Vladimir Vapnik

81 papers A* 9A 3B 4C 4Misc 1Journal 27Unranked 28

Year	Rank	Type	Title / Venue / Authors
2021	J	`jnl`	Reinforced SVM method and memorization mechanisms. Pattern Recognit. Vladimir Vapnik, Rauf Izmailov
2020	—	`conf`	Complete statistical theory of learning: learning using statistical invariants. COPA Vladimir Vapnik, Rauf Izmailov
2019	J	`jnl`	Rethinking statistical learning theory: learning using statistical invariants. Mach. Learn. Vladimir Vapnik, Rauf Izmailov
2017	J	`jnl`	Knowledge transfer in SVM and neural networks. Ann. Math. Artif. Intell. Vladimir Vapnik, Rauf Izmailov
2016	—	`conf`	Learning with Intelligent Teacher. COPA Vladimir Vapnik, Rauf Izmailov
2016	J	`jnl`	Synergy of Monotonic Rules. J. Mach. Learn. Res. Vladimir Vapnik, Rauf Izmailov
2016	A*	`conf`	Unifying distillation and privileged information. ICLR (Poster) David Lopez-Paz, Léon Bottou, Bernhard Schölkopf, Vladimir Vapnik
2015	J	`jnl`	Constructive setting for problems of density ratio estimation. Stat. Anal. Data Min. Vladimir Vapnik, Igor Braga, Rauf Izmailov
2015	J	`jnl`	Learning using privileged information: similarity control and knowledge transfer. J. Mach. Learn. Res. Vladimir Vapnik, Rauf Izmailov
2015	—	`conf`	Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer - In memory of Alexey Chervonenkis. SLDS Vladimir Vapnik, Rauf Izmailov
2015	—	`conf`	Statistical Inference Problems and Their Rigorous Solutions - In memory of Alexey Chervonenkis. SLDS Vladimir Vapnik, Rauf Izmailov
2015	J	`jnl`	V-matrix method of solving statistical inference problems. J. Mach. Learn. Res. Vladimir Vapnik, Rauf Izmailov
2014	A	`conf`	A Constructive Setting for the Problem of Density Ratio Estimation. SDM Vladimir Vapnik, Igor Braga, Rauf Izmailov
2013	J	`jnl`	Learning by Transduction CoRR Alex Gammerman, Volodya Vovk, Vladimir Vapnik
2013	B	`conf`	Multidimensional splines with infinite number of knots as SVM kernels. IJCNN Rauf Izmailov, Vladimir Vapnik, Akshay Vashist
2011	J	`jnl`	Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. NeuroImage Ilia Nouretdinov, Sergi G. Costafreda, Alexander Gammerman, Alexey Ya. Chervonenkis, Vladimir Vovk, Vladimir Vapnik, Cynthia H. Y. Fu
2010	—	`conf`	On the Theory of Learnining with Privileged Information. NIPS Dmitry Pechyony, Vladimir Vapnik
2010	—	`conf`	SMO-Style Algorithms for Learning Using Privileged Information. DMIN Dmitry Pechyony, Rauf Izmailov, Akshay Vashist, Vladimir Vapnik
2009	J	`jnl`	A new learning paradigm: Learning using privileged information. Neural Networks Vladimir Vapnik, Akshay Vashist
2009	B	`conf`	Learning using hidden information (Learning with teacher). IJCNN Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch
2008	—	`conf`	Large Margin vs. Large Volume in Transductive Learning. ECML/PKDD (1) Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
2008	J	`jnl`	Large margin vs. large volume in transductive learning. Mach. Learn. Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik
2007	—	`conf`	Learning using hidden information: Master-class learning. NATO ASI Mining Massive Data Sets for Security Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch
2006	—	`book`	Estimation of Dependences Based on Empirical Data, Second Editiontion Vladimir Vapnik
2006	A*	`conf`	Inference with the Universum. ICML Jason Weston, Ronan Collobert, Fabian H. Sinz, Léon Bottou, Vladimir Vapnik
2006	Misc	`conf`	Learning hidden information: SVM+. GrC Vladimir Vapnik
2006	—	`ch.`	Transductive Inference and Semi-Supervised Learning. Semi-Supervised Learning Vladimir Vapnik
2004	—	`conf`	Parallel Support Vector Machines: The Cascade SVM. NIPS Hans Peter Graf, Eric Cosatto, Léon Bottou, Igor Durdanovic, Vladimir Vapnik
2003	B	`conf`	Empirical inference problems. IJCNN Vladimir Vapnik
2003	A*	`conf`	Learning with Rigorous Support Vector Machines. COLT Jinbo Bi, Vladimir Vapnik
2002	J	`jnl`	Choosing Multiple Parameters for Support Vector Machines. Mach. Learn. Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee
2002	J	`jnl`	Gene Selection for Cancer Classification using Support Vector Machines. Mach. Learn. Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik
2002	—	`conf`	Kernel Dependency Estimation. NIPS Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik
2002	J	`jnl`	Model Selection for Small Sample Regression. Mach. Learn. Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio
2001	J	`jnl`	Support Vector Clustering. J. Mach. Learn. Res. Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
2000	B	`conf`	A Support Vector Clustering Method. ICPR Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik
2000	—	`conf`	A Support Vector Method for Clustering. NIPS Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
2000	J	`jnl`	Bounds on Error Expectation for Support Vector Machines. Neural Comput. Vladimir Vapnik, Olivier Chapelle
2000	—	`conf`	Feature Selection for SVMs. NIPS Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso A. Poggio, Vladimir Vapnik
2000	—	`book`	The Nature of Statistical Learning Theory Vladimir Vapnik
2000	—	`conf`	Vicinal Risk Minimization. NIPS Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik
1999	J	`jnl`	An overview of statistical learning theory. IEEE Trans. Neural Networks Vladimir Vapnik
1999	—	`conf`	Model Selection for Support Vector Machines. NIPS Olivier Chapelle, Vladimir Vapnik
1999	J	`jnl`	Model complexity control for regression using VC generalization bounds. IEEE Trans. Neural Networks Vladimir Cherkassky, Xuhui Shao, Filip Mulier, Vladimir Vapnik
1999	—	`conf`	Support Vector Method for Multivariate Density Estimation. NIPS Vladimir Vapnik, Sayan Mukherjee
1999	J	`jnl`	Support vector machines for histogram-based image classification. IEEE Trans. Neural Networks Olivier Chapelle, Patrick Haffner, Vladimir Vapnik
1999	J	`jnl`	Support vector machines for spam categorization. IEEE Trans. Neural Networks Harris Drucker, Donghui Wu, Vladimir Vapnik
1999	—	`conf`	Transductive Inference for Estimating Values of Functions. NIPS Olivier Chapelle, Vladimir Vapnik, Jason Weston
1998	A	`conf`	Learning by Transduction. UAI Alexander Gammerman, Volodya Vovk, Vladimir Vapnik
1998	—	`book`	Statistical learning theory. Vladimir Vapnik
1998	J	`jnl`	What Size Test Set Gives Good Error Rate Estimates?. IEEE Trans. Pattern Anal. Mach. Intell. Isabelle Guyon, John Makhoul, Richard M. Schwartz, Vladimir Vapnik
1997	J	`jnl`	Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. Bernhard Schölkopf, Kah Kay Sung, Christopher J. C. Burges, Federico Girosi, Partha Niyogi, Tomaso A. Poggio, Vladimir Vapnik
1997	C	`conf`	Predicting Time Series with Support Vector Machines. ICANN Klaus-Robert Müller, Alexander J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik
1997	—	`conf`	Prior Knowledge in Support Vector Kernels. NIPS Bernhard Schölkopf, Patrice Y. Simard, Alexander J. Smola, Vladimir Vapnik
1997	C	`conf`	The Support Vector Method. ICANN Vladimir Vapnik
1996	C	`conf`	Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models. ICANN Volker Blanz, Bernhard Schölkopf, Heinrich H. Bülthoff, Chris Burges, Vladimir Vapnik, Thomas Vetter
1996	—	`ch.`	Discovering Informative Patterns and Data Cleaning. Advances in Knowledge Discovery and Data Mining Isabelle Guyon, Nada Matic, Vladimir Vapnik
1996	C	`conf`	Incorporating Invariances in Support Vector Learning Machines. ICANN Bernhard Schölkopf, Chris Burges, Vladimir Vapnik
1996	A*	`conf`	Statistical Theory of Generalization (Abstract). ICML Vladimir Vapnik
1996	—	`conf`	Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. NIPS Vladimir Vapnik, Steven E. Golowich, Alexander J. Smola
1996	—	`conf`	Support Vector Regression Machines. NIPS Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alexander J. Smola, Vladimir Vapnik
1995	A*	`conf`	Capacity and Complexity Control in Predicting the Spread Between Borrowing and Lending Interest Rates. KDD Corinna Cortes, Harris Drucker, Dennis Hoover, Vladimir Vapnik
1995	—	`conf`	Estimation of dependencies based on small number of observations. CIFEr Vladimir Vapnik
1995	A*	`conf`	Extracting Support Data for a Given Task. KDD Bernhard Schölkopf, Chris Burges, Vladimir Vapnik
1995	J	`jnl`	Support-Vector Networks. Mach. Learn. Corinna Cortes, Vladimir Vapnik
1994	J	`jnl`	Boosting and Other Ensemble Methods. Neural Comput. Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik
1994	A*	`conf`	Boosting and Other Machine Learning Algorithms. ICML Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik
1994	—	`conf`	Comparison of classifier methods: a case study in handwritten digit recognition. ICPR (2) 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
1994	—	`conf`	Discovering Informative Patterns and Data Cleaning. KDD Workshop Isabelle Guyon, Nada Matic, Vladimir Vapnik
1994	J	`jnl`	Measuring the VC-Dimension of a Learning Machine. Neural Comput. Vladimir Vapnik, Esther Levin, Yann LeCun
1993	—	`conf`	Learning Curves: Asymptotic Values and Rate of Convergence. NIPS Corinna Cortes, Lawrence D. Jackel, Sara A. Solla, Vladimir Vapnik, John S. Denker
1993	J	`jnl`	Local Algorithms for Pattern Recognition and Dependencies Estimation. Neural Comput. Vladimir Vapnik, Léon Bottou
1993	A	`conf`	Writer-adaptation for on-line handwritten character recognition. ICDAR Nada Matic, Isabelle Guyon, John S. Denker, Vladimir Vapnik
1992	A*	`conf`	A Training Algorithm for Optimal Margin Classifiers. COLT Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik
1992	—	`conf`	Automatic Capacity Tuning of Very Large VC-Dimension Classifiers. NIPS Isabelle Guyon, Bernhard E. Boser, Vladimir Vapnik
1992	—	`conf`	Capacity control in linear classifiers for pattern recognition. ICPR (2) Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla
1992	—	`conf`	Computer aided cleaning of large databases for character recognition. ICPR (2) Nada Matic, Isabelle Guyon, Léon Bottou, John S. Denker, Vladimir Vapnik
1992	J	`jnl`	Local Learning Algorithms. Neural Comput. Léon Bottou, Vladimir Vapnik
1991	—	`conf`	Principles of Risk Minimization for Learning Theory. NIPS Vladimir Vapnik
1991	—	`conf`	Structural Risk Minimization for Character Recognition. NIPS Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla
1989	A*	`conf`	Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures). COLT Vladimir Vapnik