SVM Classification Source Code Implemented in MATLAB

This text presents a MATLAB-based source code implementation of Support Vector Machine (SVM) algorithm, designed for robust feature extraction and data classification tasks. The implementation leverages MATLAB's built-in SVM functions (such as fitcsvm for classification problems) with comprehensive parameter customization capabilities. Users can modify data types, kernel functions (linear, polynomial, RBF), and hyperparameters (like box constraint and kernel scale) to adapt to diverse datasets and analytical requirements. The source code demonstrates high flexibility through configurable preprocessing steps and model training procedures, making it suitable for various data analysis, pattern recognition, and machine learning scenarios. The SVM implementation employs structural risk minimization principles to enhance classification accuracy and computational efficiency. Key algorithmic features include support vector selection, margin optimization, and kernel trick implementation for non-linear separation. For technical implementation, the code typically involves data normalization, model training with cross-validation options, and performance evaluation using metrics like accuracy and confusion matrices. This MATLAB-based SVM solution enables researchers to achieve superior results in understanding data patterns and improving classification performance across different domains.