MATLAB Implementation of SVM Algorithm with Code Examples and Technical Documentation

This article provides a detailed MATLAB implementation methodology for the Support Vector Machine (SVM) algorithm, complete with practical examples and comprehensive documentation. We systematically explain how to implement data classification using SVM algorithms on the MATLAB platform, utilizing key functions such as fitcsvm for model training and predict for classification. The content covers fundamental SVM principles including linear and non-linear classification using kernel functions (linear, polynomial, RBF), data preprocessing techniques involving feature scaling with normalize or zscore functions, model training with cross-validation using crossval, and performance evaluation through metrics like accuracy score and confusion matrix. Additionally, we include practical application scenarios and experimental results demonstrating SVM's effectiveness in pattern recognition and predictive modeling. The implementation showcases MATLAB's Statistics and Machine Learning Toolbox capabilities while providing custom code snippets for hyperparameter optimization using bayesopt and kernel function customization. This resource serves as an integrated platform for mastering SVM algorithms, enabling readers to effectively apply them to real-world data classification and prediction problems with proper code optimization techniques.