good-turing

A unified framework for estimating the probability of data-dependent events, covering missing mass, adaptive data analysis, and test-error estimation, with new hybrid Good–Turing style estimators and bounds based on VC dimension and mutual information.

本文提出一种针对小样本场景下缺失质量与未见物种计数估计的“脆弱性感知”正则化框架，通过严格凹的熵-KL优化提供闭式解，作为对 Good-Turing 等经典估计器的轻量级修正，在长尾、单例主导的小样本场景中显著提升稳定性。