NONPARAMETRIC BAYESIAN FEATURE SELECTION FOR MULTI-TASK LEARNING

被引：0

作者：

Li, Hui ^{[1
]}

Liao, Xuejun ^{[2
]}

Carin, Lawrence ^{[1
,2
]}

机构：

[1] Signal Innovat Grp Inc, Durham, NC 27703 USA

[2] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA

来源：

2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2011年

关键词：

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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 beta-Bernoulli hierarchical base measure. The posterior inference is accomplished efficiently using a Gibbs sampler. Experimental results are presented on simulated as well as real data.

引用

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页码：2236 / 2239

页数：4

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