Soares Junior Amilcar, Renso Chiara, Matwin Stan
IEEE Comput Graph Appl. 2017;37(5):28-39. doi: 10.1109/MCG.2017.3621221.
The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.
定位设备可用性和使用的不断增加产生了大量轨迹数据。然而,此类数据的语义标注通常由领域专家添加,这是一项耗时的任务。机器学习算法可以通过从标记数据集学习来帮助从轨迹数据中推断语义标注。具体而言,主动学习方法可以在保持良好性能指标的同时,将需要标注的轨迹集最小化。基于网络的交互式工具ANALYTiC在视觉上引导用户完成此标注过程。