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ML
2006
ACM
142views Machine Learning» more  ML 2006»
15 years 2 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....
TEC
2010
120views more  TEC 2010»
14 years 9 months ago
Benefits of a Population: Five Mechanisms That Advantage Population-Based Algorithms
This paper identifies five distinct mechanisms by which a population-based algorithm might have an advantage over a solo-search algorithm in classical optimization. These mechanism...
Adam Prügel-Bennett
139
Voted
BIBE
2006
IEEE
119views Bioinformatics» more  BIBE 2006»
15 years 8 months ago
Large Grain Size Stochastic Optimization Alignment
DNA sequence alignment is a critical step in identifying homology between organisms. The most widely used alignment program, ClustalW, is known to suffer from the local minima pro...
Perry Ridge, Hyrum Carroll, Dan Sneddon, Mark J. C...
150
Voted
W2GIS
2004
Springer
15 years 8 months ago
Expansion-Based Algorithms for Finding Single Pair Shortest Path on Surface
Abstract. Finding single pair shortest paths on surface is a fundamental problem in various domains, like Geographic Information Systems (GIS) 3D applications, robotic path plannin...
Ke Deng, Xiaofang Zhou
ECAI
2008
Springer
15 years 4 months ago
Heuristics for Dynamically Adapting Propagation
Building adaptive constraint solvers is a major challenge in constraint programming. An important line of research towards this goal is concerned with ways to dynamically adapt th...
Kostas Stergiou