Domain adaptation from multiple sources : a domain-dependent regularization approach
In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source domain adaption problem. Under this framework, we learn a robust decision function (referred to as target classifier) for label prediction of instances from the target domain by leveraging a set...
Saved in:
Main Authors: | Duan, Lixin, Xu, Dong, Tsang, Ivor Wai-Hung |
---|---|
Other Authors: | School of Computer Engineering |
Format: | Article |
Language: | English |
Published: |
2013
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/99183 http://hdl.handle.net/10220/13528 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Domain transfer multiple kernel learning
by: Duan, Lixin, et al.
Published: (2013) -
Exploiting web images for event recognition in consumer videos : a multiple source domain adaptation approach
by: Duan, Lixin, et al.
Published: (2013) -
Domain consistency regularization for unsupervised multi-source domain adaptive classification
by: Luo, Zhipeng, et al.
Published: (2023) -
Batch mode adaptive multiple instance learning for computer vision tasks
by: Li, Wen, et al.
Published: (2013) -
Domain adaptation from multiple sources via auxiliary classifiers
by: Duan, L., et al.
Published: (2013)