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• W3149294210 abstract "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." @default.
• W3149294210 created "2021-04-13" @default.
• W3149294210 creator A5000318376 @default.
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• W3149294210 date "2021-04-06" @default.
• W3149294210 modified "2023-09-28" @default.
• W3149294210 title "Multi-source fast transfer learning algorithm based on support vector machine" @default.
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• W3149294210 doi "https://doi.org/10.1007/s10489-021-02194-9" @default.
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