Ürün Doǧan
Impact in
- Artificial Intelligence top 10%
- Machine Learning and ELM
- Domain Adaptation and Few-Shot Learning
- Machine Learning and Algorithms
- Anomaly Detection Techniques and Applications
-
- Face and Expression Recognition
Papers in
-
- Machine Learning and Algorithms 4
- Domain Adaptation and Few-Shot Learning 3
- Machine Learning and ELM 3
- Text and Document Classification Technologies 2
- Neural Networks and Applications 1
-
- Face and Expression Recognition 5
- Co-authors
- Ioannis Iossifidis (1 shared paper)Johann Edelbrunner (1 shared paper)Tobias Glasmachers (2 shared papers)Marius Kloft (5 shared papers)Yunwen Lei (4 shared papers)Christian Igel (1 shared paper)Ding‐Xuan Zhou (2 shared papers)Alexander Binder (2 shared papers)
- Journals
- Statistics and Computing (1 paper)IEEE Transactions on Information Theory (1 paper)Journal of Machine Learning Research (1 paper)Machine Learning (1 paper)PLoS ONE (1 paper)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Ürün Doǧan
13 papers receiving 184 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 120
- Computer Vision and Pattern Recognition 68
- Automotive Engineering 36
- Statistics and Probability 15
- Signal Processing 17
Countries citing papers authored by Ürün Doǧan
This map shows the geographic impact of Ürün Doǧan'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 Ürün Doǧan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ürün Doǧan more than expected).
Fields of papers citing papers by Ürün Doǧan
This network shows the impact of papers produced by Ürün Doǧan. 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 Ürün Doǧan. The network helps show where Ürün Doǧan may publish in the future.
Co-authors
The 18 scholars most cited alongside Ürün Doǧan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 57 | |
| 2 | A unified view on multi-class support vector classification | 2016 | 40 |
| 3 | 2019 | 27 | |
| 4 | 2015 | 14 | |
| 5 | 2015 | 12 | |
| 6 | Accelerated Coordinate Descent with Adaptive Coordinate Frequencies | 2013 | 10 |
| 7 | Theory and Algorithms for the Localized Setting of Learning Kernels | 2015 | 8 |
| 8 | 2017 | 8 | |
| 9 | Multi-Task Learning for Contextual Bandits | 2017 | 6 |
| 10 | Decoding multitask DQN in the world of Minecraft | 2016 | 3 |
| 11 | 2022 | 2 | |
| 12 | Prediction of Bandwidth and Additive Metrics for Large Scale Network Tomography | 2016 | 2 |
| 13 | Generalization Error Bounds for Extreme Multi-class Classification. | 2017 | 1 |
| 14 | 2024 | 0 |
About Ürün Doǧan
Ürün Doǧan is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications, Automotive Engineering and Control and Systems Engineering, having authored 14 papers that have together received 190 indexed citations. Recurring topics across this work include Face and Expression Recognition (5 papers), Machine Learning and Algorithms (4 papers), Domain Adaptation and Few-Shot Learning (3 papers), Machine Learning and ELM (3 papers), Text and Document Classification Technologies (2 papers), Statistical Methods and Bayesian Inference (1 paper), Advanced Control Systems Optimization (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (120 citations), Computer Vision and Pattern Recognition (68 citations), Automotive Engineering (36 citations), Statistics and Probability (15 citations) and Signal Processing (17 citations). Ürün Doǧan has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Ioannis Iossifidis, Johann Edelbrunner, Tobias Glasmachers, Marius Kloft, Yunwen Lei, Christian Igel, Ding‐Xuan Zhou, Alexander Binder, Gilles Blanchard and Clayton Scott. Their work appears in journals such as Statistics and Computing, IEEE Transactions on Information Theory, Journal of Machine Learning Research, Machine Learning and PLoS ONE.
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.