MATLAB Code Implementation for SVM Classification

Support Vector Machine (SVM) is a powerful supervised learning algorithm widely used for classification and regression problems. Implementing SVM classification in MATLAB is straightforward, especially with the built-in Machine Learning Toolbox.

The core concept of SVM is to find an optimal hyperplane that maximizes the margin between data points of different classes. This hyperplane best separates the data into distinct categories. MATLAB provides the `fitcsvm` function for training SVM models, which offers an intuitive approach to implementation.

Typical steps for SVM classification in MATLAB include: Data Preparation: Ensure proper data formatting, typically requiring separation of features and labels. MATLAB supports various input formats, including matrices and tables. The features should be organized in rows (observations) and columns (predictors), while labels are stored as categorical arrays or numeric vectors. Model Training: Call the `fitcsvm` function by passing feature data and corresponding labels to train an SVM classifier. Key parameters like kernel function (e.g., linear, Gaussian RBF) and regularization parameter (BoxConstraint) can be adjusted to optimize model performance. For non-linear separation, kernel functions map data to higher-dimensional spaces. Model Evaluation: Use the `predict` function to classify test data and evaluate model performance through metrics like confusion matrices or accuracy scores. The trained model outputs class predictions that can be compared against ground truth labels.

MATLAB's SVM implementation is not only efficient but also supports custom parameter tuning, making it suitable for classification tasks of varying complexity. It handles both linearly separable problems and non-linear classification requiring kernel tricks with equal convenience.

Advanced users can integrate cross-validation or grid search methods to further optimize SVM hyperparameters, enhancing the model's generalization capability. The `crossval` function enables k-fold validation, while `fitcsvm` automatically supports hyperparameter optimization when specified.