NDLI: Dynamical ensemble learning with model-friendly classifiers for domain adaptation

Content Provider	IEEE Xplore Digital Library
Author	Wenting Tu Shiliang Sun
Copyright Year	2012
Description	Author affiliation: Department of Computer Science and Technology, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China (Wenting Tu; Shiliang Sun)
Abstract	In the domain adaptation research, which recently becomes one of the most important research directions in machine learning, source and target domains are with different underlying distributions. In this paper, we propose an ensemble learning framework for domain adaptation. Owing to the distribution differences between source and target domains, the weights in the final model are sensitive to target examples. As a result, our method aims to dynamically assign weights to different test examples by making use of additional classifiers called model-friendly classifiers. The model-friendly classifiers can judge which base models predict well on a specific test example. Finally, the model can give the most favorable weights to different examples. In the experiments, we firstly testify the need of dynamical weights in the ensemble learning based domain adaptation, then compare our method with other classical methods on real datasets. The experimental results show that our method can learn a final model performing well in the target domain.
Starting Page	1181
Ending Page	1184
File Size	196192
Page Count	4
File Format	PDF
ISBN	9781467322164
ISSN	10514651
e-ISBN	9784990644109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ICPR Org Committee
Subject Keyword	Adaptation models Predictive models Brain modeling Training Machine learning Educational institutions Feature extraction
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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