Vladimir Vapnik

Alexander Gammerman, Volodya Vovk, Vladimir Vapnik: Learning by Transduction. CoRR abs/1301.7375 (2013)

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)

Dmitry Pechyony, Rauf Izmailov, Akshay Vashist, Vladimir Vapnik: SMO-Style Algorithms for Learning Using Privileged Information. DMIN 2010: 235-241

Dmitry Pechyony, Vladimir Vapnik: On the Theory of Learnining with Privileged Information. NIPS 2010: 1894-1902

Vladimir Vapnik, Akshay Vashist: A new learning paradigm: Learning using privileged information. Neural Networks 22(5-6): 544-557 (2009)

Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch: Learning using hidden information (Learning with teacher). IJCNN 2009: 3188-3195

Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik: Large margin vs. large volume in transductive learning. Machine Learning 72(3): 173-188 (2008)

Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik: Large Margin vs. Large Volume in Transductive Learning. ECML/PKDD (1) 2008: 9-10

Vladimir Vapnik: Learning hidden information: SVM+. GrC 2006: 22

Jason Weston, Ronan Collobert, Fabian H. Sinz, Léon Bottou, Vladimir Vapnik: Inference with the Universum. ICML 2006: 1009-1016

Hans Peter Graf, Eric Cosatto, Léon Bottou, Igor Durdanovic, Vladimir Vapnik: Parallel Support Vector Machines: The Cascade SVM. NIPS 2004

Jinbo Bi, Vladimir Vapnik: Learning with Rigorous Support Vector Machines. COLT 2003: 243-257

Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee: Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46(1-3): 131-159 (2002)

Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik: Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning 46(1-3): 389-422 (2002)

Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio: Model Selection for Small Sample Regression. Machine Learning 48(1-3): 9-23 (2002)

Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik: Kernel Dependency Estimation. NIPS 2002: 873-880

Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik: Support Vector Clustering. Journal of Machine Learning Research 2: 125-137 (2001)

Vladimir Vapnik, Olivier Chapelle: Bounds on Error Expectation for Support Vector Machines. Neural Computation 12(9): 2013-2036 (2000)

Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik: A Support Vector Clustering Method. ICPR 2000: 2724-2727

Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik: A Support Vector Method for Clustering. NIPS 2000: 367-373

Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik: Vicinal Risk Minimization. NIPS 2000: 416-422

Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso Poggio, Vladimir Vapnik: Feature Selection for SVMs. NIPS 2000: 668-674

Vladimir Vapnik: An overview of statistical learning theory. IEEE Transactions on Neural Networks 10(5): 988-999 (1999)

Harris Drucker, Donghui Wu, Vladimir Vapnik: Support vector machines for spam categorization. IEEE Transactions on Neural Networks 10(5): 1048-1054 (1999)

Olivier Chapelle, Patrick Haffner, Vladimir Vapnik: Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks 10(5): 1055-1064 (1999)

Vladimir Cherkassky, Xuhui Shao, Filip Mulier, Vladimir Vapnik: Model complexity control for regression using VC generalization bounds. IEEE Transactions on Neural Networks 10(5): 1075-1089 (1999)

Olivier Chapelle, Vladimir Vapnik: Model Selection for Support Vector Machines. NIPS 1999: 230-236

Olivier Chapelle, Vladimir Vapnik, Jason Weston: Transductive Inference for Estimating Values of Functions. NIPS 1999: 421-427

Vladimir Vapnik, Sayan Mukherjee: Support Vector Method for Multivariate Density Estimation. NIPS 1999: 659-665

Vladimir Vapnik: Statistical learning theory. Wiley 1998, isbn 978-0-471-03003-4, pp. I-XXIV, 1-736

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)

Alexander Gammerman, Katy S. Azoury, Vladimir Vapnik: Learning by Transduction. UAI 1998: 148-155

Bernhard Schölkopf, Kah Kay Sung, Christopher J. C. Burges, Federico Girosi, Partha Niyogi, Tomaso Poggio, Vladimir Vapnik: Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing 45(11): 2758-2765 (1997)

Vladimir Vapnik: The Support Vector Method. ICANN 1997: 263-271

Klaus-Robert Müller, Alex J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik: Predicting Time Series with Support Vector Machines. ICANN 1997: 999-1004

Bernhard Schölkopf, Patrice Simard, Alex J. Smola, Vladimir Vapnik: Prior Knowledge in Support Vector Kernels. NIPS 1997

Isabelle Guyon, Nada Matic, Vladimir Vapnik: Discovering Informative Patterns and Data Cleaning. Advances in Knowledge Discovery and Data Mining 1996: 181-203

Bernhard Schölkopf, Chris Burges, Vladimir Vapnik: Incorporating Invariances in Support Vector Learning Machines. ICANN 1996: 47-52

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

Vladimir Vapnik: Statistical Theory of Generalization (Abstract). ICML 1996: 557

Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik: Support Vector Regression Machines. NIPS 1996: 155-161

Vladimir Vapnik, Steven E. Golowich, Alex J. Smola: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. NIPS 1996: 281-287

Corinna Cortes, Vladimir Vapnik: Support-Vector Networks. Machine Learning 20(3): 273-297 (1995)

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

Bernhard Schölkopf, Chris Burges, Vladimir Vapnik: Extracting Support Data for a Given Task. KDD 1995: 252-257

Vladimir Vapnik, Esther Levin, Yann LeCun: Measuring the VC-Dimension of a Learning Machine. Neural Computation 6(5): 851-876 (1994)

Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik: Boosting and Other Ensemble Methods. Neural Computation 6(6): 1289-1301 (1994)

Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik: Boosting and Other Machine Learning Algorithms. ICML 1994: 53-61

Isabelle Guyon, Nada Matic, Vladimir Vapnik: Discovering Informative Patterns and Data Cleaning. KDD Workshop 1994: 145-156

Vladimir Vapnik, Léon Bottou: Local Algorithms for Pattern Recognition and Dependencies Estimation. Neural Computation 5(6): 893-909 (1993)

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

Léon Bottou, Vladimir Vapnik: Local Learning Algorithms. Neural Computation 4(6): 888-900 (1992)

Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik: A Training Algorithm for Optimal Margin Classifiers. COLT 1992: 144-152

Isabelle Guyon, Bernhard E. Boser, Vladimir Vapnik: Automatic Capacity Tuning of Very Large VC-Dimension Classifiers. NIPS 1992: 147-155

Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla: Structural Risk Minimization for Character Recognition. NIPS 1991: 471-479

Vladimir Vapnik: Principles of Risk Minimization for Learning Theory. NIPS 1991: 831-838

Vladimir Vapnik: Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures). COLT 1989: 3-21