Improving Transfer Learning by Introspective Reasoner

摘要：

　　Traditional learning techniques have the assumption that training and test data are drawn from the same data distribution,and thus they are not suitable for dealing with the situation where new unlabeled data are obtained from fast evolving,related but different information sources.This leads to the cross-domain learning problem which targets on adapting the knowledge learned from one or more source domains to target domains.Transfer learning has made a great progress,and a lot of approaches and algorithms are presented.But negative transfer learning will cause trouble in the problem solving,which is difficult to avoid.In this paper we have proposed an introspective reasoner to overcome the negative transfer learning.Introspective learning exploits explicit representations of its own organization and desired behavior to determine when,what,and how to learn in order to improve its own reasoning.According to the transfer learning process we will present the architecture of introspective reasoner for transductive transfer learning.

关键词： Introspective reasoned Transfer learning Negative transfer

作者: Zhongzhi Shi Bo Zhang Fuzhen Zhuang

作者单位: Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Acade Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Acade

会议类型: 国际会议

会议名称: 7th IFIP TC 12 International Conference (第七届智能信息处理国际会议 (IIP 2012))

会议地点: 桂林

会议语种:英文

页码: 28-39

在线出版日期: 2012-10-12（万方平台首次上网日期，不代表论文的发表时间）