Vladimir Vapnik
Vladimir Vapnik helped establish statistical learning theory, which examines when learning from a sample can support useful conclusions about new data. He worked with Alexey Chervonenkis on the relationship between generalization and the complexity of possible rules. After a long research career at Moscow’s Institute of Control Sciences, he moved to Bell Labs. His collaborations contributed to support vector methods, including a 1992 paper with Bernhard Boser and Isabelle Guyon and the 1995 Support-Vector Networks paper with Corinna Cortes. His books organized the theory of generalization and model complexity. In 2020, the BBVA Foundation announced a shared Frontiers of Knowledge award for Vapnik, Guyon and Bernhard Schölkopf. His contribution remains a foundation for studying how models perform beyond their training data.
- Works
- 162
- Citations
- 213,116
- h-index
- 69
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
-
1958
Beginning with mathematics
Received a mathematics master’s degree from Uzbek State University.
-
Mid-1960s
Studying learning with Chervonenkis
Developed statistical learning theory with Alexey Chervonenkis.
-
1992
Developing a support vector training method
Coauthored a support vector training method with Bernhard Boser and Isabelle Guyon.
-
1995
Publishing support vector networks with Cortes
Published Support-Vector Networks with Corinna Cortes.
-
2020
Receiving a shared research award
Shared the Frontiers of Knowledge award announced in 2020 with Guyon and Schölkopf.
Sources and credits
Photo: The Franklin Institute laureate profile. License: Not stated by source. Source: fi.edu. Cropped to a square and resized. Ask us to remove or replace this photo.