TY - GEN
T1 - Nonparametric Bayesian feature selection for multi-task learning
AU - Li, Hui
AU - Liao, Xuejun
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2011/8/18
Y1 - 2011/8/18
N2 - 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. © 2011 IEEE.
AB - 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. © 2011 IEEE.
UR - http://ieeexplore.ieee.org/document/5946926/
UR - http://www.scopus.com/inward/record.url?scp=80051635353&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2011.5946926
DO - 10.1109/ICASSP.2011.5946926
M3 - Conference contribution
SN - 9781457705397
SP - 2236
EP - 2239
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -