Inductive transfer refers to the ability of a learning mechanism to improve performance on the current task after having learned a different but related concept or skill on a previous task. Transfer may additionally occur between two or more learning tasks that are being undertaken concurrently. Transfer may include background knowledge or a particular form of search bias.
As an illustration, an application of inductive transfer arises in competitive games involving teams of robots (e.g., Robocup Soccer). In this scenario, transferring knowledge learned from one task into another task is crucial to acquire skills necessary to beat the opponent team. Specifically, imagine a situation where a team of robots has been taught to keep a soccer ball away from the opponent team. To achieve that goal, robots must learn to keep the ball, pass the ball to a close teammate, etc., always trying to remain at a safe distance from the opponents....
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Baxter, J. (2000). A model of inductive learning bias. Journal of Artificial Intelligence Research, 12, 149–198.
Brazdil, P., Giraud-Carrier, C., Soares, C., & Vilalta, R. (2009). Metalearning: Applications to data mining. Springer-Verlag Berlin: Heidelberg.
Caruana, R. (1993). Multitask learning: A knowledge-based source of inductive bias. In P. E. Utgoff (Ed.), Proceedings of the tenth international conference on machine learning (pp. 41–48). San Mateo, Springer Netherlands: Morgan Kaufmann.
Dai, W., Yang, Q., Xue, G., & Yu, Y. (2007). Boosting for transfer learning. In Proceedings of the 24th annual international conference on machine learning (pp. 193–200). New York: ACM.
Evgeniou, T., Micchelli, C. A., & Pontil, M. (2005). Learning multiple tasks with kernel methods. Journal of Machine Learning Research, 6, 615–637.
Mihalkova, L., Huynh, T., & Mooney, R. J. (2007). Mapping and revising Markov logic networks for transfer learning. In Proceedings of the 22nd AAAI conference on artificial intelligence (pp. 608–614). Vancouver, BC: AAAI Press.
Oblinger, D., Reid, M., Brodie, M., & de Salvo Braz, R. (2002). Cross-training and its application to skill-mining. IBM Systems Journal, 41(3), 449–460.
Pratt, L., & Thrun, S. (1997). Second special issue on inductive transfer. Machine Learning, 28, No. 1, 5–130.
Raina, R., Ng, A. Y., & Koller, D. (2006). Constructing informative priors using transfer learning. In Proceedings of the twenty-third international conference on machine learning (pp. 713–720). Pittsburgh, PA: ACM.
Reid, M. (2004). Improving rule evaluation using multitask learning. In Proceedings of the 14th international conference on ILP (pp. 252–269). Springer-Verlag, Heidelberg.
Schmidhuber, J., Zhao, J., & Wiering M. A. (1997). Shifting inductive bias with success-story algorithm, adaptive Levin search, and incremental self-improvement. Machine Learning, 28(1), 105–130.
Stahl, I. (1996). Predicate invention in inductive logic programming. In L. De Raedt (Ed.), Advances in inductive logic programming. (pp. 34–47). IOS Press.
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Vilalta, R., Giraud-Carrier, C., Brazdil, P., Soares, C. (2011). Inductive Transfer. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_401
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8