Eugene Tuv
- Artificial Intelligence top 2%
- Signal Processing top 1%
- Computer Vision and Pattern Recognition top 5%
- Economics and Econometrics top 10%
- Statistics, Probability and Uncertainty top 2%
- Co-authors
- George C. RungerHoutao DengMustafa Gökçe BaydoğanAlexander BorisovKari TorkkolaK. TorkkolaGeorge FormanMichael E. Berens
- Topics
- Fault Detection and Control Systems (5 papers)Machine Learning and Data Classification (5 papers)Neural Networks and Applications (5 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceInformation SciencesInternational Journal of Production Research
- Partner nations
- United StatesMexicoSwitzerland
In The Last Decade
Eugene Tuv
20 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Artificial Intelligence 637
- Signal Processing 506
- Computer Vision and Pattern Recognition 161
- Economics and Econometrics 127
- Statistics, Probability and Uncertainty 126
Countries citing papers authored by Eugene Tuv
This map shows the geographic impact of Eugene Tuv'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 Eugene Tuv with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eugene Tuv more than expected).
Fields of papers citing papers by Eugene Tuv
This network shows the impact of papers produced by Eugene Tuv. 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 Eugene Tuv. The network helps show where Eugene Tuv may publish in the future.
Co-authorship network of co-authors of Eugene Tuv
This figure shows the co-authorship network connecting the top 25 collaborators of Eugene Tuv. A scholar is included among the top collaborators of Eugene Tuv 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 Eugene Tuv. Eugene Tuv is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 250 | |
| 2 | A time series forest for classification and feature extractionbreakdown → | 393 |
| 3 | 45 | |
| 4 | Active Batch Learning with Stochastic Query-by-Forest (SQBF) | 8 |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination | 197 |
| 9 | 5 | |
| 10 | 41 | |
| 11 | 19 | |
| 12 | 27 | |
| 13 | 14 | |
| 14 | 1 | |
| 15 | 0 | |
| 16 | Feature selection: We've barely scratched the surface | 14 |
| 17 | 154 | |
| 18 | 3 | |
| 19 | 6 | |
| 20 | 6 |
About Eugene Tuv
Eugene Tuv is a scholar working on Statistics, Probability and Uncertainty, Artificial Intelligence and Industrial and Manufacturing Engineering, having authored 23 papers that have together received 1.2k indexed citations. Recurring topics across this work include Fault Detection and Control Systems (5 papers), Machine Learning and Data Classification (5 papers) and Neural Networks and Applications (5 papers). The work is most often cited by research in Signal Processing (506 citations), Artificial Intelligence (637 citations) and Statistics, Probability and Uncertainty (126 citations). Eugene Tuv has collaborated with scholars based in United States, Mexico and Switzerland. Frequent co-authors include George C. Runger, Houtao Deng, Mustafa Gökçe Baydoğan, Alexander Borisov, Kari Torkkola, K. Torkkola, George Forman, Michael E. Berens, Zheng Zhao and Lance Parsons. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Information Sciences and International Journal of Production Research.
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.