An online incremental learning support vector machine for large-scale data

Support Vector Machines (SVMs) have gained outstanding generalization in many fields. However, standard SVM and most of modified SVMs are in essence batch learning, which make them unable to handle incremental learning or online learning well. Also, such SVMs are not able to handle large-scale data effectively because they are costly in terms of memory and computing consumption. In some situations, plenty of Support Vectors (SVs) are produced, which generally means a long testing time. In this paper, we propose an online incremental learning SVM for large data sets. The proposed method mainly consists of two components: the learning prototypes (LPs) and the learning Support Vectors (LSVs). LPs learn the prototypes and continuously adjust prototypes to the data concept. LSVs are to get a new SVM by combining learned prototypes with trained SVs. The proposed method has been compared with other popular SVM algorithms and experimental results demonstrate that the proposed algorithm is effective for incremental learning problems and large-scale problems.