Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology

Feature Spaces-based Transfer Learning

Authors
Hua Zuo, Guangquan Zhang, Vahid Behbood, Jie Lu
Corresponding Author
Hua Zuo
Available Online June 2015.
DOI
https://doi.org/10.2991/ifsa-eusflat-15.2015.141How to use a DOI?
Keywords
Transfer learning, deep learning, feature ex-traction, fuzzy sets.
Abstract

Transfer learning provides an approach to solve target tasks more quickly and effectively by using previously-acquired knowledge learned from source tasks. Most of transfer learning approaches extract knowledge of source domain in the given feature space. The issue is that single perspective can t mine the relationship of source domain and target domain fully. To deal with this issue, this paper develops a method using Stacked Denoising Autoencoder (SDA) to extract new feature spaces for source domain and target domain, and define two fuzzy sets to analyse the variation of prediction ac-curacy of target task in new feature spaces.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
Series
Advances in Intelligent Systems Research
Publication Date
June 2015
ISBN
978-94-62520-77-6
ISSN
1951-6851
DOI
https://doi.org/10.2991/ifsa-eusflat-15.2015.141How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Hua Zuo
AU  - Guangquan Zhang
AU  - Vahid Behbood
AU  - Jie Lu
PY  - 2015/06
DA  - 2015/06
TI  - Feature Spaces-based Transfer Learning
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 1000
EP  - 1005
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.141
DO  - https://doi.org/10.2991/ifsa-eusflat-15.2015.141
ID  - Zuo2015/06
ER  -