Generalized Prediction Intervals for Arbitrary Distributed High-Dimensional Data

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dc.creatorKuehn, Steffen
dc.date2008-09-19
dc.date.accessioned2026-07-07T10:04:02Z
dc.date.available2026-07-07T10:04:02Z
dc.descriptionThis paper generalizes the traditional statistical concept of prediction intervals for arbitrary probability density functions in high-dimensional feature spaces by introducing significance level distributions, which provides interval-independent probabilities for continuous random variables. The advantage of the transformation of a probability density function into a significance level distribution is that it enables one-class classification or outlier detection in a direct manner.
dc.description13 pages, 3 figures
dc.identifierhttps://arxiv.org/abs/0809.3352
dc.identifierhttp://arxiv.org/abs/0809.3352
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/169514
dc.subjectComputer Vision and Pattern Recognition
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.titleGeneralized Prediction Intervals for Arbitrary Distributed High-Dimensional Data
dc.typetext

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