Titlebook: Stochastic Models, Statistics and Their Applications; Dresden, Germany, Ma Ansgar Steland,Ewaryst Rafajłowicz,Ostap Okhrin Conference proce

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Lacunar-Stroke

边缘带来墨水

Stability of Random-Projection Based Classifiers. The Bayes Error Perspectivem projections. We restrict our attention to the two-class problem. Furthermore, we assume that distributions in classes come from multivariate normal distributions with the same covariance matrices, i.e., differing only in the means. This is one of the few situations when the Bayes error expression

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不溶解

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The Hotelling—Like , Control Chart Modified for Detecting Changes in Images having the Matrix Normaln the mean of multivariate Gaussian distributions. However, this chart requires to know (or to be able to estimate from historical data) at least the in-control covariance matrix. Unfortunately, even if very small images, e.g., . pixels are vectorized, the covariance matrix is of the size . and its

ROOF

Universal Confidence Sets for Solutions of Stochastic Optimization Problems—A Contribution to Quantiems, which occur if unknown quantities are replaced with estimates. The so-called universal confidence sets yield for each sample size . a conservative confidence set. The method is based on convergence properties of sequences of random closed sets. In this paper, we will show how the approach can b

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