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TSP
2010
13 years 1 months ago
Variance-component based sparse signal reconstruction and model selection
We propose a variance-component probabilistic model for sparse signal reconstruction and model selection. The measurements follow an underdetermined linear model, where the unknown...
Kun Qiu, Aleksandar Dogandzic
PODS
2004
ACM
148views Database» more  PODS 2004»
14 years 6 months ago
Deterministic Wavelet Thresholding for Maximum-Error Metrics
Several studies have demonstrated the effectiveness of the wavelet decomposition as a tool for reducing large amounts of data down to compact wavelet synopses that can be used to ...
Minos N. Garofalakis, Amit Kumar
BMCBI
2007
215views more  BMCBI 2007»
13 years 6 months ago
Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregres
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
Rainer Opgen-Rhein, Korbinian Strimmer
WWW
2009
ACM
14 years 7 months ago
idMesh: graph-based disambiguation of linked data
We tackle the problem of disambiguating entities on the Web. We propose a user-driven scheme where graphs of entities ? represented by globally identifiable declarative artifacts ...
Hermann de Meer, Karl Aberer, Michael Jost, Parisa...
ISNN
2005
Springer
14 years 6 days ago
Post-nonlinear Blind Source Separation Using Neural Networks with Sandwiched Structure
Abstract. This paper proposes a novel algorithm based on informax for postnonlinear blind source separation. The demixing system culminates to a neural network with sandwiched stru...
Chunhou Zheng, Deshuang Huang, Zhan-Li Sun, Li Sha...