Results 11 to 20 of about 297,382 (270)
Deep Gaussian Mixture Models [PDF]
Statistics and Computing, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed.
McLachlan, Geoffrey J., Viroli, Cinzia
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Model Selection for Gaussian Mixture Models [PDF]
Statistica Sinica, 2013 This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models.
Huang, Tao, Peng, Heng, Zhang, Kun
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Gaussian Mixture Models Algorithm Based on Density Peaks Clustering [PDF]
Jisuanji kexue, 2021 Due to the existence of a large number of sample data which obey the Gaussian distribution,GMM (Gaussian mixture models) is used to cluster these sample data and get more accurate clustering results.In general,EM algorithm(expectation maxi-mization ...
WANG Wei-dong, XU Jin-hui, ZHANG Zhi-feng, YANG Xi-bei
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Modeling Multivariate Spray Characteristics with Gaussian Mixture Models
Energies, 2023 With the increasing demand for efficient and accurate numerical simulations of spray combustion in jet engines, the necessity for robust models to enhance the capabilities of spray models has become imperative.
Markus Wicker +5 more
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Processing tree point clouds using Gaussian Mixture Models [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 While traditionally used for surveying and photogrammetric fields, laser scanning is increasingly being used for a wider range of more general applications.
D. Belton, S. Moncrieff, J. Chapman
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Mathematics, 2023 The mixture of experts (ME) model is effective for multimodal data in statistics and machine learning. To treat non-stationary probabilistic regression, the mixture of Gaussian processes (MGP) model has been proposed, but it may not perform well in some ...
Yurong Xie, Di Wu, Zhe Qiang
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Quantum-like Gaussian mixture model [PDF]
Soft Computing, 2021 Abstract A new concept of a quantum-like mixture model is introduced. It describes the mixture distribution with the assumption that a point is generated by each Gaussian at the same time. The decision boundary of a quantum-like mixture Gaussian corresponds as well to the separation of probabilities for the switching Kalman filter. The quantum-
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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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Anchored Bayesian Gaussian mixture models
Electronic Journal of Statistics, 2020 Finite mixtures are a flexible modeling tool for irregularly shaped densities and samples from heterogeneous populations. When modeling with mixtures using an exchangeable prior on the component features, the component labels are arbitrary and are indistinguishable in posterior analysis.
Kunkel, Deborah, Peruggia, Mario
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Gaussian mixture model‐based contrast enhancement [PDF]
IET Image Processing, 2015 In this study, a method for enhancing low‐contrast images is proposed. This method, called Gaussian mixture model‐based contrast enhancement (GMMCE), brings into play the Gaussian mixture modelling of histograms to model the content of the images. On the basis of the fact that each homogeneous area in natural images has a Gaussian‐shaped histogram, it ...
Abdoli, Mohsen +3 more
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