论文标题
班级分类器设计能够使用基于自适应理论的拓扑聚类来持续学习
Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological Clustering
论文作者
论文摘要
本文提出了一种有监督的分类算法，能够利用基于自适应共振理论（ART）基于成长的自组织聚类算法来持续学习。基于艺术的聚类算法在理论上具有连续学习的能力，并且所提出的算法将其独立地应用于每类培训数据以生成分类器。每当给出新类的额外培训数据集时，都会在不同的学习空间中定义新的基于艺术的聚类。得益于上述功能，拟议的算法实现了持续的学习能力。模拟实验表明，与基于群集的最新分类算法相比，所提出的算法具有优越的分类性能。
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.
