论文标题
通过应用于脊柱运动预测，更新迭代的自我标签域适应
Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction
论文作者
论文摘要
转移学习领域包括有监督的机器学习方法，这些方法在培训和测试数据具有不同的输入特征空间或分布时应对问题。在这项工作中，我们提出了一种新颖的转移学习算法，称为更新迭代的自我标记域适应性（RE-ISDA）。在这项工作中，我们提出了一种新颖的转移学习算法，称为更新迭代的自我标记域适应性（RE-ISDA）。
The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions. In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA). In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA).
