2005 | 42 |
| 8 | 2014 | 35 | |
| 9 | 2020 | 34 | |
| 10 | 2005 | 33 | |
| 11 | 2019 | 33 | |
| 12 | 2019 | 30 | |
| 13 | 2008 | 30 | |
| 14 | 2015 | 28 | |
| 15 | 2007 | 27 | |
| 16 | 2007 | 26 | |
| 17 | 2010 | 25 | |
| 18 | Non-Sparse Regularization and Efficient Training with Multiple Kernels | 2010 | 25 |
| 19 | 2018 | 22 | |
| 20 | 2011 | 21 |
About Ulf Brefeld
Ulf Brefeld is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Economics and Econometrics, Signal Processing and Information Systems, having authored 64 papers that have together received 1.4k indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (13 papers), Sports Analytics and Performance (13 papers), Time Series Analysis and Forecasting (8 papers), Machine Learning and Data Classification (8 papers), Face and Expression Recognition (6 papers), Data Management and Algorithms (5 papers), Topic Modeling (5 papers) and Video Analysis and Summarization (5 papers). The work is most often cited by research in Artificial Intelligence (882 citations), Computer Vision and Pattern Recognition (380 citations), Signal Processing (188 citations), Orthopedics and Sports Medicine (96 citations) and Computer Networks and Communications (231 citations). Ulf Brefeld has collaborated with scholars based in Germany, France and Belgium. Frequent co-authors include Tobias Scheffer, Konrad Rieck, Michael Kloft, Marius Kloft, Alexander Zien, Klaus‐Robert Müller, Pavel Laskov, Sören Sonnenburg, Thomas Gärtner and Stefan Wrobel. Their work appears in journals such as Machine Learning, Frontiers in Sports and Active Living, AStA Advances in Statistical Analysis, PLoS ONE and Big Data.
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.