Feature Spaces-based Transfer Learning
Hua Zuo, Guangquan Zhang, Vahid Behbood, Jie Lu
Available Online June 2015.
- 10.2991/ifsa-eusflat-15.2015.141How to use a DOI?
- Transfer learning, deep learning, feature ex-traction, fuzzy sets.
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.
- © 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 - 10.2991/ifsa-eusflat-15.2015.141 ID - Zuo2015/06 ER -