Transfer learning addresses the problem of how to leverage acquired knowledge from a source domain to improve the learning efficiency and accuracy of the target domain that has insufficient labeled data. Instead of one source domain, multiple domains could be the source domains that are available for knowledge transfer in practice. However, there are large differences between the source and target domains, how to extract the useful knowledge from these different source domains remains a problem. To solve this problem, we propose a source-target pairwise segment method for multi-source transfer regression (STPS-MTR). The STPS-MTR method adaptively segments the different source domains and the target domain into different similar parts, and it extracts the most similar part in different source domains as the transfer knowledge. The STPS-MTR method can effectively extract the transfer knowledge from different source domains even when the source domain and the target domain have relatively low similarity, and it can avoid the negative influence between different source domains to ensure the transfer performance. Experimental results using synthetic datasets and real-world datasets demonstrate that the proposed method has better performance than existing methods, particularly when there are significant differences between different source domains and the target domain.

转移学习解决了如何利用从源域获取的知识来提高学习效率和目标域的学习效率和准确性的问题，该域的标记数据不足。代替一个源域，实际上可以使用多个域来进行知识转移。但是，源域和目标域之间存在很大差异，如何从这些不同的源域中提取有用的知识仍然是一个问题。为了解决这个问题，我们提出了一种用于多源转移回归的源-目标成对分段方法（STPS-MTR）。STPS-MTR方法将不同的源域和目标域自适应地分为不同的相似部分，并提取不同源域中最相似的部分作为传输知识。STPS-MTR方法即使源域和目标域之间的相似度较低，也可以有效地从不同的源域中提取传输知识，并且可以避免不同源域之间的负面影响，从而确保传输性能。使用合成数据集和现实世界数据集的实验结果表明，所提出的方法比现有方法具有更好的性能，尤其是当不同的源域和目标域之间存在显着差异时。