MATLAB SVM Demonstration: Implementation and Algorithm Overview

This MATLAB-based SVM demonstration program provides a practical understanding of Support Vector Machine algorithm operations. The program is designed for immediate execution within your working directory, offering a convenient tool for hands-on learning. The implementation showcases SVM's capability to handle multiple machine learning tasks including classification (using binary/multi-class approaches), regression (via support vector regression), and anomaly detection (through one-class SVM). The algorithm's flexibility is demonstrated through customizable kernel functions - linear, polynomial, radial basis function (RBF), and sigmoid kernels - allowing adaptation to various data patterns. Key implementation aspects include: - Data preprocessing and feature scaling routines - SVM model training with optimization algorithms - Decision boundary visualization for 2D datasets - Cross-validation and parameter tuning mechanisms This demonstration serves as an excellent starting point for understanding SVM's mathematical foundations and practical implementation, providing code-level insights into hyperplane optimization, margin maximization, and kernel trick applications for nonlinear classification scenarios.