Vladimir Vapnik - Selected Bibliography

V.Vapnik (1995)

The Nature of Statistical Learning Theory, Springer, 1995

V.Vapnik (1996)

Structure of Statistical Learning Theory, Chapter in the Book: Computational Learning and Probabilistic Reasoning, ed. A. Gammerman, pp. 33-41, John Wiley and Sons, 1996.

I. Guyon, J. Makhoul, and V.Vapnik (1998)

What size test set gives good error rate estimates? IEEE Pattern Analysis and Machine Intelligence, Vol. 20, January 1998, 52-64

A. Gammerman, V. Vovk and V. Vapnik (1998)

Learning by transduction. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence 1998, pp.148-156, San Francisco, CA: Morgan Kaufmann.

B. Scholkopf, P. Simard, A. Smola and V.Vapnik (1998)

Prior knowledge in support vector kernels. In Advances in Neural Information Processing Systems, Vol.10, MIT Press, 1998.

V. Vapnik (1998)

The support vector method of function estimation. In J. Suykens and J. Vandewalle, ed Nonlinear Modeling: Advanced Black-Box Techniques, p55-86, Kluwer Academic Publishers, Boston 1998.

V. Vapnik (1998)

Statistical Learning Theory, John Wiley, 1998, NY, p.732.

V. Vapnik (1998)

The support vector method of function estimation NATO ASI Series, Neural Network and Machine Learning, C. Bishop (Ed.), Springer, 1998.

V. Vapnik (1999)

Three remarks on support vector function estimation. In Advanced in Kernel methods: Support Vector Learning, B. Scholkopf, B. Burges and A. Smola (Eds), The MIT Press, Cambridge, Massachusetts, 1999.

M. Stitson, A. Gammerman, V. Vapnik, V. Vovk, C. Watkins and J. Weston (1999)

Support vector regression with ANOVA decomposition kernels. In Advanced in Kernel methods: Support Vector Learning, B. Scholkoph, B. Burges and A. Smola (Eds), The MIT Press, Cambridge, Massachusetts, 1999.

J. Weston, A. Gammerman, M. Stitson, V. Vapnik, V. Vovk and C. Watkins (1999)

Support vector density estimation. In Advanced in Kernel methods: Support Vector Learning, B. Scholkoph, B. Burges and A. Smola (Eds), The MIT Press, Cambridge, Massachusetts, 1999.

V. Vapnik (1999)

An overview of statistical learning theory, IEEE transactions on Neural Networks 10, 5, 1999, pp. 988-1000.

H. Drucker, D. Wu, and V. Vapnik (1999)

Support vector machines for spam categorization. IEEE transactions on Neural Networks ,10 5, 1999, pp. 1048-1055.

O. Chapelle, P. Haffner and V.Vapnik (1999)

Support vector for histogram-based image classification, IEEE transactions on Neural Networks 10, 5, 1999, pp.1055-1065.

V. Cherkassky, X. Shao, F. Mulier, and V. Vapnik (1999)

Model complexity control for regression using VC generalization bounds, IEEE transactions on Neural Networks 10, 5, 1999, pp. 1075-1090.

P. van Trappen, M. Stitson, R. Wools, S. Barnhill, V. Vapnik, A. Gammerman and I. Jacobs (2000)

Preoperative Differentiation of Ovarian Tumors using Support Vector Machine and Risk Malignancy Index. In: Proceedings of the International Federation of Obstetrics and Gynaecology (FIGO) Conference, Washington, 2000

Asa Ben-Hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik (2001)

Support Vector Clustering,Journal of Machine Learning Research 2: 125-137.

Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio

Model Selection for Small Sample Regression. Machine Learning 48 (1-3): 9-23.

Isabelle Guyon, Jason Weston, Stephen Barnhill, Vladimir Vapnik (2002)

Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning 46 (1-3): 389-422.

Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee (2002)

Choosing Multiple Parameters for Support Vector Machines. Machine Learning 46 (1-3): 131-159.

Jason Weston, Olivier Chapelle, Andre Elisseeff, Bernhard Schoelkopf, and Vladimir Vapnik (2003)

Kernel Dependency Estimation. In Advances in Neural Information Processing Systems, ed. by S. Becker, S. Thrun, and K. Obermayer, 15.

Hans P. Graf, Eric Cosatto, Leon Bottou, Igor Durdanovic, and Vladimir Vapnik (2005)

Parallel Support Vector Machines: The Cascade SVM. In Advances in Neural Information Processing Systems, ed. by Lawrence Saul, Yair Weiss, and Leon Bottou, 17.

Vladimir Vapnik (2006)

Estimation of Dependences Based on Empirical Data. Springer.

Jason Weston, Ronan Collobert, Fabian Sinz, Leon Bottou, and Vladimir Vapnik (2006)

Inference with the Universum. In Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006).

I. Nouretdinov, S. G. Costafreda, A. Gammerman, A. Chervonenkis, V. Vovk, V. Vapnik, and C. H. Y. Fu (2011)

Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression, NeuroImage, 56, 809-813.