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14

ICASSP
2011
IEEE

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Nonparametric Bayesian feature selection for multi-task learning

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Nonparametric Bayesian feature selection for multi-task learning

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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

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Hierarchical Base Measure | ICASSP 2011 | Multi-task Learning | Nonparametric Bayesian Model | Signal Processing |

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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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