Description: EM算法，基于期望最大化原则进行密度估计-EM algorithm, based on the expectation maximization of the principle of density estimation Platform: |
Size: 3072 |
Author:丁宏锴 |
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Description: 这篇文章介绍了EM算法，并且提出了一种加速算法，很不错-This article introduced the EM algorithm, and a speed up the algorithm, very good Platform: |
Size: 1340416 |
Author:guoguo |
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Description: 最近在做毕设，是有关高斯混合模型的算法，主要采用EM算法，这片硕士论文在这方面介绍的比较详细，可以去下载研究下。-Recently completed the set up to do, is the Gaussian mixture model algorithm, the main use of EM algorithm, this Master Platform: |
Size: 1801216 |
Author: |
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Description: fit_mix_2D_gaussian - fit parameters for a 2D mixed-gaussian distribution using EM algorithm
format: [u,covar,t,iter] = fit_mix_2D_gaussian( X,M )
input: X - input samples, Nx2 vector
M - number of gaussians which are assumed to compose the distribution
output: u - fitted mean for each gaussian (each mean is a 2x1 vector)
covar - fitted covariance for each gaussian. this is a 2x2xM matrix.
t - probability of each gaussian in the complete distribution
iter - number of iterations done by the function-fit_mix_2D_gaussian - fit parameters for a 2D mixed-gaussian distribution using EM algorithm
format: [u,covar,t,iter] = fit_mix_2D_gaussian( X,M )
input: X - input samples, Nx2 vector
M - number of gaussians which are assumed to compose the distribution
output: u - fitted mean for each gaussian (each mean is a 2x1 vector)
covar - fitted covariance for each gaussian. this is a 2x2xM matrix.
t - probability of each gaussian in the complete distribution
iter - number of iterations done by the function Platform: |
Size: 2048 |
Author:resident e |
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Description: fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm
format: [u,sig,t,iter] = fit_mix_gaussian( X,M )
input: X - input samples, Nx1 vector
M - number of gaussians which are assumed to compose the distribution
output: u - fitted mean for each gaussian
sig - fitted standard deviation for each gaussian
t - probability of each gaussian in the complete distribution
iter- number of iterations done by the function- fit_mix_gaussian - fit parameters for a mixed-gaussian distribution using EM algorithm
format: [u,sig,t,iter] = fit_mix_gaussian( X,M )
input: X - input samples, Nx1 vector
M - number of gaussians which are assumed to compose the distribution
output: u - fitted mean for each gaussian
sig - fitted standard deviation for each gaussian
t - probability of each gaussian in the complete distribution
iter- number of iterations done by the function Platform: |
Size: 1024 |
Author:resident e |
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Search - EM algorithm for t-distribution