Abstract—A major assumption in many machinelearning and data mining algorithms is that the training and future data mustbe in the same feature space and have the same distribution. However, in manyreal-world applications, this assumption may not hold. For example, wesometimes have a classification task in one domain of interest, but we onlyhave sufficient training data in another domain of interest, where the latterdata may be in a different feature space or follow a different data distribution.In such cases, knowledge transfer, if done successfully, would greatly improvethe performance of learning by avoiding much expensive data-labeling efforts.In recent years, transfer learning has emerged as a new learning framework toaddress this problem. This survey focuses on categorizing and reviewing thecurrent progress on transfer learning for classification, regression, andclustering problems. In this survey, we discuss the relationship betweentransfer learning and other related machine learning techniques such as domainadaptation, multitask learning and sample selection bias, as wellas covariate shift. We also explore some potential future issues in transferlearning research.
传统机器学习的领域假设训练数据和测试数据属于相同的特征空间并在同一分布上。然而，现实应用中这种假设往往得不到满足。例如，我们对目标领域的分类问题感兴趣，却只有源领域的训练数据。但源领域数据与目标领域数据要么不在同一特征空间，要么不满足相同的数据分布，例如需要进行的文本分类语言是西班牙语，但只提供了葡萄牙语的文本。在某些情况下成功地进行知识迁移能够很大程度上提高学习的性能，也同时降低了标记目标领域数据带来的大量时间和人力成本。近年来，迁移学习已经成为一种解决知识迁移问题的新型学习框架。这篇论文讨论了使用迁移学习进行分类、回归以及聚类的一般过程，也讨论了迁移学习和其他相关的机器学习技术之间的关系，如领域适应性、多任务学习、样本选择和变量转换。
这篇综述十分详细地介绍了各类迁移学习方法，进行分类并介绍了各类方法的代表性文章。
该文将迁移学习根据领域和任务的不同进行了划分，如下图：
2.1 归纳式迁移学习（inductive transferlearning）
代表性论文引用：
Dai W, Yang Q,Xue G R, et al. Boosting for transfer learning[C]//Machine Learning, Proceedings of the Twenty-Fourth International Conference. DBLP, 2007:193-200.
2.2 直推式迁移学习（transductivetransfer learning）
代表性论文引用：
Arnold A, Nallapati R, Cohen W W. A comparativestudy of methods for transductive transfer learning[C]//Data Mining Workshops,2007. ICDM Workshops 2007. Seventh IEEE International Conference on. IEEE,2007: 77-82.
2.3 无监督迁移学习（unsupervisedtransfer learning）
代表性论文引用：
Dai W, Yang Q, Xue G R, et al. Self-taughtclustering[C]//Proceedings of the 25th international conference on Machinelearning. ACM, 2008: 200-207.
