High-Quality SVM MATLAB Implementation

This article presents a high-quality MATLAB implementation of Support Vector Machines (SVM). The source code is exceptionally user-friendly as it requires no path configuration, making it more convenient and practical than libsvm for immediate use. The implementation includes comprehensive SVM functionality with core algorithms for classification and regression tasks, featuring efficient optimization solvers and kernel function implementations (linear, polynomial, RBF). Key technical components include: - Complete data preprocessing and normalization routines - Support for multiple kernel functions with automated parameter selection - Cross-validation modules for model evaluation - Visualization tools for decision boundaries and support vectors The codebase offers significant customization potential, allowing users to modify kernel functions, optimization parameters, and add custom features based on specific research requirements. With well-structured modular design and detailed comments, developers can easily extend functionality for advanced machine learning applications. Overall, this MATLAB implementation serves as a valuable tool for machine learning practitioners, significantly enhancing productivity and result quality in data analysis projects through its robust algorithm implementation and user-centric design.