Classic Support Vector Machine (SVM) for Classification

This document provides a MATLAB implementation of a classic Support Vector Machine (SVM) for classification tasks. The program includes core SVM algorithms for data identification and categorization, with built-in parameter optimization functionality to enhance model performance. Through this implementation, users can achieve more accurate data classification and model parameter tuning using techniques like grid search and cross-validation. Key MATLAB functions such as svmtrain and svmclassify (or fitcsvm/predict for newer versions) are utilized with customizable kernel options (linear, RBF, polynomial) to handle different data characteristics. Suitable for both academic research and practical applications, this implementation provides robust support for pattern recognition and machine learning projects. The code structure emphasizes clear data preprocessing, model training with optimization loops, and performance evaluation metrics for comprehensive SVM experimentation.