研究人员开发了一种新的分数监督分类（FSC）方法，该方法可以处理提名样本，这在保留极端观测值的应用中很常见。该方法解决了现有FSC公式中假设简单随机抽样的局限性。新方法引入了潜在表示来解释类别成员资格和剩余单元的组成，从而为提名样本数据实现了合适的EM算法和加权似然程序。 AI
影响 引入了一个新的分类框架来处理特定的数据采样设计，有可能提高小众应用的模型的准确性。
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Fractionally supervised classification (FSC) offers a flexible framework for combining labeled and unlabeled data in model-based classification, but existing formulations assume simple random sampling. In many applications, however, the retained observation is an extreme order st…
arXiv:2604.25145v1 Announce Type: cross Abstract: Fractionally supervised classification (FSC) offers a flexible framework for combining labeled and unlabeled data in model-based classification, but existing formulations assume simple random sampling. In many applications, howeve…
Fractionally supervised classification (FSC) offers a flexible framework for combining labeled and unlabeled data in model-based classification, but existing formulations assume simple random sampling. In many applications, however, the retained observation is an extreme order st…