Support Vector Machine MATLAB Implementation Suite

The following are primary MATLAB implementations for Support Vector Machines: (1) Main_SVC_C.m - Implements C-SVC classification algorithm for multi-class problems. This code handles soft margin classification using the C parameter to control the trade-off between margin maximization and classification error. Typically includes kernel function implementations (linear, RBF, polynomial) and quadratic programming optimization. (2) Main_SVC_Nu.m - Nu-SVC binary classification algorithm designed specifically for two-class problems. Features nu parameter that controls the number of support vectors and training errors, providing an alternative to C-SVC with bounds on the fraction of margin errors and support vectors. (3) Main_SVM_One_Class.m - One-Class Support Vector Machine implementation for anomaly detection and novelty recognition. This algorithm learns a decision function that separates the training data from the origin in feature space, commonly used for density estimation and outlier detection applications. (4) Main_SVR_Epsilon.m - Epsilon-SVR regression algorithm for solving regression problems. Implements epsilon-intensive loss function where errors within epsilon tolerance are not penalized. The code typically includes kernel methods and optimization for finding the regression function that deviates from actual values by at most epsilon. (5) Main_SVR_Nu.m - Nu-SVR regression algorithm for regression tasks. Utilizes nu parameter to control the number of support vectors and margin errors in regression, offering automatic adaptation of the epsilon tube size based on the training data characteristics. These represent commonly used Support Vector Machine implementations in MATLAB for both classification and regression tasks, featuring various parameter controls and optimization approaches suitable for different machine learning scenarios.