Abstract

Discriminative learning methods for classification perform well when training and test data are drawn from the same distribution. In many situations, though, we have labeled training data for a source domain, and we wish to learn a classifier which performs well on a target domain with a different distribution. Under what conditions can we adapt a classifier trained on the source domain for use in the target domain? Intuitively, a good feature representation is a crucial factor in the success of domain adaptation. We formalize this intuition theoretically with a generalization bound for domain adaption. Our theory illustrates the tradeoffs inherent in designing a representation for domain adaptation and gives a new justification for a recently proposed model. It also points toward a promising new model for domain adaptation: one which explicitly minimizes the difference between the source and target domains, while at the same time maximizing the margin of the training set.

Keywords

Adaptation (eye)Domain adaptationPsychologyDomain (mathematical analysis)Computer scienceCognitive scienceMathematicsArtificial intelligenceNeuroscienceMathematical analysis

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Publication Info

Year
2007
Type
book-chapter
Pages
137-144
Citations
1963
Access
Closed

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Shai Ben-David, John Blitzer, Koby Crammer et al. (2007). Analysis of Representations for Domain Adaptation. The MIT Press eBooks , 137-144. https://doi.org/10.7551/mitpress/7503.003.0022

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DOI
10.7551/mitpress/7503.003.0022