A new incremental learning algorithm based on hyper-sphere SVM

A sample and class incremental learning algorithm based on hyper-sphere support vector machine is proposed. For every class, hyper-sphere support vector machine is used to get the smallest hyper-sphere that contains most samples of the class, which can divide the class samples from others. In the process of incremental learning, the hyper-sphere of every new class are trained, and the history hyper-spherees that have something to do with the new incremental samples are retrained. For the sample to be classified, the distances from it to the centre of every hyper-spheres are used to confirm the class that the sample belongs to. The experimental results show that the algorithm has a higher performance on training speed, classification speed, and classification precision.