Vladimir Naumovich Vapnik (Russian: Владимир Наумович Вапник; born 6 December 1936) is one of the main developers of Vapnik–Chervonenkis theory. He was born in the Soviet Union. He received his master's degree in mathematics at the Uzbek State University, Samarkand, Uzbek SSR in 1958 and Ph.D in statistics at the Institute of Control Sciences, Moscow in 1964. He worked at this institute from 1961 to 1990 and became Head of the Computer Science Research Department. At the end of 1990, he moved to the USA and joined the Adaptive Systems Research Department at AT&T Bell Labs in Holmdel, New Jersey. The group later became the Image Processing Research Department of AT&T Laboratories when AT&T spun off Lucent Technologies in 1996. Vapnik left AT&T in 2002 and joined NEC Laboratories in Princeton, New Jersey, where he currently works in the Machine Learning group. He also holds a Professor of Computer Science and Statistics position at Royal Holloway, University of London since 1995, as well as a position as Professor of Computer Science at Columbia University, New York City since 2003. He was inducted into the U.S. National Academy of Engineering in 2006. He received the 2005 Gabor Award, the 2008 Paris Kanellakis Award, the 2010 Neural Networks Pioneer Award, the 2012 IEEE Frank Rosenblatt Award, the 2012 Benjamin Franklin Medal in Computer and Cognitive Science from the Franklin Institute, the 2013 C&C Prize from the NEC C&C Foundation, and the 2014 Kampé de Fériet Award.
While at AT&T, Vapnik and his colleagues developed the theory of the support vector machine. They demonstrated its performance on a number of problems of interest to the machine learning community, including handwriting recognition.
- On the uniform convergence of relative frequencies of events to their probabilities, co-author A. Y. Chervonenkis, 1971
- Necessary and sufficient conditions for the uniform convergence of means to their expectations, co-author A. Y. Chervonenkis, 1981
- Estimation of Dependences Based on Empirical Data, 1982
- The Nature of Statistical Learning Theory, 1995
- Statistical Learning Theory, 1998
- Estimation of Dependences Based on Empirical Data, Reprint 2006 (Springer), also contains a philosophical essay on Empirical Inference Science, 2006