论文标题
宣传：概率部分标签学习
ProPaLL: Probabilistic Partial Label Learning
论文作者
论文摘要
部分标签学习是一种弱监督的学习，每个培训实例都对应于一组候选标签，其中只有一个是真实的。在本文中，我们介绍了一种针对此问题的新型概率方法，与现有方法相比，该方法至少具有三个优势：它简化了训练过程，改善了性能并可以应用于任何深层体系结构。对人工和现实世界数据集进行的实验表明，诺言的表现优于现有方法。
Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true. In this paper, we introduce ProPaLL, a novel probabilistic approach to this problem, which has at least three advantages compared to the existing approaches: it simplifies the training process, improves performance, and can be applied to any deep architecture. Experiments conducted on artificial and real-world datasets indicate that ProPaLL outperforms the existing approaches.
