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ORCIDVladimir Vapnik
Person information
- affiliation: Columbia University, New York City, USA
- affiliation: Facebook, AI Research Lab, New York, USA
- unicode name: Владимир Наумович Вапник
- award (2017): IEEE John von Neumann Medal
- award (2012): Benjamin Franklin Medal
- award (2008): Paris Kanellakis Award
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Books and Theses
- 2006
[b4]Vladimir Vapnik:
Estimation of Dependences Based on Empirical Data, Second Editiontion. Springer 2006, ISBN 978-0-387-30865-4, pp. 1-497- 2000
- [b3]Vladimir Naumovich Vapnik:
The Nature of Statistical Learning Theory, Second Edition. Statistics for Engineering and Information Science, Springer 2000, ISBN 978-0-387-98780-4, pp. I-XIX, 1-314 - [b2]Vladimir Vapnik:
The Nature of Statistical Learning Theory. Statistics for Engineering and Information Science, Springer 2000, ISBN 978-1-4419-3160-3, pp. 1-314 - 1998
- [b1]Vladimir Vapnik:
Statistical learning theory. Wiley 1998, ISBN 978-0-471-03003-4, pp. I-XXIV, 1-736
Journal Articles
- 2021
- [j27]Vladimir Vapnik, Rauf Izmailov:
Reinforced SVM method and memorization mechanisms. Pattern Recognit. 119: 108018 (2021) - 2019
- [j26]Vladimir Naumovich Vapnik:
Complete Statistical Theory of Learning. Autom. Remote. Control. 80(11): 1949-1975 (2019) - [j25]Vladimir Vapnik, Rauf Izmailov
:
Rethinking statistical learning theory: learning using statistical invariants. Mach. Learn. 108(3): 381-423 (2019) - 2017
- [j24]Vladimir Vapnik, Rauf Izmailov:
Knowledge transfer in SVM and neural networks. Ann. Math. Artif. Intell. 81(1-2): 3-19 (2017) - 2016
- [j23]Vladimir Vapnik, Rauf Izmailov:
Synergy of Monotonic Rules. J. Mach. Learn. Res. 17: 136:1-136:33 (2016) - 2015
- [j22]Vladimir Vapnik, Rauf Izmailov:
V-matrix method of solving statistical inference problems. J. Mach. Learn. Res. 16: 1683-1730 (2015) - [j21]Vladimir Vapnik, Rauf Izmailov:
Learning using privileged information: similarity control and knowledge transfer. J. Mach. Learn. Res. 16: 2023-2049 (2015) - [j20]Vladimir Vapnik, Igor Braga, Rauf Izmailov:
Constructive setting for problems of density ratio estimation. Stat. Anal. Data Min. 8(3): 137-146 (2015) - 2011
- [j19]Ilia Nouretdinov, Sergi G. Costafreda, Alexander Gammerman, Alexey Ya. Chervonenkis, Vladimir Vovk, Vladimir Vapnik, Cynthia H. Y. Fu:
Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. NeuroImage 56(2): 809-813 (2011) - 2009
- [j18]Vladimir Vapnik, Akshay Vashist:
A new learning paradigm: Learning using privileged information. Neural Networks 22(5-6): 544-557 (2009) - 2008
- [j17]Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik:
Large margin vs. large volume in transductive learning. Mach. Learn. 72(3): 173-188 (2008) - 2002
- [j16]Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee:
Choosing Multiple Parameters for Support Vector Machines. Mach. Learn. 46(1-3): 131-159 (2002) - [j15]Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik:
Gene Selection for Cancer Classification using Support Vector Machines. Mach. Learn. 46(1-3): 389-422 (2002) - [j14]Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio:
Model Selection for Small Sample Regression. Mach. Learn. 48(1-3): 9-23 (2002) - 2001
- [j13]Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik:
Support Vector Clustering. J. Mach. Learn. Res. 2: 125-137 (2001) - 2000
- [j12]Vladimir Vapnik, Olivier Chapelle:
Bounds on Error Expectation for Support Vector Machines. Neural Comput. 12(9): 2013-2036 (2000) - 1999
- [j11]Vladimir Vapnik:
An overview of statistical learning theory. IEEE Trans. Neural Networks 10(5): 988-999 (1999) - [j10]Harris Drucker, Donghui Wu, Vladimir Vapnik:
Support vector machines for spam categorization. IEEE Trans. Neural Networks 10(5): 1048-1054 (1999) - [j9]Olivier Chapelle, Patrick Haffner, Vladimir Vapnik:
Support vector machines for histogram-based image classification. IEEE Trans. Neural Networks 10(5): 1055-1064 (1999) - [j8]Vladimir Cherkassky, Xuhui Shao, Filip Mulier, Vladimir Vapnik:
Model complexity control for regression using VC generalization bounds. IEEE Trans. Neural Networks 10(5): 1075-1089 (1999) - 1998
- [j7]Isabelle Guyon, John Makhoul, Richard M. Schwartz, Vladimir Vapnik:
What Size Test Set Gives Good Error Rate Estimates?. IEEE Trans. Pattern Anal. Mach. Intell. 20(1): 52-64 (1998) - 1997
- [j6]Bernhard Schölkopf, Kah Kay Sung, Christopher J. C. Burges, Federico Girosi, Partha Niyogi, Tomaso A. Poggio, Vladimir Vapnik:
Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45(11): 2758-2765 (1997) - 1995
- [j5]Corinna Cortes, Vladimir Vapnik:
Support-Vector Networks. Mach. Learn. 20(3): 273-297 (1995) - 1994
- [j4]Vladimir Vapnik, Esther Levin, Yann LeCun:
Measuring the VC-Dimension of a Learning Machine. Neural Comput. 6(5): 851-876 (1994) - [j3]Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik:
Boosting and Other Ensemble Methods. Neural Comput. 6(6): 1289-1301 (1994) - 1993
- [j2]Vladimir Vapnik, Léon Bottou:
Local Algorithms for Pattern Recognition and Dependencies Estimation. Neural Comput. 5(6): 893-909 (1993) - 1992
- [j1]Léon Bottou, Vladimir Vapnik:
Local Learning Algorithms. Neural Comput. 4(6): 888-900 (1992)
Conference and Workshop Papers
- 2020
- [c50]Vladimir Vapnik, Rauf Izmailov:
Complete statistical theory of learning: learning using statistical invariants. COPA 2020: 4-40 - 2016
- [c49]Vladimir Vapnik, Rauf Izmailov:
Learning with Intelligent Teacher. COPA 2016: 3-19 - [c48]David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik:
Unifying distillation and privileged information. ICLR (Poster) 2016 - 2015
- [c47]Vladimir Vapnik, Rauf Izmailov:
Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer - In memory of Alexey Chervonenkis. SLDS 2015: 3-32 - [c46]Vladimir Vapnik, Rauf Izmailov:
Statistical Inference Problems and Their Rigorous Solutions - In memory of Alexey Chervonenkis. SLDS 2015: 33-71 - 2014
- [c45]Vladimir Vapnik, Igor Braga, Rauf Izmailov:
A Constructive Setting for the Problem of Density Ratio Estimation. SDM 2014: 434-442 - 2013
- [c44]Vladimir Naumovich Vapnik, Alexey Ya. Chervonenkis:
On the Uniform Convergence of the Frequencies of Occurrence of Events to Their Probabilities. Empirical Inference 2013: 7-12 - [c43]Rauf Izmailov, Vladimir Vapnik, Akshay Vashist:
Multidimensional splines with infinite number of knots as SVM kernels. IJCNN 2013: 1-7 - 2010
- [c42]Dmitry Pechyony, Rauf Izmailov, Akshay Vashist, Vladimir Vapnik:
SMO-Style Algorithms for Learning Using Privileged Information. DMIN 2010: 235-241 - [c41]Dmitry Pechyony, Vladimir Vapnik:
On the Theory of Learnining with Privileged Information. NIPS 2010: 1894-1902 - 2009
- [c40]Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch:
Learning using hidden information (Learning with teacher). IJCNN 2009: 3188-3195 - 2008
- [c39]Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik:
Large Margin vs. Large Volume in Transductive Learning. ECML/PKDD (1) 2008: 9-10 - 2007
- [c38]Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch:
Learning using hidden information: Master-class learning. NATO ASI Mining Massive Data Sets for Security 2007: 3-14 - 2006
- [c37]Vladimir Vapnik:
Learning hidden information: SVM+. GrC 2006: 22 - [c36]Jason Weston, Ronan Collobert, Fabian H. Sinz, Léon Bottou, Vladimir Vapnik:
Inference with the Universum. ICML 2006: 1009-1016 - 2004
- [c35]Hans Peter Graf, Eric Cosatto, Léon Bottou, Igor Durdanovic, Vladimir Vapnik:
Parallel Support Vector Machines: The Cascade SVM. NIPS 2004: 521-528 - 2003
- [c34]Jinbo Bi, Vladimir Vapnik:
Learning with Rigorous Support Vector Machines. COLT 2003: 243-257 - 2002
- [c33]Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik:
Kernel Dependency Estimation. NIPS 2002: 873-880 - 2000
- [c32]Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik:
A Support Vector Clustering Method. ICPR 2000: 2724-2727 - [c31]Vladimir Naumovich Vapnik:
SVM method of estimating density, conditional probability, and conditional density. ISCAS 2000: 749-752 - [c30]Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik:
A Support Vector Method for Clustering. NIPS 2000: 367-373 - [c29]Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik:
Vicinal Risk Minimization. NIPS 2000: 416-422 - [c28]Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso A. Poggio, Vladimir Vapnik:
Feature Selection for SVMs. NIPS 2000: 668-674 - 1999
- [c27]Olivier Chapelle, Vladimir Vapnik:
Model Selection for Support Vector Machines. NIPS 1999: 230-236 - [c26]Olivier Chapelle, Vladimir Vapnik, Jason Weston:
Transductive Inference for Estimating Values of Functions. NIPS 1999: 421-427 - [c25]Vladimir Vapnik, Sayan Mukherjee:
Support Vector Method for Multivariate Density Estimation. NIPS 1999: 659-665 - 1998
- [c24]Alexander Gammerman, Volodya Vovk, Vladimir Vapnik:
Learning by Transduction. UAI 1998: 148-155 - 1997
- [c23]Vladimir Vapnik:
The Support Vector Method. ICANN 1997: 263-271 - [c22]Klaus-Robert Müller, Alexander J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik:
Predicting Time Series with Support Vector Machines. ICANN 1997: 999-1004 - [c21]Bernhard Schölkopf, Patrice Y. Simard, Alexander J. Smola, Vladimir Vapnik:
Prior Knowledge in Support Vector Kernels. NIPS 1997: 640-646 - 1996
- [c20]Bernhard Schölkopf, Chris Burges, Vladimir Vapnik:
Incorporating Invariances in Support Vector Learning Machines. ICANN 1996: 47-52 - [c19]Volker Blanz, Bernhard Schölkopf, Heinrich H. Bülthoff, Chris Burges, Vladimir Vapnik, Thomas Vetter:
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models. ICANN 1996: 251-256 - [c18]Vladimir Vapnik:
Statistical Theory of Generalization (Abstract). ICML 1996: 557 - [c17]Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alexander J. Smola, Vladimir Vapnik:
Support Vector Regression Machines. NIPS 1996: 155-161 - [c16]Vladimir Vapnik, Steven E. Golowich, Alexander J. Smola:
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. NIPS 1996: 281-287 - 1995
- [c15]Vladimir Vapnik:
Estimation of dependencies based on small number of observations. CIFEr 1995: 41 - [c14]Corinna Cortes, Harris Drucker, Dennis Hoover, Vladimir Vapnik:
Capacity and Complexity Control in Predicting the Spread Between Borrowing and Lending Interest Rates. KDD 1995: 51-56 - [c13]Bernhard Schölkopf, Chris Burges, Vladimir Vapnik:
Extracting Support Data for a Given Task. KDD 1995: 252-257 - 1994
- [c12]Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik:
Boosting and Other Machine Learning Algorithms. ICML 1994: 53-61 - [c11]Léon Bottou, Corinna Cortes, John S. Denker, Harris Drucker, Isabelle Guyon, Larry D. Jackel, Yann LeCun, Urs A. Müller, Eduard Säckinger, Patrice Y. Simard, Vladimir Vapnik:
Comparison of classifier methods: a case study in handwritten digit recognition. ICPR (2) 1994: 77-82 - [c10]Isabelle Guyon, Nada Matic, Vladimir Vapnik:
Discovering Informative Patterns and Data Cleaning. KDD Workshop 1994: 145-156 - 1993
- [c9]Nada Matic, Isabelle Guyon, John S. Denker, Vladimir Vapnik:
Writer-adaptation for on-line handwritten character recognition. ICDAR 1993: 187-191 - [c8]Corinna Cortes, Lawrence D. Jackel, Sara A. Solla, Vladimir Vapnik, John S. Denker:
Learning Curves: Asymptotic Values and Rate of Convergence. NIPS 1993: 327-334 - 1992
- [c7]Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik:
A Training Algorithm for Optimal Margin Classifiers. COLT 1992: 144-152 - [c6]Nada Matic, Isabelle Guyon, Léon Bottou, John S. Denker, Vladimir Vapnik:
Computer aided cleaning of large databases for character recognition. ICPR (2) 1992: 330-333 - [c5]Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla:
Capacity control in linear classifiers for pattern recognition. ICPR (2) 1992: 385-388 - [c4]Isabelle Guyon, Bernhard E. Boser, Vladimir Vapnik:
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers. NIPS 1992: 147-155 - 1991
- [c3]Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla:
Structural Risk Minimization for Character Recognition. NIPS 1991: 471-479 - [c2]Vladimir Vapnik:
Principles of Risk Minimization for Learning Theory. NIPS 1991: 831-838 - 1989
- [c1]Vladimir Vapnik:
Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures). COLT 1989: 3-21
Parts in Books or Collections
- 2006
- [p2]Vladimir Vapnik:
Transductive Inference and Semi-Supervised Learning. Semi-Supervised Learning 2006: 452-472 - 1996
- [p1]Isabelle Guyon, Nada Matic, Vladimir Vapnik:
Discovering Informative Patterns and Data Cleaning. Advances in Knowledge Discovery and Data Mining 1996: 181-203
Informal and Other Publications
- 2013
- [i1]Alex Gammerman, Volodya Vovk, Vladimir Vapnik:
Learning by Transduction. CoRR abs/1301.7375 (2013)
Coauthor Index
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