Results 11 to 20 of about 901,615 (325)
Modeling and prediction of COVID-19 pandemic using Gaussian mixture model. [PDF]
Chaos Solitons Fractals, 2020 Highlights • Two contrasting models are presented to understand and predict the spreading of COVID-19.• Estimation of turnaround (peak active cases) day is performed using a mathematical model for India, Italy and USA.• Trend and variability are ...
Singhal A, Singh P, Lall B, Joshi SD.
europepmc +2 more sources
Variational Autoencoder With Optimizing Gaussian Mixture Model Priors
IEEE Access, 2020 The latent variable prior of the variational autoencoder (VAE) often utilizes a standard Gaussian distribution because of the convenience in calculation, but has an underfitting problem.
Chunsheng Guo +5 more
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A Novel Approach for Gaussian Mixture Model Clustering Based on Soft Computing Method
IEEE Access, 2021 Determining the number of clusters in a data set is a significant and difficult problem in cluster analysis. In this study, a new model-based clustering approach is proposed for the estimation of the number of clusters. In the proposed method, the number
Maruf Gogebakan
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Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model [PDF]
Frontiers in Neuroscience, 2022 In this paper, a method for medical image registration based on the bounded generalized Gaussian mixture model is proposed. The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images. The mixture model
Jingkun Wang +6 more
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A novel trajectory learning method for robotic arms based on Gaussian Mixture Model and k-value selection algorithm. [PDF]
PLoS ONEIn the field of robotic arm trajectory imitation learning, Gaussian Mixture Models are widely used for their ability to capture the characteristics of complex trajectories. However, one major challenge in utilizing these models lies in the initialization
Jingnan Yan +4 more
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Earthquake Phase Association Using a Bayesian Gaussian Mixture Model [PDF]
Journal of Geophysical Research: Solid Earth, 2021 Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual sesimic events and play an important role in earthquake monitoring and research.
Weiqiang Zhu +4 more
semanticscholar +1 more source
EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering [PDF]
Applied Soft Computing, 2020 The Gaussian mixture model (GMM) provides a convenient yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new model-based clustering algorithm, called EGMM (evidential GMM), in the ...
Lianmeng Jiao +3 more
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Optimality of Spectral Clustering for Gaussian Mixture Model [PDF]
Annals of Statistics, 2019 Spectral clustering is one of the most popular algorithms to group high dimensional data. It is easy to implement and computationally efficient. Despite its popularity and successful applications, its theoretical properties have not been fully understood.
Matthias Löffler +2 more
semanticscholar +1 more source
Annual Meeting of the Association for Computational Linguistics, 2020 User intent classification plays a vital role in dialogue systems. Since user intent may frequently change over time in many realistic scenarios, unknown (new) intent detection has become an essential problem, where the study has just begun.
Guangfeng Yan +6 more
semanticscholar +1 more source
Gaussian mixture model for extreme wind turbulence estimation [PDF]
Wind Energy Science, 2022 Uncertainty quantification is necessary in wind turbine design due to the random nature of the environmental inputs, through which the uncertainty of structural loads and response under specific situations can be quantified. Specifically, wind turbulence
X. Zhang, A. Natarajan
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