论文标题
基于自适应区域的主动学习
Adaptive Region-Based Active Learning
论文作者
论文摘要
我们提出了一种新的主动学习算法，该算法将输入空间适应有限数量的区域，然后为每个区域寻求一个独特的预测指标，这两个阶段都积极要求标签。我们证明了算法的概括误差和标签复杂性的理论保证，并分析了算法在某些轻度假设下定义的区域数量。我们还报告了几个现实世界数据集上的一系列实验套件的结果，这些实验表明，对现有的单区和非自适应区域的主动学习基线表明了实质性的经验益处。
We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region, both phases actively requesting labels. We prove theoretical guarantees for both the generalization error and the label complexity of our algorithm, and analyze the number of regions defined by the algorithm under some mild assumptions. We also report the results of an extensive suite of experiments on several real-world datasets demonstrating substantial empirical benefits over existing single-region and non-adaptive region-based active learning baselines.
