Spectral domain-transfer learning

KDD, pp. 488-496, 2008.

Cited by: 153|Bibtex|Views35|DOI:https://doi.org/10.1145/1401890.1401951
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Other Links: dblp.uni-trier.de|dl.acm.org|academic.microsoft.com

Abstract:

Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world applications, however, we wish to make use of the labeled data from one domain (called in-domain) to classify the unlabeled data in a different domain (out-of-doma...More

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