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 fondness of our statements.

• Type: Conference
• Year: 2007
• Where: ICMLA
• Authors: Guillaume Bouchard
• Keywords: Asymptotic Error Rate, ICMLA 2007, Inexact Density Model, Machine Learning, Optimal Bias-variance Balance