Improving Transfer Learning by Introspective Reasoner - Intelligent Information Processing VI Access content directly
Conference Papers Year : 2012

Improving Transfer Learning by Introspective Reasoner

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.
Fichier principal
Vignette du fichier
978-3-642-32891-6_7_Chapter.pdf (214.83 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01524942 , version 1 (19-05-2017)

Licence

Attribution

Identifiers

Cite

Zhongzhi Shi, Bo Zhang, Fuzhen Zhuang. Improving Transfer Learning by Introspective Reasoner. 7th International Conference on Intelligent Information Processing (IIP), Oct 2012, Guilin, China. pp.28-39, ⟨10.1007/978-3-642-32891-6_7⟩. ⟨hal-01524942⟩
37 View
120 Download

Altmetric

Share

Gmail Facebook X LinkedIn More