Computer Science Department, BINUS Online Learning, Bina Nusantara University, West Jakarta, Jakarta 11480, Indonesia.
Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
Sensors (Basel). 2023 Jan 4;23(2):583. doi: 10.3390/s23020583.
For cases in which a machine learning model needs to be adapted to a new task, various approaches have been developed, including model-agnostic meta-learning (MAML) and transfer learning. In this paper, we investigate how the differences in the data distributions between the old tasks and the new target task impact performance in regression problems. By performing experiments, we discover that these differences greatly affect the relative performance of different adaptation methods. Based on this observation, we develop ensemble schemes combining multiple adaptation methods that can handle a wide range of data distribution differences between the old and new tasks, thus offering more stable performance for a wide range of tasks. For evaluation, we consider three regression problems of sinusoidal fitting, virtual reality motion prediction, and temperature forecasting. The evaluation results demonstrate that the proposed ensemble schemes achieve the best performance among the considered methods in most cases.
对于需要将机器学习模型适应新任务的情况，已经开发了各种方法，包括模型不可知元学习（MAML）和迁移学习。在本文中，我们研究了旧任务和新目标任务之间数据分布的差异如何影响回归问题中的性能。通过实验，我们发现这些差异极大地影响了不同适应方法的相对性能。基于这一观察，我们开发了结合多种适应方法的集成方案，可以处理旧任务和新任务之间数据分布差异的广泛范围，从而为广泛的任务提供更稳定的性能。对于评估，我们考虑了正弦拟合、虚拟现实运动预测和温度预测三个回归问题。评估结果表明，在所考虑的方法中，所提出的集成方案在大多数情况下都能取得最佳性能。
