Results 271 to 280 of about 942,483 (302)

Fuzzy Gaussian Mixture Models

Pattern Recognition, 2012
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Zhaojie Ju, Honghai Liu 0001
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Gaussian Mixture Descriptors Learner

Knowledge-Based Systems, 2020
Abstract In recent decades, various machine learning methods have been proposed to address classification problems. However, most of them do not support incremental (or online) learning and therefore are neither scalable nor robust to dynamic problems that change over time.
Breno L. Freitas, Renato Moraes Silva, Tiago A. Almeida 0001   +2 more
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Learning mixtures of Gaussians

40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039), 2003
Mixtures of Gaussians are among the most fundamental and widely used statistical models. Current techniques for learning such mixtures from data are local search heuristics with weak performance guarantees. We present the first provably correct algorithm for learning a mixture of Gaussians.
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Correlation of Gaussian Mixture Tracks

2018 21st International Conference on Information Fusion (FUSION), 2018
In this paper, methods are developed and evaluated for the correlation of Gaussian mixture tracks from two sensors. The hypothesis likelihoods for the case of a single target are given using the minimum mean square error and the maximum likelihood estimates of common origin between two Gaussian mixtures.
Terrence L. Ogle, Ben P. Davis, William Dale Blair, Peter K. Willett   +3 more
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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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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 ...
Rubén Heras Evangelio, Michael Pätzold, Thomas Sikora   +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 ...
Hyoungjoo Lee, Sungzoon Cho
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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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Gaussian mixture linear prediction

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
This work introduces an approach to linear predictive signal analysis utilizing a Gaussian mixture autoregressive model. By initializing different autoregressive states of the model to approximately correspond to the target signal and the expected type of undesired signal components, such as background noise, the iterative parameter estimation ...
Jouni Pohjalainen, Paavo Alku
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Box Gaussian Mixture Filter $ $

IEEE Transactions on Automatic Control, 2010
This note presents the box Gaussian mixture filter (BGMF), which is an efficient filter for the systems with mainly linear measurements but enables utilizing highly nonlinear measurements. BGMF contains a new way to approximate the prior distributions with a Gaussian mixture, whose components have small covariances.
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