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共享隐空间迁移SVM

董爱美 王士同

董爱美, 王士同. 共享隐空间迁移SVM. 自动化学报, 2014, 40(10): 2276-2287. doi: 10.3724/SP.J.1004.2014.02276
引用本文: 董爱美, 王士同. 共享隐空间迁移SVM. 自动化学报, 2014, 40(10): 2276-2287. doi: 10.3724/SP.J.1004.2014.02276
DONG Ai-Mei, WANG Shi-Tong. A Shared Latent Subspace Transfer Learning Algorithm Using SVM. ACTA AUTOMATICA SINICA, 2014, 40(10): 2276-2287. doi: 10.3724/SP.J.1004.2014.02276
Citation: DONG Ai-Mei, WANG Shi-Tong. A Shared Latent Subspace Transfer Learning Algorithm Using SVM. ACTA AUTOMATICA SINICA, 2014, 40(10): 2276-2287. doi: 10.3724/SP.J.1004.2014.02276

共享隐空间迁移SVM

doi: 10.3724/SP.J.1004.2014.02276
基金项目: 

国家自然科学基金(61170122, 61202311),江苏省自然科学基金(BK 2012552), 山东省高等学校科技计划项目(J14LN05) 资助

详细信息
    作者简介:

    王士同 江南大学数字媒体学院教授.主要研究方向为人工智能和机器学习.E-mail: wxwangst@yahoo.com.cn

A Shared Latent Subspace Transfer Learning Algorithm Using SVM

Funds: 

Supported by National Natural Science Foundation of China (61170122, 61202311), Natural Science Foundation of Jiangsu Province (BK2012552), and the Project of Shandong Province Higher Educational Science and Technology Program (J14LN05)

  • 摘要: 在机器学习中,迁移学习被证明能有效使用一个领域信息提高另一个领域中受训模型的分类精度. 迁移学习总是假设相关领域间共享某些隐含因素,但在当前的迁移学习方法中,该部分隐含因素依然未得到充分 探讨.本研究引入低维共享隐空间的迁移学习方法,基于经典支持向量机(Support vector machine, SVM)分类模型得到融入共享隐空间的迁移支持向量机,该模型较以往相关方法能更好地利用隐空间这一有效信息,从而提高所得分类器 的泛化性能.相关实验结果亦验证了所提方法的有效性.
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出版历程
  • 收稿日期:  2013-05-07
  • 修回日期:  2014-04-10
  • 刊出日期:  2014-10-20

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