SVM Training Implementation in MATLAB

In MATLAB, the svmTrain function is used for Support Vector Machine (SVM) training, which is a powerful machine learning algorithm for classification and regression analysis. This implementation constructs an optimal hyperplane that separates data points from different classes in the training dataset. The svmTrain function automatically learns the optimal hyperplane parameters through mathematical optimization, typically using quadratic programming solvers to minimize the structural risk. The function accepts training data matrices where rows represent samples and columns represent features, along with corresponding class labels. Key parameters include the kernel type (linear, polynomial, radial basis function), regularization parameter C controlling the margin-violation trade-off, and kernel-specific parameters. By adjusting these parameters, developers can optimize the training process to achieve better classification performance through techniques like cross-validation. The function returns a trained model structure containing support vectors, bias term, and kernel parameters, which can be used with svmClassify for prediction. This makes svmTrain an efficient tool for building and optimizing SVM models within MATLAB's computational environment.