Explore MATLAB source code curated for "多分类" with clean implementations, documentation, and examples.
SVM (Support Vector Machine) algorithms for binary classification and multiclass problems including One-vs-One and One-vs-Rest approaches with complete MATLAB training and testing code implementation
Sharing a visualized nonlinear SVM multi-classification source code that is practical and easy to learn, featuring kernel function implementation and decision boundary visualization, worthy of further development.
A robust SVM classification program capable of multi-class classification, directly executable on MATLAB platform with verified performance
SVM multiclass classifier implementation featuring multi-class classification capabilities enhanced with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for parameter optimization
This MATLAB program implements a comprehensive facial recognition pipeline that first processes face images using Gabor wavelet transformation for feature extraction, then applies PCA (Principal Component Analysis) for dimensionality reduction, and finally employs an SVM (Support Vector Machine) classifier for multi-class recognition. The implementation supports the complete ORL face database and utilizes the libsvm toolbox (version: libsvm-mat-2.89-3[FarutoUltimate3.0]). Key implementation details include Gabor filter parameter configuration, PCA eigenvector computation, and SVM kernel function selection for optimal multi-class classification performance.
This MATLAB implementation of Support Vector Machine (SVM) supports multi-class classification and features a user-friendly GUI interface for intuitive operation. The implementation includes comprehensive data input/output processing, utilizing MATLAB's built-in SVM functions and custom algorithms for efficient classification.
While SVM is typically applied to binary classification, this code utilizes Chih-Jen Lin's SVM toolkit for multi-class classification. The implementation is tested on the UCI IRIS dataset, demonstrating excellent performance through one-vs-one strategy and parameter optimization techniques.
Input training dataset with labels and testing dataset with labels, output the classification accuracy rate on the test set