Explore MATLAB source code curated for "支持向量机" with clean implementations, documentation, and examples.
A multiclass classification experimental system for Support Vector Machines featuring a graphical operation interface. Fully implemented in MATLAB, this system provides diverse classification capabilities using algorithms like one-vs-one or one-vs-all approaches. This application serves as supplementary software for my graduation project.
A MATLAB program for Support Vector Machine implementation capable of performing both classification and regression tasks. This method demonstrates superior performance compared to neural networks while avoiding the curse of dimensionality, making it an excellent modeling approach for high-dimensional datasets.
Comprehensive programs and implementations for Support Vector Machine classification, regression, and fuzzy SVM approaches with practical code examples
Creating base Support Vector Machine classifiers through AdaBoost algorithm and integrating recognition results using simple voting method. Includes descriptions of SVM toolbox and AdaBoost algorithm workflow with code implementation insights.
Using Particle Swarm Optimization (PSO) to optimize Support Vector Machine (SVM) parameters -c and -g with implementation insights
LIBSVM Support Vector Machine for classification identification. This program has been successfully applied in iris recognition research. It is a C program embedded in MATLAB with implementation details for algorithm integration.
This project implements EEG classification with fuzzy C-means clustering and compares results against support vector machines using three distinct parameter optimization techniques.
Support Vector Machine automatically extracts fuzzy rules with reliability verified through simulation results, demonstrating robust pattern recognition capabilities through margin maximization and kernel functions.
Implementation of transformer fault prediction using Support Vector Machine, featuring data preprocessing, model training, and diagnostic classification with scikit-learn
A beginner-friendly support Vector Machine implementation with practical code examples and algorithm explanations