MATLAB Implementation of SVM Algorithm with Classification Visualization
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Resource Overview
This project implements a Support Vector Machine (SVM) algorithm in MATLAB, performing binary classification between two distinct point classes and presenting graphical visualization of the classification results. The implementation includes key SVM components such as kernel function selection, optimization using quadratic programming, and margin calculation.
Detailed Documentation
In this experiment, we implemented the SVM algorithm using MATLAB and performed classification on two distinct classes of data points. The implementation utilizes MATLAB's built-in functions like `fitcsvm` for model training or custom-coded quadratic programming optimization for finding the optimal hyperplane. Through graphical visualization generated using MATLAB's plotting functions (`scatter` and `plot` for decision boundaries), we can clearly observe the classification effectiveness and margin separation. Additionally, we analyzed the classification accuracy using metrics such as confusion matrices and discussed potential improvements including kernel parameter tuning (e.g., RBF kernel sigma values), handling imbalanced data, and incorporating cross-validation techniques. This experiment provides valuable hands-on experience for deeper understanding and practical application of SVM algorithms in pattern recognition tasks.
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