MATLAB Implementation of Support Vector Machine Algorithm with Code Examples

This MATLAB code provides a complete implementation of the Support Vector Machine (SVM) algorithm, serving as highly valuable reference source code. The implementation demonstrates core SVM concepts including data preprocessing, model training with optimization, and prediction mechanisms. Key algorithmic components feature the hinge loss function calculation, kernel trick implementation (linear/RBF/polynomial), and quadratic programming optimization for finding the optimal hyperplane. The code structure includes essential functions for data normalization, SVM model training using sequential minimal optimization (SMO) approach, and prediction with decision function evaluation. You can modify and optimize various parameters such as kernel selection, regularization parameter C, and tolerance settings to adapt to specific problem domains and datasets. The commented code sections explain the mathematical formulation and implementation details, making it particularly useful for understanding SVM's working principles and practical applications.