Detailed SVM Examples with Classification, Regression, and Code Implementation

This article provides detailed examples of Support Vector Machine (SVM) implementations covering both classification and regression scenarios. The examples demonstrate practical applications with MATLAB code structure, including data normalization techniques, kernel function selection (linear, RBF, polynomial), and cross-validation approaches for model optimization. For classification tasks, we explore binary and multi-class classification using one-vs-one and one-vs-all strategies, with emphasis on margin maximization and support vector identification. Regression examples focus on epsilon-insensitive loss functions and support vector regression parameters. Readers interested in machine learning applications can download and test these implementations. I welcome technical discussions regarding SVM parameter tuning and performance optimization. Additionally, I'm seeking MATLAB source code implementing the Sequential Minimal Optimization (SMO) algorithm for SVM training - if anyone has working implementation, please share for collaborative learning. Code implementation aspects include: feature scaling methods, kernel matrix computation, Lagrange multiplier optimization, and decision function formulation for both classification and regression problems.