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» A comparison of empirical and model-driven optimization
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WWW
2010
ACM
14 years 2 months ago
Empirical comparison of algorithms for network community detection
Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses...
Jure Leskovec, Kevin J. Lang, Michael W. Mahoney
GECCO
2005
Springer
156views Optimization» more  GECCO 2005»
14 years 29 days ago
Parameterized versus generative representations in structural design: an empirical comparison
Any computational approach to design, including the use of evolutionary algorithms, requires the transformation of the domain-specific knowledge into a formal design representatio...
Rafal Kicinger, Tomasz Arciszewski, Kenneth A. De ...
ENTCS
2008
87views more  ENTCS 2008»
13 years 7 months ago
A Lightweight Approach for the Semantic Validation of Model Refinements
Model Driven Engineering proposes the use of models at different levels of ion. Step by step validation of model refinements is necessary to guarantee the correctness of the final ...
Claudia Pons, Diego García
JMLR
2012
11 years 10 months ago
Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets
We propose a new method for comparing learning algorithms on multiple tasks which is based on a novel non-parametric test that we call the Poisson binomial test. The key aspect of...
Alexandre Lacoste, François Laviolette, Mar...
WSC
1998
13 years 8 months ago
An Analytical Comparison of Optimization Problem Generation Methodologies
Heuristics are an increasingly popular solution method for combinatorial optimization problems. Heuristic use often frees the modeler from some of the restrictions placed on class...
Raymond R. Hill