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不可知 PAC 机器学习模型的精确下界AI 解读

We provide an exact nonasymptotic lower bound on the minimax expected excess risk (EER) in the agnostic probably-approximately-correct (PAC) machine learning classification model and identify minimax learning algorithms as certain maximally symmetric and minimally randomized “voting” procedures. Based on this result, an exact asymptotic lower bound on the minimax EER is provided. This bound is of the simple form $c_{\infty}/\sqrt{\nu}$ as $\nu\to\infty$, where $c_{\infty}=0.16997\dots$…

对称性条件下的极值概率问题与 Hotelling T² 检验AI 解读

We consider the Hotelling $T^2$ statistic for an arbitrary $d$-dimensional sample. If the sampling is not too deterministic or inhomogeneous, then under the zero-means hypothesis the limiting distribution for $T^2$ is $\chi^2_d$. It is shown that a test for the orthant symmetry condition introduced by Efron can be constructed which does not differ essentially from the one based on $\chi^2_d$ and at the same time is applicable not only to large random homogeneous samples but…