论文标题
具有有限的目标标记数据的多源适应理论
A Theory of Multiple-Source Adaptation with Limited Target Labeled Data
论文作者
论文摘要
在公共场景中，我们提出了对多源域适应问题的理论和算法研究，其中学习者只能访问有限的标记目标数据，但是学习者可以从多个源域中获得大量标记的数据。我们表明，基于模型选择思想的新算法家族在这种情况下从非常有利的保证中受益，并讨论了一些影响某些替代技术的理论障碍。我们还通过算法报告了几个实验的结果，这些实验证明了它们的实际有效性。
We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where the learner has at disposal a large amount of labeled data from multiple source domains. We show that a new family of algorithms based on model selection ideas benefits from very favorable guarantees in this scenario and discuss some theoretical obstacles affecting some alternative techniques. We also report the results of several experiments with our algorithms that demonstrate their practical effectiveness.
