М.А. Айзерман

17 papers receiving 1.3k citations

Hit Papers

Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning 1964 · 912 citations
9120+20+41Years since publication250500750

Peers

М.А. Айзерман
Comparison fields: 5 of 148
  • General Decision Sciences 50
  • Computer Vision and Pattern Recognition 426
  • Artificial Intelligence 650
  • Signal Processing 149
  • Computational Theory and Mathematics 169
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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
#Work
1
Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning
Hit paper breakdown →
1964912
2 1976292
3 1981116
4 198550
5 195839
6 198618
7 195816
8 19787
9 19777
10 19836
11 19865
12 19605
13
Logic, automata, and algorithms
19713
14 19603
15 19652
16 19802
17 19892
18 19631
19
PROBABILITY PROBLEM ABOUT INSTRUCTION OF AUTOMATA TO DISTINGUISH CLASSES AND THE METHOD OF POTENTIAL FUNCTIONS
19650
20
The algorithmic Insolubility of the Problem of Recognizing the Representability of recursive events in finite automata
19720

About М.А. Айзерман

М.А. Айзерман is a scholar working on Control and Systems Engineering, Computational Theory and Mathematics, Information Systems, Artificial Intelligence and Statistical and Nonlinear Physics, having authored 20 papers that have together received 1.5k indexed citations. Recurring topics across this work include Mathematical Control Systems and Analysis (3 papers), Educational Technology and Optimization (3 papers), Advanced Algebra and Logic (3 papers), Fuzzy Logic and Control Systems (2 papers), Quantum chaos and dynamical systems (2 papers), Game Theory and Voting Systems (2 papers), Advanced Research in Systems and Signal Processing (2 papers) and Advanced Data Processing Techniques (2 papers). The work is most often cited by research in General Decision Sciences (50 citations), Computer Vision and Pattern Recognition (426 citations), Artificial Intelligence (650 citations), Signal Processing (149 citations) and Computational Theory and Mathematics (169 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, M. Г. Крейн, S L Sobolev, Elena Braverman, L. S. Pontryagin and Ayellet Tal. Their work appears in journals such as Mathematical Social Sciences, IEEE Transactions on Automatic Control, Social Choice and Welfare, The Quarterly Journal of Mechanics and Applied Mathematics and Journal of the Franklin Institute.

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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