Classical Support Vector Machine (SVM) for Classification

Support Vector Machine (SVM) is a robust supervised learning algorithm widely used for classification tasks, particularly excelling in pattern recognition and data analysis applications. The core principle involves finding an optimal hyperplane that maximizes the margin between different class data points, thereby enhancing the model's generalization capability for classification. Implementing SVM classification in MATLAB typically involves these key steps: Data Preparation: Begin by splitting the dataset into training and testing sets. MATLAB provides functions like cvpartition for convenient cross-validation partitioning, allowing systematic data division for model evaluation. Model Training: The fitcsvm function constructs SVM classifiers with customizable parameters. Key specifications include kernel function selection (linear, polynomial, or radial basis function/Gaussian kernel) and regularization parameters like the penalty coefficient C, which controls model complexity and overfitting tendency. Parameter Optimization: To enhance model performance, critical parameters (kernel parameters, regularization coefficients) require tuning. fitcsvm supports automatic parameter optimization through cross-validation. Alternatively, Bayesian optimization tools like bayesopt can be integrated for more efficient hyperparameter search, using probability models to navigate parameter spaces. Model Evaluation: After training, validate classification performance using test data. MATLAB's predict function generates predictions, while performance metrics like confusion matrices, accuracy rates, and recall scores provide comprehensive model assessment through functions such as confusionmat and perfcurve. MATLAB's SVM implementation offers flexibility and efficiency, particularly suitable for classification problems involving small samples, nonlinear data, and high-dimensional datasets. Through appropriate kernel selection and parameter optimization, classification accuracy can be significantly improved, making SVM applicable to pattern recognition tasks across domains like bioinformatics and image recognition. The implementation leverages MATLAB's optimized linear algebra routines for efficient hyperplane computation and margin calculation.