High dimensional gaussian classification

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dc.creatorGirard, Robin
dc.date2008-06-04
dc.date2008-07-10
dc.date.accessioned2026-07-07T09:49:17Z
dc.date.available2026-07-07T09:49:17Z
dc.descriptionHigh dimensional data analysis is known to be as a challenging problem. In this article, we give a theoretical analysis of high dimensional classification of Gaussian data which relies on a geometrical analysis of the error measure. It links a problem of classification with a problem of nonparametric regression. We give an algorithm designed for high dimensional data which appears straightforward in the light of our theoretical work, together with the thresholding estimation theory. We finally attempt to give a general treatment of the problem that can be extended to frameworks other than gaussian.
dc.description62 pages
dc.identifierhttps://arxiv.org/abs/0806.0729
dc.identifierhttp://arxiv.org/abs/0806.0729
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/164531
dc.subjectStatistics Theory
dc.subjectMachine Learning
dc.subject62C20
dc.titleHigh dimensional gaussian classification
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