Data Mining MATLAB Source Code Collection
A comprehensive MATLAB source code repository containing classic machine learning algorithms including ID3, C4.5, Neural Networks, CARD, and EM algorithms for data mining applications.
Isomap Isometric Mapping and Dimensionality Reduction
Isomap Isometric Mapping, Feature Extraction Techniques, and Machine Learning Applications
Latest Machine Learning Toolbox for MATLAB
The newest MATLAB machine learning toolbox featuring multiple learning algorithms including k-means clustering, AdaBoost, and Support Vector Machines (SVM) with code implementation examples
Multi-Frame Image Super-Resolution Reconstruction
Multi-frame image super-resolution reconstruction achieved through machine learning, demonstrating excellent performance with robust algorithm implementation.
Machine Learning Source Code Based on Information Fusion
Gentleboost is a machine learning algorithm source code based on information fusion methodology, which has been successfully implemented across various engineering information domains including image processing and pattern recognition systems.
ID3 Decision Tree and Random Forest Algorithm for Decision Forest Generation
Implementation of ID3 decision tree combined with random forest algorithm to generate decision forests using voting mechanism for decision-making; includes training dataset 'aaa' and testing dataset 'bbb'; highly suitable for machine learning beginners with clear code structure and algorithm explanations
Exercise Solutions for Andrew Ng's Machine Learning Course with Code Implementation Examples
Detailed solutions to the programming exercises from Andrew Ng's Machine Learning course, featuring comprehensive code explanations and algorithm implementations that are invaluable for machine learning beginners.
Bayesian Machine Learning for Mutual Information
Bayesian Machine Learning for mutual information calculation, entropy and joint entropy computation. Enhanced version applicable to stochastic inversion with improved uncertainty quantification.
Expectation-Maximization (EM) Algorithm for Probabilistic Models
In statistical computing, the Expectation-Maximization (EM) algorithm is an iterative method for finding maximum likelihood (MLE) or maximum a posteriori (MAP) estimates of parameters in probabilistic models that depend on unobserved latent variables. The EM algorithm is widely used in machine learning and computer vision for data clustering applications. The algorithm alternates between an expectation step (E-step), which computes the expected value of the latent variables given current parameters, and a maximization step (M-step), which updates parameters to maximize the expected log-likelihood.
Nonlinear Dimensionality Reduction Methods
Nonlinear dimensionality reduction techniques can be applied to machine learning tasks involving high-dimensional data analysis and visualization