Domain adaptation of weighted majority votes via perturbed variation-based self-labeling

1 Oct 2014 Emilie Morvant

In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thus the design of algorithms able to generalize on a new distribution, for which we have no label information... (read more)

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