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ML
2006
ACM
142views Machine Learning» more  ML 2006»
15 years 6 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....
SIGOPSE
1992
ACM
15 years 10 months ago
Names should mean what, not where
Abstract-- This paper describes the design and implementation1 of IRIS: an intentional resource indicator service. IRIS springs from the concept that end-users should not be bogged...
James O'Toole, David K. Gifford
AAAI
2008
15 years 9 months ago
Exploiting Causal Independence Using Weighted Model Counting
Previous studies have demonstrated that encoding a Bayesian network into a SAT-CNF formula and then performing weighted model counting using a backtracking search algorithm can be...
Wei Li 0002, Pascal Poupart, Peter van Beek
GECCO
2007
Springer
314views Optimization» more  GECCO 2007»
16 years 22 days ago
Variable selection for wind power prediction using particle swarm optimization
Wind energy has an increasing influence on the energy supply in many countries, but in contrast to conventional power plants it is a fluctuating energy source. For its integration...
René Jursa
PPNA
2011
14 years 9 months ago
Ad-hoc limited scale-free models for unstructured peer-to-peer networks
Several protocol efficiency metrics (e.g., scalability, search success rate, routing reachability and stability) depend on the capability of preserving structure even over the ch...
Durgesh Rani Kumari, Hasan Guclu, Murat Yuksel