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 modified SVMs are in essence batch learning, which makes them unable to handle incremental 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, Learning Prototypes (LPs) and Learning SVs (LSVs). Experimental results demonstrate that the proposed algorithm is effective for incremental learning problems and large-scale problems.