论文标题
多个学习回归模型的主动输出选择策略
Active Output Selection Strategies for Multiple Learning Regression Models
论文作者
论文摘要
主动学习表明有望减少基于模型的驾驶性校准的测试工作时间。本文提出了一种新的主动输出选择策略，该策略适合校准任务的需求。该策略是在同一输入空间中积极学习多个输出。它选择具有最高交叉验证误差的输出模型作为领先。提出的方法应用于在现实世界范围内噪声和基准数据集的三个不同的玩具示例。结果进行了分析，并将其与其他现有策略进行了比较。在最佳情况下，与顺序填充空间设计相比，提出的策略能够将积分数量减少多达30％，同时表现优于其他现有的活跃学习策略。结果是有希望的，但也表明必须改进该算法以提高嘈杂环境的鲁棒性。进一步的研究将着重于改进算法并将其应用于现实世界的示例。
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.
