Inductive transfer

Inductive transfer, or transfer learning, is the machine learning process of storing and applying knowledge gained from one problem or task to a different but related problem or task. For example, learning to walk could be used in learning to run, or learning to recognize cars could be used in learning to recognize trucks.

This terminology was developed in reference to machine learning, although the underlying idea of transfer of learning has been studied in cognitive psychology for more than a century. The process is typically most effective in machine learning when learned knowledge is stored in relational or heirarchical structures.

Algorithms have been developed to apply transfer learning in Markov logic networks and Bayesian networks. Applications that have been studied include text classification, spam filtering, and urban combat simulation.