MATLAB Implementation of Support Vector Machines (SVM) with Source Code and Exercises

This resource provides comprehensive SVM source code along with hands-on practice exercises for mastering Support Vector Machine implementation. The SVM algorithm, a powerful supervised learning method for classification and regression tasks, operates by constructing an optimal hyperplane that maximizes the margin between different classes in the feature space. The MATLAB implementation includes core functions for handling linear and non-linear separation through kernel tricks (such as RBF and polynomial kernels), with key components covering parameter optimization, support vector identification, and decision boundary calculation. The practice section offers progressive exercises that guide users through data preprocessing, model training using sequential minimal optimization (SMO) algorithms, and performance evaluation metrics. Learners will gain practical experience in implementing SVM for real-world scenarios, including parameter tuning for regularization (C parameter) and kernel selection. The code architecture demonstrates efficient matrix operations for large-scale data handling and includes visualization functions for decision boundaries. By combining theoretical foundations with executable MATLAB code, this material serves as an essential toolkit for understanding SVM's mathematical underpinnings while developing practical machine learning skills applicable to pattern recognition, bioinformatics, and predictive analytics projects.