814 search results - page 69 / 163 » Graphical Models for Graph Matching |
AI
2002
Springer
13 years 10 months ago
2002
Springer
Bayesian networks, equivalently graphical Markov models determined by acyclic digraphs or ADGs (also called directed acyclic graphs or dags), have proved to be both effective and ...
GD
2005
Springer
14 years 3 months ago
2005
Springer
Clustered graph is a very useful model for drawing large and complex networks. This paper presents a new method for drawing clustered graphs in three dimensions. The method uses a ...
GD
1998
Springer
14 years 2 months ago
1998
Springer
Abstract. Techniques for drawing graphs based on force-directed placement and virtual physical models have proven surprisingly successful in producing good layouts of undirected gr...
SEMWEB
2005
Springer
14 years 3 months ago
2005
Springer
Probabilistic inference will be of special importance when one needs to know how much we can say with what all we know given new observations. Bayesian Network is a graphical prob...
SODA
2001
ACM
13 years 11 months ago
2001
ACM
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...