一种新的兼类样本类增量学习算法

提出了一种基于超椭球的兼类样本类增量学习算法。对兼有同一类别的样本,在特征空间构建一个能包围该类尽可能多样本的最小超椭球,使各类样本之间通过超椭球球面分开。增量学习过程中,对新增样本中的每一新类别构建超椭球,对新增样本中的各历史类别重新构建超椭球,使得算法在很小的空间代价下实现了兼类样本类增量学习,同时保留了与新增样本类别无关的历史类训练结果。分类过程中,根据待分类样本是否在超椭球内或隶属度来确定其所属类别。实验结果表明,该算法较超球方法具有较快的分类速度和较高的分类精度。

New Multi-label Sample Class Incremental Learning Algorithm

To multi-label sample, a class incremental learning algorithm based on hyper ellipsoidals was proposed. For every class, the smallest hyper ellipsoidal that contains most samples of the class was structured, which can divide the class samples from others. In the process of class incremental learning, the hyper cllipsoidals of new class were structured,and the historical hyper ellipsoidal that its class exists in the incremental samples was structured again. The multi-label class incremental learning is realized in a small memory space, and the history results that has nothing to do with the new sample classes arc saved at the same time. For the sample to be classified, its class is confirmed by the hyper ellipsoidal that it belongs to or its membership. The experimental results show that the algorithm has a higher performance on classification speed and classification precision compared with hyper sphere algorithm.