Adaptation Regularization: A General Framework for Transfer Learning

Domain transfer learning, which learns a target classifier using labeled data from a different distribution, has shown promising value in knowledge discovery yet still been a challenging problem. Most previous works designed adaptive classifiers by exploring two learning strategies independently: di...

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Bibliographic Details
Published in:	IEEE transactions on knowledge and data engineering Vol. 26; no. 5; pp. 1076 - 1089
Main Authors:	Mingsheng Long, Jianmin Wang, Guiguang Ding, Pan, Sinno Jialin, Yu, Philip S.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.05.2014 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial Intelligence Classifier design and evaluation Computing Methodologies Database Applications Database Management Design Methodology Feature extraction Information Technology and Systems Joints Kernel Knowledge acquisition Learning Manifolds Mining methods and algorithms Modeling structured Pattern Recognition Probability distribution Risk management textual and multimedia data manifold regularization distribution adaptation Transfer learning generalization error adaptation regularization
ISSN:	1041-4347, 1558-2191
Online Access:	Get full text
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