Inductive transfer

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Inductive transfer, or transfer learning, is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.^[1] For example, the abilities acquired while learning to walk presumably apply when one learns to run, and knowledge gained while learning to recognize cars could apply when recognizing trucks. This area of research bares some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited.

Notably, scientists have developed algorithms for inductive transfer in Markov logic networks^[2] and Bayesian networks.^[3] Furthermore, researchers have applied techniques for transfer to problems in text classification,^[4][5] spam filtering,^[6] and urban combat simulation.^{[7] [8]}

[edit] See also

Multi-task learning

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