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PAMI
2010

Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy

8 years 10 months ago
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
—This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target-domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy.
Lorenzo Bruzzone, Mattia Marconcini
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PAMI
Authors Lorenzo Bruzzone, Mattia Marconcini
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