Parsimonious model selection for tissue classification: a DTI study of zebrafish

2007 ◽  
Author(s):  
Raisa Z. Freidlin ◽  
Michal E. Komlosh ◽  
Murray H. Loew ◽  
Peter J. Basser
Keyword(s):  
Model Selection ◽  
Tissue Classification ◽  
Selection For ◽  
Parsimonious Model

Parsimonious Model Selection for Tissue Segmentation and Classification Applications: A Study Using Simulated and Experimental DTI Data

2007 ◽  
Vol 26 (11) ◽  
pp. 1576-1584 ◽  
Author(s):  
R.Z. Freidlin ◽  
E. Ozarslan ◽  
M.E. Komlosh ◽  
Lin-Ching Chang ◽  
Cheng Guan Koay ◽  
...  
Keyword(s):  
Model Selection ◽  
Tissue Segmentation ◽  
Selection For ◽  
Parsimonious Model

scholarly journals Optimization of Aerosol Model Selection for TROPOMI/S5P

2021 ◽  
Vol 13 (13) ◽  
pp. 2489
Author(s):  
Lanlan Rao ◽  
Jian Xu ◽  
Dmitry S. Efremenko ◽  
Diego G. Loyola ◽  
Adrian Doicu
Keyword(s):  
Model Selection ◽  
Insufficient Information ◽  
Satellite Measurements ◽  
Model Uncertainties ◽  
Bayesian Algorithm ◽  
Layer Height ◽  
Aerosol Model ◽  
Selection For ◽  
Temporal Inhomogeneity ◽  
Aerosol Properties

To retrieve aerosol properties from satellite measurements, micro-physical aerosol models have to be assumed. Due to the spatial and temporal inhomogeneity of aerosols, choosing an appropriate aerosol model is an important task. In this paper, we use a Bayesian algorithm that takes into account model uncertainties to retrieve the aerosol optical depth and layer height from synthetic and real TROPOMI O2A band measurements. The results show that in case of insufficient information for an appropriate micro-physical model selection, the Bayesian algorithm improves the accuracy of the solution.

Multiple order model migration and optimal model selection for online glucose prediction in Type 1 diabetes

AIChE Journal ◽  
2017 ◽  
Vol 64 (3) ◽  
pp. 822-834 ◽  
Author(s):  
Hong Zhao ◽  
Chunhui Zhao ◽  
Chengxia Yu ◽  
Eyal Dassau
Keyword(s):  
Type 1 Diabetes ◽  
Model Selection ◽  
Optimal Model ◽  
Order Model ◽  
Model Migration ◽  
Selection For ◽  
Glucose Prediction

Multi-class particle swarm model selection for automatic image annotation

2012 ◽  
Vol 39 (12) ◽  
pp. 11011-11021 ◽  
Author(s):  
Hugo Jair Escalante ◽  
Manuel Montes ◽  
L. Enrique Sucar
Keyword(s):  
Model Selection ◽  
Image Annotation ◽  
Particle Swarm ◽  
Automatic Image Annotation ◽  
Selection For
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