A Survey on Transfer Learning
A Survey on Transfer Learning
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DOI：
10.1109/tkde.2009.191
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发表时间：
2010-10-01
影响因子：
8.9
通讯作者：
Yang, Qiang
中科院分区：
文献类型：
--
作者：
Pan, Sinno Jialin;Yang, Qiang
许多机器学习和数据挖掘算法中的一个主要假设是，训练数据和未来数据必须在相同的特征空间中，并且具有相同的分布。然而，在许多实际应用中，这种假设可能不成立。例如，我们有时在一个感兴趣的领域中有一个分类任务，但我们在另一个感兴趣的领域中只有足够的训练数据，其中后者的数据可能在不同的特征空间中或遵循不同的数据分布。在这种情况下，知识转移，如果成功地完成，将大大提高学习的性能，避免昂贵的数据标记工作。近年来，迁移学习已经成为解决这一问题的一种新的学习框架。本调查的重点是分类和审查目前的进展转移学习分类，回归和聚类问题。在这项调查中，我们讨论了迁移学习与其他相关机器学习技术之间的关系，如领域适应，多任务学习和样本选择偏差，以及协变量转移。我们还探讨了迁移学习研究中一些潜在的未来问题。
A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.