Particle Filter and Unscented Particle Filter Algorithms
Implementation code for particle filter and unscented particle filter algorithms, including detailed Gaussian mixture model parameter configuration
EM Algorithm: A Fundamental Approach in Machine Learning
The EM algorithm is a widely used technique in machine learning. This implementation demonstrates its most basic form applied to Gaussian Mixture Models, featuring clear code structure with separate E-step and M-step functions for educational purposes.
Gaussian Mixture Model for Clustering
A ready-to-use Gaussian Mixture Model implementation for clustering tasks, featuring 3 practical examples with code-based demonstrations
Speaker Recognition Implementation Using Gaussian Mixture Models
Source code for speaker recognition utilizing Gaussian Mixture Models with excellent recognition performance, featuring feature extraction and model training implementation
Powerful Statistical Pattern Recognition Toolbox
A comprehensive statistical pattern recognition toolbox featuring Gaussian classifier, Gaussian mixture models (GMM), principal component analysis (PCA), support vector machines (SVM), and other common classification algorithms with detailed implementation guidance.
MATLAB Implementation of Gaussian Mixture Model with Source Code
Complete source code for Gaussian Mixture Model (GMM) implemented in MATLAB environment. GMM is widely applied in various fields, particularly in signal processing applications. This implementation serves as an excellent reference for beginners, demonstrating core algorithms including Expectation-Maximization (EM) for parameter estimation and probability density function calculations using multivariate Gaussian distributions.
Gaussian Mixture Model with MDL-Based Cluster Determination
A MATLAB-implemented Gaussian Mixture Model for clustering analysis, featuring automatic optimal cluster number selection using Minimum Description Length (MDL) criterion, complete with experimental datasets from international research papers.
Image Segmentation Using Gaussian Mixture Model and Markov Tree Algorithm
Image segmentation based on Gaussian Mixture Model and Markov Tree algorithm implementation approaches and technical advantages.
Building Gaussian Mixture Models (GMM) for Computer Vision Applications
This implementation constructs a Gaussian Mixture Model (GMM) designed for computer vision applications including video object detection, video surveillance, motion detection, moving object detection, and video object tracking. The code features parameter optimization and expectation-maximization algorithm implementation for robust multi-modal data modeling.
Gaussian Mixture Model Parameter Initialization
Gaussian mixture model parameter initialization procedure utilizing KMEANS algorithm for pre-modeling initialization with clustering-based parameter estimation