Small Support Vector Machine Example for Prediction
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This is a small MATLAB-based Support Vector Machine (SVM) example designed for prediction tasks using Least Squares (LS). SVM is a popular machine learning algorithm that, through training on datasets, can predict unknown data patterns. In this implementation, we utilize MATLAB's built-in SVM functions or custom implementations to create a predictive model where LS (Least Squares) serves as the optimization method for prediction. The example demonstrates key SVM components including kernel function selection (likely linear or RBF), parameter tuning for better generalization, and the LS approach for minimizing prediction errors through squared residual minimization. Through this practical example, users can gain deeper insights into SVM's working mechanism, including how decision boundaries are constructed and how the algorithm handles both linear and non-linear classification/regression tasks, along with understanding LS applications in machine learning contexts.
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