MATLAB Implementation of Support Vector Machine Algorithm

Support Vector Machine (SVM) is a widely-used machine learning algorithm applied to both classification and regression problems. In MATLAB, SVM models can be conveniently implemented using the built-in Statistics and Machine Learning Toolbox.

The implementation begins with preparing training data, consisting of a feature matrix and corresponding label vector. The core SVM algorithm focuses on finding the optimal hyperplane that maximizes the margin between data points of different classes. MATLAB provides the `fitcsvm` function for training SVM models, supporting various kernel options including linear kernels and Gaussian (RBF) kernels. The function syntax typically follows: `SVMModel = fitcsvm(X,Y,'KernelFunction','rbf')` where X represents the feature matrix and Y contains the class labels.

After training, the `predict` function enables classification predictions on new data using the syntax: `labels = predict(SVMModel,newX)`. Additionally, MATLAB supports cross-validation techniques and parameter tuning (such as penalty parameter C and kernel parameter gamma) to enhance model performance. Through methods like grid search or Bayesian optimization, SVM hyperparameters can be further optimized using functions like `fitcsvm` with hyperparameter optimization options, ultimately achieving optimal classification performance.