Transfer of learning

Mobile robot

Feature (linguistics)

Multi-task learning

Mathematics

Domain (mathematical analysis)

Semi-supervised learning

Online machine learning

Machine learning

Mathematical analysis

Philosophy

Artificial intelligence

Management

Robot learning

Economics

Instance-based learning

Computer science

Linguistics

Task (project management)

Cluster analysis

Inductive transfer

Feature vector

Robot

IEEE Transactions on Knowledge and Data Engineering

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.

A Survey on Transfer Learning