MATLAB Toolkit for Support Vector Machines (SVM) and Kernel Functions

Support Vector Machine (SVM) is a widely-used machine learning algorithm that demonstrates excellent performance in both classification and regression tasks. SVM employs kernel functions to map data from lower-dimensional spaces to higher-dimensional feature spaces, enabling more effective separation of complex patterns. This MATLAB toolkit provides numerous implementation examples and utility functions for SVM and kernel methods, including code for linear, polynomial, and radial basis function (RBF) kernels. The collection contains practical implementations of key SVM components such as the optimization solver for margin maximization, kernel matrix computation, and cross-validation routines. These ready-to-use code examples and tools facilitate rapid understanding and application of SVM methodologies, helping researchers and practitioners solve real-world problems efficiently through customizable MATLAB implementations that demonstrate core concepts like the kernel trick and support vector selection.