99 search results - page 10 / 20 » Learning Causal Structure from Overlapping Variable Sets |
IJAR
2010
13 years 6 months ago
2010
We recall the basic idea of an algebraic approach to learning Bayesian network (BN) structures, namely to represent every BN structure by a certain (uniquely determined) vector, c...
CVPR
1999
IEEE
14 years 9 months ago
1999
IEEE
In this paper, a novel framework for the recovery of 3D surfaces of faces from single images is developed. The underlying principle is shape from recognition, i.e. the idea that p...
ICDM
2003
IEEE
14 years 1 months ago
2003
IEEE
Learning Bayesian network structure from large-scale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements ...
JMLR
2012
11 years 10 months ago
2012
Standard compressive sensing results state that to exactly recover an s sparse signal in Rp , one requires O(s · log p) measurements. While this bound is extremely useful in prac...
ASPDAC
2005
ACM
13 years 9 months ago
2005
ACM
－ It is a hotly research topic to eliminate irrelevant variables from counterexample, to make it easier to be understood. BFL algorithm is the most effective Counterexample minim...