Results 231 to 240 of about 297,382 (270)

Parsimonious Gaussian mixture models

Statistics and Computing, 2008
Parsimonious Gaussian mixture models are developed using a latent Gaussian model which is closely related to the factor analysis model. These models provide a unified modeling framework which includes the mixtures of probabilistic principal component analyzers and mixtures of factor of analyzers models as special cases.
Paul David McNicholas, Thomas Brendan Murphy   +1 more
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Gaussian Mixture Models

2014
In this chapter we first introduce the basic concepts of random variables and the associated distributions. These concepts are then applied to Gaussian random variables and mixture-of-Gaussian random variables. Both scalar and vector-valued cases are discussed and the probability density functions for these random variables are given with their ...
Dong Yu, Li Deng
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Hierarchical Gaussian mixture model

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
Gaussian mixture models (GMMs) are a convenient and essential tool for the estimation of probability density functions. Although GMMs are used in many research domains from image processing to machine learning, this statistical mixture modeling is usually com- plex and further needs to be simplified.
Vincent Garcia, Frank Nielsen, Richard Nock   +2 more
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Edgeworth-Expanded Gaussian Mixture Density Modeling

Neural Computation, 2005
Instead of increasing the order of the Edgeworth expansion of a single gaussian kernel, we suggest using mixtures of Edgeworth-expanded gaussian kernels of moderate order. We introduce a simple closed-form solution for estimating the kernel parameters based on weighted moment matching.
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Gaussian process modelling with Gaussian mixture likelihood

Journal of Process Control, 2019
Abstract Gaussian Process (GP), as a probabilistic non-linear multi-variable regression model, has been widely used in nonparametric Bayesian framework for the data based modelling of complex processes. The noise dynamics in standard GP regression is assumed to follow a Gaussian distribution.
Atefeh Daemi, Hariprasad Kodamana, Biao Huang   +2 more
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Combining Gaussian Mixture Models

2004
A Gaussian mixture model (GMM) estimates a probability density function using the expectation-maximization algorithm. However, it may lead to a poor performance or inconsistency. This paper analytically shows that performance of a GMM can be improved in terms of Kullback-Leibler divergence with a committee of GMMs with different initial parameters ...
Hyoung-joo Lee, Sungzoon Cho
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Gaussian Mixture Models

The slides introduce Gaussian Mixture Models (GMMs) and extend to mixtures of Bernoulli distributions. They begin with the formulation of GMMs as weighted sums of Gaussian components, describing latent variables, prior and conditional distributions, and posterior responsibilities.
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Gaussian Mixture Model Cluster Forest

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
Random Forest (RF) classification algorithm is widely used in the area of information retrieval and became a basis for some extended branches of classification and/or regression algorithms. Cluster Forest (CF) represents a particular branch, and brings usually better results than individual clustering algorithms.
Jan Janouek, Petr Gajdo, Michal Radecky, Vaclav Snael   +3 more
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Splitting Gaussians in Mixture Models

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, 2012
Gaussian mixture models have been extensively used and enhanced in the surveillance domain because of their ability to adaptively describe multimodal distributions in real-time with low memory requirements. Nevertheless, they still often suffer from the problem of converging to poor solutions if the main mode stretches and thus over-dominates weaker ...
Ruben Heras Evangelio, Michael Patzold, Thomas Sikora   +2 more
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Gaussian Process Regression for Materials and Molecules

Chemical Reviews, 2021
Volker L Deringer, Albert P Bartók, Noam Bernstein   +2 more
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