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Department of Machine Learning
Books
Vapnik, Vladimir: Estimation of Dependences Based on Empirical Data. Springer Verlag (2006) Vapnik, Vladimir: Statistical Learning Theory. John Wiley & Sons (1998) Vapnik, Vladimir: The Nature of Statistical Learning Theory. Springer Verlag (1995) Articles and Conference PapersVladimir Vapnik and Akshay Vashist and Natalya Pavlovic: Learning Using Hidden Information: Master Class Learning. Proc of NATO workshop on Mining Massive Data Sets for Security (2008) Weston, Jason and Collobert, Ronan and Sinz, Fabian and Bottou, Léon and Vapnik, Vladimir: Inference with the Universum. Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006) (2006) (link) Vapnik, Vladimir: Transductive Inference and Semi-Supervised Learning. Semi-Supervised Learning (2006) Graf, Hans Peter and Cosatto, Eric and Bottou, Léon and Durdanovic, Igor and Vapnik, Vladimir: Parallel Support Vector Machines: The Cascade SVM. Advances in Neural Information Processing Systems (2005) (link) Weston, Jason and Chapelle, Olivier and Elisseeff, Andre and Schölkopf, Bernhard and Vapnik, Vladimir: Kernel Dependency Estimation.. Advances in Neural Information Processing Systems (2003) (link) Guyon, Isabelle and Weston, Jason and Barnhill, Steven and Vapnik, Vladimir: Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning (2002) (link) Chapelle, Olivier and Vapnik, Vladimir and Weston, Jason: Transductive Inference for Estimating Values of Functions. Advances in Neural Information Processing Systems 12 (2000) (link) Chapelle, O. and Weston, Jason and Bottou, Léon and Vapnik, Vladimir: Vicinal Risk Minimization. Advances in Neural Information Processing Systems (2000) (link) Weston, Jason and S. Mukherjee and Chapelle, Olivier and M. Pontil and T. Poggio and Vapnik, Vladimir: Feature Selection for SVMs. Advances in Neural Information Processing Systems (2000) (link) Stitson, Mark and Gammerman, Alex and Vapnik, Vladimir and Vovk, Volodya and Watkins, Chris and Weston, Jason: Support Vector Regression with {ANOVA} Decomposition Kernels. Advances in Kernel Methods --- Support Vector Learning (1999) Bi, J. and Vapnik, Vladimir: Learning with rigorous support vector machines. Proceedings of the 16th Annual Conference on Learning Theory Tech ReportsWeston, Jason and Chapelle, Olivier and Elisseeff, Andre and Schölkopf, Bernhard and Vapnik, Vladimir: Kernel Dependency Estimation. Max Planck Institute for Biological Cybernetics (98) (2002) (link) Bottou, Léon and LeCun, Yann and Vapnik, Vladimir: Report: Predicting Learning Curves without the Ground Truth Hypothesis. (1999) (link) Bottou, Léon and Cortes, Corinna and Vapnik, Vladimir: On the Effective VC Dimension.. (bottou-effvc.ps.Z) (1994) (link) |
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