Logistic regression for transductive transfer learning from multiple sources

Authors:
Yuhong Zhang;Xuegang Hu;Yucheng Fang
Affiliations:
School of Computer and Information, Hefei University of Technology, Hefei, China;School of Computer and Information, Hefei University of Technology, Hefei, China;School of Computer and Information, Hefei University of Technology, Hefei, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Year:
2010

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Abstract

Recent years have witnessed the increasing interest in transfer learning. And transdactive transfer learning from multiple source domains is one of the important topics in transfer learning. In this paper, we also address this issue. However, a new method, namely TTLRM (Transductive Transfer based on Logistic Regression from Multi-sources) is proposed to address transductive transfer learning from multiple sources to one target domain. In term of logistic regression, TTLRM estimates the data distribution difference in different domains to adjust the weights of instances, and then builds a model using these re-weighted data. This is beneficial to adapt to the target domain. Experimental results demonstrate that our method outperforms the traditional supervised learning methods and some transfer learning methods.