М.А. Айзерман

21 papers receiving 1.7k citations

М.А. Айзерман's Hit Papers

Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning 1964 · 1.1k citations
1.1k0+20+41Years since publication2505007501000

Peers

М.А. Айзерман
Comparison fields: 5 of 150
  • General Decision Sciences 62
  • Computer Vision and Pattern Recognition 545
  • Artificial Intelligence 838
  • Signal Processing 180
  • Computational Theory and Mathematics 218
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М.А. Айзерман relative to Wynn C. Stirling United States Wynn C. Stirling's profile →
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Citations per year

Countries citing papers authored by М.А. Айзерман

Since Specialization
Citations

This map shows the geographic impact of М.А. Айзерман'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 М.А. Айзерман with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites М.А. Айзерман more than expected).

Fields of papers citing papers by М.А. Айзерман

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by М.А. Айзерман. 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 М.А. Айзерман. The network helps show where М.А. Айзерман may publish in the future.

Co-authors

The 10 scholars most cited alongside М.А. Айзерман, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with М.А. Айзерман Line = papers co-authored together М.А. Айзерман links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning
Hit paper breakdown →
19641091
2 1976409
3 1981136
4 198572
5 195841
6 198622
7 195817
8 19839
9 19789
10 19778
11 19877
12 19606
13 19866
14
Logic, automata, and algorithms
19714
15 19653
16 19603
17 19872
18 19892
19 19802
20 19632

About М.А. Айзерман

М.А. Айзерман is a scholar working on Control and Systems Engineering, Computational Theory and Mathematics, Artificial Intelligence, Information Systems and Statistical and Nonlinear Physics, having authored 24 papers that have together received 1.9k indexed citations. Recurring topics across this work include Mathematical Control Systems and Analysis (4 papers), Advanced Algebra and Logic (3 papers), Advanced Data Processing Techniques (3 papers), Educational Technology and Optimization (3 papers), Game Theory and Voting Systems (3 papers), Fuzzy Logic and Control Systems (2 papers), Quantum chaos and dynamical systems (2 papers) and Advanced Research in Systems and Signal Processing (2 papers). The work is most often cited by research in General Decision Sciences (62 citations), Computer Vision and Pattern Recognition (545 citations), Artificial Intelligence (838 citations), Signal Processing (180 citations) and Computational Theory and Mathematics (218 citations). М.А. Айзерман has collaborated with scholars based in Russia. Frequent co-authors include King‐Sun Fu, F.R. GANTMAKHER, Fuad Aleskerov, Felix R. Gantmacher, Ye.S. Pyatnitskiy, Elena Braverman, Ayellet Tal, L. S. Pontryagin, S L Sobolev and M. Г. Крейн. Their work appears in journals such as IEEE Transactions on Automatic Control, Mathematical Social Sciences, The Quarterly Journal of Mechanics and Applied Mathematics, Systems & Control Letters and Journal of Mathematical Analysis and Applications.

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.

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