Fast data selection for SVM training using ensemble margin

Support Vector Machine (SVM) is a powerful classification method. However, it suffers a major drawback: the high memory and time complexity of the training which constrains the application of SVM to large size classification tasks. To accelerate the SVM training, a new ensemble margin-based data selection approach is proposed. It relies on a simple and efficient heuristic to provide support vector candidates: selecting lowest margin instances. This technique significantly reduces the SVM training task complexity while maintaining the accuracy of the SVM classification. A fast alternative of our approach we called SVIS (Small Votes Instance Selection) with great potential for large data problem is also introduced.