Vladimir Naumovich Vapnik

NEC, Princeton, N.J., Princeton, NJ, United States 
 Computer Science Columbia University, New York, NY 
Machine Learning, Empirical Inference, Statistical Learning Theory
"Vladimir Vapnik"

Vapnik-Chervonenkis theory

Mean distance: 15.54 (cluster 29)
Cross-listing: MathTree - Computer Science Tree

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Vapnik VN. (2019) Complete Statistical Theory of Learning Automation and Remote Control. 80: 1949-1975
Vapnik V, Izmailov R. (2019) Rethinking statistical learning theory: learning using statistical invariants Machine Learning. 108: 381-423
Vapnik V, Izmailov R. (2017) Knowledge transfer in SVM and neural networks Annals of Mathematics and Artificial Intelligence. 81: 3-19
Nouretdinov I, Costafreda SG, Gammerman A, et al. (2011) Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. Neuroimage. 56: 809-813
Vapnik V, Vashist A. (2009) 2009 Special Issue: A new learning paradigm: Learning using privileged information Neural Networks. 22: 544-557
Corfield D, Schölkopf B, Vapnik V. (2009) Falsificationism and statistical learning theory: Comparing the popper and vapnik-chervonenkis dimensions Journal For General Philosophy of Science. 40: 51-58
El-Yaniv R, Pechyony D, Vapnik V. (2008) Large margin vs. large volume in transductive learning Machine Learning. 72: 173-188
Bi J, Vapnik VN. (2003) Learning with rigorous support vector machines Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2777: 243-257
Chapelle O, Vapnik V, Bengio Y. (2002) Model selection for small sample regression Machine Learning. 48: 9-23
Chapelle O, Vapnik V, Bousquet O, et al. (2002) Choosing multiple parameters for support vector machines Machine Learning. 46: 131-159
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