Domain adaptation is the supervised learning setting in which the training
and test data are sampled from different distributions: training data is
sampled from a source domain, whilst test data is sampled from a target domain.
This paper proposes and studies an approach, called feature-level domain
adaptation (FLDA), that models the dependence between the two domains by means
of a feature-level transfer model that is trained to describe the transfer from
source to target domain. Subsequently, we train a domain-adapted classifier by
minimizing the expected loss under the resulting transfer model. For linear
classifiers and a large family of loss functions and transfer models, this
expected loss can be computed or approximated analytically, and minimized
efficiently. Our empirical evaluation of FLDA focuses on problems comprising
binary and count data in which the transfer can be naturally modeled via a
dropout distribution, which allows the classifier to adapt to differences in
the marginal probability of features in the source and the target domain. Our
experiments on several real-world problems show that FLDA performs on par with
state-of-the-art domain-adaptation techniques.

该论文提出并研究了一种称为特征级域自适应（FLDA）的方法，其模拟了源域到目标域的转换，并通过最小化所得到的转移模型下的期望损失来训练适应于目标域的分类器，该方法在真实世界的多个问题上和现有领先的域自适应技术相匹敌。