SVM Toolbox Implementation for Classification and Regression Algorithm Function Fitting

This implementation provides four distinct SVM toolboxes for classification and regression algorithms. Below is the detailed description of each toolbox: 1) Toolbox: LS_SVMlab - Classification_LS_SVMlab.m: Implements multi-class classification using Least Squares Support Vector Machines, which solves linear systems instead of quadratic programming problems for faster computation - Regression_LS_SVMlab.m: Handles function fitting tasks with regularization parameters and kernel functions optimization 2) Toolbox: OSU_SVM3.00 - Classification_OSU_SVM.m: Performs multi-class classification through one-vs-all or one-vs-one strategies with various kernel options including linear, polynomial, and RBF 3) Toolbox: stprtool - Classification_stprtool.m: Provides multi-class classification capabilities with statistical pattern recognition approaches and support for different SVM formulations 4) Toolbox: SVM_SteveGunn - Classification_SVM_SteveGunn.m: Specializes in binary classification problems using standard SVM formulation with sequential minimal optimization - Regression_SVM_SteveGunn.m: Addresses function fitting through support vector regression with epsilon-insensitive loss function If you find these toolboxes helpful for your work, please consider supporting this post to keep it visible in the community. Your engagement helps maintain the accessibility of these resources for other researchers and developers. Thank you for your support