Domain Adaptation for Statistical Classifiers
نویسندگان
چکیده
منابع مشابه
Domain Adaptation for Statistical Classifiers
The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the “in-domain” test data is drawn from a distribution that is related, but not identical, to the “out-of-domain” distribution of the training data. We consider the common case in which labeled out-of-domain data ...
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The domain adaptation problem, especially domain adaptation in natural language processing, started gaining much attention very recently [Daumé III and Marcu, 2006, Blitzer et al., 2006, Ben-David et al., 2007, Daumé III, 2007, Satpal and Sarawagi, 2007]. However, some special kinds of domain adaptation problems have been studied before under different names such as class imbalance [Japkowicz a...
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Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applicat...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2006
ISSN: 1076-9757
DOI: 10.1613/jair.1872