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 |