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Vladimir Naumovich Vapnik - Publications

Affiliations: 
NEC, Princeton, N.J., Princeton, NJ, United States 
 Computer Science Columbia University, New York, NY 
Area:
Machine Learning, Empirical Inference, Statistical Learning Theory
Website:
http://www.nec-labs.com/research/machine/ml_website/main/bio.php?person=vlad
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24 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2019 Vapnik VN. Complete Statistical Theory of Learning Automation and Remote Control. 80: 1949-1975. DOI: 10.1134/S000511791911002X  0.383
2019 Vapnik V, Izmailov R. Rethinking statistical learning theory: learning using statistical invariants Machine Learning. 108: 381-423. DOI: 10.1007/S10994-018-5742-0  0.498
2017 Vapnik V, Izmailov R. Knowledge transfer in SVM and neural networks Annals of Mathematics and Artificial Intelligence. 81: 3-19. DOI: 10.1007/S10472-017-9538-X  0.363
2011 Nouretdinov I, Costafreda SG, Gammerman A, Chervonenkis AY, Vovk V, Vapnik V, Fu CHY. Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. Neuroimage. 56: 809-813. PMID 20483379 DOI: 10.1016/J.Neuroimage.2010.05.023  0.378
2009 Vapnik V, Vashist A. 2009 Special Issue: A new learning paradigm: Learning using privileged information Neural Networks. 22: 544-557. PMID 19632812 DOI: 10.1016/J.Neunet.2009.06.042  0.445
2009 Corfield D, Schölkopf B, Vapnik V. Falsificationism and statistical learning theory: Comparing the popper and vapnik-chervonenkis dimensions Journal For General Philosophy of Science. 40: 51-58. DOI: 10.1007/S10838-009-9091-3  0.504
2008 El-Yaniv R, Pechyony D, Vapnik V. Large margin vs. large volume in transductive learning Machine Learning. 72: 173-188. DOI: 10.1007/S10994-008-5071-9  0.43
2003 Bi J, Vapnik VN. Learning with rigorous support vector machines Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2777: 243-257.  0.363
2002 Chapelle O, Vapnik V, Bengio Y. Model selection for small sample regression Machine Learning. 48: 9-23. DOI: 10.1023/A:1013943418833  0.4
2002 Chapelle O, Vapnik V, Bousquet O, Mukherjee S. Choosing multiple parameters for support vector machines Machine Learning. 46: 131-159. DOI: 10.1023/A:1012450327387  0.345
2000 Vapnik V, Chapelle O. Bounds on Error Expectation for Support Vector Machines Neural Computation. 12: 2013-2036. PMID 10976137 DOI: 10.1162/089976600300015042  0.385
1999 Cherkassky V, Shao X, Mulier FM, Vapnik VN. Model complexity control for regression using VC generalization bounds. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 10: 1075-89. PMID 18252610 DOI: 10.1109/72.788648  0.353
1999 Chapelle O, Haffner P, Vapnik VN. Support vector machines for histogram-based image classification. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 10: 1055-64. PMID 18252608 DOI: 10.1109/72.788646  0.316
1999 Drucker H, Wu D, Vapnik VN. Support vector machines for spam categorization. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 10: 1048-54. PMID 18252607 DOI: 10.1109/72.788645  0.366
1999 Vapnik VN. An overview of statistical learning theory. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 10: 988-99. PMID 18252602 DOI: 10.1109/72.788640  0.481
1998 Guyon I, Makhoul J, Schwartz R, Vapnik V. What size test set gives good error rate estimates Ieee Transactions On Pattern Analysis and Machine Intelligence. 20: 52-64. DOI: 10.1109/34.655649  0.314
1997 Schölkopf B, Sung KK, Burges CJC, Girosi F, Niyogi P, Poggio T, Vapnik V. Comparing support vector machines with gaussian kernels to radial basis function classifiers Ieee Transactions On Signal Processing. 45: 2758-2765. DOI: 10.1109/78.650102  0.437
1995 Cortes C, Vapnik V. Support-Vector Networks Machine Learning. 20: 273-297. DOI: 10.1023/A:1022627411411  0.436
1994 Drucker H, Cortes C, Jackel LD, LeCun Y, Vapnik V. Boosting and Other Ensemble Methods Neural Computation. 6: 1289-1301. DOI: 10.1162/Neco.1994.6.6.1289  0.338
1994 Vapnik V, Levin E, Cun YL. Measuring the VC-Dimension of a Learning Machine Neural Computation. 6: 851-876. DOI: 10.1162/Neco.1994.6.5.851  0.44
1993 Vapnik V, Bottou L. Local algorithms for pattern recognition and dependencies estimation Neural Computation. 5: 893-909. DOI: 10.1162/Neco.1993.5.6.893  0.423
1993 Vapnik V. Three fundamental concepts of the capacity of learning machines Physica a: Statistical Mechanics and Its Applications. 200: 538-544. DOI: 10.1016/0378-4371(93)90558-L  0.36
1992 Bottou L, Vapnik V. Local learning algorithms Neural Computation. 4: 888-900. DOI: 10.1162/Neco.1992.4.6.888  0.375
1968 Vapnik VN, Lerner AY, Chervonenkis AY. Learning Methods in Problems of Diagnosis Ifac Proceedings Volumes. 2: 741-747. DOI: 10.1016/S1474-6670(17)68922-5  0.395
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