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Improving Transfer Learning by Introspective Reasoner

  • Zhongzhi Shi
  • Bo Zhang
  • Fuzhen Zhuang
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 385)

Abstract

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.

Keywords

Introspective reasoned Transfer learning Negative transfer 

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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Zhongzhi Shi
    • 1
  • Bo Zhang
    • 1
    • 2
  • Fuzhen Zhuang
    • 1
  1. 1.Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Graduate School of the Chinese Academy of SciencesBeijingChina

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