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.[1] 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.[2]
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.[3] The process is typically most effective in machine learning when learned knowledge is stored in relational or heirarchical structures.[3]
Algorithms have been developed to apply transfer learning in Markov logic networks[4] and Bayesian networks.[5] Applications that have been studied include text classification,[6][7] spam filtering,[8] and urban combat simulation.[9]
See Also
References
- ↑ West, Jeremy, Dan Ventury, and Sean Warnick. Spring Research Presentation: A Theoretical Foundation for Inductive Transfer (Abstract Only). Brigham Young University, College of Physical and Mathematical Sciences. 2007. Retrieved on 2007–08-05.
- ↑ Silver, Danny. Inductive Transfer: 10 Years Later. NIPS 2005 Workshop. Retrieved on 2007–08-05.
- ↑ 3.0 3.1 ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning (Informational website on the workshop). 2006. Retrieved on 2007–08-05.
- ↑ Mihalkova, Lilyana, Tuyen Huynh, and Raymond J. Mooney. Mapping and Revising Markov Logic Networks for Transfer Learning. Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI-2007), Vancouver, BC, pp. 608-614, July 2007. Retrieved on 2007–08-05.
- ↑ Niculescu-Mizil, Alexandru, and Rich Caruana. Inductive Transfer for Bayesian Network Structure Learning. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS 2007), March 21-24, 2007. Retrieved on 2007–08-05.
- ↑ Do, Cuong B. and Andrew Y. Ng. Transfer learning for text classification. Neural Information Processing Systems Foundation, NIPS*2005 Online Papers. Retrieved on 2007–08-05.
- ↑ Raina, Rajat, Andrew Y. Ng, and Daphne Koller. Constructing Informative Priors using Transfer Learning Proceedings of the Twenty-third International Conference on Machine Learning, 2006. Retrieved on 2007–08-05.
- ↑ Bickel, Steffen. ECML-PKDD Discovery Challenge 2006 Overview Proceedings of the ECML-PKDD Discovery Challenge Workshop, 2006. Retrieved on 2007–08-05.
- ↑ Gorski, Nicholas A., and John E. Laird. Experiments in Transfer Across Multiple Learning Mechanisms. Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning. Pittsburgh, PA. Retrieved on 2007–08-05.
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