Logistic Regression for Transductive Transfer Learning from Multiple Sources

摘要：

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.

关键词： transductive transfer learning classification multiple sources logistic regression

作者: Yuhong Zhang Xuegang Hu Yucheng Fang

作者单位: School of Computer and Information, Hefei University of Technology, Hefei 230009, China

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 175-182

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）