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» Model Selection and Error Estimation
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121
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ICML
2006
IEEE
16 years 1 months ago
Convex optimization techniques for fitting sparse Gaussian graphical models
We consider the problem of fitting a large-scale covariance matrix to multivariate Gaussian data in such a way that the inverse is sparse, thus providing model selection. Beginnin...
Onureena Banerjee, Laurent El Ghaoui, Alexandre d'...
123
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IPPS
2003
IEEE
15 years 6 months ago
System-Level Modeling of Dynamically Reconfigurable Hardware with SystemC
To cope with the increasing demand for higher computational power and flexibility, dynamically reconfigurable blocks become an important part inside a system-on-chip. Several meth...
Antti Pelkonen, Kostas Masselos, Miroslav Cup&aacu...
76
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TIP
2008
139views more  TIP 2008»
15 years 1 months ago
Model-Based 2.5-D Deconvolution for Extended Depth of Field in Brightfield Microscopy
Abstract--Due to the limited depth of field of brightfield microscopes, it is usually impossible to image thick specimens entirely in focus. By optically sectioning the specimen, t...
François Aguet, Dimitri Van De Ville, Micha...
104
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TASLP
2010
118views more  TASLP 2010»
14 years 11 months ago
Time-Frequency Sparsity by Removing Perceptually Irrelevant Components Using a Simple Model of Simultaneous Masking
Abstract—We present an algorithm for removing timefrequency components, found by a standard Gabor transform, of a “real-world” sound while causing no audible difference to th...
Péter Balázs, Bernhard Laback, Gerha...
111
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GECCO
2006
Springer
161views Optimization» more  GECCO 2006»
15 years 4 months ago
The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems
1 Learnable Evolution Model (LEM) is a form of non-Darwinian evolutionary computation that employs machine learning to guide evolutionary processes. Its main novelty are new type o...
Janusz Wojtusiak, Ryszard S. Michalski