Amir Navot
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Molecular Biology
- Computational Theory and Mathematics top 10%
- Information Systems
- Co-authors
- Naftali TishbyRan Gilad-BachrachKoby CrammerEilon VaadiaLavi ShpigelmanEyal KrupkaG. GvaryahuM. E. El Halawani
- Topics
- Machine Learning and Data Classification (4 papers)Machine Learning and Algorithms (3 papers)Face and Expression Recognition (3 papers)
- Journals
- Journal of Machine Learning ResearchBritish Poultry ScienceNeural Information Processing Systems
In The Last Decade
Amir Navot
9 papers receiving 538 citations
Peers
Comparison fields: 5 of 102
- Artificial Intelligence 353
- Computer Vision and Pattern Recognition 268
- Molecular Biology 78
- Computational Theory and Mathematics 50
- Information Systems 42
Countries citing papers authored by Amir Navot
This map shows the geographic impact of Amir Navot'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 Amir Navot with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amir Navot more than expected).
Fields of papers citing papers by Amir Navot
This network shows the impact of papers produced by Amir Navot. 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 Amir Navot. The network helps show where Amir Navot may publish in the future.
Co-authorship network of co-authors of Amir Navot
This figure shows the co-authorship network connecting the top 25 collaborators of Amir Navot. A scholar is included among the top collaborators of Amir Navot 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 Amir Navot. Amir Navot is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 4 | |
| 3 | Learning to Select Features using their Properties | 13 |
| 4 | Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity | 69 |
| 5 | Query by Committee Made Real | 52 |
| 6 | 292 | |
| 7 | 15 | |
| 8 | Margin Analysis of the LVQ Algorithm | 133 |
| 9 | 1 |
About Amir Navot
Amir Navot is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aquatic Science, having authored 9 papers that have together received 583 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (4 papers), Machine Learning and Algorithms (3 papers) and Face and Expression Recognition (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (268 citations), Artificial Intelligence (353 citations) and Media Technology (35 citations). Amir Navot has collaborated with scholars based in Israel, France and India. Frequent co-authors include Naftali Tishby, Ran Gilad-Bachrach, Koby Crammer, Eilon Vaadia, Lavi Shpigelman, Eyal Krupka, G. Gvaryahu, M. E. El Halawani, Avi Rosenstrauch and I. Rozenboim. Their work appears in journals such as Journal of Machine Learning Research, British Poultry Science and Neural Information Processing Systems.
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.