Feature-based inductive transfer learning through minimum encoding

Hao Shao, Einoshin Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

This paper proposes an Extended Minimum Description Length Principle (EMDLP) for feature-based inductive transfer learning, in which both the source and the target data sets contain class labels and relevant features are transferred from the source domain to the target one. Despite numerous works on this topic, few of them have a solid theoretical framework and are parameter-free. Our EMDLP overcomes these flaws and allows us to evaluate the inferiority of the results of transfer learning with the add-sum of the code lengths of five components: the corresponding two hypotheses, the two data sets with the help of the hypotheses, and the set of the transferred features. We design a code book to build the connections between the source and the target tasks. Extensive experiments using both real and artificial data sets show that EMDLP is robust against noise and performs better on the classification accuracy than the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011
PublisherSociety for Industrial and Applied Mathematics Publications
Pages259-270
Number of pages12
ISBN (Print)9780898719925
DOIs
Publication statusPublished - Jan 1 2011
Event11th SIAM International Conference on Data Mining, SDM 2011 - Mesa, AZ, United States
Duration: Apr 28 2011Apr 30 2011

Publication series

NameProceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Other

Other11th SIAM International Conference on Data Mining, SDM 2011
CountryUnited States
CityMesa, AZ
Period4/28/114/30/11

All Science Journal Classification (ASJC) codes

  • Software

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  • Cite this

    Shao, H., & Suzuki, E. (2011). Feature-based inductive transfer learning through minimum encoding. In Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011 (pp. 259-270). (Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611972818.23