2021
Reinforced SVM method and memorization mechanisms.
Pattern Recognit., 2021
2020
Complete statistical theory of learning: learning using statistical invariants.
Proceedings of the Conformal and Probabilistic Prediction and Applications, 2020
2019
Rethinking statistical learning theory: learning using statistical invariants.
Mach. Learn., 2019
Complete Statistical Theory of Learning.
Autom. Remote. Control., 2019
2017
Knowledge transfer in SVM and neural networks.
Ann. Math. Artif. Intell., 2017
2016
Synergy of Monotonic Rules.
J. Mach. Learn. Res., 2016
Unifying distillation and privileged information.
Proceedings of the 4th International Conference on Learning Representations, 2016
Learning with Intelligent Teacher.
Proceedings of the Conformal and Probabilistic Prediction with Applications, 2016
2015
Constructive setting for problems of density ratio estimation.
Stat. Anal. Data Min., 2015
Learning using privileged information: similarity control and knowledge transfer.
J. Mach. Learn. Res., 2015
V-matrix method of solving statistical inference problems.
J. Mach. Learn. Res., 2015
Statistical Inference Problems and Their Rigorous Solutions - In memory of Alexey Chervonenkis.
Proceedings of the Statistical Learning and Data Sciences - Third International Symposium, 2015
Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer - In memory of Alexey Chervonenkis.
Proceedings of the Statistical Learning and Data Sciences - Third International Symposium, 2015
2014
A Constructive Setting for the Problem of Density Ratio Estimation.
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014
2013
Multidimensional splines with infinite number of knots as SVM kernels.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013
On the Uniform Convergence of the Frequencies of Occurrence of Events to Their Probabilities.
Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013
2011
Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.
NeuroImage, 2011
2010
On the Theory of Learnining with Privileged Information.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010
SMO-Style Algorithms for Learning Using Privileged Information.
Proceedings of The 2010 International Conference on Data Mining, 2010
2009
A new learning paradigm: Learning using privileged information.
Neural Networks, 2009
Learning using hidden information (Learning with teacher).
Proceedings of the International Joint Conference on Neural Networks, 2009
2008
Large margin vs. large volume in transductive learning.
Mach. Learn., 2008
2007
Learning using hidden information: Master-class learning.
Proceedings of the Mining Massive Data Sets for Security, 2007
2006
Inference with the Universum.
Proceedings of the Machine Learning, 2006
Learning hidden information: SVM+.
Proceedings of the 2006 IEEE International Conference on Granular Computing, 2006
Estimation of Dependences Based on Empirical Data, Second Editiontion
Springer, ISBN: 978-0-387-30865-4, 2006
Transductive Inference and Semi-Supervised Learning.
Proceedings of the Semi-Supervised Learning, 2006
2004
Parallel Support Vector Machines: The Cascade SVM.
Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004
2003
Empirical inference problems.
Proceedings of the International Joint Conference on Neural Networks, 2003
Learning with Rigorous Support Vector Machines.
Proceedings of the Computational Learning Theory and Kernel Machines, 2003
2002
Gene Selection for Cancer Classification using Support Vector Machines.
Mach. Learn., 2002
Choosing Multiple Parameters for Support Vector Machines.
Mach. Learn., 2002
Model Selection for Small Sample Regression.
Mach. Learn., 2002
Kernel Dependency Estimation.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002
2001
Support Vector Clustering.
J. Mach. Learn. Res., 2001
2000
Bounds on Error Expectation for Support Vector Machines.
Neural Comput., 2000
Feature Selection for SVMs.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000
Vicinal Risk Minimization.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000
A Support Vector Method for Clustering.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000
SVM method of estimating density, conditional probability, and conditional density.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2000
A Support Vector Clustering Method.
Proceedings of the 15th International Conference on Pattern Recognition, 2000
The Nature of Statistical Learning Theory
Statistics for Engineering and Information Science, Springer, ISBN: 978-1-4757-3264-1, 2000
The Nature of Statistical Learning Theory, Second Edition.
Statistics for Engineering and Information Science, Springer, ISBN: 978-0-387-98780-4, 2000
1999
An overview of statistical learning theory.
IEEE Trans. Neural Networks, 1999
Support vector machines for spam categorization.
IEEE Trans. Neural Networks, 1999
Model complexity control for regression using VC generalization bounds.
IEEE Trans. Neural Networks, 1999
Support vector machines for histogram-based image classification.
IEEE Trans. Neural Networks, 1999
Support Vector Method for Multivariate Density Estimation.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999
Transductive Inference for Estimating Values of Functions.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999
Model Selection for Support Vector Machines.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999
1998
What Size Test Set Gives Good Error Rate Estimates?.
IEEE Trans. Pattern Anal. Mach. Intell., 1998
Learning by Transduction.
Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998
Statistical learning theory.
Wiley, ISBN: 978-0-471-03003-4, 1998
1997
Comparing support vector machines with Gaussian kernels to radial basis function classifiers.
IEEE Trans. Signal Process., 1997
Prior Knowledge in Support Vector Kernels.
Proceedings of the Advances in Neural Information Processing Systems 10, 1997
The Support Vector Method.
Proceedings of the Artificial Neural Networks, 1997
Predicting Time Series with Support Vector Machines.
Proceedings of the Artificial Neural Networks, 1997
1996
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing.
Proceedings of the Advances in Neural Information Processing Systems 9, 1996
Support Vector Regression Machines.
Proceedings of the Advances in Neural Information Processing Systems 9, 1996
Statistical Theory of Generalization (Abstract).
Proceedings of the Machine Learning, 1996
Incorporating Invariances in Support Vector Learning Machines.
Proceedings of the Artificial Neural Networks, 1996
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models.
Proceedings of the Artificial Neural Networks, 1996
Discovering Informative Patterns and Data Cleaning.
Proceedings of the Advances in Knowledge Discovery and Data Mining., 1996
1995
Extracting Support Data for a Given Task.
Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995
Capacity and Complexity Control in Predicting the Spread Between Borrowing and Lending Interest Rates.
Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995
Estimation of dependencies based on small number of observations.
Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering, 1995
1994
Measuring the VC-Dimension of a Learning Machine.
Neural Comput., 1994
Boosting and Other Ensemble Methods.
Neural Comput., 1994
Discovering Informative Patterns and Data Cleaning.
Proceedings of the Knowledge Discovery in Databases: Papers from the 1994 AAAI Workshop, 1994
Comparison of classifier methods: a case study in handwritten digit recognition.
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Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994
Boosting and Other Machine Learning Algorithms.
Proceedings of the Machine Learning, 1994
1993
Local Algorithms for Pattern Recognition and Dependencies Estimation.
Neural Comput., 1993
Learning Curves: Asymptotic Values and Rate of Convergence.
Proceedings of the Advances in Neural Information Processing Systems 6, 1993
Writer-adaptation for on-line handwritten character recognition.
Proceedings of the 2nd International Conference Document Analysis and Recognition, 1993
1992
Local Learning Algorithms.
Neural Comput., 1992
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers.
Proceedings of the Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30, 1992
Computer aided cleaning of large databases for character recognition.
Proceedings of the 11th IAPR International Conference on Pattern Recognition, 1992
Capacity control in linear classifiers for pattern recognition.
Proceedings of the 11th IAPR International Conference on Pattern Recognition, 1992
A Training Algorithm for Optimal Margin Classifiers.
Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, 1992
1991
Principles of Risk Minimization for Learning Theory.
Proceedings of the Advances in Neural Information Processing Systems 4, 1991
Structural Risk Minimization for Character Recognition.
Proceedings of the Advances in Neural Information Processing Systems 4, 1991
1989
Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures).
Proceedings of the Second Annual Workshop on Computational Learning Theory, 1989