B. Igelnik
Impact in
- Artificial Intelligence top 2%
- Machine Learning and ELM
- Neural Networks and Applications
- Domain Adaptation and Few-Shot Learning
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- Face and Expression Recognition
Papers in
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- Neural Networks and Applications 10
- Fuzzy Logic and Control Systems 2
- Machine Learning and ELM 2
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- Face and Expression Recognition 2
- Co-authors
- Yoh‐Han Pao (6 shared papers)Steven R. LeClair (7 shared papers)Y.-H. Pao (4 shared papers)Nidhi Parikh (1 shared paper)Jinyan Li (1 shared paper)Yaakov Kogan (2 shared papers)E. G. Coffman (1 shared paper)Mark E. Oxley (2 shared papers)
- Journals
- Engineering Applications of Artificial Intelligence (3 papers)Journal of Alloys and Compounds (2 papers)Queueing Systems (1 paper)IEEE Transactions on Information Theory (1 paper)Applied Mathematics and Computation (1 paper)
- Partner nations
- United StatesIsraelJapan
In The Last Decade
B. Igelnik
19 papers receiving 994 citations
B. Igelnik's Hit Papers
Peers
Comparison fields: 5 of 100
- Artificial Intelligence 651
- Computer Vision and Pattern Recognition 240
- Control and Systems Engineering 251
- Computational Theory and Mathematics 138
- Statistical and Nonlinear Physics 58
Countries citing papers authored by B. Igelnik
This map shows the geographic impact of B. Igelnik'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 B. Igelnik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites B. Igelnik more than expected).
Fields of papers citing papers by B. Igelnik
This network shows the impact of papers produced by B. Igelnik. 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 B. Igelnik. The network helps show where B. Igelnik may publish in the future.
Co-authors
The 24 scholars most cited alongside B. Igelnik, 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 | Stochastic choice of basis functions in adaptive function approximation and the functional-link net Hit paper breakdown → | 1995 | 725 |
| 2 | 2001 | 63 | |
| 3 | 1997 | 58 | |
| 4 | 1999 | 47 | |
| 5 | 2003 | 34 | |
| 6 | 1991 | 24 | |
| 7 | 2000 | 15 | |
| 8 | 2001 | 13 | |
| 9 | 1995 | 12 | |
| 10 | 1996 | 9 | |
| 11 | 2011 | 8 | |
| 12 | 1998 | 4 | |
| 13 | 1998 | 4 | |
| 14 | 2005 | 4 | |
| 15 | 1998 | 4 | |
| 16 | 2001 | 3 | |
| 17 | 2002 | 2 | |
| 18 | 1995 | 1 | |
| 19 | 1999 | 1 |
About B. Igelnik
B. Igelnik is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Materials Chemistry, Statistical and Nonlinear Physics and Electrical and Electronic Engineering, having authored 19 papers that have together received 1.0k indexed citations. Recurring topics across this work include Neural Networks and Applications (10 papers), X-ray Diffraction in Crystallography (3 papers), Face and Expression Recognition (2 papers), Machine Learning in Materials Science (2 papers), Control Systems and Identification (2 papers), Fuzzy Logic and Control Systems (2 papers), Thin-Film Transistor Technologies (2 papers) and Machine Learning and ELM (2 papers). The work is most often cited by research in Artificial Intelligence (651 citations), Computer Vision and Pattern Recognition (240 citations), Control and Systems Engineering (251 citations), Computational Theory and Mathematics (138 citations) and Statistical and Nonlinear Physics (58 citations). B. Igelnik has collaborated with scholars based in United States, Israel and Japan. Frequent co-authors include Yoh‐Han Pao, Steven R. LeClair, Y.-H. Pao, Nidhi Parikh, Jinyan Li, Yaakov Kogan, E. G. Coffman, Mark E. Oxley, Shuichi Iwata and A. G. Jackson. Their work appears in journals such as Engineering Applications of Artificial Intelligence, Journal of Alloys and Compounds, Queueing Systems, IEEE Transactions on Information Theory and Applied Mathematics and Computation.
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.