MATLAB Implementation of Support Vector Machine with Multi-Class Classification Support

This documentation discusses the MATLAB implementation of Support Vector Machine that supports multi-class classification problems through a user-friendly GUI interface, making it easily accessible and understandable. The implementation handles both input and output data processing using MATLAB's fitcsvm function and multiclass extension techniques like one-vs-all or one-vs-one approaches. We provide detailed explanations on how to utilize this implementation for various classification problems, including data preprocessing steps such as feature scaling using zscore normalization and model validation through cross-validation techniques. The GUI interface allows users to interactively load datasets (supported formats: .mat, .csv, .xlsx), configure SVM parameters (kernel type: linear, polynomial, RBF; penalty parameter C; kernel coefficients), visualize decision boundaries, and evaluate performance metrics (accuracy, precision, recall, F1-score). Through this documentation, you will gain deep insights into SVM principles including maximum margin classification and kernel trick implementation, while learning practical applications for classification tasks in MATLAB environment with complete code examples for training (svmtrain) and prediction (svmpredict) workflows.