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ICDM
2010
IEEE
127views Data Mining» more  ICDM 2010»
15 years 3 months ago
Learning Markov Network Structure with Decision Trees
Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima d...
Daniel Lowd, Jesse Davis
ICML
2009
IEEE
16 years 6 months ago
On primal and dual sparsity of Markov networks
Sparsity is a desirable property in high dimensional learning. The 1-norm regularization can lead to primal sparsity, while max-margin methods achieve dual sparsity. Combining the...
Jun Zhu, Eric P. Xing
ICML
2009
IEEE
16 years 6 months ago
Learning Markov logic network structure via hypergraph lifting
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning ML...
Stanley Kok, Pedro Domingos
ICML
2008
IEEE
16 years 6 months ago
Laplace maximum margin Markov networks
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...
Jun Zhu, Eric P. Xing, Bo Zhang
CSSE
2008
IEEE
16 years 7 days ago
A Markov Game Theory-Based Risk Assessment Model for Network Information System
—Risk assessment is a very important tool to acquire a present and future security status of the network information system. Many risk assessment approaches consider the present ...
Cui Xiaolin, Xiaobin Tan, Zhang Yong, Hongsheng Xi