Learning Algorithms for Domain Adaptation


A fundamental assumption for any machine learning task is to have training and test data instances drawn from the same distribution while having a sufficiently large number of training instances. In many practical settings, this ideal assumption is invalidated as the labeled training instances are scarce and there is a high cost associated with labeling… (More)
DOI: 10.1007/978-3-642-05224-8_23

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