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115
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MICCAI
2010
Springer
14 years 11 months ago
Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction
Abstract. We apply sparse Bayesian learning methods, automatic relevance determination (ARD) and predictive ARD (PARD), to Alzheimer’s disease (AD) classification to make accura...
Li Shen, Yuan Qi, Sungeun Kim, Kwangsik Nho, Jing ...
TIP
2011
231views more  TIP 2011»
14 years 7 months ago
Variational Bayesian Super Resolution
—In this paper, we address the super resolution (SR) problemfromasetofdegradedlowresolution(LR)imagestoobtain a high resolution (HR) image. Accurate estimation of the sub-pixel m...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
73
Voted
CSDA
2010
122views more  CSDA 2010»
15 years 21 days ago
A comparison of design and model selection methods for supersaturated experiments
Various design and model selection methods are available for supersaturated designs having more factors than runs but little research is available on their comparison and evaluati...
Christopher J. Marley, David C. Woods
CL
2000
Springer
15 years 5 months ago
Logic, Knowledge Representation, and Bayesian Decision Theory
In this paper I give a brief overview of recent work on uncertainty inAI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks...
David Poole
103
Voted
CVPR
1997
IEEE
15 years 4 months ago
Empirical Bayesian EM-based Motion Segmentation
A recent trend in motion-based segmentation has been to rely on statistical procedures derived from ExpectationMaximization (EM) principles. EM-based approaches have various attra...
Nuno Vasconcelos, Andrew Lippman