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1. Mixture Model Averaging for Clustering

Wei, Yuhong,

Model-based clustering is based on a finite mixture of distributions, where each mixture component corresponds to a different group, cluster, subpopulation, or part thereof. Gaussian mixture distribut...

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http://hdl.handle.net/10214/3538

2. Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and KMeans

Rajkumar G.V.S. Srinivasa Rao K. Srinivasa Rao P.,

A new Image Segmentation method based on Finite Doubly Truncated Bivariate Gaussian Mixture Model is proposed in this paper. The Truncated Bivariate Gaussian Distribution includes several of the skewe...

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http://www.ijcaonline.org/volume25/number4/pxc3874087.pdf

3. A new approach to calculate and forecast dynamic conditional correlation - the use of a multivariate heteroskedastic mixture model

Lu, Cheng,

Much research in finance has been directed towards forecasting time varying volatility of unidimensional macroeconomic variables such as stock index, exchange rate and interest rate. However, comparat...

complete description

http://eprints.soton.ac.uk/172585/1/Final_PhD_thesis%2DCheng_Lu%2DJan2011.pdf

4. Data assimilation with Gaussian mixture models using the dynamically orthogonal field equations

Sondergaard, Thomas, S.M. Massachusetts Institute of Technology,

Data assimilation, as presented in this thesis, is the statistical merging of sparse observational data with computational models so as to optimally improve the probabilistic description of the field ...

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http://hdl.handle.net/1721.1/68954

5. Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox

Heyns, T, Heyns, PS, De Villiers, JP,

This paper investigates how Gaussian mixture models (GMMs) may be used to detect and trend fault induced vibration signal irregularities, such as those which might be indicative of the onset of gear d...

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http://www.sciencedirect.com/science/article/pii/S0888327012002221

6. Model-Based clustering of microarray expression data via latent Gaussian mixture models

McNicholas, Paul D., Murphy, Thomas Brendan,

In recent years, work has been carried out on clustering gene expression microarray data. Some approaches are developed from an algorithmic viewpoint whereas others are developed via the application o...

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http://hdl.handle.net/10197/2836

7. Wavelet-Based Denoising Using Hidden Markov Models

Borran, Mohammad Jaber, Nowak, Robert David,

Hidden Markov models have been used in a wide variety of wavelet-based statistical signal processing applications. Typically, Gaussian mixture distributions are used to model the wavelet coefficients ...

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http://hdl.handle.net/1911/19741

8. A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses

Huang, Y., Englehart, K. B., Hudgins, B. S., Chan, A. D. C.,

This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus of this work is to optimize the co...

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http://hdl.handle.net/1882/24342

9. Performance Evaluation on the Effect of Combining DCT and LBP on Face Recognition System

Dasari Haritha, Kraleti Srinivasa Rao, Chittipotula Satyanarayana,

In this paper, we introduce a face recognition algorithm based on doubly truncated multivariate Gaussian mixture model with Discrete Cosine Transform (DCT) and Local binary pattern (LBP). Here, the in...

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http://www.mecs-press.org/ijmecs/ijmecs-v4-n11/v4n11-2.html

10. Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model and Hierarchical Clustering Algorithm

V Sailaja K. Srinivasa Rao K.V.V.S. Reddy,

In this paper we propose a Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model with Hierarchical Clustering. Each speaker speech spectra are characteriz...

complete description

http://www.ijcaonline.org/volume11/number11/pxc3872187.pdf

1. Mixture Model Averaging for Clustering

2. Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and KMeans

3. A new approach to calculate and forecast dynamic conditional correlation - the use of a multivariate heteroskedastic mixture model

4. Data assimilation with Gaussian mixture models using the dynamically orthogonal field equations

5. Combining synchronous averaging with a Gaussian mixture model novelty detection scheme for vibration-based condition monitoring of a gearbox

6. Model-Based clustering of microarray expression data via latent Gaussian mixture models

7. Wavelet-Based Denoising Using Hidden Markov Models

8. A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses

9. Performance Evaluation on the Effect of Combining DCT and LBP on Face Recognition System

10. Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model and Hierarchical Clustering Algorithm

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