[1]陆 兵,周国华,顾晓清,等.迁移拉普拉斯总间隔支持向量机[J].南京理工大学学报(自然科学版),2020,44(01):40-48.[doi:10.14177/j.cnki.32-1397n.2020.44.01.007]
 Lu Bing,Zhou Guohua,Gu Xiaoqing,et al.Transfer learning Laplacian total margin support vector machine[J].Journal of Nanjing University of Science and Technology,2020,44(01):40-48.[doi:10.14177/j.cnki.32-1397n.2020.44.01.007]
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迁移拉普拉斯总间隔支持向量机()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
44卷
期数:
2020年01期
页码:
40-48
栏目:
出版日期:
2020-02-29

文章信息/Info

Title:
Transfer learning Laplacian total margin support vector machine
文章编号:
1005-9830(2020)01-0040-09
作者:
陆 兵13周国华12顾晓清3殷新春2
1.常州工业职业技术学院 信息工程系,江苏 常州 213164; 2.扬州大学 信息工程学院,江苏 扬州 225127; 3. 常州大学 信息科学与工程学院,江苏 常州 213164
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
关键词:
迁移学习 支持向量机 分类 半监督 总间隔 最大均值差异度量
Keywords:
transfer learning support vector machine classification semi-supervised total margin maximum mean discrepancy
分类号:
TP391.4
DOI:
10.14177/j.cnki.32-1397n.2020.44.01.007
摘要:
为了提高半监督分类器在已标记和未标记样本的数量均不足时的分类性能,该文在迁移学习的基础上,提出了一种迁移拉普拉斯总间隔支持向量机。首先提出了联合最大均值差异度量准则,从全局和局部两方面衡量不同领域间的分布差异,并将迁移学习的思想引入半监督学习框架,提出了迁移拉普拉斯总间隔支持向量机。实现源域的知识到目标域的迁移,提高了目标域分类器的性能。8个迁移数据集上的实验结果证明,该方法能处理目标域标记和未标记数据均不足场景下的分类任务。
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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备注/Memo

备注/Memo:
收稿日期:2018-11-29 修回日期:2019-01-12
基金项目:国家自然科学基金(61472343; 61806026); 江苏省自然科学基金(BK20180956); 常州工业职业技术学院创新团队项目(YB201813101005)
作者简介:陆兵(1967-),男,副教授,主要研究方向:计算机应用技术及计算机控制,E-mail:lub@czili.edu.cn。
引文格式:陆兵,周国华,顾晓清,等. 迁移拉普拉斯总间隔支持向量机[J]. 南京理工大学学报,2020,44(1):40-48.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2020-02-29