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Adaptation versus adaptability
http://nlpers.blogspot.com/ 2008/ 05/ adaptation-versus-adaptability.html
Domain adaptation is, roughly, the following problem: given labeled data drawn from one or more source domains, and either (a) a little labeled data drawn from a target domain or (b) a lot of unlabeled data drawn from a target domain; do the following. Produce a classifier (say) that has low expected loss on new data drawn from the target domain. (For clarity: we typically assume that it is the data distribution that changes between domains, not the task; that would be standard multi-task learning.)Obviously I think this is an fun problem (I publish on it, and blog about it reasonably frequently).
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