论文标题
积极学习：问题设置和最新发展
Active Learning: Problem Settings and Recent Developments
论文作者
论文摘要
在监督学习中，获取预测模型的标记培训数据可能非常昂贵，但是获取大量未标记的数据通常很容易。主动学习是一种通过自适应选择样品以有限成本获得高精度的预测模型的方法。本文解释了积极学习和最新研究趋势的基本问题设置。特别是，突出了有关学习采集功能的研究，以从数据，主动学习算法上的理论工作以及为顺序数据获取的停止标准中选择样本。引入了材料开发和测量的应用示例。
In supervised learning, acquiring labeled training data for a predictive model can be very costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is a method of obtaining predictive models with high precision at a limited cost through the adaptive selection of samples for labeling. This paper explains the basic problem settings of active learning and recent research trends. In particular, research on learning acquisition functions to select samples from the data for labeling, theoretical work on active learning algorithms, and stopping criteria for sequential data acquisition are highlighted. Application examples for material development and measurement are introduced.
