Vladimir Vapnik

2008
44	EE	Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik: Large Margin vs. Large Volume in Transductive Learning. ECML/PKDD (1) 2008: 9-10
43	EE	Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik: Large margin vs. large volume in transductive learning. Machine Learning 72(3): 173-188 (2008)
2006
42	EE	Jason Weston, Ronan Collobert, Fabian H. Sinz, Léon Bottou, Vladimir Vapnik: Inference with the Universum. ICML 2006: 1009-1016
2004
41	EE	Hans Peter Graf, Eric Cosatto, Léon Bottou, Igor Durdanovic, Vladimir Vapnik: Parallel Support Vector Machines: The Cascade SVM. NIPS 2004
2003
40	EE	Jinbo Bi, Vladimir Vapnik: Learning with Rigorous Support Vector Machines. COLT 2003: 243-257
2002
39	EE	Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik: Kernel Dependency Estimation. NIPS 2002: 873-880
38		Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee: Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46(1-3): 131-159 (2002)
37		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)
36		Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio: Model Selection for Small Sample Regression. Machine Learning 48(1-3): 9-23 (2002)
2001
35	EE	Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik: Support Vector Clustering. Journal of Machine Learning Research 2: 125-137 (2001)
2000
34	EE	Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik: A Support Vector Clustering Method. ICPR 2000: 2724-2727
33		Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik: A Support Vector Method for Clustering. NIPS 2000: 367-373
32		Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik: Vicinal Risk Minimization. NIPS 2000: 416-422
31		Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso Poggio, Vladimir Vapnik: Feature Selection for SVMs. NIPS 2000: 668-674
30		Vladimir Vapnik, Olivier Chapelle: Bounds on Error Expectation for Support Vector Machines. Neural Computation 12(9): 2013-2036 (2000)
1999
29	EE	Olivier Chapelle, Vladimir Vapnik: Model Selection for Support Vector Machines. NIPS 1999: 230-236
28	EE	Olivier Chapelle, Vladimir Vapnik, Jason Weston: Transductive Inference for Estimating Values of Functions. NIPS 1999: 421-427
27	EE	Vladimir Vapnik, Sayan Mukherjee: Support Vector Method for Multivariate Density Estimation. NIPS 1999: 659-665
26	EE	Harris Drucker, Donghui Wu, Vladimir Vapnik: Support vector machines for spam categorization. IEEE Transactions on Neural Networks 10(5): 1048-1054 (1999)
25	EE	Olivier Chapelle, Patrick Haffner, Vladimir Vapnik: Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks 10(5): 1055-1064 (1999)
24	EE	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)
23	EE	Vladimir Vapnik: An overview of statistical learning theory. IEEE Transactions on Neural Networks 10(5): 988-999 (1999)
1998
22	EE	Alexander Gammerman, Katy S. Azoury, Vladimir Vapnik: Learning by Transduction. UAI 1998: 148-155
21	EE	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
20		Vladimir Vapnik: The Support Vector Method. ICANN 1997: 263-271
19		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
18		Bernhard Schölkopf, Patrice Simard, Alex J. Smola, Vladimir Vapnik: Prior Knowledge in Support Vector Kernels. NIPS 1997
1996
17		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
16		Bernhard Schölkopf, Chris Burges, Vladimir Vapnik: Incorporating Invariances in Support Vector Learning Machines. ICANN 1996: 47-52
15		Vladimir Vapnik: Statistical Theory of Generalization (Abstract). ICML 1996: 557
14	EE	Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik: Support Vector Regression Machines. NIPS 1996: 155-161
13	EE	Vladimir Vapnik, Steven E. Golowich, Alex J. Smola: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. NIPS 1996: 281-287
12		Isabelle Guyon, Nada Matic, Vladimir Vapnik: Discovering Informative Patterns and Data Cleaning. Advances in Knowledge Discovery and Data Mining 1996: 181-203
1995
11		Bernhard Schölkopf, Chris Burges, Vladimir Vapnik: Extracting Support Data for a Given Task. KDD 1995: 252-257
10		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
9		Corinna Cortes, Vladimir Vapnik: Support-Vector Networks. Machine Learning 20(3): 273-297 (1995)
1994
8		Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik: Boosting and Other Machine Learning Algorithms. ICML 1994: 53-61
7		Isabelle Guyon, Nada Matic, Vladimir Vapnik: Discovering Informative Patterns and Data Cleaning. KDD Workshop 1994: 145-156
1993
6	EE	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
5	EE	Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik: A Training Algorithm for Optimal Margin Classifiers. COLT 1992: 144-152
4	EE	Isabelle Guyon, Bernhard E. Boser, Vladimir Vapnik: Automatic Capacity Tuning of Very Large VC-Dimension Classifiers. NIPS 1992: 147-155
1991
3	EE	Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla: Structural Risk Minimization for Character Recognition. NIPS 1991: 471-479
2	EE	Vladimir Vapnik: Principles of Risk Minimization for Learning Theory. NIPS 1991: 831-838
1989
1	EE	Vladimir Vapnik: Inductive Principles of the Search for Empirical Dependences (Methods Based on Weak Convergence of Probability Measures). COLT 1989: 3-21

Coauthor Index

1	Katy S. Azoury	[22]
2	Stephen Barnhill	[37]
3	Asa Ben-Hur	[33] [34] [35]
4	Yoshua Bengio	[36]
5	Jinbo Bi	[40]
6	Volker Blanz	[17]
7	Bernhard E. Boser	[3] [4] [5]
8	Léon Bottou	[3] [32] [41] [42]
9	Olivier Bousquet	[38]
10	Heinrich H. Bülthoff	[17]
11	Christopher J. C. Burges (Chris Burges)	[11] [14] [16] [17]
12	Olivier Chapelle	[25] [28] [29] [30] [31] [32] [36] [38] [39]
13	Vladimir Cherkassky	[24]
14	Ronan Collobert	[42]
15	Corinna Cortes	[6] [8] [9] [10]
16	Eric Cosatto	[41]
17	John S. Denker	[6]
18	Harris Drucker	[8] [10] [14] [26]
19	Igor Durdanovic	[41]
20	Ran El-Yaniv	[43] [44]
21	André Elisseeff	[39]
22	Alexander Gammerman (Alex J. Gammerman)	[22]
23	Steven E. Golowich	[13]
24	Hans Peter Graf	[41]
25	Isabelle Guyon	[3] [4] [5] [7] [12] [21] [37]
26	Patrick Haffner	[25]
27	Dennis Hoover	[10]
28	David Horn	[33] [34] [35]
29	Lawrence D. Jackel	[6] [8]
30	Linda Kaufman	[14]
31	Jens Kohlmorgen	[19]
32	Yann LeCun	[8]
33	John Makhoul	[21]
34	Nada Matic	[7] [12]
35	Sayan Mukherjee	[27] [31] [38]
36	Filip Mulier	[24]
37	Klaus-Robert Müller	[19]
38	Dmitry Pechyony	[43] [44]
39	Tomaso Poggio	[31]
40	Massimiliano Pontil	[31]
41	Gunnar Rätsch	[19]
42	Bernhard Schölkopf	[11] [16] [17] [18] [19] [39]
43	Richard M. Schwartz	[21]
44	Xuhui Shao	[24]
45	Hava T. Siegelmann	[33] [34] [35]
46	Patrice Y. Simard (Patrice Simard)	[18]
47	Fabian H. Sinz	[42]
48	Alexander J. Smola (Alex J. Smola)	[13] [14] [18] [19]
49	Sara A. Solla	[3] [6]
50	Thomas Vetter	[17]
51	Jason Weston	[28] [31] [32] [37] [39] [42]
52	Donghui Wu	[26]

Colors in the list of coauthors