Online SVM Incremental Learning Method

This is an online Support Vector Machine (SVM) incremental learning code implementation based on MATLAB's built-in SVMTrain model training function. The algorithm enables real-time adaptation of SVM models by incrementally updating model parameters when new data samples arrive. The implementation efficiently handles large-scale datasets while maintaining model accuracy and computational efficiency through sequential optimization techniques. Key features include batch-incremental learning support, kernel function compatibility, and automated hyperparameter adjustment. Users can easily deploy this code to build and train adaptive SVM models that dynamically evolve with changing data distributions, making it suitable for streaming data applications and real-time pattern recognition systems. The core functionality revolves around MATLAB's SVMTrain function with modifications for incremental weight updates and support vector retention strategies.