SVM Classifier Algorithm Implementation with Source Code and Documentation

This documentation package contains complete source code implementation with detailed technical explanations provided in both TXT and Word formats. The package includes comprehensive instructions for utilizing the SVM toolbox, along with thorough explanations of various SVM classification algorithms. Beyond the standard SVM classification implementation, the package introduces an advanced newsvm classification approach that incorporates optimized parameter tuning and kernel function selection. The codebase features two practical examples demonstrating different classification scenarios and includes an optimizer module that implements gradient descent and cross-validation techniques for model performance enhancement. Key functions include kernel matrix computation, support vector identification, and decision boundary optimization algorithms, all implemented with clear code structure and extensive commenting for easy integration and modification.