Explore MATLAB source code curated for "高斯混合模型" with clean implementations, documentation, and examples.
This MATLAB implementation solves parameter estimation for Gaussian Mixture Models using the Expectation-Maximization (EM) algorithm. The program modularly separates mean, covariance, and weight estimation into independent functions saved as .M files. The main execution point is through main.m, with sample data provided in spreadsheet format for immediate testing and customization.
MATLAB implementation of a classic image processing approach combining Gaussian Mixture Model background modeling and skin color detection algorithms for facial detection applications
MATLAB source code for Gaussian Mixture Model (GMM) implementation featuring Expectation-Maximization algorithm for clustering and density estimation tasks.
A program for calculating prior and posterior probabilities in Gaussian Mixture Models utilizing large matrix operations to significantly boost computational efficiency.
Implementation of preprocessing and classification training for Gaussian Mixture Models using MATLAB, featuring complete pipeline development with practical code examples.
MATLAB-based implementation of the Expectation-Maximization algorithm for estimating means, variances, and weights in Gaussian Mixture Models, featuring iterative optimization and parameter estimation capabilities.
MATLAB implementation for data clustering analysis using Gaussian Mixture Models, featuring the Statistics and Machine Learning Toolbox with comprehensive code examples
This paper presents an effective implementation of Gaussian Mixture Models (GMM), a classic speaker recognition algorithm, using the Expectation-Maximization (EM) algorithm. The study primarily simulates GMM's noise robustness performance under various acoustic environments, yielding valuable insights for practical applications. Key implementation aspects include parameter initialization strategies and convergence criteria for the EM iteration process.
Expectation-Maximization Algorithm for Maximum Likelihood Estimation of Gaussian Mixture Models
MATLAB-based implementation of Gaussian Mixture Model algorithm for advanced image processing applications including image enhancement, edge detection, and segmentation