111 search results - page 10 / 23 » Perspectives on Sparse Bayesian Learning |
111
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JMLR
2010
14 years 6 months ago
2010
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
ICIAP
2007
ACM
15 years 11 months ago
2007
ACM
In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key issue is the achievement of a sparse model, i.e., a model in which all irreleva...
ICASSP
2010
IEEE
14 years 12 months ago
2010
IEEE
Sparse signal recovery algorithms utilizing multiple measurement vectors (MMVs) are known to have better performance compared to the single measurement vector case. However, curre...
103
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CVPR
2010
IEEE
15 years 8 months ago
2010
IEEE
Markov random fields (MRFs) are popular and generic probabilistic models of prior knowledge in low-level vision. Yet their generative properties are rarely examined, while applica...
JMLR
2010
14 years 6 months ago
2010
We study the problem of learning an optimal Bayesian network in a constrained search space; skeletons are compelled to be subgraphs of a given undirected graph called the super-st...