论文标题
IALE：模仿活跃的学习者合奏
IALE: Imitating Active Learner Ensembles
论文作者
论文摘要
主动学习（AL）优先考虑最有用的数据样本的标签。但是，AL启发术的性能取决于基础分类器模型和数据的结构。我们提出了一种模仿学习方案，该方案在基于批处机池的设置中模仿了AL周期的每个阶段中最佳专家启发式的选择。我们使用匕首在数据集上训练策略，然后将其应用于来自类似域的数据集。鉴于AL过程的当前状态，该政策以专家的多种启发式为专家，能够反映出最佳AL启发式方法的选择。我们对知名数据集的实验表明，我们既优于艺术模仿学习者和启发式方法。
Active learning (AL) prioritizes the labeling of the most informative data samples. However, the performance of AL heuristics depends on the structure of the underlying classifier model and the data. We propose an imitation learning scheme that imitates the selection of the best expert heuristic at each stage of the AL cycle in a batch-mode pool-based setting. We use DAGGER to train the policy on a dataset and later apply it to datasets from similar domains. With multiple AL heuristics as experts, the policy is able to reflect the choices of the best AL heuristics given the current state of the AL process. Our experiment on well-known datasets show that we both outperform state of the art imitation learners and heuristics.
