NDLI: Transfer Learning from Unlabeled Data via Neural Networks

Content Provider	Springer Nature Link
Author	Zhang, Huaxiang Ji, Hua Wang, Xiaoqin
Copyright Year	2012
Abstract	A machine learning framework which uses unlabeled data from a related task domain in supervised classification tasks is described. The unlabeled data come from related domains, which share the same class labels or generative distribution as the labeled data. Patterns in the unlabeled data are learned via a neural network and transferred to the target domain from where the labeled data are generated, so as to improve the performance of the supervised learning task. We call this approach self-taught transfer learning from unlabeled data. We introduce a general-purpose feature learning algorithm producing features that retain information from the unlabeled data. Information preservation assures that the features obtained will be useful for improving the classification performance of the supervised tasks.
Starting Page	173
Ending Page	187
Page Count	15
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	36
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2012-06-05
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Transfer learning Neural networks Base transfer Mapping function Statistical Physics, Dynamical Systems and Complexity Artificial Intelligence (incl. Robotics) Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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Transfer Learning from Unlabeled Data via Neural Networks