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Noisy classification with boundary assumptions
Abstract : 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.
https://hal.archives-ouvertes.fr/hal-00843776
Contributor : Sébastien Loustau <>
Submitted on : Friday, July 12, 2013 - 10:24:45 AM
Last modification on : Monday, March 9, 2020 - 6:16:02 PM
Long-term archiving on: : Sunday, October 13, 2013 - 4:25:09 AM
Contributor : Sébastien Loustau <>
Submitted on : Friday, July 12, 2013 - 10:24:45 AM
Last modification on : Monday, March 9, 2020 - 6:16:02 PM
Long-term archiving on: : Sunday, October 13, 2013 - 4:25:09 AM
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