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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.

[edit] See also

[edit] 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

    nderstand completely "Exploring Regularized Feature Selection for Person Specific Face Verification". This is a paper on multi-task feature selection. Peipei Yang讲通过比如2,1范数是一种实现多任务的方式,还有其他方式。
    Multi-Task Learning_ Theory, Algorithms, and Applications.pptx的第五页的例子很好地解释了什么是Multi-Task Learning. Partial Face Recognition (TPAMI 2013)的第三节也是multi-task。 relaxed collaborative representation的公式6(说成multi-task,也可以视为multi-view)