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Noisy classification with boundary assumptions

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We address the problem of classification when data are collected from two samples with measurement errors. This problem turns to be an inverse problem and requires a specific treatment. In this context, we investigate the minimax rates of convergence using both a margin assumption, and a smoothness condition on the boundary of the set associated to the Bayes classifier. We establish lower and upper bounds (based on a deconvolution classifier) on these rates.
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hal-00843776 , version 1 (12-07-2013)



Sébastien Loustau, Clément Marteau. Noisy classification with boundary assumptions. 2013. ⟨hal-00843776⟩
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