Adaptive Knowledge Transfer Based on Classification-error Consensus Regularization

Shuang LIANG; Wenlong HANG; Wei FENG; Xuejun LIU

doi:10.11999/JEIT181054

Volume 41 Issue 11

Nov. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(11): 2736-2743

Shuang LIANG, Wenlong HANG, Wei FENG, Xuejun LIU. Adaptive Knowledge Transfer Based on Classification-error Consensus Regularization[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2736-2743. doi: 10.11999/JEIT181054

Citation:

Shuang LIANG, Wenlong HANG, Wei FENG, Xuejun LIU. Adaptive Knowledge Transfer Based on Classification-error Consensus Regularization[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2736-2743. doi: 10.11999/JEIT181054

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Adaptive Knowledge Transfer Based on Classification-error Consensus Regularization

doi: 10.11999/JEIT181054

1.
School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2.
Department of Computer and Technology, Nanjing Tech University, Nanjing 211816, China

Funds: The National Nature Science Foundation of China (61802177), The Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (18KJB520020), NUPTSF (NY219034), Key Research and Development Program of Jiangsu Province (BE2015697)

Received Date: 2018-11-20
Rev Recd Date: 2019-04-30

Available Online: 2019-05-16

Publish Date: 2019-11-01

Abstract

Abstract

Most current transfer learning methods are modeled by utilizing the source data with the assumption that all data in the source domain are equally related to the target domain. In many practical applications, however, this assumption may induce negative learning effect when it becomes invalid. To tackle this issue, by minimizing the integrated squared error of the probability distribution of the source and target domain classification errors, the Classification-error Consensus Regularization (CCR) is proposed. Furthermore, CCR-based Adaptive knowledge Transfer Learning (CATL) method is developed to quickly determine the correlative source data and the corresponding weights. The proposed method can alleviate the negative transfer learning effect while improving the efficiency of knowledge transfer. The experimental results on the real image and text datasets validate the advantages of the CATL method.
- Transfer learning,
- Negative transfer,
- Probability distribution,
- Classification-error Consensus Regularization (CCR)

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References(25)

References

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