Vladimir Naumovich Vapnik
Affiliations: | NEC, Princeton, N.J., Princeton, NJ, United States | ||
| Computer Science | Columbia University, New York, NY |
Area:
Machine Learning, Empirical Inference, Statistical Learning TheoryWebsite:
http://www.nec-labs.com/research/machine/ml_website/main/bio.php?person=vladGoogle:
"Vladimir Vapnik"Bio:
Vapnik-Chervonenkis theory
Cross-listing: Neurotree - Computer Science Tree
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Publications
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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 |