  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.
