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. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited.
In 1997, the journal Machine Learning published a special issue devoted to transfer learning, and by 1998, the field had advanced to include multi-task learning, along with a more formal analysis of its theoretical foundations. Learning to Learn, edited by Pratt and Sebastian Thrun, is a 1998 review of the subject.
Transfer learning has also been applied in cognitive science, with the journal Connection Science publishing a special issue on reuse of neural networks through transfer in 1996.
Algorithms are available for transfer learning in Markov logic networks and Bayesian networks. Transfer learning has also been applied to cancer subtype discovery,  building utilization, general game playing, text classification and spam filtering.
- Crossover (genetic algorithm)
- Domain adaptation
- General game playing
- Multi-task learning
- Multitask optimization
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- Pratt, L. (1996). "Special Issue: Reuse of Neural Networks through Transfer". Connection Science. 8 (2). Retrieved 2017-08-10.
- Mihalkova, Lilyana; Huynh, Tuyen; Mooney, Raymond J. (July 2007), "Mapping and Revising Markov Logic Networks for Transfer" (PDF), Learning Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-2007), Vancouver, BC, pp. 608–614, retrieved 2007-08-05
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- Hajiramezanali, E. & Dadaneh, S. Z. & Karbalayghareh, A. & Zhou, Z. & Qian, X. Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada. https://arxiv.org/pdf/1810.09433.pdf
- Arief-Ang, I.B.; Salim, F.D.; Hamilton, M. (2017-11-08). DA-HOC: semi-supervised domain adaptation for room occupancy prediction using CO2 sensor data. 4th ACM International Conference on Systems for Energy-Efficient Built Environments (BuildSys). Delft, Netherlands. pp. 1–10. doi:10.1145/3137133.3137146. ISBN 978-1-4503-5544-5.
- Arief-Ang, I.B.; Hamilton, M.; Salim, F.D. (2018-12-01). "A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data". ACM Transactions on Sensor Networks (TOSN). 14 (3–4): 21:1–21:28. doi:10.1145/3217214. Retrieved 2018-12-31.
- Banerjee, Bikramjit, and Peter Stone. "General Game Learning Using Knowledge Transfer." IJCAI. 2007.
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