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Probabilistic Joint Feature Selection for Multi-task Learning
[chapter]
2007
Proceedings of the 2007 SIAM International Conference on Data Mining
We study the joint feature selection problem when learning multiple related classification or regression tasks. By imposing an automatic relevance determination prior on the hypothesis classes associated with each of the tasks and regularizing the variance of the hypothesis parameters, similar feature patterns across different tasks are encouraged and features that are relevant to all (or most) of the tasks are identified. Our analysis shows that the proposed probabilistic framework can be seen
doi:10.1137/1.9781611972771.30
dblp:conf/sdm/XiongBRC07
fatcat:tn35xokpbfa3tn5wsjl5wjwyoi