PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bias-Variance tradeoff in Hybrid Generative-Discriminative models
Guillaume Bouchard
Proceedings of the Sixth International Conference on Machine Learning and Applications (ICMLA'07) 2007.


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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:3070
Deposited By:Guillaume Bouchard
Deposited On:05 December 2007