Gao Peng, Wu Weifei, Li Jingmei
College of Computer Science and Technology, Harbin Engineering University, Harbin, China.
Technology Development Cente, Heilongjiang Broadcasting Station, Harbin, China.
Appl Intell (Dordr). 2021;51(11):8451-8465. doi: 10.1007/s10489-021-02194-9. Epub 2021 Apr 6.
Knowledge in the source domain can be used in transfer learning to help train and classification tasks within the target domain with fewer available data sets. Therefore, given the situation where the target domain contains only a small number of available unlabeled data sets and multi-source domains contain a large number of labeled data sets, a new Multi-source Fast Transfer Learning algorithm based on support vector machine(MultiFTLSVM) is proposed in this paper. Given the idea of multi-source transfer learning, more source domain knowledge is taken to train the target domain learning task to improve classification effect. At the same time, the representative data set of the source domain is taken to speed up the algorithm training process to improve the efficiency of the algorithm. Experimental results on several real data sets show the effectiveness of MultiFTLSVM, and it also has certain advantages compared with the benchmark algorithm.
源域中的知识可用于迁移学习，以帮助在可用数据集较少的情况下训练目标域内的分类任务。因此，针对目标域仅包含少量可用未标记数据集且多源域包含大量标记数据集的情况，本文提出了一种基于支持向量机的新型多源快速迁移学习算法（MultiFTLSVM）。基于多源迁移学习的思想，利用更多的源域知识来训练目标域学习任务，以提高分类效果。同时，采用源域的代表性数据集来加速算法训练过程，提高算法效率。在多个真实数据集上的实验结果表明了MultiFTLSVM的有效性，并且与基准算法相比也具有一定优势。
