论文标题
综合学习
Integrated Weak Learning
论文作者
论文摘要
我们介绍了综合弱学习，这是一个原则上的框架，将薄弱的监督整合到机器学习模型的培训过程中。我们的方法共同训练末端模型和标签模型，该模型汇总了多种弱监督源。我们介绍了一个标签模型，该标签模型可以学会以不同的数据点的方式汇总薄弱的监督源，并考虑训练期间最终模型的性能。我们表明，我们的方法在一组6个基准分类数据集中优于现有的弱学习技术。当出现少量标记的数据和弱监督时，性能的提高既一致又大，并且可靠地获得了2-5点测试F1得分在非整合方法中获得的增长。
We introduce Integrated Weak Learning, a principled framework that integrates weak supervision into the training process of machine learning models. Our approach jointly trains the end-model and a label model that aggregates multiple sources of weak supervision. We introduce a label model that can learn to aggregate weak supervision sources differently for different datapoints and takes into consideration the performance of the end-model during training. We show that our approach outperforms existing weak learning techniques across a set of 6 benchmark classification datasets. When both a small amount of labeled data and weak supervision are present the increase in performance is both consistent and large, reliably getting a 2-5 point test F1 score gain over non-integrated methods.
