MATLAB Implementation of Support Vector Machine with Code Examples
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Resource Overview
MATLAB-based implementation of Support Vector Machine (SVM) that automatically performs data classification tasks using built-in functions and customizable parameters.
Detailed Documentation
Support Vector Machine (SVM) is a widely-used machine learning algorithm that offers convenient implementation methods in MATLAB. Through SVM algorithms, we can automatically classify data to achieve efficient data analysis and processing. The implementation typically involves using MATLAB's Statistics and Machine Learning Toolbox, which provides functions like fitcsvm for training classification models. Key implementation steps include data preprocessing, kernel selection (linear, polynomial, or RBF), parameter optimization using cross-validation, and model evaluation through functions like predict and loss. The algorithm works by finding the optimal hyperplane that maximizes the margin between different classes, with support vectors determining the decision boundary. MATLAB's SVM implementation supports both binary and multi-class classification through error-correcting output codes (ECOC) framework.
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