Vladimir Vapnik
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Face and Expression Recognition
- Image Retrieval and Classification Techniques
- Artificial Intelligence top 0.01%
- Neural Networks and Applications
- Text and Document Classification Technologies
- Anomaly Detection Techniques and Applications
Papers in
-
- Neural Networks and Applications 48
- Machine Learning and Algorithms 19
- Machine Learning and Data Classification 10
-
- Face and Expression Recognition 31
- Image Retrieval and Classification Techniques 6
- Co-authors
- Corinna Cortes (8 shared papers)Isabelle Guyon (12 shared papers)Bernhard E. Boser (4 shared papers)Jason Weston (6 shared papers)Stephan R. Sain (1 shared paper)Alex Smola (3 shared papers)Olivier Chapelle (9 shared papers)Harris Drucker (7 shared papers)
- Journals
- Machine Learning (7 papers)Neural Computation (5 papers)Technometrics (2 papers)NeuroImage (1 paper)Physica A Statistical Mechanics and its Applications (1 paper)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Vladimir Vapnik
87 papers receiving 145.4k citations
Vladimir Vapnik's Hit Papers
Peers
Comparison fields: 5 of 239
- Computer Vision and Pattern Recognition 37.4k
- Artificial Intelligence 56.9k
- Signal Processing 12.5k
- Media Technology 7.9k
- Analytical Chemistry 7.0k
Countries citing papers authored by Vladimir Vapnik
This map shows the geographic impact of Vladimir Vapnik'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 Vladimir Vapnik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vladimir Vapnik more than expected).
Fields of papers citing papers by Vladimir Vapnik
This network shows the impact of papers produced by Vladimir Vapnik. 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 Vladimir Vapnik. The network helps show where Vladimir Vapnik may publish in the future.
Co-authors
The 25 scholars most cited alongside Vladimir Vapnik, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 89 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Support-vector networks Hit paper breakdown → | 1995 | 30645 |
| 2 | The Nature of Statistical Learning Theory Hit paper breakdown → | 1995 | 28133 |
| 3 | Support-Vector Networks Hit paper breakdown → | 1995 | 22409 |
| 4 | Statistical Learning Theory Hit paper breakdown → | 1999 | 18532 |
| 5 | The Nature of Statistical Learning Theory Hit paper breakdown → | 2000 | 7629 |
| 6 | A training algorithm for optimal margin classifiers Hit paper breakdown → | 1992 | 7594 |
| 7 | Gene Selection for Cancer Classification using Support Vector Machines Hit paper breakdown → | 2002 | 6945 |
| 8 | An overview of statistical learning theory Hit paper breakdown → | 1999 | 4486 |
| 9 | The Nature of Statistical Learning Theory Hit paper breakdown → | 1996 | 3566 |
| 10 | Support Vector Regression Machines Hit paper breakdown → | 1996 | 3109 |
| 11 | Support Vector Method for Function Approximation, Regression Estimation and Signal Processing Hit paper breakdown → | 1996 | 2049 |
| 12 | The Nature of Statistical Learning Hit paper breakdown → | 1995 | 1703 |
| 13 | Choosing Multiple Parameters for Support Vector Machines Hit paper breakdown → | 2002 | 1602 |
| 14 | On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities Hit paper breakdown → | 1971 | 1525 |
| 15 | Support vector machines for histogram-based image classification Hit paper breakdown → | 1999 | 1056 |
| 16 | Comparing support vector machines with Gaussian kernels to radial basis function classifiers Hit paper breakdown → | 1997 | 1001 |
| 17 | Support vector machines for spam categorization Hit paper breakdown → | 1999 | 992 |
| 18 | Estimation of Dependences Based on Empirical Data Hit paper breakdown → | 2006 | 991 |
| 19 | Support vector clustering Hit paper breakdown → | 2002 | 911 |
| 20 | Pattern recognition using generalized portrait method Hit paper breakdown → | 1963 | 805 |
About Vladimir Vapnik
Vladimir Vapnik is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Signal Processing and Statistics and Probability, having authored 89 papers that have together received 154.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (48 papers), Face and Expression Recognition (31 papers), Machine Learning and Algorithms (19 papers), Control Systems and Identification (10 papers), Machine Learning and Data Classification (10 papers), Advanced Statistical Methods and Models (7 papers), Blind Source Separation Techniques (7 papers) and Image Retrieval and Classification Techniques (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (37.4k citations), Artificial Intelligence (56.9k citations), Signal Processing (12.5k citations), Media Technology (7.9k citations) and Analytical Chemistry (7.0k citations). Vladimir Vapnik has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Corinna Cortes, Isabelle Guyon, Bernhard E. Boser, Jason Weston, Stephan R. Sain, Alex Smola, Olivier Chapelle, Harris Drucker, Alexey Chervonenkis and Steven E. Golowich. Their work appears in journals such as Machine Learning, Neural Computation, Technometrics, NeuroImage and Physica A Statistical Mechanics and its 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.