Vladimir Vapnik

	2010
54		Dmitry Pechyony, Rauf Izmailov, Akshay Vashist, Vladimir Vapnik: SMO-Style Algorithms for Learning Using Privileged Information. DMIN 2010: 235-241
53		Dmitry Pechyony, Vladimir Vapnik: On the Theory of Learnining with Privileged Information. NIPS 2010: 1894-1902
	2009
52		Vladimir Vapnik, Akshay Vashist, Natalya Pavlovitch: Learning using hidden information (Learning with teacher). IJCNN 2009: 3188-3195
51		Vladimir Vapnik, Akshay Vashist: A new learning paradigm: Learning using privileged information. Neural Networks 22(5-6): 544-557 (2009)
	2008
50		Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik: Large Margin vs. Large Volume in Transductive Learning. ECML/PKDD (1) 2008: 9-10
49		Ran El-Yaniv, Dmitry Pechyony, Vladimir Vapnik: Large margin vs. large volume in transductive learning. Machine Learning 72(3): 173-188 (2008)
	2006
48		Vladimir Vapnik: Learning hidden information: SVM+. GrC 2006: 22
47		Jason Weston, Ronan Collobert, Fabian H. Sinz, Léon Bottou, Vladimir Vapnik: Inference with the Universum. ICML 2006: 1009-1016
	2004
46		Hans Peter Graf, Eric Cosatto, Léon Bottou, Igor Durdanovic, Vladimir Vapnik: Parallel Support Vector Machines: The Cascade SVM. NIPS 2004
	2003
45		Jinbo Bi, Vladimir Vapnik: Learning with Rigorous Support Vector Machines. COLT 2003: 243-257
	2002
44		Jason Weston, Olivier Chapelle, André Elisseeff, Bernhard Schölkopf, Vladimir Vapnik: Kernel Dependency Estimation. NIPS 2002: 873-880
43		Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee: Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46(1-3): 131-159 (2002)
42		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)
41		Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio: Model Selection for Small Sample Regression. Machine Learning 48(1-3): 9-23 (2002)
	2001
40		Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik: Support Vector Clustering. Journal of Machine Learning Research 2: 125-137 (2001)
	2000
39		Asa Ben-Hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik: A Support Vector Clustering Method. ICPR 2000: 2724-2727
38		Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik: A Support Vector Method for Clustering. NIPS 2000: 367-373
37		Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik: Vicinal Risk Minimization. NIPS 2000: 416-422
36		Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso Poggio, Vladimir Vapnik: Feature Selection for SVMs. NIPS 2000: 668-674
35		Vladimir Vapnik, Olivier Chapelle: Bounds on Error Expectation for Support Vector Machines. Neural Computation 12(9): 2013-2036 (2000)
	1999
34		Olivier Chapelle, Vladimir Vapnik: Model Selection for Support Vector Machines. NIPS 1999: 230-236
33		Olivier Chapelle, Vladimir Vapnik, Jason Weston: Transductive Inference for Estimating Values of Functions. NIPS 1999: 421-427
32		Vladimir Vapnik, Sayan Mukherjee: Support Vector Method for Multivariate Density Estimation. NIPS 1999: 659-665
31		Harris Drucker, Donghui Wu, Vladimir Vapnik: Support vector machines for spam categorization. IEEE Transactions on Neural Networks 10(5): 1048-1054 (1999)
30		Olivier Chapelle, Patrick Haffner, Vladimir Vapnik: Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks 10(5): 1055-1064 (1999)
29		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)
28		Vladimir Vapnik: An overview of statistical learning theory. IEEE Transactions on Neural Networks 10(5): 988-999 (1999)
	1998
27		Vladimir Vapnik: Statistical learning theory. Wiley 1998: I-XXIV, 1-736
26		Alexander Gammerman, Katy S. Azoury, Vladimir Vapnik: Learning by Transduction. UAI 1998: 148-155
25		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
24		Vladimir Vapnik: The Support Vector Method. ICANN 1997: 263-271
23		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
22		Bernhard Schölkopf, Patrice Simard, Alex J. Smola, Vladimir Vapnik: Prior Knowledge in Support Vector Kernels. NIPS 1997
	1996
21		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
20		Bernhard Schölkopf, Chris Burges, Vladimir Vapnik: Incorporating Invariances in Support Vector Learning Machines. ICANN 1996: 47-52
19		Vladimir Vapnik: Statistical Theory of Generalization (Abstract). ICML 1996: 557
18		Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alex J. Smola, Vladimir Vapnik: Support Vector Regression Machines. NIPS 1996: 155-161
17		Vladimir Vapnik, Steven E. Golowich, Alex J. Smola: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. NIPS 1996: 281-287
16		Isabelle Guyon, Nada Matic, Vladimir Vapnik: Discovering Informative Patterns and Data Cleaning. Advances in Knowledge Discovery and Data Mining 1996: 181-203
	1995
15		Bernhard Schölkopf, Chris Burges, Vladimir Vapnik: Extracting Support Data for a Given Task. KDD 1995: 252-257
14		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
13		Corinna Cortes, Vladimir Vapnik: Support-Vector Networks. Machine Learning 20(3): 273-297 (1995)
	1994
12		Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik: Boosting and Other Machine Learning Algorithms. ICML 1994: 53-61
11		Isabelle Guyon, Nada Matic, Vladimir Vapnik: Discovering Informative Patterns and Data Cleaning. KDD Workshop 1994: 145-156
10		Vladimir Vapnik, Esther Levin, Yann LeCun: Measuring the VC-Dimension of a Learning Machine. Neural Computation 6(5): 851-876 (1994)
9		Harris Drucker, Corinna Cortes, Lawrence D. Jackel, Yann LeCun, Vladimir Vapnik: Boosting and Other Ensemble Methods. Neural Computation 6(6): 1289-1301 (1994)
	1993
8		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
7		Vladimir Vapnik, Léon Bottou: Local Algorithms for Pattern Recognition and Dependencies Estimation. Neural Computation 5(6): 893-909 (1993)
	1992
6		Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik: A Training Algorithm for Optimal Margin Classifiers. COLT 1992: 144-152
5		Isabelle Guyon, Bernhard E. Boser, Vladimir Vapnik: Automatic Capacity Tuning of Very Large VC-Dimension Classifiers. NIPS 1992: 147-155
4		Léon Bottou, Vladimir Vapnik: Local Learning Algorithms. Neural Computation 4(6): 888-900 (1992)
	1991
3		Isabelle Guyon, Vladimir Vapnik, Bernhard E. Boser, Léon Bottou, Sara A. Solla: Structural Risk Minimization for Character Recognition. NIPS 1991: 471-479
2		Vladimir Vapnik: Principles of Risk Minimization for Learning Theory. NIPS 1991: 831-838
	1989
1		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	[26]
2	Stephen Barnhill	[42]
3	Asa Ben-Hur	[38] [39] [40]
4	Yoshua Bengio	[41]
5	Jinbo Bi	[45]
6	Volker Blanz	[21]
7	Bernhard E. Boser	[3] [5] [6]
8	Léon Bottou	[3] [4] [7] [37] [46] [47]
9	Olivier Bousquet	[43]
10	Heinrich H. Bülthoff	[21]
11	Christopher J. C. Burges (Chris Burges)	[15] [18] [20] [21]
12	Olivier Chapelle	[30] [33] [34] [35] [36] [37] [41] [43] [44]
13	Vladimir Cherkassky	[29]
14	Ronan Collobert	[47]
15	Corinna Cortes	[8] [9] [12] [13] [14]
16	Eric Cosatto	[46]
17	John S. Denker	[8]
18	Harris Drucker	[9] [12] [14] [18] [31]
19	Igor Durdanovic	[46]
20	Ran El-Yaniv	[49] [50]
21	André Elisseeff	[44]
22	Alexander Gammerman (Alex J. Gammerman)	[26]
23	Steven E. Golowich	[17]
24	Hans Peter Graf	[46]
25	Isabelle Guyon	[3] [5] [6] [11] [16] [25] [42]
26	Patrick Haffner	[30]
27	Dennis Hoover	[14]
28	David Horn	[38] [39] [40]
29	Rauf Izmailov	[54]
30	Lawrence D. Jackel	[8] [9] [12]
31	Linda Kaufman	[18]
32	Jens Kohlmorgen	[23]
33	Yann LeCun	[9] [10] [12]
34	Esther Levin	[10]
35	John Makhoul	[25]
36	Nada Matic	[11] [16]
37	Sayan Mukherjee	[32] [36] [43]
38	Filip Mulier	[29]
39	Klaus-Robert Müller	[23]
40	Natalya Pavlovitch	[52]
41	Dmitry Pechyony	[49] [50] [53] [54]
42	Tomaso Poggio	[36]
43	Massimiliano Pontil	[36]
44	Gunnar Rätsch	[23]
45	Bernhard Schölkopf	[15] [20] [21] [22] [23] [44]
46	Richard M. Schwartz	[25]
47	Xuhui Shao	[29]
48	Hava T. Siegelmann	[38] [39] [40]
49	Patrice Y. Simard (Patrice Simard)	[22]
50	Fabian H. Sinz	[47]
51	Alexander J. Smola (Alex J. Smola)	[17] [18] [22] [23]
52	Sara A. Solla	[3] [8]
53	Akshay Vashist	[51] [52] [54]
54	Thomas Vetter	[21]
55	Jason Weston	[33] [36] [37] [42] [44] [47]
56	Donghui Wu	[31]

Colors in the list of coauthors

Last update Tue Feb 14 23:09:10 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page