Support Vector Machine Toolkit

In this technical documentation, the Support Vector Machine (SVM) represents a powerful computational tool for machine learning applications. This toolkit provides both classification and regression functionalities designed for MATLAB R2021a (previously known as MATLAB 6.5) environment. Support Vector Machine is a widely adopted algorithm in machine learning that employs kernel methods for nonlinear decision boundaries and structural risk minimization principle. The implementation includes key functions for data preprocessing, model training using quadratic programming optimization, and prediction modules with cross-validation support. Through SVM utilization, researchers can efficiently perform binary/multi-class classification and regression analysis on complex datasets. The toolkit's robustness and flexibility, featuring various kernel options (linear, polynomial, RBF) and parameter tuning capabilities, make it a preferred choice for engineers and researchers. Whether in academic research or practical applications like pattern recognition and predictive modeling, SVM plays a crucial role in handling high-dimensional data and achieving generalization performance. Mastering SVM implementation with this toolkit, including understanding kernel selection and regularization parameters (C/gamma), provides valuable insights that enhance research capabilities and create new opportunities in data-driven projects.