On generalization in moment-based domain adaptation

Werner Zellinger; Bernhard A Moser; Susanne Saminger-Platz

doi:10.1007/s10472-020-09719-x

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arXiv · 2002.08260

On generalization in moment-based domain adaptation

Werner Zellinger·Bernhard A Moser·Susanne Saminger-Platz

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Abstract

Domain adaptation algorithms are designed to minimize the misclassification risk of a discriminative model for a target domain with little training data by adapting a model from a source domain with a large amount of training data. Standard approaches measure the adaptation discrepancy based on distance measures between the empirical probability distributions in the source and target domain. In this setting, we address the problem of deriving generalization bounds under practice-oriented general conditions on the underlying probability distributions. As a result, we obtain generalization bounds for domain adaptation based on finitely many moments and smoothness conditions.

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BibTeX RIS

Werner Zellinger, Bernhard A Moser, Susanne Saminger-Platz. 2021-07-26. On generalization in moment-based domain adaptation. https://doi.org/10.1007/s10472-020-09719-x

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