MATLAB Source Code for Training Samples in Support Vector Machine (SVM)

Support Vector Machine (SVM) is a supervised learning algorithm widely used for classification and regression problems. In MATLAB, you can utilize built-in SVM functions or third-party toolboxes to train models effectively. ### Training Sample Preparation Before training an SVM model, you need to prepare training sample data consisting of feature matrices and corresponding labels. The feature matrix is typically an N×D matrix where N represents the number of samples and D denotes the feature dimensionality. Labels can be binary class or multi-class categorical identifiers. In MATLAB implementation, you would typically store features in a numeric matrix and labels in a categorical array or numeric vector for optimal processing. ### SVM Implementation in MATLAB MATLAB provides the `fitcsvm` function for training Support Vector Machine classification models. This function supports various kernel types including linear kernels and Gaussian (RBF) kernels, allowing adjustment of key parameters like the penalty parameter C and kernel-specific parameters to optimize model performance. The function implements the sequential minimal optimization (SMO) algorithm for efficient training, with options for different solver configurations. ### Training Workflow Data Preprocessing: Normalize or standardize your data using functions like `zscore` or `normalize` to enhance training efficiency and model convergence. Model Training: Call the `fitcsvm` function with specified hyperparameters including kernel type, regularization parameter C, and kernel scale. For example: `svmModel = fitcsvm(X_train, y_train, 'KernelFunction', 'rbf', 'BoxConstraint', C)`. Model Evaluation: Validate classification accuracy using cross-validation (via `crossval` function) or hold-out test sets with prediction functions like `predict` to generate performance metrics. Parameter Tuning: Optimize hyperparameters such as C and γ (gamma for RBF kernel) through grid search (using `fitcsvm` with different parameter combinations) or Bayesian optimization methods available in MATLAB's Statistics and Machine Learning Toolbox. SVM implementation in MATLAB demonstrates high efficiency, particularly suitable for classification tasks with small to medium-sized datasets. For extended applications, you can enhance model performance by combining feature selection methods (like sequential feature selection) or ensemble learning techniques (such as bagging or boosting with SVM base learners) to address more complex pattern recognition challenges.