Support Vector Machine Classification Algorithm with MATLAB Implementation

The following MATLAB source code examples demonstrate implementations of Support Vector Machine (SVM) algorithms, including classification algorithms, regression algorithms, and One-Class SVM. These specific implementations cover SVC_C (C-Support Vector Classification), SVC_Nu (Nu-Support Vector Classification), SVR_Epsilon (Epsilon-Support Vector Regression), SVR_Nu (Nu-Support Vector Regression), and One-Class SVM algorithms. These code implementations allow you to perform various machine learning tasks such as classification and regression analysis. Support Vector Machines represent a powerful machine learning methodology applicable across diverse domains. The provided implementations include kernel function options (linear, polynomial, RBF), parameter optimization approaches, and model validation techniques. Through these algorithms, you can classify data patterns and make predictions with high accuracy. The code structure features clear function definitions, parameter handling, and result visualization components suitable for both educational and production environments. Whether you are a beginner exploring machine learning concepts or an experienced developer seeking ready-to-use implementations, these code examples provide practical insights into SVM algorithm application. Each implementation includes comments explaining key mathematical concepts like margin maximization, kernel tricks, and optimization constraints, along with practical usage examples demonstrating data preprocessing, model training, and performance evaluation workflows.