论文标题
关于基于力矩的域适应性的概括
On generalization in moment-based domain adaptation
论文作者
论文摘要
域的适应算法旨在最大程度地降低目标域的判别模型的错误分类风险，而训练数据几乎没有训练数据，并通过从源域中调整具有大量培训数据的模型。标准方法基于源和目标域中的经验概率分布之间的距离度量来衡量适应性差异。在这种情况下，我们解决了在基本概率分布的面向实践的一般条件下得出概括界限的问题。结果，我们基于有限的许多矩和平滑度条件获得了域适应性的概括。
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.
