Optimized MATLAB Code for SVM Multi-Class Classification

This package provides a carefully tuned MATLAB implementation of a multi-class Support Vector Machine (SVM) classifier. The code includes comprehensive preprocessing routines, kernel function implementations (linear, RBF, polynomial), and one-vs-rest multi-class strategy. Key features include automated parameter optimization using grid search, cross-validation setup for model evaluation, and integrated data normalization procedures. The included sample dataset demonstrates proper data formatting and allows for immediate testing of the classification pipeline. The modular code structure enables easy customization of kernel parameters, cost functions, and optimization algorithms to suit specific research requirements. Users can leverage the built-in visualization functions for decision boundaries and performance metrics analysis. Thank you for using this implementation - we hope it facilitates your machine learning projects and provides a solid foundation for further algorithmic enhancements.