The test bench time needed for model-based calibration can be reduced with active learning methods for test design. This paper presents an improved strategy for active output selection. This is the task of learning multiple models in the same input dimensions and suits the needs of calibration tasks. Compared to an existing strategy, we take into account the noise estimate, which is inherent to Gaussian processes. The method is validated on three different toy examples. The performance compared to the existing best strategy is the same or better in each example. In a best case scenario, the new strategy needs at least 10% less measurements compared to all other active or passive strategies. Further efforts will evaluate the strategy on a real-world application. Moreover, the implementation of more sophisticated active-learning strategies for the query placement will be realized.

翻译：以模型为基础的校准所需的测试台时间可以随着测试设计的积极学习方法而缩短。本文件为积极的输出选择提供了一个更好的战略。 这是学习相同投入层面的多种模型的任务, 并适合校准任务的需求。 与现有的战略相比, 我们考虑到高斯进程所固有的噪音估计值。 这种方法在三个不同的玩具例子中得到验证。 与现有最佳战略相比,每个例子的性能相同或更好。 在最好的情况下, 新战略需要比所有其他主动或被动战略至少少10%的测量值。 进一步的努力将评价现实世界应用的战略。 此外, 将实现为查询定位实施更复杂的主动学习战略。