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ICASSP
2011
IEEE

Nonparametric Bayesian feature selection for multi-task learning

10 years 5 months ago
Nonparametric Bayesian feature selection for multi-task learning
We present a nonparametric Bayesian model for multi-task learning, with a focus on feature selection in binary classification. The model jointly identifies groups of similar tasks and selects the subset of features relevant to the tasks within each group. The model employs a Dirchlet process with a betaBernoulli hierarchical base measure. The posterior inference is accomplished efficiently using a Gibbs sampler. Experimental results are presented on simulated as well as real data.
Hui Li, Xuejun Liao, Lawrence Carin
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Hui Li, Xuejun Liao, Lawrence Carin
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