  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.
