MATLAB Code Implementation of Support Vector Machines
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Resource Overview
MATLAB-based implementation of Support Vector Machines (SVM) handling both linearly separable and non-separable cases, including nonlinear SVM with common kernel functions for enhanced classification and prediction analysis.
Detailed Documentation
This MATLAB implementation of Support Vector Machines (SVM) effectively handles both linearly separable and linearly non-separable datasets through soft-margin classification using optimization techniques. The code includes nonlinear SVM capabilities with multiple kernel functions such as linear, polynomial, radial basis function (RBF), and sigmoid kernels. Key algorithm implementations include sequential minimal optimization (SMO) for efficient training, kernel trick application for nonlinear transformations, and cross-validation support for parameter tuning. The solution provides comprehensive functionality for data classification and predictive analysis with proper error handling and visualization components.
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