A survey on heterogeneous transfer learning

A survey on heterogeneous transfer learning

Abstract Transfer learning has been demonstrated to be effective for many real-world applications as it exploits knowledge present in labeled training data from a source domain to enhance a model’s performance in a target domain, which has little or no labeled target training data. Utilizing a label...

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Bibliographic Details
Main Authors: Oscar Day, Taghi M. Khoshgoftaar
Format: Article
Language:English
Published: SpringerOpen 2017-09-01
Series:Journal of Big Data
Subjects:
Transfer learning
Heterogeneous transfer learning
Knowledge transfer
Supervised learning
Semisupervised learning
Unsupervised learning
Online Access:http://link.springer.com/article/10.1186/s40537-017-0089-0

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http://link.springer.com/article/10.1186/s40537-017-0089-0

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