This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm. The ART-based clustering algorithm is theoretically capable of continual learning, and the proposed algorithm independently applies it to each class of training data for generating classifiers. Whenever an additional training data set from a new class is given, a new ART-based clustering will be defined in a different learning space. Thanks to the above-mentioned features, the proposed algorithm realizes continual learning capability. Simulation experiments showed that the proposed algorithm has superior classification performance compared with state-of-the-art clustering-based classification algorithms capable of continual learning.

翻译：本文提出一种利用基于自适应共振理论（ART）的成长型自组织聚类算法的监督分类算法，该算法具备持续学习能力。基于ART的聚类算法在理论上能够实现持续学习，而所提算法将这一特性独立应用于训练数据的每个类别以生成分类器。每当新增某一类别的额外训练数据集时，将在不同学习空间中定义新的基于ART的聚类。得益于上述特性，所提算法实现了持续学习能力。仿真实验表明，与当前最先进的具备持续学习能力的基于聚类的分类算法相比，所提算法具有更优的分类性能。