Implementation of Support Vector Machine Learning Algorithm Using Stochastic Sub-Gradient Descent

Support Vector Machines (SVMs) are among the most robust supervised machine learning algorithms for classification and regression tasks. They aim to find the optimal hyperplane that maximizes the margin between classes in a dataset. However, traditional quadratic programming-based optimization of SVMs can be computationally intensive for large-scale problems. This paper provides a detailed, step-by-step implementation of the SVM learning algorithm using the Stochastic Sub-Gradient Descent (SSGD) optimization technique. The study discusses the mathematical formulation, algorithmic structure, convergence properties, and computational advantages of SSGD-based SVMs. Empirical results and pseudo-code illustrations demonstrate the efficiency and scalability of the approach.