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» On the Optimality of the Dimensionality Reduction Method
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IJON
2007
114views more  IJON 2007»
15 years 6 months ago
Ridgelet kernel regression
In this paper, a ridgelet kernel regression model is proposed for approximation of high dimensional functions. It is based on ridgelet theory, kernel and regularization technology ...
Shuyuan Yang, Min Wang, Licheng Jiao
CEC
2008
IEEE
15 years 7 months ago
A technique for the visualization of population-based algorithms
— A technique for the visualization of stochastic population–based algorithms in multidimensional problems with known global minimizers is proposed. The technique employs proje...
Konstantinos E. Parsopoulos, Voula C. Georgopoulos...
FGCN
2008
IEEE
125views Communications» more  FGCN 2008»
16 years 24 days ago
Coordinating System Software for Power Savings
Power consumption is becoming a primary concern as a result of tremendous increasing in computer power usage. Innumerable methods and techniques have been exploited to address thi...
Lingxiang Xiang, Jiangwei Huang, Tianzhou Chen
GECCO
2008
Springer
131views Optimization» more  GECCO 2008»
15 years 7 months ago
Rigorous analyses of fitness-proportional selection for optimizing linear functions
Rigorous runtime analyses of evolutionary algorithms (EAs) mainly investigate algorithms that use elitist selection methods. Two algorithms commonly studied are Randomized Local S...
Edda Happ, Daniel Johannsen, Christian Klein, Fran...
APPROX
2005
Springer
111views Algorithms» more  APPROX 2005»
15 years 12 months ago
Sampling Bounds for Stochastic Optimization
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...
Moses Charikar, Chandra Chekuri, Martin Pál