Multi-source fast transfer learning algorithm based on support vector machine

被引:15
作者
Gao, Peng [1 ,2 ]
Wu, Weifei [1 ]
Li, Jingmei [1 ]
机构
[1] Harbin Engn Univ, Coll Comp Sci & Technol, Harbin, Peoples R China
[2] Technol Dev Ctr, Heilongjiang Broadcasting Stn, Harbin, Peoples R China
关键词
Multi-source transfer learning; Support vector machine; Classification; ADAPTATION;
D O I
10.1007/s10489-021-02194-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:8451 / 8465
页数:15
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