Multi-task learning

From Wikipedia, the free encyclopedia

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

• Artificial Neural Networks
• Evolutionary computation
• Human-based genetic algorithm

[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

[edit] External links

• The Biosignals Intelligence Group at UIUC