Active learning shows promise to decrease test bench time for model-based drivability calibration. This paper presents a new strategy for active output selection, which suits the needs of calibration tasks. The strategy is actively learning multiple outputs in the same input space. It chooses the output model with the highest cross-validation error as leading. The presented method is applied to three different toy examples with noise in a real world range and to a benchmark dataset. The results are analyzed and compared to other existing strategies. In a best case scenario, the presented strategy is able to decrease the number of points by up to 30% compared to a sequential space-filling design while outperforming other existing active learning strategies. The results are promising but also show that the algorithm has to be improved to increase robustness for noisy environments. Further research will focus on improving the algorithm and applying it to a real-world example.

中文翻译：

---

多种学习回归模型的主动输出选择策略

主动学习表明有望减少基于模型的可驾驶性校准的测试工作台时间。本文提出了一种用于主动输出选择的新策略，该策略适合校准任务的需求。该策略是在相同的输入空间中主动学习多个输出。它选择交叉验证误差最大的输出模型作为前导模型。所提出的方法应用于在现实世界范围内具有噪声的三个不同玩具示例以及基准数据集。分析结果并将其与其他现有策略进行比较。在最佳情况下，与连续的空间填充设计相比，所提出的策略能够将点数减少多达30％，而性能却优于其他现有的主动学习策略。结果令人鼓舞，但也表明必须对算法进行改进，以提高嘈杂环境的鲁棒性。进一步的研究将集中在改进算法并将其应用于实际示例中。