Results 281 to 290 of about 942,483 (302)

Learning mixtures of arbitrary gaussians

Proceedings of the thirty-third annual ACM symposium on Theory of computing, 2001
Mixtures of gaussian (or normal) distributions arise in a variety of application areas. Many techniques have been proposed for the task of finding the component gaussians given samples from the mixture, such as the EM algorithm, a local-search heuristic from Dempster, Laird and Rubin~(1977).
Sanjeev Arora, Ravi Kannan
openaire   +2 more sources

Modelling profiles with a mixture of Gaussians

Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), 2002
Point distribution models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a principle component analysis to the data, to reduce the dimensionality of the representation. However, a single multivariate Gaussian model of the probability density, estimated from the principle covariances, can be
James Orwell, Darrel Greenhill, Jonathan D. Rymel, Graeme A. Jones   +3 more
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Density Boosting for Gaussian Mixtures

2004
Ensemble method is one of the most important recent developments in supervised learning domain. Performance advantage has been demonstrated on problems from a wide variety of applications. By contrast, efforts to apply ensemble method to unsupervised domain have been relatively limited.
Xubo B. Song, Kun Yang, Misha Pavel
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The Infinite Gaussian Mixture Model.

2000
In a Bayesian mixture model it is not necessary a priori to limit the number of components to be finite. In this paper an infinite Gaussian mixture model is presented which neatly sidesteps the difficult problem of finding the ``right'' number of mixture components.
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Probability density function for wave elevation based on Gaussian mixture models

Ocean Engineering, 2020
Zhe Gao, Shuxiu Liang
exaly  

A Novel Approach for Gaussian Mixture Model Clustering Based on Soft Computing Method

IEEE Access, 2021
Maruf Gogebakan
exaly  

Bayesian estimation of finite mixtures of Gaussian mixtures

1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
BILANCIA, Massimo, POLLICE, Alessio
openaire   +1 more source

Gaussian mixture density modeling, decomposition, and applications

IEEE Transactions on Image Processing, 1996
Kannappan Palaniappan, Xinhua Zhuang
exaly  

Bounded generalized Gaussian mixture model

Pattern Recognition, 2014
Q M Jonathan Wu, Thanh Nguyen
exaly  

Gaussian process modelling with Gaussian mixture likelihood

Journal of Process Control, 2019
Biao Huang, Hariprasad Kodamana, Atefeh Daemi   +2 more
exaly