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Ma and Grimson [4] used a single Gaussian to model the features and a mixture of Gaussians (MoG) to model feature positions.
Unlike the relativistic disk model, the features are formed in an accelerating wind at a relatively large distance from the central black hole.
Specifically, luminal and basal-A lines appear to best model the features of luminal-B and basal-like tumors, while basal-B lines might inform stem cell biology.
Next, we describe how we build the click-word model, the features used for characterizing the click-words, the learning algorithm and the evaluation measures.
It has been shown that Natural Cubic Smoothing Splines (NCSS) can model the features of lactation curves with greater flexibility than the traditional parametric methods.
In 1999, White and colleagues [ 2] proposed and demonstrated that Natural Cubic Smoothing Splines (NCSS) can model the features of lactation curves with greater flexibility than the traditional parametric methods.
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We consider a classifier based on Independent Component Analysis Mixture Modelling (ICAMM) to model the feature joint-probability density.
The model consists of the integrated platform system, information model, part model, geometric modelling and the feature model.
In this section, we modeled the features using Gaussian mixture model (GMM), which are widely used statistical classifier.
This model combines the features in Models 2 and 3 to form the most comprehensive model for capturing CVD burden.
The model considers the features and gain of an AC.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com