LIBSVM: A Simple MATLAB Interface for Support Vector Machine Implementation

LIBSVM is a simple MATLAB interface developed by authors at National Taiwan University, specifically designed for Support Vector Machine (SVM) training and prediction tasks. The interface provides essential functions and methods that enable users to efficiently handle datasets, configure model parameters, perform cross-validation, and execute prediction operations. Key functions include svmtrain() for model training with customizable kernel parameters (linear, polynomial, RBF) and svmpredict() for classification/regression tasks with probability estimates. The implementation follows a modular design with built-in parameter optimization and multi-class classification support using one-vs-one strategy. LIBSVM features an intuitive design suitable for both beginners and professionals, offering command-line utilities and MATLAB wrapper functions for seamless integration. Widely adopted in machine learning and pattern recognition domains, it has become a preferred tool for researchers and engineers due to its robust implementation of SVM algorithms including C-SVC, nu-SVC, epsilon-SVR, and nu-SVR variants.