Multi-task learning

Multi-task learning

Multi-task learning is an approach to machine learning that learns a problem together with other related problems at the same time, using a shared representation. This often leads to a better model for the main task, because it allows the learner to use the commonality among the tasks. Therefore, multi-task learning is a kind of inductive transfer.

See also

References

  • Baxter, J. (2000). A model of inductive bias learning. Journal of Artificial Intelligence Research, 12:149--198, On-line paper
  • Caruana, R. (1997). Multitask learning: A knowledge-based source of inductive bias. Machine Learning, 28:41--75. Paper at Citeseer
  • Thrun, S. (1996). Is learning the n-th thing any easier than learning the first?. In Advances in Neural Information Processing Systems 8, pp. 640--646. MIT Press. Paper at Citeseer

External links


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