MATLAB Implementation of SVM Classification with Multi-Class Support

This powerful SVM classification program offers multi-class classification capabilities and can be executed directly on the MATLAB platform. The implementation has been thoroughly tested, demonstrating reliable performance and effectiveness. The code features a user-friendly interface with straightforward operational steps, making it highly accessible for users at all levels. The program utilizes MATLAB's built-in functions and custom algorithms to handle SVM classification tasks, including feature scaling, kernel function selection (linear, polynomial, or RBF), and cross-validation for parameter optimization. For multi-class classification, it implements either the one-vs-one or one-vs-all strategy through efficient coding structures. Regardless of whether you're a beginner or an experienced user, you can easily run this program to obtain accurate and reliable classification results. The code includes clear commenting and modular design, allowing for straightforward customization of parameters and algorithmic approaches. This SVM classification program serves as a valuable tool for both academic research and practical applications, providing effective solutions for various classification problems including pattern recognition, data mining, and machine learning tasks. Key implementation features include: - Automated data preprocessing and normalization routines - Flexible kernel parameter configuration - Comprehensive result visualization capabilities - Detailed performance metrics calculation (accuracy, precision, recall) - Support for different data formats and dataset sizes