SVM Implementation in MATLAB

Support Vector Machine (SVM) is a powerful machine learning algorithm primarily used for classification and regression tasks. In the MATLAB environment, SVM implementation typically relies on built-in toolboxes or third-party libraries to enhance model training and prediction efficiency.

The five folders mentioned likely correspond to different SVM implementation approaches, potentially including the following scenarios: MATLAB's built-in SVM tools: Functions like `fitcsvm` (for classification) and `fitrsvm` (for regression) belong to the Statistics and Machine Learning Toolbox, featuring automatic hyperparameter optimization and cross-validation support for rapid prototyping. LIBSVM MATLAB interface: LIBSVM is a widely-used SVM library where the MATLAB version calls its core functions through .m files, particularly suitable for complex classification problems with customizable kernel functions. Custom SVM implementations: May contain code written from scratch manually implementing kernel functions (e.g., linear, RBF) and optimization processes (like Sequential Minimal Optimization algorithm), demonstrating fundamental SVM mathematics. Variants and extensions: Examples include multi-class SVM implementations (One-vs-One, One-vs-All strategies), Support Vector Regression (SVR), or weighted SVM for handling imbalanced datasets through class-specific penalty parameters.

Usage recommendations: Prioritize MATLAB's built-in functions (e.g., `fitcsvm`) for their ease of use and high integration with MATLAB's ecosystem. For greater flexibility (e.g., custom kernel functions), consider the LIBSVM interface which allows direct kernel matrix input. Optimize model performance by tuning key parameters such as the penalty coefficient `C` (controlling misclassification tolerance) and kernel function types using MATLAB's automatic hyperparameter optimization features.