Explore MATLAB source code curated for "参数训练" with clean implementations, documentation, and examples.
This approach utilizes the Expectation-Maximization (EM) algorithm for parameter training in wavelet domain Hidden Markov Models (HMMs), demonstrating improved efficiency in training time compared to alternative methods. The implementation involves iterative estimation of latent state probabilities and optimization of model parameters through maximum likelihood estimation.
MATLAB implementation of the Expectation-Maximization (EM) algorithm, a classic method for parameter training in stochastic process models like Hidden Markov Models (HMMs), featuring code structure and key function explanations
Master function for HMT parameter training code implementation in MATLAB environment
gmmTrain: Parameter estimation and model fitting for Gaussian Mixture Models using EM algorithm implementation
MATLAB-based parameter training code implementation for Hidden Markov Tree (HMT) models with enhanced algorithm descriptions
gmmTrain: Parameter Training for Gaussian Mixture Model (GMM) using Expectation-Maximization Algorithm