论文标题
测深编辑的实验设计
Experimental Design for Bathymetry Editing
论文作者
论文摘要
我们描述了机器学习到现实世界计算机辅助标签任务的应用。我们的实验结果暴露了与机器学习中常用的IID假设的显着偏差。这些结果表明，将所有数据的常见随机分配到训练和测试中通常会导致性能差。
We describe an application of machine learning to a real-world computer assisted labeling task. Our experimental results expose significant deviations from the IID assumption commonly used in machine learning. These results suggest that the common random split of all data into training and testing can often lead to poor performance.
