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References in periodicals archive ?
In this experiment, we evaluate our method by cooperating with Gaussian mixture models (GMMs), which is a standard model for image representation, for image recognition.
Improved adaptive Gaussian mixture model for back-ground subtraction," in Proc.
Keywords: Clustering, Expected Maximization, Gaussian Mixture Model, Maximum Likelihood
The multivariate gaussian mixture model for abnormality-enhanced visualization was proposed by Grim et al.
We propose a regression approach based on Gaussian Mixture Models for building energy prediction and uncertainty quantification that has the following advantages (1) response surface modeling is integrated with local uncertainty quantification, therefore a secondary process of localized or global confidence estimation does not need to be performed (2) impact of correlated regressors is low in this approach (3) sensitivity to sparse data density is low, and (4) there is a principled approach to model structure selection as opposed to being based purely on domain knowledge.
The benefit of the achieved relevant zone maps is tested in an acoustic-to-articulatory regression system based on Gaussian mixture models (GMMs).
A Gaussian mixture model (GMM) is a weighted combination of Gaussian probability density functions.
Keywords: Wireless sensor networks, localization algorithms, non-localizable problem, Gaussian mixture model, collaborative and predictive schemes
1], Hidden Markov model (HMM) [2], Gaussian mixture model (GMM) [3], Harmonic Noise Model (HNM) [4].
At that time only the Gaussian mixture model was replaced with a deep neural network (DNN), keeping everything else the same.
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