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» Algorithmic Theories of Everything
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117
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ICDM
2003
IEEE
134views Data Mining» more  ICDM 2003»
15 years 6 months ago
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conve...
Bianca Zadrozny, John Langford, Naoki Abe
83
Voted
GECCO
2006
Springer
137views Optimization» more  GECCO 2006»
15 years 4 months ago
Inside a predator-prey model for multi-objective optimization: a second study
In this article, new variation operators for evolutionary multiobjective algorithms (EMOA) are proposed. On the basis of a predator-prey model theoretical considerations as well a...
Christian Grimme, Karlheinz Schmitt
80
Voted
GECCO
2008
Springer
118views Optimization» more  GECCO 2008»
15 years 1 months ago
Theoretical analysis of diversity mechanisms for global exploration
Maintaining diversity is important for the performance of evolutionary algorithms. Diversity mechanisms can enhance global exploration of the search space and enable crossover to ...
Tobias Friedrich, Pietro Simone Oliveto, Dirk Sudh...
94
Voted
COMGEO
1999
ACM
15 years 12 days ago
Optimal triangulation and quadric-based surface simplification
Many algorithms for reducing the number of triangles in a surface model have been proposed, but to date there has been little theoretical analysis of the approximations they produ...
Paul S. Heckbert, Michael Garland
CORR
2012
Springer
183views Education» more  CORR 2012»
13 years 8 months ago
Learning Determinantal Point Processes
Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among...
Alex Kulesza, Ben Taskar