142 search results - page 1 / 29 » Learning Graphical Model Structure Using L1-Regularization P... |
AAAI
2007
15 years 22 days ago
2007
Sparsity-promoting L1-regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the ...
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ICMLA
2009
14 years 8 months ago
2009
This paper introduces a new algorithm, namely the EquiCorrelation Network (ECON), to perform supervised classification, and regression. ECON is a kernelized LARS-like algorithm, b...
ICML
2009
IEEE
15 years 11 months ago
2009
IEEE
In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the dependency structure than directed graphical mode...
JMLR
2010
14 years 5 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...
CORR
2010
Springer
14 years 10 months ago
2010
Springer
In this paper we propose an approximated structured prediction framework for large scale graphical models and derive message-passing algorithms for learning their parameters effic...