中文摘要：

提出一种新的基于超椭球的类增量学习算法。对每一类样本,在特征空间求得一个包围该类尽可能多样本的最小超椭球,使得各类样本之间通过超椭球隔开。类增量学习过程中,只对新增类样本进行训练。分类时,通过计算待分类样本是否在超椭球内判定其所属类别。实验结果证明,该方法较超球方法提高了分类精度和分类速度。

英文摘要：

A new Class Incremental Learning（CIL） algorithm based on hyper ellipsoidal is proposed.For every class,the smallest hyper ellipsoidal that contains most samples of the class is structured,which can divide the class samples from others.In the process of CIL,only are the samples that belong to the new incremental class trained.For the sample to be classified,its class be confirmed by the hyper ellipsoidal that it belong to.The experimental results show that the algorithm has a higher performance on classification speed and classification precision compared with hyper sphere algorithm.