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ICMLA
2007

Bias-variance tradeoff in hybrid generative-discriminative models

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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.
Guillaume Bouchard
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ICMLA
Authors Guillaume Bouchard
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