MATLAB Source Code Implementation for Training Samples in Support Vector Machines
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MATLAB source code implementation for training samples in Support Vector Machines. Support Vector Machine (SVM) is a supervised learning algorithm primarily used for classification and regression analysis. The algorithm works by finding an optimal hyperplane that separates sample space into distinct categories, maximizing the margin between different classes while keeping samples of the same category closely clustered. This MATLAB implementation provides detailed code-level insights into SVM algorithm execution, including key functions for data preprocessing, kernel selection (linear, polynomial, or RBF), and optimization using quadratic programming approaches. The code demonstrates practical implementation of support vector selection, margin calculation, and classification decision functions, enabling researchers to better understand and apply the algorithm through hands-on coding examples that illustrate parameter tuning and performance evaluation techniques.
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