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» Embedding Heterogeneous Data Using Statistical Models
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ICTAC
2010
Springer
14 years 11 months ago
Formal Modelling of Separation Kernel Components
Abstract. Separation kernels are key components in embedded applications. Their small size and widespread use in high-integrity environments make them good targets for formal model...
Andrius Velykis, Leo Freitas
101
Voted
NIPS
2003
15 years 2 months ago
Ambiguous Model Learning Made Unambiguous with 1/f Priors
What happens to the optimal interpretation of noisy data when there exists more than one equally plausible interpretation of the data? In a Bayesian model-learning framework the a...
Gurinder S. Atwal, William Bialek
CIVR
2004
Springer
117views Image Analysis» more  CIVR 2004»
15 years 6 months ago
Using Maximum Entropy for Automatic Image Annotation
In this paper, we propose the use of the Maximum Entropy approach for the task of automatic image annotation. Given labeled training data, Maximum Entropy is a statistical techniqu...
Jiwoon Jeon, R. Manmatha
CVPR
2005
IEEE
16 years 2 months ago
A Bayesian Approach to Unsupervised Feature Selection and Density Estimation Using Expectation Propagation
We propose an approximate Bayesian approach for unsupervised feature selection and density estimation, where the importance of the features for clustering is used as the measure f...
Shaorong Chang, Nilanjan Dasgupta, Lawrence Carin
101
Voted
BIBM
2007
IEEE
159views Bioinformatics» more  BIBM 2007»
15 years 7 months ago
Predicting Future High-Cost Patients: A Real-World Risk Modeling Application
Health care data from patients in the Arizona Health Care Cost Containment System, Arizona’s Medicaid program, provides a unique opportunity to exploit state-of-the-art data pro...
Sai T. Moturu, William G. Johnson, Huan Liu