论文标题
基于力矩的领域适应：学习界限和算法
Moment-Based Domain Adaptation: Learning Bounds and Algorithms
论文作者
论文摘要
本论文为机器学习中的新兴领域的域适应性基础做出了贡献。与经典的统计学习相反，域适应性的框架考虑了培训和应用程序设置中的概率分布之间的偏差。域的适应性适用于更广泛的应用范围，因为将来的样本通常遵循与训练样本不同的分布。一个决定性的观点是关于分布相似性的假设的普遍性。因此，在本文中，我们研究了在有限的许多时刻可以建模的弱相似性假设下的域适应问题。
This thesis contributes to the mathematical foundation of domain adaptation as emerging field in machine learning. In contrast to classical statistical learning, the framework of domain adaptation takes into account deviations between probability distributions in the training and application setting. Domain adaptation applies for a wider range of applications as future samples often follow a distribution that differs from the ones of the training samples. A decisive point is the generality of the assumptions about the similarity of the distributions. Therefore, in this thesis we study domain adaptation problems under as weak similarity assumptions as can be modelled by finitely many moments.
