Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)
Olivier Chapelle, Jason Weston, Léon Bottou, Vladimir Vapnik
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Mini(cid:173) mization Principle such as Support Vector Machines or Statistical Reg(cid:173) ularization. We explain how VRM provides a framework which inte(cid:173) grates a number of existing algorithms, such as Parzen windows, Support Vector Machines, Ridge Regression, Constrained Logistic Classifiers and Tangent-Prop. We then show how the approach implies new algorithm(cid:173) s for solving problems usually associated with generative models. New algorithms are described for dealing with pattern recognition problems with very different pattern distributions and dealing with unlabeled data. Preliminary empirical results are presented.