Generalized Prediction Intervals for Arbitrary Distributed High-Dimensional Data

09/19/2008

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by   Steffen Kuehn, et al.

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This 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.