|Table of Contents|

Transfer learning Laplacian total margin support vector machine(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2020年01期
Page:
40-48
Research Field:
Publishing date:

Info

Title:
Transfer learning Laplacian total margin support vector machine
Author(s):
Lu Bing13Zhou Guohua12Gu Xiaoqing3Yin Xinchun2
1.Department of Information Engineering,Changzhou Institute of Industry Technology,Changzhou213164,China; 2.College of Information Engineering,Yangzhou University,Yangzhou 225127,China; 3.School of Information Science and Engineering,Changzhou University,Changzhou 213164,China
Keywords:
transfer learning support vector machine classification semi-supervised total margin maximum mean discrepancy
PACS:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2020.44.01.007
Abstract:
To further promote the performance of semi-supervised classifier with insufficient labeled and unlabeled samples,a transfer learning Laplacian total margin support vector machine called TL-Lap-TSVM is studied here. The joint projected maximum mean discrepancy is proposed to measure the distribution differences between the different domains from the global and local aspects. The TL-Lap-TSVM is proposed by introducing the idea of transfer learning into the semi-supervised learning framework. This paper realizes the transferring knowledge from the source domain to the target domain and promotes the performance of classifier in the target domain. The experimental results on eight transfer learning datasets show that the TL-Lap-TSVM can deal with the task of classification in the scenario that both labeled and unlabeled data in the target domain are insufficient.

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Last Update: 2020-02-29