Support Vector Machine (SVM) Toolbox for MATLAB

Support Vector Machine (SVM) is a robust supervised learning algorithm widely used for classification and regression tasks. MATLAB users can leverage specialized toolboxes to streamline SVM implementation, typically through functions like `fitcsvm` for classification and `fitrsvm` for regression.

This toolbox is specifically designed for MATLAB 6.5, providing complete SVM functionality. For classification tasks, it efficiently handles both linearly and non-linearly separable data by optimizing decision boundaries through kernel functions (e.g., Gaussian RBF kernel, polynomial kernel). The implementation involves solving quadratic programming problems using sequential minimal optimization (SMO) algorithm. In regression problems, the toolbox utilizes Support Vector Regression (SVR) with epsilon-insensitive loss function to fit data, making it suitable for high-dimensional feature prediction analysis.

The toolbox enables straightforward model training through `svmtrain` function, parameter tuning via cross-validation, and prediction evaluation using `svmpredict`. Key parameters like kernel type, box constraint, and kernel scale can be optimized using grid search approaches. This facilitates rapid deployment of machine learning solutions, enhancing workflow efficiency for both academic research and industrial applications.