Support Vector Machine (SVM) Classification Algorithm Implementation with MATLAB Code

In the following content, you will find MATLAB source code implementing the Support Vector Machine (SVM) classification algorithm. This algorithm represents a highly popular machine learning technique renowned for its exceptional performance in handling high-dimensional data. The implementation demonstrates how SVM constructs optimal hyperplanes to separate different classes within datasets.

MATLAB serves as an ideal programming environment for scientific computing and data analysis, making this SVM source code particularly valuable for practical implementation. The code includes key components such as data preprocessing, kernel function selection (linear, polynomial, or RBF), optimization solving using quadratic programming, and margin maximization techniques. The implementation showcases how to train SVM models, handle support vectors, and perform classification predictions on new data points.

This MATLAB implementation provides comprehensive insights into SVM's mathematical foundation, including the formulation of the objective function, constraint handling, and the use of Lagrangian multipliers. The code structure allows users to modify kernel parameters, adjust regularization constants, and visualize decision boundaries, making it an excellent educational resource for understanding SVM's inner workings and applying the technique to real-world classification problems.