This paper addresses the general problem of domain adaptation which arises in
a variety of applications where the distribution of the labeled sample
available somewhat differs from that of the test data. Building on previous
work by Ben-David et al. (2007), we introduce a novel distance between
distributions, discrepancy distance, that is tailored to adaptation problems
with arbitrary loss functions. We give Rademacher complexity bounds for
estimating the discrepancy distance from finite samples for different loss
functions. Using this distance, we derive novel generalization bounds for
domain adaptation for a wide family of loss functions. We also present a series
of novel adaptation bounds for large classes of regularization-based
algorithms, including support vector machines and kernel ridge regression based
on the empirical discrepancy. This motivates our analysis of the problem of
minimizing the empirical discrepancy for various loss functions for which we
also give novel algorithms. We report the results of preliminary experiments
that demonstrate the benefits of our discrepancy minimization algorithms for
domain adaptation.

本文介绍了一种针对域自适应问题的新型分布距离度量 —— 偏差距离，并给出了不同损失函数的估计方法以及普遍推广界限。同时，本文也探讨了偏差距离减少和多种损失函数的最小化算法，并验证了其在不同应用场景的有效性。