MATLAB Implementation of Support Vector Machine (SVM) with Source Code

This article presents a MATLAB-implemented support Vector Machine (SVM) source code suitable for feature classification and extraction. Support Vector Machine represents a powerful machine learning algorithm capable of solving both classification and regression problems. The core implementation involves identifying an optimal hyperplane that maximizes the margin between different class data points, achieving accurate classification through mathematical optimization techniques. The MATLAB code implements key SVM components including kernel function handling (linear, polynomial, RBF), quadratic programming optimization for margin maximization, and support vector identification. It effectively handles high-dimensional feature datasets while maintaining strong generalization capabilities through regularization parameters. The implementation also enables feature extraction by analyzing feature importance through weight vectors, identifying the most informative features to simplify datasets and enhance classification accuracy. Key functions include data normalization preprocessing, kernel matrix computation, and decision function implementation for prediction. By utilizing this MATLAB SVM source code, researchers and engineers can apply machine learning algorithms across various domains to better understand and process complex data patterns while maintaining implementation transparency through well-documented code structure.