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Gaussian mixture models.
Figure 20 Performance of proposed features using Gaussian mixture models.
The mixture models a C5 raffinate cut from a refinery.
To date, few mixture models have been designed.
We trained three-state tied Gaussian Mixture Models (GMM).
Mixture models benefit from the smoothing property of ensemble methods.
In this paper, I present several related mixture models.
Figure 2 Mixture models in remotely sensed hyperspectral imaging.
Gaussian mixture models (GMMs) are trained for each chunk.
Moreover, mixture models are an interesting and flexible model family.
Bayesian mixture models are the most common algorithms for predictor design (see Bayesian mixture models and redundancy-capacity theorem for optimality analysis [20,23,28,31]).
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