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ICML
2009
IEEE
14 years 9 months ago
Learning structurally consistent undirected probabilistic graphical models
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...
Sushmita Roy, Terran Lane, Margaret Werner-Washbur...
GECCO
2009
Springer
159views Optimization» more  GECCO 2009»
14 years 1 months ago
Bayesian network structure learning using cooperative coevolution
We propose a cooperative-coevolution – Parisian trend – algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It i...
Olivier Barrière, Evelyne Lutton, Pierre-He...
AAAI
2007
13 years 11 months ago
Learning Graphical Model Structure Using L1-Regularization Paths
Sparsity-promoting L1-regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the ...
Mark W. Schmidt, Alexandru Niculescu-Mizil, Kevin ...
ICDCS
2005
IEEE
14 years 2 months ago
Using a Layered Markov Model for Distributed Web Ranking Computation
The link structure of the Web graph is used in algorithms such as Kleinberg’s HITS and Google’s PageRank to assign authoritative weights to Web pages and thus rank them. Both ...
Jie Wu, Karl Aberer
CSB
2004
IEEE
115views Bioinformatics» more  CSB 2004»
14 years 13 days ago
PoPS: A Computational Tool for Modeling and Predicting Protease Specificity
Proteases play a fundamental role in the control of intra- and extracellular processes by binding and cleaving specific amino acid sequences. Identifying these targets is extremel...
Sarah E. Boyd, Maria J. García de la Banda,...