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 generalization bounds for domain adaptation based on finitely many moments and smoothness conditions.

中文翻译：

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基于矩域适应的泛化

域自适应算法旨在通过对来自具有大量训练数据的源域的模型进行自适应，从而最大限度地降低具有很少训练数据的目标域的判别模型的错误分类风险。标准方法基于源域和目标域中经验概率分布之间的距离度量来度量适应差异。在这种情况下，我们解决了在面向实践的一般条件下根据潜在概率分布推导学习界限的问题。结果，我们获得了基于有限多个矩和平滑条件的域适应的泛化边界。