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English(EN) Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification

新研究探讨用于分类的依赖于不确定性的缺失标签

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文探讨了一种半监督分类方法，其中标签缺失的概率取决于观测到的特征和分类器的不确定性。这种方法将缺失不仅仅视为丢失的信息，而是视为由与分类器相关的机制生成的信号。该论文为这些依赖于不确定性的缺失标签开发了一种基于似然的信息论，并在各种模型规范下推导出了费舍尔信息分解和协方差分区。通过高斯混合模型和医学诊断示例进行的计算说明了该方法如何在固定的标记预算内提高估计和分类效率。 AI

影响 这项研究可以通过利用标签缺失作为信息信号来开发更有效的半监督学习方法。

排序理由 该集群包含一篇提交到arXiv的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新研究探讨用于分类的依赖于不确定性的缺失标签

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该集群包含一篇提交到arXiv的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · You-Gan Wang, Jinran Wu, Geoffrey J. McLachlan ·

    从依赖不确定性的缺失标签中学习用于半监督分类

    arXiv:2608.23960v1 Announce Type: cross Abstract: Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of label missingness depends on the observed features through posterior classificat…

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