Learning Bounds for Moment-Based Domain Adaptation

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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 learning bounds under practice-oriented general conditions on the underlying probability distributions. As a result, we obtain learning bounds for domain adaptation based on finitely many moments and smoothness conditions.

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