A survey of multi-source domain adaptation
A survey of multi-source domain adaptation
复制标题
多源域适应综述
DOI：
10.1016/j.inffus.2014.12.003
复制
发表时间：
2015-07-01
影响因子：
18.6
通讯作者：
Wu, Yuanbin
中科院分区：
文献类型：
--
作者：
Sun, Shiliang;Shi, Honglei;Wu, Yuanbin
在许多机器学习算法中，一个主要的假设是训练样本和测试样本在相同的特征空间中，并且具有相同的分布。然而，对于许多实际应用来说，这一假设并不成立。本文研究了训练样本和测试样本来自不同分布的问题。这个问题可以称为领域自适应。训练样本总是有标签，从所谓的源域获得，而测试样本通常没有标签或只有几个标签，从所谓的目标域获得。源域和目标域是不同的，但在一定程度上是相关的，学习者可以从源域中学习到一些信息来学习目标域。我们主要研究多源域自适应问题，其中有一个以上的源域可用，但只有一个目标域。一个关键问题是如何为适应选择好的来源和样本。在这篇综述中，我们回顾了多源域自适应问题的一些理论结果和成熟的算法。我们还讨论了在今后的工作中可以探索的一些有待解决的问题。(C)2014爱思唯尔B.V.保留所有权利。
In many machine learning algorithms, a major assumption is that the training and the test samples are in the same feature space and have the same distribution. However, for many real applications this assumption does not hold. In this paper, we survey the problem where the training samples and the test samples are from different distributions. This problem can be referred as domain adaptation. The training samples, always with labels, are obtained from what is called source domains, while the test samples, which usually have no labels or only a few labels, are obtained from what is called target domains. The source domains and the target domains are different but related to some extent; the learners can learn some information from the source domains for the learning of the target domains. We focus on the multisource domain adaptation problem where there is more than one source domain available together with only one target domain. A key issue is how to select good sources and samples for the adaptation. In this survey, we review some theoretical results and well developed algorithms for the multi-source domain adaptation problem. We also discuss some open problems which can be explored in future work. (C) 2014 Elsevier B.V. All rights reserved.