Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization
This addresses the challenge of training classifiers with only label proportions in instance sets, which is incremental as it builds on prior LLP methods.
The paper tackles the problem of Learning from Label Proportions (LLP) by proposing a novel method based on online pseudo-labeling with regret minimization, which effectively handles large bag sizes and demonstrates effectiveness on benchmark datasets.
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.