Implementation of Support Vector Machines (SVM) in MATLAB

MATLAB 1.49M 199 views 0 downloads 1 credits
Tags:

Resource Overview

This project demonstrates the MATLAB implementation of Support Vector Machines (SVM), organized into five distinct folders representing different SVM variants with corresponding code examples and algorithm implementations.

Detailed Documentation

Support Vector Machine (SVM) is a machine learning algorithm implemented in the MATLAB environment. This implementation contains five separate folders, each representing different types of SVM models. The code structure includes implementations for linear SVM, nonlinear SVM with kernel functions (such as RBF and polynomial kernels), multi-class classification extensions, and regression variants (SVR). Each folder contains MATLAB scripts (.m files) demonstrating data preprocessing, model training using optimization techniques like quadratic programming, parameter tuning procedures, and performance evaluation metrics. Key functions involve MATLAB's built-in optimization solvers for solving the convex optimization problem inherent in SVM formulation, with custom implementations for kernel computations and decision boundary visualization.

You May Also Like