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» A Parallel Algorithm for Gene Expressing Data Biclustering
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115
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JMLR
2008
209views more  JMLR 2008»
15 years 12 days ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger
91
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AAAI
2008
15 years 2 months ago
Generating Application-Specific Benchmark Models for Complex Systems
Automated generators for synthetic models and data can play a crucial role in designing new algorithms/modelframeworks, given the sparsity of benchmark models for empirical analys...
Jun Wang, Gregory M. Provan
105
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CORR
2007
Springer
128views Education» more  CORR 2007»
15 years 12 days ago
Model Selection Through Sparse Maximum Likelihood Estimation
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to...
Onureena Banerjee, Laurent El Ghaoui, Alexandre d'...
BMCBI
2010
118views more  BMCBI 2010»
15 years 16 days ago
Identifying differentially regulated subnetworks from phosphoproteomic data
Background: Various high throughput methods are available for detecting regulations at the level of transcription, translation or posttranslation (e.g. phosphorylation). Integrati...
Martin Klammer, Klaus Godl, Andreas Tebbe, Christo...
IJHPCA
2010
105views more  IJHPCA 2010»
14 years 11 months ago
A Pipelined Algorithm for Large, Irregular All-Gather Problems
We describe and evaluate a new, pipelined algorithm for large, irregular all-gather problems. In the irregular all-gather problem each process in a set of processes contributes in...
Jesper Larsson Träff, Andreas Ripke, Christia...