Support Vector Machine Toolbox

This document introduces the Support Vector Machine Toolbox, which contains fundamental functions and MATLAB demonstration programs. The toolbox includes kernel function computation modules (implementing linear, polynomial, and RBF kernels), SVM training functions utilizing optimization algorithms like sequential minimal optimization, and cross-validation functions for hyperparameter tuning. By leveraging this toolbox, researchers can streamline SVM development and application processes. Support Vector Machines represent a powerful machine learning methodology applicable to both classification and regression tasks, with widespread implementation in computer vision, natural language processing, and bioinformatics domains. The toolbox enables rapid prototyping of SVM solutions with flexible parameter configuration and kernel selection capabilities. All functions are optimized for MATLAB environment with detailed code comments and usage examples. We anticipate this toolbox will facilitate your research and practical machine learning projects!