70 search results - page 4 / 14 » Bottom-Up Learning of Markov Network Structure |
108
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JMLR
2008
15 years 1 months ago
2008
We show how a generic feature selection algorithm returning strongly relevant variables can be turned into a causal structure learning algorithm. We prove this under the Faithfuln...
100
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ICML
2008
IEEE
16 years 1 months ago
2008
IEEE
We propose Laplace max-margin Markov networks (LapM3 N), and a general class of Bayesian M3 N (BM3 N) of which the LapM3 N is a special case with sparse structural bias, for robus...
114
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AAAI
2007
15 years 3 months ago
2007
Transfer learning addresses the problem of how to leverage knowledge acquired in a source domain to improve the accuracy and speed of learning in a related target domain. This pap...
103
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NIPS
1997
15 years 2 months ago
1997
We address the problem of learning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidimensional density estimation exploit...
115
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ICML
2009
IEEE
16 years 1 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...