MATLAB Code Implementation for SVM Classification

In this document, I present a MATLAB program for implementing Support Vector Machine (SVM) classification. This implementation is fully compatible with MATLAB versions 7.0 and higher. SVM, or Support Vector Machine, is a widely-used machine learning algorithm employed for both classification and regression tasks. The program demonstrates how to utilize MATLAB's built-in functions or custom implementations to classify data points into distinct categories based on training datasets.

The implementation includes key components such as data preprocessing, kernel function selection (linear, polynomial, or RBF), parameter optimization, and model evaluation. You can modify and extend this code to suit specific project requirements, whether you're working with different datasets or experimenting with various SVM configurations. The code structure emphasizes clear separation of training and testing phases, with proper validation techniques to ensure model accuracy.

This program serves as an excellent starting point for both machine learning beginners and experienced researchers looking to deepen their understanding of SVM classification principles and practical implementation in MATLAB. The code includes comments explaining critical algorithmic steps and offers insights into decision boundary formation and support vector identification.