Bias-Variance tradeoff in Hybrid Generative-Discriminative models
Given any generative classifier based on an inexact density model, we can define a discriminative counterpart that reduces its asymptotic error rate, while increasing the estimation variance. An optimal bias-variance balance might be found using Hybrid Generative-Discriminative (HGD) approaches. In these paper, these methods are defined in a unified framework. This allow us to find sufficient conditions under which an improvement in generalization performances is guaranteed. Numerical experiments illustrate the well foundness of our statements.
The Sixth International conference on Machine Learning and Applications (ICMLA 07), Cincinnati, Ohio, USA, 13-15 Dec.2007.