MATLAB Code Implementation for Support Vector Machines

This documentation provides comprehensive MATLAB source code implementations for Support Vector Machines (SVM). The codebase includes core SVM algorithms designed for pattern recognition tasks and solving nonlinear classification problems through kernel methods. For beginners or developers seeking quick implementation insights, these codes offer practical starting points with clear structural organization. The implementation covers essential SVM components including: - Linear and nonlinear kernel functions (RBF, polynomial, sigmoid) - Optimization routines using quadratic programming solvers - Margin calculation and support vector identification algorithms - Multi-class classification extensions using one-vs-one or one-vs-all strategies Detailed comments and usage examples demonstrate proper parameter configuration, data preprocessing techniques, and result interpretation methods. The code architecture emphasizes modular design, separating kernel computations, optimization processes, and prediction modules for easy customization. Additional validation scripts illustrate performance evaluation metrics like accuracy calculation and confusion matrix generation, enabling users to systematically verify implementation correctness while deepening their understanding of SVM operational mechanisms.