Recent advances in visual analytics have enabled us to learn from user interactions and uncover analytic goals. These innovations set the foundation for actively guiding users during data exploration. Providing such guidance will become more critical as datasets grow in size and complexity, precluding exhaustive investigation. Meanwhile, the machine learning community also struggles with datasets growing in size and complexity, precluding exhaustive labeling. Active learning is a broad family of algorithms developed for actively guiding models during training. We will consider the intersection of these analogous research thrusts. First, we discuss the nuances of matching the choice of an active learning algorithm to the task at hand. This is critical for performance, a fact we demonstrate in a simulation study. We then present results of a user study for the particular task of data discovery guided by an active learning algorithm specifically designed for this task.

翻译：视觉分析的最新进展使我们得以从用户互动中学习,并发现分析目标。这些创新为在数据探索期间积极指导用户奠定了基础。提供这种指导将随着数据集的大小和复杂性的增加而变得更加重要,排除了详尽的调查。与此同时,机器学习界也与规模和复杂性的数据集挣扎不休,排除了详尽的标签。积极学习是为积极指导模型在培训期间开发的多种算法。我们将考虑这些类似的研究重点的交叉点。首先,我们讨论将积极学习算法的选择与手头任务相匹配的细微差别。这对于业绩至关重要,我们在模拟研究中展示了这一事实。然后,我们介绍了由专门设计的积极学习算法指导的特定数据发现任务的用户研究结果。