MATLAB Implementation of Support Vector Machine with Code Examples

This document presents MATLAB implementation code for Support Vector Machines along with comprehensive algorithm explanations.

Support Vector Machine (SVM) is a widely-used machine learning algorithm that performs classification tasks by finding an optimal hyperplane to separate samples from different classes. The fundamental principle of SVM is based on maximum margin classification, which aims to identify a hyperplane that maximizes the separation margin between different classes of data points.

In MATLAB, you can implement SVM training and classification using the following code structure:

% Load dataset load('data.mat'); % Train SVM model using fitcsvm function % fitcsvm implements linear or kernel-based SVM classification % X: feature matrix, Y: class labels model = fitcsvm(X, Y); % Perform predictions on test data % predict method applies the trained model to new data Y_pred = predict(model, X_test);

The fitcsvm function supports various kernel functions (linear, polynomial, RBF) and allows parameter customization for different classification scenarios. The algorithm automatically handles both binary and multi-class classification problems through one-vs-one or one-vs-all strategies.

This provides a fundamental introduction to Support Vector Machines and MATLAB implementation code, which should serve as a practical starting point for your machine learning projects!