1542 search results - page 194 / 309 » The metamathematics of random graphs |
118
Voted
WEA
2010
Springer
15 years 6 months ago
2010
Springer
Maximizing the quality index modularity has become one of the primary methods for identifying the clustering structure within a graph. As contemporary networks are not static but e...
104
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APPROX
2006
Springer
15 years 5 months ago
2006
Springer
Inspired by Feige (36th STOC, 2004), we initiate a study of sublinear randomized algorithms for approximating average parameters of a graph. Specifically, we consider the average ...
113
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SODA
2001
ACM
15 years 3 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 ...
117
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UAI
2004
15 years 3 months ago
2004
Based on a recent development in the area of error control coding, we introduce the notion of convolutional factor graphs (CFGs) as a new class of probabilistic graphical models. ...
127
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ECCV
2006
Springer
16 years 3 months ago
2006
Springer
Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...