MATLAB Source Code for SVM Classification
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Resource Overview
Implementation of SVM classification using MATLAB source code, including a sample dataset for simulation experiments with detailed algorithm explanations.
Detailed Documentation
This is a MATLAB source code implementation of an SVM (Support Vector Machine) classifier. The code demonstrates core SVM concepts including kernel function selection (linear, polynomial, or RBF), hyperparameter optimization, and decision boundary calculation. A sample dataset is provided for simulation experiments, allowing users to test classification performance and visualize results. Through this implementation, you'll learn how to utilize MATLAB's SVM functions for data classification and prediction tasks, including dataset preprocessing, model training with fitcsvm function, and prediction using predict method. This practical example will enhance your understanding of SVM algorithms, expand your machine learning knowledge, and provide hands-on experience for real-world applications.
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