Support Vector Machine SMO Algorithm Implementation

This document presents a MATLAB implementation of the Sequential Minimal Optimization (SMO) algorithm for Support Vector Machines. As a fundamental algorithm in machine learning, this implementation serves as an excellent reference for study and practical application. The SMO algorithm efficiently solves the quadratic programming optimization problem in SVM training by breaking it down into smallest possible subproblems, significantly improving computational efficiency. Beyond the core implementation, I provide explanations of key SVM concepts including hyperplane construction, margin maximization, and kernel function applications. The code demonstrates practical techniques for handling classification tasks with clear documentation on parameter selection and convergence criteria. Support Vector Machines represent a powerful supervised learning method for both classification and regression analysis, widely applied in domains such as image recognition, text categorization, and financial forecasting. The implementation includes optimization strategies for large-scale datasets and demonstrates proper handling of both linear and non-linear classification problems through appropriate kernel functions. For those interested in machine learning and SVMs, this document offers valuable learning resources with practical code examples and algorithm explanations. I hope you find this material beneficial for your machine learning journey!