This paper proposes a novel and efficient method for Learning from Label Proportions (LLP), whose goal is to train a classifier only by using the class label proportions of instance sets, called bags. We propose a novel LLP method based on an online pseudo-labeling method with regret minimization. As opposed to the previous LLP methods, the proposed method effectively works even if the bag sizes are large. We demonstrate the effectiveness of the proposed method using some benchmark datasets.

翻译：本文提出了一种新颖且高效的学习方法，用于处理标签比例学习（LLP）问题，其目标仅通过利用实例集（称为包）的类别标签比例来训练分类器。我们提出了一种基于在线伪标签方法与遗憾最小化的新型LLP方法。与以往的LLP方法不同，即使包的大小很大，所提出的方法也能有效工作。我们使用一些基准数据集证明了所提出方法的有效性。