Abstract When the labelling information is not deterministic, traditional supervised learning algorithms cannot be applied. In this case, stochastic supervision models provide a valuable alternative to classification. However, these models are restricted in several aspects, which critically limits their applicability. In this paper, we provide four generalisations of stochastic supervision models, extending them to asymmetric assessments, multiple classes, feature-dependent assessments and multi-modal classes, respectively. Corresponding to these generalisations, we derive four new EM algorithms. We show the effectiveness of our generalisations through illustrative examples of simulated datasets, as well as real-world examples of three famous datasets, the MNIST dataset, the CIFAR-10 dataset and the EMNIST dataset.

中文翻译：

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随机监督模型的推广

摘要 当标签信息不确定时，传统的监督学习算法无法应用。在这种情况下，随机监督模型提供了一种有价值的分类替代方案。然而，这些模型在几个方面受到限制，这严重限制了它们的适用性。在本文中，我们提供了随机监督模型的四种概括，分别将它们扩展到非对称评估、多类、依赖于特征的评估和多模态类。对应于这些概括，我们推导出四种新的 EM 算法。我们通过模拟数据集的说明性示例以及三个著名数据集（MNIST 数据集、CIFAR-10 数据集和 EMNIST 数据集）的真实示例展示了我们概括的有效性。