Vicinal Risk Minimization 论文

摘要

The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Minimization Principle such as Support Vector Machines or Statistical Regularization. We explain how VRM provides a framework which integrates 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 algorithms 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. 1 Introduction Structural Risk Minimisation (SRM) in a learning system can be achieved using constraints on the parameter vectors, using regularization terms in the cost function, or using Support Vector Machines (SVM). All these principles have lead...