Feature Spaces-based Transfer Learning

DOI

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)

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  -