Generalization Bounds for Domain Adaptation

Understand

In this paper, we provide a new framework to obtain the generalization bounds of the learning process for domain adaptation, and then apply the derived bounds to analyze the asymptotical convergence of the learning process.

• Without loss of generality, we consider two kinds of representative domain adaptation: one is with multiple sources and the other is combining source and target data.
• In particular, we use the integral probability metric to measure the difference between two domains.
• For either kind of domain adaptation, we develop a related Hoeffding-type deviation inequality and a symmetrization inequality to achieve the corresponding generalization bound based on the uniform entropy number.